”NIH!WWW“!!! fiEE WWW!WINIWUHIUIIHIHI , ‘ LIBRARY 1M Michigan State University This is to certify that the thesis entitled CREATING A NORTH AMERICAN STURGEON INFORMATION INFRASTRUCTURE: IMPLICATIONS FOR COMPOSITE DATABASES AS A MANAGEMENT TOOL presented by TRACY L. KOLB has been accepted towards fulfillment of the requirements for the degree In FISHERIES AND WILDLIFE Ma 22¢ Major Professor’s Sig 5/4/22 Date MSU is an affirmative—action. equal-opponunity employer 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 5/08 K:IProj/Acc&Pres/CIRCIDateDueindd CREATING A NORTH AMERICAN STURGEON INFORMATION INFRASTRUCTURE: IMPLICATIONS FOR COMPOSITE DATABASES AS A MULTIJURISDICTIONAL MANAGEMENT TOOL By Tracy L. Kolb A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Fisheries and Wildlife 2008 ABSTRACT CREATING A NORTH AMERICAN STURGEON INFORMATION INFRASTRUCTURE: IMPLICATIONS FOR COMPOSITE DATABASES AS A MULTIJURISDICTIONAL MANAGEMENT TOOL By Tracy L. Kolb Concerns about the decline of fisheries resources is becoming a mainstream policy issue. At the core of addressing fisheries decline is the need to maintain and publish knowledge about fish species, their distributions, and relative abundances. Compiling this knowledge entails a need for Sharing information across multiple jurisdictions and disciplines. In order to address this need, I developed a nationwide pilot status and trends information system for lake sturgeon, called the Sturgeon Information Infrastructure (SH). 811 combines historical and current sturgeon status and trends data from state federal, and tribal agencies, academic institutions, and private organizations. Data were collected, standardized and entered into an online relational database, where they are searchable and mappable. Constraints on creating 811 include lack of a standardization and classification system for lake sturgeon status and trends, lack of available and standardized georeferenced hydrography information, reluctance to share data amongst data providers, and lack of historical datasets about distribution and population abundances of lake sturgeon. ACKNOWLEDGEMENTS First and foremost I want to acknowledge Dr. William Taylor for accepting me in the Fisheries and Wildlife family at Michigan State. His confidence in my abilities has never wavered and he has continually pushed me to be a better and more balanced person. A project of this magnitude does not happen without the collaboration of many individuals. I am indebted to Doug Beard, Jack Liu, Scott McGill, Andrea Ostroff and Andy Loftus for their guidance and support throughout this project. Thanks to Amy Schueller, Dan Hayes, Dana Infante, and Jon Hansen. Without their support, encouragement, and love I could not have completed this journey. I also owe a great deal of gratitude to Tim Boyd and Ed Bissell for stoically enduring my initial attempts at project management and having limitless patience during the design and redesign of the website. Thanks also to the numerous individuals who gave me support: my lab mates, the Lake Huron Technical Committee, Jim Johnson, Jim Boase, Andrew McAnich, Chris Geddes, Tracy Hill, Nick Mandrak, Robert McLaughlin, Jeff Schaefer, Scott Nelson, Bruce Manny, Emily Zollweg, Gary Whelan, Randy Claramunt, Tim Haxton and Dave Clapp, my steering committee, and all of the participants that took the time and effort to take my data sharing survey. Lastly, I want to thank my parents David and Maxine Kolb, my sisters Rae Beth and Natalie, and Kendra, the love of my life. You are my past, present and future. I love you. iii TABLE OF CONTENTS LIST OF TABLES ........................................................................................................... viii LIST OF FIGURES ............................................................................................................ ix INTRODUCTION ............................................................................................................... 1 Composite databases and fisheries management ..................................................... 2 Building a sturgeon information infrastructure (SII) ............................................... 3 Lake sturgeon as a template species ........................................................................ 4 811 Goals and objectives .......................................................................................... 5 METHODS .......................................................................................................................... 6 Status and trends definition ...................................................................................... 6 Data collection ......................................................................................................... 7 Data standardization ................................................................................................. 9 Development of $11 database ................................................................................. 10 Development of SII website application ................................................................ 12 Evaluation of 811 .................................................................................................... 1.4 RESULTS .......................................................................................................................... 15 Amount, types and sources of data collected ......................................................... 15 Description of 811 ................................................................................................... 19 Lake sturgeon status and trends according to the 811 ............................................ 24 Results of $11 evaluation ........................................................................................ 39 DISCUSSION .................................................................................................................... 43 APPENDIX A —— UCRIHS- approved study consent form ................................................ 51 APPENDIX B — Replication of 2004-2005 survey instrument ......................................... 53 APPENDIX C — Standardized waterbody and data names in the 811 ................................ 57 WORKS CITED ................................................................................................................ 62 iv LIST OF TABLES Table 1. Synopsis of the information contained in each of the tables in the 811 database .............................................................................................................................. l 1 Table 2. Names, affiliations and areas of expertise of steering committee, convened in January 2008 to evaluate project progress and future needs .............................................. 14 Table 3. Agency name and type of information collected, from surveys sent to US Fish and Wildlife Service (USFWS), United States Geological Survey (USGS), Michigan (MDNR), Wisconsin (WDNR) and Ohio Department of Natural Resources (ODNR), and New York State Department of Environmental Conservation (NY DEC) ......................... 15 Table 4. Amount, sources and types of data collected for the 811. Numbers number of sources from each agency and total records contributed for Presence/Absence (P/A), status, population estimates (PE) and research. Sources are Department of Fisheries and Oceans Canada (DFO), Michigan Department of Natural Resources (MDNR), New York State Department of Conservation (NYDEC), Ontario Ministry of Natural Resources (OMNR), United States Fish and Wildlife Agency (USFWS), United States Geological Survey (USGS), Wisconsin Department of Natural Resources (WDNR), Great Lakes tribes, Great Lakes universities and various state, federal, international, and private interests Various are grouped because they contributed an average of 1 record each. Incomplete represents records in the database that cannot be traced back to the original source because of lack of metadata ................................................................................... 18 Table 5. Names of basins and waterbodies that had historical populations of lake sturgeon .............................................................................................................................. 27 Table 6. Names of basins and waterbodies in Lakes Michigan and Superior that have current populations of lake sturgeon .................................................................................. 28 Table 7. Names of basins and waterbodies in Lakes Huron, Erie and Ontario that have current populations of lake sturgeon .................................................................................. 29 Table 8. Names of basins and waterbodies in the St. Lawrence River that have current populations of lake sturgeon .............................................................................................. 30 Table 9. Names of basins and waterbodies with remnant populations of lake sturgeon for Lakes Michigan and Superior. Remnant populations are defined as having spawning populations of adults that range from 10 — 1,000 individuals ............................................ 32 Table 10. Names of basins and waterbodies with remnant populations of lake sturgeon for Lakes Huron, Erie, and Ontario and the St. Lawrence River. Remnant populations are defined as having spawning populations of adults that range from 10 — 1,000 individuals .......................................................................................................................... 33 Table 11. Names of basins and waterbodies with extirpated populations of lake sturgeon for Lakes Michigan, Superior and Huron. Extirpated populations are defined as having spawning populations of adults that are less than 10 individuals ...................................... 34 Table 12. Names of basins and waterbodies with extirpated populations of lake sturgeon for Lakes Erie, and Ontario and the St. Lawrence River. Extirpated populations are defined as having spawning populations of adults that are less than 10 individuals ......... 35 Table 13. Names of basins and waterbodies with unknown populations of lake sturgeon for Lakes Michigan, Superior, Huron, Erie, and Ontario and the St. Lawrence River ...... 35 Table 14. Population estimates or ranges of estimates for Great Lakes basin from United States Fish and Wildlife Service (U SFWS), Enviro-Science Inc., Wisconsin Department of Natural Resources (WDNR), New York State Department of Environmental Conservation (NYDEC), Ontario Ministryof Natural Resources (OMNR), Central Michigan University (CMU), Michigan Technical University, Michigan Department of Natural Resources (MNDR), Consumer’s Energy Environmental Department, University of Georgia, Purdue University, and unknown sources ...................................................... 37 Table 15. Population estimates or ranges of estimates for Great Lakes basin from Ontario Ministry of Natural Resources (ONMR), Michigan Department of Natural Resources (MDNR), and the Service de l'amenagement et de l'exploitation de la faune ................... 38 Table 16. Completed research projects carried out by agencies across the Great Lakes basin. Research categories include: Basic biological data collection (BB), population estimate (PE), telemetry (TL), tagging (TG), genetics (G), contaminant studies (C), and other scientific studies (SS). DNR stands for Department of Natural Resources and USFWS stands for United States Fish and Wildlife Service ............................................. 38 Table 17. Ongoing research projects carried out by agencies across the Great Lakes basin. Research categories include: Basic biological data collection (BB), population estimate (PE), telemetry (TL), tagging (TG), genetics (G), contaminant studies (C), and other scientific studies (SS). DNR stands for Department of Natural Resources and USFWS stands for United States Fish and Wildlife Service ........................................................... 39 vi LIST OF FIGURES Figure 1. Data in tables that have the name but different meanings are separated during the database standardization process. In this example both tables have data called abundance, but the data within the tables were collected using different methodology ...... 9 Figure 2: Data in tables with different names having the same definitions are combined during the database standardization process. In this example tables are named differently but hold the same information ............................................................................................. 9 Figure 3: Design of the 811 database indicating the data tables, relationships between tables, and fields used as the keys in the relationships ...................................................... 1 1 Figure 4. Willingness to share data from 22 surveys, sent to Great Lakes biologists and supervisors, asking yes or no are you willing to contribute lake sturgeon data that your agency collects to a composite database ............................................................................ 16 Figure 5. List of reasons that 22 Great Lakes sturgeon technicians, biologists, or supervisors were unwilling to share data. Reasons are: they lack the time, fear‘data misuse, fear being scooped by other scientists, fear they will lose control of their data, fear sturgeon poachers will access location data and use it to illegally poach sturgeon, lack the money, fear that their data are in poor condition, and/or have political or legal reasons for refraining from contributing data. Scientists were able to choose or not respond for as many reasons as they wished ..................................................................... 17 Figure 6. 811 sturgeon main page. Toolbars are from left to right: about nbii, project funding, about us, links, and contact us. Along the right side is a general disclaimer and a disclaimer about data reliability. In the bottom lefi corner is a drop down box for selecting a species, and in the bottom middle is a log in section for data providers ......... 20 Figure 7. Lake sturgeon main page. Toolbars are from left to right: participating agencies, about lake sturgeon, queries/reports, lake sturgeon links, and return to main ................... 21 Figure 8. Lake sturgeon query page. At the top a user must select a location, which can be all locations, basin, state, province, or waterbody name. The user can specify a time period. The user can also select a status type, present, healthy, remnant, extirpated, unknown, and can narrow their search by reintroduced, introduced and supplemented populations. The user can also query by research type, life history, management, or additional record information ............................................................................................ 22 Figure 9. Table with a partial listing of records that met criteria Basin = Michigan, Status = remnant. Note that each record ID is hyperlinked to its metadata. At the top of the table the user has the option to see his or her query retumed as a map ...................................... 23 Figure 10. Distribution of lake sturgeon from before 1975 according to records in the $11 ....................................................................................................................................... 25 vii Figure 1 1. Current distribution of lake sturgeon according to records in the 811 .............. 26 Figure 12. Status of lake sturgeon populations in the Great Lakes basin. Healthy populations are defined as having spawning populations of adults that range from 1,000 - 10,000 individuals. Remnant populations are defined as having spawning populations of adults that range from 10 — 1,000 individuals. Extirpated populations are defined as having spawning populations of less than 10 individuals. Unknown populations have unknown amounts of spawning adults ............................................................................... 31 viii INTRODUCTION Research directed at addressing global issues such as bio-complexity, sustainability, and ecosystem change, while supporting regional and national policy and management decisions is needed in order to address emerging threats to natural resource conservation (Porter 2004; Hale et al. 2003; Brunt et al. 2002). These types of research transcend traditional disciplinary, jurisdictional, Spatial, and/or temporal boundaries, deal with issues that require the input of more then just one or a few individuals, and require synthesizing and combining data that are collected at finer ecological scales (Baker et al. 2005; Han et a1. 2002). AS such, integrating datasets from multiple sources can be a powerful scientific tool for using diverse information in ways that support the decision making process or address broad scale issues (Porter 2004; Hale et a1. 2003). A common tool for integrating diverse datasets is the composite database (McLaughlin et al. 2001). A database is a collection of data or records that have structure imposed on them. I have coined the term “composite” database to refer to databases that standardize and integrate data from multiple sources. Composite databases are used because they imbue a project with several advantages. First, data cost less to reuse than to re-collect, reducing the need for both personnel and fiscal resources (Porter 1998). Second, many types of historical datasets can’t be recreated. Hence, there is utility to preserving historical datasets as they provide valuable information on past environmental conditions that can never be re-collected. Third, composite databases prevent or slow “data decay”. Data decay refers to data that have been traditionally collected and analyzed by a Single individual or small group, and over time, our ability to locate and interpret those data has been diminished or lost through lack of documentation (Porter and Ramsey 2005). Most importantly, composite databases can be used for a wide variety of scientific inquiry, including: long-term studies, which use databases to retain project history; syntheses, which combine data for purposes then they were otherwise intended; and integrated multidisciplinary projects (McLaughlin et al. 2001; Porter 1998). Composite databases and fisheries management For North American fisheries resources, one essential need that a composite database project can accomplish is to map the spatial distribution of fish populations. Understanding current and historical distribution patterns is an essential prerequisite for determining causal relationships between ecosystem change and aquatic processes (Watson et al. 2004). Any plan that attempts to address management strategies for mitigating fisheries loss must have insight into historical as well as current species distribution, so scientists can determine where species are and how they are doing relative to past conditions and provide policy makers with needed general characterizations of a resource across time and space. The United States Geologic Survey (USGS) and the United States Fish and Wildlife Services (USFWS) have mission statements that require them to preserve and protect natural resources at a national level to ensure the continued availability of those resources for ecosystem services and human uses. In order to fulfill those mission statements natural resources need to be inventoried and characterized so that threats can be identified, mitigated, and potential benefits optimized. There are a number of programs to help federal natural resources agencies meet their missions. One of these programs is the Fisheries and Aquatic Resources Node (FAR), initiated in 2001. The goals of this program are to provide an integrated, web-based resource that will: coordinate and link to fishery databases across the United States, as well as provide data on fisheries distribution and trends through time. In 2004 the USGS in concert with FAR developed a 5-year strategic plan to help meet agency missions. They solicited input from peers within and outside of the USGS, including other Department of the Interior (DOI) bureaus, federal and state agencies, and non-governmental organizations. They found there was an immediate need to assess the status and trends of the nation’s biological resources by moving beyond a “large collection of projects”, towards an integrated effort to maintain and publish knowledge about species, their distributions, and relative abundances, that is, their status and trends. Building a sturgeon information infrastructure (SI!) To address these issues the USGS solicited a prototype, intemet-based information system that allows for assessment of status and trends of an aquatic species across its entire range. AS completed, the information system was meant to be used as a template for development of additional information systems as well as could be used as a tool to manage inland freshwater aquatic resources. This system was developed in order to build a model for tracking status and trends of a Species across its range, not to conduct a complete status report on the template Species itself. Sturgeons were chosen as the template species because they occur in the continental United States, and are a species that the USGS is interested in characterizing. The USGS is interested in sturgeons because there is significant public interest in collecting information and maintaining information about their status and trends. Additionally, the majority of sturgeon biologists are nearing retirement age, so there appeared to be a need to provide for the long-term care of their data, making “data decay’ a looming concern, (D. Beard, USGS, personal communication). Lake sturgeon as a template species The first step in creating the prototype database was to convene a meeting of “sturgeon experts” from around the country. The experts raised serious logistical concerns about data availability and personnel, with regards to integrating information from all 8 Species of North American sturgeon. Based on the validity of these concerns the project was further streamlined to include only the single species lake sturgeon (Acipenserfulvescens). The lake sturgeon information system could still serve as a prototype by combining multiple datasets from diverse sources across the Great Lakes basin. This prototype system built for lake sturgeon is known as the Sturgeon Information System (811). The lake sturgeon is a late-maturing, slow-growing, long-lived fish (Cook et al. 1987). Lake sturgeon are found in many large rivers and lakes in North America. While there are some remnant or introduced populations in the southern and central United States, most sturgeon populations are in the Great Lakes basin including the Canadian provinces of Ontario and Quebec (Auer 1999, Baker 1980). Lake sturgeons travel within a home range and return to spawn in natal tributaries in spring (Sandilands 1987, Dumont et al. 1987, Priegel and Wirth 1971). Lake sturgeons are an ideal candidate for establishing this prototype system, because in 2003 scientists agreed on a common scheme for classifying lake sturgeon relative abundances (Zollweg et al. 2003). 511 Goals and objectives The lack of standardized biological information on aquatic species available at national scales makes it essential that we build research and management tools with the ability to integrate what local or regional data already exist. Therefore the overarching goals for this project were to develop an intemet-based information system for the scientists at USGS, that combined lake sturgeon status and trends information from multiple sources across the Great Lakes basin. Specific objectives were to build a composite, geospatial, database, for lake sturgeon with the ability to store existing data and integrate new data as they become available for: current and historical distributions of lake sturgeon, current and historical changes in lake sturgeon abundance, and types of locations of current and historical research projects related to lake sturgeon. METHODS Status and trends definition The first step in creating the 811 was to define a unit of measurement for lake sturgeon. Lake sturgeon are potadromous, and home to their natal streams during reproduction (Baker 1980; Harkness and Dymond 1961). Therefore the most natural unit of measurement of status and trends is at the level of populations. All SII status and trends information reflects the health of geographically distinct populations, defined by natal spawning tributaries. For the purpose of measuring status and trends the following definitions were used: Status information is delineated by three general scales of information. At a coarse scale, status is defined as simply the presence or absence of a lake sturgeon population. At a finer scale, status is classified by categories denoting the relative abundances of lake sturgeon populations. At the finest scale, status is recorded as a population estimate in absolute numbers or ranges of numbers. Status was never inferred from a course scale to a finer scale, but if status was available at the abundance level, the status and presence of the record was inferred. Trends were defined by changes in relative abundances through time, which at the coarsest scale can be characterized as a change from present to absent or vice versa, at a finer scale as a change from one status classification to another, and at the finest scale as a change in numbers of sturgeon present in a given population. Information on sturgeon research was classified into broad categories representing the most common types of research. Research types were categorized as follows. First basic biological data collection, where researchers collected characteristics of sturgeon during a sampling period such as age, gender, length, and weight. Population estimates, where researchers actively used quantitative methodology to determine a population estimate for sturgeon during a sampling period. Telemetry studies, where researchers implanted telemetn'c tags to study the movements of sturgeon during the course of a given sampling period. Tagging studies, where researchers implanted tags in or on sturgeon, tracking their spatial movement over long time periods. Genetics, where researchers took genetic samples from the sturgeon populations. Contaminant studies, where researchers collected information about the amount or source of different contaminants in a given area during a sampling period. And finally, other scientific studies, which served as a bin for organizing research that didn’t fit into the other categories such as sampling environmental conditions, habitat characteristics, other species present, etc. Data collection Information on available data was collected systematically by sending 25 surveys to all federal and state agency researchers across the Great Lakes basin inquiring as to the types of sturgeon data they collected and if they were willing to make that information available. Researchers were identified by the Great Lakes Sturgeon Website. Federal and state agency researchers were chosen because they were thought to have previously complied large data sets on lake sturgeon. Data were also collected opportunistically by speaking to lake sturgeon scientists with the Michigan Department of Natural Resources, United States Geological Survey and the United States Fisheries and Wildlife Service, attending lake sturgeon coordination and American Fisheries Society meetings, and combing through peer- reviewed literature, government reports, online databases and unpublished datasets from the states of Minnesota, Wisconsin, Indiana, Illinois, Michigan, Ohio, Pennsylvania, New York and Vermont, the provinces of Ontario and Quebec, the federal governments of Canada and the United States, Michigan State, Michigan Technical, Central Michigan, State University of New York, Cornell, Wisconsin, St. Mary’s, and Purdue Universities, as well as various Great Lakes tribal authorities and private entities (Baker 2006; Zollweg et al 2003; Auer and Baker 2002; Auer 1999; Bruch 1999; Dumont et al. 1987; Baker 1980; Harkness and Dymond 1961) . Types of data sought included information about historical and current presence. Information pertaining to an existing classification schemes denoting relative abundances of current and historical populations of lake sturgeon, and information pertaining to current and historical research projects. Additionally, where available, citations or methodology used to generate data were sought and compiled. Additional types of information included in the 811 were referenced to hatchery-reared populations, endemic populations, successfully reproducing populations, and if sturgeon were present, which life-history stages had been observed: eggs, larvae, juveniles, sub adults or adults. Lastly, information was compiled on which types of harvest could take place at the Spawning tributaries. These auxiliary data were determined to be useful additions to the status and trends database, because they allow researchers subset criteria when querying the database for more refined synthesis and analysis (Michael Parsley, USGS, and Mark Collins, South Carolina DNR, personal communication). Data standardization After data were collected they were standardized for the 811 database. The standardization process included two steps: creating a common naming format and creating a common geo-referencing data scheme. In order to create a common naming scheme, data from different source datasets with the same name but containing the different types information were streamlined and separated (Fig 1). Data with different names that had the same information were combined (Fig 2). Abundance Table population size estimate total biomass Abundance Table population density estimate biomass density 0 Figure 1. Data in tables that have the name but different meanings are separated during the database standardization process. In this example both tables have data called abundance, but the data within the tables were collected using different methodology. Relative abundance Table Healthy Status Table Healthy 0 Figure 2: Data in tables with different names having the same definitions are combined during the database standardization process. In this example tables are named differently but hold the same information. When creating a common geo-classification scheme, all data were geo-referenccd using the USGS National Hydrographic Dataset (NHD). NHD is a comprehensive set of digital spatial data containing information about surface water features such as lakes, ponds, streams, rivers, Springs and wells. Within the NHD, surface water features are combined to form "reaches," which provide the framework for linking water-related data to the NHD surface water drainage network. Reaches delineate sections of rivers that join at a confluence. Each reach has a unique 14 digit identification number called the reachcode. Each record in the S11 database is linked to one or more reachcodes. SII geospatial information using NHD was based on 1:100,000-Scale data. Because data from Canada is not available in NHD form, a shapefile containing Canadian waterbodies, the National Scale Frameworks Hydrology (N SFH), was appended to the NHD. Canadian waterbodies were georeferenced by hand and assigned special case names. Special case names were then manually linked to line features within the NSFH. Canadian hydrography was provided by the Ontario Ministry of Natural Resources (Tim Haxton, personal communication). Development of 51] database The $11 database was developed in Microsoft Access, using relational database rules in order to maximize database integrity (Hernandez 2003). Relational database rules include: 1) tables that are constructed properly and efficiently, i.e.: each table represents a single object, is comprised of distinct fields, keeps redundant data to a minimum and is identified throughout the database by a field with unique values; 2) data integrity that is imposed at the field, table, and relationship level. Information and a schematic of the design is provided in Figure 3, information included in the database is listed in Table 1. 10 MAIN Record ID Contact ID Reach ID Citation ID CONTACT 00 Contact ID LOCATION 00 Reach ID CITATION ID 00 Citation ID Figure 3: Design of the $11 database indicating the data tables, relationships between tables, and fields used as the keys in the relationships. Table 1. Synopsis of the information contained in each of the tables comprising the SII database. 1. Main 3. Location 0 start date 0 waterbody name 0 end date 0 basin name 0 presence/absence o state/province name 0 status type 0 abundance 4. contact 0 life history 0 contact name 0 research type ’ agency 0 endemism 0 address 0 reproduction successful ' phone 0 metadata ' email 2. Citation 0 citation reference 0 year 11 Data quality was checked using several different methods. All records were verified with the original data. Also as mentioned previously, data were standardized by ensuring that fields with the same names in different source datasets contained the same information in the same format, and fields with different names but containing the same data were unified, because using existing structure from inherited datasets is not considered a “best practice” and should be avoided if possible (McLaughlin et al. 2001, Hernandez 1997). Many problems including poor design (tables that aren’t properly linked), and insufficient data integrity (redundancy) that can arise from creating a single database can be compounded when combining multiple datasets. Additionally where possible, information regarding how the record was sampled, including any citations was included. Development of 51] website application After the $11 was created in Access (part of Microsoft Office Professional Edition, 2003, Microsoft Corporation), it was copied to Microsoft SQL Server (2003, Microsoft Corporation), an application that allows a database to be searchable online. A website was created to serve database information. The website also included sections on funding agencies, data providers, information about lake sturgeon, links to other lake sturgeon websites, and a report on the status and trends of lake sturgeon according to the 811 database. The main function of the webpage however, is to allow users to search the $11 database online. The query page has fields that allow users to search by location: waterbody name, basin, state or province, status type: presence/absence, status 12 classification, or abundance, research type. All search fields can be narrowed by time period, endemic populations only, successfully reproducing populations only, or by specific life history stages observed. Once the database is queried, all records which meet the criteria are returned in tabular form, with each record hyperlinked to its citations and/or metadata. The records can also be mapped using an interactive mapping site (IM S), created by the MSU Geography department for this project. Essentially IMS is an ASP.NET website built on the ESRI ArcGIS Web ADF (Application Developer Framework) that uses ArcGIS Server to publish GIS maps to the intemet. The IMS sits on a separate server which contains a copy of the NHD and the NSFH with the special case names as fields within the file. IMS is housed at MSU Remote Sensing & Geographic Information Science & Outreach Services (RS&GIS). The IMS can be thought of as the front-end or user interface that allows a user to interact with the geographic data by supplying search criteria that are then processed by IMS. When a query is performed, a querystring in the form of a unique URL is passed to IMS server through the URL specifying the criteria the user has chosen. The querystring identifies locations to display within a specified geographic area. Applicable geographic areas include standard geolocators such as states, provinces, basins, HUCS (Hydrologic Cataloging Units), Component Basins (Canadian hydrologic units) and waterbodies (named water features). The smallest unit that locations are identified to is the reach level. The smallest unit that users are able to search on is the waterbody which consists of one or many reaches. Once the user chooses to map the results of a query, IMS selects the location criteria passed through the URL and it appears highlighted on 13 the screen. The user then has the option to pan around, or zoom in and out, of the map SCI'CCII. Evaluation ofSII To evaluate the $11 a steering committee of sturgeon biologists and policy experts was reconvened after development (Table 2). The steering committee was asked to evaluate the system by addressing the questions: what is useful about 811? What needs improvement? What is unnecessary? Is there information or functionality that is missing? Is it useful for biologists, policy makers, public, other scientists, NGO’s or industry? Is appropriate access provided to the various groups that you would expect to use the system? Would reports containing status and trends of sturgeon be useful? Are the useful characteristics of status and trends reports that are currently being used in research and decision making reflected in capabilities of the S11? Do you think a status and trends information system such as this would be useful on a regional or national scale? Table 2. Names, affiliations and areas of expertise of steering committee, convened in January 2008 to evaluate project progress and future needs. Names Affiliation Expertise William Taylor Michigan State University multijurdisctional management Andrea Ostroff USGS information management Douglas Beard USGS policy, information management Michael Parsley USGS white sturgeon Patrick Braaten USGS pallid and shovelnose sturgeon Vaughn Paragamian Idaho Fish and Game white sturgeon Andy Loftus Loftus Consulting information management Jarrod Kosa USFWS multijurdisctional management l4 RESULTS Amount, types and sources of data collected Out of the 25 surveys that were sent to great lakes lake sturgeon scientists, 22 responded. When asked what lake sturgeon data had been or was being collected, surveys showed that information on both distribution and abundance were available through USFWS, USGS, Michigan (MDNR), Wisconsin (WDNR), and Ohio Departments of Natural Resources (ODNR), and New York State Department of Environmental Conservation (NYDEC) (Table 3). However, fewer entities collected abundance information than distribution information. Table 3. Agency name and type of information collected, from surveys sent to US Fish and Wildlife Service (USFWS), United States Geological Survey (USGS), Michigan (MDNR), Wisconsin (WNDR), and Ohio Department of Natural Resources (ODNR), and New York State Department of Environmental Conservation (NY DEC). Information Type Agency Name Distribution Population Estimate USFWS WNDR MDNR NYDEC OMNR USGS ><><><><><>< ><><><><>< Of great importance to this study we found that, only 10 of 22 scientists were willing to contribute their data to the S11 (Figure 4). Reluctance to share data became a bottleneck in the data collection process and represents a critical obstacle to building composite fisheries databases. When asked why they were unwilling to share, the majority of scientists cited issues such as lack of time and fear of data misuse (Figure 5). 15 yes no Figure 4. Willingness to share data from 22 surveys, sent to Great Lakes biologists and supervisors, asking yes or no are you willing to contribute lake sturgeon data that your agency collects to a composite database. Given the reluctance of many professionals to share data, access to large unpublished datasets was limited. However, I was able to access two major sources of data, the Great Lakes lake Sturgeon Tributary Database and Geographic Information System compiled in 2002 by the USFWS, and an unpublished summary report that defined status classifications for relative abundances of Lake sturgeon (Zollweg et al 2003). Because these sources were large in volume they were ideal for the 811. From those sources, I compiled 364 records from 79 scientists, representing 41 agencies, organizations, universities, or tribes. Of the 364 records, 242 record historical presence or absence, 180 record status, 32 record population abundance or estimates and 364 record historical and current research efforts (Table 4). 16 60:23 >2: mm 8832 ESE mm 8 98%»: Ho: .8 38:0 8 03m 825 83:28 .83 mix—£550 80¢ magma»: com 8588 Ewfl no 3358 96: SEE“ £02280 coon E 8m 8% :05 8: 80m $288 2: 308 .aooweam sowed 3:“on 8 .: um: ES 8% 5:82 38% E? 80:33 coowcam Sum .83 :2: mo 38:8 32 EB >05 Row .38 >2: 80.89 8% :2: :o :83an 53 83:23. 550 85 Sub 83:28 .550 c3 @28on wEon Row .8288 8% Sum .08: on... x08 >2: ”Be 388% .88 88% 8 wEEBS. 053 883395 co 8&w2ofi 5:83:58 coowcam mews EEO mm 85 8538 mo 65 .m oSmE V v O x/ O O x/ as 00% Res be say» as soy. Ac Ax +9 00.... z» 0:7 v QIW IV 90 0 CV I: 00 s so 0 o 4 e O 0 AV OO 0...0 0& $01. 00 Q00 2va 00 00V. 7 00. 00 SO 9/ 29 AW 0O 90» l .d a Q _ m a — — _ — W E 0— D O.— n_ #02 D E 0— Q 0.. Q I 8st 9:35 “o: .8 mcozmzuos. Table 4. Amount, sources and types of data collected for the S11. Numbers number of sources from each agency and total records contributed for Presence/Absence (P/A), status, population estimates (PE) and research. Sources are Department of Fisheries and Oceans Canada (DFO), Michigan Department of Natural Resources (MDNR), New York State Department of Conservation (NY DEC), Ontario Ministry of Natural Resources (OMNR), United States Fish and Wildlife Agency (U SF WS), United States Geological Survey (USGS), Wisconsin Department of Natural Resources (WDNR), Great Lakes tribes, Great Lakes universities and various state, federal, international, and private interests Various are grouped because they contributed an average of 1 record each. Incomplete represents records in the database that cannot be traced back to the original source because of lack of metadata. Agency No. sources P/A Status PE Research DF O 3 32 2 1 41 MDNR 5 29 12 3 44 NYDEC 7 9 9 1 13 OMNR 5 50 15 7 50 USFWS l 1 28 20 5 38 USGS 3 6 4 0 6 WDNR 8 9 20 4 19 Tribes 1 l 1 1 13 0 18 Universities 16 19 24 4 25 Various 10 22 20 7 29 Incomplete n/a 27 41 0 81 In 2003 lake sturgeon researchers from across the Great Lakes basin developed a classification scheme for lake sturgeon populations denoting their relative abundance (Zollweg et al. 2003). This was the classification scheme that 811 used for defining status. There are four major classification types, based on observations of adult sturgeon numbers as they entered tributaries to Spawn in spring. Healthy: denotes populations of 1,000 — 10,000 adult spawners. Remnant denotes populations of 10-1,000 adults spawners. Extirpated denotes populations of less than 10 adult spawners, and unknown denotes populations that have unknown amounts of adult spawners. Because this 18 classification scheme wasn’t developed until 2003, it couldn’t be applied to records from before 2003, unless those records had population abundance estimates. Description of 511 The $11 is available at http://ntwebl 1.ais.msu.edu/sturgeon/SturgeonLogin.asp. The main page serves has several functions. It serves as a pathway for accessing specific Species pages, links to more information about S11, and allows data providers to add or enter data by logging into the system. (Figure 6). Once lake sturgeon is selected. The main lake sturgeon page of the S11 allows a user to view lake sturgeon data contributors, link to important lake sturgeon websites, query the S11 database, view a report on the 811 status and trends database, and learn more about lake sturgeon (Figure 7). The query page allows users to search by location, status type, population estimate, and research type. All of these search fields can be narrowed by time period, endemic populations only, successfully reproducing populations only, specific life history stages sampled, management type, and by introduced, reintroduced or supplemented populations only. A definition of the field appears when the user moves the cursor over that field name (Figure 8). Once a record is queried. For example Basin = Michigan, Status = remnant a table of data meeting the criteria is returned to the user (Fig 9). A user can then click on the ID number of a Specific record to obtain its metadata, citations associated with the record, and if available, how population estimates were obtained. The user can also map the record by clicking on the Map it! tool. 19 802.600 880 :8 00:08 E wofi a a 202:: 80:09 on: E 000 .020on 0 3:028 :8 :00 :300 00:: a 8 000.80 02 E0009 0:: E 5:50:00 880 00000 0000886 0 0:0 50:88:: .00000w 0 mm 020 Emu 05 m02< .00 8808 0:0 .83: .0: Son: .mfivfia 000.800 .:0: 00090 ”Emu 8 $2 50¢ 2: 88:00.0 Swan 50:: :OQwEm :m .0 oSwE m _ .. . .. .. i. ,. .... . rt. Ins... . l.....;.... . .2 In. u............ a .2: ....._.”,..1....I.2 _ 2.003 _ ..... . llli _li :mtbflmm ., H .Eoimuam 00.1.20me 2&3 .xm: $50 :9: S cm 0000 E05? 00.: 0000 005an 00300035 >00 00 3:00 0:) >500 00 03000 b? €2.30 60 .0 300005 00 >80 00:00:25 503 C0 009000. 00:00:20. 0030 .0 0000.300: 5:50:00. 3.0022053. 383000 05 .0254, 0:333:ng 00.30020. 000935 0020 00103000000 2303:0050. 30m. 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'6. blill- ..I.I.lll.,l.<.l..hir. -‘lcilfll luvfllc .9. . . _ .11 :2 .Ci . . Q. 1 22 .38 a ma BEBE Easy 8: Ho m5 com 9 cacao 2: was How: 05 038 05 mo a8 2: 2 3.692: m: 8 @3559»: a e 282 some 85 082 .3388 n 293m 6me22 H £QO «:85 BE 35 $88“ mo mam: Etna m 53> 2an .o 035 Sam 8% 52m 335%: H 88 32 83 SEWEE 9m fl 80m wmmfi Spa 4.388” um NW 88 $2 :53 Mam mm 88 83 865 NM NOON 52m 3.05%: film. NOON 82m quGOQoaA WwM ugh. zoEumom vcm taum mafim vanth; a _ 39:0 953me __ wfiwotoaxm __ :_ Q02 _ 3.0an new $225 gamma“ 9:3 ”.0 >m_am_n_ op 2:00.03 23 Lake sturgeon status and trends according to the SI] The 811 database contains 242 records of presence information of which 34 (14%) are historical records. Historical information was defined as information collected prior to or during 1975 and current information was defined as after 1975 (J. Crossman, Michigan State University, personal communication). Prior to 1975, the $11 contains 34 records of geographically distinct populations of lake sturgeon occurring in 16 tributaries to the Great Lakes basin (Figure 10, Table 5). Lake sturgeon populations in and around the Great Lakes region were estimated to number in the hundreds of thousands, if not millions (Tody 1974, Kinietz 1965, Slade and Auer 1997). Therefore it is important to note that the small number of tributaries reported by the 811 is more reflective of a lack of historical information available to the database than the true amount of tributaries supporting historical sturgeon populations. According to the SII post 1975 there are 208 records of geographically distinct populations of lake sturgeon occurring in 160 tributaries or landlocked bodies of water to the Great Lakes basin (Figure 11). In Lake Michigan there are 23 tributaries supporting populations of lake sturgeon, and in Lake Superior there are 13 tributaries or inland lakes that are currently supporting lake sturgeon populations (Table 6). In Lake Huron there are 19 tributaries currently supporting lake sturgeon populations. In both Lakes Erie and Ontario there are 5 tributaries or inland lakes that are currently supporting lake sturgeon populations. (Table7). In the St. Lawrence River there are 14 tributaries that are supporting lake sturgeon populations (Table 8). 24 .:m 2: E $808 8 @65on 39 20.62 Eob coownam 83 mo 5:25:55 .3 unswE 93 28935 30:22: 55> 29$ 11): ccwmoA 25 :m 2: E $802 8 @6808 coowpam 83 mo cozzngmfi E230 ._ _ Esmi c0356 5:5 202m 15):: ucmmoJ \ 26 Table 5. Names of basins and waterbodies that had historical populations of lake sturgeon. Basin Waterbody name Michigan Fox River Muskegon River Menominee River OcontoRiver Peshtigo River Huron Garden River Mississagi River Thessalon River Superior Amnicon River Bad River Black Sturgeon River Goulais River Iron River Michipicoten River Ontonagon River Sturgeon River 27 Table 6. Names of basins and waterbodies in Lakes Michigan and Superior that have current populations of lake sturgeon. Basin Waterbody name Michigan Superior Bear Creek Big Manistique Lake Cedar River Fox River Grand River Indian Lake Kalamazoo River Green Bay Ludington Shoal Manistee River Manistique River Menominee River Millecoquins River Muskegon River Oconto River Pere Marquette River Peshtigo River Pike River Sheboygan River St. Joseph River St. Joseph Shoal Sturgeon Bay Wolf River Bad River Batchawana River Black Sturgeon River Chippewa River Goulais River Kaministiquia River Nipigon River Ontonagon River Pic River Pigeon River St Louis River Sturgeon River White River 28 Table 7. Names of basins and waterbodies for Lakes Huron, Erie and Ontario that have current populations of lake sturgeon. Basin Waterbody name Huron Black Lake Blue Point Burt Lake Carp River ‘ Cheboygan River French River Garden River Magnetawan River Mississagi River Moon River Mullett Lake Naiscoot River, Nottawasaga River Rifle River Saginaw River Sauble River Severn River St. Marys River Thessalon River. Erie Detroit River Eastern Basin Lake St. Clair St. Clair River Upper Niagara River Ontario Black River Genesee River Niagara River Oneida/Cayuga Lakes Trent River 29 Table 8. Names of basins and waterbodies in the St. Lawrence River that have current populations of lake sturgeon. Basin Waterbody name St. Lawrence River Batiscan River Black Lake Des Milles Iles River Des Prairies River Detroit River Grasse River Lake Champlain L'Assomption River Oswegatchie River Ottawa River Raquette River St. Francois River St. Lawrence River St. Maurice River Status — classification 811 provides a current snapshot of the status of sturgeon populations in the Great Lakes region (Figure 12). There are 8 tributaries recorded in the 811 that have healthy populations of lake sturgeon: Wolf River, in the Lake Winnebago watershed, the Des Prairies, St. Maurice, and St. Lawrence Rivers, and Ottawa River’s Allumette Lake and Lac Coulonge. There are 63 tributaries with remnant populations of lake sturgeon (Tables 9 and 10). Lake sturgeon have been extirpated from 43 tributaries (Tables 11 and 12). Additionally, there are 17 tributaries where lake sturgeon are present but their status is unknown (Table 13). 30 .mzsnm wSEEwmw .«o 355:8 E50515 02E mecca—smog 850523 2323?: o. :2: 32 mo macaw—smog @63QO megs: 3 Backup 8m macaw—smog onmESxm .2963me coo; I 3 8on ems“. 35 3.2% CO macaw—smog wEEZwmm meta: mm @263 2m macaw—smog Ema—Sod $33222: 25.3 I coo; 88w owcfl :2... 3:6“ mo 20:22.8 wEEZEm meta: mm became 8w macaw—smog 328m .Emmn moxaq 380 2: E macaw—smog :oomcfim 83 mo mafim .2 2&3 535...: 383:3 25:83". 1.35. 3:8... I)». Ucomo4 _-“\ do 31 Table 9. Names of basins and waterbodies with remnant populations of lake sturgeon for Lakes Michigan and Superior. Remnant populations are defined as having spawning populations of adults that range from 10 — 1,000 individuals. Basin Waterbody name Michigan Big Manistique Lake Black Lake Fox River Grand River Indian Lake Kalamazoo River Green Bay Manistee River Manistique River Menominee River Millecoquins River Muskegon River Oconto River Peshtigo River Pike River St. Joseph River Superior Bad River Batchawana River Black Sturgeon River Chippewa River Goulais River Kaministiquia River Michipicoten River Nipigon River Pic River Pigeon River St. Louis River Sturgeon River White River 32 Table 10. Names of basins and waterbodies with remnant populations of lake sturgeon for Lakes Huron, Erie, and Ontario and the St. Lawrence River. Remnant populations are defined as having spawning populations of adults that range from 10 — 1,000 individuals. Basin Waterbody name Huron Erie Ontario St. Lawrence River Black Lake Burt Lake Carp River Cheboygan River French River Garden River Magnetawan River Mississagi River Moon River Mullett Lake Naiscoot River Nottawasaga River Saginaw River Severn River St. Marys River Thessalon River Detroit River Eastern basin Lake St. Clair St. Clair River Upper Niagara River Black River Niagara River Trent River Batiscan River Des Milles Iles River Detroit River Grasse River Lake Champlain L'Assomption River Ottawa River Raquette River St. Francois River St. Lawrence River St. Regis River 33 Table 11. Names of basins and waterbodies with extirpated populations of lake sturgeon for Lakes Michigan, Superior and Huron. Extirpated p0pulations are defined as having spawning populations of adults that are less than 10 individuals. Basin Waterbody name Michigan Superior Huron Barr Creek Boardman River Chicago Reef complex East/W est Twin Rivers Escanaba River Kewaunee River Manitowoc River Menominee River Milwaukee River Root River Sturgeon Bay Sturgeon River Whitefish River Wolf River Gravel River Harmony River Montreal River Ontonagon River Prairie River Stokely Creek Tahquamenon River Wolf River Ausable River Black River Blind River Go Home River Manitou River Root River Saugeen River Seguin River Serpent River Sturgeon River Thunder Bay River 34 Table 12. Names of basins and waterbodies with extirpated populations of lake sturgeon for Lakes Erie, and Ontario and the St. Lawrence River. Extirpated populations are defined as having spawning populations of adults that are less than 10 individuals. Basin Waterbody name Erie Cattaraugus Creek Huron River Maumee River Raisin River Sandusky River Ontario St. Lawrence Genesee River Napanee River Oswego River Salmon River Salmon River Table 13. Names of basins and waterbodies with unknown populations of lake sturgeon for Lakes Michigan, Superior, Huron, Erie, and Ontario and the St. Lawrence River. Basin Waterbody name Michigan Huron Ontario St. Lawrence Bear Creek Cedar River Ludington Shoal Manitowoc River Pere Marquette River Sheboygan River St. Joseph Shoal White River Blue Point Echo River Key River Koshkawong River Rifle River Sauble River Spanish River Amherst Island Shoal Oswegatchie River 35 Status — numbers SII also provides a population estimate or range of estimates in absolute numbers for lake sturgeon. The SII records 31 estimates of population abundances for 22 tributaries (Table 14) and 5 lakes (Table 15). All population estimates are from 2002 or later. Generally there were only one set of population estimates that exist for each tributary, however in the Fox and Menominee Rivers, state and federal agencies are both measuring population abundances. The 811 was unable to detect trends in lake sturgeon using historical information. However, the status classification scheme developed by Zollweg et al. had time built in, so 811 could detect trends by using the extirpated category. Using the extirpated category 811 inferred that at least 131 tributaries to the Great Lakes basin did at one time support lake sturgeon populations, and that currently at least 43 (32%) of those tributaries have become extirpated. Furthermore, of the 88 remaining tributaries supporting lake sturgeon populations only 8 (9%) tributaries have populations of greater than 1000 adult spawners, 63 (72%) have populations of less then 1000 adult spawners, and the remaining 17 (19%), are unknown. Using a large scale composite system to track status and trends requires some knowledge of what research is and has been done. However, tracking down that information requires resources. Therefore, the 811 also contains information on, past research projects (Table 16) and ongoing research projects (Table 17). 36 Table 14. Population estimates or ranges of estimates for Great Lakes basin from United States Fish and Wildlife Service (USFWS), Enviro-Science Inc., Wisconsin Department of Natural Resources (WDNR), New York State Department of Environmental Conservation (NY DEC), Ontario Ministry of Natural Resources (OMNR), Central Michigan University (CMU), Michigan Technical University, Michigan Department of Natural Resources (MNDR), Consumer’s Energy Environmental Department, University of Georgia, Purdue University, and unknown sources. River Name Population Estimate Source Bad 250 USFWS Des Prairies 7000 Enviro-Science Inc. Detroit 50-150 Enviro-Science Inc. Fox 200-300 WDNR Fox 100-200 USFWS Grasse 10-20 NYDEC Kaministiquia 140-175 ONMR L'Assomption 50-150 Enviro-Science Inc. Manistee 1-50 CMU Manistique 1-50 Michigan Tech. Menominee 200 WDNR Menominee 200-1000 USFWS Millecoquins < 10 MDNR Mississagi 150 Consumer’s Energy Muskegon 1-25 University of Georgia Oconto 1-50 USFWS Peshti go 1-200 Purdue University St. Francois 100 Enviro-Science Inc. St. Lawrence 100-200 OMNR St. Maurice 1250 Enviro-Science Inc. St. Regis 1-100 Unknown Sturgeon 200 Michigan Tech White 15-1000 USFWS Wolf 22000 WDNR 37 Table 15. Population estimates or ranges of estimates for Great Lakes basin from Ontario Ministry of Natural Resources (ONMR), Michigan Department of Natural Resources (MDNR), and the Service de l'amenagement et de l'exploitation de la faune. Lake Name Population Estimate Source Allumette 10,000 OMNR Black 60 MNDR Lac Coulonge 10,000 OMNR Lac St. Pierre 10,000 Service de l’amengement Table 16. Completed research projects carried out by agencies across the Great Lakes basin. Research categories include: Basic biological data collection (BB), population estimate (PE), telemetry (TL), tagging (TG), genetics (G), contaminant studies (C), and other scientific studies (SS). DNR stands for Department of Natural Resources and USFWS stands for United States Fish and Wildlife Service. Agency Name Research Type Central Michigan University Cornell University Department Fisheries Oceans- Canada Enviro-Science Great Lakes Indian Fish and Wildlife Commission Little River Band of Ottawa Indians Michigan DNR — Marquette Fisheries Station Michigan DNR — Mt. Clemens Fisheries Station Michigan Technological University New York State Department of Conservation Ontario Ministry of Natural Resources Purdue University SUNY College of New York USFWS — Alpena Fisheries Resource Office USFWS — Ashland Fisheries Resources Office USFWS- Marquette Biological Station USFWS — Green Bay Fishery Resource Office University of Georgia United States Army Corps of Engineers United States Geological Survey Vermont Department of Fisheries and Wildlife Wisconsin DNR BB, PE, TL, TG, G, O BB, TG, O BB, PE, O BB, PE BB, TL, 0 PE, TL, TG, C, 0 BB, PE, TL, C BB, PE, TL, TG, G, C BB, PE, TL, TG, G, O BB, PE, TL, TG, G, 0 BB, PE, TL, TG, G, C, O BB, PE, TL, TG, G, C, O BB, PE, TL, TG, G, 0 BB, PE, TL, TG, G, C BB, PE, TL, TG, G, C, O BB, PE, TG, G, BB, PE, TL, TG, G, C, O BB, PE, TL, TG, G, O BB, TL, TG, O BB, PE, TG, C, 0 BB, O BB, PE, TL, TG, G, O 38 Table 17. Ongoing research projects carried out by agencies across the Great Lakes basin. Research categories include: Basic biological data collection (BB), population estimate (PE), telemetry (TL), tagging (TG), genetics (G), contaminant studies (C), and other scientific studies (SS). DNR stands for Department of Natural Resources and USFWS stands for United States Fish and Wildlife Service. Agency Name Research Type Central Michigan University BB, TL, TG, G, 0 Department Fisheries Oceans- Canada BB, PE Enviro-Science BB, PE Fon Du Lac Band PE Grand Portage Band PE Grand Portage Chippewa Resource Program PE Great Lakes Indian Fish and Wildlife Commission PE Little River Band of Ottawa Indians Michigan DNR — Marquette Fisheries Station Michigan DNR — Mt. Clemens Fisheries Station Michigan State University Michigan Technological University New York State Department of Conservation Ontario Ministry of Natural Resources Service de l'amenagement et de l'exploitation de la faune SUNY College of New York USFWS — Alpena Fisheries Resource Office USFWS — Ashland Fisheries Resources Office USFWS — Green Bay Fishery Resource Office USFWS- Marquette Biological Station United States Geological Survey Vermont Department of Fisheries and Wildlife Wisconsin DNR TL, TG, G, C, O BB, PE, TL, G, C, 0 BB, TG G BB, PE, TG, G, O BB, PE, TG, G, O BB, PE, TG G C PE BB BB, TG BB, PE, TG BB, PE, TG, G, C, O BB, PE, TG BB, PE, TG, C, O BB, PE, G, O BB, PE, TL, TG, G, O 9 9 Results of SI] evaluation The steering committee agreed the SII was useful because of the following reasons: it provide a quick synopsis of status, saving users time on searching for that information and it provided a centralized clearinghouse for organizing and maintaining data on status and trends. The mapping application, with its visual display of status, is especially attractive and useful to many types of users. Also, SII helps to identify who is 39 doing what research, and where research is taking place, which means less time searching for available datasets. 811 provided a meaningful way to discuss conservation and status of individual species by standardizing available information into a common reference scheme, while creating options for tracking the products of a specific agency, as 811 tracks and compiles data from a variety of sources, not just a single source. The development of the 311 also provided valuable experience that can be used towards other data compilation or sharing projects, such as documented awareness that there unwillingness amongst scientists to contribute data to a composite database, uncertainty amongst database administrators on how to grant access to composite datasets that have already been compiled, awareness of the extent of which there is a lack of documented historical information about status and trends as well as a lack of metadata about many biological records. The 811 can also be used as model when developing similar products as it helps developers visualize how status and trends information can be displayed and organized for management purposes. The committee suggested that the $11 needed the following improvements, most of which have already been implemented: species management plans should be added to site content, species information should be deep-linked to FishBase — an online database that provides a lot of basic biological information about fish. The steering committee also agreed that the 811 also needs to ultimately provide the ability to perform mapping by multiple layers instead of by a single layer and that metadata and links to original records of sampling should be included where possible. Additional comments included: methods for deriving status should be made clear to the user, users should have the choice of querying by a single year or by a specific time period, locations where harvest is taking 40 place should be identified and harvest should be delineated by type. The committee felt as much information as possible should be included in the query results, including agency and quality of data, and users should ultimately be able to query by map instead of data fields. The committee suggested that a way to capture peer-reviewed literature that is not status and trends information was missing. They agreed that a method for communicating to users where to get more information; e.g., link to Google Scholar with text query, and other sources would be valuable as an information tool. The committee, also wanted to see links to data citations from data results that direct people to the original data source and a way to query by congressional districts. The committee concluded that a status report using information from the S11 would be of limited use to scientists because of the lack of detail in the data, but that the citation and research information would be very helpful. They recommended that 811 could be very helpful by allowing scientists to track the status of single rivers through time; e. g., watching an extirpated river become healthy after stocking. The committee recommended that 811 would help a scientist when addressing public information queries, and would be useful to new scientists taking over data in positions where a predecessor has retired or left. The committee agreed that 811 information would be useful to policy makers as a decision making- tool to see where management efforts have been successful, as an important communication tool for answering questions from Congress, and as an “expose” or transparency to the decision making process. The committee agreed that 811 would be useful to the public in terms of outreach, enabling the public to search research, 41 conservation and management inquires. Finally the committee recommended 811 would be especially helpful if there were ways to capture threats and/or provide “public knowledge” of threats. The committee noted that 811, in its current form, didn’t provide appropriate access to data. They suggested that having limited access would only cause trouble when restricting access to data, and that at its current level of data, the 811 should be accessible to everyone. The committee agreed that the useful characteristics of status and trends reports that are currently being used in research and decision making are reflected in capabilities of the 811 in a coarse way, but suggested other metrics:— genetics, successful reproduction, etc as proxies for measuring status and trends as well. They also recommended that 811 should include management goals —e.g., number of fish to reach “healthy” status; or a probability of extinction statement in order to provide a framework for assessing the meaning of status and trends. Ultimately the committee concluded that $11 was a very usefiJl prototype. Additional comments and suggestions were that the system may be better for short-lived species where status and trends were more evident over a shorter time frame, and that it must be both field tested, and presented to other species researchers in order to be more fully developed. 42 DISCUSSION Despite spending hundreds of millions of dollars on environmental monitoring and research each year, the United States does not know the full extent or condition of our natural resources on a national basis, or how they are changing (Bricker and Ruggiero 1998). Based on SII’s ability to say little about the status and trends of sturgeon, despite collecting and piecing together hundreds of research records from across the Great Lakes. a fundamental change in our approach to environmental monitoring and reporting is needed if we are to meet the challenges facing us in assuring conservation of our fish populations into the future. Based on 811 development it appears that current federal programs are too piecemeal, intermittent, and short term to provide the long-tenn information needs for understanding status and trends of species across an entire range. Organization is an emergent property for any complex system, and efforts like the 811 are necessary in order to develop that organization. Creating the $11 was far from simple and during its evaluation, while it was agreed that it a worthwhile endeavor, it’s real value was that its development provided a roadmap for the types of obstacles a similar future project would face. While creating the SII, I faced two types of general obstacles: technical and human-related (Hale et al. 2003; Pinkerton 1999). Technical barriers were things such as lack of consistency in resources, technology, data collection methods, experimental design, data quality standards, and laboratory procedures that hindered efforts to collect and integrate data. Human obstacles included difficulty finding unpublished data and reluctance to share data by data generators. Specific technical challenges faced when developing the 811 included: a lack of historical datasets about distribution and population abundances of lake sturgeon, 43 inconsistency in methodology for determining status and trends, a lack of status and trends data for populations of lake sturgeon outside the Great Lakes basin with a dearth of metadata or citations for data that was available, and a lack of available and standardized georeferenced hydrography information. The lack of available historical data impeded the 811’s ability to detect trends in populations because time is inherent in trends data. 811 has only 7 records of sturgeon information from pre-1960 and 24 records from pre-1975. However, based on a few publications and anecdotal evidence, there is general consensus that sturgeon populations have declined much from their historic levels (Baker 2006). However, it is fundamental to the missions of natural resource agencies that products like the 811 have the capability to capture historic data in order to effectively characterize trends. Unfortunately, finding that historical data is difficult, because, based on personal data searching experience, historical information is not available electronically and is rarely associated with metadata or citations. In the Great Lakes Region, sturgeon researchers are currently compiling historic records for a rewrite of the Michigan Lake Sturgeon Rehabilitation Plan (Gary Whelan, Michigan Department of Natural Resources, personal communication). To compile these records they are combing through county records and noting all observations of sturgeon, even anecdotal evidence. While, this type of detailed search is an inappropriate use of time and resources for the current version of the 811, I do recommend that future species composite database projects make sure to incorporate the information reflected in species management plans, because they are a large source of historical information. Clearly, the general lack of ability to find historical data is evidence that a system like the $11 is 44 needed and can be used to contain and preserve current data sets to slow or prevent further data decay. A lack of consistency in methodology needed for the determination of status and trends was the biggest constraint for the creation of the $11. For systems such as the 811 to function smoothly and reliably there needs to be an integrated effort amongst data contributors towards common data standards, mutually supporting data collection, and common reporting/distribution formats. Unfortunately commonality is the exception among datasets used for the $11. For example, lake sturgeon population estimates in the $11 reflect 6 types of different methodologies: survey catches, observation, harvest, occasional angler catch, and mark-recapture through electrofishing surveys or gillnet survey captures. Furthermore, the majority of population estimate information doesn’t have a corresponding citation. Therefore while the 811 can display these different population estimates, it can’t guide users on how to compare population estimates derived from different methodologies. Ultimately, composite database projects can create common organization schemes that group data for searching and displaying, but they can’t firndamentally change data so that they can be universally compared, because of the different assumptions and methodologies used to collect the original data. Lake sturgeon trends could not be assessed using SII data, because the status classification used in the 811 wasn’t created until 2003, and is only available for lake sturgeon in the Great Lakes basin. Lake sturgeon in the Mississippi River, northern Ontario, and Quebec were not included in the classification scheme. Because the 811 isn’t a decision making tool, it can’t classify lake sturgeon populations that haven’t already been classified by biologists. Additionally lake sturgeon populations haven’t been 45 reclassified since the original classification scheme was developed, making detecting trends impossible. The scientist that spearheaded classifying lake sturgeon populations has changed positions and there are no immediate plans to update the scheme (Emily Zollweg, New York Department of Environmental Conservation, personal communication). If the 811 is to stay relevant then once a classification scheme has been adopted, plans need to be put in place to keep it current. Without regular data updates, SII cannot fiilfill USGS goals and objectives. Another difficulty in creating a system based primarily on large unpublished datasets is that metadata documenting data collection is rare. In the 811’s approximate 300 records only about 15% have citations associated with them. While state and federal agencies are increasingly requiring their data to be referenced, this practice isn’t universal and the standards for documentation can vary largely between agencies. Therefore it is incumbent upon a system such as the SII to determine which and if documentation is appropriate and to ensure that each record incorporated meets those standards. Lastly, the lack of available and standardized georeferenced hydrography information made the construction of the 811 very cumbersome, because data layers had to manually appended. Because standardized datasets for North American are non- existent, this will be a problem for any composite database that seeks to track the status and trends of an aquatic species across its entire range, when that range extends outside of the United States. There are three potential solutions to this issue: the first is to simply make these projects United States based only. This option is reasonable because the agencies themselves are not international in nature and their mission statements declare their responsibility with the United States only. The second option is to collect what 46 spatial datasets do exist internationally (sometimes at cost) and manually geo-reference and append spatial data to the overall geo-classification scheme. The last option would be to engage international governments to create a common geo-classification scheme. Human—related barriers included identifying pertinent status and trends data and collecting those from diverse sources. Unfortunately, the process of data discovery and data collection is challenging and poorly understood (Hale et al. 2003). Identifying existing data sets is very difficult. No library of data sets exists where a researcher can simply look through a card catalog and pick the data sets that are most appropriate to address an analytical question. Most of these data sets exist at diverse locations and are not public knowledge (Hale et al. 2003). For a scientist to be able to decide if a particular dataset is appropriate for answering a research topic, the researcher must address such questions as: what are the data, who has permission to release these data, who maintains these data, how were these data collected, and what is the spatial and temporal extent of these data? These questions can be hard to get definitive answers to because even if data can be found many data generators are also reluctant to share information about the state of their datasets (Porter 2004). Identifying available data sets to incorporate into the 811 was a very time consuming process, it took approximately one year to identify pertinent datasets. That time was spent searching through peer-reviewed literature, grey literature, government reports, online databases and unpublished datasets, speaking to scientists, attending meetings and by sending surveys to fishery professionals across the Great Lakes basin. Finding out information on who was performing research and how the research was being done was next to impossible when that research remained unpublished. In fact, only by 47 word of mouth did that information become accessible. However, there is a need to bridge the gap between what research takes place and what research is reported. If work is to further take place on standardizing the sampling and analysis of aquatic populations, then laying the groundwork for what research takes place to begin with is absolutely fundamental. In recognition that the search process is long and cumbersome, the $11 includes information about historical and ongoing research projects, because they serve as a proxy for identifying pertinent datasets. Integrating that information alone makes 811 a very valuable asset for tracking and identifying relevant datasets. Once a researcher has identified a dataset, access can be difficult because the data generator often will not share the data (Porter 2004; Hale et al. 2003; Pinkerton 1999). The results of the surveys sent to sturgeon researchers across the Great Lakes basin indicate that at least 40% of scientists were unwilling to contribute their data to a common database. However, not all unwillingness to share data is equal, reluctance to share data due to fears of data misuse or being “scooped” by other scientists require redress differently than reasons such as limited time or money. In one conversation with a survey participant, he informed me that at his annual review he was not rewarded for sharing data, he was rewarded for publishing papers, clearly a disincentive for scientists to collaborate to a common database. If scientists are rewarded for publishing individual manuscripts, and furthermore perceive that by contributing data to a composite database their research might get scooped, then sharing data presents a critical obstacle to building composite databases. A tool that can be used to address these issues is the data sharing agreement, which outlines how the data will be managed and accessible to others, before any data is ever 48 contributed. The fact that the federal agencies are spearheading attempts to consolidate and standardize data, but that scientists at the ground are unwilling to share data, suggests improved communication from top-down federal agency administration as one possible intervention point. Another obvious solution is for federal agencies to consider moving beyond the traditional reward system for publishing papers towards a reward system that includes acknowledgement to scientists for contributing to composite databases. Developing the 811, despite its obstacles, was ultimately a meaningful endeavor, because the topic of status and trends is both relevant and topical. Attempts to consolidate and display information about the status of a species gets at the core of a very simple, intuitive, and relevant public concern about biodiversity and the state of our natural resources. Most people outside the science world do not have the ability to generate or gather this information, but because natural resources are communally owned, it is the responsibility of natural resources agencies to make basic biological information about resources accessible to any member of the community. Additionally, understanding the state and change of our nations fishery assets is imperative if management is to address changes happening beyond a local or regional scale. Development of the 811 also probes at a number of underlying systemic issues relating to natural resource management within the United States. In particular fundamental issues associated with addressing large scale research questions which included: resistance to sharing data, how and if agencies cooperate and communicate with each other and with their own employees, how to preserve and use historical datasets, the general lack of biological standardization, and assessing if the creation of these databases yields returns enough to justify their investment in resources. 49 Given the worth of creating this type of database, my recommendation would be to maintain its content. There are few large-scale successes in the fisheries world that that can be pointed to as an example for the worth of databases, but this project has the potential to be the success by which others are defined. I would recommend that more than one individual be assigned to the project. By their very nature, these projects are collaborative and having a sole individual responsible for the entire process is unrealistic. I would also recommend that if further development is to take place, then understanding the research for a species should take place prior to compiling information on the status of a species. I think that by understanding the research one begins to understand what the research is able to tell us about the species. USGS is the appropriate agency to develop and manage a national fisheries database, and on-line library of fisheries datasets, because USGS has dual role of science generator and user. But USGS needs to examine ways to improve efficiency (reduce fragmentation) in federal fisheries research, reduce the duplication of effort in data collection, and spearhead efforts to standardize fishery data standards at a national level in order to adequately transfer scientific information to all of its stakeholders and policy makers. Fish don’t acknowledge management boundaries and if we manage them by our own jurisdictional narrow goals, rather than by their natural distributions, then we can never expect to fully characterize their status or understand their trends. This can only be accomplished if natural resource agencies must move towards a common goal of sharing. documenting and ultimately standardizing how fisheries data is collected and reported. 50 APPENDIX A 51 APPENDIX A UCRIHS- approved study consent form T OFFICE OF REGULATORY AFFAIRS Human Research Protection Programs BIOMEDICAL 5 HEALTH INSTITUTIONAL REVIEW BOARD (BIRB) COMMUNITY RESEARCH INSTITUTIONAL REVIEW BOARD (CRIRB) SOCIAL SCIENCE} BEHAVIORAL I EDUCATION INSTITUTIONAL REVIEW BOARD Isms) 202 Otds Hal East Lansmg. Mocmgan 48824-1046 517-355-2180 Fax: 517-432-4503 wwwhumanresearchmsuedu SIRS 8 SIRS: IR8@msu.edu CRIRS; crirb@msu.edu O MSU i: an nfirmarive-arrlon nIImI-wpuumrrrr inrlrlunoa. February 14, 2008 Tracy Kolb 41 Natural Resources Dear Ms. Kolb: In May 2007, you submitted an initial IRB application for your study titled “Developing an Information Infrastructure for North American Sturgeon.” At that time, it was determined that your study did not need [RB review and approval and an c-mail was sent to you and Dr. Taylor indicating this determination. Recently, you contacted our office and requested a letter to present to the Graduate School. Thus, I am writing to confirm that it has been determined that your project does not meet the definition of “human subject” as defined by the DHHS Federal Regulations. “Human Subject” means “a living individual about whom an investigator (whether professional or student) conducting research Obtains: (1) Data through intervention or interaction with the individual, or (2) Identifiable private information.” [45 CFR 46.102(f)]. Accordingly, the federal regulations for the protection of human participants would not apply to your project and you do not need IRB approval to proceed. While you are collecting data through interaction with the individual, you are not collecting information about that individual’s personal attitudes, behaviors, or thoughts. You are collecting infomiation through these individuals to learn more about the agency. Therefore, your project does not need review by the Social Science/Behavioral/Education Institutional Review Board (IRB). Thank you for erring on the side of caution. If you have any further questions regardingthese comments, please do not hesitate to call me at 517-355-2180 x 228. Sincerel , M673 Karalyn Burt Administrator, Social Science/BehavioraVEducation Institution Review Board (SIRB) Human Research Protection Program Cc: William Taylor 7 Natural Resources 52 APPENDIX B 53 APPENDIX B Replication of 2004-2005 survey instrument You are being asked to participate in a study on the state of research pertaining to North American sturgeon. The information that you provide is to assist the United States Geological Survey (USGS) in understanding how sturgeon status and trends data are managed and maintained. Your completion of the survey is completely voluntary. You are free to not answer any question or to stop participating at any time. There are no risks or individual benefits associated with taking this survey. By completing this survey, you indicate your voluntary consent to participate in this study and have your answers included in the project data set. If you have any questions about this study you may call or email me, Tracy Kolb, at 517.281.8722, kolbtrac@msu.edu, or my advisor William W. Taylor, at 517.353.0647, taylom’@msu.edu. If you have any questions or concerns regarding your rights as a study participant, you may contact Peter Vasilenko, PhD, Director of Human Subject Protection Programs at Michigan State University (517.355.2180, Fax: 517.432.4503, irb@msu.edu , 202 Olds Hall, East Lansing 48824). Please note that the purpose of this questionnaire is only to determine the availability and extent of data for North American sturgeon. Completion of this questionnaire does not obligate the respondent in any way. 1A. Name: 1B. Agency: 1C. Address and telephone: 1D. E-mail: 1E. How long have you been working for this agency? 1F. How long have you been working in fisheries? 1G. What is your position at the agency (circle/bold one): Technician Biologist Supervisor Other (please specify 2A. What species of sturgeon do you collect information on? 54 2B. Do you collect information on the following? If so, is this information publicly available at either a detailed level or as summarized information? Please circle/bold your I’CSPOHSC. Data type 1. Distribution . Population Abundance . Age composition . Size composition . Genetic information . Telemetry information . Specific catch locations . Other OO\IO\&JI4>UJI\) If other, please describe: Collect? Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No Available? Detail Summary Detail Summary Detail Summary Detail Summary Detail Summary Detail Summary Detail Summary Detail Summary Not Available Not Available Not Available Not Available Not Available Not Available Not Available Not Available Please use the following definition: a composite database stores multiple datasets that have overlapping data content, but whose original database structures are incompatible with one another. The composite system integrates and standardizes these data sets so that all of the data can be analyzed together. 3A. Have you been asked to contribute to a composite database before? (please circle/bold) yes no 38. If so, what were your expectations for the composite database? 3C. Were you satisfied with the result of your contribution? (Please circle/bold) Very satisfied Neither satisfied nor dissatisfied Dissatisfied 4A. Would you be willing to contribute any of the sturgeon information listed above to a composite database (please specify)? 48. What would you expect from your contribution to the composite database? 5A. Do you know of a composite sturgeon database? (please circle/bold) yes no SB Do you use it? (please circle/bold) 5C. Would you use it? (please circle/bold) yes no no 6A. Please rank the following as impediments to sharing the data mentioned above: Please circle/bold your answer. 1. Time Critical impediment Minor Impediment No Impediment 2. Money Critical impediment Minor Impediment No Impediment 3. Fear of data Critical impediment Minor Impediment No Impediment misuse 4. Fear of being Critical impediment Minor Impediment No Impediment “scooped” 5. Fear of poachers Critical impediment Minor Impediment No Impediment accessing data 6. Past experience Critical impediment Minor Impediment No Impediment with data contribution project 7. Lack of control Critical impediment Minor Impediment No Impediment over data that you contribute 8. Fear that your Critical impediment Minor Impediment No Impediment data are in poor condition 9. Political reasons Critical impediment Minor Impediment No Impediment 10. Legal reasons Critical impediment Minor Impediment No Impediment l 1. Other Critical impediment Minor Impediment No Impediment 6B. If other, please describe: 6C. Do you have any suggestions for overcoming any of the impediments listed above? 56 APPENDIX C 57 APPENDIX C Waterbody names in the SI] Amherst Island Shoal Amnicon River Ausable River Bad River Barr Creek Batchawana River Batiscan River Bear Creek Big Manistique Lake Black Lake Black River Black Sturgeon River Blind River Blue Point Boardman River Bronte Creek Burt Lake Carp River Cattaraugus Creek Cayuga Lake Cedar River Cheboygan River Chequamegon Bay Chicago Reef complex Chippewa River Des Milles Iles River Des Prairies River Detroit River East/West Twin Rivers Eastern Basin Echo River Escanaba River Fox River French River Garden River Genesee River Go Home River Goulais River Grand River Grasse River Gravel River 58 Great Lakes Basin Lake Michigan —Green Bay Harmony River Huron River Indian Lake Indiana shoreline Iron River Kalamazoo River Kaministiquia River Kewaunee River Key River Koshkawong River Lake Champlain Lake Huron- Georgian Bay Lake Huron- Main Basin Lake Huron- North Channel Lake Michigan -Grand Traverse Bay Lake Michigan -Little Traverse Bay Lake St. Clair Lake Winnebago L'Assomption River Little Sturgeon Bay Ludington Shoal Magnetawan River Manistee River Manistique River Manitou River Manitowoc River Maumee River Menominee River Michigan shoreline Michipicoten River Millecoquins River Milwaukee River Mississagi River Montreal River Moon River Mullett Lake Muskegon River Naiscoot River Napanee River Niagara River Nipigon River Nottawasaga River Oconto River Oneida Lake Oneida/Cayuga Lakes Ontonagon River Oswegatchie River Oswego River Otsego Lake Ottawa River Ottawa River -Allumette Lake Ottawa River -Holden Lake Ottawa River -Lac Coulonge Ottawa River -Lac des Chats Ottawa River -Lac des Deux Montagnes Ottawa River -Lac Deschenes Ottawa River -Lac Dollard des Orrneaux Ottawa River -Lac Deux Rocher Fendu Ottawa River -Lac la Cave Ouareau River Pere Marquette River Peshtigo River Pic River Pigeon River Pike River Portage Lake Prairie River Raisin River Raquette River Rifle River Root River Saginaw Bay Saginaw River Salmon River Sandusky River Sauble River Saugeen River 59 Seguin River Serpent River Severn River Sheboygan River Spanish River St Louis River St. Clair River St. Francois River St. Joseph River St. Joseph Shoal St. Lawrence River St. Lawrence River— La Prairie Basin St. Lawrence River -Lac St. Louis St. Lawrence River -Lac St. Pierre St. Lawrence River -Lake St. Francis St. Lawrence River -Lake St. Lawrence St. Lawrence River —Thousand Islands St. Louis River St. Marys River St. Maurice River St. Regis River Stokely Creek Sturgeon Bay Sturgeon Bay area shoals Sturgeon River Tahquamenon River Thessalon River Thunder Bay River Trent River Upper Niagara River western Keweenaw penninsula western Wisconsin waters White River Whitefish Bay Whitefish River Wisconsin shoreline Wolf River Keweenaw Bay Mississagi River (landlocked) Standardized Data Names in the 811 Status - data type: Boolean Present: documented records of lake sturgeon Healthy: spawning populations from 1,000-10,000 individuals Remnant: spawning populations from 10-1,000 individuals Extirpated: spawning populations of less than 10 individuals Unknown: unknown amounts of adults in spawning populations Status — data type: number Population estimate lower bound: estimated lower range of adult spawners Population estimate upper bound: estimated upper range of adult spawners Record and study criteria — data type Boolean Research: record of research Monitoring: record of monitoring Planned: observation was planned Incidental: observation was incidental Telemetry: researchers placed telemetric tags on lake sturgeon to monitor movement Tagging: researchers placed on lake sturgeon tags to monitor movement Genetics: researchers are studying lake sturgeon genetics Basic biological stats: researchers collected lake sturgeon morphological information Population estimate: researchers determined a lake sturgeon population estimate Contaminant: researchers determined the amount or source of different contaminants Stocking: researchers are doing lake sturgeon stocking studies Other: researchers collected other information such as associated species, habitat information etc. Life Stages Observed — data type Boolean Spawning: spawning lake sturgeon were observed Larvae: larval lake sturgeon were Observed Juveniles: juvenile lake sturgeon were observed (0-5 yrs of age) Subadult: subadult lake sturgeon were observed (5-15 yrs of age) Juvenile lake sturgeon were observed (0-5 yrs of age) Adult: adult lake sturgeon were observed (> 15 yrs of age) Management — data type Boolean Tribal harvest: harvest of lake sturgeon allocated to tribes at study location Recreational harvest: lake sturgeon are harvested recreationally at study location Commercial harvest: lake sturgeon are harvested by a commercial fishery at study location 60 Search modifiers — data type Boolean Reintroduced: lake sturgeon populations have been stocked at study site Reproductive success: lake sturgeon populations are self-sustaining at study site Endemic: lake sturgeon populations are endemic to study site 61 WORKS CITED Auer, N. 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