”sown. . A 3 V. H— ; ‘2‘ i 700583 5611907“? 0 uJ \ ) \JV This is to certify that the dissertation entitled The role of compartments in food-web structure and changes following biological invasions in southeast Lake Michigan presented by Ann Elizabeth Krause has been accepted towards fulfillment of the requirements for the Doctoral degree in Fisheries and Wildlife M 47%, Major Professor’s 8" nature . MSU is an Affirmative Action/Equal Opportunity Institution UBRARY l Michigan State University PLACE IN RETURN Box to remove this checkout from your record. TO AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 6/01 cJCIRC/DateDue.p65«p.15 THE ROLE OF COMPARTMENTS IN FOOD-WEB STRUCTURE AND CHANGES FOLLOWING BIOLOGICAL INVASIONS IN SOUTHEAST LAKE MICHIGAN By Ann Elizabeth Krause A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Fisheries and Wildlife 2004 ABSTRACT THE ROLE OF COMPARTMENTS IN FOOD-WEB STRUCTURE AND CHANGES FOLLOWING BIOLOGICAL INVASIONS IN SOUTHEAST LAKE MICHIGAN By Ann Elizabeth Krause Compartments in food webs are subgroups of taxa where many, strong interactions occur within the subgroups and few, weak interactions occur between the subgroups. Theoretically, compartments increase the stability in networks, such as food webs, however, compartments have been difficult to detect in empirical food webs. We used a method for detecting compartments from the social networking science. This method identified significant compartments in three out of five complex, empirical food webs. We found that detection of compartments was influenced by food web resolution, such as interactions with weights. A graphical presentation reveals systemic relationships and taxa-specific positions as structured by compartments. In addition, we explore two scenarios of disturbance to develop a hypothesis for testing how compartmentalized interactions increase stability in food webs. We examined the food-web structure of southeast Lake Michigan and determined the changes in structure that occurred after two biological invasions, Bythotrephes and zebra mussels. We tested for compartmentalization and measured the structure using four indices: weighted connectance, quantitative connectance, diversity of interactions, and diversity of taxa. We found that the structure was significantly compartmentalized, where the compartments represented a range of biotic habitats. Connectivity and diversity of interactions were greater within compartments than between compartments. The two biological invaders affected the food-web structure both at the local compartment-level and at the overall structure-level. The overall structure demonstrated resistance to the invaders in compartment membership and three of its indices. The fourth index, quantitative connectivity, demonstrated a detectable decline from its pro-invasion status. This decline indicated that those taxa with large biomass in the post-invasion structure had fewer effective interactions than those in the pre-invasion structure, providing fewer effective interactions as alternative pathways to absorb disturbance affects. These fewer effective interactions could act as buffers for unaffected compartments from the disturbance with fewer pathways to transfer the effects. At the local compartment-level, indices identified one invaded compartment as the weaker compartment compared to the other invaded compartment and indeed, the weaker compartment demonstrated less resistant to the invasion in its indices. Ecosystem managers should continue to monitor the food-web structure and its resistance to future disturbances. Dedicated to the memories of Maxine Louella Yost and Rebecca Ruth Bridgeman-Ferhat iv ACKNOWLEDGEMENTS This publication is the result of research funded by the Great Lakes Fishery Commission, the Environmental Science and Policy Program at Michigan State University, and NCAA-Great Lakes Environmental Research Laboratory. My major professor, Dr. William W. Taylor, has been a solid foundation to this research. He has provided the contacts, the pep-talks, and the guidance needed to achieve this life-long goal. Most importantly, be granted me all the fieedom and support that I required. For all of this, I will be forever grateful. All of my committee members have assisted me in this accomplishment. In particular, Dr. Ken Frank has worked closely with me by training me on the complex methodology he developed during his dissertation and by contributing considerably to Chapter 2. Without his enthusiasm and patience, this dissertation would not have happened. Along with providing entertaining discussions, Dr. Mike Jones helped me to weigh the interactions and taxa of the food web appropriately. My other committee members, Drs. Alan Tessier and Gary Mittelbach, have also provided valuable insights along the way. I would also like to thank Dr. Robert Ulanowicz and Dr. Doran Mason for thoughtful discussions and advice. Constructing a food web takes a lot of people’s effort to accomplish. I am very grateful to the many folks from a variety of institutions who contributed their data and expertise to the building of the southeast Lake Michigan food web: Tom Nalepa (NOAA-GLERL), Rick Barbiero (GLNPO- EPA/Dynocorp), Charles Madenjian (U 868- GLSC), Marlene Evans (EC), Hank Vanderploeg (NOAA-GLERL), Emily Smith (MSU), Jim Bence (MSU), Steve Pothoven (UM-CILER), Phil Schneeberger (MI-DNR), Gavin Christie (GLF C), Gary F ahnenstiel (NOAA-GLERL), Darryl Hondorp (UM-CILER), Mark Ebener (CORA), Glenn Carter (NOAA-GLERL), and Dave Clapp (MDNR). The Great Lakes Fishery Commission (GLFC) has been instrumental throughout my degree. They funded a major portion of this research by hiring me as a part-tirne contract officer and part-tirne research assistant. Randy Eshenroder, my mentor at GLF C, provided a unique understanding of the Great Lakes ecosystem and broadened my appreciation for the Great Lakes considerably. Chris Goddard and Gavin Christie also provided endless feedback, for which I am very grateful. Robert Young, Jill Finster, Sean Wolford, Katie Amatangelo, Elise Zipkin, and Bill Alguire were my substitute graduate student colleagues that provided lots of necessary fun. I thank the teachers who inspired me along the long path leading up to my dissertation: Miss Henry (Hillel Academy 1983-4), Mr. Holstein (Friends School of Detroit 1986), and Dr. Michael Wiley (School of Natural Resources, University of Michigan, 1992-3). Finally, I thank my husband, Big T, with whom it is a lot of fun to go out on a boat, whether for sampling or sailing. vi TABLE OF CONTENTS LIST OF TABLES ........................................................................................................... viii LIST OF FIGURES ........................................................................................................... ix CHAPTER 1 PROLOGUE - AN INTRODUCTION TO FOOD-WEB STRUCTURE ........................... 1 CHAPTER 2 COMPARTMENTS REVEALED IN FOOD-WEB STRUCTURE ................................... 8 Methods ........................................................................................................................ 20 CHAPTER 3 STRUCTURE OF THE FOOD WEB FOR SOUTHEAST LAKE MICHIGAN AND ITS CHANGES IN RELATION TO TWO BIOLOGICAL INVASIONS ............. 25 Introduction .................................................................................................................. 25 Study Site and Data Acquisitions ................................................................................ 28 Methods ........................................................................................................................ 33 Compartment Analysis ........................................................................................... 38 Network Indices ..................................................................................................... 42 Results .......................................................................................................................... 45 Compartments and Structure Indices ..................................................................... 45 Structural Changes ................................................................................................. 58 Discussion .................................................................................................................... 6O Food-web Structure ................................................................................................ 60 Structural Changes ................................................................................................. 65 CHAPTER 4 SUMMARY OF RESULTS AND CONCLUSIONS ........................................................ 74 APPENDICES ................................................................................................................... 77 REFERENCES ................................................................................................................ 210 vii Table 1 Table 2 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 LIST OF TABLES Association between compartment membership and occurrence of interactions between taxa ........................................ 12 Compartment analysis for five food webs ..................................... 14 Years, seasons and depth zones collected by the main long- term monitoring programs for datasets .......................................... 31 Association between compartment membership and occurrence of interactions between taxa ........................................ 4O Compartment assignment, taxa identification number, and scientific name. Invasive taxa are in bold italics .......................... 47 The results for the indices of connectance, CW and Cq, where ‘within’ indicates those interactions between taxa within the same compartment, ‘between’ indicates those interactions between taxa between different compartments, and letters indicate the compartment identification and a combination of letters indicate interactions between those two compartments .......................................................................... 56 The results for the indices of diversity, effective interactions (EI) and effective taxa (ET), where EI and ET are reported as proportions of the actual interactions (AI) and actual taxa (AT). Note that ‘within’ indicates those interactions between taxa within the same compartment, ‘between’ indicates those interactions between taxa between different compartments, and letters indicate the compartment identification and a combination of letters indicate interactions between those two compartments ................. 57 viii Figure 1 Figure 3.1 Figure 3.2 Figure 3.3 LIST OF FIGURES Graphical display of the results for the Chesapeake Bay food web with 45 taxa and weighted by interaction strength. Units are relative distances based on the inverse of the density of interactions. Within compartment distances were decreased by a factor of 6.2 for aesthetic purposes. Circles indicate compartment boundaries and numbers identify taxa (yellow = within compartment A, blue = within compartment B). Arrows indicate interactions between taxa (red solid = within compartment, green dashed = between compartments, thickness = rank of associated interaction strength) and point from predator to prey. Images in this dissertation are presented in color. A larger font version of the embedded table follows on next page ................................................................................................ 16 A map of southern Lake Michigan with the latitude and longitude boundaries of study outlined .......................................... 29 A graphical display of the placement of the six compartments in relation to each other based on the compartment assignment of the pre-invasion food web and the taxa and interactions of the post-invasion food web. The units on the axes are relative distances based on the inverse of the density of interactions between compartments. The letters indicate the identification of the compartment. Circles represent a compartment ............................ 52 A graphical display of taxa within their compartments based on the compartment assignment of the pre-invasion food web and the taxa and interactions of the post-invasion food web. The units on the axes are relative distances based on the inverse of the density of interactions between compartments. The letters indicate the identification of the compartment. Circles represent a compartment and numbers identify the taxa (see table X). To facilitate understanding, compartments A and B were moved +115 units along the y-axis and compartment D was moved -55 units along the y-axis ..................................................................... 54 ix CHAPTER ONE Prologue - An Introduction to Food-web Structure Measurement of ecosystem properties has become more important as policies for managed resources, such as forests and fisheries, are requiring an ecosystem approach (Callicott et al. 1999). An ecosystem is defined as all of the biotic community (plants and animals) found within a self-contained entity and their interactions with each other and abiotic factors (Lawrence 1989, Begon et a1. 1990; Walker 1995). Measuring properties of ecosystems is a difficult task for community ecologists for both the basic and applied levels and often studies address the properties of subsets of the species in an ecosystem (Tilrnan 1999; Diaz and Cabio 2001). Ecologists have found that the properties measured at a species or sub-community level often are not linearly related to whole ecosystem properties (U lanowicz 1997; Kay and Regier 2000; Diaz and Cabio 2001). Thus, only by analyzing a system at all levels of organization can scientists and managers best understand an ecosystem (Gaedke 1995). Of particular interest to scientists and managers is the response of an ecosystem to disturbance, i.e., an external event that perturbs components of a system, such as biological invasions, where the response also must be analyzed at various levels of organization (Vitosek 1990; Grimm 1996). An important level for evaluating the impact of a disturbance in the ecosystem is the changes in structure of the food web, which integrates the biotic components of an ecosystem (Cohen et al. 1993; Gaedke 1995). Ecosystem structure is its organization; where material (e.g., biomass, energy, nutrients) is distributed among biota (Walker 1995; Ulanowicz 1997; Diaz and Cabio 2001). Because food webs are constructed of the feeding interactions among taxa (Pimm 2002), the definition of food-web structure is the organization and distribution of material and interactions among taxa. Another advantage to examining the food-web level of an ecosystem is that food webs figure prominently in the diversity versus stability debate (McCann 2000). Early theory by Charles Elton and others proposed that more species diversity in an ecosystem would increase the stability of the community during a disturbance event, that is, it would show fewer or weaker effects. Additionally, Elton proposed that greater diversity would increase the resistance of ecosystems to invasion by providing fewer open niches for the invader (Tilman 1999; Ricciardi 2001; Shea and Chesson 2002). In the early 19703, Robert May published theoretical work based on simulated food webs, which demonstrated that increased diversity in communities decreased stability (May 1973). One corollary of this work was that the arrangement of interactions within the community had an effect on stability. If species with many or strong interactions with each other were arranged into subgroups with few or weak interactions between subgroups, then the stability of that community was increased. A value placed on an interaction between two taxa that is relatively large is considered to be a strong interaction and a relatively small value placed on an interaction is a weak interaction. These values can be measures of energy flow as a predator consumes a prey, the preference of a prey item for a predator or other measures that reflect relative differences among taxa interactions (McCann 2000; Berlow et a1. 2004). Stuart Pimm followed on May’s work by renaming subgroups compartments (Pimm 1979; Pirnm and Lawton 1980). He refined the theory by finding that communities with compartments that were still connected to each other through weak or few interactions were more stable than communities with compartments that had no interactions between them and communities that were uncompartrnentalized (Pirnm 1979). Recent work has complemented this finding by demonstrating the importance of the number and arrangement of weak interactions to food-web stability (McCann et a1. 1998; Neutel et al. 2002). The weak interactions buffer disturbance effects within food- web structure. This theory of compartments as structural elements that increase stability for networks has been methodologically difficult to test on empirical food webs. Methodologies for identifying compartments in empirical food webs have all had weaknesses. Some studies used methods that did not match the definition of compartments, that is, the method did not explicitly detect concentrations of interactions within subgroup of taxa (Pirnm and Lawton 1980; Yodiz 1982; Raffaelli and Hall 1992; Dicks and waell 2002). Rather than identifying groups of taxa which interact with each other, these approaches identified groups of taxa that were most similar to each other in terms of their interactions. The groups did not have taxa that explicitly interacted with each other. Two methods (Moore and Hunt 1988; Girvan and Newman 2002) did identify groups of highly interacting taxa, but their results could not be tested against a null model, a feature necessary to be statistically rigorous according to Pirnm and Lawton (1980). As the ecology literature did not provide sufficient methodology, I borrowed a method from the social networking sciences that met the requirements necessary for detecting compartments in empirical food webs. Social science has a similar theory on the stabilizing effects of compartments within networks of people, known as cohesive subgroups (Sirnmel 1950; Simon 1965; Blau 1977). They have also struggled to find appropriate methodology for identifying these subgroups. Recently, a method has been developed within the social sciences which maximizes a function that explicitly accounts for the interactions between nodes (people or taxa) within a network and places the nodes into subgroups in which interactions are concentrated (Frank 1995, 1996). The method also does not require a priori specification of compartments, does not place nodes into more than one compartment, and tests results against a null model, three important criteria for compartment identification (Pimm and Lawton 1980). It was also calibrated by applying it extensively to simulated data with known compartment assignments. Finally, the method has a graphical display associated with it where compartment boundaries are embedded to help facilitate my understanding of the role of compartments in food-web structure (Frank and Yasumoto 1998). In this research, I tested the applicability of this method for identifying compartments in empirical food webs. After testing the applicability of the method, I examined the food-web structure of Lake Michigan and the effect of invasion as a disturbance event on the connectivity and diversity of its structure as defined by compartments. Lake Michigan is the second largest lake by volume of the Laurentian Great Lakes. The Laurentian Great Lakes contain one-fifth of the world’s freshwater. In addition, the Lake Michigan ecosystem is economically important in that it supports a world-class sport fishery as well as commercial fisheries (Madenjian et al. 2002). Managing the Lake Michigan ecosystem has been a challenge as it has undergone extensive disturbances associated with cultural eutrophication, overfishing, and multiple species introductions, where 139 introduced species have been documented as established within the Laurentian Great Lakes (Mills et al. 1993; Madenjian et al. 2002). Government agencies have long-term monitoring programs in an effort to understand the patterns and processes of effects of these disturbances on the ecosystem. I used data from these monitoring programs to construct a food web of southeastern Lake Michigan, where the monitoring has been particularly intensive over time, in order to evaluate the changes in food-web structure when an ecosystem is confronted with biological invasions. I examined the structure of the food web for southeast Lake Michigan, before and after the invasion of Bythotrephes cederstoemii, a cladoceran zooplankter, in 1986 (Evans 1988) and Dreissena polymorpha, zebra mussels, in 1989 (Lauer and McComish 2001). Bythotrephes, a predator on zooplankton, has out-competed its closest native competitor Leptodora kindtii by having a higher consumption rate and larger size range of prey (Shultz and Yurista 1999). In addition, they are also thought to be competitors with small fishes, are unpalatable to small fish, and difficult to digest for larger fish (Bamhisel and Harvey 1995; Parker et al. 2001; Vanderploeg et al. 2002). Zebra mussels have a high filtering rate and have the ability to filter a wide range of particle sizes unlike the native fingernail clams (Vanderploeg et al. 2002). As a result, they have increased water clarity. In addition, they increase nutrient deposition by depositing pseudofaeces into the sediments (V anderploeg et al. 2002). While the impacts of these two species have been studied on the levels of populations for one or a few species in the ecosystem, no study has taken the integrative approach of understanding these disturbances from the level of food-web structure. The goals of this research are to identify compartments in empirical food-webs and to understand food-web structure of southeast Lake Michigan and changes in structure occurring after two biological invasions. Three objectives were developed: 1. To determine an appropriate methodology for identifying compartments within the structure of empirical food-webs: 0 is the social-science method able to identify compartments in previously published food webs that are complex in number of taxa and interactions? is the detection of compartments affected by level of aggregation for taxa? is the detection of compartments affected by weights on interactions? what information is provided about food-web structure when viewed from a compartment perspective? 0 what is the potential for compartments to inform me about stability in food-web structure? 2. To determine the structure of the food-web for southeast Lake Michigan. 0 is the structure significantly compartmentalized: 0 what information about structure is provided when viewed from a compartment perspective? what is the connectivity as structured by compartments? what is the diversity in interactions and taxa as structured by compartments? 3. To compare the food-web structure before the two biological invasions of Bythotrephes and zebra mussels to the structure alter the two invaders had established large populations within the food web: did compartment membership of taxa significantly changed? what compartment(s) did the two invaders invade? did connectivity and diversity decline for compartments after the invasion? 0 did connectivity and diversity decline for food-web structure afier the invasion? 0 what implications are there for the stability of the food-web structure for which ecosystem managers should take into account in the event of future disturbances? In the following two chapters, the goals and objectives are evaluated. Chapter 2 applies methodology from the social sciences for identifying compartments to five complex, empirical food-webs. From this analysis, I learned what food-web properties worked for identifying empirical food-webs and what did not. I applied this knowledge in Chapter 3 when I built a food-web of southeastern Lake Michigan using monitoring data from government agencies. I then evaluated the food-web structure so that I could determine the impacts on connectivity and diversity after the invasion of the two invaders. Results in both studies were related to the potential implications each had to the stability of food-webs. In Chapter 4, the results and conclusions are summarized. CHAPTER TWO This chapter was published in Nature and is copyrighted by Nature. The formatting follows the guidelines for Nature. Images in this dissertation are presented in color, in particular, Figure 1 of this chapter. The full citation is: Krause, Ann E., Kenneth A. Frank, Doran M. Mason, Robert E. Ulanowicz, and William W. Taylor. 2003. Compartments revealed in food-web structure. Nature. 426:282-285. Compartments revealed in food-web structure Keywords: food webs, compartments, stability, trophic structure, connectance Ann E. Krause', Kenneth A. Frank", Doran M. Mason‘, Robert E. Ulanowicz§, and William w. Taylor. ' Department of Fisheries & Wildlife; and rDepartment of Counseling, Educational Psychology, and Special Education, Michigan State University, East Lansing, MI 48824 ‘t NOAA, Great Lakes Environmental Research Laboratory, 2205 Commonwealth Blvd, Ann Arbor, MI 48105 § Chesapeake Biological Laboratory, University of Maryland, Solomons, Ilfl) 20688- 0038 Compartments‘ in food webs are subgroups of taxa where many, strong interactions occur within the subgroups and few, weak interactions occur between the subgroups”. Theoretically, compartments increase the stability in networks”, such as food webs. Compartments have been difficult to detect in empirical food webs because of incompatible approaches“9 or insufficient methodological rigors’m“. Here we show that a method for detecting compartments from the social networking scienceu'“ identified significant compartments in three out of five complex, empirical food webs. Detection of compartments was influenced by food web resolution, such as interactions with weights. Because the method identifies compartmental boundaries in which interactions are concentrated, it is compatible with the definition of compartments. The method is rigorous because it maximizes an explicit function, identifies the number of non-overlapping compartments, assigns membership to compartments, and tests the statistical significance of the results'“‘. A graphical presentation" reveals systemic relationships and taxa-specific positions as structured by compartments. From this graphic, we explore two scenarios of disturbance to develop a hypothesis for testing how compartmentalized interactions increase stability in food webs‘s'". Similarity between human social networks and food webs has recently been noted through exchanges between ecologists and sociologistsls’w. In the social sciences, cohesive subgroups in human communities have been an important concept since the 19503, when it was proposed that social systems were more efiicient and durable when comprised of subgroups in which interactions were concentrated 3'20’2‘. The concept of cohesive subgroups has had strong theoretical support, but the methodologies needed to apply the concept to communities were lacking‘z'”. This is also the case for food-web compartments in ecology, wherein methods for identifying compartments often have emphasized the similarity of prey and predators among taxa”, which results in little direct interaction or carbon exchange within compartments. In this study, we use a recently developed social network method”’” for identifying cohesive subgroups to detect compartments in empirical food webs. The method identifies compartments in which interactions are concentrated, thus conserving the flow of organic material, energy, etc. within compartments, just as information and influence flow primarily within human subgroups in which interactions are concentrated. Although the algorithm identifies compartments in which interactions are concentrated, interactions are not exclusively confined within compartments. Thus there are critical cross-compartment interactions that integrate the compartments into a food web‘, just as interactions between people in different subgroups sustain social systems and societies3'20’21. 10 Key to the network method is the criterion defining the concentration of interactions within compartments, where an interaction is predator taxon i consuming prey taxon i’. The criterion is the increase in the odds of an interaction occurring (versus not occurring) given that two taxa are in the same compartment versus in different compartments. This odds ratio corresponds to [(AxD)/(BxC)] as defined in Table 1 and can be interpreted as a comparison of density of interactions within versus between compartments and is thus associated with key parameters for social network models”. Statistical significance of the odds ratio is determined by Monte Carlo simulations (see Methods). In the food web literature”, density is analogous to interactive connectance (IC), with overall IC defined by [(B+D)/(A+B+C+D)] and interpreted as the proportion of realized interactions out of all possible interactions. 12-” critical for our application6'23. First, The algorithm we employed has four features taxa are assigned to non-overlapping compartments as a result of the odds ratio being a flexible criterion‘z‘”. Thus, the assignment of each taxon contributes to the concentration of interactions within all of the compartments of the food web". Second, the algorithm generally does not require a priori specification of the number of compartments. This feature removes a key subjective decision from the procedure and assists in simulating a sampling distribution for the odds ratio. Third, the algorithm was calibrated by applying to extensive simulated data with known compartment assignmentsn‘”. Fourth, compartment boundaries can be embedded in a graphical presentation of the food web”, thus facilitating interpretation of the roles of compartments and their taxa within food webs. None of the previous methodologies in either social networks1 1'” or ecology‘s'11 have all four of these important features. We applied this method to five food webs: Ythan Estuary“, Little Rock Lakezz’zs, St. Martin Island”, Chesapeake Ba 7’28, and a cypress wetland (Supplement A). Seventeen separate versions of these food webs that vary with respect to level of 11 Table 1. Association between compartment membership and occurrence of interactions between taxa. Interaction occurring No Yes Compartment Different A B n(n-1) - Z9 ng(ng-1) membership Same C D Z, ng(n9-1) "(ll-1% 212' Xlr {it Xu' n(n-1) For the symbols: X... represents the presence(=1)labsence(=0) of an interaction between taxon i and taxon i', n represents the number of taxa in the food web, and n9 represents the number of taxa in compartment 9. Interactions between predator taxon iand prey taxon i ’can be weighted by integers from O to MW, the maximum weight across all ii ’interactions. Weights are included in the above table by multiplying X" by the weight assigned to the ii' interaction and by multiplying [n(n-1)] and [£9 ng(ng-1)] by MW13.A represents unrealized interactions between compartments, B represents realized interactions between compartments. C represents unrealized interactions within compartments and D represents realized interactions within compartments. 12 aggregation of taxa, weight of interactions, and season were considered. Seven of them yielded odds ratios that were statistically significantly greater than would be expected by chance alone (Table 2; a = 0.05). Six of these would still be statistically significant when adjusting for the number of tests using a Bonferonni correction of a. As we expected, IC within compartments was higher and IC between compartments was lower than the overall IC (by a factor of 1.9 to 3.5 and 0.003 to 0.27 respectively). The algorithm did not detect compartments in St. Martin Island, a narrow food web focused on two lizard species and not likely to have compartments, but did detect compartments in broader food webs (e.g., Chesapeake Bay). This is consistent with our claim that the method is able to detect the presence or absence of compartments with reasonable accuracy. The resolution of a food web can affect the detection of compartments. We detected compartrnentalization in only 1 of 14 less complex food webs originally analyzed by Pimm and Lawton‘s’8 (Supplement B) as opposed to 3 of 5 more complex food webs presented here. No compartments were detected in the aggregated version of Little Rock Lake or in the unweighted versions of Chesapeake Bay and the cypress wetland whereas compartments were detected in alternative versions. These inconsistencies suggest that ignoring weights when aggregating taxa decreases the number of analyzed interactions and can obscure strong relationships that contribute to compartmentalization. Note that the range in weights for interactions in all of our food web versions was at the upper limit for the method. Ideally, the range should be narrower (e.g., 1-100). Wide range in weights (e.g., 1-99,999) can result from high aggregation in the basal taxa and low aggregation in top predators, such as in our weighted food webs. The graphical presentation (Figure 1) shows the compartments] structure of the Chesapeake Bay food web with 45 taxa and weighted by interaction strength. 13 Table 2. Compartment analysis for five food webs. . o... aims. as" “‘18“ 3°12“ Ythan Estuary 134 None 4.19 30.999 3 0.033 - — Little Rock Lake 92 None 3.14 30.999 2 0.12 — - 181 None 10.04 50.001‘ 4 0.072 0.17 0.020 St. Martin Island 44 None 4.20 50.907 5 0.11 - - 44 Frequency 28.93 50.803 6 0.0065 — - Chesapeake 33 None 8.61 50.751 3 0.067 — - Bay 33 Strength 642.55 50001" 2 0.0029 0.0059 0.0000093 33 Carbon 618.75 50.012' 2 0.0035 0.0071 0.000012 45 None 9.63 50.200 4 0.069 - - 45 Strength 114.92 50.001' 2 0.0052 0.0099 0.000087 45 Carbon 165.84 50001“ 3 0.0018 0.0044 0.000026 Cypress wetland Dry Season 64 None 5.52 50.285 2 0.11 - - 64 Strength 11.93 50.918 3 0.0021 - — 64 Carbon 228.18 50.001‘ 5 0.00057 0.0020 0.0000088 Wet Season 64 None 7.81 _<_0.001 1 0.1 1 — - 64 Strength 12.34 50.910 4 0.0026 - — 64 Carbon 384.81 50.001“ 3 0.00044 0.0013 0.0000033 For the calculations of the odds ratio. overall 10. and definitions of A. B, C, and D, refer to the text and Table 1. 10 within compartments is calculated as D/(C+D). and IC between compartments is calculated as B/(A+B). " indicates significance at a = 0.05. 14 Figure 1. Graphical display of the results for the Chesapeake Bay food web with 45 taxa and weighted by interaction strength. Units are relative distances based on the inverse of the density of interactions. Within compartment distances were decreased by a factor of 6.2 for aesthetic purposes. Circles indicate compartment boundaries and numbers identify taxa (yellow = within compartment A, blue = within compartment B). Arrows indicate interactions between taxa (red solid = within compartment, green dashed = between compartments, thickness = rank of associated interaction strength) and point from predator to prey. Images in this dissertation are presented in color. A larger font version of the embedded table follows on next page. 15 2 5.3 lmenSIon D -60. ~70 - Figure l. ~80. In! Scientific noun or W W1 . 2 ”MI- 8 Batons <1 um (small) 4 Bacteria >1 4 um (m) 5 We >2 um (large) 6 tom: 7 Micro em a Macro sill-tea 9 Prod-nous alleles 10 Chryswra (Sea nettle) 11 ' ' (00m 10'!) B 12 Non-optic bachei (Jew) 13 14 Other modem 15 Malice mill larvae (W) 16 mm ’m 17 Flirt luv. 18 SOOMS wide: (Port/chaste) 19 r , ”an.-. 20 Helsrmastus lilitounes (Ohgochaete) 21 01her Potychaetes 22 um Iacustras (WM 23 Laplocherers plumulosus (NHDhiPOG) 24 Other metolauna 25 M ' (Baltic clam) Macoma Mitchell: (Rosy clam) 27 angle ouneata (Wedge darn) 20 Milton lat-rails (Coot elem) i 29 Mrs alumna (Soft-shelled clam) 30 Crasostrea Virginica (Oyster) ‘ 31 C lhnecte‘ s sapktus (Blue crab) 32 much/[Ii (Bay 606W) um (Croak ) 34 Tnnectes muculates ( aker) 35 Lainstomus tanthulus (Spot) 36 Cynosion rogalis (Weakfish) A 37 Alosa sapidisima (American shad) Ales: Wm (We) 39 Ales. aestivafis (Blurb-cl: herrhg) 40 (Menhsdsn) 4 Morons alumna (White perch) 42 Morons saxatilis (Striped bass) stomus saltab'ix (Bluefish) M Faralichlhyes dentalus (Flounder) l l l l I I l I I I I I I 60-5040-30-20-100102030405060 Dimension 1 :— U NNNNNN—u—tn—nn—ot—p—eu—u—n—u—o MthNu—oemqa\m&w~_o‘oWQGMDUNv—n Table embedded within Figure 1 Scientific name or classification Phytoplankton Benthic producers Bacteria < l um (small) Bacteria >1 <2 um (medium) Bacteria >2 um (large) Acartia tonsa (copepod) Micro ciliates Macro ciliates Predaceous ciliates Chrysaora quinquecirrha (sea nettle) Mnemiopsis leidyi (comb jelly) Nemopsis bachaei (jellyfish) Cladocera Other zooplankton Anchoa mitchilli larvae (anchovy) Anchoa mitchilli eggs Fish larvae Marenzelleria viridis (polychaete) Nereis succinea (polychaete) Hetermastusfiliformis (oligochaete) Other polychaetes Corophium lacustre (amphipod) Leptocheirus plumulosus (amphipod) Other meiofauna Macoma balthica (Baltic clam) Macoma mitchelli (rosy clam) Rangia cuneata (wedge clam) Mulinia lateralis (coot clam) Mya arenaria (soft-shelled clam) Crassostrea virginica (oyster) Callinectes sapidus (blue crab) Anchoa mitchilli (bay anchovy) Micropogon undulatus (croaker) Trinectes maculates (hogchoaker) Leiostomus xanthurus (spot) Cynoscion regalis (weakfish) Alosa sapidissima (American shad) Alosa pseudoharengus (alewife) Alosa aestivalis (blue-back herring) Brevoortia tyranus (menhaden) Morone americana (white perch) Morone saxatilis (striped bass) Pomatomus saltatrix (bluefish) Paralichthys dentatus (flounder) Ariusfelis (catfish) l7 Although the detail in this graphic appears complex”, the image reveals an intuitive understanding of the food web. Compartment A has 28 taxa, most of which would be considered pelagic (water column) and compartment B has 17 taxa that are primarily benthic (sediments). Some taxa placements may seem counterintuitive when considering the physical component of habitat. For example, clams (27, 28, 29) and oyster (30) physically reside in the benthos, like the clams (25, 26) in compartment B. Our results demonstrate that the biotic habitat of 27, 28, 29, and 30 is in the pelagia because of their strong interactions with bacteria (3, 4, 5) and ciliates (7, 8, 9) in A, which supports previous research”. Compartments should measure biotic habitat“"°'1 1, and compartment membership from our other significant results (Supplement C) lend additional support. At the system level, A and B are linked through cross-compartrnent interactions. The few, weak interactions indicate the level of isolation between the two compartments. The interactions B has with A are more evenly dispersed within its compartment where 65% of its taxa interact with A. Conversely, only 30% of the taxa in A interact with B and these interactions are concentrated within specific areas of A. Placement of a taxon indicates its role within its compartment. For compartment B, taxa 2, 22, and 45 are centrally located indicating their importance to compartrnental interactions, particularly compared to those taxa around the periphery of compartment B, such as 21, 33, and 41. Central to compartment A is a food chain [1, 6, & 14]. Of the twenty-five other taxa in A, twenty-three interact with one of these three taxa. Peripheral taxa placed near to another compartment relate more strongly to that compartment than peripheral taxa placed farther away. For example, peripheral taxon 16 has two interactions within A and interacts with taxa that only interact within A, thus 16’s position is far from B. Conversely, peripheral taxon 41 has one interaction within B and one that goes to A, thus its position within B is close to A. Taxon 36 has 18 the role of a bridging taxon between A and B, where the majority of the interactions between A and B are attributable to 36. Because previous work relates compartments to stability”, we developed a hypothesis of stability by simulating two disturbance scenarios on our food web in Figure l. The first scenario removes weakfish (36), which could occur with overfishing. This disturbance15 translates to a reduction in the number of taxa (-2% = taxa loss). Our overall IC, a variable of interest1 5'”, was -1 .5x the taxa loss. The IC within A, where 36 was a member, was -3x the taxa loss whereas IC within B showed no change. Between IC was +18x the taxa loss because 36 was a dominant bridging taxon. In our second scenario, we considered replacing Acartia tonsa (6), a central taxon in A, with an invading zooplankter, a peripheral taxon, that preys only on large bacteria (5) and macro ciliates (8) in A and is unpalatable to predators. This new invader reduces realized interactions (-5% = interaction loss). In response to this disturbance, overall IC changed by the same percent (1 x, as expected) as did A’s within IC. B’s within IC was -O.3x the interaction loss and the between IC was 1.5x the interaction loss. Because the factoral change in ICs in relation to the disturbance index (taxa or interaction loss) indicates resistance”, we hypothesize that B would be the most resistant and the exchange between A and B would be the least resistant to both disturbance events. That is, compartmentalization retains the impacts of a disturbance within a single compartment, minimizing impacts on other compartments and thus providing the stabilizing structure to food webs. This hypothesis is consistent with previous studies“5 that found weak interactions buffer the effect of disturbances, demonstrated by B’s resistance to disturbances occurring in A. An empirical test29 of hypotheses such as ours requires longitudinal data for multiple food webs, compartmentalized and uncompartmentalizedl's, that have undergone disturbance events15 . The changes in variables of interest (e.g., IC) should be assessed 19 for stability properties, such as resistance”. Our results demonstrate that this method is a rigorous and effective way to analyze food web structure and provide the initial steps in understanding the relationship between compartments and stability”. Methods Interactions were weighted either by interaction frequency, carbon flow, or interaction strength. Interaction frequency was estimated as acts of predation per hectare per day“. Carbon flow fi'om prey i’to predator i, Witt, was estimated as gC/mZ/yr 27”. Interaction strength was the geometric mean of the interaction strengths between predator i and prey 1"”. The interaction strength of predator i on prey i’was measured as —(W u/Bi), where B, is the biomass of predator i (gC/mz), and the interaction strength of prey i’on predator i was measured as Rix( W W /B,v), where Bit is the biomass of prey i’(gC/m2) and R, is the production to consumption ratio of predator i 3°. With weights, the definition of IC becomes the proportion of possible interactions, each with maximum weight, that are realized (with no cannibalistic interactions). We used the software KliqueFinder ‘2 to identify compartments. Because KliqueFinder operates on an integer scale from 1-99999, we modified the interactions for weighted versions of the food webs to fit the scale. The modifications had little to no effect on the results. To test if the concentration of interactions within identified compartments was greater than what was likely to have occurred by chance alone, for each web we conducted Monte-Carlo simulations. First, we randomly reassigned interactions, constraining the row marginal (sum of each row in a matrix) to be equal to the row marginal of the original food web where rows represented predators and columns represented prey. We then applied KliqueFinder and recorded the odds ratio. We then repeated this process 1000 times to obtain a sampling distribution against which we could compare the empirical odds ratio. Our constraints insured that the simulated food webs had the same number of predators, the same number (and 20 weight) of interactions associated with a predator, and the same total number (and weight) of realized interactions as the original food web. Basal taxa (taxa with no prey) did vary in simulations for some food web versions which did change the overall 1C from the original for some food webs but we found that there was little to no effect on the p—values and no effect on statistical inference. Although the range of calibrating simulations”'‘4 (the third feature of the methodology) did not allow direct assessment of the performance of the algorithm for our data (because of large n and weighted interactions), typically the algorithm performs well when there is evidence of compartments in non-weighted data12 . Cannibalism and taxa that interacted with only one other taxon were dropped from the analysis when optimizing the odds ratio because these interactions do not add information to the relative assignments of taxa to compartments. Dropped taxa were added back to the food web for the calculations in Table 2. Coordinates for the graphic in Figure 1 were generated by employing multidimensional scaling within and between subgroups13 , and SAS proc gplot was used to generate the figure. Because of the large magnitude of the difference between the smallest and largest interaction strength weightings (~10,000X), the lines were weighted by the rank of the associated interaction strength, where the smallest interaction strength was given a rank of 1 and the largest interaction strength was given a rank of 137 (the maximal number of realized interactions). The units are based on the inverse of the between compartment density (0.0097). In our scenarios for exploring compartments and stability, we made one simple assumption: the predators of the taxon involved in the disturbance compensated for the loss in their interactions by increasing their interaction strength with their remaining prey items. For the first hypothetical scenario, all interactions with 36 were removed and the predators on 36 had their interaction strengths associated with 36 21 redistributed proportionally to their other prey interactions. For the second hypothetical scenario, all interactions associated with 6 were removed except for those with taxa 5 and 8. The interactions of the predators on 6 were modified in the same manner as scenario 1. 22 10. ll. 12. 13. 14. 15. 16. 17. REFERENCES . Pimm, S. L. The structure of food webs. Theor. Popul. Biol. 16, 144-158 (1979). May, R. M. Stability and Complexity in Model Ecosystems. (Princeton Univ. Press, Princeton, 1973). . Simon, H.A. in General Systems: Yearbook of the Society for General Systems (Eds. von Bertalanfi'y, L. and Rapaport, A.) Vol. 10 pp. 63-7 6 (The Society, Ann Arbor, MI, 1965) McNaughton, S. J. Stability and diversity of ecological communities. Nature 274, 251-252 (1978). Simmel, G. 1950. The sociology of Georg Simmel, (translated & ed., Kurt H. Wolff, Free Press, Glencoe, Ill. 1950). Pimm, S. L. & Lawton, J. H. Are food webs divided into compartments? J. Anim. Ecol. 49, 879-898 (1980). Yodiz, P. The compartrnentation of real and assembled ecosystems. Am. Nat. 120, 551-570 (1982). . Raffaelli, D. & Hall, S. J. Compartments and predation in an estuarine food web. J. Anim. Ecol. 61, 551-560 (1992). Dicks, L. V., Corbet, S. A., & waell, R. F. Compartrnentalization in plant-insect flower visitor webs. J. Anim. Ecol. 71, 32-43 (2002). Moore, J. C. & Hunt, H. W. Resource compartmentation and the stability of real ecosystems. Nature 333, 261-263 (1988). Girvan, M. and Newman, M. E. .1. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 8271-8276 (2002). Frank, K. Identifying cohesive subgroups. Social Networks 17, 27-56 (1995). Frank, K. Mapping interactions within and between cohesive subgroups. Social Networks 18, 93-119 (1996). Frank, K. & Yasumoto, J. Y. Linking action to social structure within a system: social capital within and between groups. Am. J. Sociology 104, 642-686 (1998) Grimm, V. A down-to-earth assessment of stability concepts in ecology: dreams, demands, and the real problems. Senckenbergiana maritima 27, 215-226 (1996) McCann, K. S. The diversity-stability debate. Nature 405, 228-233 (2000). Dunne, J. A., Williams, R. J ., & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecology Letters 5, 558-567 (2002). 23 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. Johnson, J. C., Borgatti, S. P., Luczkovich. J. J., & Everett, M. G. Network role analysis in the study of food webs: an application of regular role coloration. J. Soc. Structure 2: published online at http://zeeb.library.cmu.edu:7850/JoSS/johnson/RoleAnalysis.html (2001) McMahon, S. M., Miller, K. H. and Drake J. Networking tips for social scientists and ecologists. Science 293, 1604-1605 (2001). Simmel, G. Conflict and the web of group affiliations (K. Wolff, Trans, Free Press, Glencoe, Ill, 1955). Blau, P. M. Inequality and heterogeneity. (Macmillan, New York, 1977) Martinez, N. D. Artifacts or attributes? Effects of resolution on the Little Rock Lake food web. Ecol. Monog. 61, 367-392 (1991). Raffaelli, D. From Elton to mathematics and back again. Science 296, 1035- 1037 (2002). Huxharn, M., Beaney, S., and Raffaelli, D. Do parasites reduce the chances of triangulation in a real food web? Oikos 76, 284-300 (1996). Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180-183 (2000). Goldwasser, L. & Roughgarden, J. Construction and analysis of a large Caribbean food web. Ecology. 74, 1216-1233 (1993). Ulanowicz, R. E. & Baird, D. Nutrient controls on ecosystem dynamics: the Chesapeake mesohaline community. J. Marine Systems. 19, 159-172 (1999). Abarca-Arenas, L. G. & Ulanowicz, R. E. The effects of taxonomic aggregation on network analysis. Ecol. Model]. 149, 285-296 (2002). Polis, G.A. Complex trophic interactions in deserts: an empirical critique of food-web theory. Am. Nat. 138, 123-155 (1991). Neutel, A. M., Heesterneek, J. A. P., & de Ruiter, P. C. Stability in real food webs: weak links in long loops. Science 296, 1120-1123 (2002). 24 CHAPTER THREE Structure of the food web for southeast Lake Michigan and its changes in relation to two biological invasions Introduction The negative impacts of biological invasions on ecosystems resulting in economic losses to society have been well documented (for reviews, see Vitosek 1990; Kolar and Lodge 2001; and Shea and Chesson 2002). Ecosystem changes resulting from disturbances such as biological invasions need to be evaluated at multiple levels, from the ecosystem-level to the single species level (V itosek 1990). Only by analyzing a system at all levels of organization can I best understand how an ecosystem has reacted to a disturbance event (Gaedke 1995; Grimm 1996). An important level for evaluating the impact of a disturbance in the ecosystem is the changes in structure of the food web, which integrates the biotic components of an ecosystem (Cohen and others 1993; Gaedke 1995). Structure is defined as the distribution of material among taxa Walker 1995; Ulanowicz 1997; Diaz and Cabio 2001). Because food webs are constructed of the feeding interactions among taxa (Pimm 2002), the definition of food-web structure is extended to include the distribution of interactions among taxa. Food webs figure prominently in the diversity versus stability debate, that is, does more diversity of species in an ecosystem create more stability for the biota of that system (McCann 2000)? A more stable ecosystem demonstrates fewer effects by a disturbance. The distribution of interactions within food webs, particularly the arrangement of weak interactions, is important for food-web stability (May 1972; Pimm 1979; McCann 2000; Neutel and others 2002). Compartments are one such arrangement 25 of weak interactions and have been shown to increase stability in simulated food web structure (May 1972). Compartments are subgroups of taxa within a food web that share many or strong interactions; few or weak interactions exist between compartments (May 1972; Pimm 1979). The weak interactions between compartments should buffer disturbance effects within food-web structure. Diversity is also thought to increase the resistance of ecosystems to invasion by providing fewer open niches for the invader (Tilman 1999; Ricciardi 2001; Shea and Chesson 2002). When measuring diversity in food-web structure, two important indices are connectance and the Shannon diversity index (May 1972; Martinez 1991; Gaedke 1995; Bersier 1999; Zorach and Ulanowicz 2003). Connectance is the ratio of realized interactions to potential interactions in a food web. The Shannon diversity index is a classic measure of diversity and provides information about how diversity has changed in an ecosystem when applied to the distribution of interactions and biomass (Shannon 1948; Hurlbert 1971; Ulanowicz 1997; Zorach and Ulanowicz 2003). In order to evaluate the changes in food-web structure when confronted with a biological invasion, I examined the structure of the food web for southeast Lake Michigan, before and after the establishment of two biological invaders. Since the early 18805, 139 introduced species have been documented as established within the Laurentian Great Lakes (Mills and others 1993). In my study area, 15 exotic species had become established prior to the invasion of Bythotrephes cederstoemii, a cladoceran zooplankter, in 1986 (Evans 1988) and Dreissena polymorpha, zebra mussels, in 1989 (Lauer and McComish 2001). Bythotrephes is a predator on zooplankton with a higher consumption rate and larger range of prey size than its closest native competitor, 26 Leptodora kindtii (Shultz and Yurista 1999). They are also thought to be competitors with small fishes (Vanderploeg and others 2002). Unlike Leptodora, they have a long, spiky spine that is unpalatable to small fish and difficult to digest for larger fish in the Great Lakes (Bamhisel and Harvey 1995; Parker and others 2001). Zebra mussels have a high filtering rate and have the ability to filter a wide range of particle sizes unlike the native fingernail clams (Vanderploeg and others 2002). These attributes allowed for a dramatic increase in water clarity while rapidly moving organic matter from the water column into the benthic zone as zebra mussels deposit pseudofaeces into the sediments (V anderploeg and others 2002). These two impacts have likely resulted in a restructuring of the pathways for nutrient cycling and energy flow in the systems that zebra mussels have invaded. The impacts of these two species have been studied on the levels of populations for one or a few species in the ecosystem but not on the integrative level of food-web structure (for review, see Vanderploeg and others 2002). My goal was to determine how food-web structure changed afier the invasion of Bythotrephes and zebra mussels for southeastern Lake Michigan. I measured the structure of the food web by first determining if it was significantly compartmentalized. Then, I determined the distribution of interactions and taxa as organized by compartments using two indices of connectivity and two indices of diversity to evaluate the structure of the food web. I compared these measures on a food web representing the pre-invasion structure to measures on a post-invasion structure. A comparison of one index of connectivity and an index of interaction diversity allowed me to evaluate changes in the organization and distribution of interactions directly resulting from the addition of the two invading taxa. The second index of connectivity and diversity of taxa 27 did not directly measure changes associated with these two invasions because they incorporated changes in the biomass of taxa, which may have changed as a result of many other factors, such as nutrient loading or internal dynamics of populations. If my indices declined, it would indicate an increase in dominance of a few interactions and taxa within the food web and reduced diversity. A reduction in diversity may lead to reduced resistance of the ecosystem to future disturbance. This analysis provides a comparison for evaluating impacts of future invaders or other disturbances (Vanderploeg and others 2002). This study is the first to provide an analysis of the structural changes at the food- web level that occur to an ecosystem after the disturbance of biological invasions. Study Site and Data Acquisitions The food web represented southeastern Lake Michigan in spring and summer and bottom depths of 15m to 110m over the years of 1980-84 (pre-invasion food web) and 1995-99 (post-invasion food web). The time frame of 1980-84 was selected because it was before either Bythotrephes or zebra mussels had been detected in Lake Michigan waters. The second time frame of 1995-99 was selected because both species had high populations within the food web for several years by that time period (Makarewicz and others 1995; Barbiero and others 2001; Fleischer and others 2001). Although by 1999, several new exotic species had been detected in the lake, they had yet to reach high levels of density (Vanderploeg and others 2002). I focused on data collected between the latitudes of 43° 15' N and 41° 45' N and east of longitude 87° 00' W (Figure 3.1). I set the general time frame of the food web from April 1 to September 30. I used June 15th as the approximate first day of summer. June is the time when the lake starts to stratify, at the 28 - Figure 3.1. A map of southern Lake Michigan with the latitude and longitude boundaries of study outlined. .\- - ' ‘s. -._ ‘\. x.-.” a/ '1‘ \ \ " e e l a u."" ‘e . .cu - '.' . - - o . .. u I ..-—_ .m... , NW— "- 9""- —-—.— y.- ‘W‘- it": "'7—" -—~\-—'. 1 Muskegon Grand Haven 43°00‘- 42°00'- Figure courtesy of NCAA-Great Lakes Environmental Research Laboratory, Ann Arbor, MI 29 beginning of June, conditions are usually still at the mixing stage and by the end of June the lake has usually set up a therrnocline. The bottom depth range of 15-110m was broken into three horizontal depth zones based on previous studies: 15-30m Zone 1, 31- 50m Zone 2, and 51-110m Zone 3 (Nalepa 1989; Agy 2001). The restrictions in season, bottom depth, and location within the lake were dictated by the long-term monitoring datasets that I was able to locate. Southeastern Lake Michigan has the most long-term datasets available within the lake and most of these are collected in the spring and summer between the bottom depths of 15m to 110m. The primary information I used from the datasets were taxa presence during my time periods and their associated relative abundance (Table 3.1). Phytoplankton data were provided by the Great Lakes National Program Office (GLNPO) of the US-EPA. GLNPO collected phytoplankton data to monitor water quality and has been consistent in its data collection methods (Barbiero and Tuchrnan 2001). Although they collect in only one depth zone,I could not find evidence that there were large changes in phytoplankton among depth zones except for Nitschia spp. (Munawar and Munawar 1976; Lowe 2003). The zooplankton data for the pre-invasion food web was provided by Dr. Marlene Evans (Environment Canada, National Hydrology Research Institute, Saskatoon, SK) collected for the Cook Power Plant study conducted by the University of Michigan, Ann Arbor, MI (Evans 1986). The post-invasion food web was provided by NCAA-Great Lakes Environmental Research Laboratory (GLERL), Ann Arbor, MI and was collected for their Episodic Events in Great Lakes Ecosystems (http://www.glerl.meamv/eegle/data/datahtml; Agy 2001). These two datasets were considered to be comparable (personal communication, Dr. Henry Vanderploeg, 30 Table 3.1. Years, seasons and depth zones collected by the main long-term monitoring programs for datasets. Group Phytoplankton Zooplankton Benthic Invertebrates Fish Pre-invasion Post-invasion Years 1983-4, seasons spring & summer, depth zone 3 Years 1980-1, seasons spring & summer, depth zones 1, 2 Years 1980-81, seasons spring & summer, depth zones 1,2,3 Years 1980-4, season fall, depth zones 1,2,3 31 Years 1995-9, seasons spring & summer, depth zone 3 Years 1998-9, seasons spring & summer, depth zones 1,2,3 Years 1998-9, seasons spring & summer, depth zones 1,2,3 Years 1995-9, season fall, depth zones 1,2,3 GLERL). Bythotrephes were collected in a separate sampling program by GLERL within all three zones and in summer because they are not present in the spring (Pothoven 2001). Benthic invertebrates were provided by GLERL and collected for a monitoring program using consistent methods (N alepa 1989). Fish data were provided by USGS-Great Lakes Science Center, Ann Arbor, MI from their long-term monitoring program of forage fish (Krause and others 2002). Although samples were taken in the fall, the fish species only have one cohort per year as opposed to the other taxa so these data were adequate for my purposes. Individual fish species that were not collected in the previous dataset but where data were available for both time periods were lake Whitefish, Chinook, coho, steelhead (rainbow trout), brown trout, lake trout, and sea lamprey. Lake Whitefish data were provided by Michigan Department of Natural Resources for their lake Whitefish management unit 8. Salmon and trout data provided by Michigan State University, East Lansing, M1 for salmon management unit 8. These management units did not perfectly overlap with my boundaries. Sea lamprey data were provided by the Great Lakes Fishery Commission (Ann Arbor, MI). Although I could not find consistent datasets for Mysis relicta, opossum shrimp, they are too important as predators and prey within the food web to not include (Lehman and others 1990; Eshenroder and Bumham-Curtis 1999). A comprehensive dataset was available from GLERL for 1995-6 and 1998 for zones 2 and 3 and for both seasons (Pothoven and others 2000). For the 1980s time period, I used estimates from Lehman and others (1990) and McDonald and others (1990); when combined the data included years 1984-5, zones 1-3, and summer. 32 Methods From these datasets, I assembled 244 taxa for the pre-invasion food web. The post-invasion food web had the same 244 taxa with the two additional invaders for a total of 246 taxa. Some taxa collected in the pre-invasion time period were not collected in the post-invasion time period and vice verse. I could not find any studies to suggest that these taxa had been extirpated from the system nor were they introduced to the system during that time period. Because my goal was to test differences in food-web structure in relation to invasive species, I took a conservative approach and kept the taxa composition the same for both time periods with the exception of the two invasive species. Exclusive of the invaders, there were 136 phytoplankton taxa, 27 zooplankton taxa, 49 benthic invertebrate taxa, and 22 fish taxa. Generally, taxa were defined at the species level. Aggregation into higher level taxonomic groups was avoided where possible because of its potential biases, including reduced detection of compartments (Martinez 1991; Cohen and others 1993; Gaedke 1995; Krause and others 2003). Aggregation beyond the species level in the food web only occurred where sampling was reported at a higher taxonomic level with the exception of phytoplankton. Because of the difficulty in taxonomy at the species-level, potential for misidentification, and large munbers of species within phytoplankton, the 269 species of phytoplankton found in the survey data were aggregated to 136 taxa groups based on their group type (for example, centric diatoms) and on having similar characteristics in size, shape, colonial attributes, motility, toxicity, nitrogen-fixing ability, silica, and spatial and temporal presence (W eithoff 2003). For zooplankton and fish, some species, for example Leptodora kindtii, had more 33 than one taxon associated with them based on ontogenetic shifts in feeding interactions because there can be greater differences in feeding interactions that occur with these shifts than between species (Gaedke 1995). These ontogenetic shifis were included where the data collection made it possible. In the benthic invertebrate data, I disaggregated a group, the Spheariidae, into three taxonomic groups because the taxa represented by the group were dissimilar in their depth distributions and diets (Dr. Tom Nalepa, GLERL-NCAA, personal communication). The number of interactions was 9,476 in the pre-invasion food web and 9,638 in the post-invasion food web, which included only the addition of interactions associated with two invasive species. I weighted my interactions based on selectivity of the predator on the prey, horizontal depth overlap, and vertical depth overlap using a relative measure where every interaction had the potential to have the same maximum value of 100. Weighting interactions is important for detecting compartments (Krause and others 2003) and for constructing food webs in general to differentiate between strong and weak interactions (Cohen and others 1993). The quality of information available for this weighting method was higher than for weighting methods such as interaction strength (see Berlow and others 2004) or energy flow (Ulanowicz 1997). The values used to calculate the weights were docmnented in a database along with the associated references (over 175 references total; Appendices B-H). Using previously published diet studies, I determined if an interaction was present between two taxa and then evaluated the selectivity of each taxa pair (Appendix G). If diet data suggested that a predator preferred a prey taxon over most other prey taxa, then the selectivity of that interaction was given a score of 100 (sensu Vanderploeg 1994). If a prey taxon was only an 34 occasional prey item or actively selected against (e.g., toxic phytoplankton), then selectivity was given a score of 10 to represent a low selectivity. All other interactions were given a score of 50 for medium selectivity. For spatial overlap, I collected information on taxa depth preferences from the literature. Because some taxa’s horizontal distribution can shift from spring to summer, particularly for the more mobile fish taxa, each taxon’s presence was recorded for the three horizontal depth zones in spring and in summer, including their zone preference (Appendix E). Vertical space was divided into zones for each season because of the difference in thermal structures (Appendix F). Spring had four zones: 1 = upper water column, 2 = bottom water column, 3 = sediments surface to 1 cm below, 4 = deeper sediments. Summer had six zones: 1 = epilirnnion, 2 = therrnocline area, 3 = hypolimnion, 4 = nephloid area, 5 = sediments surface to 1 cm below, 6 = deeper sediments (Drs. Thomas Nalepa and Gary Fahnenstiel, GLERL, personal communication). Taxa presence in the vertical zones was recorded for spring and summer. For both seasons, presence was also recorded for day and night to allow for the die] vertical migration some taxa undertake, for example, Mysis. For each taxa pair the scores for horizontal depth overlap and vertical depth overlap were calculated in four steps. First, the maximum range of depth zones in which the taxa pair could interact was calculated by subtracting the lower value of the minimum depth zones for the taxa pair from the higher value of the maximum depth zones for the taxa pair and then adding one to the result. This is an estimate of the maximum number of depth zones within which the taxa pair could interact. Second, the higher value of the minimum depth zones for the pair was subtracted from the lower value of the maximum 35 depth zones for the pair. Again, one was added to this value to get the actual number of depth zones in which the taxa pair interacted or the actual range of zones. Third, the actual range was divided by the actual range; this result calculated the proportion of actual zones to potential zones of interaction. Finally, a percent value was multiplied to this proportion based on their depth preferences, where preferences were equal = 100%; preferences were not equal but still within the actual range = 75%; one preference was in the actual range and the other preference was not = 50%; and if neither preference was in the actual range = 25%. For example, taxon i and taxon j interact. Taxon i is present in depth zones 1-2 and prefers l. Taxon j is present in depth zones 2-3 and prefers 3. The score would then be calculated accordingly: [(2-2+1)/(3-1+1)]*25% or [(minimum range)/(maximum range)]*depth preference value. Temporal adjustments were made to the spatial values, taking into account the number of days in spring and summer and number of hours in day and night. A horizontal value was calculated for both spring and summer. These two numbers were averaged by multiplying the spring value by the proportion of days in spring to the total number of days within time boundaries and multiplying the summer value by the proportion of days in summer and adding these two values together. If a taxon was not present within the entire a season (e.g., the hatching of Bythotrephes in June; Pothoven and others 2001), then the value was also adjusted for the temporal overlap of the interacting taxa by calculating the potential number of days of interaction and actual number of days of interaction same as the calculation of spatial overlap. Vertical values were also adjusted for season and had the additional adjustment for day and night. A vertical value was calculated for day and for night for each season. The average was 36 calculated by multiplying the day vertical value by the proportion of hours of daylight in 24 hours and multiplying the night vertical value by the proportion of hours of night in 24 hours and adding these two values together. Thus, the maximum Spatial overlap score was 100 for both horizontal and vertical, which indicated that the interacting taxa pair had 100% spatial overlap and 100% temporal overlap. The three scores of selectivity, horizontal depth overlap, and vertical depth overlap were then averaged for each taxa pair for a final weight for that interaction (Xip). The highest interaction value was 100 and the lowest value was 6. The average interaction value was 35 and the median value was 30 for both time periods. Because I did not find evidence in the literature to suggest that the information used to calculate these three scores changed between time periods, the weight on individual interactions was the same for the pre- and post-invasion food web (Appendix D). The pro-invasion food web had total weighted interactions = 333,228 and the post-invasion food web had total weighted interactions = 339,587. Mean biomass was estimated for each taxon for each year that data were available. These estimates were not assumed to be absolute values of biomass. Instead, I assumed they were reasonable relative estimates to calculate relative changes in my selected indices because the collection methods were consistent across time periods. Most biomass estimates of taxa were included in the data. The exception was the benthic invertebrate data where data were only available in terms of number of individuals. Literature values were used to convert these data into biomass (Appendix I). For Mysis, mean biomass fi'om the 1990s data was used to convert the 19803 data. Biomass estimates were all converted to a common unit of mg of dry weight/m2 (Appendix I). Because seasonal data were available for phytoplankton, Mysis, zooplankton, and benthic 37 invertebrates, estimates were calculated based on the proportion of time in spring and summer. For zooplankton, mysis, benthic invertebrates, and fish (USGS-GLSC), means were calculated using weights based on the percent area each depth zone represented within the latitude and longitude boundaries (Holcombe and others 1996). Because there were no depth zone 3 samples in the 19803 data for zooplankton but there were in the 19903, I assumed that there was no difference in biomass between the two depth zones and adjusted the 19803 means accordingly. A previous study conducted an ANOVA of the 19903 data to determine if there were significant differences between depth zones and for these two depth zones during the time periods, no significant differences were found (Agy 2001). For the disaggregated sphaeriids, I assumed equal biomass among the taxon for each horizontal depth zone based on their presence within that depth zone. From the yearly estimates of biomass, a mean and associated standard deviation was calculated racross years for the 1980-84 time period and the 1995-99 time period. Because I kept taxa collected only in one time period in the analyses of both time periods, I assigned a minimum biomass value based on 0.5*minimum biomass for their general group (for example, phytoplankton). Standard deviations for these biomasses were estimated by regressing biomass and standard deviation for those in its group where biomass was detected. Compartment Analysis In order to detect compartments 1 first analyzed the pre- and post-invasion food webs using the odds ratio method employed by KliqueFinder (Frank 1995, 1996). This method seeks compartments within a network structure by iteratively reassigning taxa to 38 compartments to maximize the odds that interactions occur within compartments. For all of the potential interactions that can occur between taxa in a network, the method sums up (Table 3.2) all of weights associated with realized interactions (an interaction occurs between two taxa) that occur within compartments (D) and all of weights associated with realized interactions that occur between compartments (B). Unrealized interactions are calculated by summing the weights associated with taxa pairs that do not interact, which are assigned the maximum weight (in this study maximum weight = 100) and the difference between the maximum weight and the weight of a realized interaction. Unrealized interactions are calculated for taxa pairs that are both within the same compartment compartments (C), and for taxa pairs that are in different compartments compartments (A). According to the definition of compartments, if realized interactions occur, most should be occurring within compartments and the remaining unrealized interactions out of potential interactions should be occurring primarily between compartments. After each compartment assignment, the method follows this definition by calculating the odds that realized interactions occur within compartments and unrealized interactions occur between compartments versus realized interactions occur between compartments and unrealized interactions occur within compartments. That is, the odds ratio is calculated by [(AxD)/(BxC)]. The method then reassigns compartments until this odds ratio is maximized. This method has previously been shown to successfully identify compartments in food webs (Krause and others 2003). Compartment assignment between the pre- and post-invasion food webs was tested to determine if compartment assignment was different from each. I added up the number of taxa that shared the same compartment in both food webs (E), the number of 39 Table 3.2. Association between compartment membership and occurrence of interactions between taxa. Interaction occurring No Yes X11 2 0 X11 = 1 n(n-l) Xm Different 0 A B Compartment — [28 ng(ng-l)] Xmax membership Same 1 C D [23 ng(ng-l)] Xmx n(n—1)Xmax 2121' Xii' n(n-l) Xm - 2121' Xii' For the symbols: Xiy represents the weight of an interaction between taxon i and taxon i', Xm represents the maximum weight out of all Xiiv, n represents the number of taxa in the food web, and 118 represents the number of taxa in compartment g. 40 taxa that did not share the same compartment in both food webs (H), the number of taxa that shared the same compartment in the pre-invasion but not the post-invasion food web (G), and the number of taxa that did not share the same compartment in the pre-invasion but did in the post-invasion (F). I calculated the odds that E and H occurred versus G and F, that is [(ExH)/(GxF)]. That is, I wanted to determine the odds that a pair of taxa were in the same compartment or in different compartments in both time periods. The odds ratio was 167 which translated to taxa that shared the same compartment in the pre- invasion food web had a 99% chance of sharing the same compartment in the post- invasion food web. This high value indicated that the two invaders did not change the compartment membership of the pre-invasion food-web structure. Thus, I concluded for the subsequent analyses that compartment assignment was the same for all 244 taxa between the pre- and post-invasion food webs. Because compartment assignment was highly similar between the two food webs, I only tested the pre-invasion food web for significance by conducting a Monte Carlo simulation (Frank 1995; Frank 1996). First, I randomly assigned interactions, constraining the sum of each row to be equal to the row marginal of the pre-invasion food web, where row represented predators and columns represented prey (Frank 1995; Krause and others 2003). Then, KliqueFinder was applied to the randomized food web and an odds ratio was calculated. This process was repeated 500 times to obtain a sampling distribution against which I could compare the odds ratio of the pre-invasion food web to see if it was significantly different from random (or = 0.05). A diagram of compartments and a diagram of compartments with their taxa were generated using SAS proc gplot (Frank and Yasumoto 1998). The dimensions and coordinates were based on a nested 41 analysis of multidimensional scaling using the interaction density within and between compartments. Network Indices I calculated four indices to determine the distribution of interactions and taxa for the overall food web and within and between compartments for evaluating differences in structure between the pre- and post-invasion food web. My first index is connectance or C, which is the proportion of realized interactions to potential interactions. Because I have weights on my interactions, they get incorporated to calculate Cw. Connectance is equivalent to (B+D)/(A+B+C+D) in Table 3.2 for a food web overall. My second index is the diversity of interactions (H1) index using Shannon’s diversity index (Shannon 1948; Hurlbert 1971; Zorach and Ulanowicz 2003). where X” is the sum of the weights of interactions, equivalent to 2in X5): in Table 3.2. For my third index, I use the same equation only for biomass as a weight on taxa to calculate the diversity of taxa (HT). _ " .BL HT: met 8 B. i=1 - 42 where B, is the biomass of taxon k and B_ is the sum of all the taxa biomasses. These diversity indices reflect the degree of departure from an even distribution of weights on interactions and weights on taxon. The next step was to take the exponent of H) and HT: E1 = e”, and ET = e”, where E1 is effective interactions and ET is effective taxa. If all the weights on interactions were equal, then EI would equal the actual number of interactions and if all the weights on taxa, that is, the biomasses, were equal, then ET would equal the actual number of taxa. The more uneven weights are, the lower the value of E1 and ET, which indicates a few interactions or a few taxa are dominating the food web structure. My fourth index was developed by Bersier and others 2002, known as quantitative connectance or Cq, combined both the weights on interactions and the biomass of taxa. This index determined the diversity of interactions of individual taxon weighted by their proportion of biomass to the overall biomass. To calculate Cq, first the diversity of interactions coming into taxon k (HM) is calculated: " X- X- HR,k =‘Z—X'k 111% i=1 1: .k where X”, is the weight of an interaction where k is the predator on prey i and X), is the sum of all the weights of interactions where k is the predator. Then, the diversity of interactions going out from taxon k (Hr-,1.) is calculated: 43 n X k,- X .. HPJ; = - Z-X— 3f— i'=l k. k. where X“ is the weight of an interaction where k is the prey of predator i' and X... is the sum of all the weights of interactions where k is the prey. These diversity measures are then used in the final equation: q : %[i%k_eflp,k + iflgeflm k=l - k=l - This calculation of connectance weights the diversity of interactions by the proportion of biomass taxon k to total biomass. Higher values of Cq indicate that those taxa with high biomasses also have high diversity of interactions, indicating higher complexity. I adjusted the four indices for compartments to calculate separate measures for the interactions that occur within compartments and for the interactions that occur between compartments. For Cq, n was adjusted by substracting 1 in keeping with the definition of Cw, that is, what are all potential interactions one taxon has with all the other taxa within the food web or within a compartment. By keeping compartment assignments constant between time periods, I could compare the differences in Cw and H1 that resulted directly from introducing two new taxa into the structure of the food web. For HT and Cq, I conducted Monte Carlo simulations for the biomass estimates. I assruned a normal distribution for all taxon biomass based on their mean biomass and standard deviation. I then randomly generated 1000 biomass estimates for each taxon based on the distribution for the pre-invasion food web and the post-invasion food web. I did not want 0 or negative biomasses thus, if a value went below 0.0005 (3 value less than the lowest detected biomass estimate), it was assumed to equal 0.0005. Assruning log-normal distributions produced unrealistic high values of biomass so this truncation of the distribution was preferable. I used these simulated biomasses to estimate the distributions for H7 and Cq. The methods for estimating these distributions did not lend themselves to formal statistical testing. Thus, to determine if the distributions between time periods were different from each other, I compared the distribution with the greater mean value with the distribution of the lesser mean value. If the 90‘h percentile of the lesser mean distribution was less than the minimum value of the greater mean distribution and if the 10‘h percentile of the greater mean distribution was greater than the maximum value of the lesser mean distribution, then the mean values of the pre-invasion and post-invasion were considered to be different. Results Compartments and Structure Indices The pro-invasion food web was significantly compartmentalized with an odds ratio of 4.75 (p < 0.002). The analysis of the post-invasion food web produced an odds ratio of 4.72. When I assumed the same compartment assignments of the pre-invasion for the post-invasion, the odds ratio only slightly declined (4.71). For the pre-invasion food web, there were six compartments in all: compartment A (76 taxa), compartment B (24 taxa), compartment C (34 taxa), compartment D (54 taxa), compartment E (19 taxa), and compartment F (19 taxa). There were 18 taxa unaffiliated with any compartment. I determined that 97% of the 74 other compartment members with zebra mussels in the 45 post-invasion food web were found to be members of Compartment A in the pre-invasion food web, thus I concluded that zebra mussels invaded Compartment A. For Bythotrephes, 100% of its 19 other compartment members in the post-invasion food web were members of Compartment F in the pre-invasion analysis. As such, I concluded that Bythotrephes invaded Compartment F. Table 3.3 reports the taxa assignments to each compartment. Phytoplankton taxa were found in all six compartments. Zooplankton taxa were found in compartments A and E. Benthic invertebrates were found in compartments B, C, D, and F. Fish were found in compartments A, D, E, and F. Figure 3.2 shows the compartment arrangement using multidimensional scaling. Compartments A, C, and D were placed close together indicating their high density of interactions among each other. Compartments B, E, and F were placed farther away indicating their lower density of interactions with other compartments. Even though B and F overlap, it is more due to their similarity in interactions with the other compartments than their density of interactions which is low (Table 3.3). Figure 3.3 rearranges the compartments so that taxa identification numbers can be seen within compartments. Both graphics are based on the post-invasion taxa and the pre-invasion compartment assignments with the 18 unaffiliated taxa removed. All compartments had interactions with all the other compartments. I compared the results of the pre- and post-invasion food webs for the overall food web, all compartments grouped together for the measures within compartments (overall within) and between compartments (overall between), measures of the compartments separately (within compartment, between compartment), and finally, measures of the interactions between specific compartments (specific between compartments). The 46 Table 3.3. Compartment assignment, taxa identification number, and scientific name. Invasive taxa are in bold italics Compartment A Compartment A (cont) ID it Name ID # Name Phytoplankton — Phytoplankton - Cryptophyceae Centric Bacillariophyceae 112 Chroomonas (Rhodomonas spp. 2 Stephanodiscus spp. 1 l3 Cryptomonas spp. 3 C yclostephanos & C yclotella spp. 1 14 Rhodomonas spp. 4 Rhizosolenia sp. 1 15 Coptomonas spp. 9 Rhizosolenia eriensis 1 l6 Cryptomonas rostratifirmis 18 Stephanodtsats hantzschi i l 17 Cryptomonas curata 19 Cyclotella comensis l 18 Cryptomonas oata Phytoplankton - Phytoplankton - Cyanophyceae Pennate Bacillariophyceae 120 Anacystis dynechoccus spp. 23 Achnanthes eigua 125 Chroococcus spp. 28 Cocconeis placentula ar. euglypta 129 Chroococcus limneticus PhytOplankton — Cblorophyceae Phytoplankton — Dinophyceae 64 Chlamydocapsa sp. 130 Amphidinium sp. 69 F ranceia Henkinia spp. 131 ,Gnnodinium (Peridinium spp. 70 Monoraphidium spp. 132 Peridinium sp. 71 Tetraedron d‘reubaria spp. 133 Gnodinium sp. 72 Sphaerellocystis spp. Crustacean Zooplankton 73 Carteria cfhlamydomonas spp. 137 Alana spp. 74 Stichococcus sp. 138 Bosmina longirostris 78 Monoraphidium spp. 139 Ceriodaphia spp. 87 sttis spp. 140 Chydorus spaericus 88 él'ystis spp. 141 Daphnia galeata mendotae Phytoplankton — 144 Diaphanosoma spp. Chrysophyceae & Protozoa 145 Eubosima coregoni 91 Protozoa 146 Holopedium gibberum 94 Chromulina fliromonas 147 Leptodora kindti 95 Dinobryon spp. 148 Polyphemus pediculus 96 Protozoa 149 Nauplii 97 Protozoa 150 Cyclops spp. 98 Haptophyceae 151 Acanthocyclops smalis 99 Spinifiromonas sp. 152 Diacyclops thomasi 100 Paraphysomonas sp. 153 Mesocyclops edax 101 Rhizochtysis sp. 154 Tropocyclops prasinus meranus 102 Bitrichia chodatii 155 Epischura lacustris 103 Bicoeca spp. 157 Diaptomus spp. copepodites 104 Chrysolykos spp. 158 Leptodiaptomus ashlandi 105 Chrysococcus sp. 159 Leptodiaptomus minutus 106 @hyrion (Pseudokephyrion spp. 160 Leptodiaptomus sicilis 107 Chrysosphaerella longispina 161 Limnocalanus macrurus copepodites 108 Dinobryon spp. 162 Limnocalanus macrurus adults 109 Mallomonas sp. 8 163 Skistodiaptomus oregonensis 1 10 Mallomonas sp. Fish 1 1 l firhyrion spp. 223 Alarm pseudoharengus larvae Zebra Mussel 246 Dreissena polymorpha 47 Table 3.3 (cont) Compartment B ID # Name Phytoplankton - Centric Bacillariophyceae 5 Melosira spp. 17 Stephanodiscus niagarae Phytoplankton - Pennate Bacillariophyceae 36 F ragilaria intermedia nr. fillax 37 Nitzschia spp. 42 Nitzschia conifiris 44 Nitzschia acicularis 46 Nitzschia gracilis 48 Nitzschia rostellata 50 Cymatopleura solea 51 Nitzschia acuta 53 Surirella augusta 54 Nitzschia spp. 57 Nitzschia lauenburgiana 58 Synedra delicatissima Phytoplankton - Chrysophyceae 92 Stichogloea sp. 93 Hyalobryon sp. Snails 165 Amnicola limnosa 166 Mata sincera 167 Pseudosuccinea columnella Isopods 173 Caecidotea racoitzai Dipteran Larvae 182 Chironomus sp. . Oligochaetes 197 Enchytraeidae spp. 198 Stylodrilus heringianus 199 A rcteonais lomondi Compartment C ID # Name Phytoplankton - Centric Bacillariophyceae 6 Cyclotella spp. 8 Stephauodiscus binder-anus l l Stephanodtscus alpinus 12 Actinocyclus (E'yclotella spp. 14 Halassiosira weisflogii 20 Stephanodiscus spp. Phytoplankton - Pennate Bacillariophyceae 22 F ragilaria pinnata 24 Achnanthes spp. 25 F ragilaria spp. 26 Nitzschia spp. 27 Meridian circulare 29 Cymbella microcephala 30 F ragilaria spp. 31 Synedra amphicephala ar. austriaca 32 Amphora daicula spp. 33 Nitzschia spp. 38 F ragilaria capucina 39 Synedraflifirmis ar. edis 40 Nitzschia para 41 Tabellariafnestrata 47 F ragilaria crotonensis 60 anhonema oliaceum Phytoplankton - Dinophyceae 134 finnodinium heleticum ar. achroum 135 Ceratium hirundinella Opposum shrimp 164 Mysis relicta Fingernail clam 168 Sphaerium spp. Amphipod 172 Diporeia hoyi Dipteran Larvae 180 Chironomus anthracinus 181 Chironomusfitiatilis 200 Chaetogaster sp. 201 Piguetiella michiganensis 202 Stylaria lacustris 203 bbinais uncinata 204 lidoskyella intermedia 48 Table 3.3 (cont.) Compartment D ID # Name Phytoplankton — Centric Bacillarlophyceae l Aulacoseria subarctica 7 Stephanodiscus hantzschii f tenuis 10 Melosira islandica 15 Stephanodiscus subtransilanicus l6 Stephanodiscus transilanicus 21 Aulacoseria italica Phytoplankton — Pennate Bacillariophyceae 35 Diatoma sp. 43 Tabellariafocculosa 45 Asterionellafirmosa 52 Synedra spp. 5 5 Synedra spp. Dipteran Larvae 174 Procladius sp. 175 Potthastia cf longimanus 176 Monodiamesa tuberculata 177 Heterotrissocladius changi 178 Heterotrissocladius olieri 179 Ohocladius sp. 183 Cryptochironomus cf digitatus 184 Cryptochironomus cf filus 185 Cryptochironomus sp. 186 Demicryptochironomus sp. 187 Cladopelma sp. 188 Paracladopelma winnelli 189 Paracladopelma camptolabis 190 Paracladopelma undine 191 Polypedilum scalaenum 192 Polypedilum nereis 193 Polypedilum tuberculum 194 Robackia cf demeijerei 195 Micropsectra sp. 196 Tanytarsus sp. 49 Compartment D (cont.) ID # Name Oligochaetes 205 Aulodrilus americanus 206 Aulodrilus pluriseta 207 llyodrilus templentoni 208 M'ichaetadrilus angustipenis 209 Limnodrilus claparedianus 210 Limnodrilus hofiteisteri 21 1 Limnodrilus spiralis 212 Limnodrilus proflndicola 213 Limnodrilus udekemianus 214 Isochaetidesfeyi 215 @strodrilus multisetosus 216 Spirosperma nikolskyi 217 Tasserkidrilus superiorensis 218 Potamothrixnoldaiensis 219 Potamothrbcjdovkyi 220 Tasserkidrilus americanus 221 Tubtfxubifix Leech 222 Helobdella stagnalis Fish 225 Notropis hudsonius 228 Coregonus clupeafirmis 237 Percopsis omiscomaycus 239 Pungitius pungitius 242 Etheostoma nigrum Tat-l; 62' 123 136 117 123 136 111 143 226 Mg 49 63 67 68 7S 77 79 81 Table 3.3 (cont.) Compartment E [D # Name Phytoplankton - Centric Bacillariophyceae l3 Rhizosolenia longiseta Phytoplankton - Pennate Bacillariophyceae 34 Naicula sp. 56 Amphipleura pelliucdia Phytoplankton - Chlorophyceae 62 Planktonema lauterbornii 65 Scenedesmus spp. 76 Microspora sp. 80 Ankistrodesmusfilcatus ar. mirabilis 83 Ankistrodesmusfilcatus 9O Ankistrodesmus gelifictum Phytoplankton - Cyanophyceae 123 A nabaena fosaquae 126 Anabaena spp. 127 Qillatoria limnetica 128 Qillatoria sp. Phytoplankton - Euglenophyceae 136 E uglena sp. Crustaceous zooplankton 142 Daphnia pulicaria 143 Daphnia retrocura 156 Emytemora aflinis Fish 226 Osmerus mordax larvae 229 Coregonus hoyi larae Unassociated Taxa 49 63 66 67 68 75 77 79 81 82 Phytoplankton - Pennate Bacillariophyceae Synedra radians Phytoplankton — Chlorophyceae Crucigeniafieocystis spp. Eudorina elegans ldthrbsp. Dictyosphaerium ehrenbergianum Elakatothrbsp. dbystis spp. Ankistrodesmusfilcatus ar. mirabilis Monoraphidium tortile Ankistrodesmus gracilis 50 Compartment F [D # Name Phytoplankton - Pennate Bacillariophyceae 59 Synedra delicatissima ar. augustissima 61 Synedra ulna ar. chaseana Fingernail clam 169 Pisidium henslowanum 170 Pisidium spp. Amphipod l 71 Gnmarus sp. Fish 224 Alosa pseudoharengus adult 227 Osmerus 1010er adult 230 Coregonus hoyi adult 231 Oncorhynchus kisutclt 232 Oncorhynclms mykiss 233 Oncorhynchus ahawyrscha 234 Salmo tratta 235 Salalinus namaycushjuanile 236 Salalinus namaycush adult 238 Lota lota 240 Cottus cognatus 241 Myoocephalus thompsonii 243 Percafaascens 244 Petromyzon marinas Crustaceans zooplankton 24S Bythotrephes cederstmemii Unassociated Taxa (cont.) Phytoplankton - Chlorophyceae 84 Monoraphidium irregulare 85 Closteriopsis longissima 86 Crucigenia spp. 89 62313th borgei Phytoplankton - Cyanophyceae 1 19 A nacystis,Aphanothece, Aphanocapsa spp. 121 anhosphaeria spp. 122 Mcrocystis spp. 124 flillatoria spp. Figure 3.2. A graphical display of the placement of the six compartments in relation to each other based on the compartment assignment of the pre-invasion food web and the taxa and interactions of the post-invasion food web. The units on the axes are relative distances based on the inverse of the density of interactions between compartments. The letters indicate the identification of the compartment. Circles represent a compartment. 51 Figure 3.2. 200 150 E5 100 50 Dimension 2 -100 -150 -200 -250 -100 -50 0 50 100 150 200 250 300 350 Dimension 1 52 Figure 3.3. A graphical display of taxa within their compartments based on the compartment assignment of the pre-invasion food web and the taxa and interactions of the post-invasion food web. The units on the axes are relative distances based on the inverse of the density of interactions between compartments. The letters indicate the identification of the compartment. Circles represent a compartment and numbers identify the taxa (see table X). To facilitate understanding, compartments A and B were moved +115 units along the y-axis and compartment D was moved -55 units along the y-axis. 53 Figure 3.3. 100 1mension 2 D -100 -150 -200 -250 1 130113 13631 414117;; 146 23 53 19.197 17 51 113 166 165 54 ‘8 42 36 182 1‘7 199 241 2135 2322“ 238 230 240 245 23}, 2y57 22‘ 170 -100 -50 0 50 100 150 200 250 300 Dimension 1 54 results for the connectance indices, Cw and Cq, showed that connectance was greater within compartments than between compartments for the overall food web (Table 3.4). When the connectance within and between compartments for individual compartments were compared, three compartments (A,D,&F) had within CW greater than the between Cw whereas the remaining three did not. All of the compartments did have within Cq greater than between Cq. Both indices indicated that compartment A bad the highest within connectance of all of the compartments. Compartments B had the lowest within connectance according to Cw whereas compartment E had the lowest within connectance according to C... Compartment C had the highest between connectance in the structure; it had the highest between Cw and Cq overall and the highest connectance indices between specific compartments (AC, BC, CD, and BC). Between connectance for compartment F indicated that it was the least connected of the compartments where its between connectance indices were the lowest overall and the lowest of the specific Cq between compartments (BF). Compartment E was also not well connected as indicated by its low Cq between compartments and also the lowest Cw between specific compartments (BE). For comparative purposes, the diversity indices of effective interactions (EI) and effective taxa (ET) were reported as a proportion of the actual number of interactions (AI) and actual number of taxa (AT) each index represented (Table 3.5). The higher the proportion of E1 to AI and ET to AT indicated the more even the distribution of weights across interactions or taxa and thus, greater diversity. Proportions of E1 to AI ranged from 0.97 to 0.84 and proportions of ET to AT ranged from 0.28 to 0.07. There was a greater diversity of interactions within compartments than between compartments for both the overall food web and at an individual compartrnent-level. Compartment B had 55 Table 3.4. The results for the indices of connectance, Cw and Cq, where ‘within’ indicates those interactions between taxa within the same compartment, ‘between’ indicates those interactions between taxa between different compartments, and letters indicate the compartment identification and a combination of letters indicate interactions between those two compartments. CW C.I Pre Post Pre Post Overall 0.056 0.056 0.198 0.151 Wrin 0.148 0.148 0.315 0.287 Between 0.035 0.036 0.171 0.132 Compartments Wu'n A 0.182 0.182 0.342 0.317 B 0.050 0.050 0.312 0.321 C 0.083 0.083 0.346 0.272 D 0.142 0.142 0.311 0.311 E 0.054 0.054 0.176 0.060 F 0.099 0.097 0.214 0.238 Between A 0.083 0.084 0.143 0.163 B 0.068 0.069 0.156 0.159 C 0.095 0.096 0.250 0.147 D 0.078 0.078 0.134 0.124 E 0.055 0.056 0.097 0.041 F 0.041 0.041 0.076 0.069 Specifc Between AB 0.062 0.063 0.287 0.341 AC 0.1 15 0.117 0.473 0.385 AD 0.089 0.089 0.274 0.272 AE 0.099 0.100 0.336 0.265 AF 0.047 0.050 0.161 0.166 BC 0.1 12 0.112 0.582 0.476 BD 0.102 0.102 0.423 0.400 BE 0.020 0.020 0.150 0.089 BF 0.031 0.030 0.083 0.052 CD 0.115 0.115 0.381 0.380 CE 0.056 0.056 0.420 0.189 CF 0.047 0.045 0.355 0.190 DE 0.032 0.032 0.131 0.109 DF 0.050 0.048 0.154 0.155 EF 0.032 0.036 0.133 0.069 56 131 | 13.11 am 536' co left Cor Table 3.5. The results for the indices of diversity, effective interactions (EI) and effective taxa (ET), where EI and ET are reported as proportions of the actual interactions (AI) and actual taxa (AT). Note that ‘within’ indicates those interactions between taxa within the same compartment, ‘between’ indicates those interactions between taxa between different compartments, and letters indicate the compartment identification and a combination of letters indicate interactions between those two compartments. El EI/Al ET/AT Pre Post Pre Post Pre Post Overall 8083 8236 0.85 0.85 0.1 1 0.13 Within 2950 3006 0.92 0.92 na na Between 5448 5542 0.85 0.86 na na Compartments Within A 1705 1752 0.95 0.95 0.23 0.26 B 125 125 0.97 0.97 0.10 0.09 C 251 251 0.93 0.93 0.07 0.14 D 833 833 0.91 0.91 0.19 0.10 E 36 36 0.92 0.92 0.25 0.25 F 61 69 0.90 0.90 0.28 0.18 Between A 3040 3134 0.88 0.88 na na B 1409 1425 0.89 0.89 na na C 2168 2190 0.86 0.86 na na D 2945 2956 0.87 0.87 na na E 612 627 0.84 0.84 na na F 501 519 0.85 0.85 na na Specific Between AB 388 403 0.92 0.93 na na AC 820 842 0.90 0.90 na na AD 1246 1256 0.90 0.90 na na AB 295 307 0.94 0.94 na na AF 166 182 0.85 0.86 na na BC 319 319 0.94 0.94 na na ED 590 590 0.89 0.89 na na BE 48 48 0.92 0.92 na na BF 70 70 0.93 0.93 na na CD 773 773 0.84 0.84 na na CE 120 120 0.87 0.87 na na CF 89 89 0.81 0.81 na na DE 139 139 0.86 0.86 na na DF 157 158 0.91 0.91 na na EF 28 30 0.84 0.83 na na 57 the highest diversity in interactions both within its compartment and in interactions between its compartment and other compartments where its interactions with compartment C had one of the highest diversities over E13 specifically between compartments. The lowest diversities in interactions were associated with compartment F. On average, compartments A and E had the highest diversity of taxa out of all of the compartments. Compartments B had the lowest diversity of taxa on average. Overall, compartments ranged from compartment A with high within connectance and high diversity of interactions to compartment F with low between connectance and low diversity of interactions. Structural changes The most notable change between the pre-invasion food web and the post- invasion food web is a 24% decline in the connectance of the overall food web as measured by Cq. When interaction weights were used to calculate connectance (Cw), no change was detected. The decline in Cq, a combination of the diversity of the interactions and the biomasses of taxa, indicated that those taxa with low diversity of interactions increased in biomass and taxa with high diversity of interactions declined in biomass. Only one other change was detected in Cq given my criteria, that was a 55% decline in connectance between compartment C and E. There were no notable changes in the diversity of taxa as indicated by the proportions of E1 to AI between the two time periods. The only notable change in the diversity of taxa was in compartment D which showed a 46% decline in diversity since the pre-invasion food web. While all of the ETs did show 58 some change between time periods, they were not large enough to be determined as different given my criteria. Using Cw allows me to evaluate those changes in connectance that have occurred as a result of the invaders directly. This evaluation is possible because the interactions between taxa and their associated weights were constant between time periods. Thus, the only differences in interactions and their weights between the two time periods was a result of adding the interactions associated with the invaders. Other than taxa within its invaded compartment, F, Bythotrephes was also a predator and a prey for taxa in three other compartments (A,D,&E). From the pre-invasion food web to the post-invasion food web, Compartment F ’3 Cw within its compartment declined by 2% and its Cw between F and B, C, and D declined by 5, 5, and 4% respectively. Its CW with compartment E increased by 12% and Cw with compartment A increased by 6%. Zebra mussels interacted with taxa in all the other compartments in the food web in addition to those taxa within its own compartment (A). Zebra mussels caused smaller changes in Cw for its own compartment (A) than Bythotrephes for its compartment F. As well, the impact on other compartments was less than Bythotrephes. There was a 2% increase in Cw between compartments A and B and compartments A and C and a 1% increase in Cw between compartments A and E. The 6% increase in CW between compartments A and F was due to both invaders. Because Bythotrephes and zebra mussels did not have any direct interactions within compartments B, C, D or E, the within Cw for these compartments did not show any change nor did their interactions with each other change (BC, BD, BE, CD, CE, DE). 59 Discussion Food web structure My analysis provides novel insights into how the food web of southeastern Lake Michigan is structured. The 18 taxa found to be unassociated with any compartment represented phytoplankton species that were generally large or asymmetric colonials so their weak interactions with the food web which lead to their subsequent disassociation were not surprising. Consistent with previous research, taxa membership in the six compartments related to biotic habitat, that is, habitat as defined by the biota (Raffaelli and Hall 1992; Pimm 2002; Krause and others 2003). The two largest compartments, A and D, represented the two major habitats within a lake ecosystem: the pelagia (water column) by compartment A and the benthos (sediments) by compartment D. Compartment A members were protozoa (for example, 91), phytoplankton, particularly flagellated plankton (for example, 101) and predatory plankton (for example, 98), crustacean zooplankton (for example, 138), and fish larvae (223). There are three roles a taxon can have within their compartment: central taxa, peripheral taxa, and bridging taxa (Krause and others 2003). Central taxa interact with many of their compartment members and have strong interactions, that is, high weights on interactions. As such, they are located within the center of their compartment in the graphical display of the food web (Figure 3.3). Conversely, a peripheral taxon is located along the edges which indicate it has few interactions and weak interactions, that is, small weights on interactions with the other taxa. The third role is the bridging taxon which has interactions with taxa in another compartment thereby connecting its compartment to another compartment. A bridging taxon can be important for bringing in resources into 60 its compartment that taxa within its compartment may not otherwise be able to access. A protozoa group (97) and a predatory chrysophyte (103) have central roles in their compartrnent’s structure as they are both predators and prey for other members of their group. A group of green phytoplankton (87) is an example of a peripheral taxon in compartment A, likely because they tend to form large colonies. The small diaptomids (160 and 163) act as important bridging taxa for compartment A by having the most interactions with other compartments. The taxa that were members of compartment D were also generally found in the sediments of the lake, such as oligochaetes (for example, 205-222) and dipterean, or fly, larvae (for example, 174-179). Several diatom groups (for example 1, 35) were also found in compartment D. Diatoms are phytoplankton with heavy silica shells so they eventually settle to the sediment surface and are consumed by the fauna associated with the sediments. The five fish species that were members of compartment D are known to be demersal (Scott and Crossman 1973; F abrizio and others 1997). The most central taxa within compartment D were two dipteran larvae in Diamesinae family (175, 176). A leech (222) was the most peripheral taxon. Dipteran larvae, Cryptochironomus spp. (183-5) were prominent bridging taxa and are one of the few dipteran larvae taxa to be predators on other benthic invertebrates. With its high connectance with both A and D, compartment C appeared to couple the benthic and pelagic habitats. Mysis (164) and Diporeia (172) had dual roles as both important central taxa and bridging taxa. The roles for these taxa were appropriate given that they are known for their strong connections between predators and prey in the food web. Mysis undertake extensive vertical migrations from the sediments to the upper 61 water column at night, thereby integrating the interactions between the pelagic and benthic habitat as both a predator and a prey (Pothoven and others 2000). Diporeia are an important prey item for fish taxa, particularly demersal taxa, in the lake and consume a high quantities of freshly-settled diatoms (N alepa and others 2000). A large representation by diatom groups (for example, 6) and the only oligochaete nadid (202) known to vertically migrate off the bottom at night lend support for the integrative role of this compartment as well. Chironomus spp. (180-1) were an additional important bridging taxon by having an abundance of interactions with other compartments as predators and prey. These three groups cover all of the horizontal depth zones in my study; Mysis prefer the deepest zone (zone 3), Diporeia prefer the middle zone (zone 2), and Chironomus spp. prefer the shallow zone (zone 1). Thus, compartment C is critical for coupling the benthic and pelagic biotic habitats across horizontal depth zones. Compartment F was the most weakly connected to the other compartments in the food web. This compartment contained many of the fish taxa, particularly those taxa that have been accidentally or intentionally introduced to the ecosystem. This compartment had the highest percentage of introduced taxa within its compartment membership at 37% and almost half of the introduced taxa within the food web at 47%. Four of the taxa (231-4) were intentionally introduced in large part to create a sport fishery that is now very important economically and to convert the biomass of an unintentionally introduced fish, alewife (223, 224) into useable biomass by preying on them (Bence and Smith 1999). One of these taxa is the most peripheral taxon of the compartment (and perhaps the whole food web), brown trout (234). Alewife (224) is a central taxon in this compartment. It is also the major prey item of several intentional introductions 62 (Madenjian and others 2002). In addition, an important taxon for commercial and recreational fisheries, yellow perch (243), also has a central role as a predator and as a prey of the other fish within the compartment (Madenjian and others 2002). The bridging taxa for this compartment are not fish but rather are fingernail clams, Pisidium spp. (170). This compartment had the lowest diversity in interactions indicating that a few taxa are dominating the interactions associated with the compartment. Both compartment B and E had small memberships and their members represented habitats similar to for D and A respectively. Compartment B had taxa that were associated with the sediments. Similar to D, it had many diatom groups, particularly the more benthic oriented Nitzschia spp. (for example, 37) and Chironomus sp. (182), which served as an important bridging taxon for its compartment the same way its genus served as bridging taxon for D. It also had oligochaetes (197-9), including the most abundant taxon of oligochaetes, Stylodrilus heringianus. Unlike compartment D, compartment B had snails (165-7), which were a central taxon, and an isopod (173). Its highest connectance between compartments was associated with both D and the benthic- pelagic coupler, compartment C. Compartment E had herbivorous zooplankton (142-3), Daphnia spp., which were both central taxon and the primary bridging taxon as both predators on phytoplankton and prey for fish and invertebrates. It also had various types of phytoplankton (for example, 13), a introduced zooplankton (156), and two larval or young-of-the-year stage fish (226 and 229). These taxa all inhabit the pelagia, similar to those in compartment A and it had its highest between connectance with A. Both compartment B and E had lower connectance within their compartments than the other compartments and one of the lowest connectance between each other. Although in the 63 graphical display (Figure 3.2) compartment B and F overlap, their Cq was the lowest, indicating that their position had more to do with their similarity in their interactions with the other compartments rather than strong interactions with each other. My four indices provided information about different aspects of connectivity and diversity in my food-web structure. My connectance index based on weights of interactions, Cw, was low in comparison to connectance calculated with unweighted interactions or by Cq but was within the range of other previously published food webs (Krause and others 2003). That the within Cw was greater than the overall Cw, which was greater than the between Cw of the overall food web was expected as well (Krause and others 2003). These patterns indicate that there was greater complexity of interactions within compartments than overall or between. Interestingly, the effective interaction index (E1) also showed a similar pattern in the within and between measures, indicating again greater diversity in interactions within compartments than between. The values of El were quite high where 92% of the actual numbers of interactions were effective within compartments and 85-6% of the actual numbers of interactions were effective between compartments. For the effective taxa index (ET), I found low diversity of taxa throughout the food-web structure where values ranged from 7% of the actual number of taxa to 28% of the actual number of taxa. The index of quantitative connectance, Cq, was higher than its corresponding Cw, which is to be expected as the value of Cq should approach the unweighted connectance (proportion of realized number of interactions out of potential number of interactions; Bersier and others 2002). These two indices measure two different aspects of connectance in the food web, resulting in a difference in values. While both take the weight on interactions into account, Cw uses the weight to calculate the density of interactions whereas Cq uses the weight to calculate the diversity of the interactions and then weights those diversity measures by biomass of taxa. The index of Cq provides a measure to evaluate if those taxa who have a high diversity of interactions also have high biomass or if they do not and vice versa for those taxa with low diversity of interactions. Both tell me how my taxa are connected to each other in the food-web structure. Cq had patterns similar to CW in its overall food web measures for within and between connectance. Additionally, within and between Cq for individual compartments showed that the connectance between compartments is lower than within compartments, which was not shown by C... This result in Cq confirmed that my compartments were areas of high connectance within my food-web structure. Structural changes If diversity does increase stability, then those compartments with higher diversity indices should be more stable in the presence of a disturbance. However, it is the pattern of that diversity that is important for maintaining stability, particularly in the arrangement of weak interactions (May 1972; Pimm 1979; McCann 2000; Neutel and others 2002). There seems to be tension in this arrangement where weak interactions buffer the effects of the disturbance but too few interactions reduces the ability for a taxon to find alternative pathways in a food web which may increase the effect of the disturbance on it and its closely interacting taxa. The diversity in the interactions would then appear to play an important role for maintaining stability. When interactions become dominated by a few taxa, then the diversity of taxa is reduced and the ability for taxa to find alternative pathways in the food web is impaired. Weak interactions between compartments should 65 buffer other compartments when one compartment is disturbed, but the diversity of interactions should be maintained between compartments so that members of that compartment have optional pathways outside of their compartment (Pimm 1979; McCann 2000). Diversity of interactions within a compartment should be high so that taxa have many pathways in which to help them resist the disturbance, however, the effects of disturbance can be passed on through strong interactions within compartments. It is the pattern of strong and weak interactions and their diversity that should maintain the stability of a system. Two indices of food-web structure, CW and EI, are direct measures of the impacts of invasion of Bythotrephes and zebra mussels into southeast Lake Michigan. Given my results for Cw, the measure of density of interactions, I did not detect an impact in the overall structure nor in the within or between structure of the overall food web. I did not detect changes in the diversity of interactions that directly related to either taxa indicating that interactions and associated weights of the taxa did not deviate from the distribution of interactions already established within and between compartments. Another direct measure of their effect on food web structure was the analysis for compartments. The addition of these taxa did not change the overall compartment assignment. A significant change in the compartment assignment would have been an indication that a catastrophic shift in structure had occurred (Scheffer and others 2001). Although the structure of the overall food-web was not directly affected by these introductions, they did have direct effects on their invaded compartments. Bythotrephes invaded the compartment (F) where indices had suggested it may be less resistant to disturbance than other compartments. The indices measured a low 66 diversity of interactions, low between Cw, and a high percentage of previous invaders, all indications of potentially less resistance. The effect of the invasion on this compartment was to weaken the connectance, as measured by Cw, it had with the benthic compartments (B&D) and the benthic-pelagic coupling compartment (C). The decline in connectance as measured by Cq between compartments F and C was very close to meeting my criteria for having a detectable difference supporting this decline. The addition of Bythotrephes strengthened the connectance with the pelagic compartments of A and E, which contain most of the prey for Bythotrephes (Schultz and Yurista 1999). The lower connectance with benthic compartments to higher connectance with pelagic connections balanced out for compartment F resulting in no change in the measure of connectance between F and all other compartments. Bythotrephes caused a small decline in the Cw of its compartment. The changes in both within Cw and Cw between its compartment and other individual compartements was higher for compartment F than for the compartment that zebra mussels invaded, A. Thus, Bythotrephes had a larger local effect on its compartment than zebra mussels did. The impacts of zebra mussels on its compartment (A) were not as large as Bythotrephes and these impacts were small increases in diversity for compartment A. As zebra mussels filter a wide size range of phytoplankton, its role of a bridging taxon for its compartment is understandable given that phytoplankton taxa were found in all compartments (V anderploeg and others 2002). Zebra mussels invading a pelagic compartment may seem counterintuitive because they physically reside on the sediments (V anderploeg and others 2002). Their physical habitat may be the benthos, but my analysis clearly showed that their biotic habitat was with those taxa found within the 67 water column of the lake. This invasion is an example of how the physical and biological habitats of taxa may be different thus both should be taken into account when defining a taxon’s niche within an ecosystem (Shea and Chesson 2001). While there were no other taxa within A that were sessile filter feeders, there were a range of zooplankton that also feed on phytoplankton. The high within connectance and high diversity of taxa for compartment A indicated that it would be more resistant to an invasion. Traditional invasion theory hypothesized that species rich communities, or those with high diversity, would be more difficult to invade than less diverse communities which may have more niche opportunities (Ricciardi 2001; Shea and Chesson 2002). Although it was not resistant to having the invader establish itself as a member of its compartment, it did demonstrate smaller impacts in diversity than compartment F did. Another local change in structure occurred for compartment D with a decline in its diversity of taxa (ET), although I cannot directly attribute the decline to the invaders because the distribution of biomass could be affected by a number of environmental and biological factors. The decline in ET indicated an increase in the dominance in biomass of a few taxa. Both Bythotrephes and zebra mussels did have direct interactions with this compartment but their influence on this decline would need further investigation. Zebra mussels have been implicated in the decline of Diporeia, a member of compartment C, by removing its major food source, diatoms (Nalepa and others 2000, Vanderploeg and others 2002). Many of the chironomid and oligochaete taxa do consume diatoms so the filtering of zebra mussels may also be affecting them as well although they are not as strongly dependant on diatoms as Diporeia (Gardner and others 1985). Or, given the 68 strong connectance between compartments C and D, it could be that the members of that compartment are reacting to the loss of Diporeia by increasing their biomass. The decline in the quantitative connectance (Cq) for the overall food-web indicated that the structure was not resistant between the two time periods. Because Cq included biomass, I could not attribute the decline directly to the disturbance associated with the invaders. Other factors, such as declines in phosphorous loadings or population dynamics in species, would also contribute to the decline (Madenjian and others 2002). This decline indicated that taxa associated with large biomasses in the post-invasion structure were also associated with fewer effective interactions compared to the taxa with large biomasses in the pre-invasion structure. How this decline affects the future of resistance of the food web to disturbance is unknown. If another disturbance were to affect the taxa with higher biomass, the structure may be less resistant to the disturbance because there are fewer effective interactions to provide alternative pathways for the taxa to absorb the disturbance effects. Alternatively, these fewer effective interactions could also help to buffer unaffected compartments from the disturbance by providing fewer pathways for it to be transferred. Continued monitoring of the food web structure is necessary for fully understanding the implications of the decline in Cq to the resistance of the ecosystem. I expected changes in Cq to occur at the compartment level but that the few, weak interactions between compartments help to buffer the effects on the overall food web (Pimm 1979; McCann 2000). Instead, I only found one detectable decline in the Cq between compartments C and E. I doubt that this decline in an exchange between two compartments that accounts for 1% of the interactions could be the cause for the decline 69 in the overall food web. Rather, I saw small declines that were not detectable at the compartment-level Cq measures within compartments and between specific compartments. Likely, it was the accumulation of all these declines which led to the decline in Cq as an emergent property of the system. Systems may show emergent properties that cannot be directly measured using a subset of the system (Ulanowicz 1986, Gaedke 1995, Diaz and Cabido 2001, Vitosek 1990). Because I did not detect similar declines in Cw, diversity of interactions (E1), or diversity of taxa (ET) which each used a part of the same information that was combined in the calculation of Cq, I conclude that the Cq index provides valuable insights for food-web structure in terms of the distribution of biomass and interactions together that are not captured by these other indices. One compartment, F, contained ahnost half of the introduced taxa found within my food web. The other half was dispersed in three other compartments and two compartments remained free of any introductions. For one compartment to have such a high percentage of introductions indicated that introductions are forming new compartments or restructuring old compartments within the food-web structure. The recent introductions including Bythotrephes and zebra mussels have been referred to as a potential ‘invasional meltdown’ (Ricciardi 2001). The ‘invasional meltdown’ theory proposes that when one invading species is successful, it opens up niches for other species to invade, particularly those from its region of origin (Ricciardi 2001). In this analysis, both taxa invaded compartments that already had invasive taxa within them. Bythotrephes invaded the compartment with the highest percentage of invasive taxa. This compartment may be indicative of the niches provided by previous introductions, 70 where many of the introduced taxa have strong interactions with each other (Shea and Chesson 2002). Some of these strong interactions were a result of management where pacific salmon and brown trout were intentionally introduced to be strong predators on unintentional introductions of alewife and rainbow smelt (Madenjian and others 2002). The newest introduced species, an amphipod (Echinogammerus ischnus), round goby (Neogobius melanostomus), a cladoceran zooplankton (Cercopagis pengoi), and quagga mussel (Dreissena bugensis), seem to have strong interactions with each other as well as already present introduced taxa (Ricciardi 2001; Vanderploeg and others 2002). These additional new taxa have the potential to increase the percentage of introduced taxa within one compartment or forming a new compartment, thus restructuring compartments within the food web. They may even become members of compartment F, the compartment with the highest percentage of introductions. Because of the increasing rate of the introduction and establishment of taxa (Mills and others 1993), the native taxa may be unable to adapt to such a restructuring. Such restructuring has the potential to significantly change the compartment assignments of the other taxa, indicating a catastrophic shift in the food-web structure (Scheffer and others 2001). I followed some general guidelines for assembling the food web (Cohen et al. 1993). While the priority of long-term monitoring program is generally related to one taxanomic group, they were appropriate for constructing a food web because they maintained similar collecting methods across time. I set defined boundaries for the data including latitude and longitude lines, bottom depth, and season, to aid future studies that may want to compare results. For all groups except for phytoplankton, we used the lowest level of aggregation for taxa reported in the data because taxa can always be 71 aggregated for subsequent analyses but are much more difficult or impossible to disaggregate and aggregation can remove important differences in strength of interactions. Given that the monitoring programs did not collect diet information nor measured energy flow or interaction strength for interactions between taxa, I could not directly observe links in the food web, the ideal method for connecting and weighing taxa. Thus, I relied on prior publications (Appendices G&H) for identifying links and weighting interactions using a novel approach. All information used to construct the food web was documented and all the references were catalogued into a database (Appendices OH). This thorough documentation provides a good basis for future comparisons involving this food web. This analysis of structure at the food-web level provides ecosystem managers three valuable insights into the system they are managing. First, they may have to deal with more uncertainty through greater changes in food-web structure if the decline in Cq is indicative of less resistance. Monitoring of the structure should be continued for a better understanding of structural resistance. Second, many of their commercial and recreational fisheries are dependant on a compartment with indices of low stability that has demonstrated less resistance in structure to invasion than a compartment with indices of higher stability. This property may lead to greater uncertainty in the processes of the fish taxa involved. Third, there are impacts from disturbance that may accumulate over time within the food-web structure that cannot be detected at either the compartment or overall level until these cumulative impacts lead to declines in structural resistance. These cumulative impacts may eventually lead to catastrophic shifis in food web 72 structure; the implication for managers is they have to revise their understanding of the system. 73 CHAPTER FOUR Summary of Results and Conclusions I identified compartments in empirical food-webs and gained a better understanding of food-web structure for southeast Lake Michigan, including the structural changes in connectivity that occurred after two biological invasions. In Chapter 2, I successfully detected significant compartmentalization in three of five previously published food webs. I found that connectance within compartments was greater than connectance of the overall food web, which was greater than connectance between compartments. These results indicate that social science methods were the first appropriate methods for empirical food webs and for the definition of compartments. Methods from the social sciences were better at detecting compartments in food webs with weights on the interactions than their unweighted counterparts. Similarly, these methods detected compartments in a food web with its taxa defined to the species-level but not in its counterpart where species were aggregated into taxonomic units. Thus, the methods are more successful at detection with unaggregated, weighted food webs. A graphical display of one food web, Chesapeake Bay, provided an understanding of the structure of the food-web in terms of compartments and the roles of taxa within those compartments. This display provided new insights into food-web structure. From the analysis of Chesapeake Bay, I simulated the removal of taxa to develop hypotheses about the role of compartments for increasing the buffering effect to disturbance for the resilience or stability of the system. In Chapter 3, the food-web structure of southeast Lake Michigan was significantly compartmentalized. The six compartments that were identified represented a range of 74 biotic habitats. Two compartments had mainly flagellated algae and zooplankton, representing the pelagia or water column of the lake. Two compartments had mainly benthic algae and benthic invertebrates, representing the benthos or sediments of the lake. One compartment helped to couple the larger pelagic compartment and larger benthic compartment together, with taxa that had strong interactions with both environments. Finally, although a few fish taxa were found in both of the pelagic compartments and one benthic compartment, most fish were found in a compartment with snails and algae. This compartment demonstrated that introduced fish in the food web have strong interactions with each other because all of the fish taxa that had been previously introduced into the food web (with the exception of larval stages) were members of this compartment. Finally, I found that within the structure, the connectivity and diversity of interactions was greater within compartments than between compartments. The two biological invaders, Bythotrephes and zebra mussels, affected the food- web structure at the compartment level. The overall structure demonstrated resistance to the invaders in its compartment membership, weighted connectivity, and diversity in both interactions and taxa. At the local compartment-level, I found the indices identified the compartment invaded by Bythotrephes as the weaker compartment compared to the compartment invaded by zebra mussels. The weaker compartment was less resistant to the invasion by demonstrating larger changes in its connectivity. Most of the taxa that support economically important sport and commercial fisheries can be found in this weaker compartment, thus ecosystem managers should find that a less resistant compartment supporting their fishery will make their jobs more difficult by adding more uncertainty in their predictions. Although difficult to prevent, more biological invasions, 75 including those already detected, have potential to further restructure the food web based on this understanding of the structure, where the least resistant compartment to invaders contained previously introduced fish that strongly interacted with each other. The quantitative connectivity demonstrated a detectable decline in at overall food- web level from the pre-invasion status. Because the measure includes shifts in biomass of taxa, the decline cannot be directly attributed to the two invasive species but is indicative of how the structure has changed since the two invasions. This decline indicated that those taxa with large biomass in the post-invasion structure had fewer effective interactions than those in the pre-invasion structure. If another disturbance were to affect the taxa with higher biomass, the structure may be less resistant to the disturbance because there are fewer effective interactions to provide alternative pathways for the taxa to absorb the disturbance effects. These fewer effective interactions could also help to buffer unaffected compartments from the disturbance by providing fewer pathways for it to be transferred. Continued monitoring of the food-web structure will help in the understanding of whether this lower value of quantitative connectance increases or decreases the resistance of food-web structure to future disturbances. 76 APPENDICES 77 APPENDIX A Supplemental Material for Chapter 2 78 79 DRY SEASON WET SEASON Biomass Biomass Predator Exchange Predator Exchange Prey ID # ID # ( C/m2/y) Prey ID # ID # /m2/ ) 1 15 . I 15 ngmifffi 1 16 7.6OE-02 1 16 1.87E-01 1 17 2.72E+00 1 17 2.71E+00 1 18 7.65E-01 1 18 8.52E-01 1 20 2.63E-01 1 20 2361-3-01 1 21 7.02E-03 1 21 7.00E-03 1 22 7025-04 1 22 6.25E-04 1 32 4.33E-O4 1 32 1 .0213-03 2 15 5.31E-01 2 15 4.44E-01 2 16 1.86E-02 2 16 4.7OE-02 2 17 5.42E-01 2 17 5.48E-01 2 18 7.62E-01 2 18 8.52E-01 2 20 1.05E-01 2 20 9.4OE-02 2 21 3.50E-03 2 21 4.005-03 2 22 6.99E-04 2 22 6.63E-04 2 32 2165-04 2 32 5.1013-04 3 1 7.12E-02 3 1 1.99E-01 3 15 4.87E-Ol 3 15 4.44E-01 3 16 1.71 E-02 3 16 4.70E-02 3 17 1 87E+00 3 17 2.06E+00 3 18 1 05E+OO 3 18 1.28E+00 3 20 2408-01 3 20 2.36E-01 3 21 8.558-03 3 21 9.00E-03 3 32 1 77E-03 3 32 3.06E-03 4 20 6 77E-02 4 20 7 .10E-02 4 21 2 ODE-03 4 21 2.00E-03 4 24 7 64E-02 4 24 5.42E-02 4 34 1 SSE-O4 4 34 2.03E-O2 5 2 2 81E+01 5 2 4.07E+01 5 15 1 38E+00 5 15 1.33E+00 5 16 1 13E-01 5 16 3.27E-01 5 17 5 96E+00 5 17 6.99E+00 5 18 9.85E-01 5 18 1.28E+00 5 20 2.26E-01 5 20 2.36E-01 5 21 8.03E-03 5 21 9008-03 5 24 1 05E-Ol 5 24 9.36E-02 5 32 1 11E-03 5 32 4.59E-03 6 15 1 82E+00 6 15 1.78E+00 6 20 2 25E-01 6 20 2.36E-01 6 21 2 DOE-03 6 21 2.00E-03 6 24 4 42E-02 6 24 5.03E-02 6 34 1 495-04 6 34 1.88E-02 7 20 1 13E—01 7 20 1.18E-01 7 21 1 99E-03 7 21 2.00E-03 8 19 2 59E+00 8 19 9.74E-01 8 34 2 47E-O4 8 34 2.18E-02 8 37 3 60E-02 8 37 2885-02 8 46 2 77E-O6 8 46 1.48E-O7 8 47 l 7lE-03 8 47 1.37E-03 8 48 l 19E-03 8 48 2.45E-04 8 50 8 7OE-02 8 50 3.55E-02 8 53 5 29E-03 8 53 2.12E-O3 8 54 7 41E-04 8 54 6.65E-05 8 55 3.45E-02 8 55 2.44E-03 8 6O 3.74E-02 8 6O 2.45E-02 DRY SEASON Biomass Predator Exchange Prey ID # ID # (ElmZ/E) 8 61 . - 8 62 6.53E-02 8 63 1.3OE-01 8 64 1.84E-03 8 65 2225-03 9 l9 1.58E+OO 9 60 2.71E-O4 10 19 2.16E+00 10 34 3805-04 10 37 2028-02 10 46 1.55E-06 10 47 9.6OE-04 10 48 6.70E-04 10 50 4.87E-02 10 53 2.96E-03 10 54 4.17E-O4 10 55 1938-02 10 60 2.10E-02 10 61 6.47E-03 10 62 3.66E-02 10 63 7.26E-02 10 64 1038-03 10 65 1.24E-03 11 19 1.44E+00 11 60 6.50E-03 12 19 1.44E-01 13 19 1.445-01 14 19 28713-0] 15 22 3.62E-O3 15 23 4345-02 15 24 1.21E-01 15 26 3.20E-02 15 27 4.26E-02 15 28 1.96E-02 15 29 4.59E-02 15 3O 9.62E-03 15 33 5.53E-03 15 36 3.47E-03 15 38 4.75E-02 15 39 9.15E-03 15 4O 4.20E-02 15 41 5.69E-02 15 42 3.14E-02 15 43 7.38E-04 15 44 6.09E-03 15 55 2.69E-02 15 56 1.95E-03 15 57 1.00E-01 16 21 1.06E-02 16 22 2.39E-03 16 23 1.97E-O3 16 24 5.49E-03 16 26 1.45E-03 16 27 1.93E-03 16 28 8.86E-04 WET SEASON Biomass Predator Exchange Prey ID # ID # (%lm2/E) 8 61 . - 8 62 5.23E—O3 8 63 5.19E-O2 8 64 7.74E-04 8 65 1.97E-04 9 l9 5.95E-Ol 9 60 3.00E-04 10 19 8.12E-01 10 34 2.06E-02 10 37 2.73E-02 10 46 1.40E-07 10 47 1.30E-03 10 48 2.31E-04 10 50 3.35E-02 10 53 2018-03 10 54 6305-05 10 55 2.3 lE-03 10 6O 2.32E-02 10 61 4.24E-03 10 62 4.95E-O3 10 63 4.92E-02 10 64 7.33E-04 10 65 1.87E-04 11 19 5.41E-01 11 6O 8.46E-03 12 19 5.41E-02 13 19 5.4lE-02 14 19 1.08E-01 15 22 7 .95E-03 15 23 2.59E-02 15 24 1.02E-01 15 26 2238-02 15 27 3818-02 15 28 1.34E-02 15 29 3.15E-02 15 30 6.6OE-03 15 33 3.51E-04 15 36 1.56E-O3 15 38 8.40E-03 15 39 1.07E-03 15 40 7.38E-04 15 41 4.025-05 15 42 1.21E-02 15 43 9.08E-05 15 44 5.50E-03 15 55 2.14E-03 15 56 2.75E-05 15 57 5.63E-02 16 21 9905-03 16 22 1.99E-03 16 23 2.6OE-03 16 24 1.14E-02 16 26 2.64E-03 16 27 3.83E-03 16 28 1.22E-03 8O DRY SEASON Biomass Predator Exchange Prey ID # ID # (gg/ng) 16 29 . - 16 30 4.37E-04 16 36 1578-04 16 38 2168-03 16 39 4.1SE-04 16 40 1.91E-03 16 41 2.58E-03 16 42 1.42E-03 16 43 3.35E-05 16 44 2.77E-04 16 55 1.22E-03 16 56 8858-05 16 57 4575-03 17 21 3.49E-02 17 22 2025-02 17 23 3.18E-02 17 24 8.86E-02 17 26 2345-02 17 27 3.12E-02 17 28 1.43E-02 17 29 3.37E-02 17 30 7045-03 17 33 4.05E-03 17 36 2.54E-03 17 38 3.47E-02 17 39 6.7OE-03 17 40 3.08E-02 17 4| 4.17E-02 17 42 2.3OE-02 17 43 5.40E-O4 17 44 4.47E-03 17 55 1.97E-02 17 56 1.43E-03 17 57 7.35E-02 18 20 8.76E-01 18 21 9.46E-02 18 22 1.96E-02 18 23 8.55E-03 18 24 2.38E-02 18 26 6.28E-03 18 27 8.38E-03 18 28 3.8SE-03 18 29 9048-03 18 30 1.89E-03 18 31 2.41E-03 18 33 1.09E-03 18 36 6.83E-04 18 38 9.34E-03 18 39 1.80E-03 18 40 8.29E-03 18 41 1.12E-02 18 42 6.18E-03 18 43 1.45E-04 18 44 1.20E-03 18 55 5.30E-03 WET SEASON Biomass Predator Exchange PreyID# ID# 16 29 16 30 16 36 16 38 16 39 16 40 16 41 16 42 16 43 16 44 16 55 16 56 16 57 17 21 17 22 17 23 17 24 17 26 17 27 17 28 17 29 17 30 17 33 17 36 17 38 17 39 17 40 17 41 17 42 17 43 17 44 17 55 17 56 17 57 18 20 18 21 18 22 18 23 18 24 18 26 18 27 18 28 18 29 18 30 18 31 18 33 18 36 18 38 18 39 18 40 18 41 18 42 18 43 18 44 18 55 81 (gfi/mZ/y 6.02E-04 1 .5 7E-04 8 .43E-04 9.79E-05 6.73E-05 3 .67E-06 l .2 1 E-03 8.28E-06 5 .5 1 E-04 l .95E-04 2.5 1 153-06 5 .65E-03 2.70E-02 1 . 1 9E-02 I .64E-02 6.45 E-02 l .40E-02 2.40E-02 8.46E-03 I .99E-02 4. l 6E-03 2.2 1 E-04 9.86E-O4 5 .29E-03 6.76E-04 4.65E-04 2.54E-05 7.63 E-O3 5 .72E-05 3 .46E-03 l .3 5E-03 1 .73E-05 3 .55E-02 7.08E-01 8.00E-02 2.19E-02 5 .44E-03 2.1 5E-02 4.85E-03 8.01E-03 2.82E-03 6.63 E-03 1 .39E-O3 5. 12E-03 7.3 8E-05 3 .29E-04 1 .76E-03 2.25 E-04 l .5 5E-04 8.46E-06 2.54E-03 l .91 E-OS l . 15E-03 4.5 0E-04 DRY SEASON Biomass Predator Exchange Prey ID # ID # ( /m2/y) 18 56 . - 18 57 7.35E-02 19 20 12713-0] 19 21 3.38E-03 19 22 1.36E-03 19 23 1.68E-02 19 24 4.67E-02 19 25 2.93E-01 19 26 1245-02 19 27 1.6413-02 19 28 7.55E-03 19 29 1.78E-02 19 3O 3.71E-03 19 34 4.85E-05 19 36 1.34E-03 19 37 2.71E-02 19 38 1.83E-02 19 39 3.54E-03 19 40 1.63E-02 19 41 2.20E-02 19 42 1.22E-02 19 43 2.85E-O4 19 44 2.35E-03 19 45 3928-03 19 46 4.86E-06 19 47 2.95E-03 19 48 20913-03 19 49 3.62E-03 19 50 1.65E-01 19 51 4.52E-03 19 52 1.54E-06 19 53 1.34E-03 19 54 3.89E-03 19 55 1.04E-02 19 56 7.54E-O4 19 57 3.87E-02 19 61 9.63E-03 19 64 2.10E-03 19 65 2.57E-02 20 21 2075-02 20 22 1.57E-02 20 23 3.01E-01 20 24 5.72E-02 20 26 1.75E-01 20 27 1.68E-O2 20 28 1.37E-02 20 33 4.10E-04 2O 36 2.57E-04 20 38 3.52E-03 20 39 6785-04 20 40 3.12E-03 20 41 4.22E-03 20 42 2335-03 20 43 5.47E-05 20 44 4.53E-04 WET SEASON Biomass Predator Exchange Prey ID # ID # ( /m2/y) 18 56 . - 18 57 1.18E-02 19 20 1.18E-01 19 21 4.00E-03 19 22 1.33E-O3 19 23 3.94E-03 19 24 1.56E-02 19 25 2.6OE-01 19 26 3.38E-O3 19 27 5.80E-03 19 28 2.04E-03 19 29 4.79E-03 19 30 1.00E-03 19 34 9.05E-O3 19 36 2.38E-04 19 37 2.41E-02 19 38 1.2813-03 19 39 1.63E-04 19 40 1.12E-04 19 41 6.12E-06 19 42 1.84E-03 19 43 1.38E-05 19 44 8.35E-04 19 45 1.74E-03 19 46 2.87E-07 19 47 2678-03 19 48 4.76E-O4 19 49 1.42E-03 19 50 6.62E-02 19 51 3.00E-O4 19 52 1.37E-06 19 53 5675-04 19 54 3.89E-04 19 55 3268-04 19 56 4.18E-06 19 57 8.55E-03 19 61 4.21E-03 19 64 9.92E-04 19 65 2.55E-03 20 21 1.98E-02 20 22 9.50E-03 20 23 2535-01 20 24 4.81E-02 20 26 1.56E-01 20 27 1.41E-02 2O 28 8.00E-03 20 33 2.13E-05 20 36 9.51E-05 20 38 5.6213-04 20 39 6.52E-05 20 40 4.49E-05 20 41 2.45E-06 20 42 8.09E-04 20 43 5.52E-06 20 44 3.68E-04 82 DRY SEASON Biomass Predator Exchange Prey ID # ID # (ElmZIy) 20 55 . 20 56 2.175-05 20 57 1.125-03 21 22 1.015-02 21 23 3.915-02 21 24 7.415-03 21 26 2.275-02 21 27 2.175-03 21 28 1.785-03 21 33 5.325-05 21 36 3335-05 21 38 4.565-04 21 39 8 785-05 21 40 4 045-04 21 41 5 475-04 21 42 3 025-04 21 43 7 085-06 21 44 5 875-05 21 55 1 735-03 21 56 1 255-04 21 57 6 425-03 22 23 2 505-02 22 24 4 735-03 22 26 1 455-02 22 36 2 135-05 22 39 1 185-05 22 41 7 485-05 22 44 3 745-05 22 55 1 655-04 22 56 1 195-05 22 57 6 175-04 23 24 3 535-02 23 26 4 615-02 23 36 3 825-04 23 39 3.465-04 23 41 2.205-03 23 44 6.725-04 24 23 1.005-01 24 33 6.415-04 24 36 2.625-04 24 40 3.175-03 24 41 4.295-03 24 44 4.605-04 24 53 2985-04 25 23 2.495-02 25 26 6 725-03 25 33 1.605-04 25 36 6495-05 25 38 8.865-04 25 39 1.715-04 25 40 7865-04 25 41 1.065-03 25 42 5.765-03 25 43 2225-04 25 44 1.145-04 WET SEASON Biomass Predator Exchange Prey ID # ID # ( lm2/y) 20 55 . - 20 56 1.675-06 20 57 3.765-03 21 22 6.635-03 21 23 3.805-02 21 24 1.305-02 21 26 2.375-02 21 27 2.115-03 21 28 1.325-03 21 33 3.525-06 21 36 1.575-05 21 38 8.435-05 21 39 1.085-05 21 40 7.405-06 21 41 4.045-07 21 42 1.215-04 21 43 9.115-07 21 44 5.515-05 21 55 2155-05 21 56 2.765-07 21 57 5.655-04 22 23 2.175-02 22 24 8.235-03 22 26 1.135-02 22 36 1.195-05 22 39 3.185-05 22 41 1.215-06 22 44 4.205-05 22 55 1.645-05 22 56 2.105-07 22 57 3.225-04 23 24 1.525-02 23 26 1.855-02 23 36 8.755-05 23 39 2.335-04 23 41 8.885-06 23 44 3.075-04 23 53 5.165-05 23 55 4 125-05 23 56 2 715-07 24 23 5 025-02 24 33 2 505-05 24 36 6 265-05 24 40 2 955-05 24 41 1 615-06 24 44 2 205-04 24 53 5 165-05 24 55 2 955-05 24 56 1 945-07 25 23 1 885-02 25 26 7 065-03 25 33 8 135-06 25 36 2 045-05 25 38 1 265-04 25 39 1 405-05 83 DRY SEASON WET SEASON Biomass Biomass Predator Exchange Predator Exchange Pre 1]) # ID # (ES/ME) Prey ID # ID # lm2/ ) g6 23 . - 25 4o ($998150? 26 24 1.225-02 25 41 5255-07 26 27 1 .065-02 25 42 2.255-03 26 33 5.765-04 25 43 4.745-05 26 36 2.355-04 25 44 7.175-05 26 38 3.205-03 26 23 4.785-02 26 39 6.185-04 26 24 5.085-03 26 40 2.835-03 26 27 1 .715-03 26 41 3.845-03 26 33 2.075-05 26 42 2.085-02 26 36 5195-05 26 44 4.125-04 26 38 3.205-04 26 53 1.075-04 26 39 3 .565-05 27 23 2935-02 26 40 2.455-05 27 24 6.405-03 26 41 1 .345-06 27 26 2.015-02 26 42 5.735-03 27 33 1.665-04 26 44 2.105-04 27 36 6.715-05 26 53 5.165-05 27 38 9225-04 26 55 2.445-05 27 39 1 .775-04 26 56 1 .615-07 27 40 8.155-04 27 23 3 .525-02 27 41 1.105-03 27 24 4.805-03 27 42 5.965-03 27 26 1 .505-02 27 44 1 . 185-04 27 33 1.135-05 28 23 6.905-03 27 36 2.835-05 28 26 4.1 15-02 27 38 1295-04 28 33 3.905-05 27 39 1 .945-05 28 36 1 .595-05 27 40 1 .345-05 28 44 2795-05 27 41 7295-07 28 55 1.235-04 27 42 2.315-03 29 23 9495-03 27 44 9955-05 29 24 4.885-03 28 23 4.785-03 29 26 5.625-02 28 26 1 .775-02 29 33 5.365-05 28 33 3255-06 29 36 2.185-05 28 36 8.165-06 29 38 2985-04 28 44 2.875-05 29 39 5.755-05 28 55 1.125-05 29 40 2.645-04 29 23 6.815-03 29 41 3575-04 29 24 1.875-03 29 42 1.935-03 29 26 3.185-02 29 43 7465-05 29 33 4.645-06 29 44 3.835-05 29 36 1.165-05 29 55 1695-04 29 38 6.255-05 29 57 1.465-03 29 39 7985-06 30 23 1.995-03 29 40 5.485-06 30 24 1.025-03 29 41 2.995-07 30 26 1.185-02 29 42 4.475-04 30 33 1 .125-05 29 43 2.705-05 3O 36 4.565-06 29 44 4.095-05 30 38 6.245-05 29 55 1 .595-05 30 39 1.205-05 29 57 8215-04 30 40 5.545-05 30 23 6.425-04 30 41 7.475-05 30 24 1.765-04 30 42 4065-04 30 26 7.155-03 30 43 1 .565-05 30 33 9705-07 30 57 3.065-04 30 36 2.445-06 84 DRY SEASON WET SEASON Biomass Biomass Predator Exchange Predator Exchange Pre ID # ID # (ElmZ/E) Prey ID # ID # (gfi/mZ/y) 31 26 . - 30 38 . - 31 43 1685-06 30 39 1.675-06 32 23 3 .085-04 30 40 1.155-06 32 26 1.835-03 30 41 6.275-08 32 42 6315-05 30 42 9365-05 32 43 2.425-06 3O 43 5665-06 37 23 8.475-04 30 44 8.565-06 37 24 5 .005-03 30 57 2.155-04 37 26 4.855-03 31 23 6.035-04 37 54 2875-04 31 26 1.725-03 37 55 1.335-04 31 27 6.455-04 37 56 1.335-04 31 42 6095-05 37 59 1.135-04 31 43 1.475-06 37 64 5595-06 32 23 2.825-04 37 65 3.115-05 32 24 1.135-03 38 23 1 .465-03 32 26 4595-03 38 26 8.375-03 32 27 4.975-04 38 54 6.575-04 32 42 9265-05 38 55 3.045-04 32 43 2.245-06 38 56 8.925-06 34 23 1595-03 38 64 1.1 15-05 34 26 3.305-03 38 65 6195-05 34 54 9.995-05 39 26 1.885-03 34 64 4.885-06 39 54 1.685-04 34 65 5.685-06 39 55 7.775-05 37 23 1 .425-03 40 23 1 .445-03 37 24 4.955-03 40 26 8.255-03 37 26 2.935-03 40 36 3.915-06 37 54 8.855-05 40 44 6.355-06 37 59 1.235-04 40 54 9585-04 37 64 4345-06 40 55 4.465-04 37 65 5055-06 40 56 9575-06 38 23 3835-03 40 64 1.195-05 38 26 7.93 5-03 40 65 6.625-05 38 54 2.175-05 41 26 1.1 15-02 39 54 2.935-06 41 54 1.675-03 40 54 2.195-06 41 55 7.775-04 42 23 7795-03 41 56 1.155-05 42 26 1 .615-02 41 64 1.435-05 42 54 4.1 15-05 41 65 7955-05 42 64 2.025-06 42 23 8.445-04 42 65 2355-06 42 26 4.815-03 43 54 9.785-08 42 54 3295-03 45 38 9.075-05 42 55 1 .535-03 45 40 8.105-06 42 56 9935-06 47 26 1 .955-04 42 64 1 .245-05 47 38 2.795-04 42 65 6.895-05 47 40 2.495-05 43 54 1 .865-04 48 24 2025-04 43 55 9.385-05 48 26 1 .265-04 45 40 1 .495-04 48 36 1 .805-06 47 26 3.345-04 48 38 4.335-05 47 40 2.015-04 48 39 1.005-06 48 24 2.185-04 48 40 3.875-06 48 26 1 .845-04 48 41 4995-08 48 36 2915-06 48 44 6.005-06 85 DRY SEASON Biomass Predator Exchange Prey ID # ID # ( Im2/ ) 48 38 . - 48 39 6.125-06 48 40 1.115-04 48 41 4245-05 48 44 4.725-06 48 49 4.725-06 48 54 3.455-04 48 55 1.745-04 48 59 4.275-06 48 61 8.755-06 49 24 1965-04 49 26 1.635-04 49 36 2.625-06 49 38 3.325-05 49 39 5.505-06 49 40 8695-05 49 41 3815-05 49 44 4.245-06 49 54 5825-04 49 55 2945-04 49 59 3.355-06 49 61 7.875-06 51 24 1.455-07 51 26 1.025-06 51 40 3.775-08 51 54 3.465-05 51 59 1.075-06 55 58 6935-04 56 54 5.735-04 56 59 1.765-05 60 54 4.805-03 60 59 1.475-04 61 23 5.495-05 61 24 1.055-03 61 26 7.395-03 61 36 7915-07 61 38 2695-05 61 39 2.145-06 61 40 2.715-04 61 41 1.505-05 61 44 1.285-06 61 49 5.325-07 61 51 3.935-05 61 54 2.075-04 61 56 2.665-07 61 58 1.065-06 61 59 6.365-06 62 23 2.545-03 62 36 5.845-06 62 38 1.245-03 62 40 1.265-02 62 44 1.035-05 62 54 6.225-03 62 56 1.875-06 62 58 7.035-05 WET SEASON Biomass Predator Exchange Prey ID # ID # ( /m2/ ) 48 49 . - 48 54 2.905-07 48 59 1.275-06 48 61 5.815-06 49 24 2455-04 49 26 1.535-04 49 36 1.805-06 49 38 6.815-05 49 39 1.005-06 49 40 6.085-06 49 41 4995-08 49 44 6.005-06 49 54 4.565-07 49 59 2.005-06 49 61 5.815-06 51 38 7095-06 51 40 7.175-07 51 54 3395-06 55 58 2.975-04 56 54 2.285-06 60 54 1.285-03 60 59 1.365-04 61 23 1.015-04 61 24 1.805-03 61 26 7.035-04 61 36 2.545-06 61 38 8.165-05 61 39 1.415-06 61 40 8.255-05 61 41 7.065-08 61 44 8.475-06 61 49 1.695-05 61 51 1.585-05 61 54 1 705-04 61 56 2 815-08 61 58 2 975-06 61 59 1 805-05 62 23 7 785-04 62 36 3 595-06 62 38 1 835-04 62 40 1 855-04 62 41 9 985-08 62 44 1 205-05 62 53 1 035-04 62 54 2 695-04 62 56 2 815-08 62 58 5 205-05 62 59 2 875-05 63 53 1.035-04 63 58 6.515-04 63 59 7.635-04 64 53 1.035-04 64 58 3.425-04 65 58 1.415-04 86 DRY SEASON WET SEASON Biomass Biomass Predator Exchange Predator Exchange Prey ID # ID # ( C/m2/y) Prey ID # ID # (gC/mZ/L) 62 59 1915-04 63 53 1.015-04 63 58 1.275-03 63 59 1.585-03 64 53 9475-05 64 58 6.245-04 65 53 1.165-04 65 58 3.165-04 87 Supplement B. Here, we provide the results from using the methodology on the same food webs used in two seminal papers on compartments in empirical food webs [Pimm, S. L. & Lawton, J. H. Are food webs divided into compartments? J. Anim. Ecol. 49, 879-898 (1980); Raffaelli, D. & Hall, S. J. Compartments and predation in an estuarine food web. J. Anim. Ecol. 61, 551-560 (1992)] Name n Oqu P-value IC ratio Askew (1961)“ 62 5.63 50.996 0.05 Bird (1930) Prairie3 15 5.21 50.819 0.13 Bird (1930) Willowa 12 2.41 50.940 0.14 Jones (1949)“ 12 2.77 50.922 0.20 Koepcke & Koepcke (1952)‘ll 46 11.02 50.967 0.04 Menge & Mauzey (1978)b 22 2.81 50.998 0.11 Milne & Dunnet (1972) Mudflata 12 3.89 _<_0.945 0.14 Milne & Dunnet (1972) Mussel bed8 10 5.93 50.814 0.18 Minshall (1967)a 13 1.60 50.991 0.23 Niering (1963)3 27 8.21 50.544 0.06 Paine (1966) 13 2.11 50.990 0.18 Summerhayes & Elton (1923)c 29 18.09 $0.00” 0.06 Tilly (1968)“ 11 3.83 50.820 0.18 Zaret & Paine (1974)a 13 9.03 50.519 0.10 ' These webs were taken from: Cohen, J. E. (compiler). 1989. Ecologists' Co-Operative Web Bank. Version 1.00. Machine-readable data base of food webs. New York: The Rockefeller University. t’I'his web was taken from: Cohen, J .E. 1978. Food Webs and Niche Space. Princeton University Press, Princeton, New Jersey. c This web was taken from: Pimm, S.L. and J .H. Lawton. 1980. Are food webs divided into compartments? J. of Animal Ecology. 49:879-898 88 Our results for nine of these food webs sustained the same conclusion of Pimm and Lawton (1980) and Raffaelli and Hall (1992). That is, these nine food webs did not have odds ratios higher than what was expected by chance, and therefore they were not compartmentalized. The lack of compartrnentalization is likely due to how these food webs were constructed rather than a lack of compartments in the actual food webs or limitations in the method. The interactions in all of these webs were unweighted and our analysis demonstrated the difficulty in identifying compartments in unweighted food webs. In addition, these food webs likely represented a fraction of the species within the actual food web or aggregated species in a way that might obscure compartments. For example, two species of fish may interact with ten species of zooplankton for a total of ten realized interactions. If the fish are aggregated into one taxanomic group and the zooplankton are aggregated into another, then the ten interactions are reduced to one interaction. The method would no longer have the same information for identifying compartments. Weighting the one interaction would help to alleviate this problem. Our conclusions diverge from the previous studies with the Menge & Mauzey (1978) and Paine (1966) food webs. Both of these food webs were found to be significantly compartmentalized with 2 compartments. In our analysis, these food webs did not have odds ratios higher than expected by chance. A similarity index was used in both of the Pimm and Lawton (1980) and Raffaelli and Hall (1992) analyses and similarity indices have been used to identify trophic levels. Thus these analyses may have picked up on trophic levels. Koepcke & Koepcke (1952), Niering (1963), and Summerhayes & Elton (1923) were food webs where Pimm and Lawton (1980) were a priori defined compartments. They compared realized interactions between two compartments against an estimate (based on the average number of interactions per species and number of species within each compartment) using a chi-square distribution. They found that all of these food webs had lower than expected realized interactions between compartments. Our results indicate that there are only significant compartments than by chance alone for Summerhayes & Elton (1923). The Askew (1961) food web was published in the Pimm and Lawton (1980) paper but Pimm and Lawton were unable to analyze it due to lack of computing power. The Sumrnerhayes and Elton (1923) food web had an interactive connectance within compartments = 0.12 and interactive connectance between compartments = 0.0072. This food web was found to have two compartments (see table below). Pimm and Lawton (1980) reported a priori placement of the taxa from the Summerhayes and Elton food web into three separate compartments. In their study, one compartment contained marine taxa, a second contained terrestrial taxa, and a third contained freshwater taxa. In our arrangement, all of the marine taxa, four terrestrial taxa, and six freshwater taxa are in compartment A and thirteen terrestrial taxa and three freshwater taxa are in compartment B. Only three interactions are shared between the two compartments of the potential 416 interactions which produce a very low between interactive connectance. The density of interactions between and within our compartments produced an odds ratio lower than the Pimm and Lawton’s a priori assignments. In fact, the odds ratio of the original analysis was smaller than 983 of the 89 simulated food webs of this web and therefore would not have been significant in our analysis. Summerhayes and Elton (1923) food web divided into compartments based on our analysis Compartment A Compartment B marine plankton Plants marine animals Worms Seals Geese Sea-birds Colembola ducks and divers Diptera (terrestrial) skua and glaucous gull Mites benthic algae (freshwater) Hymenoptera Protozoa snow-bunting Diptera (freshwater) purple sandpiper other invertebrates Ptarmigan Lepidurus Spiders polar bear arctic fox decaying matter planktonic algae (freshwater) Protozoa invertebrates (freshwater) dead plants 90 REFERENCES Original Citations for the food webs: Askew, R. R.. On the biology of the inhabitants of oak galls of Cynipidae (Hymenoptera) in Britain. Trans. Soc. Brit. Entomol. 14:237-268 (1961) Bird, R. D. Biotic communities of the Aspen Parkland of central Canada, Ecology, 11:356-442 (1930). Bird, R. D. Biotic communities of the Aspen Parkland of central Canada, Ecology, 11:356-442 (1930). Jones, J. R. E. A further ecological study of calcareous streams in the "Black Mountain" district of South Wales, J. Anim. Ecol. 18:142-159 (1949). Koepcke, H. W. and M. Koepcke, Sobre el proceso de transforrnacion de la material organica en las playas arenosas marinas del Peru, Zoologie Serie A, No. 8, Pub]. Univ. Nae. Mayer, San Marcos (1952). Milne H. and G. M. Dunnet, Standing crop, productivity and trophic relations of the fauna of the Ythan estuary. In: The Estuarine Environment, R. S. K. Barnes and J. Green, Eds., Applied Science Publications, Edinburgh, Scotland (1972) Minshall, G. W. Role of allochthonous detritus in the trophic structure of a woodland springbrook community, Ecolog 48(1): 139-149 (1967). Niering, W. A. Terrestrial ecology of Kapingamarangi Atoll, Caroline Islands, Ecol. Monogr. 33(2):13 l-160 (1963). Paine, R. Food web complexity and species diversity. Am. Nat. 100:65-75 (1966) Summerhayes, V.S. and CS. Elton. Contributions to the ecology of Spitsbergen and Bear Island. J. Ecology. 11:214-286 (1923) Tilly, L. J. The structure and dynamics of Cone Spring. Ecol. Monogr. 38(2): 169-197 (1968) Zaret, T. M. and R. T. Paine, Species introduction in a tropical lake, Science 182:449-455 (1973) 91 Supplement C. Compartment membership for all taxa for the six remaining food-webs versions with significant odds ratios Little Rock Lake with 181 Taxa Compartment A Genus Species Common Name Clirnacia -- neuroptera Spongilla lacustris porifera Ephydatia muelleri porifera Corvomyenia everetti porifera -- -- benthic copepods -- -- tubellaria Compartment B Genus Species Common Name Perca flavescens YCIIOW perch juv Perca flavescens YCHOW perch Micropterus salrnoides 13f gemouth bass juv. Micropterus salrnoides largemouth bass Ambloplites rupestris I'OCk bass juv. Ambloplites rupestris I'OCk bass Pomoxis nigromaculatus black crappie JUV- Pomoxis nigromaculatus black crappie Umbra -- mudrninnow -- -- fish eggs Alona affinis benthic cladocera Alona quadrangularis benthic cladocera Alona rustica benthic cladocera Alona intermedia benthic cladocera Alona excisa benthic cladocera Disparalona acutirostris benthic cladocera Chydorus spl benthic cladocera Chydorus sp2 benthic cladocera Acantholeberis curvirostris benthic cladocera Ophryoxus gracilis benthic cladocera Scapholeberis kingi benthic cladocera Macrocyclops albidus benthic 00p6p0ds Eucyclops serrulatus benthic copepods Acanthocyclops -- benthic capepods Microcyclops rubellus benthic COPCPOdS -- -- benthic copepods 92 Hydroporus Caenis Oecetis Mystacides Lirnnephilus Agrypnia Banksiola Molanna Polycentropus Anisopetra Libellula Sympetrum Enallagrna Sialis Eoparagyractis Bezzia Sphaeromais Choaborus Chaoborus Albabesmyia Clinotanypus Djalmabatista Guttipelopia Larsia Macropelopis Procladius Chaetocladius Corynoneura Cricotopus Nanocladius Micropsectra Paratanytarsus Tanytarsus Chironomus Cladopelma Cryptochironomus Endochrionomus Glyptotendipes Microtendipes Parachironomus Paratendipes albatus punctipennis coleoptera ephemeroptera trichoptera trichoptera trichoptera trichoptera trichoptera trichoptera trichoptera odonata odonata odonata odonata megaloptera lepidoptera diptera diptera diptera diptera diptera chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid chironomid 93 Polypedilum -- chironomid Pseudochironomus -- chironomid Stenochironomus -- chironomid Stictochironomus -- chironomid Xenochironomus -- chironomid -- -- mollusc Campeloma decisum 1110111180 -- -- mollusc -- -- annelid -- -- annelid -- -- arthropod Synedra -- chrysophyceae Tabellaria -- chrysophyceae fine organic matter Compartment C Genus Species Common Name Sida crystallina benthic cladocera Leptophlebia -- ephemeroptera Crangonyx gracilis amphipod Barnbusina -- filamentous algae Batrachospermum -- filamentous algae Binuclearia -- filamentous algae Bulbochaete -- filamentous algae Desmidiurn -- filamentous algae Geminella -- filamentous algae Groenbladia -- filamentous algae Hapalosiphon -- filamentous algae Hyalotheca -- filamentous algae Lyngbya -- filamentous algae Microchaete -- filamentous algae Microcoleus -- filamentous algae Mougeotia -- filamentous algae Oedogonium -- filamentous algae Oscillatoria -- filamentous algae Phormidium -— filamentous algae Plectonema -- filamentous algae Radiofilum -- filamentous algae Rhizoclonium -- filamentous algae Schizothrix -- filamentous algae 94 Scytonema -- filamentous algae Sphaerozosma -- filamentous algae Spirogyra -- filamentous algae Tribonema -- filamentous algae Zygnema -- cyanobacteria Compartment D Genus Species Common Name Notemigonus crysoleucus Shiner Diaptomus minutus pelagic copepod Diacyclops thomasi pelagic copepod Mesocyclops edax pelagic copepod Tropocyclops prasinus pelagic copepod Epischura lacustris pelagic copepod Bosmina longirostris pelagic cladocera Eubosmina -- pelagic cladocera Daphnia galeata pelagic cladocera Daphnia parvula pelagic cladocera Diaphanosoma birgei pelagic cladocera Holopedium gibberum pelagic cladocera Leptodora kindtii pelagic cladocera Polyphemus pediculuc pelagic cladocera Conochilus unicornis pelagic rotifers Conochiloides -- pelagic rotifers Kellicottia longispina pelagic rotifers Kellicottia bostoniensis pelagic rotifers Keratella cochlearis pelagic rotifers Keratella taurocephala pelagic rotifers Keratella crassa pelagic rotifers Keratella hiemalis pelagic rotifers Polyarthra remata pelagic rotifers Polyarthra vulgaris pelagic rotifers Trichocerca cylindrica pelagic rotifers Asplanchna -- pelagic rotifers Gastropus -- pelagic rotifers Synchaeta -- pelagic rotifers Nauplii -- juvenile pelagic zooplankton -- -- juvenile pelagic zooplankton -- -- juvenile pelagic zooplankton Gerris -- hemiptera 95 Chroococcus Gloeothece Merismopedia Aphanocapsa Gomphosphaeria Coelosphaerium Rhabdoderma Aphanothece Anabaena Arthrodesmus Cosmarium Crucigenia Euastrum Oocystis Pediastrum Quadrigula Schroederia Spondylosium Stauastrum Tetraedron Ankistrodesmus Xanthidium Elaktothrix Scenedesmus Phacus Trachelomonas Chroomonas Cryptomonas Asterionella Dinobryon Mallomonas Synura hemiptera hemiptera cyanobacteria cyanobacteria cyanobacteria cyanobacteria cyanobacteria cyanobacteria cyanobacteria cyanobacteria cyanobacteria green algae green algae green algae green algae green algae green algae green algae green algae green algae green algae green algae green algae green algae green algae green algae euglenophyta euglenophyta cryptophyta cryptophyta chrysophyceae chrysophyceae chrySOphyceae chrysophyceae 96 Chesapeake Bay Interaction Strength with 33 Taxa Compartment A Compartment B Phytoplankton Bacteria in sediment POC Bacteria in suspended POC Benthic diatoms Free bacteria Other polychaetes Heterotrophic microflagellates Nereis Ciliates Macoma spp. Zooplankton Meiofauna Ctenophores Crustacean deposit feeder Sea nettle Blue crab Other suspension feeders Croaker Mya arenaria Hogchoaker Oysters Spot Fish larvae White perch Alewife & Blue herring Catfish Bay anchovy Bluefish Menhaden Shad Weakfish Summer flounder Striped bass Chesapeake Bay Energy Flow with 33 Taxa Compartment A Compartment B Phytoplankton Bacteria in sediment POC Bacteria in suspended POC Benthic diatoms Free bacteria Heterotrophic microflagellates Ciliates Zooplankton Ctenophores Sea nettle Other suspension feeders Mya arenaria Oysters Other polychaetes Nereis Macoma spp. Meiofauna Crustacean deposit feeder Blue crab Croaker Hogchoaker Spot 97 Fish larvae White perch Alewife & Blue herring Catfish Bay anchovy Bluefish Menhaden Shad Weakfish Summer flounder Striped bass Chesapeake Bay Energy Flow with 45 Taxa Compartments A Compartments B Compartments C Phytoplankton Benthic Producers Other Polychaetes Bacteria <1 um Scololepides virides Cynosion regalis Bacteria >1 <2 um Nereis succinea Alosa pseudoharengus Bacteria >2 um Hetermastusfiliformes Morone saxatilis Acartia tonsa Micro ciliates Macro ciliates Predaceous ciliates Chrysoura quinquecirrha Mnemiopsis leiajri Nemopsis bachei Cladocera Other zooplankton Anchoa mitchilli larvae Anchoa mitchilli eggs Fish larvae Rangia cuneata Mulinea lateralis Mya arenaria Crasostrea virginica Anchoa mitchilli Alosa sapidisima Alosa aestivalis Brevortia tyranus C orophium Iacustrae Paralichthyes dentatus Leptochereis plumulosus Other meiofauna Macoma balthica Macoma mitchelli Callinectes sapidus Micropogon undulatus T rinectes maculatus Leiostomus xanthurus Morone americana Pomatomus saltatrix Ariusfelis 98 Cypress Wetland Energy Flow in Wet Season Compartment A Compartment B Compartment C Understory Living POC Salamander L Vine Leaves Living sediment Kites & Hawks Hardwoods Leaves Phytoplankton Great blue heron Cypress Leaves Float. vegetation Other herons Cypress Wood Periphyton/Macroalgae Wood stork HW Wood Macrophytes Gruiformes Roots Epiphytes Caprimulgiformes Ter. Invertebrates Crayfish Woodpeckers Lizards Apple Snail Passeriformes omniv. Pelecanifonnes Prawn Passeriformes pred. Anseriformes Aquatic Invertebrates Shrews Galliformes Small Fish, herb + omniv G. Fox Hummingbirds Small Fish, prim. carniv Mink Opossum Large Fish Bobcat Bats Alligators Black Bear Turtles Raccoon Snakes Florida Panther Salamanders Squirrels Large Frogs Mice & Rats Medium Frogs Rabbits Small Frogs White-Tailed Deer Tadpoles Hogs Egrets Armadillo White ibis Owls Otter 99 Cypress Wetland Energy Flow in Dry Season Compartment A Understory Vine Leaves Hardwoods Leaves Cypress Leaves Cypress Wood HW Wood Roots Ter. Invertebrates Lizards Anseriforrnes Galliformes Caprimulgiformes Hummingbirds Woodpeckers Opossum Bats Raccoon Squirrels Mice & Rats Rabbits White-Tailed Deer Armadillo Compartment B Living POC Living sediment Phytoplankton Float. vegetation Periphyton/Macroalgae Macrophytes Epiphytes Crayfish Apple Snail Prawn Aquatic Invertebrates Small Fish, herb + omniv Small Fish, prim. carniv Large Fish Alligators Turtles Snakes Salamanders Large Frogs Medium Frogs Egrets Other herons Wood stork White ibis Otter Compartment C Small Frogs Pelecanifonnes Kites & Hawks Great blue heron Owls Passeriformes onniv. Compartment D Salamander L Tadpoles Gruiformes Passeriformes pred. G. Fox Mink Compartment E Shrews Black Bear Florida Panther Bobcat Hogs 100 APPENDIX B Taxa Identification Numbers and Names 101 NOTE: FWID refers to id #s in Chapter 3 & App. D. ID refers to id # in appendices E-I Species with multiple life-stages represented in the food web have the following additions to main id number: .2 = nauplii, larval, young-of-the year, .3 Juvenile/Copepodite, .4 Adult, .5 Juvenile and Adult FWID ID Common Name Genus Species 245 I Bythotrephes Bythotrephes cederstroemii 137 8 Cladoceran Alona spp. 1 38 1 0 Cladoceran Bosmina longirostris 139 l l Cladoceran Ceriodaphia spp. 140 14 Cladoceran Chydorus spaericus 141 16 Cladoceran Daphnia galeata mendotae 142 1 9 Cladoceran Daphnia pulicaria 143 20 Cladoceran Daphnia retrocurva 144 2 1 Cladoceran Diaphanosoma spp. 145 25 Cladoceran Eubosima coregoni 146 28 Cladoceran Holopedium gibberum 147 32 Cladoceran Leptodora kindti 148 39 Cladoceran Polyphemus pediculus 150 50.3 Cyclopoid Copepod Cyclops spp. 151 51.4 Cyclopoid Copepod Acanthocyclops vernalis 152 59.4 Cyclopoid Copepod Diacyclops thomasi 153 68.5 Cyclopoid Copepod Mesocyclops edax 154 76.5 Cyclopoid Copepod Tropocyclops prasinus mexicanus 155 111.5 Calanoid Copepod Epischura lacustris 156 112.5 Calanoid Copepod Eurytemora affinis 158 113.4 Calanoid Copepod Leptodiaptomus ashlandi 159 1 14.4 Calanoid Copepod Leptodiaptomus minutus 160 115.4 Calanoid Copepod Leptodiaptomus sicilis 157 1 16.3 Calanoid Copepod Diaptomus spp. 161 117.3 Calanoid Copepod Limnocalanus macrurus 162 117.4 Calanoid Copepod Limnocalanus macrurus 163 120.4 Calanoid Copepod Skistodiaptomus oregonensis 149 121.2 Nauplii 164 122 Mysis Mysis relicta 22 201 Pennate Diatom 23 202 Pennate Diatom 24 203 Pennate Diatom 25 204 Pennate Diatom 26 205 Pennate Diatom 27 206 Pennate Diatom 28 207 Pennate Diatom 29 208 Pennate Diatom 30 209 Pennate Diatom 31 210 Pennate Diatom 32 211 Pennate Diatom 33 212 Pennate Diatom 34 213 Pennate Diatom 35 214 Pennate Diatom 36 215 Pennate Diatom 37 216 Pennate Diatom 38 217 Pennate Diatom 102 FWID ID Common Name 39 218 Pennate Diatom 40 219 Pennate Diatom 41 220 Pennate Diatom 42 221 Pennate Diatom 43 222 Pennate Diatom 44 223 Pennate Diatom 45 224 Pennate Diatom 46 225 Pennate Diatom 47 226 Pennate Diatom 48 227 Pennate Diatom 49 228 Pennate Diatom 50 229 Pennate Diatom 51 230 Pennate Diatom 52 231 Pennate Diatom 53 232 Pennate Diatom 54 233 Pennate Diatom 55 234 Pennate Diatom 56 235 Pennate Diatom 57 236 Pennate Diatom 58 237 Pennate Diatom 59 238 Pennate Diatom 60 239 Pennate Diatom 61 240 Pennate Diatom 62 301 Green algae 63 302 Green algae 64 303 Green algae 65 304 Green algae 66 305 Green algae 67 306 Green algae 68 307 Green algae 69 308 Green algae 70 309 Green algae 71 310 Green algae 72 311 Green algae 73 312 Green algae 74 313 Green algae 75 314 Green algae 76 315 Green algae 77 316 Green algae 78 317 Green algae 79 318 Green algae 80 319 Green algae 81 320 Green algae 82 321 Green algae 83 322 Green algae 84 323 Green algae 85 324 Green algae 86 325 Green algae 87 326 Green algae 88 327 Green algae 103 FWID ID Common Name Genus Species 89 328 Green algae 90 329 Green algae 91 401 Chrysophyte 92 402 Chrysophyte 93 403 Chrysophyte 94 404 Chrysophyte 95 405 Chrysophyte 96 406 Chrysophyte 97 407 Chrysophyte 98 408 Chrysophyte 99 409 Chrysophyte 100 410 Chrysophyte 101 41 1 Chrysophyte 102 412 Chrysophyte 103 413 Chrysophyte 104 414 Chrysophyte 105 415 Chrysophyte 106 416 Chrysophyte 107 417 Chrysophyte 108 418 Chrysophyte 109 419 Chrysophyte 110 420 Chrysophyte 111 421 Chrysophyte 1 12 501 Cryptophyte 1 13 502 Cryptophyte 1 14 503 Cryptophyte 1 15 504 Cryptophyte 1 16 505 Cryptophyte 1 17 506 Cryptophyte 1 18 507 Cryptophyte l 19 601 Cryptophyte 120 602 Cryptophyte 121 603 Blue-green algae 122 604 Blue-green algae 123 605 Blue-green algae 124 606 Blue-green algae 125 607 Blue-green algae 126 608 Blue-green algae 127 609 Blue-green algae 128 610 Blue-green algae 129 611 Blue-green algae 130 701 Dinoflagellate 131 702 Dinoflagellate 132 703 Dinoflagellate 133 704 Dinoflagellate 134 705 Dinoflagellate 135 706 Dinoflagellate 136 707 Euglenoid l 801 Centric Diatom 2 802 Centric Diatom 104 FWID ID Common Name Genus Species 3 803 Centric Diatom 4 804 Centric Diatom 5 805 Centric Diatom 6 806 Centric Diatom 7 807 Centric Diatom 8 808 Centric Diatom 9 809 Centric Diatom 10 810 Centric Diatom 11 811 Centric Diatom 12 812 Centric Diatom 13 813 Centric Diatom 14 814 Centric Diatom 15 815 Centric Diatom 16 816 Centric Diatom 17 817 Centric Diatom 18 818 Centric Diatom 19 819 Centric Diatom 20 820 Centric Diatom 21 821 Centric Diatom 165 916 Gast. Hydro. Amnicola limnosa 166 924 Gast. Hydro. Valvata sincera 167 928 Gast. Lymn. Pseudosuccinea colurnnella 246 944 Pele. Drei. Dreissena polymorpha 168 950 Sphaeriidae Sphaerium spp. 169 958 Sphaeriidae Pisidium henslowanum 170 962 Sphaeriidae Pisidium spp. l7 1 977 Amph. Gamm. Gammarus sp. 172 978 Amph. Pont. Diporeia hoyi 173 982 Isop. Asel. Caecidotea racovitzai 174 986 Dipt. Tanp. Procladius sp. 175 993 Dipt Diam. Potthastia cf. longimanus 176 997 Dipt. Diam. Monodiamesa tuberculata 177 1003 Dipt. Ortho. Heterotrissocladius changi 178 1004 Dipt. Ortho. Heterotrissocladius oliveri 179 1010 Dipt. Ortho. Orthocladius sp. 180 1014 Dipt. Chir. Chironomus anthracinus 181 1017 Dipt Chir. Chironomus fluviatilis 182 1018 Dipt Chir. Chironomus sp. 183 1019 Dipt. Chir. Cryptochironomus cf. digitatus 184 1020 Dipt. Chir. Cryptochironomus cf. fulvus 185 1021 Dipt. Chir. Cryptochironomus sp. 186 1023 Dipt. Chir. Demicryptochironomus sp. 187 1025 Dipt Chir. Cladopelma sp. 188 1039 Dipt. Chir. Paracladopelma winnelli 189 1040 Dipt. Chir. Paracladopelma camptolabis 190 1043 Dipt. Chir. Paracladopelma undine 191 1049 Dipt. Chir. Polypedilum scalaenum 192 1050 Dipt. Chir. Polypedilum nereis 193 1052 Dipt. Chir. Polypedilum tuberculum 194 1054 Dipt. Chir. Robackia cf. demeijerei 105 FWID ID Common Name Genus Species 195 1057 Dipt. Tany. Micropsectra sp. 196 1059 Dipt. Tany. Tanytarsus sp. 197 1081 Oli g. Ench Unknown spp. 198 1082 Olig. Lumb Stylodrilus heringianus 199 1084 Olig. Nadid Arcteonais lomondi 200 1085 011 g. Nadid Chaetogaster sp. 201 1089 Olig. Nadid Piguetiella michiganensis 202 1092 Olig. Nadid Stylaria lacustris 203 1093 Olig. Nadid Uncinais uncinata 204 1094 011 g. Nadid Vej dovskyella intermedia 205 1095 Olig. Tubif Aulodrilus americanus 206 1098 Olig. Tubif Aulodrilus pluriseta 207 1099 Olig. Tubif Ilyodrilus templentoni 208 l 100 Olig. Tubif Varichaetadrilus angustipenis 209 l 102 Olig. Tubif Limnodrilus claparedianus 210 l 103 011 g. Tubif Limnodrilus hoffmeisteri 21 l 1104 Olig. Tubif Limnodrilus spiralis 212 1 105 011 g. Tubif Limnodrilus profundicola 213 1 106 Olig. Tubif Limnodrilus udekemianus 214 1 108 Olig. Tubif Isochaetides freyi 2 15 1 109 Olig. Tubif Quistrodrilus multisetosus 216 1 11 1 Olig. Tubif Spirosperrna nikolskyi 217 1 1 12 Olig. Tubif Tasserkidrilus superiorensis 218 l 1 l4 Olig. Tubif Potamothrix moldaviensis 219 1 1 15 Olig. Tubif Potamothrix vejdovskyi 220 11 19 Olig. Tubif Tasserkidrilus americanus 221 1 120 Olig. Tubif Tubifex tubifex 244 1 125 sea lamprey Petromyzon marinas 223 1133.2 alewife Alosa pseudoharengus 224 1133.5 alewife Alosa pseudoharengus 225 l 152 spottail Shiner Notropis hudsonius 226 1188.2 rainbow smelt Osmerus mordax 227 1188.5 rainbow smelt Osmerus mordax 228 l 190 lake Whitefish Coregonus clupeaforrnis 229 1191.2 bloater Coregonus hoyi 230 1191.5 bloater Coregonus hoyi 231 1194 coho salmon Oncorhynchus kisutch 232 1195 rainbow trout Oncorhynchus mykiss 233 1196 chinook salmon Oncorhynchus tshawytscha 234 1198 brown trout Salmo trutta 235 1200.3 lake trout Salvelinus namaycush 236 1200.4 lake trout Salvelinus namaycush 237 1201 trout-perch Percopsis omiscomaycus 238 1202 burbot Lota lota 239 1208 ninespine stickleback Pungitius pungitius 240 1209 slimy sculpin Cottus cognatus 241 1210 deepwater sculpin Myoxocephalus thompsonii 242 1225 johnny darter Etheostoma nigrum 243 1226 yellow perch Perca flavescens 222 1242 Hirun. Gloss. Helobdella stagnalis 106 APPENDIX: Unaggregated phytoplankton identification numbers, their corresponding aggregated identification numbers, and their scientific names 107 ID1 ID2 Group Genus Species 231 648 Pennate Diatom Synedra sp. 232 634 Pennate Diatom Surirella augusta 233 586 Pennate Diatom Nitzschia recta 233 597 Pennate Diatom Nitzschia subacicularis 233 598 Pennate Diatom Nitzschia sublinearis 234 640 Pennate Diatom Synedra ostenfeldii 234 642 Pennate Diatom Synedra acus 234 644 Pennate Diatom Synedra ulna 235 418 Pennate Diatom Amphipleura pelliucdia 236 579 Pennate Diatom Nitzschia lauenburgiana 237 639 Pennate Diatom Synedra delicatissima 238 649 Pennate Diatom Synedra delicatissima var. augustissima 239 510 Pennate Diatom Gomphonema olivaceum 240 638 Pennate Diatom Synedra ulna var. chaseana 301 280 Green Planktonema lauterbomii 302 217 Green Crucigenia rectancularis 302 235 Green Chlamydocapsa planktonica 302 236 Green Gloeocystis sp. 303 234 Green Chlamydocapsa sp. 304 287 Green Scenedesmus bijuga 304 288 Green Scenedesmus ecomis 304 289 Green Scenedesmus sp. 305 224 Green Eudorina elegans 306 309 Green Ulothrix sp. 307 219 Green Dictyosphaerium ehrenbergianum 307 220 Green Dictyosphaerium pulchellum 308 230 Green Franceia droescheri 308 237 Green Golenkinia radiata 308 239 Green Golenkinia radiata var. brevispina 309 255 Green Monoraphidium minutum 309 257 Green Monoraphidium skujae 310 301 Green Tetraedron minimum 310 302 Green Tetraedron minimum var. tetralobulatum 310 307 Green Treubaria planktonica 311 298 Green Sphaerellocystis lacustris 311 299 Green Sphaerellocystis lateralis 312 186 Green Carteria sp. 312 190 Green Chlamydomonas sp. 312 191 Green Chlamydomonas globosa 313 300 Green Stichococcus sp. 314 227 Green Elakatothrix lacustris 314 228 Green Elakatothrix gelatinosa 314 229 Green Elakatothrix viridis 315 248 Green Microspora sp. 316 262 Green Nephrocytium limneticum 316 268 Green Oocystis lacustris 316 270 Green Oocystis submarina 316 275 Green Oocystis solitaria 317 249 Green Monoraphidium braunii 317 250 Green Monoraphidium contortum 108 ID] ID2 Group Genus Species 318 245 Green Kirchneriella contorta 319 172 Green Ankistrodesmus falcatus var. mirabilis 320 259 Green Monoraphidium tortile 321 174 Green Ankistrodesmus gracilis 322 171 Green Ankistrodesmus falcatus 323 254 Green Monoraphidium irregulare 324 197 Green Closteriopsis longissima 325 214 Green Crucigenia quadrata 325 216 Green Crucigenia irregularis 326 264 Green Oocystis parva 326 265 Green Oocystis sp. 327 269 Green Oocystis sp. #1 327 274 Green Oocystis pusilla 328 267 Green Oocystis borgei 329 175 Green Ankistrodesmus gelifactum 401 335 Chrysophyte Desmarella brachycalyx 401 391 Chrysophyte Sphaeroeca volvox 402 396 Chrysophyte Stichogloea sp. 403 357 Chrysophyte Hyalobryon sp. 404 321 Chrysophyte Chromulina sp. 404 379 Chrysophyte Ochromonas sp. 404 380 Chrysophyte Ochromonas sp. #4 405 338 Chrysophyte Dinobryon sp. 405 341 Chrysophyte Dinobryon sociale var. americanrun 405 342 Chrysophyte Dinobryon sociale var. stipitatum 405 343 Chrysophyte Dinobryon acuminaturn 405 344 Chrysophyte Dinobryon bavaricurn 405 345 Chrysophyte Dinobryon borgei 405 346 Chrysophyte Dinobryon eurystoma 405 348 Chrysophyte Dinobryon stokesii 405 353 Chrysophyte Dinobryon utriculus var. tabellariae 406 372 Chrysophyte Lagneoeca ovata 406 373 Chrysophyte Salpingoeca amphorae 407 317 Chrysophyte Aulomonas purdyi 407 377 Chrysophyte Monosiga sp. 407 378 Chrysophyte Monosiga ovata 407 395 Chrysophyte Stelexomonas dichotoma 408 324 Chrysophyte [Haptophyceae] 409 394 Chrysophyte Spiniferomonas sp. 410 381 Chrysophyte Paraphysomonas sp. 411 390 Chrysophyte Rhizochrysis sp. 412 316 Chrysophyte Bitrichia chodatii 413 318 Chrysophyte Bicoeca campanulata 413 319 Chrysophyte Bicoeca socialis 413 320 Chrysophyte Bicoeca sp. 414 327 Chrysophyte Chrysolykos angulatus 414 329 Chrysophyte Chrysolykos planktonicus 414 330 Chrysophyte Chrysolykos sp. 415 326 Chrysophyte Chrysococcus sp. 416 359 Chrysophyte Kephyrion boreale 109 ID] ID2 Group Genus Species 416 361 Chrysophyte Kephyrion rubri-claustri 416 362 Chrysophyte Kephyrion doliolum 416 363 Chrysophyte Kephyrion littorale 416 364 Chrysophyte Kephyrion ovale 416 368 Chrysophyte Kephyrion sp. #2 416 369 Chrysophyte Kephyrion sp. #3 416 385 Chrysophyte Pseudokephyrion conicum 416 386 Chrysophyte Pseudokephyrion entzii 416 387 Chrysophyte Pseudokephyrion sp. #1 416 388 Chrysophyte Pseudokephyrion millerense 417 332 Chrysophyte Chrysosphaerella longispina 418 339 Chrysophyte Dinobryon divergens 418 340 Chrysophyte Dinobryon sociale 418 347 Chrysophyte Dinobryon cylindricum 419 376 Chrysophyte Mallomonas sp. #3 420 375 Chrysophyte Mallomonas sp. 421 360 Chrysophyte Kephyrion cuplifromae 421 366 Chrysophyte Kephyrion sp. 501 659 Cryptophyte Chroomonas acuta 501 660 Cryptophyte Chroomonas caudata 501 661 Cryptophyte Chroomonas nordstedtii 501 683 Cryptophyte Rhodomonas minuta var. nannoplanctica 501 686 Cryptophyte Rhodomonas sp. 501 687 Cryptophyte Rhodomonas lacustris 502 664 Cryptophyte Cryptomonas phaseolus 502 665 Cryptophyte Cryptomonas lobata 502 667 Cryptophyte Cryptomonas caudata 502 670 Cryptophyte Cryptomonas marssonii 502 675 Cryptophyte Cryptomonas pyreniodifera 502 678 Cryptophyte Cryptomonas sp. 502 680 Cryptophyte Cryptomonas tenius 503 682 Cryptophyte Rhodomonas minuta (pusilla) (Cryptomonas) 503 685 Cryptophyte Rhodomonas lens 504 662 Cryptophyte Cryptomonas erosa 504 666 Cryptophyte Cryptomonas brevis 504 671 Cryptophyte Cryptomonas tetrapyrenoidosa 504 676 Cryptophyte Cryptomonas reflexa 504 681 Cryptophyte Cryptomonas erosa var. reflexa 505 669 Cryptophyte Cryptomonas rostratiformis 506 668 Cryptophyte Cryptomonas curvata 507 663 Cryptophyte Cryptomonas ovata 601 124 Blue-green Agmenellum quadruplicatum 601 132 Blue-green Anacystis thermalis 601 136 Blue-green Aphanothece elabens (Coccochloris) 601 137 Blue-green Aphanocapsa delicatissima 601 138 Blue-green Aphanothece clathrata 601 139 Blue-green Aphanothece gelatinosa 602 131 Blue-green Anacystis montana f. minor 110 IDl ID2 Group Genus Species 602 140 Blue-green Synechoccus sp. 603 152 Blue-green Gomphosphaeria aponina var. delicatula 603 153 Blue-green Gomphosphaeria lacustris (naegelianum) (Coelosphaerium) 603 154 Blue-green Gomphosphaeria naegelianum 604 160 Blue-green Microcystis aeruginosa 604 161 Blue-green Microcystis elachista (montana) (Anacycstis) 605 126 Blue-green Anabaena flos-aquae 606 159 Blue-green Oscillatoria minima 606 165 Blue-green Oscillatoria subbrevis 606 166 Blue-green Oscillatoria tenuis 607 141 Blue-green Chroococcus dispersus 607 143 Blue-green Chroococcus sp. 608 127 Blue-green Anabaena sp. 608 130 Blue-green Anabaena spiroides var. crassa 609 164 Blue-green Oscillatoria limnetica 610 156 Blue-green Oscillatoria sp. 611 142 Blue-green Chroococcus lirnneticus 701 688 Dinoflagellate Amphidinium sp. 702 693 Dinoflagellate Gymnodinium sp. 702 700 Dinoflagellate Peridinium cinctum 703 701 Dinoflagellate Peridinium sp. 704 692 Dinoflagellate Glenodinium sp. 705 696 Dinoflagellate Gymnodinium helveticum var. achroum 706 689 Dinoflagellate Ceratium hirundinella 707 702 Euglenoid Euglena sp. 801 427 Centric Diatom Aulacoseria subarctica 802 609 Centric Diatom Stephanodiscus sp. 802 614 Centric Diatom Stephanodiscus minutus 802 617 Centric Diatom Stephanodiscus subtilis 802 621 Centric Diatom Stephanodiscus parvus 802 623 Centric Diatom Stephanodiscus sp. #03 802 624 Centric Diatom Stephanodiscus sp. #21 802 626 Centric Diatom Stephanodiscus sp. #51 802 627 Centric Diatom Stephanodiscus sp. #5 803 442 Centric Diatom Cyclostephanos invisitatus 803 443 Centric Diatom Cyclostephanos tholiforrnis 803 445 Centric Diatom Cyclotella CRL#1 803 446 Centric Diatom Cyclotella CRL#2 803 447 Centric Diatom Cyclotella ocellata 803 449 Centric Diatom Cyclotella antiqua 803 450 Centric Diatom Cyclotella kuetzingiana var. planetophora 803 451 Centric Diatom Cyclotella stelligera 803 452 Centric Diatom Cyclotella michiganiana 803 453 Centric Diatom Cyclotella cryptica 803 454 Centric Diatom Cyclotella atomus 803 462 Centric Diatom Cyclotella pseudostelligera 803 463 Centric Diatom Cyclotella sp. 803 464 Centric Diatom Cyclotella sp. #1 111 ID] ID2 Group Genus Species 803 466 Centric Diatom Cyclotella sp. #4 804 605 Centric Diatom Rhizosolenia sp. 805 520 Centric Diatom Melosira ambigua 805 527 Centric Diatom Melosira varians 805 528 Centric Diatom Melosira italica var. tenuissima 806 457 Centric Diatom Cyclotella kuetzingiana 806 458 Centric Diatom Cyclotella meneghiniana 807 615 Centric Diatom Stephanodiscus hantzschii f. tenuis 808 616 Centric Diatom Stephanodiscus binderanus 809 603 Centric Diatom Rhizosolenia eriensis 810 522 Centric Diatom Melosira (Aulacoseria) islandica 811 613 Centric Diatom Stephanodiscus alpinus 812 406 Centric Diatom Actinocyclus normanii 812 448 Centric Diatom Cyclotella comta 813 604 Centric Diatom Rhizosolenia longiseta 814 653 Centric Diatom Thalassiosira weisflogii 815 631 Centric Diatom Stephanodiscus subtransilvanicus 816 628 Centric Diatom Stephanodiscus transilvanicus 817 612 Centric Diatom Stephanodiscus niagarae 818 611 Centric Diatom Stephanodiscus hantzschii f. hantzschii 819 456 Centric Diatom Cyclotella comensis 820 618 Centric Diatom Stephanodiscus conspicueporus 820 619 Centric Diatom Stephanodiscus lucens 820 622 Centric Diatom Stephanodiscus sp. #10 820 625 Centric Diatom Stephanodiscus sp. #31 821 521 Centric Diatom Aulacoseria italica 112 NOTE: ID] corresponds with Appendices B, G, H, & l IDs, ID2 corresponds wiith Appendices E& F IDs ID] ID2 Group Genus Species 201 505 Pennate Diatom Fragilaria pinnata 202 403 Pennate Diatom Achnanthes exigua 203 402 Pennate Diatom Achnanthes affinis 203 404 Pennate Diatom Achnanthes minutissima 203 405 Pennate Diatom Achnanthes sp. 204 496 Pennate Diatom Fragilaria brevistriata 204 500 Pennate Diatom Fragilaria construens 204 501 Pennate Diatom F ragilaria sp. 205 554 Pennate Diatom Nitzschia amphibia 205 565 Pennate Diatom Nitzschia angustatula 206 515 Pennate Diatom Meridion circulare 207 437 Pennate Diatom Cocconeis placentula var. euglypta 208 423 Pennate Diatom Cymbella microcephala 209 494 Pennate Diatom Fragilaria intermedia 209 502 Pennate Diatom Fragilaria vaucheriae 210 643 Pennate Diatom Synedra amphicephala var. austriaca 211 422 Pennate Diatom Amphora sp. 211 535 Pennate Diatom Navicula costulata 211 536 Pennate Diatom Navicula cryptocephala 211 537 Pennate Diatom Navicula integra 212 556 Pennate Diatom Nitzschia kuetzingiana 212 560 Pennate Diatom Nitzschia tropica 212 571 Pennate Diatom Nitzschia fonticola 213 538 Pennate Diatom Navicula sp. 214 487 Pennate Diatom Diatoma tenue var. elongatum 214 488 Pennate Diatom Diatoma tenue 215 497 Pennate Diatom Fragilaria intermedia var. fallax 216 555 Pennate Diatom Nitzschia angustata 216 557 Pennate Diatom Nitzschia palea 216 570 Pennate Diatom Nitzschia dissipata 216 591 Pennate Diatom Nitzschia sp. 217 498 Pennate Diatom Fragilaria capucina 218 635 Pennate Diatom Synedra filiforrnis var. exilis 219 558 Pennate Diatom Nitzschia pura 220 650 Pennate Diatom Tabellaria fenestrata 221 569 Pennate Diatom Nitzschia conifinis 222 652 Pennate Diatom Tabellaria flocculosa 223 561 Pennate Diatom Nitzschia acicularis 224 426 Pennate Diatom Asterionella forrnosa 225 575 Pennate Diatom Nitzschia gracilis 226 499 Pennate Diatom Fragilaria crotonensis 227 559 Pennate Diatom Nitzschia rostellata 228 647 Pennate Diatom Synedra radians 229 471 Pennate Diatom Cymatopleura solea 230 563 Pennate Diatom Nitzschia acuta 231 636 Pennate Diatom Synedra nana 231 641 Pennate Diatom Synedra filiforrnis 113 APPENDIX) Food web matrix with weights including two invaders 114 l’nenia433r It) 1 2 3 4 s 6 7 8 9 10 11 12 13 14 15 16 17 18 51 78 95 95 70 e4 68 83 83 59 95 43 56 56 37 as 59 78 78 53 81 81 100 100 73 as 68 83 83 59 99 68 83 83 48 83 76 49 59 100 59 78 78 43 78 70 44 53 103 81 100 100 73 105 68 83 83 59 107 45 61 61 26 61 53 27 40 108 65 78 78 78 57 113 59 75 75 75 68 115 62 73 73 53 70 32 73 45 70 11s 32 45 45 26 36 59 45 75 36 117 32 45 45 26 36 59 45 75 36 118 59 73 73 53 68 32 73 45 68 130 56 73 73 73 48 131 57 68 68 54 49 26 68 49 132 55 70 70 55 47 26 70 55 68 55 47 134 26 39 39 25 22 57 39 26 39 25 22 135 30 46 46 60 25 10 46 30 47 60 30 30 25 137 47 50 50 50 33 39 53 33 50 33 53 138 64 69 56 56 42 51 77 43 69 64 69 70 43 51 69 69 77 139 19 51 51 44 23 7 35 22 8 30 23 140 64 83 83 83 41 67 92 58 83 64 53 41 37 92 141 59 65 64 64 39 35 52 27 64 59 52 51 40 35 52 52 65 142 45 50 50 50 24 52 37 11 50 45 37 36 24 52 37 50 143 64 69 68 68 44 39 45 14 68 64 39 39 44 39 39 39 75 144 29 60 60 55 33 9 43 30 9 42 33 145 73 77 78 78 46 58 69 47 78 73 64 34 45 69 14s 46 59 59 61 40 17 59 46 61 30 61 40 147 42 42 59 42 14s 28 41 41 41 25 7 41 25 14s 62 75 56 53 32 62 62 42 62 53 150 151 152 153 154 155 156 52 44 14 51 38 51 28 52 38 38 157 53 73 73 30 73 53 44 158 49 63 63 36 49 25 63 39 49 159 52 64 64 35 60 27 64 40 60 160 78 65 51 51 56 48 65 4o 64 78 65 63 40 48 65 65 65 161 61 79 79 65 69 37 79 61 78 54 65 69 162 47 60 60 46 47 24 60 47 60 37 46 47 163 73 64 50 50 56 48 64 40 64 73 64 66 40 48 64 64 64 164 63 62 62 62 48 52 62 48 62 63 62 62 48 52 62 62 48 62 185 28 27 28 28 22 24 30 22 28 28 27 27 22 24 27 27 24 30 166 36 36 36 36 26 29 41 26 36 36 36 36 26 29 36 36 29 41 167 28 27 28 28 22 24 30 22 28 28 27 27 22 24 27 27 24 30 168 36 36 36 36 26 29 41 26 36 36 36 36 26 29 36 36 29 41 168 15 14 14 14 9 11 17 9 14 15 14 14 9 11 14 14 10 17 170 31 31 31 31 17 22 31 17 31 31 31 31 17 22 31 31 17 31 171 23 22 23 23 13 16 28 13 23 23 22 22 13 16 22 22 16 28 172 72 69 69 69 53 55 69 52 69 72 69 68 52 55 69 69 52 69 173 29 28 28 28 16 20 22 16 28 29 28 28 16 20 28 28 13 22 174 22 22 22 22 13 16 28 13 22 22 22 22 13 16 22 22 15 28 175 23 23 23 23 13 16 29 13 23 23 23 23 13 16 23 23 16 29 176 23 23 23 23 13 16 29 13 23 23 23 23 13 16 23 23 16 29 177 31 3o 30 30 17 21 39 17 30 31 30 30 17 21 30 30 21 39 17s 31 30 30 30 17 21 30 17 30 31 30 30 17 21 30 30 17 30 1115 Predator ll) 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 81* 89’ 49 49 49 94 73 36 36 36 95 53 13 14 13 96 70 27 27 27 97 89 49 49 49 88 73 36 36 36 88 73 36 76 36 36 36 36 28 36 49 45 36 36 49 28 49 83 36 100 70 27 70 27 27 14 14 11 14 44 43 I4 14 44 ll 44 78 14 103 89 49 49 49 105 73 36 36 36 107 53 14 53 14 14 14 14 11 14 27 26 14 14 27 11 27 61 14 108 73 35 36 35 35 36 28 35 49 45 113 81 32 32 32 32 32 32 32 45 41 115 81 32 32 32 32 32 32 32 45 41 32 32 45 32 116 49 59 59 62 62 59 62 62 7s 67 59 62 75 62 75 45 59 117 49 59 59 62 62 59 62 62 7s 67 59 62 75 62 75 45 59 118 83 32 32 32 32 32 32 32 45 41 32 32 45 32 130 66 38 38 38 38 38 31 38 55 52 131 60 26 26 26 26 26 19 26 39 37 132 60 26 26 26 26 26 19 26 40 36 26 26 40 19 134 35 57 55 55 55 55 40 55 68 62 55 55 68 40 135 41 10 10 10 10 10 8 10 24 23 10 10 24 8 137 53 33 47 20 33 33 20 25 20 33 37 20 20 33 25 33 50 20 138 65 43 64 43 43 43 43 45 43 43 41 43 43 43 45 43 69 43 138 56 7 8 8 8 8 8 8 21 20 8 8 21 140 92 58 64 58 58 33 28 58 59 28 41 33 41 67 28 141 65 4o 59 57 40 40 27 21 26 40 35 27 26 40 21 40 64 27 142 49 24 45 24 24 24 11 9 11 24 23 11 11 24 9 24 50 11 143 75 44 64 28 44 44 14 14 14 44 42 14 27 44 14 44 68 14 144 63 9 9 9 9 9 9 9 23 21 9 9 30 145 69 47 73 47 47 34 29 34 47 47 34 34 29 34 65 34 146 55 17 17 17 17 17 30 28 17 17 30 147 35 12 11 148 40 7 7 7 7 7 6 7 20 19 14s 67 32 32 32 32 32 28 32 45 32 32 32 28 32 62 32 150 151 152 153 154 155 156 14 14 18 14 28 16 14 14 28 18 28 51 14 157 65 53 44 39 44 44 73 158 63 25 25 25 25 25 21 25 39 35 25 25 39 21 39 63 25 159 73 27 27 26 26 27 25 26 40 35 27 26 40 25 40 64 27 160 50 40 78 40 40 40 21 27 40 35 40 27 40 21 40 64 40 161 86 37 37 38 38 37 39 38 54 51 37 38 54 39 162 60 24 24 24 24 24 21 24 37 35 24 24 37 21 183 52 40 73 4o 40 40 21 26 40 35 40 26 40 21 40 64 40 164 62 48 63 48 48 48 48 50 48 48 50 48 48 48 50 48 62 48 165 30 22 28 22 22 22 22 25 22 22 24 22 22 22 25 22 28 22 166 41 26 36 26 26 26 26 31 26 26 28 26 26 26 31 26 36 26 167 30 22 28 22 22 22 22 25 22 22 24 22 22 22 25 22 28 22 168 41 26 36 26 26 26 26 31 26 26 28 26 26 26 31 26 36 26 169 17 9 15 9 9 9 9 12 9 9 11 9 9 9 12 9 14 9 170 31 17 31 17 17 17 17 19 17 17 19 17 17 17 19 17 31 17 171 28 13 23 13 13 13 13 17 13 13 15 13 13 13 17 13 23 13 172 68 52 72 52 52 52 52 55 52 52 55 52 52 52 55 52 69 52 173 22 16 29 16 16 16 16 15 16 16 17 16 16 16 15 16 28 16 174 28 13 22 13 13 13 13 17 13 13 14 13 13 13 17 13 22 13 175 29 13 23 13 13 13 13 18 13 13 15 13 13 13 18 13 23 13 178 29 13 23 13 13 13 13 18 13 13 15 13 13 13 18 13 23 13 177 39 17 31 17 17 17 17 22 17 17 18 17 17 17 22 17 30 17 178 30 17 31 17 17 17 17 18 17 17 18 17 17 17 18 17 30 17 1163 l’reniatcu' It) 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 81 94 85 86 87 88 88 28 68 48 28 45 28 68 28 68 28 65 28 36 28 70 45 28 100 11 45 70 11 68 11 45 11 45 11 43 11 14 11 48 43 11 103 105 107 11 45 70 11 68 11 45 11 45 11 43 11 14 11 48 26 11 108 113 115 116 62 117 62 32 13 62 12 118 130 131 132 134 26 11 40 11 135 33 47 8 48 8 33 8 33 8 34 8 137 25 37 26 25 26 25 37 25 37 25 37 25 20 25 37 37 25 138 45 70 51 45 52 45 138 7 7 140 33 53 33 37 33 53 33 53 33 53 33 33 53 42 33 141 21 51 35 21 36 21 51 21 51 21 51 21 26 21 50 35 21 142 9 36 52 9 51 9 36 9 36 9 34 9 11 9 9 36 23 9 143 14 39 53 14 40 14 39 14 39 14 39 14 27 12 14 51 42 14 144 8 8 146 29 64 29 44 29 64 29 64 29 63 29 33 29 33 148 147 146 148 28 62 42 28 42 150 151 152 153 154 155 156 18 157 25 25 158 21 50 36 21 35 21 158 25 51 35 25 36 25 180 21 63 48 35 48 35 63 35 63 35 63 35 40 51 35 64 51 35 161 162 163 21 66 48 35 50 35 66 35 66 35 65 3s 40 51 35 64 51 35 184 50 62 52 50 52 50 62 50 62 50 62 50 48 50 50 62 50 50 185 25 27 24 25 24 25 27 25 27 25 28 25 22 24 25 28 24 25 166 31 36 29 31 29 31 36 31 36 31 36 31 26 28 31 36 28 31 167 25 27 24 25 24 25 27 25 27 25 28 25 22 24 25 28 24 25 168 31 36 29 31 29 31 36 31 36 31 36 31 26 28 31 36 28 31 168 12 14 11 12 10 12 14 12 14 12 14 12 9 11 12 14 11 12 170 19 31 22 19 21 19 31 19 31 19 31 19 17 19 19 31 19 19 171 17 22 16 17 16 17 22 17 22 17 23 17 13 15 17 23 15 17 172 55 68 55 55 54 55 68 55 68 55 69 55 52 55 55 69 55 55 173 15 28 20 15 19 15 28 15 28 15 28 15 16 17 15 28 17 15 174 17 22 16 17 16 17 22 17 22 17 22 17 13 14 17 22 14 17 175 18 23 16 18 16 18 23 18 23 18 23 18 13 15 18 23 15 18 176 18 23 16 18 16 18 23 18 23 18 23 18 13 15 18 23 15 18 177 22 30 21 22 21 22 30 22 30 22 30 22 17 18 22 30 18 22 178 18 30 21 18 21 18 3o 18 30 18 3o 18 17 18 18 30 18 18 llf7 l’riud4883r It) 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 ’81 49 43 85 85 85 66 84 39 30 71 71 71 52 85 55 12 49 49 49 68 86 71 25 69 69 69 88 87 52 44 88 88 88 69 8s 39 30 71 71 71 52 88 70 49 28 36 36 36 70 39 30 71 71 71 52 100 48 44 11 14 14 14 48 71 25 69 69 69 88 103 52 44 88 88 88 69 105 39 30 71 71 71 52 107 48 27 11 14 14 14 48 58 12 52 52 52 71 108 35 36 30 68 68 68 49 113 32 34 26 63 63 63 47 115 32 32 26 61 61 61 45 116 62 11 49 40 40 40 24 117 62 11 49 40 40 40 24 118 32 32 26 61 61 61 45 130 38 131 26 132 26 134 55 135 10 137 37 33 25 20 20 20 37 35 16 35 13 35 35 35 29 138 43 59 44 44 59 44 59 38 46 46 46 31 138 8 8 37 9 56 56 56 67 140 53 41 33 28 28 52 26 52 52 22 69 69 69 56 141 50 40 21 26 26 26 50 84 35 35 84 35 84 31 54 54 54 35 142 36 24 9 11 11 11 36 50 50 36 143 51 44 14 27 27 14 51 52 52 39 144 9 9 45 10 60 60 60 75 145 34 34 64 34 64 64 27 64 64 64 48 146 60 60 60 62 147 41 41 41 62 148 7 27 27 6 41 41 41 41 148 66 66 150 64 151 49 152 58 153 61 164 66 155 35 156 14 157 64 64 55 77 77 77 55 158 25 35 49 29 67 67 67 49 158 26 35 48 31 68 68 68 48 160 40 21 27 85 35 35 85 35 68 31 55 55 55 35 161 38 46 41 79 79 79 62 162 24 29 27 59 59 59 43 163 40 21 26 84 35 35 84 35 68 31 54 54 54 35 164 62 48 50 48 48 48 62 165 28 22 25 22 22 22 28 166 36 26 31 26 26 26 36 167 28 22 25 22 22 22 28 188 36 26 31 26 26 26 36 168 14 9 12 9 9 9 14 170 31 17 19 17 17 17 31 171 23 13 17 13 13 13 23 172 69 52 55 52 52 52 69 32 21 21 32 21 32 18 32 32 32 21 173 28 16 15 16 16 16 28 174 22 13 17 13 13 13 22 175 23 13 18 13 13 13 23 20 15 15 20 15 20 12 20 20 20 15 176 23 13 18 13 13 13 23 20 15 15 20 15 20 12 20 20 20 15 177 30 17 22 17 17 17 30 178 30 17 18 17 17 17 30 1113 l’rentauor It) 73 74 7s 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 7’81 85 66 52 71 84 71 52 36 55 85 49 68 58 39 86 69 88 68 49 87 88 69 49 68 88 71 52 36 55 88 71 52 36 55 100 69 88 68 49 103 88 69 49 68 105 71 52 36 55 107 52 71 55 36 108 68 49 39 68 39 58 58 113 63 47 31 63 31 46 46 115 61 45 31 61 26 39 31 46 46 32 116 40 24 10 40 49 63 49 10 26 26 11 117 40 24 10 40 49 63 49 26 10 26 26 11 118 61 45 32 61 26 39 32 48 48 32 130 131 132 134 135 137 35 29 29 16 35 16 22 22 138 46 31 59 44 44 59 59 38 38 38 59 38 38 44 59 59 44 138 56 67 40 39 9 9 40 29 29 140 69 56 39 26 69 22 35 22 39 35 26 39 39 26 141 54 35 54 65 35 84 54 44 61 44 68 61 61 31 35 55 55 48 142 69 50 25 25 25 50 25 25 69 143 54 29 29 52 54 144 60 75 44 43 10 10 44 29 29 145 64 48 48 33 64 27 27 27 51 27 33 49 49 34 146 60 62 60 32 147 41 62 41 148 41 41 31 41 31 31 31 148 82 49 66 28 28 28 52 36 150 80 151 56 152 70 153 61 154 85 165 54 158 30 42 12 25 12 28 30 157 94 55 41 77 49 35 158 83 49 35 67 29 42 29 53 42 35 53 53 35 158 84 48 35 68 31 44 31 54 44 35 55 55 35 160 55 35 55 65 34 68 55 31 44 31 68 61 44 31 34 53 53 35 161 79 62 44 79 41 57 44 61 61 46 162 59 43 29 59 27 40 29 46 46 29 163 54 35 54 65 36 68 54 31 44 31 68 61 44 31 36 56 56 35 164 165 166 167 168 168 170 171 172 32 21 32 21 21 32 32 18 18 18 32 18 18 18 21 32 32 21 173 174 175 20 15 20 15 15 20 20 12 12 12 20 12 12 12 15 20 20 15 176 20 15 20 15 15 20 20 12 12 12 20 12 12 12 15 20 20 15 177 178 1159 l’neniaflxar ID 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 ”81* 78 61 73 95 95 95 73 95 73 95 95 95 73 84 78 43 61 83 83 83 61 83 61 83 83 83 61 85 61 43 78 56 56 56 78 56 78 56 56 56 78 86 73 61 78 78 78 78 100 78 100 78 78 78 100 87 95 83 56 78 100 100 78 100 78 100 100 100 78 88 78 70 43 61 83 83 61 83 61 83 83 83 61 88 78 7o 43 61 83 83 61 83 61 83 83 83 61 108 73 61 78 100 78 78 78 78 100 78 78 78 100 103 95 83 56 78 100 100 100 78 100 78 100 100 78 105 78 70 43 61 83 83 83 61 83 61 83 83 61 187 56 48 65 83 61 61 61 83 61 83 61 61 61 83 108 83 65 48 56 78 78 78 56 78 56 78 78 78 56 113 70 62 37 56 75 75 75 56 75 56 75 75 75 56 57 115 70 59 37 53 73 73 73 53 73 53 73 73 73 53 57 116 45 32 12 26 45 45 45 26 45 26 45 45 45 26 31 117 45 32 12 26 45 45 45 26 45 26 45 45 45 26 31 118 73 59 40 53 73 73 73 53 73 53 73 73 73 53 59 130 131 132 134 135 137 37 26 39 50 50 50 39 50 39 50 50 50 39 37 138 56 51 51 69 51 38 56 56 56 38 56 38 56 56 56 38 51 69 138 21 32 60 51 51 51 60 51 60 51 51 51 60 23 148 53 37 67 83 83 83 67 83 67 83 83 83 67 53 141 50 35 48 64 64 64 48 64 48 64 64 64 48 51 142 65 50 50 50 65 50 65 50 50 50 65 34 143 38 4o 53 51 51 51 53 51 53 51 51 51 53 39 144 30 41 72 60 60 60 72 60 72 6o 60 60 72 29 145 63 65 43 58 78 78 78 58 78 58 78 78 78 58 63 146 61 59 59 59 61 59 61 59 59 59 61 147 39 59 42 42 42 59 42 59 42 42 42 59 148 32 28 32 41 41 41 41 41 41 41 41 41 41 41 32 148 75 42 75 56 72 92 92 62 42 75 92 62 92 72 42 75 150 94 57 94 74 74 94 94 94 74 94 94 94 74 57 94 151 62 52 62 52 52 62 62 62 52 62 62 62 52 52 62 152 84 68 84 68 68 84 84 84 68 84 84 84 68 68 84 153 61 62 62 61 62 62 62 62 62 62 62 62 62 62 61 154 81 66 81 66 66 81 81 81 66 81 81 81 66 66 81 155 52 48 50 36 35 50 50 50 35 50 50 50 35 48 52 156 38 157 55 90 71 90 90 71 73 55 90 90 90 71 158 49 80 65 80 80 66 63 49 80 80 80 66 36 158 48 80 65 80 80 65 64 48 80 80 80 65 35 160 63 35 48 64 47 35 51 51 51 35 51 35 51 51 51 35 48 63 161 62 46 48 62 79 79 65 79 65 79 79 79 65 60 162 47 33 46 60 60 60 46 60 46 60 60 60 46 47 163 65 35 48 64 50 35 50 50 50 35 50 35 50 50 50 35 48 65 164 165 28 24 24 28 24 24 28 28 28 24 28 24 28 28 28 24 24 28 166 36 29 29 36 29 29 36 36 36 29 36 29 36 36 36 29 29 36 167 28 24 24 28 24 24 28 28 28 24 28 24 28 28 28 24 24 28 168 23 16 16 23 16 16 23 23 23 16 23 16 23 23 23 16 16 23 168 14 11 11 14 11 11 14 14 14 11 14 11 14 14 14 11 11 14 170 31 22 22 31 22 22 31 31 31 22 31 22 31 31 31 22 22 31 171 23 16 16 23 16 16 23 23 23 16 23 16 23 23 23 16 16 23 172 39 25 25 39 25 25 39 39 39 25 39 25 39 39 39 25 25 39 173 28 20 20 28 20 20 28 28 28 20 28 20 28 28 28 20 20 28 174 35 16 16 22 16 29 35 22 22 16 22 16 22 22 22 16 16 22 175 23 16 16 23 16 16 23 23 23 16 23 16 23 23 23 16 16 23 176 23 16 16 23 16 16 23 23 23 16 23 16 23 23 23 16 16 23 177 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 176 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 121) Fwiulamor I!) 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 81 95 95 85 68 84 83 81 71 58 85 56 56 49 36 86 78 75 69 52 87 100 98 88 71 88 83 81 71 58 88 83 81 71 58 100 78 75 69 52 103 100 98 88 71 105 83 81 71 58 107 61 59 52 39 106 56 78 78 78 68 55 113 56 75 73 73 63 49 115 53 73 73 75 75 61 48 116 26 45 45 45 45 40 26 117 26 45 45 45 45 40 26 116 53 73 73 73 73 61 48 138 131 132 134 135 137 39 50 50 50 50 35 27 26 22 27 138 51 69 56 56 77 69 77 47 47 78 59 46 59 46 41 38 59 41 138 44 35 51 49 33 56 26 148 67 83 83 83 83 69 37 35 39 37 141 48 50 64 65 65 54 54 54 54 42 44 54 42 142 65 36 50 50 50 63 25 63 143 53 38 51 52 44 52 45 16 16 45 65 29 65 144 55 43 60 59 42 60 30 145 58 78 78 77 77 64 44 41 51 44 146 61 59 59 59 59 60 147 59 42 42 42 42 41 148 41 41 41 41 41 41 27 148 42 62 92 92 92 66 150 74 94 94 94 69 94 69 43 43 70 151 52 62 62 62 68 62 68 50 50 68 152 68 84 84 84 84 84 84 58 58 85 153 62 62 62 61 67 61 66 38 38 67 154 66 81 81 81 90 81 90 61 61 91 155 35 50 50 50 72 50 72 44 44 74 156 52 51 51 157 55 73 9o 89 72 77 35 35 35 158 49 63 80 79 50 62 49 25 25 50 67 53 158 48 64 80 82 59 65 60 31 31 60 68 54 168 48 64 51 51 64 65 65 40 40 63 55 55 55 55 60 61 55 60 161 65 79 79 78 78 79 62 162 46 60 60 60 60 59 46 163 48 64 50 50 64 64 64 40 40 66 54 54 54 54 58 61 54 58 164 165 24 28 28 27 31 27 30 24 24 30 166 29 36 36 36 42 36 41 29 29 41 167 24 28 28 27 31 27 30 24 24 30 168 16 23 23 22 28 22 28 16 16 28 168 11 14 14 14 17 14 17 10 10 17 170 22 31 31 31 31 31 31 17 17 31 171 16 23 23 22 28 22 28 16 16 28 172 25 39 39 39 39 39 39 22 22 38 32 32 32 32 22 18 32 22 173 20 28 28 28 23 28 22 13 13 22 174 16 22 22 22 28 22 28 15 15 28 175 16 23 23 23 29 23 29 16 16 29 20 20 20 20 15 12 20 15 176 16 23 23 23 29 23 29 16 16 29 20 20 20 20 15 12 20 15 177 21 30 30 30 39 30 39 21 21 39 178 21 30 3o 30 30 30 30 17 17 30 121 thulahor II) 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 81 49 84 39 85 55 86 71 87 52 88 39 88 39 100 71 103 52 105 39 187 58 108 36 59 113 34 48 115 32 48 62 60 116 11 34 35 36 66 117 11 34 35 36 66 118 32 49 60 60 130 131 132 134 135 137 38 35 16 29 37 12 138 67 59 44 49 58 58 58 34 39 138 37 40 35 24 24 9 11 148 61 52 26 62 71 40 40 19 23 141 68 68 35 52 53 51 51 25 142 50 50 37 40 40 40 11 143 58 52 44 42 42 16 35 18 144 45 32 45 33 33 9 10 145 56 64 34 55 64 51 51 24 3o 20 146 48 62 62 62 147 29 45 48 62 45 40 60 45 78 146 27 35 44 39 36 148 73 82 65 65 38 34 150 72 81 80 80 54 44 50 70 20 151 52 56 56 56 44 47 46 70 42 31 152 62 72 71 71 49 55 49 85 33 26 153 51 64 65 65 37 56 38 65 38 36 154 69 82 82 82 55 60 59 88 41 38 155 39 51 51 51 25 30 29 70 66 156 38 44 43 43 26 157 55 64 6O 73 74 74 29 25 158 35 50 63 64 64 36 36 158 35 52 66 65 65 38 42 160 85 85 35 51 66 82 82 55 27 54 161 46 64 77 78 78 27 56 25 55 53 39 46 35 162 29 48 60 60 60 35 60 30 62 75 56 53 41 163 84 84 35 53 65 81 81 55 30 55 164 76 42 75 76 62 58 165 166 167 168 168 170 171 172 32 32 21 30 32 31 31 17 20 17 173 174 175 26 20 15 20 20 20 12 14 176 26 20 15 20 20 20 12 14 177 178 1122 Predator 178 I45 52 39 59 47 I46 I47 I48 I49 52 75 61 83 77 38 47 77 47 36 150 IS] 52 47 74 42 61 77 36 33 32 34 39 152 47 59 46 36 123 153 47 21 28 4O 37 46 36 154 155 156 53 47 59 53 63 67 51 52 62 67 67 45 36 43 68 40 157 57 67 58 73 67 89 67 83 67 158 52 41 51 71 55 69 38 159 53 47 54 81 62 37 160 54 45 38 71 55 72 43 161 73 51 I62 59 Predator ID 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 147 52 150 46 151 41 153 49 154 74 161 53 162 67 164 37 45 174 80 80 72 61 67 69 124 Predator ID 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 95 97 100 103 107 113 141 125 Predator ID 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 174 79 85 85 76 96 79 96 88 79 93 96 79 79 79 88 96 80 69 175 63 55 46 59 63 46 46 46 55 63 47 36 177 55 49 58 53 55 58 58 58 49 55 63 45 126 Predator ID 95 97 100 103 105 107 109 113 115 116 117 179 178 217 218 219 220 221 79 58 58 79 58 58 69 36 45 50 79 46 58 58 222 223 224 225 226 227 228 229 230 231 33 31 40 40 127 232 233 234 Predator ID 235236237238239240241242243245246 103 105 107 113 128 129 Fatudator ll) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ""'118 14 14“ 14 14 9 10 17 9 14“ 14 14 14 9 10 714' 14 10 17 160 45 45 45 45 31 35 53 31 45 45 45 45 31 35 45 45 35 53 181 30 32 31 31 23 26 34 24 31 30 32 32 24 26 32 32 25 34 182 27 27 27 27 22 23 30 22 27 27 27 27 22 23 27 27 23 30 183 164 185 186 22 22 22 22 13 16 28 13 22 22 22 22 13 16 22 22 15 28 187 31 30 30 30 17 21 30 17 30 31 30 30 17 21 30 30 17 30 186 31 30 3o 30 17 21 30 17 30 31 30 30 17 21 30 30 17 30 188 22 22 22 22 13 16 28 13 22 22 22 22 13 16 22 22 15 28 180 22 22 22 22 13 16 24 13 22 22 22 22 13 16 22 22 15 24 181 14 14 14 14 9 10 17 9 14 14 14 14 9 10 14 14 10 17 182 22 22 22 22 13 16 28 13 22 22 22 22 13 16 22 22 15 28 183 31 30 30 30 17 21 39 17 30 31 30 30 17 21 30 30 21 39 184 14 14 14 14 9 10 17 9 14 14 14 14 9 10 14 14 10 17 185 31 30 30 30 17 21 35 17 30 31 30 30 17 21 30 30 17 35 186 31 30 30 30 17 21 39 17 30 31 30 30 17 21 30 30 21 39 187 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 186 30 30 30 30 16 21 30 16 30 30 3o 30 16 21 30 30 16 30 188 27 26 26 26 22 23 29 22 26 27 26 26 22 23 26 26 23 29 200 43 43 43 43 30 34 51 30 43 43 43 43 30 34 43 43 34 51 201 43 43 43 43 30 34 51 30 43 43 43 43 30 34 43 43 34 51 202 27 27 27 27 22 23 30 22 27 27 27 27 22 23 27 27 23 30 203 35 35 35 35 26 29 40 26 35 35 35 ' 35 26 29 35 35 28 40 284 43 43 43 43 30 34 43 30 43 43 43 43 30 34 43 43 30 43 205 22 21 21 21 12 15 27 12 21 22 21 21 12 15 21 21 15 27 206 22 21 21 21 12 15 21 12 21 22 21 21 12 15 21 21 12 21 207 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 208 22 21 21 21 12 15 25 12 21 22 21 21 12 15 21 21 13 25 208 22 21 21 21 12 15 27 12 21 22 21 21 12 15 21 21 15 27 210 30 30 30 30 16 21 3o 16 30 30 30 30 16 21 30 30 16 30 211 30 3o 30 30 16 21 30 16 30 30 30 3o 16 21 30 30 16 30 212 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 213 22 21 21 21 12 15 21 12 21 22 21 21 12 15 21 21 12 21 214 22 21 21 21 12 15 27 12 21 22 21 21 12 15 21 21 15 27 215 30 30 30 30 16 21 35 16 30 30 3o 30 16 21 30 30 17 35 216 22 22 22 22 13 15 16 13 22 22 22 22 13 15 22 22 10 16 217 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 218 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 218 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 220 22 22 22 22 13 15 16 13 22 22 22 22 13 15 22 22 10 16 221 30 30 30 30 16 21 30 16 30 30 30 30 16 21 30 30 16 30 222 223 30 34 32 32 32 30 32 30 34 33 32 34 34 30 224 225 226 227 228 228 230 231 232 233 234 235 236 237 238 238 240 241 242 It) 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 178’ 17 9 14 9 9 9 9 11 9 9 10 9 9 9 11 9 14 9 180 53 31 45 31 31 31 31 36 31 31 32 31 31 31 36 31 45 31 181 34 24 30 24 24 24 24 27 24 24 25 24 24 24 27 24 31 24 182 30 22 27 22 22 22 22 24 22 22 23 22 22 22 24 22 27 22 183 184 185 186 28 13 22 13 13 13 13 17 13 13 14 13 13 13 17 13 22 13 187 30 17 31 17 17 17 17 18 17 17 18 17 17 17 18 17 30 17 188 30 17 31 17 17 17 17 18 17 17 18 17 17 17 18 17 30 17 188 28 13 22 13 13 13 13 17 13 13 14 13 13 13 17 13 22 13 180 24 13 22 13 13 13 13 16 13 13 14 13 13 13 16 13 22 13 181 17 9 14 9 9 9 9 11 9 9 10 9 9 9 11 9 14 9 182 28 13 22 13 13 13 13 17 13 13 14 13 13 13 17 13 22 13 183 39 17 31 17 17 17 17 22 17 17 18 17 17 17 22 17 30 17 184 17 9 14 9 9 9 9 11 9 9 1o 9 9 9 11 9 14 9 185 35 17 31 17 17 17 17 19 17 17 18 17 17 17 19 17 30 17 186 39 17 31 17 17 17 17 22 17 17 18 17 17 17 22 17 30 17 187 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 188 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 188 29 22 27 22 22 22 22 24 22 22 22 22 22 22 24 22 26 22 200 51 30 43 30 30 30 30 35 30 30 31 30 30 30 35 30 43 30 201 51 30 43 30 30 30 30 35 3o 30 31 30 30 30 35 30 43 30 202 30 22 27 22 22 22 22 24 22 22 23 22 22 22 24 22 27 22 203 40 26 35 26 26 26 26 29 26 26 26 26 26 26 29 26 35 26 204 43 30 43 30 30 30 30 31 30 30 31 30 30 30 31 30 43 30 205 27 12 22 12 12 12 12 16 12 12 13 12 12 12 16 12 21 12 206 21 12 22 12 12 12 12 13 12 12 13 12 12 12 13 12 21 12 287 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 288 25 12 22 12 12 12 12 13 12 12 13 12 12 12 13 12 21 12 208 27 12 22 12 12 12 12 16 12 12 13 12 12 12 16 12 21 12 210 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 211 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 212 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 213 21 12 22 12 12 12 12 13 12 12 13 12 12 12 13 12 21 12 214 27 12 22 12 12 12 12 16 12 12 13 12 12 12 16 12 21 12 215 35 16 30 16 16 16 16 18 16 16 17 16 16 16 18 16 30 16 216 16 13 22 13 13 13 13 11 13 13 13 13 13 13 11 13 22 13 217 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 218 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 218 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 220 16 13 22 13 13 13 13 11 13 13 13 13 13 13 11 13 22 13 221 30 16 30 16 16 16 16 17 16 16 17 16 16 16 17 16 30 16 222 223 29 30 32 224 225 226 227 228 228 238 231 232 233 234 235 236 237 238 238 240 241 242 13() Predator 1131 II) 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 ""'178 11 14 10 11 10 11 14 11 14 11 14 11 9 10 11 14 10 11 180 36 45 35 36 35 36 45 36 45 36 45 36 31 32 36 45 32 36 161 27 32 26 27 27 27 32 27 32 27 31 27 24 25 27 31 25 27 182 24 27 23 24 23 24 27 24 27 24 27 24 22 23 24 27 23 24 183 184 165 186 17 22 16 17 16 17 22 17 22 17 22 17 13 14 17 22 14 17 187 18 30 21 18 21 18 30 18 30 18 30 18 17 18 18 30 18 18 188 18 30 21 18 21 18 30 18 30 18 30 18 17 18 18 30 18 18 188 17 22 16 17 16 17 22 17 22 17 22 17 13 14 17 22 14 17 188 16 22 16 16 16 16 22 16 22 16 22 16 13 14 16 22 14 16 181 11 14 10 11 10 11 14 11 14 11 14 11 9 10 11 14 10 11 182 17 22 16 17 16 17 22 17 22 17 22 17 13 14 17 22 14 17 183 22 30 21 22 21 22 30 22 30 22 30 22 17 18 22 30 18 22 184 11 14 10 11 10 11 14 11 14 11 14 11 9 10 11 14 10 11 185 19 30 21 19 21 19 30 19 30 19 30 19 17 18 19 30 18 19 186 22 30 21 22 21 22 30 22 30 22 30 22 17 18 22 30 18 22 187 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 188 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 188 24 26 23 24 23 24 26 24 26 24 26 24 22 22 24 26 22 24 200 35 43 34 35 34 35 43 35 43 35 43 35 30 31 35 43 31 35 281 35 43 34 35 34 35 43 35 43 35 43 35 30 31 35 43 31 35 202 24 27 23 24 23 24 27 24 27 24 27 24 22 23 24 27 23 24 203 29 35 29 29 29 29 35 29 35 29 35 29 26 26 29 35 26 29 204 31 43 34 31 34 31 43 31 43 31 43 31 30 31 31 43 31 31 205 16 21 15 16 15 16 21 16 21 16 21 16 12 13 16 21 13 16 206 13 21 15 13 15 13 21 13 21 13 21 13 12 13 13 21 13 13 207 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 208 13 21 15 13 15 13 21 13 21 13 21 13 12 13 13 21 13 13 208 16 21 15 16 15 16 21 16 21 16 21 16 12 13 16 21 13 16 210 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 211 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 212 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 213 13 21 15 13 15 13 21 13 21 13 21 13 12 13 13 21 13 13 214 16 21 15 16 15 16 21 16 21 16 21 16 12 13 16 21 13 16 215 18 30 21 18 21 18 30 18 30 18 30 18 16 17 18 30 17 18 216 11 22 15 11 15 11 22 11 22 11 22 11 13 13 11 22 13 11 217 17 30 21 17 21 17 30 17 30 17 3o 17 16 17 17 30 17 17 216 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 218 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 220 11 22 15 11 15 11 22 11 22 11 22 11 13 13 11 22 13 11 221 17 30 21 17 21 17 30 17 30 17 30 17 16 17 17 30 17 17 222 223 33 32 33 33 33 34 32 224 225 226 227 226 228 230 231 232 233 234 235 236 237 238 238 240 241 242 l’nentateu' 1212 It) 55 56 57 58 59 60 61 62 63 64 65 66 67 6s 69 78 71 72 ""'178’ 14 9 11 9 9 9 14 188 45 31 36 31 31 31 45 29 20 20 29 20 29 16 29 29 29 20 181 31 24 27 24 24 24 31 14 10 10 14 10 14 8 14 14 14 10 182 27 22 24 22 22 22 27 163 184 165 186 22 13 17 13 13 13 22 187 30 17 18 17 17 17 30 188 30 17 18 17 17 17 30 188 22 13 17 13 13 13 22 188 22 13 16 13 13 13 22 181 14 9 11 9 9 9 14 182 22 13 17 13 13 13 22 183 30 17 22 17 17 17 30 184 14 9 11 9 9 9 14 185 30 17 19 17 17 17 30 186 30 17 22 17 17 17 30 187 30 16 17 16 16 16 30 186 30 16 17 16 16 188 26 22 24 22 22 288 43 30 35 30 30 281 43 30 35 30 30 282 27 22 24 22 22 12 9 9 12 9 12 8 12 12 12 9 283 35 26 29 26 26 284 43 30 31 30 30 285 21 12 16 12 12 286 21 12 13 12 12 287 30 16 17 16 16 288 21 12 13 12 12 288 21 12 16 12 12 218 30 16 17 16 16 211 30 16 17 16 16 212 30 16 17 16 16 213 21 12 13 12 12 214 21 12 16 12 12 215 30 16 18 16 16 216 22 13 11 13 13 217 30 16 17 16 16 218 30 16 17 16 16 218 30 16 17 16 16 228 22 13 11 13 13 221 30 16 17 16 16 222 223 32 32 27 27 27 27 27 27 27 27 27 27 224 225 226 227 228 228 238 231 232 233 234 235 236 237 238 238 248 241 242 74 75 76 78 83 84858687888990 235 237 239 239 241 29 27 20 10 27 29 27 20 27 20 28 29 27 29 14 27 133 29 14 27 16 8 l6 8 20 10 28 29 14 28 29 14 28 20 10 27 Predator It) 9] 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 179 14 10 10 14 10 10 l4 l4 14"410 14 10 14 14 14 10 10 14 180 31 22 22 31 22 22 31 31 31 22 31 22 31 31 31 22 22 31 181 18 13 13 18 l3 13 18 18 18 13 18 13 18 18 18 l3 l3 18 182 14 10 IO 14 10 10 l4 l4 I4 10 14 10 14 l4 14 10 10 14 183 184 185 186 22 16 16 22 16 16 22 22 22 16 22 16 22 22 22 l6 16 22 187 30 21 21 3O 21 21 30 30 30 21 30 21 30 30 30 21 21 30 188 30 21 21 30 21 21 30 3O 30 21 30 21 30 30 3O 21 21 30 189 22 l6 16 22 16 16 22 22 22 16 22 16 22 22 22 16 16 22 190 22 l6 16 22 16 16 22 22 22 16 22 16 22 22 22 l6 16 22 191 14 10 10 14 10 10 l4 l4 I4 10 14 10 l4 14 14 10 10 14 192 22 l6 16 22 l6 16 22 22 22 16 22 16 22 22 22 16 16 22 193 30 21 21 3O 21 21 30 30 3O 21 30 21 30 30 3O 21 21 30 194 14 10 10 14 10 10 I4 l4 14 10 14 10 l4 14 14 10 10 14 195 30 21 21 30 21 21 30 30 3O 21 30 21 30 30 30 21 21 30 196 30 21 21 30 21 21 30 30 30 21 30 21 30 3O 30 21 21 30 197 30 21 21 30 21 21 3O 30 30 21 30 21 30 30 3O 21 21 30 198 30 21 21 30 21 21 30 30 30 21 30 21 30 3O 30 21 21 30 199 13 10 10 13 10 10 13 13 13 10 13 10 l3 13 13 10 10 13 200 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 201 30 21 21 3O 21 21 30 30 30 21 30 21 30 30 30 21 21 30 202 14 10 10 14 10 10 14 l4 14 10 14 10 14 14 14 10 10 14 203 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 204 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 205 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 200 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 207 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 208 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 209 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 210 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 211 30 21 21 30 21 21 30 30 30 21 30 21 3O 30 3O 21 21 30 212 30 21 21 30 21 21 30 30 30 21 30 21 30 30 3O 21 21 30 213 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 214 21 15 15 21 15 15 21 21 21 15 21 15 21 21 21 15 15 21 215 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 216 22 15 15 22 15 15 22 22 22 15 22 15 22 22 22 15 15 22 217 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 218 30 21 21 30 21 21 30 30 30 21 30 21 30 30 30 21 21 30 219 30 21 21 30 21 21 30 30 30 21 30 21 30 30 3O 21 21 30 220 22 15 15 22 15 15 22 22 22 15 22 15 22 22 22 15 15 22 221 30 21 21 30 21 21 30 30 3O 21 3O 21 30 30 30 21 21 30 222 223 34 32 32 32 34 32 32 32 32 32 32 32 32 32 32 32 32 34 224 225 229 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 134 135 II) 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 ""'178 10 14 14 14 17 14 17 10 10 17 188 22 31 31 31 40 31 40 21 21 40 29 29 29 29 20 16 29 20 181 13 18 18 18 20 18 21 12 12 21 14 14 14 14 10 8 14 10 182 10 14 14 14 17 14 17 1o 10 17 183 184 165 186 16 22 22 22 28 22 28 15 15 28 187 21 30 30 30 30 30 30 17 17 30 188 21 30 30 30 30 30 30 17 17 30 188 16 22 22 22 28 22 28 15 15 28 188 16 22 22 22 24 22 24 15 15 24 181 10 14 14 14 17 14 17 10 10 17 182 16 22 22 22 28 22 28 15 15 28 183 21 30 3o 30 39 30 39 21 21 39 184 10 14 14 14 17 14 17 10 10 17 185 21 30 30 30 35 30 35 17 17 35 186 21 30 30 30 39 30 39 21 21 39 187 21 30 30 30 30 30 30 16 16 30 186 21 30 30 30 30 30 30 16 16 30 188 10 13 13 13 16 13 16 10 10 16 288 21 30 30 30 38 30 38 20 20 38 281 21 30 30 3o 38 30 38 20 20 38 282 10 14 14 14 16 14 16 10 10 16 12 12 12 12 9 8 12 9 283 15 21 21 21 27 21 27 15 15 27 284 21 30 30 30 30 3o 30 16 16 30 285 15 21 21 21 27 21 27 15 15 27 286 15 21 21 21 21 21 21 12 12 21 287 21 30 30 30 30 30 30 16 16 30 288 15 21 21 21 25 21 25 13 13 25 288 15 21 21 21 27 21 27 15 15 27 218 21 30 30 30 30 30 3o 16 16 30 211 21 30 30 30 30 30 30 16 16 30 212 21 30 30 30 30 30 30 16 16 30 213 15 21 21 21 21 21 21 12 12 21 214 15 21 21 21 27 21 27 15 15 27 215 21 30 30 30 35 30 35 17 17 35 216 15 22 22 22 16 22 16 10 10 16 217 21 30 30 30 30 30 30 16 16 30 218 21 30 30 30 30 30 30 16 16 30 218 21 30 30 30 30 30 30 16 16 30 228 15 22 22 22 16 22 16 10 10 16 221 21 30 30 30 30 30 30 16 16 30 222 223 32 32 32 34 28 34 30 29 27 27 27 27 24 27 24 224 225 226 227 228 228 238 231 232 233 234 235 236 237 238 239 248 241 242 197 210 211 212 213 215 216 217 218 219 232 236 237 239 239 241 127 37 17 23 29 14 27 20 10 27 128 129 130 13 22 I31 29 14 12 31 I32 29 l4 12 30 29 I4 30 16 8 136 133 I34 135 136 25 31 24 27 59 56 51 42 41 48 16 37 32 37 I37 I38 I39 140 39 37 72 50 I41 41 63 78 69 62 31 52 21 32 32 I42 87 49 23 37 22 I43 41 71 73 50 46 28 42 24 30 I44 23 45 48 17 21 145 I46 I47 148 I49 150 15] 152 153 154 155 156 157 158 159 160 I6] I62 62 70 52 69 40 48 4O 31 52 37 53 30 24 31 49 42 35 62 28 22 26 39 26 37 15 46 67 49 37 54 73 47 71 39 38 52 77 69 51 48 51 38 49 39 32 29 57 62 28 69 70 137 50 55 27 62 70 68 7O 48 42 37 32 70 40 74 71 57 52 35 27 29 65 45 62 33 41 63 40 80 63 63 47 62 31 33 41 88 68 70 46 57 37 32 70 41 81 63 63 53 67 31 33 63 39 71 40 35 47 34 74 39 78 52 65 28 35 Predator I63 164 165 166 167 I68 169 170 I7] 172 I73 I74 I75 I76 I77 I78 I79 210 211 212 213 214 215 219 217 219 219 221 8883899988988 235 98 55589 40 79 63 61 48 59 31 32 62 58 76 59 80 47 58 I7 33 ll 39 75 28 30 22 23 58 17 33 39 28 3O 52 33 42 37 l9 I7 33 41 25 47 26 31 16 69 48 37 53 33 138 75 13 42 28 22 23 22 29 62 67 72 59 52 63 69 53 63 69 60 53 61 83 34 60 56 56 75 27 83 61 45 41 62 14 24 48 58 58 80 17 53 62 32 1338238 Predator m 181 I82 183 184 185 186 187 188 189 I90 19] I92 I93 I95 I96 I98 1” 190 191 192 193 184 195 196 197 198 199 190 191 192 193 194 195 199 197 198 199 200 201 202 203 204 205 209 207 209 209 210 211 212 213 214 215 219 217 219 219 220 221 223 225 226 227 228 230 231 232 233 235 239 237 239 239 241 242 76 76 8! 46 62 19 24 49 83 83 61 62 41 62 14 24 48 61 8 62 41 83 61 8 62 41 62 14 24 48 61 61 17 51 31 67 67 72 13 59 52 56 35 59 56 75 17 43 35 56 75 3t: 69 34 139 67 72 62 69 &X&%8$ 83 61 62 14 24 48 67 72 61 83 31 83 83 61 30 62 41 62 14 24 48 59 80 32 61 83 34 68 68 88 3O 38 2 199 3‘88 30 20] 74 74 96 30 202 93 71 79 85 21 204 68 88 38 83 21 206 207 41 75 27 140 67 38 80 24 83 21 210 2]] 212 38 4438 4438 67 67 67 38 38 38 213 41 75 27 214 49 83 21 215 41 68 30 216 55 27 Predator 217 218 219 220 221 22223224225226227228229230231 232233234 237 239 241 38 38 67 67 38 38 38 67 38 39 45 49 45 55 27 38 67 38 28 20 42 26 ll 24 31 58 87 39 93 44 7 l 60 76 37 141 69 43 57 63 69 25 52 31 45 14 69 67 35 59 72 55 235 236237 238 239 240 241 242243245246 7O 31 47 35 39 58 45 43 16 47 49 67 28 35 25 62 142 47 58 43 Prey ID Predator ID123456789101112131415161718 243 244 245 246 49 48 65 48 33 54 48 33 48 49 486433 54 34 3419 48 Prey ID Predator ID 192021222324252627282930313233343536 243 244 245 246 6433 49 33 33 33 33 37 33 33 37 3333 33 37 33 48 33 Prey ID Predator ID 373839404142434445464748495051525354 243 244 245 246 37 34 24 23 23 23 34 23 34 23 35 23 19 23 23 35 23 23 Prey ID Predator ID 555657585960616263646566676869707172 243 244 245 246 35 19 23 19 19 33 35 29 20 34 29 20 29 30 43 43 43 34 Prey ID Predator ID 737475767778798081828384858687888990 243 244 245 246 43 34 29 20 34 43 29 30 30 16 29 16 16 16 34 43 43 34 Prey ID Predator ID 9]9293949596979899100101102103104105106107108 243 244 245 246 48 37 37 48 37 37 48 48 48 37 48 37 48 48 48 37 54 65 Prey ID Predator ID 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 243 244 245 246374848486548644949642943292920164320 143 Prey ID Predator ID123456789101112131415161718 Prey ID Predator ID 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 243 50 4o 24 16 23 244 245 47 74 99 74 246 29 29 34 59 59 59 46 29 Prey ID Predator ID 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 243 32 32 17 31 244 245466058 46 5244545352585256 246 Prey ID Predator ID 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 243 20 35 32 35 32 35 19 32 55 26 244 245 52 246 Prey ID Predator ID 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 243 244 245 248 Prey ID Predator ID 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 243 244 245 246 Prey ID Predator ID 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 243 30 46 36 57 67 30 244 92 84 75 47 83 47 245 246 Prey ID Predator ID 235236237238239240241242243245246 243 56 39 56 40 29 244 85 85 67 40 245 246 144 APPENDIX Horizontal depth values with references 145 Note: Season 1 = spring; 2 = summer; 88888 = not present; 8 = no preference ID Season Minimum Maximum Preference Datasource 1 1 15 110 110 91,94,95, l 2 15 110 110 91,94,95, 8 l 15 3O 8 4,172,167, 8 2 15 30 8 4,172,167, 10 l 15 110 15 4,91,164,171,172, 10 2 15 110 15 4,91,164,171,172, 11 l 15 50 15 4,93,17l,172,97, ll 2 15 50 15 4,93,171,172,97, l4 1 15 110 15 164,166,4,171,172, l4 2 15 110 15 l64,l66,4,l71,l72, 16 1 15 110 110 91,170,171, 16 2 15 110 110 91,170,171, 19 1 15 110 110 95,171, 19 2 15 110 110 95,171, 20 l 15 110 15 4,170,171,172,97, 20 2 15 110 15 4,170,171,172,97, 21 1 15 50 15 170,172,93,171, 21 2 15 50 15 170,172,93,l71, 25 1 15 110 8 91,164,171,172, 25 2 15 110 8 91,164,171,172, 28 l 15 1 10 8 4,91,170,93,172, 28 2 15 l 10 8 4,91,170,93,172, 32 l 15 110 8 4,91,170,93,171,172, 32 2 15 110 8 4,91,170,93,171,172, 39 1 88888 88888 88888 4,170,171,172, 39 2 15 50 8 4,170,171,172, 50.3 1 15 110 8 I64,91,171,172, 50.3 2 15 110 8 I64,91,171,172, 51.4 1 15 50 15 164,4,172,93, 51.4 2 15 50 15 164,4,172,93, 59.4 1 15 110 45 91,170,171,4, 59.4 2 15 110 45 91,170,171,4, 68.5 1 88888 88888 88888 170,171,4, 68.5 2 15 110 15 170,171,4,97, 76.5 1 15 110 15 91,4,171,172,93, 76.5 2 15 110 15 91,4,171,172,93, 111.5 1 15 110 15 91,170,171,172,93, 111.5 2 15 110 15 91,170,171,172,93, 112.5 1 15 110 15 166,164,93,171,172,4, 112.5 2 15 110 15 166,164,93,171,172,4, 113.4 1 15 110 45 91,170,93,l71,172, 113.4 2 15 110 45 91,170,93,l71,172, 114.4 1 15 110 15 91,170,171,172, 114.4 2 15 110 15 91,170,171,172, 115.4 1 15 110 45 91,170,171,172, 115.4 2 15 110 80 91,170,171,172,97, 116.3 1 15 110 8 91,171,172, 116.3 2 15 110 8 91,171,172, 146 [D 117.3 117.3 117.4 117.4 120.4 120.4 121.2 121.2 122 122 124 124 126 126 127 127 130 130 131 131 132 132 136 136 137 137 138 138 139 139 140 140 141 141 142 142 143 143 152 152 153 153 154 154 156 156 159 159 160 160 Season N—N—N—N—N—Nu—Nn—nN—N—oN—aNu—Nu—N~N~N~N~N—N—N—N—N~N—N—N—nN— Minimum Maximum Preference Datasource 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 20 20 110 110 45 45 8 8 110 110 08 ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooocoooooooooo 147 4,91,170,171,172, 4,91,170,171,172, 4,91,170,171,172, 4,91,170,171,172, 91,170,4,172, 91,170,4,172, 164,171,172, 164,171,172, 165,169, 165,169, 114;115;118;120;140;1 16;107; 114;] 15;118;120;140;1 16;107; 114;]15;118;120;140;116;107; 114;]15;118;120;140;1]6;107; 114;115;118;120;140;1 16;107; 1l4;115;118;]20;140;116;107; 114;115;118;120;140;1 16;107; 114;] 15;118;120;140;1 16;107; 114;]15;118;120;140;1]6;107; 114;115;1]8;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;1 16;107; 114;115;118;120;140;116;107; 114;115;118;120;l40;116;107; 114;]15;118;]20;140;116;107; 114;]15;118;120;140;1]6;107; 114;115;1 18;]20;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;]15;118;120;140;1]6;107; 114;]15;118;120;140;1]6;107; 114;]15;118;120;140;1]6;107; 114;]15;118;120;140;116;107; 114;115;118;120;140;]16;107; 114;]15;118;120;140;1]6;107; 114;115;118;120;l40;116;107; 114;115;118;]20;140;116;107; 114;115;1 18;]20;140;116;107; 114;]15;118;120;140;1]6;107; 114;115;118;]20;140;116;107; 114;115;118;120;140;116;107; 114;]15;118;120;140;1]6;107; 114;] 15;118;120;140;1 16;107; 114;]15;118;120;140;1]6;107; 114;]15;118;]20;140;116;107; 114;]15;118;120;140;1]6;107; 114;115;118;120;140;116;107; 114;]15;118;120;140;1]6;107; 114;115;118;]20;140;116;107; ID 161 161 164 164 165 165 166 166 171 171 172 172 I74 174 175 175 186 186 190 190 191 191 197 197 214 214 216 216 217 217 219 219 220 220 224 224 227 227 228 228 229 229 230 230 234 234 235 235 236 236 Season N—Nu—N—N—Nr—N~N~N~N~N~N~N~N~N~N~N~N~N~N-~N~N~N~N~N~ Minimum Maximum Preference Datasource 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 148 8 8 15 15 oo ooooooooooooooooooooooooocoooooooooooooooooooooooooooooooooooooooooooooooooooooeoooooooooo 114;115;118;120;140;116;107; 114;115;118;]20;140;116;107; 114;115;1 18;]20;140;116;107;97; 114;115;1 l8;120;140;ll6;107;97; 114;115;1 18;]20;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;]20;140;116;107; 114;]15;118;120;140;1]6;107; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; l14;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;1 16;105; 114;]15;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; l14;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;1]8;120;l40;116;105; 114;115;118;120;140;1 16;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;1 18;120;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 1l4;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 1l4;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;1 18;]20;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; ID Season Minimum Maximum Preference Datasource 237 237 239 239 245 245 248 248 249 249 250 250 254 254 255 255 257 25 7 259 259 262 262 264 264 265 265 267 267 268 268 269 269 270 270 274 274 275 275 280 280 287 287 288 288 289 289 298 298 299 299 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 149 oooooooooooooooooocooooooooooooooooooooooooooooooooooooooooooooeoooooooooooooooooooooooooooooooooooo 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;]15;118;]20;140;116;105; l14;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; l14;]15;118;120;140;1]6;105; 114;115;118;120;l40;116;105; 114;]15;ll8;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;1 18;]20;140;116;105; 114;115;118;120;140;1 16;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;1 18;]20;140;116;105; 114;]15;118;120;140;1]6;105; I14;]15;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;l16;105; 114;]15;118;120;140;1]6;105; 114;]15;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;]16;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;105; 114;115;118;]20;140;116;105; 114;115;1 18;]20;140;116;105; 114;]15;118;120;140;1]6;105; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 1l4;115;118;120;140;l16;104; 114;]15;118;120;140;1]6;104; 114;]15;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;115;1]8;120;140;1 16;104; 114;115;1 18;]20;140;116;104; 114;]15;118;120;140;1]6;104; l14;]15;118;]20;140;116;104; 114;]15;118;120;140;116;104;]19; 114;]15;]18;]20;140;116;104;]19; 114;115;1]8;120;140;1 16;104; 114;]15;118;120;140;116;104; 114;115;118;]20;140;116;104; 114;115;118;]20;140;116;104; 114;115;1 18;]20;140;116;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;1 18;]20;140;116;104; 114;115;118;]20;140;116;104; 114;115;118;]20;140;116;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;116;104; 114;]15;118;120;140;1]6;104; ID Season Minimum Maximum Preference Datasource 300 1 15 110 8 300 2 15 110 8 301 1 15 110 8 301 2 15 110 8 302 1 15 110 8 302 2 15 110 8 307 1 15 110 8 307 2 15 110 8 309 1 15 110 8 309 2 15 110 8 316 1 15 110 8 316 2 15 110 8 317 1 15 110 8 317 2 15 110 8 318 1 15 110 8 318 2 15 110 8 319 1 15 110 8 319 2 15 110 8 320 1 15 110 8 320 2 15 110 8 321 1 15 110 8 321 2 15 110 8 324 1 15 110 8 324 2 15 110 8 326 1 15 110 8 326 2 15 110 8 329 1 15 110 8 329 2 15 110 8 330 1 15 110 8 330 2 15 110 8 332 1 15 110 8 332 2 15 110 8 335 1 15 110 8 335 2 15 110 8 338 1 15 110 8 338 2 15 110 8 339 l 15 110 8 339 2 15 110 8 340 1 15 110 8 340 2 15 110 8 34] l 15 110 8 341 2 15 110 8 342 1 15 110 8 342 2 15 110 8 343 l 15 110 8 343 2 15 110 8 344 1 15 110 8 344 2 15 110 8 345 1 15 110 8 345 2 15 110 8 150 114;115;1 18;120;l40;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1 16;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;115;1 18;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;116;104; 114;115;118;]20;140;116;104; 114;115;118;120;140;116;104; l14;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;l18;120;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;116;104; 114;]15;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;1 18;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;116;104; ll4;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; l14;115;118;120;140;l16;104; 114;]15;118;120;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 1]4;115;118;120;140;116;104; ID Season Minimum Maximum Preference Datasource 346 1 15 110 8 346 2 15 110 8 347 1 15 110 8 347 2 15 110 8 348 l 15 110 8 348 2 15 110 8 353 l 15 110 8 353 2 15 110 8 357 1 15 110 8 357 2 15 110 8 359 1 15 110 8 359 2 15 110 8 360 1 15 110 8 360 2 15 110 8 361 1 15 110 8 361 2 15 110 8 362 1 15 110 8 362 2 15 110 8 363 1 15 110 8 363 2 15 110 8 364 1 15 110 8 364 2 15 110 8 366 1 15 110 8 366 2 15 110 8 368 1 15 110 8 368 2 15 110 8 369 1 15 110 8 369 2 15 110 8 372 1 15 110 8 372 2 15 110 8 373 1 15 110 8 373 2 15 110 8 375 1 15 110 8 375 2 15 110 8 376 1 15 110 8 376 2 15 110 8 377 l 15 110 8 377 2 15 110 8 378 1 15 110 8 378 2 15 110 8 379 l 15 110 8 379 2 15 110 8 380 1 15 110 8 380 2 15 110 8 381 1 15 110 8 381 2 15 110 8 385 1 15 110 8 385 2 15 110 8 386 1 15 110 8 386 2 15 110 8 151 114;115;1 18;120;140;l 16;104; ID Season Minimum Maximum Preference Datasource 387 387 388 388 390 390 391 391 394 394 395 395 396 396 402 402 403 403 404 405 405 406 406 41 8 41 8 422 422 423 423 426 426 427 427 437 437 442 442 443 443 445 445 446 446 447 447 448 448 449 449 -N~N~NHN~N—4N—N—Nn—4Nn—4NI—4Nc—4Nn—4Nn—oN—aNu—aN—Nu—N—Na—INa—IlN—N~N-~ 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 152 8 ooooooooooooooooocooooooooooooooooooooooooooooooocoooooooooooooooooooooooooooooooocooooooooooooooe 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;116;104; 1l4;115;118;120;140;l16;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;]20;140;1 16;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;1]6;111;106; ll4;115;118;120;140;116;111;106; 114;]15;118;120;140;1]6;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;]20;140;116;]11;106; 114;]15;118;120;140;1]6;l11;]06; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;1]6;111;106; 114;115;118;]20;140;1 16;111;106; 114;115;118;]20;140;116;]]1;106; 114;]15;118;120;140;1]6;111;106; 114;115;118;]20;140;116;111;106; 114;]15;118;120;140;1]6;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;1]6;111;106; l14;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;]06; 1l4;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;]15;118;120;l40;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106;97; 114;115;118;120;140;116;111;106;97; 114;]15;118;120;140;116;111;106; l14;]15;118;120;140;116;111;106; 114;]15;118;]20;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;] 15;118;120;140;116;111;106;133; 114;115;118;120;140;116;111;106;133; l14;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;]06; 114;115;118;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;]06; 114;115;118;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;] 15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; ID Season Minimum Maximum Preference Datasource 450 1 15 110 8 450 2 15 110 8 451 l 15 110 8 451 2 15 110 8 452 1 15 110 8 452 2 15 110 8 453 1 15 110 8 453 2 15 110 8 454 1 15 110 8 454 2 15 110 8 456 1 15 110 27 456 2 15 110 27 457 1 15 110 8 457 2 15 110 8 458 1 15 110 8 458 2 15 110 8 462 1 15 110 8 462 2 15 110 8 463 l 15 110 8 463 2 15 110 8 464 1 15 110 8 464 2 15 110 8 466 l 15 110 8 466 2 15 110 8 471 l 15 110 8 471 2 15 110 8 487 1 15 110 8 487 2 15 110 8 488 1 15 110 8 488 2 15 110 8 494 1 15 110 8 494 2 15 110 8 496 1 15 110 8 496 2 15 110 8 497 1 15 110 8 497 2 15 110 8 498 1 15 110 8 498 2 15 110 8 499 1 15 110 8 499 2 15 110 8 500 1 15 110 8 500 2 15 110 8 501 1 15 110 8 501 2 15 110 8 502 1 15 110 8 502 2 15 110 8 505 1 15 110 8 505 2 15 110 8 510 1 15 110 8 510 2 15 110 8 153 ll4;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;]06; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;1 1 1;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;]06; 114;]15;118;120;140;116;11];106; 114;]15;118;120;140;116;111;106; 114;115;118;]20;140;116;]11;]06; l14;]15;118;120;140;116;1 1 1;106; 114;]15;118;120;140;116;111;]06; 114;]15;]18;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;]06; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;1l6;111;106;l33; 114;115;118;]20;140;1l6;111;106;l33; 114;]15;118;120;140;116;11l;106;133; 114;] 15;118;120;140;1l6;111;106;l33; 114;115;118;]20;140;1l6;111;106;l33; 114;115;118;]20;140;1l6;111;106;l33; 114;]15;1]8;120;140;116;111;]06;133; 114;115;118;]20;140;1l6;111;106;l33; 114;]15;]18;120;140;116;11l;106;133; 114;]15;118;120;140;116;11l;106;133; 114;]15;118;120;140;1l6;111;106;l33; 114;] 15;]18;120;140;116;11l;106;133; 114;]15;118;120;140;116;111;]06;133; l14;]15;118;120;140;1l6;111;106;l33; 114;]15;118;120;140;116;11l;106;133; 114;]15;]18;120;140;116;11l;106;133; 114;115;118;]20;140;1l6;111;106;l33; 114;]15;118;120;140;116;11l;106;133; 114;115;118;120;140;1l6;111;106;l33; 114;]15;]18;]20;140;1l6;111;106;l33; 114;115;118;120;140;116;11l;106;133; 114;]15;118;120;140;116;111;]06;133; 114;115;118;120;140;116;11l;106;133; 114;115;118;120;140;116;l11;]06;133; 114;]15;118;120;140;1l6;111;106;l33; l14;]15;118;120;140;116;]]l;106;133; l14;]15;118;120;140;116;111;]06;133; 114;]15;118;120;140;116;11l;106;133; 114;]15;118;120;140;1l6;111;106;l33; ID Season Minimum Maximum Preference Datasource 515 l 15 110 8 515 2 15 110 8 520 l 15 110 8 520 2 15 110 8 521 1 15 110 8 521 2 15 110 8 522 1 15 110 8 522 2 15 110 8 527 1 15 110 8 527 2 15 110 8 528 l 15 110 8 528 2 15 110 8 535 1 15 110 8 535 2 15 110 8 536 1 15 110 8 536 2 15 110 8 537 1 15 110 8 537 2 15 110 8 538 1 15 110 8 538 2 15 110 8 554 1 15 110 15 554 2 15 110 15 555 l 15 110 15 555 2 15 110 15 556 1 15 110 15 556 2 15 110 15 557 l 15 110 15 557 2 15 110 15 558 1 15 110 15 558 2 15 110 15 559 1 15 110 15 559 2 15 110 15 560 1 15 110 15 560 2 15 110 15 561 1 15 110 15 561 2 15 110 15 563 1 15 110 15 563 2 15 110 15 565 1 15 110 15 565 2 15 110 15 569 1 15 110 15 569 2 15 110 15 570 1 15 110 15 570 2 15 110 15 571 1 15 110 15 571 2 15 110 15 575 1 15 110 15 575 2 15 110 15 579 1 15 110 15 579 2 15 110 15 154 114;]15;118;120;140;1l6;111;106;l33; ID Season Minimum Maximum Preference Datasource 586 586 591 591 597 597 598 598 603 603 605 605 609 609 611 611 612 612 613 613 614 614 615 615 616 616 617 617 618 618 619 619 621 621 622 622 623 623 624 624 625 625 626 626 627 627 628 628 NH“—NHN—eN—aN—NflN—eN—N—nw—N—N—Nu—N—N—N—N—N—aN—N—N—oN—nNI—IN— 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110 15 15 15 15 15 15 15 15 l1”,";aoooeoooocooooooe N 0000me 8—4—4 MM OOOOWWWOOWOOWOOOOOOOOOOWWOOOOWWWWOOOO 155 114;115;118;]20;140;116;111;106;]33; 114;]15;]18;]20;140;1l6;111;106;l33; 114;]15;118;120;140;116;11l;106;133; 114;115;118;]20;140;1l6;111;106;l33; 114;115;118;120;140;116;111;]06;133; 114;]15;]18;120;140;116;l11;106;133; 114;] 15;118;120;140;1 l6;111;106;l33; 114;]15;118;120;140;116;11l;106;133; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106;97; 114;]15;118;120;140;116;111;]06;97; 114;]15;118;120;140;116;111;]06;97; 114;]15;]18;120;140;116;111;]06;97; 114;]15;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106;97; 114;115;118;120;140;116;111;]06;97; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;] 15;118;120;140;116;111;106; 114;] 15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; l14;]15;]18;120;140;116;111;106; l14;]15;118;120;140;116;111;106; 114;115;1 18;120;140;116;11l;106;133; 114;115;1 18;120;140;116;11 l;106;133; 114;115;118;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;]20;140;1 16;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;115;118;]20;140;116;]11;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;]]1;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;115;118;]20;140;116;111;106; 114;]15;118;120;140;116;111;106; 114;]15;118;120;140;116;]11;]06; 114;115;118;]20;140;116;]]1;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;l11;106; 114;]15;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116;97; 114;115;1 18;120;140;116;97; 114;115;1 18;120;140;116;97; 114;115;118;120;140;116;97; 114;115;118;]20;140;116;97; 114;]15;118;120;140;116;97; l14;]15;118;120;140;116;97; 114;115;1 18;120;140;116;97; 114;115;118;120;140;116;97; ID Season Minimum Maximum Preference Datasource 631 1 15 110 8 631 2 15 110 8 634 l 15 110 8 634 2 15 110 8 635 1 15 110 8 635 2 15 110 8 636 1 15 110 8 636 2 15 110 8 638 1 15 110 8 638 2 15 110 8 639 1 15 110 8 639 2 15 110 8 640 1 15 110 8 640 2 15 110 8 641 1 15 110 8 641 2 15 110 8 642 l 15 110 8 642 2 15 110 8 643 1 15 110 8 643 2 15 110 8 644 l 15 110 8 644 2 15 110 8 647 1 15 110 8 647 2 15 110 8 648 1 15 110 8 648 2 15 110 8 649 1 15 110 8 649 2 15 110 8 650 1 15 110 8 650 2 15 110 8 652 1 15 110 8 652 2 15 110 8 653 1 15 110 8 653 2 15 110 8 659 1 15 110 8 659 2 15 110 8 660 1 15 110 8 660 2 15 110 8 661 1 15 110 8 661 2 15 110 8 662 1 15 110 15 662 2 15 110 15 663 1 15 110 15 663 2 15 110 15 664 1 15 110 15 664 2 15 110 15 665 1 15 110 15 665 2 15 110 15 666 1 15 110 15 666 2 15 110 15 156 114;115;1 l8;120;140;116;97; ID Season Minimum Maximum Preference Datasource 667 l 15 110 15 114;]15;]18;120;140;116;97; 667 2 15 110 15 l14;]15;118;120;140;116;97; 668 1 15 110 15 114;]15;118;120;140;116;97; 668 2 15 110 15 114;115;118;]20;140;116;97; 669 1 15 110 15 114;]15;118;120;140;116;97; 669 2 15 110 15 1l4;115;118;120;140;116;97; 670 l 15 110 15 114;]15;118;120;140;116;97; 670 2 15 110 15 114;115;118;]20;140;116;97; 671 1 15 110 15 l14;]15;118;120;140;116;97; 671 2 15 110 15 114;]15;118;120;140;116;97; 675 l 15 110 15 114;]15;118;120;140;116;97; 675 2 15 110 15 114;115;118;120;140;116;97; 676 1 15 110 15 114;]15;118;120;140;116;97; 676 2 15 110 15 114;]15;118;120;140;116;97; 678 l 15 110 15 114;]15;118;120;140;116;97; 678 2 15 110 15 114;115;118;]20;140;116;97; 680 1 15 110 15 114;l15;118;120;140;116;97; 680 2 15 110 15 114;115;118;120;140;116;97; 681 1 15 110 15 114;]15;118;120;140;116;97; 681 2 15 110 15 114;115;118;120;140;116;97; 682 l 15 110 8 114;115;118;120;140;116; 682 2 15 110 8 114;115;118;120;140;116; 683 1 15 110 8 114;115;118;120;140;116; 683 2 15 110 8 114;115;118;120;140;116; 685 1 15 110 8 114;115;118;120;140;116; 685 2 15 110 8 114;115;118;120;140;116; 686 1 15 110 8 114;115;118;120;140;116; 686 2 15 110 8 114;115;118;120;140;116; 687 1 15 110 8 114;115;118;120;140;116; 687 2 15 110 8 114;115;118;120;140;116; 688 l 15 110 8 l14;]15;118;120;140;116;]l9;181 688 2 15 110 8 114;115;118;120;140;ll6;119;181 689 1 15 110 8 114;115;1l8;120;140;116;119;181 689 2 15 110 8 114;115;118;120;140;1l6;119;181 692 1 15 110 8 ll4;115;118;120;140;116;119;l81 692 2 15 110 8 114;115;118;120;140;l16;119;181 693 l 15 110 8 114;115;118;]20;140;116;119;181 693 2 15 110 8 114;]15;]18;120;140;116;119;181 696 l 15 110 8 1I4;115;118;120;140;116;119;181 696 2 15 110 8 114;115;118;120;l40;116;119;181 700 1 15 110 8 l14;]15;]18;120;140;116;119;181 700 2 15 110 8 114;]15;118;120;140;116;119;181 701 1 15 110 8 114;115;118;120;140;116;119;l8l 701 2 15 110 8 114;115;118;]20;140;116;119;181 702 l 15 110 15 160; 702 2 15 110 15 160; 916 l 15 20 19 9,11,12, 916 2 15 20 19 7,11,12 924 1 15 47 19 9,11,12, 924 2 15 47 19 7,1],12 157 HD 928 928 944 944 950 950 958 958 962 962 977 977 978 978 982 982 986 986 993 993 997 997 1003 1003 1004 1004 1010 1010 1014 1014 1017 1017 1018 1018 1019 1019 1020 1020 1021 1021 1023 1023 1025 1025 1039 1039 1040 1040 1043 1043 Season fl N'—NHN—NF‘N'—N'—Nfi-‘N'dN'—N—‘N—N—N'—N—‘N-‘ND—N—‘N—‘N—‘N—N—‘N—‘N—‘NHN Minimum Maximum Preference Datasource 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 20 20 68 68 37 37 16 i6 110 110 36 36 110 110 45 45 47 47 37 37 45 45 77 77 110 110 20 20 110 110 20 20 20 20 19 19 20 20 110 110 46 46 77 77 110 110 37 37 37 37 19 19 36 36 l7 17 15 15 37 37 19 19 37 37 8 8 19 l9 19 19 19 19 19 19 68 68 19 19 20 20 19 19 19 19 19 I9 19 19 19 19 19 19 77 77 35 35 19 19 18 35 158 9,11,12, 7,11,12 11, 11, 11,12,14 11.12.14 11,12,14 11,12,14 11,12,14 11,12,14 11,12 11,12 11,12,39, 11,12,39, 11, 11, 9,11,12,148 9,11,12,148 9,11,12,13,]48 9,11,12,13,]48 9,11,12,13,]48 9,11,12,13,]48 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,13,]48 9,11,12,13,]48 9,11,12,13,]48 9,11,12,13,]48 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 ID Season Minimum Maximum Preference Datasource 1049 1 15 20 20 9,11,12,148 1049 2 15 20 20 9,11,12,148 1050 1 15 46 18 9,11,12,148 1050 2 15 46 18 9,11,12,148 1052 l 15 77 19 9,11,12,148 1052 2 15 77 19 9,11,12,148 1054 1 15 24 19 9,11,12,148 1054 2 15 19 19 9,11,12,148 1057 1 15 110 37 9,11,12,148 1057 2 15 110 19 9,11,12,148 1059 1 15 73 18 9,11,12,148 1059 2 15 73 18 9,11,12,148 1081 1 15 110 93 8,11,12 1081 2 15 110 93 8,11,12 1082 1 15 110 35 8,11,12 1082 2 15 110 35 8,11,12 1084 l 15 19 19 8,11,12 1084 2 15 19 19 8,11,12 1085 1 15 68 19 8,11,12 1085 2 15 68 19 8,11,12 1089 l 15 110 19 8,11,12 1089 2 15 110 19 8,11,12 1092 1 15 20 18 8,11,12,13 1092 2 15 20 18 8,11,12,13 1093 1 15 37 18 8,11,12 1093 2 15 37 18 8,11,12 1094 1 15 110 46 8,11,12 1094 2 15 110 46 8,11,12 1095 1 15 36 20 8,11,12 1095 2 15 36 20 8,11,12 1098 1 15 46 40 8,11,12 1098 2 15 46 40 8,11,12 1099 1 15 68 36 8,11,12 1099 2 15 68 36 8,11,12 1100 1 15 46 46 8,11,12 1100 2 15 46 20 8,11,12 1102 l 15 37 19 8,11,12 1102 2 15 37 19 8,11,12 1103 l 15 110 50 8,11,12 1103 2 15 110 50 8,11,12 1104 1 15 110 40 8,11,12 1104 2 15 110 40 8,11,12 1105 1 15 110 46 8,11,12 1105 2 15 110 46 8,11,12 1106 1 15 37 37 8,11,12 1106 2 15 37 37 8,11,12 1108 1 15 37 18 8,11,12 1108 2 15 37 18 8,11,12 1109 l 15 55 55 8,11,12 1109 2 15 55 19 8,11,12 159 ID Season Minimum Maximum Preference Datasource 1111 1111 1112 1112 1114 1114 1115 1115 1119 1119 1120 1120 1125 1125 1133.2 1133.2 1133.5 1133.5 1152 1152 1188.2 1188.2 1188.5 1188.5 1190 1190 1191.2 1191.2 1191.5 1191.5 1194 1194 1195 1195 1196 1196 1198 1198 1200.3 1200.3 1200.4 1200.4 1201 1201 1202 1202 1208 1208 1209 1209 N—‘N—‘NI—‘Nt—N~NHN~N~NHN~N—N-—Nt—NF—‘N—‘N—‘N—‘N—‘NHN—N—‘N—‘N—N—NH 35 35 15 15 15 15 15 15 32 32 15 15 15 15 88888 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 18 18 15 27 110 110 110 110 73 73 110 110 110 110 110 110 110 110 88888 110 110 91 37 15 15 110 110 55 73 73 110 110 110 110 110 110 30 30 110 86 30 30 68 91 68 91 55 22 82 82 110 110 110 110 40 40 46 46 40 40 35 35 46 46 40 40 40 40 88888 8 27 18 15 8 15 20 18 18 31 31 90 90 46 55 40 40 15 15 75 17 15 15 31 40 31 40 15 15 8 8 8 8 37 50 160 8,11,12 8,1 1,12 8,11,12 8,1 1,12 8,1 1,12 8,11,12 8,11,12 8,1 1,12 8,11,12 8,11,12 8,1 1,12 8,1 1,12 158, 158, 41 ,46,68, 41,46,155,156,157, 42,54,48,70,155,156,157, 42,54,48,70,155,156,157, 42,68,78, 42,68,78, 45,41,63, 45,41 ,63, 42,100,54,70,155,156,157, 42,100,54,70,155,156,157, 100,79,158, 100,79,158, 42,43,41,55,58,44, 42,43,41,55,58,44, 42,70,54,58,57,73,155,156,157, 42,70,54,58,57,73,155,156,157, 87, 87, 87, 87, 87,158, 87,158, 87, 87, 87,89,82,83,158,159, 87,89,82,83,158,159, 87,89,82,83,158,159, 87,89,82,83,158,159, 42,100,66, 42,100,66, 100,76,77, 100,76,77, 42,70,68,100, 42,70,68,100, 42,72,62,48,60,73, 42,72,62,48,60,73, HD 1210 1210 1225 1225 1226 1226 1242 1242 Season 1 N—‘N—‘Ni—N Minimum Maximum Preference Datasource 31 37 15 15 15 15 15 15 110 110 64 64 46 27 37 37 161 91 82 22 22 15 8 19 19 42,72,62,48,60,73, 42,72,62,48,60,73, 42,100,70,68, 42,100,70,68, 42,68,154, 42,68,154, 7,1 1, 7,1 1 , APPENDIX Vertical depth values with references 162 Ky to vertical depth categories: Vertical Depth Category - Spring 1 Upper Water column Bottom Water column Sediments Surface 1cm above - 1cm below Sediment >1 cm Not present = 88888 No preference = 8 Vertical Depth Category - Summer Epilimnion Thermocline Area Hypolimnion Nephloid Area - up to 10 m from bottom Datasource - 32,33,34] Sediments Surface 1cm above - lcm below Sediment >1 cm For season, Spring = 1 Summer = 2 163 Note: Double digits indicate two depth zones of preference 50.3 50.3 51.4 51.4 59.4 59.4 68.5 68.5 76.5 76.5 1 1 1.5 l l 1.5 1 12.5 112.5 1 13.4 1 13.4 1 14.4 1 14.4 88 88 1900 J’ 96% 00 164 o 9 °& 69 ‘39 $80 ‘90 4> k‘" «8 4' 0 4" 0 0 ~49 ~89 V” 53‘ .3: Q’ Q’ é‘o’ '6‘ s" 2 1 1 2 1 4 2 1 4 2 3 3 1 3 3 5 5 1 5 5 3 8 1 3 1 5 8 1 5 12 1 1 1 1 1 2 1 1 2 1 3 23 1 3 8 5 45 1 5 4 2 1 1 2 1 4 23 1 4 1 2 1 1 2 1 4 23 1 4 2 2 1 1 1 1 4 23 1 4 1 2 2 1 2 1 4 2 1 4 1 3 23 1 3 1 5 45 1 5 2 2 1 1 2 1 4 12 1 4 12 2 8 1 2 1 4 23 1 4 2 88 88888 88888 88888 88888 88888 4 1 1 4 1 3 23 l 3 l 5 45 1 5 12 4 3 1 4 1 6 5 1 6 12 3 23 1 3 3 5 45 1 5 12 88 88888 88888 88888 88888 88888 4 2 1 4 1 2 1 1 2 1 4 12 1 4 12 2 1 1 2 1 4 12 1 4 1 4 3 1 4 3 6 5 1 6 5 2 2 1 2 1 4 2 1 4 1 2 1 1 2 1 4 23 1 4 1 91,94,95, 91,94,95, 4,172,167, 4,172,167, 4,91,164,171,172, 4,91,164,171,172, 4,93,171,172,97, 4,93,171,172,97, 164,166,4,171,172, 164,166,4,171,172, 91,170,171, 91,170,171, 95,171, 95,171, 4,170,171,172,97, 4,170,171,172,97, 17o,172,93,171, 17o,172,93,171, 91,164,171,172, 91,164,171,172, 4,91,170,93,172, 4,91,170,93,172, 4,91,170,93,171,172, 4,91,170,93,171,172, 4,170,171,172, 4,170,171,172, 164,91,171,172, 164,91,171,172, 164,4,172,93, 164,4,172,93, 91,170,171,4, 91,170,171,4, 170,171,4, 170,171,4,97, 91,4,171,172,93, 91,4,171,172,93, 91,170,171,172,93, 91,170,171,172,93, 166,164,93,171,172,4, 166,164,93,171,172,4, 91,170,93,171,172, 91,170,93,171,172, 91,170,171,172, 91,170,171,172, '5 115.4 115.4 116.3 116.3 117.3 117.3 117.4 117.4 120.4 120.4 121.2 121.2 122 122 124 124 126 126 127 127 130 130 131 131 132 132 136 136 137 137 138 138 139 139 140 140 141 141 142 142 143 143 152 152 153 153 ”a ”a ”’6- ”a 6;, 1'00 o i» ”6% 0 ’4 a" ’4 ’e 0 0 MNWNWNWNWNWNWNWNWMia-3ND)NWNUJNWNWNWNQ-fiMWANkoN-hNbN Nv—tNt—IN—N—N—N—N—INHN—NHN—INHNn—oNu—N—Nu—uNI—IN—Na—N—Nu—Nu—N— Nu—nNh—INv—oN—nN—Nu—nN—Nr—N—IN—‘Nu—N—m—nm—m—N—B 91,170,171,172, 91,170,171,172,97, 91,171,172, 91,171,172, 4,91,170,171,172, 4,91,170,171,172, 4,91,170,171,172, 4,91,170,171,172, 91,170,4,172, 91,170,4,172, 164,171,172, 164,171,172, 165,169, 165,169, 114;115;118;120;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;]15;118;120;140;1]6;107; l14;]15;118;120;140;1]6;107; ll4;l15;118;120;140;1]6;107; 114;115;118;120;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;120;140;116;107; ll4;115;118;120;140;116;107; 114;] 15;118;120;140;116;107; 114;115;118;]20;140;116;107; 114;]15;118;120;140;116;107; 114;115;118;120;140;1 16;107; 114;115;118;]20;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;] 15;118;120;140;116;107; 114;115;l18;]20;140;116;107; 114;115;1 18;]20;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;]20;140;116;107; 114;115;118;120;140;1 16;107; 114;115;118;]20;140;116;107; 114;115;1 18;]20;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;]15;118;120;140;1]6;107; 114;115;118;]20;140;116;107; ID 154 154 156 156 159 159 160 160 161 161 164 164 165 165 166 166 171 171 172 172 174 174 175 175 186 186 190 190 191 191 197 197 214 214 216 216 217 217 219 219 220 220 224 224 227 227 '9 N—N—N—N—N—‘Ni—eN—N—‘N—N~N—N—N—N—N—N—N—N—N—Ne—Nt—N—‘N—‘Pg '“o e 0 0 Q90 °¢ $°0 «'99 9° 3° c.“ e“ 30 .4} Q ‘5 .5 ‘5 é 38° 6 65" 4° 9 a a? --H---~Hhflhflfl#Hflflfl—-_~H--fl--_~fl~_~_~ WNWNWNWNWNWNWNWNWNWNWNWNWNWNWNWNWNWNUNNNWNWNWN N—‘N—‘N—‘N—‘NF‘N—‘N—‘N—‘N'—N—‘Ni—‘N—‘N—‘N—‘N—‘NHN—‘N—‘N_N~N_N~N—‘ ----~HHH~H-----~_~_~_~fl—flflfl—flflflflflflflflu 166 114;115;118;120;140;116;107; l14;]15;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107;97; li4;115;118;120;140;116;107;97; 114;l15;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;107; 114;115;118;120;140;116;105; 114;115;1 18;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;l 15;118;120;140;116;105; 114;115;118;120;140;116;105; ll4;l15;118;120;140;116;105; 114;115;1 18;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;l15;118;120;140;116;105; 114;]15;118;120;140;116;105; 1l4;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;1 18;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;1 18;120;140;116;105; 114;115;1 18;120;140;116;105; ll4;l15;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;118;120;140;116;105; ID 228 228 229 229 230 230 234 234 235 235 236 236 237 237 239 239 245 245 248 248 249 249 250 250 254 254 255 255 257 257 259 259 262 262 264 264 265 265 267 267 268 268 269 269 270 270 _H-~—-~_HHHHflHflH-----~_~__—~_~_--H-~ b)NWNUNWNWNWNWNWNWNUJNUJNWNWNWNWNWNNNWNWNWNWNWNMN u—oy—nt—ou—o—an—Iu—tn—tn—nn—Ir—np—oN—nNu—N—nNu—N—Nv—nN—N—N—Nt—INt—oN—N—nNr—‘N—N—IN— flfluflb—h—ap—pfi—a—pap—ap—a—a—ay—au—p—g—ag—ap—a-‘y—a—aud—p—ap—a—a—a—ay—ap—n-a-a-ay—nu—n-a—nfl—flfl— 167 Q9 114;115;118;120;140;116;105; l14;l15;118;120;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; l14;]15;118;120;140;116;105; ll4;115;118;120;i40;l16;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;116;105; 114;]15;118;120;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;116;105; 114;]15;118;120;140;116;105; ll4;l15;118;120;140;116;105; 1l4;115;118;120;140;116;105; 1l4;115;118;120;l40;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;116;105; l 14;l 15;1 18;]20;140;1 16;105; 114;]15;118;120;140;116;105; 114;115;1 18;]20;140;116;105; 114;115;118;120;140;116;105; 114;115;1 18;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;116;105; i14;]15;118;120;140;116;105; 114;115;118;]20;140;116;105; l14;]15;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;l15;118;120;140;116;105; ID 274 274 275 275 280 280 287 287 288 288 289 289 298 298 299 299 300 300 301 301 302 302 307 307 309 309 316 316 317 317 318 318 319 319 320 320 321 321 324 324 326 326 329 329 330 330 {,0 0" 0 J' ”6% or,» 33+, o “a «3,, Q 4%" G 484’; 099 I ”a % Nt—N—eN—IN—N-—Nh—NHN~Nv—-N—Nr—Nt—N—N—N—‘N—N—N—Nt—N—N-—-N~N-—-d:3 ....._.._.._.._.._.._.._.._.._.._.._.._................_.._....._.._.._.._.._....._.._.._.._.._......_.._.._.._.._....._....._.._.._._....... mumuuumwuwmwuwuwmuuwwuwuwmumuNwmwwwnwmunumwnwu N—N—N—N---~Nv—Nu—m—N—N—N—N—N—N—N—N—N—N—N--—-—-—-- ....._.._....._......_.._.._......_....._.._._._.._.._.._.._....._.._.._.._.._....._.._.._.._.._........._.._.._.._............_....._....._. mumwmwmwmwmwmwmumwmwwwwwwNumwmwnwwwuwwwuwuwwwwdééé ll4;115;118;120;140;116;105; il4;115;118;120;140;116;105; 114;115;118;120;140;116;105; i l4;l15;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; 114;]15;118;120;140;116;105; l14;]15;118;120;140;116;105; 114;115;118;]20;140;116;105; l14;]15;118;120;140;116;105; il4;115;118;120;140;116;105; 114;115;118;120;140;l16;105; 114;]15;118;120;140;116;105; 114;115;118;]20;140;1 16;105; 1l4;l15;118;120;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;]15;118;120;140;116;105; 114;115;118;120;140;116;105; 114;115;118;]20;140;116;105; ll4;115;118;120;140;l16;105; 114;115;118;]20;140;116;105; 114;115;118;]20;140;116;105; 114;115;118;120;140;116;105; ll4;l15;118;120;140;116;105; 114;115;118;120;140;116;104; 1l4;115;l18;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;115;118;120;140;i16;104; 114;]15;118;120;140;1]6;104; l14;]15;118;120;140;1]6;104; l14;l15;118;120;140;116;104; 114;115;118;]20;140;116;104; 114;115;118;120;140;116;104; l14;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;l16;104; 114;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;l16;104; 114;]15;118;120;140;116;104; l14;115;118;120;140;116;104; 114;]15;118;120;140;1]6;104; ID 332 332 335 335 338 338 339 339 340 340 341 341 342 342 343 343 344 344 345 345 346 346 347 347 348 348 353 353 357 357 359 359 360 360 361 361 362 362 363 363 364 364 366 366 368 368 9 9 0 $9 ° $9 5’ 18° 5" Q45 Q65 Ni—‘Ni—Ni—‘NF-‘NF‘NHN—‘N—‘Ni—N—N—‘Nh‘NF‘ND—N—‘N—Nl—NiflN—N—‘N'HNHN—Q --fl---~_--~—_——-flfl_~_—#_-—-—~—~—~fl- mwmwmwmwmwmumwmumumwmumwmwmwmwmwmwmwmwmwmwmuuw N—oNn—nNa—an—‘Nu—tNr—IN—N—N—Nn—nN—Ne—IN—N—Nu—N—ou-‘u—n—u—nu—ou—nN—nNu—u—Ic—iNr-n fl-fl-----~—~—-#_-—~H-H~_~fi~—~————~—_H~ 169 N—NI—INr—IN—N—Ns—INr—nNI—tNt—nNn—nNi—nN—nN—Nr—INt—tN—o—I—nn—nn—oe—e—INt—NI—ou—n—N— Q (c e 0 9 0°" sf ¢° 4'? 4 § b R" 5‘ K“ 0 ‘9 é 0 0 Q ~e‘ ‘ ‘ 9'? 8" 4° 114;115;118;120;140;1 16;104;119; 114;]15;]18;120;140;116;104;119; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;1 16;104; 114;115;118;120;140;1 16;104; 114;115;118;120;140;116;104; l14;115;118;120;140;116;104; l14;115;118;120;140;116;104; 114;115;118;]20;140;1 16;104; 114;115;118;120;140;116;104; 114;115;118;120;140;l 16;104; 114;]15;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;]20;140;116;104; 114;115;118;]20;140;116;104; 114;115;1 18;120;140;116;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;1 18;120;140;116;104; l14;l15;118;120;140;1]6;104; 114;115;118;]20;140;116;104; 114;!15;118;120;140;116;104; 1l4;115;118;120;140;116;104; 114;115;1 18;120;140;116;104; 114;]15;118;120;140;116;104; 114;115;1 18;120;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;1 18;120;140;116;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;]20;140;1 16;104; 114;115;1 18;120;140;116;104; 114;115;1 18;120;140;1 16;104; 114;115;1 18;120;140;116;104; 114;115;118;]20;140;116;104; 114;]15;118;120;140;1]6;104; 114;]15;118;120;140;1]6;104; 114;115;118;120;140;116;104; 114;115;118;120;140;116;104; 114;115;1 18;120;140;116;104; “a 8 006 ,0 49 ,6 f f 6“ 6* Q 4' o e ‘ 6 ° 0 O Q5 a. a '3 9° *1? 9‘3 45 9'3 '41? '41? '8? 9‘" 11) 9‘ Q Q Q e e Q Q 369 1 1 3 l 1 3 1 114;]15;118;120;140;1]6;104; 369 2 1 5 2 1 5 2 114;115;118;]20;140;116;104; 372 1 l 3 1 1 3 1 114;115;118;120;140;116;104; 372 2 1 5 2 1 5 2 114;l15;118;120;140;116;104; 373 1 1 3 1 1 3 l 114;]15;118;120;140;116;104; 373 2 l 5 2 1 5 2 114;115;118;120;140;116;104; 375 1 1 3 1 l 3 l 114;115;118;120;140;116;104; 375 2 1 5 2 l 5 2 114;115;l18;120;140;116;104; 376 1 1 3 1 1 3 1 114;115;118;120;140;116;104; 376 2 1 5 2 l 5 2 114;115;118;]20;140;116;104; 377 1 1 3 1 1 3 1 114;115;118;]20;140;116;104; 377 2 1 5 2 1 5 2 114;115;118;120;140;116;104; 378 l l 3 1 1 3 1 114;115;118;]20;140;116;104; 378 2 1 5 2 1 5 2 114:115;118;120;140;1]6;104; 379 1 1 3 1 1 3 1 114;115;118;]20;140;116;104; 379 2 1 5 2 1 5 2 114;115;118;]20;140;116;104; 380 1 1 3 1 1 3 1 114;115;118;120;140;116;104; 380 2 1 5 2 1 5 2 114;l15;118;120;140;1]6;104; 381 1 1 3 1 1 3 1 114;115;118;120;140;l16;104; 381 2 1 5 2 1 5 2 114;115;118;120;140;116;104; 385 1 1 3 1 1 3 1 114;115;118;]20;140;116;104; 385 2 1 5 2 1 5 2 114;115;118;120;140;l16;104; 386 l 1 3 1 l 3 l 114;115;118;120;140;116;104; 386 2 1 5 2 1 5 2 114;]15;118;120;140;116;104; 387 1 1 3 1 1 3 1 114;115;118;]20;140;116;104; 387 2 1 5 2 1 5 2 114;115;118;120;140;116;104; 388 1 1 3 1 1 3 1 114;115;118;120;140;116;104; 388 2 1 5 2 1 5 2 114;115;118;120;140;1]6;104; 390 1 1 3 1 1 3 1 114;]15;118;120;140;116;104; 390 2 1 5 2 l 5 2 114;115;118;120;140;1]6;104; 391 1 1 3 l 1 3 1 114;115;118;]20;140;116;104; 391 2 1 5 2 l 5 2 114;]15;118;120;140;116;104; 394 l 1 3 l 1 3 l 1l4;115;l18;120;140;116;104; 394 2 1 5 2 l 5 2 114;]15;118;120;140;1]6;104; 395 1 1 3 1 1 3 1 114;115;118;120;140;116;104; 395 2 1 5 2 1 5 2 114;]15;118;120;140;116;104; 396 1 l 3 1 1 3 1 114;115;118;]20;140;116;104; 396 2 1 5 2 1 5 2 114;115;118;120;140;116;104; 402 1 1 3 1 l 3 l 114;115;118;120;140;116;111;106; 402 2 l 5 2 1 5 2 114;115;118;120;140;116;111;106; 403 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 403 2 1 5 2 1 5 2 114;115;118;]20;140;116;111;106; 404 1 l 3 1 1 3 1 114;]15;118;120;140;116;111;106; 404 2 1 5 2 1 5 2 114;115;118;120;140;116;111;106; 405 l 1 3 1 l 3 1 114;115;118;]20;140;116;111;106; 405 2 1 5 2 1 5 2 114;115;118;120;140;116;111;106; 170 ID 406 406 41 8 41 8 422 422 423 423 426 426 427 427 437 437 442 442 443 443 445 445 446 446 447 447 448 448 449 449 450 450 451 45 l 452 452 453 453 454 454 456 456 457 457 458 458 462 462 ——--fl~fl-~fl-—fl—”-———-—~—~—_—~N__-——-fl_~ 0 9‘" 4° Q8 «9‘9 9° ‘0 &¢ $¢ “‘5 ‘0 3 3’ e e "’ 4‘ 0 Q5 a. s \3 '9 9‘3 95 '6? \3: '5? 9" Q 4 e e e 3 1 1 3 1 114;115;118;120;140;116;“1;106; 5 2 1 5 2 114;115;118;120;140;116;111;106; 3 1 1 3 l 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;]15;118;120;140;116;”1;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;115;118;120;140;116;111;106; 3 3 1 3 3 114;115;118;120;140;116;111;106; 5 5 1 5 5 114;]15;118;120;140;116;”1;106; 3 1 1 3 1 114;l15;118;120;140;116;“1;106; 5 12 1 5 12 114;115;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 3 2 5 3 114;115;118;]20;140;116;111;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;]15;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;]15;118;120;140;116;111;106; 3 1 1 3 1 114;]15;118;120;140;116;111;106; 5 2 1 5 2 114;]15;118;120;140;116;111;106; 3 1 1 3 1 114;]15;118;120;140;116;111;106; 5 2 l 5 2 1l4;115;118;120;l40;116;111;106; 3 1 1 3 1 114;115;118;]20;140;116;111;106; 5 2 1 5 2 114;115;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;]20;140;116;]11;106; 5 2 1 5 2 114;115;118;120;140;116;111;106; 3 l 1 3 1 114;115;118;]20;140;116;111;106; 5 12 l 5 12 114;115;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;115;118;120;140;116;]11;106; 3 1 1 3 1 114;]15;118;120;140;116;111;106; 5 2 1 5 2 114;]15;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;115;118;]20;140;116;111;106; 3 1 1 3 1 1l4;115;ll8;120;140;1l6;111;106; 5 2 1 5 2 114;115;118;120;140;116;111;106; 3 l l 3 1 114;115;118;120;140;116;111;106; 5 2 1 5 2 114;]15;l18;120;140;116;111;106; 3 l 1 3 1 114;]15;118;120;140;116;“1;106; 5 2 l 5 2 114;]15;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;]20;140;116;]11;106;97; 5 12 1 5 12 114;]15;118;120;140;116;111;106;97; 3 l 1 3 l 114;]15;118;120;140;116;111;106; 5 2 1 5 2 114;115;118;120;140;116;111;106; 3 1 1 3 1 114;115;118;120;140;116;111;106; 5 2 l 5 2 ll4;115;118;120;140;116;111;106; 3 1 1 3 1 114;]15;]18;120;140;116;111;106; 5 2 l 5 2 114;]15;]18;120;140;116;111;106; 171 f $5 4“ $3 .+ 49‘" .5" ° O e Q5 s ‘9 \Q ‘? .~° .5 .5 .5 4? 41? 4? 5’ ID “J“ 9 Q Q Q Q Q 9 463 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 463 2 1 5 2 1 5 2 114;]15;118;120;140;116;”1;106; 464 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 464 2 1 5 2 1 5 2 114;115;118;120;140;116;111;106; 466 1 1 3 1 1 3 1 114;]15;118;120;140;116;”1;106; 466 2 l 5 2 1 5 2 114;]15;118;120;140;116;”1;106; 471 1 1 3 3 1 3 3 114;]15;118;120;140;116;]11;106;133; 471 2 1 5 5 1 5 5 114;115;118;120;140;1l6;111;106;l33; 487 1 l 3 1 1 3 1 114;115;l18;120;140;116;111;106; 487 2 1 5 2 l 5 2 114;115;118;]20;140;116;111;106; 488 l 1 3 1 1 3 l 114;115;118;120;140;116;“1;106; 488 2 1 5 2 1 5 2 1l4;115;118;120;140;116;111;106; 494 1 1 3 1 1 3 1 114;]15;118;120;140;116;]11;106; 494 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 496 1 1 3 1 l 3 1 114;115;118;120;140;116;111;106; 496 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 497 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 497 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 498 1 1 3 1 l 3 1 114;115;118;120;140;116;111;106; 498 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 499 l 1 3 1 1 3 1 114;]15;118;120;140;116;111;106; 499 2 1 5 1 1 5 1 114;]15;118;120;140;116;111;106; 500 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 500 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 501 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 501 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 502 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 502 2 1 5 12 1 5 12 114;115;118;120;140;116;“1;106; 505 1 1 3 1 1 3 1 ll4;l15;118;120;140;116;“1;106; 505 2 1 5 12 1 5 12 114;115;118;120;140;116;111;106; 510 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 510 2 1 5 2 1 5 2 114;115;118;120;140;1l6;111;106; 515 1 l 3 1 l 3 1 1l4;115;118;120;140;116;“1;106; 515 2 1 5 2 1 5 2 114;115;118;120;140;116;111;106; 520 1 1 3 l 1 3 1 114;115;118;120;140;116;111;106; 520 2 2 5 3 2 5 3 114;]15;118;120;140;116;111;106; 521 1 l 3 l l 3 1 114;115;118;120;140;116;111;106; 521 2 2 5 3 2 5 3 114;115;118;120;140;116;111;106; 522 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 522 2 2 5 3 2 5 3 114;115;118;120;140;116;111;106; 527 1 1 3 1 1 3 1 114;115;118;120;140;116;111;106; 527 2 2 5 3 2 5 3 114;]15;118;120;140;116;“1;106; 528 1 1 3 1 1 3 1 114;]15;118;120;140;116;111;106; 528 2 2 5 3 2 5 3 114;]15;118;120;140;116;111;106; 535 1 1 3 1 1 3 1 114;115;118;120;l40;116;111;106; 535 2 1 5 2 1 5 2 114;115;118;120;140;116;111;106; 172 ID 536 536 537 537 538 538 554 554 555 555 556 556 557 557 558 558 559 559 560 560 561 561 563 563 565 565 569 569 570 570 571 571 575 575 579 579 586 586 591 591 597 597 598 598 603 603 Nu—nNi—aNp—Nr—nNt—aN—nN—Nu—tNi—aN—Ns—Nr—oN—Nt—nNs—nNr—Ns—ON—IN—tN—N—Nu—nN—I% 0 ‘5 -~—-----H-~flfl—--H~H~H-H-H-~H~fl-- 0 0 ’4 mwmwmumwmumwmwuwmwmumwmwuwmwmwmwmwuwmwuwmwmwmw 98 6°“ 5°? 5° .9 .5 e " S 4" 9 Q 8 ‘ * e 43* .9 é" N—‘MMMWMWMWMUJMWMWMWMMMWMWMMMWMWMWMWMMMWMWNt-‘N—Nh‘ H--~H---~fl”H-flfl---fl--------- 173 N-‘MMMWMMMWMMMMUD-DU}WMWMWMWMWMWMWMWMMMWMMMWN—‘N—N— 114;115;118;120;140;116;111;106; 114;]15;]18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;”1;106; 1l4;115;118;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114;]15;118;120;140;116;”l;106;133; 114;115;1 18;]20;140;116;111;106;133; 114;]15;118;120;140;116;“l;106;133; 114;115;1 18;120;140;116;! 1 l;106;133; 114;] 15;] 18;120;140;116;11l;106;133; 114;115;118;]20;140;1 16;111;106;133; 114;115;1 18;]20;140;1 16;111;106;133; l14;115;118;120;140;116;11 l;106;133; 114;]15;118;120;140;116;“l;106;133; 114;115;118;]20;140;116;111;106;133; 114;115;118;120;140;116;]1l;106;133; 114;115;118;120;140;l l6;111;106;l33; 114;] 15;118;120;140;1 16;111;106;133; 114;115;118;120;140;116;”l;106;133; 114;115;118;120;140;116;111;106;133; 114;]15;118;120;140;116;“l;106;133; 114;l 15;118;120;140;116;] 1 l;106;133; 114;115;118;120;140;116;l 1 l;106;133; 114;115;118;]20;140;116;111;106;133; 114;115;1 18;]20;140;1 16;] l l;106;133; 114;]15;118;120;140;116;”l;106;133; 114;115;118;]20;140;116;111;106;133; ll4;115;118;120;140;116;11l;106;133; 114;115;118;120;140;116;11l;106;133; 114;115;118;]20;140;116;111;106;133; 114;115;118;120;140;116;11l;106;133; 114;115;118;120;140;116;11l;106;133; 114;115;118;120;l40;116;l1l;106;133; ll4;115;118;120;140;ll6;11l;106;133; ll4;l15;118;120;140;116;”l;106;133; 114;115;118;]20;140;116;11 l;106;133; 114;115;118;120;140;116;11l;106;133; 114;]15;118;120;140;116;]11;106;133; 114;]15;118;120;140;116;” l;106;133; 114;l 15;118;120;140;116;1 l l;106;133; 114;]15;118;120;140;116;” l;106;133; 114;]15;118;120;140;116;“l;106;133; 114;]15;118;120;140;116;“l;106;133; 114;115;118;120;140;116;111;106; l14;115;118;120;l40;ll6;1l1;106; ID 604 605 605 609 609 611 611 612 612 613 613 614 614 615 615 616 616 617 617 618 618 619 619 621 621 622 622 623 623 624 624 625 625 626 626 627 627 628 628 631 631 634 634 635 635 QO“ .89." 3.5 .5 9‘0 Q 11 3 21 5 11 3 21 5 11 3 21 5 ll 3 21 5 ll 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 ll 3 21 5 ll 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 ll 3 21 5 ll 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 11 3 21 5 c." .9 Q¢ ‘9‘? ‘8? $5 $9 $5 a. ‘0“ § 9 Q30 éé 5° a? 3* o 3 e 4 «3‘ 6* 1 l 3 l ll4;115;118;120;140;116;111;106; 2 1 5 2 114;115;118;120;140;116;111;106; l l 3 1 114;115;118;120;140;116;111;106; 2 1 5 2 114;]15;118;120;140;116;111;106; 1 1 3 1 114;115;118;120;140;116;111;106; 23 1 5 23 114;115;118;120;140;116;111;106; 1 1 3 1 114;!15;118;120;140;116;111;]06;97; 23 1 5 23 114;l15;118;120;140;116;”1;]06;97; 1 1 3 1 114;115;118;120;140;116;111;]06;97; 23 1 5 23 114;]15;118;120;140;116;111;106;97; 1 1 3 1 114;115;118;120;140;116;111;106; 23 1 5 23 114;115;118;120;l40;116;111;106; 1 l 3 l l14;115;118;120;140;116;”1;106; 23 1 5 23 114;]15;118;120;140;116;“1;106; 1 1 3 1 114;115;1l8;120;140;116;111;106;97; 23 1 5 23 114;115;118;120;140;116;111;106;97; 1 1 3 1 114;]15;118;120;140;116;“1;106; 23 l 5 23 114;115;118;]20;140;116;111;106; 1 1 3 1 114;115;118;120;140;116;111;106; 23 1 5 23 114;115;118;120;140;116;111;106; 1 1 3 1 114;115;118;120;l40;116;111;106; 23 1 5 23 114;115;118;]20;140;116;111;106; 1 1 3 1 114;]15;118;120;140;116;111;106; 23 1 5 23 114;115;118;120;140;116;111;106; 1 1 3 1 114;115;118;120;140;116;111;106; 23 1 5 23 1l4;115;118;120;140;l16;111;106; l l 3 1 114;115;118;120;140;116;111;106; 23 1 5 23 114;]15;118;120;140;116;111;106; 1 1 3 l 114;l15;118;120;140;116;“1;106; 23 l 5 23 114;115;118;120;140;l16;111;106; l 1 3 1 1l4;115;118;120;140;116;111;106; 23 1 5 23 l14;]15;118;120;140;1l6;111;106; 1 1 3 1 l14;115;118;120;140;116;111;106; 23 1 5 23 114;]15;118;120;140;116;111;106; 1 1 3 1 114;]15;118;120;140;116;111;106; 23 1 5 23 114;]15;118;120;140;116;111;106; 1 l 3 l 114;]15;]18;120;140;116;111;106; 23 1 5 23 114;115;118;]20;140;1l6;1l1;106; 1 1 3 1 114;]15;118;120;140;116;111;106; 23 1 5 23 114;115;118;120;140;116;111;106; 1 1 3 1 114;115;118;120;140;116;111;106; 23 1 5 23 114;]15;118;120;140;116;111;106; 3 1 3 3 l14;]15;118;120;140;116;”l;106;133; 5 1 5 5 114;]15;118;120;140;116;“l;106;133; 1 1 3 1 ll4;115;118;120;140;116;111;106; 2 1 5 2 114;115;118;120;140;116;111;106; 174 ID 636 636 638 638 639 639 640 640 641 641 642 642 3.3.8888 648 648 649 649 650 650 652 652 653 653 659 659 660 660 661 661 662 662 663 663 664 664 665 665 666 666 667 667 Q 9" 08 Q" a"? $3 4" y 58 8‘" a» ,8 .4 .5" i .9 4 5* ‘ 43* 8" e‘ 4*" 4* ---~H_g—au—a—a——a_u—nu—‘y—nu—n—a—a—u—a—a--~fi-------- -—_.—- N N u—ONv—nN—Nv—oN—o —- N NHL» ND-‘N—‘N—Nv-e ----~H-fl-----~_——__———~—--_——_-~_ 175 HN—N—nN—N—Nu—N—N—N—N—N—N— _ I—O N'HN B—N—nN—N—INi—o 5H N—Nu—IN—Ni—n 114:115;118;120;140;116;“1;106; 114:1 15:1 18;120;140;116;111;106; 114;115;118;120;140;116;111;106; 114:115;118;120;140;116;“1:106: 114:115;118;120;140;116;”1;106; 114:115;118;120;140;116;“1:106: 114;115;118;120;140;116;111;106; 114:1 15:1 18;120;140;116;111;106; 114:115;118;120;140;116;”1:106: 114;115;118;120;140:116;111;106; 114:115:118;120;140;116;111;106; 114:115:118;120;140;116;111;106; 114:115;118:120:140:116;111:106: 114:115:118:120;140;116;1l1:106: 114;115;118;120;140;116;”1:106: 114;115;1 18;120;140;116;111;106; 114:115;118;120;140;116;“1:106: 114:115:118;120;140;116;111;106; 114;115;1 18;120;140;116;111;106; 114:115;118;120;140;116;111;106; 114:115:118;120;140;116;111;106; 114:115;118;120;140;116;111:106: 114:115;118;120;140;116;]11:106: 114:115;118;120;140;116;]11:106; 114:115;118;120;140;116:111:106; 114;115;118;120;140;116;111;106; 114;115;118;120;140:116;111;106; 114;115;118;120;140;116;111;106; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116; 114;115;118;120;140;116;97; 114;115;118;120;140:116;97; 114;115;118;120;140;116;97; 114:115:118;120:l40;116;97; 114:115:118:120:140;116:97; 114;115;118;120;140;116;97; 114;115;118;120;140;116;97; 114:115;118;120;140;116;97; 114:115;118;120;140;116;97; 114:115;118;120;140;1l6;97; 114;115;1l8;120;140;116;97; 114;115;1 18;120;140;116;97; ID 668 668 669 669 670 670 67 l 67 l 675 675 676 676 678 678 680 680 68 l 68 l 682 682 683 683 685 685 686 686 687 687 688 688 689 689 692 692 693 693 696 696 700 700 701 70 1 702 702 916 916 '9 1'00 O *4 4% ’4 uN—nN—N—N—N—Nu—Nu—eNa—n N N N ~UJ—‘UJ—UJ N—‘N—‘N—‘N—‘N—‘NflN—N—N—‘N—‘N—‘N'flNh‘N—‘N—NHN-‘N'flN—‘NF‘N—‘N—‘N—‘g N \I‘JI-h"A—‘A'-‘AHAW-he"Mid"i—LAMMWLAWMWMMMWMWMWMWMNMWMUMMMW mw~H—np—op—a—au—ae—aHp—ip—au—ap—nmu—ap—ap—a—‘p—a—na—ag—a—y—ay—a_‘~u—ap—a_a—a—a_a—a—a—a—a—au—nu—a-—_ \lkllht-eJiv-‘bt-e-hV-‘At-eAv-‘Nt-‘t-‘t-‘(JIMLIIUJMWMWMMMWMMMMMWMMMWMWMWMU (e6 mun—o—NHNr—t I—nNt—INh—INt—IN—INI—OND—INt—IN—fi % 23 thJD—u—N—Nu—n ’0. ,4? 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{f x \f’ 43 43 ~48 “s5: '8? e e e e e 34 34 56 56 34 34 56 56 34 34 56 56 34 34 56 56 34 34 56 56 34 34 56 56 34 34 56 56 34 34 56 56 34 34 56 56 \IMQMQMQMQMQM\ILII\JM\IM\IMQMQM\IMQMQMQMQMNMQMQMQMNMQM ONhGAONAabo-hON-ba‘#G-fiOAO‘bOAGthON-h £11WMWMWMWMWMWMWWW-hNMUJMWMWMWMWMWMWMWMWMWMUMWMWMW \IMNMQMQMNMQMQMflUi\JMQMQMNMQMQMQMQMQMQMQMQMQM\ILIIQLII 178 O‘AO‘bQ-fiababathNbG-h03-hOA05AObO5-D-ON-h 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 9,11,12,148 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12,13 8,11,12,13 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 8,11,12 II) E? 1104 1 1104 2 1105 1 1105 2 1106 1 1106 2 1108 1 1108 2 1109 1 1109 2 1111 1 1111 2 1112 1 1112 2 1114 l 1114 2 1115 1 1115 2 1119 l 1119 2 1120 1 1120 2 1125 l 1125 2 1133 1 1133 2 1134 1 1134 2 1152 1 1152 2 1188 l 1188 2 1189 l 1189 2 1190 1 1190 2 1191 1 1191 2 1192 1 1192 2 1194 1 1194 2 1195 1 1195 2 1196 1 1196 2 88 u—n—nu—Au—u—nu—N—I—n—nt—tv—v—et—H---OO-dMWMWMWMUMWMWMMMWMWMWMM WQMQMQMQMNMQMQMQMQMNMQM M 88 88888 88888 88888 88888 88 5 MWMUMWMUMWMWMWMWMWMW GhCB-ha-hO‘ACI-b b) b) mo\-h°‘&Q&O5AON-ho5& oo 3 t-‘k-N—‘N-hNhN-hN-bNUJN 1.» N bNN"w -MWMWMMMWMWMWMWWWMWMWMW $6, 1 -H-~NH------ 4, WQMQMNMQMNMQMQMQMQMQMNM 5 5 MWMWMWMWMWMWMWMWMWMW 42, 179 on-haboJx/béz 88 -WOOOOO545OJ>O5¥QJ>O5AO5¢OKAON 12 Q. 8,] 1 ,12 8,1 1,12 8,1 1,12 8,1 1,12 8,1 1 ,12 8,1 1,12 8,1 1,12 8,1 1,12 8,1 1,12 8,1 1,12 8,11,12 8,1 1,12 8,11,12 8,11,12 8,1 1 ,12 8,] 1,12 8,11,12 8,1 1 , 12 8,1 1,12 8,] 1 ,12 8,1 1,12 8,1 1,12 158, 158, 41,46,68, 41 ,46,155, 156,157, 42,54,48,70,155,156,157, 42,54,48,70,155,156,157, 42,68,78, 42,68,78, 45,41,63, 45,41,63, 42,100,54,70,155,156,157, 42,100,54,70,155,156,1S7, 100,79,158, 100,79,158, 42,43,41,55,58,44, 42,43,41,55,58,44, 42,70,54,58,57,73,1 55,156,] 57, 42,70,54,58,57,73,155,156,157, 87, 87, 87, 87, 87,158, 87,158, 1198 1198 1200 1200 1200 1200 1201 1201 1202 1202 1208 1208 1209 1209 1210 1210 1225 1225 1226 1226 1242 1242 49 ~78 \9 ’5" .4 .4 9 9 Q23 65‘ Q95 1 3 1 1 5 4 2 3 2 2 5 4 2 3 2 2 5 4 2 3 8 4 5 8 1 3 23 1 5 23 2 3 8 4 5 8 2 4 3 4 6 5 2 4 23 4 6 45 2 3 2 4 5 8 1 3 8 1 5 8 3 5 4 5 7 6 §° 69 $09 .91" ‘0 $0 +\ ‘6 9‘ 3' '~ 3* 4" 4‘ n8 «.8 '3 9° 36° 4? 33 V’ .4 e 4* 4* 1 3 1 87, 1 5 4 87, 2 3 2 87,89,82,83,158,159, 2 5 3 87,89,82,83,158,159, 2 3 2 87,89,82,83,158,159, 2 5 3 87,89,82,83,158,159, 2 3 8 42J0066, 4 5 8 42J0066, 1 3 23 100,76,77, 1 5 23 100,76,77, 2 3 8 42,70,68, 100, 4 5 8 42,70,68,100, 2 4 3 42,72,62,48,60,73, 4 6 5 42,72,62,48,60,73, 2 4 23 42,72,62,48,60,73, 4 6 45 42,72,62,48,60,73, 2 3 2 42,100,70,68, 4 5 8 42,100,70,68, 2 3 8 42,68,154, 4 5 8 42,68,154, 3 5 4 7,1 1, 5 7 6 7J1, 180 APPENDIX} Diet references for Taxa 181 Phytoplankton Zooplankton ID References for diets 1D References for diets 401 127:]08:162:163: l 96, 404 109:117; 8 167, 405 109:117: 10 4, 406 127:]08:162:163: 11 2, 407 127:]08:162:163: 14 4,1,167, 408 109:117: 16 4,230,144,173, 409 1 12:107:109: 19 4,2,30, 410 112:108: 20 4,2,30,173, 413 127:]08: 21 4,2,3, 415 109:117: 25 1,2,4, 417 112:]07:109: 28 4, 418 109:117: 32 146,4, 502 109;]17:135: 39 4, 504 109:]17:135; 5] 144,145,1,4,173, 505 109:117:l35: 59 144,145,1,4,173, 506 109:117:135: 68 144,145,1,4,173, 701 134:109:117;138:139: 76 144,145,1,4,173, 702 134:109;117:138:139: 111 4,173,]77,]78,]79 705 134:109:117:138;139: 112 4,3,177, 706 l34;109:]17:138:139: 113 4,1,2,30,38,5,174,175,176, 114 4,1,2,30,38,5,174,175,176, Fish 115 4,1,2,30,38,5,174,175,176,177, ID References for diets 116 173,175, 1125 142, 117 4,1,2,30,38,5,173,177,179 1 133 49,40,50,53,59, 120 4,1,2,30,38,5,174,l75,176,177, 1133 50,40,48,47,7 1 ,59, 121 4,30,173,175, 1152 40,50, 122 35,36,37, 1188 53,51, 1188 51,50,52, 1190 79,69, 1191 56,55,5 8, 1191 59,55,71,73,58, 1194 78,81,84,85, 1195 78,80, 1196 85,78,71,86,88, 1198 . 78, 1200 78,89,82, 1200 78,89,82, 1201 40,50, 1202 77, 1208 101,102,103, 1209 40, 1210 40,60,61, 1225 68, 1226 40,50,94,64,75,67, 182 Benthic Invertebrates ID References for diets 916 31, 924 3 l , 928 31, 944 28,29 950 14,20,31, 958 14,20,31, 962 14,18,20,31, 977 150,152,149, 978 23,24,25,26,27, 982 31,150, 986 19,20,148, 147, 993 19,20,148, 147, 997 19,20,148, 147, 1003 19,20,148, 147, 1004 19,20,148,]47, 1010 19,20,148,]47, 1014 19,20,148,]47, 1017 19,20,148,]47, 1018 19,20,148,]47, 1019 9,19,20,21,]48,147, 1020 9,19,20,21 , 148, 147, 1021 9,19,20,21 ,148,]47, 1023 19,20,148, 147, 1025 19,20,148, 147, 1039 19,20,148, 147, 1040 19,20,148,]47, 1043 19,20,148, 147, 1049 9,19,20,148,147, 1050 19,20,148,]47, 1052 19,20,148,]47, 1054 19,20,148,]47, 1057 9,19,20,148,147, 1059 19,20,148,]47, 1081 148, 1082 8, 1084 16,148, 1085 15, 1089 16,148, 1092 15,148, 1093 16,148, 1094 16,148, 1095 8,16, 1098 8,16, 1099 8,16, 1100 8,16, 1 102 8,16, 1 103 8,16, 1104 8,16, 1 105 8,16, Benthic Invertebrates ID References for diets l 106 8,16, 1 108 8,16, 1 109 8,16, 111] 8,16, 1112 8,16, 1114 8,16, 1115 8,16, 1119 8,16, 1120 8,16, 1242 17, 183 APPENDIX H References with Numbers associated with Appendices E-G 184 APPENDIX H References with Numbers associated with Appendices E-G 185 REFERENCES . 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Sandgren, Editor. 1988, Cambridge University Press: Cambridge. p.442. 201 APPENDIX Biomass estimates of taxa for pre-invasion and post-invasion food webs and associated references 202 Pre-invasion Biomass Standard ID Biomass Deviation 8 0.0037 0.0052 10 229.2522 20.5564 1 1 0.2794 0.3758 14 0.11 0.0795 16 140.9856 43.8469 19 0.0007 0.000403] 20 94.946 44.5884 21 25.9515 35.4546 25 3.7913 4.2876 28 8.9477 1.9995 32 6.1653 0.1346 39 0.3421 0.0167 50.3 144.6925 37.2414 51.4 0.9376 0.6096 59.4 120.0438 18.596] 68.5 0.0087 0.0123 76.5 8.4678 9.394 111.5 51.9126 0.0388 1 12.5 14.4413 5.9263 113.4 234.592 129.7001 1 14.4 49.6024 14.6733 1 15.4 342.9791 27.7674 116.3 1123.9528 672.4728 117.3 84.6629 18.1402 1 17.4 626.6224 486.5596 120.4 28.1397 14.6205 121.2 105.6925 7.6084 122 102.16328 52.331182 201 0.0166 0.0235 202 0.1775 0.251 203 0.3304 0.4569 204 0.1713 0.2422 205 0.8481 1.1995 206 0.344 0.4865 207 0.00615 0.0072235 208 0.1158 0.1638 209 33.3787 41.6977 210 0.00615 0.0072235 21 1 0.4333 0.6128 212 0.2255 0.1654 213 0.0123 0.0173 214 1.2078 1.7081 215 0.00615 0.0072235 216 0.5264 0.2721 217 1.3882 1.9632 218 0.00615 0.0072235 219 0.2793 0.395 220 0.00615 0.0072235 Post-invasion Biomass 203 Standard ID Biomass Deviation Datasource 8 0.0007 0.000403] 23,24 10 51 .6209 17.4674 23,24 11 0.0007 0.000403] 23,24 14 0.0007 0.000403] 23,24 16 590.5072 92.4439 23,24 19 0.3517 0.0432 23,24 20 0.5838 0.8256 23,24 2] 0.0085 0.012 23,24 25 0.0014 0.002 23,24 28 0.0007 0.000403] 23,24 32 0.0356 0.0504 23,24 39 0.0122 0.0172 23,24 50.3 140.5866 80.5454 23,24 51.4 0.0007 0.000403] 23,24 59.4 138.0603 134.5451 23,24 68.5 0.5052 0.7145 23,24 76.5 5.1148 3.3451 23,24 1 11.5 43.6755 0.5979 23,24 1 12.5 0.5225 0.4363 23,24 1 13.4 174.064 47.651 23,24 1 14.4 58.1849 1.9968 23,24 115.4 105.0161 89.1596 23,24 1 16.3 550.3447 543.9594 23,24 117.3 31.1079 22.8532 23,24 117.4 331.6161 154.1356 23,24 120.4 9.4274 2.7028 23,24 121.2 25.22 19.1714 23,24 122 302.7252] 90.94030] 28 201 0.2534 0.5666 13,22 202 0.00615 0.0072235 13,22 203 0.0198 0.0443 13,22 204 0.061 0.1364 13,22 205 0.132 0.2477 13,22 206 0.00615 0.0072235 13,22 207 0.0773 0.1729 13,22 208 0.00615 0.0072235 13,22 209 0.5756 0.832 13,22 210 0.0307 0.0687 13,22 21] 0.1369 0.2956 13,22 212 0.1243 0.1823 13,22 213 0.00615 0.0072235 13,22 214 6.3774 9.9801 13,22 215 6.5675 9.4707 13,22 216 5.1787 5.9846 13,22 217 0.2987 0.3867 13,22 218 0.0129 0.0288 13,22 219 0.00615 0.0072235 13,22 220 0.2159 0.4828 13,22 Pre-invasion Biomass Standard ID Biomass Deviation 221 0.3154 0.446 222 92.5159 2.9496 223 0.00615 0.0072235 224 22.0868 31.0673 225 0.00615 0.0072235 226 63.9279 78.3385 227 0.3507 0.496 228 0.00615 0.0072235 229 140.501 198.6984 230 0.6927 0.9796 231 1.4907 1.909 232 0.00615 0.0072235 233 1.121 1.5853 234 23.4306 33.1358 235 0.00615 0.0072235 236 21.7636 30.7784 237 0.00615 0.0072235 238 0.00615 0.0072235 239 0.2001 0.283 240 0.00615 0.0072235 30] 1.3269 1.8765 302 18.398] 26.0188 303 6.5829 5.3475 304 0.0994 0.1406 305 0.00615 0.0072235 306 0.00615 0.0072235 307 2.7505 3.8898 308 0.00615 0.0072235 309 0.00615 0.0072235 310 0.00615 0.0072235 31] 0.7219 1.021 312 0.00615 0.0072235 313 1.1016 0.2201 314 3.0614 0.5393 315 0.00615 0.0072235 316 22.7662 16.226 317 7.0264 0.1999 318 0.5027 0.71 1 3 19 0.00615 0.0072235 320 0.00615 0.0072235 32] 0.00615 0.0072235 322 0.00615 0.0072235 323 0.1181 0.1671 324 0.00615 0.0072235 325 1.1315 1.6002 326 0.00615 0.0072235 327 2.3952 1.8986 328 72.044 90.0415 Post-invasion Biomass Standard ID Biomass Deviation Datasource 221 0.0284 0.0635 13,22 222 51.5494 62.8673 13,22 223 0.4198 0.4033 13,22 224 48.8199 55.1899 13,22 225 26.5137 55.9773 13,22 226 947.6124 884.8199 13,22 227 0.00615 0.0072235 13,22 228 1.0952 1.8203 13,22 229 12.4216 23.9814 13,22 230 1.148 2.567 13,22 23] 40.4493 52.2136 13,22 232 0.5923 1.3245 13,22 233 8.5001 6.6157 13,22 234 2.3108 4.1192 13,22 235 0.5675 1.2689 13,22 236 19.9453 16.4755 13,22 237 1.2545 2.8051 13,22 238 5.6952 5.4104 13,22 239 0.00615 0.0072235 13,22 240 62.4708 77.9188 13,22 30] 63.7379 142.5222 13,22 302 3.9763 5.5051 13,22 303 0.00615 0.0072235 13,22 304 13.4472 14.3004 13,22 305 33.229] 74.3025 13,22 306 85.822 150.9683 13,22 307 4.4481 8.117 13,22 308 10.8896 14.852 13,22 309 8.0152 7.3444 13,22 310 1.6126 1.6878 13,22 31] 0.00615 0.0072235 13,22 312 20.8065 20.9456 13,22 313 9.2756 20.7409 13,22 314 1.8555 3.5407 13,22 315 27.6466 61.8197 13,22 316 12.8312 21.2057 13,22 317 0.6958 1.0658 13,22 318 0.00615 0.0072235 13,22 319 0.3068 0.6859 13,22 320 0.0346 0.0773 13,22 32] 0.1025 0.1444 13,22 322 1.0243 1.6791 13,22 323 0.0779 0.1743 13,22 324 21.5468 40.8747 13,22 325 3.2707 3.821 1 13,22 326 10.3078 8.8439 13,22 327 34.5915 60.9947 13,22 328 6.9367 9.6848 13,22 204 Pre-invasion Biomass Standard ID Biomass Deviation 329 0.1749 0.2473 401 0.00615 0.0072235 402 0.00615 0.0072235 403 0.00615 0.0072235 404 298.6066 163.0771 405 91.9466 56.4617 406 0.4962 0.7017 407 10.5506 14.9208 408 17.0513 1.0681 409 0.00615 0.0072235 410 3.4401 4.865 411 0.00615 0.0072235 412 3.3639 4.7573 413 0.00615 0.0072235 414 0.00615 0.0072235 415 0.00615 0.0072235 416 8.2818 2.1868 417 0.00615 0.0072235 418 266.8601 36.8229 419 8.7762 12.4114 420 24.2166 34.2475 421 1.0531 1.0125 501 25.2115 10.2678 502 99.0041 65.8501 503 403.0132 98.2692 504 324.5982 24.6194 505 12.0973 17.1082 506 0.00615 0.0072235 507 4.443 6.2833 601 4.8598 2.0086 602 84.7504 39.8351 603 1 .6246 2.2975 604 22.677 12.6463 605 0.00615 0.0072235 606 16.8071 17.9012 607 0.00615 0.0072235 608 41.0466 4.9596 609 7.4149 0.3289 610 75.2274 99.6922 61 1 0.00615 0.0072235 701 21.6998 26.8756 702 77.7374 5.3749 703 14.155] 20.0183 704 0.00615 0.0072235 705 0.00615 0.0072235 706 212.31 300.2516 707 3.6753 5.1976 801 0.00615 0.0072235 Post-invasion Biomass Standard ID Biomass Deviation Datasource 329 0.00615 0.0072235 13,22 401 4.3651 8.3931 13,22 402 1.0633 2.3777 13,22 403 0.4347 0.9721 13,22 404 88.9993 25.3219 13,22 405 57.125 47.6561 13,22 406 0.2243 0.5015 13,22 407 8.6607 5.1651 13,22 408 71.6817 17.1096 13,22 409 1.8675 1.8129 13,22 410 0.9498 2.1238 13,22 411 20.4016 20.5689 13,22 412 5.2539 3.6824 13,22 413 10.0223 18.2992 13,22 414 3.2079 4.4806 13,22 415 13.2692 9.7751 13,22 416 13.2178 9.4996 13,22 417 1.7722 3.9628 13,22 418 66.5476 68.1545 13,22 419 0.00615 0.0072235 13,22 420 87.4365 54.943 13,22 421 4.6573 5.1439 13,22 501 81.887 33.9367 13,22 502 205.0652 229.9953 13,22 503 150.5369 59.267 13,22 504 450.7825 1 19.3444 13,22 505 0.00615 0.0072235 13,22 506 20.9538 46.8541 13,22 507 21 1.9367 317.7186 13,22 601 565.7067 1140.6492 13,22 602 81.9604 38.2388 13,22 603 4.1647 4.823 13,22 604 56.1668 69.7081 13,22 605 12.2456 16.9472 13,22 606 6.2694 9.0514 13,22 607 8.1164 7.6996 13,22 608 6.5129 14.5633 13,22 609 14.9297 10.7191 13,22 610 11.9591 16.7319 13,22 611 16.1535 16.3226 13,22 701 2.9886 6.6827 13,22 702 202.9559 126.0164 13,22 703 178.0559 235.7192 13,22 704 35.3965 48.6165 13,22 705 260.8515 199.4367 13,22 706 1159.9723 1633.7742 13,22 707 0.00615 0.0072235 13,22 801 196.4694 232.0701 13,22 205 Pre-invasion Biomass Post-invasion Biomass Standard Standard ID Biomass Deviation ID Biomass Deviation Datasource 802 13.2959 10.8571 802 19.8971 11.3814 13,22 803 46.8867 3.4364 803 46.8271 29.0906 13,22 804 0.00615 0.0072235 804 26.4511 40.1538 13,22 805 0.00615 0.0072235 805 5.8693 9.1113 13,22 806 5.8182 6.3861 806 0.00615 0.0072235 13,22 807 6.2176 4.4409 807 0.3963 0.6518 13,22 808 0.00615 0.0072235 808 0.4307 0.9631 13,22 809 9.2969 13.1478 809 9.9755 12.1408 13,22 810 225.8063 138.9271 810 990.1226 1522.4791 13,22 811 350.335 355.3995 811 452.8221 483.2129 13,22 812 6.122 1.4642 812 211.1746 158.1296 13,22 813 14.314 20.2431 813 12.7619 12.0942 13,22 814 0.00615 0.0072235 814 0.2439 0.5455 13,22 815 0.00615 0.0072235 815 394.0574 416.3766 13,22 816 299.9245 144.9515 816 0.00615 0.0072235 13,22 817 51.5692 72.9299 817 17.2655 24.0509 13,22 818 11.1582 1.9208 818 3.6248 4.429 13,22 819 6.803 9.1032 819 103.3061 81.6007 13,22 820 0.242 0.3422 820 0.221 1 0.271 1 13,22 821 127.79 89.173 821 0.00615 0.0072235 13,22 916 0.2270852 0.321147 916 0.0005432 0.0001279 3,8,11,15,30, 924 15.999466 3.8522252 924 15.027121 15.08083 3,8,11,15,30, 928 2.436658 2.1613668 928 0.0005432 0.0001279 3,8,11,15,30, 950 430.29189 67.902203 950 108.75208 49.611628 3,15,17,12,20,30, 958 41.545991 4.9080763 958 4.9222382 2.6820351 3,15,17,12,20,30, 962 222.54608 6.4913765 962 143.04049 10.729923 3,15,17,12,20,30, 977 0.0005432 0.0001279 977 0.1828227 0.2585504 3,15,17,12,20,30, 978 4825.6219 954.9333 978 1091.8555 769.48645 2,3,21,30, 982 0.2925501 0.0795039 982 0.0005432 0.0001279 15,21,30, 986 0.4293193 0.1544859 986 0.4892305 0.1972567 18,15,19,21,30, 993 0.190383 0.1815972 993 0.4102858 0.44415 3,6,10,15,30, 997 2.8761752 0.4528064 997 0.5692364 0.0329 3,6,10,15,30, 1003 9.3911014 3.5759515 1003 4.9168363 0.4699411 3,6,10,15,30, 1004 75.118955 7.2739675 1004 16.675004 12.38818 3,6,10,15,30, 1010 0.0005432 0.0001279 1010 1.226439 1.4938655 3,6,10,15,30, 1014 0.7690969 1.0876673 1014 18.814905 26.038974 3,6,18,15,30, 1017 40.632692 54.481769 1017 5.781491 4.3967991 3,6,18,15,30, 1018 1.7952451 2.1490359 1018 2.8373928 1.6050718 3,6,18,15,30, 1019 0.0005432 0.0001279 1019 0.2151848 0.1014391 3,6,18,15,30, 1020 1.5093222 0.4092855 1020 0.0005432 0.0001279 3,6,18,15,30, 1021 0.0920881 0.1302323 1021 2.7845522 2.1345224 3,6,18,15,30, 1023 0.0005432 0.0001279 1023 0.6657974 0.4456216 3,6,10,15,30, 1025 0.026405 0.0373423 1025 0.0177734 0.0251354 3,6,10,15,30, 1039 1.220133 0.0529107 1039 1.6833034 0.3341209 3,6,10,15,30, 1040 0.0005432 0.0001279 1040 0.0661107 0.0221705 3,6,10,15,30, 1043 0.0191305 0.0270546 1043 0.2659329 0.1880631 3,6,10,15,30, 1049 0.0240777 0.0110011 1049 0.2129442 0.0654471 3,6,10,15,30, 1050 0.0005432 0.0001279 1050 0.0473608 0.0433438 3,6,10,15,30, 206 Pre-invasion Biomass ID Biomass Standard Deviation 1052 1054 1057 1059 1081 1082 1084 1085 1089 1092 1093 1094 1095 1098 1099 1100 1102 1103 1104 1105 1106 1108 1109 1111 1112 1114 1115 1119 1120 1125 1133.2 1133.5 1152 1188.2 1188.5 1190 1191.2 1191.5 1194 1195 1196 1198 1200.3 1200.4 1201 1202 1208 1209 1.2578057 0.0194248 6.5135076 0.0291372 70.924866 870.54656 0.013039 0.0795512 0.1361682 0.3224196 0.7404242 0.1 136712 0.2514467 3.358084 0.2194291 1.4001994 0.0758492 344.5971 1 4.5677511 1 1.371764 0.13365 0.0005432 0.8677425 21.377445 4.55981 1 1 20.602424 244.43873 4.3464942 56.593326 0.0014908 158.198 170.555 5.731 2.295 37.854 49.59509 70.132 762.268 2.8814772 12.992743 29.167016 3.4945188 24.703755 80.849894 6.059 5.48 0.004 8.22 0.210601 0.0091569 2.9913639 0.0412062 31.882447 132.68297 0.01844 0.0194888 0.0479625 0.4559701 0.7606952 0.0781562 0.3555994 2.03 84288 0.3103196 1.6021617 0.107267 10.810416 1.5008145 1.5936504 0.1890097 0.0001279 1.2271731 9.1790689 2.0946475 1.7621349 81.485818 2.3413328 16.806485 0.00031 12 226.22 152.394 2.746 1.785 17.994 17.756196 62.844 243.045 0.7453501 0.895593 3.3943064 0.2435209 1.0559019 5.7680095 6.973 7.786 0.009 6.551 Post-invasion Biomass Standard ID Biomass Deviation Datasource 1052 0.5409808 0.0223739 3,6,10,15,30, 1054 0.0151909 0.0214832 3,6,10,15,30, 1057 3.373225 1.5707555 3,6,10,15,30, 1059 0.1083492 0.0729892 3,6,10,15,30, 1081 7.5539673 0.2409882 3,15,30, 1082 735.57479 33.839811 4,30, 1084 0.3859362 0.2010828 7,9,15,30,34, 1085 2.9299429 3.5146651 7,9,15,30,34, 1089 1.0537342 0.3222409 7,9,15,30,34, 1092 0.5759529 0.535607 7,9,15,30,34, 1093 0.6701776 0.0739602 7,9,15,30,34, 1094 0.2272452 0.1095184 7,9,15,30,34, 1095 11.807035 4.0366332 3,4,5,15,21,30,33, 1098 1.0846101 0.9449849 3,4,5,15,21,30,33, 1099 3.4832134 0.1917164 3,4,5,15,21,30,33, 1100 3.3970979 0.8178719 3,4,5,15,21,30,33, 1102 0.5774503 0.6077599 3,4,5,15,21,30,33, 1103 210.68075 98.467619 3,4,5,15,21,30,33, 1104 0.0005432 0.0001279 3,4,5,15,21,30,33, 1105 4.9440814 1.7386864 3,4,5,15,21,30,33, 1106 0.2953983 0.4177563 3,4,5,15,21,30,33, 1108 2.9993111 2.5605748 3,4,5,15,21,30,33, 1109 1.3533158 1.6194348 3,4,5,15,21,30,33, 1111 4.0311582 0.9851361 3,4,5,15,21,30,33, 1112 0.1616848 0.2286568 3,4,5,15,21,30,33, 1114 15.157355 3.4031723 3,4,5,15,21,30,33, 1115 745.3255 106.23574 3,4,5,15,21,30,33, 1119 0.7779925 1.1002475 3,4,5,15,21,30,33, 1120 16.475403 0.9449115 3,4,5,15,21,30,33, 1125 0.0010865 0.0001701 14,29 1 133.2 6.742 9.45 25 1133.5 142.167 104.653 25 1152 1.49 1.389 25 1188.2 0.997 1.635 25 1188.5 15.159 8.276 25 1190 60.917475 1.3966614 27 1 191.2 0.079 0.093 25 1191.5 2138.767 784.515 25 1194 1.4515934 0.3679341 26 1195 24.135487 0.356224 26 1196 29.902646 6.5592941 26 1198 5.2321762 0.2088364 26 1200.3 8.0883221 0.8406123 26 1200.4 21.98599 1.4656158 26 1201 0.375 0.43 25 1202 44.21 27.173 25 1208 0.074 0.068 25 1209 41.009 48.9 25 207 Pre-invasion Biomass Post-invasion Biomass Standard Standard ID Biomass Deviation ID Biomass Deviation Datasource 1210 559.944 352.6 1210 538.806 192.796 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