INFLUENCES OF DIET, SPATIAL SCALE, AND SOCIALITY ON AVIAN FORAGING BEHAVIOR AND HABITAT USE IN CULTIVATED SWEET CHERRY ORCHARDS AND THE RESULTING IMPLICATIONS FOR MANAGEMENT    By Rachael Ann Eaton         A DISSERTATION Submitted to  Michigan State University in partial fulfillment of the requirements for the degree of  Integrative Biology Ð Doctor of Philosophy Ecology, Evolutionary Biology and Behavior Ð Dual Major  2016!ABSTRACT "! #!INFLUENCES OF DIET, SPATIAL SCALE, AND SOCIALITY ON AVIAN FORAGING $!BEHAVIOR AND HABITAT USE IN CULTIVATED SWEET CHERRY ORCHARDS AND %!THE RESULTING IMPLICATIONS FOR MANAGEMENT  &! '!By (!Rachael Ann Eaton )!Agricultural crops provide foods that attract a variety of foragers. In particular, fruit crops *!attract many fruit eating bird species because fruits are densely available, energy-rich, and "+!readily accessible. Agricultural systems have important implications for avian foraging because ""!they manipulate the availability and quality of food. Avian fruit orchard use and crop "#!consumption represent a major conflict between humans and wild birds. Despite nearly a century "$!of attention paid to understanding the nature of this conflict, many inconsistencies and avenues "%!for research remain. In this dissertation, I utilized a variety of techniques including radio "&!tracking, focal observation, and bioenergetic modeling to understand more fully where, how, and "'!to what extent wild fruit eating birds use cultivated fruit resources in northwest Michigan, as well "(!resulting implications for crop producers. Species-specific information on crop damage and ")!habitat use is essential for better-informed pest management programs and damage mitigation. "*!Bird damage to fruit crops amounts to tens of millions of dollars in losses annually. Yet, #+!the development of successful damage-mitigation strategies for fruits is hindered by a lack of #"!species-specific damage information. In chapter 2, I used bioenergetic modeling that integrated ##!species-specific data on energetic demands and diet to estimate sweet cherry (Prunus avium) #$!consumption by American robins (Turdus migratorius) and cedar waxwings (Bombycilla #%!cedrorum). I then developed economic models to quantify species-specific financial loss due to #&!bird damage. Individual waxwings consumed significantly more sweet cherry and caused seven #'!!! times the financial loss than robins. I estimated economic losses at $US1.8 million and #(!$US147,000 from the waxwing and robin populations, respectively.  #)! Species-specific variation in diet preferences could result in varying use of orchards and #*!impacts on the fruit-producing industry by different bird species. However, species-specific $+!studies of avian orchard use are lacking, particularly throughout the fruit-growing season. $"!Cultivated sweet cherries are high in sugar and low in proteins and lipids; American robins $#!typically prefer lipid-rich fruits, while cedar waxwings choose sugary fruits. Differences in diet $$!preferences may translate into species-specific patterns of habitat use for birds in fruit crops. In $%!chapter three, I used radio telemetry to quantify frequency of daily bird visits to orchards and the $&!amount of time birds spent visiting orchards each day over the fruit-ripening season. I found that $'!waxwings visited orchards a greater percentage of days than robins and spent more time in $(!orchards each day.  $)! Birds forage in habitats where food abundance varies at multiple spatial scales; relative $*!resource abundance between hierarchical spatial scales likely influences within-patch foraging. %+!For frugivorous birds, fruit-growing agricultural regions provide a system of readily available %"!food resources heterogeneously distributed at increasingly broad hierarchical scales. In chapter %#!four, I conducted foraging observations and quantified fruit abundance at three spatial scales to %$!evaluate influences of fruit abundance at multiple spatial scales, and influences of sociality, on %%!avian behavior in sweet cherry orchards. Fruit abundance across multiple scales interacted to %&!influence patch residence time and proportion time spent feeding at sweet cherry trees; these %'!patterns differed between species. In addition, fruit abundance at large spatial scales influenced %(!patch residence time in robins and proportion time feeding by waxwings more strongly for birds %)!in large foraging groups than for those in small groups. %*!iv This thesis is dedicated to all the kind people in my life who were willing to put up with me &+!while I completed it. Thank you for everything. &"!v ACKNOWLEDGEMENTS &#! &$! Completing this dissertation has been a long road. Thankfully, innumerable colleagues, &%!friends, and family walked this road with me and played essential roles in helping me undertake &&!and complete my dissertation work. First, I must thank my graduate advisor, Catherine Lindell. &'!As a mentor, Catherine offered me the perfect balance of guidance and independence. As a &(!mentor she guided me and taught me how to be a scientist. Most importantly, she encouraged me &)!through many challenges and worked with me over the many years to help me succeed. Thank &*!you also to the lab group of fellow Òbird nerdsÓ that Catherine has assembled. Thank you to Sean '+!Williams, Megan Shave, Steve Roels, and Melissa Brady Hannay for the many years of '"!encouragement, feedback, and commiseration (when necessary). I would like to acknowledge my '#!Michigan State graduate advising committee of Kay Holekamp, Fred Dyer, and Brian Mauer. '$!Special thanks as well to George Linz of the USDA National Wildlife Research Center in North '%!Dakota. From hundreds of miles away, George served as an additional member of my advising '&!committee, provided valuable guidance on studying birds in agricultural systems, and lent me ''!both equipment and personnel to conduct my research.  '(! I could not have completed this dissertation without the invaluable in-field assistance of ')!Megan Shave, Shayna Weiferich, Kate Howard, Ben Hawes, Bryant Dossman, and Emily Oja.   '*!Completing this project also hinged upon generous partnerships with sweet cherry growers of (+!northwest Michigan. In particular, thanks to Francis Otto of Cherry Bay Orchards for allowing ("!me access to cherry orchards. Thank you to the staff of the Northwest Michigan Horticultural (#!Research Station for use of orchards, equipment, and administrative support. I also thank Lars ($!!vi  Brudvig and Jason Gallant of Michigan State University for use of laboratory space and (%!equipment. (&! I would also like to acknowledge the many sources that funded my dissertation research: ('!The Specialty Crop Research Initiative of the U.S. Department of Agriculture, The Wilson ((!Ornithological Society, the George J. and Martha C. Wallace Endowed Scholarship, the ()!Michigan State University College of Natural Science, the Michigan State University Ecology, (*!Evolutionary Biology, and Behavior program.  )+! Finally to B”n” Bachelot and all of my family and friends, I would really like to write )"!something nice for you here to express how valuable each of you has been to me, but this is my )#!final section to complete and after writing 31,000 words all I can manage is a brief but sincere )$!Òthank youÓÉfor everything.  )%! )&! )'! )(! ))! )*! *+! *"! *#! *$! *%! *&! *'! *(! *)! **! "++! "+"! "+#! "+$! "+%! "+&! "+'! "+(! "+)!!vii  TABLE OF CONTENTS "+*! ""+!LIST OF TABLES ix """!LIST OF FIGURES x ""#!CHAPTER 1: A REVIEW OF THE NEGATIVE AND POSITIVE  ""$!IMPLICATIONS OF BIRDS IN FRUIT AGRICULTURE 1 ""%! Introduction 2 ""&! Methods 3 ""'! Negative Implications of Avian Fruit Orchard Use 4 ""(! Bird damage to fruit by direct consumption 4 "")!  Species causing damage 5 ""*!  Types of fruit damaged and field-based damage estimates 7 "#+!  Spatial patterns of field-based damage estimates 12 "#"!  Economic damage estimates 14 "##! Indirect damage to fruit crops 15  "#$! Bird damage to fruit increases susceptibility to other pests 15 "#%! Birds consume invertebrate crop pollinators 17  "#&! Birds consume fruit tree flower buds 18 "#'! Positive Implications of Avian Fruit Orchard Use 19 "#(! Birds consume orchard pests 19 "#)! Invertebrate orchard pests 19 "#*! Mammalian orchard pests 22 "$+! Addressing Gaps in Avian Use of Cultivated Fruit Orchards 24 "$"! LITERATURE CITED 28 "$#! "$$!CHAPTER 2: ESTIMATING FRUIT DAMAGE AND ECONOMIC LOSS DUE TO BIRDS "$%!WITH A BIOENERGETIC APPROACH 35 "$&! Abstract 36 "$'! Introduction 36 "$(! Methods 39 "$)! Bioenergetic models of avian sweet cherry consumption 40 "$*!Estimating proportion of sweet cherries in diets 42 "%+!Population sizes 44 "%"!Economic models of avian damage to sweet cherries 45 "%#! Results 47 "%$!Bioenergetic models of avian sweet cherry consumption 47  "%%!Economic models of avian damage to sweet cherries 47 "%&! Discussion 48  "%'! LITERATURE CITED 54 "%(! "%)! "%*!!viii  CHAPTER 3: AMERICAN ROBINS AND CEDAR WAXWINGS VARY IN USE OF "&+!CULTIVATED CHERRY ORCHARDS 60 "&"! Abstract 61 "&#! Introduction 61 "&$! Methods 64 "&%! Study Area & Species 64 "&&! Capture & Radio Deployment 66 "&'! Data Collection 67 "&(! Data Preparation 68 "&)! Day-to-day & Within-day Orchard Use 69 "&*! Statistical Analyses 69 "'+! Results 71 "'"! Study Demographics 71 "'#! Day-to-day & Within-day Orchard Use 72 "'$! Discussion 75 "'%!Day-to-day Orchard Use 75 "'&!Within-day Orchard Use 77 "''! LITERATURE CITED 81 "'(! "')!CHAPTER 4: FOOD ABUNDANCE AT MULTIPLE SPATIAL SCALES INFLUENCES "'*!FORAGING BEHAVIORS 86 "(+! Abstract 87 "("! Introduction 88 "(#! Methods 92 "($!Behavioral Observations 93 "(%!Fruit Abundance 94 "(&! Tree-scale fruit abundance 95 "('! Orchard-scale fruit abundance 95 "((! Landscape-scale fruit abundance 96 "()!Statistical Analyses 97 "(*! Results 98 ")+! Fruit abundance at the tree scale and orchard scale interacted to affect   ")"! proportion time feeding but not patch residence time  99 ")#! Fruit abundance at the orchard scale and landscape scale interacted to   ")$! affect proportion time feeding, but not patch residence time 99 ")%! Sociality altered the influence of fruit abundance at large spatial scales on   ")&! foraging behavior 101 ")'! Discussion 104 ")(!Fruit abundance at the tree scale and orchard scale interacted to affect   "))! proportion time feeding but not patch residence time 105 ")*!Fruit abundance at the orchard scale and landscape scale interacted to   "*+! affect proportion time feeding, but not patch residence time 107 "*"!Sociality altered the influence of fruit abundance at large spatial scales on   "*#! foraging behavior 108 "*$!  Conclusion 109 "*%! LITERATURE CITED 110 "*&!!ix  LIST OF TABLES "*'! "*(!Table 2.1  Types and sources of AMC values used to calculate proportion of sweet cherries "*)! in diet 44 "**! #++!Table 2.2  Estimated sweet cherry consumption by American robins and cedar waxwings in  #+"! northwest Michigan orchards and resulting economic losses 47 #+#! #+$!Table 3.1  Study orchard area, 2013 harvest date, and land cover types adjacent to study   #+%! orchards 71 #+&! #+'!Table 3.2  Generalized liner mixed models exploring the relationships between within-day  #+(! orchard use of American robins and cedar waxwings, relative to species,  #+)! orchard, and days-to-harvest 73 #+*! #"+!Table 4.1  Group sizes, patch residence times, and proportions time feeding for American  #""! robins and cedar waxwings 102 #"#! #"$!Table 4.2  Outcomes of species-specific generalized linear mixed models of patch residence  #"%! time and proportion time feeding 103 #"&! #"'! #"(! #")! #"*! ##+! ##"! ###! ##$! ##%! ##&! ##'! ##(! ##)! ##*! #$+! #$"! #$#! #$$! #$%! #$&! #$'! #$(! #$)! #$*!!x  LIST OF FIGURES #%+! #%"!Figure 2.1  Map of Leelanau County study region with mist netting and point count locations  #%#! identified 42 #%$! #%%!Figure 3.1  Map of Leelanau County study region and focal sweet cherry orchards 65 #%&! #%'!Figure 3.2  Percent of days American robins and cedar waxwings visited cherry orchards   #%(! relative to their respective tracking periods 72 #%)! #%*!Figure 3.3  Percent of the daylight period that American robins and cedar waxwings visited  #&+! cherry orchards on a given day 74 #&"! #&#!Figure 3.4  Percent of the daylight period that American robins and cedar waxwings visited  #&$! cherry orchards on a given day relative to days-before-harvest 75 #&%! #&&!Figure 4.1 Schematic of the tree, orchard, and landscape spatial scales 91 #&'! #&(!Figure 4.2  The effects of fruit abundance across multiple spatial scales on American robin  #&)! foraging behavior 100   #&*! #'+!Figure 4.3  The effects of fruit abundance between spatial scales on cedar waxwing  #'"! foraging behavior 101#'#!!!1 CHAPTER 1 #'$! #'%!NEGATIVE AND POSITIVE IMPLICATIONS OF BIRDS IN FRUIT AGRICULTURE #'&! #''! #'(!Rachael A. Eaton#')!2 Introduction #'*! #(+! Bird use of agricultural habitat is long-documented (Beal 1915, McDowell and Pillsbury #("!1959). Indeed Òeconomic ornithologyÓ, the study of bird-agriculture interactions and conflict, #(#!began officially within the USDA in 1885 (Henderson and Preble 1935). Many bird species #($!utilize agricultural habitats for food resources, with disparate consequences for crop producers #(%!(Drake and Grande 2002, Retamosa et al. 2008). In one regard, avian use of agriculture can be #(&!beneficial. Birds can provide ecosystem services, natural processes that provide some benefit to #('!humans (Whelan et al. 2008), in the form of biological control and pest reduction, including both #((!invertebrate and vertebrate crop pests (e.g. Mols and Visser 2002). In contrast, avian use of #()!agriculture can have negative consequences, such as depredation or damage to the crops #(*!themselves, that result in financial losses for crop producers (Retamosa et al. 2008, Lindell et al. #)+!2012, Anderson et al. 2013). Thus, avian use of agricultural systems represents a complex #)"!interaction between humans and wildlife with both positive and negative implications (Messmer #)#!2009). The negative and positive implications of birds in agriculture have been studied in #)$!numerous crops and in growing regions around the world (Dolbeer et al. 1994, Peisley et al. #)%!2015). Yet, much of the research addressing costs or benefits of birds in agriculture has focused #)&!on major crops (e.g. corn, grains; Dolbeer et al. 1994), while bird use of less abundant but high-#)'!value crops such as fruits has historically received less attention. In this introductory chapter, my #)(!objective was to explore the complex interaction between birds and fruit agriculture by #))!synthesizing existing literature on the negative and positive implications of wild birds in #)*!cultivated fruit crops, evaluating our current understanding, and suggesting future directions for #*+!fruit crop management and research.  #*"!!!3  Cultivated fruit crops provide abundant, dense, and readily accessible food resources for #*#!many birds (Sallabanks 1993). However, the manner in which wild birds utilize fruit agriculture #*$!for food can result in either damage to fruit crops and costs for crop growers, or protection #*%!against other crop pests, benefiting crop producers. Historically, attempts to characterize the #*&!relationship between birds and fruit agriculture have emphasized the negative consequences of #*'!avian use of fruit orchards and potential for crop damage by birds (DeHaven 1974, Gebhardt et #*(!al. 2011). Birds consume a wide variety of cultivated fruits (Johnson et al. 1989, Nelms et al. #*)!1990, Somers and Morris 2002, Lindell et al. 2016). In addition to direct fruit consumption, birds #**!cause indirect damage to fruiting plants, such as flower bud damage (Summers and Pollack $++!1978), affecting fruit production (Wright and Summers 1960). The positive implications of avian $+"!fruit orchard use are primarily viewed in the context of ecosystem services birds may provide. $+#!The use of non-chemical biological agents to regulate or control pest species abundance, known $+$!as biological control, is utilized in a variety of fruit crops both in the U.S. and Europe. In $+%!agricultural settings, some birds provide regulating services by consuming other orchards pests $+&!such as invertebrate herbivores or crop-damaging small mammals (Whelen et al. 2008, Wenny et $+'!al. 2011). The majority of research in this area has assessed the role of birds in controlling $+(!herbivorous insects. Considerably less information is available about predatory birds and the $+)!beneficial consumption of avian and mammalian pests. $+*! $"+!Methods $""! I searched the Web of Science database with relevant combinations of the keywords: $"#!bird, orchard, avian, crop, damage, fruit, and vineyard to generate an initial list of papers for this $"$!review. To this list I added any papers I knew of but that had not been produced by the database $"%!!!4 search. Last, I added relevant references cited within or by any of these papers. Database $"&!searches were conducted between January 28, 2016 and February 8, 2016.  $"'!From the database results, I included relevant studies that took place in fruit agriculture in $"(!temperate regions of North America and Eurasia. I focused my review on these regions because $")!of their high contribution to global fruit production and their long-standing, but sporadic, history $"*!of research into avian use of fruit agriculture. I included only studies that evaluated the role of $#+!birds in fruit agriculture and excluded studies that were interested in agricultureÕs effects on birds $#"!or bird communities. I also excluded studies that took place exclusively in laboratory settings $##!and studies that reported on the efficacy of different bird deterrent techniques but provided no $#$!data for bird damage in the absence of such techniques.  $#%! $#&!Negative Implications Of Avian Fruit Orchard Use $#'!Bird damage to fruit by direct consumption $#(! Studies reporting bird damage to fruit via direct consumption of the fruit itself occurred $#)!in North America but not Europe, while European studies reported alternative mechanisms of $#*!damage such as bud eating (Summers and Pollack 1978) or pollinator consumption (Galeotti and $$+!Inglisa 2001). Most of the work in this area has been conducted in the United States, but some $$"!studies have taken place in fruit growing regions of Ontario, Quebec, and British Columbia in $$#!Canada. This geographic difference in foci could be attributed to long-standing infusion of $$$!funding and research effort by national agencies such as the United States Department of $$%!Agriculture into studies of bird damage to crops, economic losses, and loss mitigation. Within $$&!the United States, research has occurred in major fruit-growing regions like Michigan, $$'!California, New York, Washington, and Florida.  $$(!!!5 Species causing damage $$)! In fruit-growing regions, orchards offer birds rich patches of foraging habitat with $$*!numerous, perennial fruit resources (Dolbeer et al. 1994, Simon 2008, Lindell et al. 2012). $%+!Cultivated fruits are attractive food resources for many species of birds because fruits are $%"!abundant, energy-rich, and easily accessible. A variety of bird species consume and damage $%#!cultivated fruit. Such damaging species range from predominantly frugivorous species such as $%$!cedar waxwings (Bombycilla cedrorum; Stone 1974, Lindell et al. 2012) to omnivorous species $%%!such as American robins (Turdus migratorius; Lindell et al. 2012) and common starlings $%&!(Sturnus vulgarius; Stone 1973, Guarino et al. 1974). To a lesser extent, some studies identify $%'!birds that are typically insectivores like woodpeckers and flycatchers (Boudreau 1972, DeHaven $%(!and Hothem 1981) and granivores like house finches (DeHaven 1974, DeHaven and Hothem $%)!1981, Tobin et al. 1989) as fruit damaging birds.  $%*! Among this diversity of avian fruit crop consumers, the most frequently cited fruit crop $&+!pests across an array of fruit types are the omnivorous American robin and common starling $&"!(Stevenson and Virgo 1971, Stone 1973, Guarino et al. 1974, Anderson et al. 2013) and the $&#!frugivorous cedar waxwing. Birds of these species are frequent orchard visitors (Guarino et al. $&$!1974, Lindell et al. 2012, Eaton et al. 2016) and often consume substantial amounts of fruit $&%!(Boudreau 1972, Guarino et al. 1974, Lindell et al. 2012). These three species are considered $&&!significant fruit pests in wine grapes (Vitis spp.; Stevenson and Virgo 1971, Bourdeau 1972), $&'!sweet and tart cherries (Prunus avium and Prunus cerasus; Stone 1973, Guarino et al. 1974, $&(!Lindell et al. 2012), and blueberries (Vaccinium corybosum; Lareau and Vincent 1985, Nelms et $&)!al. 1990, Avery et al. 1991). American robins and common starlings, along with American crows $&*!(Corvus brachyrhynchos), are significant pests in apples (NASS 1999, Anderson et al. 2013). A $'+!!!6 survey of apple growers across the U.S. in 1998 estimated that common starlings and American $'"!robins cause 16% and 9% of the damage to apple crops, respectively (NASS 1999). In Ontario, $'#!starlings comprised 42% of all birds identified in sweet cherry orchards and caused 60% of $'$!observed damaged (Virgo 1971). In Michigan cherry orchards, cedar waxwings were responsible $'%!for  >60% of all avian sweet cherry consumption, while American robins were responsible for $'&!>40% of tart cherry consumption (Lindell et al. 2012). Most recently, apple, grape, blueberry, $''!and cherry growers from New York, Michigan, and the Pacific Northwest identified starlings and $'(!robins as two of the most damaging bird species in fruit crops (Anderson et al. 2013).  $')! Several features of American robins, common starlings and cedar waxwings contribute to $'*!their role as principle fruit-damaging bird species. All three species frequently live and forage in $(+!human-modified environments, including agricultural environments (Homan et al. 2010, Lindell $("!et al. 2012). These species take advantage of readily available fruit food resources; for example, $(#!when foraging in high-fruit areas American robins increase the proportion of fruit in their diets $($!(Wheelwright 1986). Cedar waxwings are also highly frugivorous; 84% of their annual diet $(%!consists of fruit (Witmer 1996). In addition, cedar waxwings show preferences for sugar-rich $(&!fruits, like many cultivated crops, over lipid-rich fruits, like many wild fruits (Witmer and Van $('!Soest 1998). In addition, cedar waxwings and common starlings forage in large groups in $((!orchards and vineyards, which can contribute to the heavy degree of fruit consumption and $()!damage, making them particularly troublesome pests (Stone 1974, Nelms et al. 1990, Lindell et $(*!al. 2012). $)+! Much of the data on the identity of avian fruit crop pests comes from fruit grower reports $)"!and species surveys of birds in fruit crops. For instance, a survey of >1500 fruit growers in New $)#!York, Michigan, and the Pacific Northwest identified American robins and common starlings as $)$!!!7 the top species responsible for bird damage to blueberry, wine grape, and cherry crops (Anderson $)%!et al. 2013). However, the association between bird species frequently observed in orchards and $)&!those that actually cause crop damage is not always clear. Virgo (1971) noted a high density of $)'!robins present in Ontario sweet cherry orchards (28% of all birds observed); however, robins $)(!were responsible for a relatively small proportion (~5%) of the damage. Critical components of $))!wildlife damage-control programs include understanding the identity and ecology of species $)*!causing the damage (Somers and Morris 2002, Tracey et al. 2007). Recent work has emphasized $*+!feeding observations of birds actually consuming fruit crops over survey-based studies or $*"!grower-identified assessments to evaluate the role of different species in fruit damage (Lindell et $*#!al. 2012). In order to understand better the extent of bird damage to fruit crops, more targeted $*$!research studies are needed to identify the birds causing crop damage and robustly quantify $*%!species-specific damage. Additional research that identifies problem species in particular regions $*&!or crops of interest will be important to generate efficient and effective deterrent techniques. $*'!Such an approach will be more efficient and economical than attempting to deter all birds since $*(!not all birds cause problematic levels of damage. In the third chapter of my dissertation, I $*)!explored this by quantifying and comparing the species-specific crop damage of two common $**!fruit-consuming species, American robins and cedar waxwings.  %++! %+"!Types of fruit damaged and field-based damage estimates %+#! The majority of research into estimating avian fruit crop consumption took place between %+$!the 1970s and early 1990s (e.g. Virgo 1971, Stone 1974, Guarino et al. 1974, DeHaven and %+%!Hothem 1981, Nelms et al. 1990, Avery et al. 1993, Vincent and Lareau 1993). Prior to this time, %+&!bird consumption of cultivated fruit was noted as a growing concern but relatively little research %+'!!!8 had investigated the problem (Virgo 1971). The current body of literature on the extent of bird %+(!consumption of fruit crops comes from in-field damage assessments, as well as some direct %+)!surveys of fruit growers. These studies reveal that a variety of cultivated tree, vine, and shrub %+*!fruits are damaged by birds (Lindell et al. 2016), with most studies focused on North American %"+!wine grapes (DeHaven and Hotherm 1981), sweet and tart cherries (e.g. Virgo 1971), and %""!blueberries (e.g. Avery et al. 1992). These fruits are economically valuable and thus there is %"#!strong economic motivation to evaluate the extent of avian crop damage. For example, in the %"$!United States wine grape production has a value of  >2.5 billion dollars annually (NASS 2016), %"%!and CanadaÕs wine industry generates 1.1 billion dollars annually (Agriculture and Agri-Food %"&!Canada 2016).  %"'! Wine grapes, cherries, and blueberries also share several characteristics that make them %"(!attractive food resources for fruit-consuming birds (Avery 2002). Grapes, cherries, and %")!blueberries are high in sugar and energy rich. They also have thin skins and soft pulp, enabling %"*!easy consumption by birds (Avery 2002). In terms of quantifying bird damage and consumption, %#+!other fruit crops have received considerably less attention in the literature than wine grapes, %#"!cherries, and blueberries. A limited number of studies reporting bird damage exist for apples %##!(Tobin et al. 1989) and citrus fruit (Johnson et al. 1989). Several major patterns emerge from the %#$!studies of avian fruit crop consumption.  %#%! The first major patterns is that most studies report low bird damage levels (e.g. <10%), %#&!and this is evident across multiple crop types. Bird damage affects a relatively small proportion %#'!of the overall fruit crop in apples (Malus pumila; Tobin et al. 1989), grapefruit (Citrus x %#(!paradise; Johnson et al. 1989), wine grapes (DeHaven 1974), and cherries (Stone 1974, Lindell %#)!et al. 2016). A recent multi-year field study of damage to Honeycrisp apples in Michigan, New %#*!!!9 York, and Washington estimated damage at 2% (Lindell et al. 2016). My literature search %$+!revealed one study of grapefruit (Citrus paradisi) damage in Texas (Johnson et al. 1989); %$"!estimated damage across 30 grapefruit groves was 8%. In Ontario vineyards Stevenson and %$#!Virgo (1971) found that 85% of 108 vineyards had some degree of bird damage, but most (69%) %$$!showed damage levels of 10% or less. In Michigan tart cherries, Stone (1974) estimated damage %$%!at 7.4% and Lindell et al. (2016) recently found a three-year average for damage was 2.6% %$&!(Lindell et al. 2016). Sweet cherry crops tend to incur greater damage than those of tart cherries; %$'!however, overall bird damage to sweet cherry is still relatively low (Virgo 1971, Lindell et al. %$(!2016). Virgo (1971) estimated damage to sweet cherries in Ontario as 3%. Lindell et al. (Lindell %$)!et al. 2016) generated three-year average estimate of 9% across multiple orchards in New York, %$*!Michigan, and Washington. Notably, one study found higher bird damage in tart cherries than in %%+!sweet cherries, in contrast to most reports (Guarino et al. 1974). However, this study was limited %%"!to four trees in two orchards and may thus reflect local trends rather than large-scale patterns. %%#!One important caveat regarding these reports of relatively low levels of bird damage is the %%$!existence of considerable year-to-year variation among in-field damage estimates (Lindell et al. %%%!2016). For example, damage is much higher in years of low overall crop yield (i.e. due to poor %%&!weather or growing conditions) compared to years when crop yield is high (Lindell et al. 2016). %%'! The relatively low damage in apples and citrus fruits may be explained in part by the %%(!presence of a thick, tough outer skin of these fruits. A thicker fruit skin is likely more difficult to %%)!break for birds with smaller bills, thus relatively few bird species may be capable of damaging %%*!apple and grapefruit crops. Indeed, apple growers report that larger-billed birds like American %&+!crows and wild turkeys (Meleagris gallopavo) are among the most significant apple pests (Tobin %&"!et al. 1989, Anderson et al. 2013), while great-tailed grackles (Quiscalus mexicanus) are major %&#!!!10 grapefruit consumers (Johnson et al. 1989). With fewer possible apple and grapefruit problem %&$!species, overall damage estimates in these crops tend to be low. The reports of low bird damage %&%!in wine grapes and cherries are somewhat surprising given the soft-skinned texture and high-%&&!energy content that make these fruits attractive to wild foraging birds (Avery 2002). Estimated %&'!bird damage could also be low across many studies and crop types because the high density of %&(!available fruit in cultivated systems exceeds avian resource demands. In many fruit-growing %&)!regions, orchards and vineyards occur in close proximity to other fruit agriculture. Thus, the %&*!availability of fruit on a broad scale may be high enough that overall damage is low because fruit %'+!resources are broadly available. This phenomenon is already reflected in instances of temporal %'"!variation in fruit abundance; bird damage is lower in years with high fruit yield than in years %'#!with low fruit yield (Lindell et al. 2016). The effect of fruit resource abundance at broad spatial %'$!scales and the interaction between fruit abundance across multiple scales (e.g. an orchard and the %'%!surrounding landscape) on avian foraging behavior have not been well explored and I %'&!investigated these ideas further in my fourth dissertation chapter.  %''! The second notable pattern among published studies is that damage estimates are highest %'(!in blueberries compared to other crops. Damage estimates in blueberries range from of 17% in %')!Florida (Nelms et al. 1990) to as high as 85% in Michigan (Avery et al. 1993). Bird preferences %'*!for particular fruit characteristics may explain the substantially high level of damage in blueberry %(+!crops. Abundant fruit-consuming birds like American robins have demonstrated preferences for %("!blue fruits over red, green or yellow options (Willson 1994). In addition, highly frugivorous %(#!birds like cedar waxwings demonstrate preferences for relatively small fruits that were similar in %($!diameter to ripening blueberries (~7 mm), compared to larger fruits (McPherson 1988, Avery et %(%!!!11 al. 1993). Given their small size, blueberries are accessible to a variety of fruit-consuming %(&!species of many sizes.  %('! The third major pattern in bird damage to fruit is that early ripening fruit varieties incur %((!greater damage than varieties that ripen later. Early-ripening varieties of sweet cherries (Virgo %()!1971), blueberries (Nelms et al. 1990), and apples (Tobin et al. 1989) incur more damage than %(*!their later-ripening counterparts. Early-ripening blueberry crops in Florida incurred damage as %)+!high as 75% (Nelms et al. 1990). Tobin et al. (1989) reported that apple varieties that displayed %)"!red coloration early in the season suffered the most damage. Several factors may contribute to %)#!high damage among early-ripening fruits. Fruit color in an important factor in the avian fruit %)$!selection process (Sallabanks 1993). Early-ripening fruits that turn from colors indicating unripe %)%!fruit like green and yellow to ÒripeÓ colors like red and blue likely stand out among other fruit %)&!options and catch the attention of foraging birds. This effect is also supported by differences in %)'!damage among varieties of different colors. For example, susceptibility to damage is higher %)(!among darker varieties of grapes than lighter varieties (Boudreau 1972, DeHaven 1974). %))! Fruit varieties that ripen before others also represent an attractive but sparse resource in %)*!orchards and vineyards. Such varieties likely incur heavy damage because alternative fruit %*+!options in the foraging area are low at the time of ripening. Heavy damage to early-ripening %*"!fruits can have particularly negative consequences for growers because these fruits are first to hit %*#!markets and can have high economic value (Nelms et al. 1990). Patterns of heavy damage in %*$!early-ripening varieties suggest that fruit growers could mitigate potential bird damage by %*%!avoiding planting some varieties that ripen or color much earlier other varieties in the field.  %*&! %*'! %*(!!!12 Spatial patterns of field-based damage estimates %*)! Bird damage to fruit crops shows several interesting spatial patterns. First, damage can be %**!vertically stratified within plants; evidence for this comes from studies of grapes, cherries, and &++!grapefruit. For example, DeHaven (1974) and Somers and Morris (2002) observed greater levels &+"!of damage on grape bunches growing high on a vine, further from the ground. Similarly, cherry-&+#!consuming birds in Michigan were observed almost exclusively in the top half of trees, &+$!suggesting that damage will be heavier in the higher parts of cherry trees than in lower parts &+%!(Lindell et al. 2012). However, like many trends in bird damage, there are inconsistencies in the &+&!pattern of vertical stratification of damage. Virgo (1971) found no evidence of such stratification &+'!in Ontario sweet cherry orchards. The upper parts of plants may incur greater damage because &+(!fruit density and nutritional quality is often higher in the upper parts of fruit trees, making those &+)!parts more attractive to fruit-eating birds (Houle et al. 2014). Future field assessments of bird &+*!damage to fruit should account for this vertical stratification to avoid biased estimates of &"+!damage. For example, sampling only in plant areas that are easily accessible to humans could &""!lead to underestimated damage.  &"#! Second, habitat features adjacent to fruit crops influence the degree of damage; however, &"$!such patterns are inconsistent among studies. Anecdotal reports from fruit producers suggests &"%!that orchards that are spatially isolated from other fruit agriculture suffer greater damage than &"&!those that are near to other cultivated fruit fields. My literature search generated only two studies &"'!to corroborate this point; Johnson et al. (1989) found more damage in isolated grapefruit &"(!orchards than in orchards surrounded largely by other orchards. Isolated orchards may be &")!especially attractive because they offer birds a high density of food resources in an area that may &"*!otherwise be limited in high-energy fruit resources. Lindell et al. (2016) recently determined that &#+!!!13 sweet cherry blocks in Michigan sustained less damage when surrounded to a greater degree (e.g. &#"!on 1 or more sides) by other sweet cherry orchards compared to isolated blocks. In contrast, &##!Virgo (1971) found no indication that the habitat surrounding a cherry orchard influenced the &#$!amount of bird damage. Some studies report that crops surrounded by a relatively high amount of &#%!non-fruit crops, such as forest, tend to have less damage than those with little surrounding forest &#&!(Lindell et al. 2016). This pattern may due in part because prominent avian fruit pests, like &#'!common starlings are not forest-dwelling species and tend to avoid fruit crops surrounded by &#(!forests (Boudreau 1972). Man-made habitat features like power lines can also affect the extent of &#)!bird damage because they offer perching places for many birds and draw in large numbers of &#*!crop-damaging birds to adjacent fruit crops (Bourdreau 1972). For example, plantings of &$+!Honeycrisp apples suffered slightly greater damage if utility wires ran overhead compared to &$"!those without overhead wires (Lindell et al. 2016). &$#! Some studies also report the existence of edge effects, wherein plants nearer to the edge &$$!of an orchard or vineyard have greater damage than plants nearer to the interior (Somers and &$%!Morris 2002). Texas grapefruit orchard edges had higher damage than interior areas (Johnson et &$&!al. 1989). However, support for edge effects is not consistent within or among crop types &$'!(Lindell et al. 2016). Somers and Morris (2002) found that damage levels in vineyards in Ontario &$(!were greater near edges and declined closer to the vineyard interior. In contrast, DeHaven and &$)!Hothem (1981) found no evidence of edge effects in California vineyards. This variation among &$*!studies regarding the presence of edge effects is consistent with the larger literature on the edge &%+!effect phenomenon, which has been difficult to generalize (Ries and Sisk 2004). Given that these &%"!few studies in fruit crops suggest higher vulnerability to bird damage along field edges, crop &%#!producers may experience more efficient damage mitigation by targeting edge areas. However, it &%$!!!14 is clear that additional studies are needed to determine the circumstances under which edge &%%!effects occur, and to what extent these effects vary among pest bird species.  &%&! These spatial effects contribute to variation in damage incurred by different fruit growers. &%'!Situations in which many growers have little or no damage, while few growers have substantial &%(!damage, can lead to relatively low costs to the industry overall despite significant challenges to &%)!some growers (Virgo 1971). Collectively, these spatial patterns in bird damage suggest that fruit &%*!producers may benefit from targeting bird deterrent efforts toward particular edges or growing &&+!plots that are more likely to suffer bird damage, how such foci are likely to vary among fruit &&"!types and growing locations. Therefore, the variability among studies and the relatively low &&#!amount of research into bird damage to fruit crops suggest that further work is needed to &&$!elucidate generalizable patterns of bird damage; especially considering most of the published &&%!literature is over 20 years old.  &&&! &&'!Economic damage estimates &&(! Some studies have translated in-field damage estimates into economic consequences for &&)!fruit producers; however, such estimates are sparse throughout the past 40 years. The most &&*!prominent pattern regarding economic estimates is the high degree of variability in costs incurred &'+!by different fruit growers. Despite the generally low levels of bird damage reported in the &'"!literature, some individual growers can suffer high financial losses. Johnson et al. (1989) &'#!quantified US$ 2.2 million in damage in Texas grapefruit groves. However, bird damage was &'$!highly variable among groves; some growers experienced loss of as little as $10 per ha, others &'%!incurred as much as $1900 per ha.  &'&!!!15  The high degree of variability in field-based damage estimates suggests that alternative &''!mechanisms to overcome such variability may be important to generate more broadly applicable &'(!damage estimates and resulting values of economic loss. Accurate loss estimates are important &')!for growers to evaluate whether, and to what extent, money and effort should be spent on bird &'*!damage mitigation. Bioenergetic-based crop damage estimates that incorporate data on bird diets &(+!and energy needs, along with the energy content of crops have gained prominence in the &("!literature in recent decades (e.g. Wiens and Dyer 1975, Peer et al. 2003, and Homan et al. 2011). &(#!A bioenergetics method is not based upon damage levels at different orchards or vineyards and is &($!thus subject to less variance. Such an approach has not yet been applied to fruit agriculture. In &(%!the second chapter of my dissertation, I utilized a bioenergetics approach to estimate bird &(&!damage to Michigan cherry crops.  &('! &((!Indirect damage to fruit crops &()! There are considerably fewer studies of non-consumptive bird damage to fruit crops and &(*!fruit plants compared to studies of avian crop consumption. Beyond direct consumption, negative &)+!implications of avian orchard use include increased susceptibility of damaged fruit to other &)"!pathogens (e.g. Ioriatti et al. 2015), pollinator consumption (Golawski and Golawska 2013), and &)#!bud consumption (Greig-Smith and Wilson 1984).  &)$!  &)%!Bird damage to fruit increases susceptibility to other pests  &)&!  Birds often damage fruits but do not consume them fully. These injuries to fruit skin &)'!provide entry points for invertebrate pests such as fruit flies (Stevenson and Virgo 1971, Ioriatti &)(!et al. 2015) or fungal pathogens (Xu et al. 2001, Holb and Sherm 2008). The earliest mention of &))!!!16 this damage type comes from Stevenson and Virgo (1971); they posited that bird-damaged &)*!grapes attracted fruit flies in Ontario vineyards but provided no data to that effect. Iorriati et al. &*+!(2015) tested the likelihood of bird damage to increase spotted wing Drosophila (Drosophila &*"!suzukii) infestation of wine grapes in Oregon and Italy. D. suzukii is a common invertebrate pest &*#!that feeds on and lays eggs in cultivated fruits. Iorriati et al. (2015) experimentally incised grapes &*$!to mimic bird damage and found a higher incidence of D. suzukii in these grapes than in un-&*%!manipulated grapes.  &*&! Bird damage also invites fungal infestation such as brown rot, Monilinia fructigena (van &*'!Leeuwen et al. 2000, Xu et al. 2001, Holb and Sherm 2008); studies addressing this type of &*(!damage take place primarily in apple crops. Brown rot is the most significant fungal pathogen of &*)!stone fruits, fruits with a fleshy outer tissue that surrounds a hard ÒstoneÓ around the seed, &**!throughout the warm climates of the world (Ritchie 2000). Brown rot caused an 11% loss in '++!yield in Polish apple orchards; bird damage was responsible for 29% of new brown rot '+"!inoculations (Xu et al. 2001). In Hungary, brown rot occurred in 94% of all injured apples. '+#!However, insects were responsible for much more damage than birds; bird damage comprised '+$!only 25% of the fruit injury (Holb and Sherm 2008). In Poland, birds caused more damage to '+%!pears than insects or typical growth cracking (Xu et al. 2001); approximately 70% of new M. '+&!fructigena inoculations stemmed from bird damage (Xu et al. 2001). '+'! Only a few studies have explored the capacity for birds to increase fruitÕs susceptibility to '+(!other pathogens and drawing general patterns is difficult. Overall, such research is recent and '+)!agrees that birds contribute to higher incidences of insect and fungal pathogens by damaging '+*!fruit crops. There is some disagreement as to whether bird-caused injury in fruits leads to higher '"+!pathogen incidence than insect-caused injury. Additional research is necessary to elucidate more '""!!!17 clearly the relative roles of birds and fruit-damaging insects in increased pathogen susceptibility. '"#!In addition, no study attempted to quantify the financial consequences of increased pathogens '"$!due to bird damage. Doing so would provide fruit producers and managers a more holistic '"%!picture of the economic costs of bird damage to fruit.  '"&! '"'!Birds consume invertebrate crop pollinators  '"(! Some insectivorous birds in orchards consume fruit tree pollinators like bees (Bosch and '")!Trostle 2006, Galeotti and Inglisa 2001, Golawski and Golawska 2013). This type of damage '"*!could adversely affect crop growth and yield and would be especially problematic for crops that '#+!require cross-pollination. However, I found no study that directly quantified the effect of '#"!pollinator consumption on crop yield. Among studies, the extent to which birds consume '##!pollinator bees varies. For instance, bees (Apidae) are more important for pollination of fruit '#$!trees than other hymenopterans (Galeotti and Inglisa 2001). Galeotti and Inglisa (2001) analyzed '#%!stomach contents of European bee-eaters (Merops apiaster) foraging in orchard-rich regions of '#&!Italy and found that honeybees comprised up to 63% of the diet. In contrast, bees (Family: '#'!Apidae) comprised just 2% of the red-backed shrike diet in Polish apple orchards (Golawski and '#(!Golawska 2013). Bosch and Trostle (2006) noted that bluejays and American robins consumed '#)!orchard mason bees (Osmia lignaria) emerging from hives in Utah sweet cherry orchards, '#*!negatively affecting bee population growth. These varied results and the limited research in this '$+!area invite further exploration to understand the extent of avian pollinator consumption and '$"!specifically quantify the potential yield losses due to pollinator consumption. If bird '$#!consumption of pollinators adversely affects crop yield, then bird damage mitigation strategies '$$!should be expanded to protect crops during the flowering period.  '$%!!!18 Birds consume fruit tree flower buds '$&! A few studies from the 1960s through the 1980s explored the negative consequences of '$'!avian consumption of fruit tree buds (Newton 1964, Summers and Pollack 1978, Greig-Smith '$(!and Wilson 1984, Greig-Smith 1987). However, research in this area is limited in scope and has '$)!looked exclusively at Eurasian bullfinches (Pyrrhula pyrrhula) feeding on dormant flower buds '$*!in English pear orchards (Summers and Pollack 1978). Wild tree fruit buds are a common foods '%+!for Eurasian bullfinches and these birds take advantage of the abundance of buds in cultivated '%"!fruits (Newton 1964), which explains why research on bud consumption focuses on this species. '%#!Other noted bud-consuming species are tit species (Parus spp.) and house sparrows (Passer '%$!domesticus; Newton 1964).  '%%! Eurasian bullfinches consume the center of the bud itself, which is a highly nutritious '%&!food item (Summers and Pollock 1978). The type of indirect damage can adversely affect fruit '%'!production because when birds consume flower buds, fewer flowers are available for pollination '%(!and fruit production, which can lower yields. Yield loss is proportional to the severity of bud loss '%)!(Summers and Pollock 1978). Summers and Pollock (1978) experimentally removed >50% of '%*!buds from trees in a pear orchard to mimic bird damage and recorded yield losses between 50 '&+!and 85%. Bud damage, like direct fruit consumption, displays spatial and temporal patterns '&"!(Wright and Summers 1960, Newton 1964, Summers and Pollack 1978). For example, '&#!bullfinches removed 100% of buds from one pear variety, but only 12% of another in 1961 '&$!(Newton 1964). In contrast, bud removal the following year was less than 2% in both varieties '&%!(Newton 1964). The exterior areas of orchards incur greater damage via bud loss than interior '&&!areas (Summers and Pollack 1978, Greig-Smith and Wilson 1984). Greig-Smith and Wilson '&'!(1984) documented up to 80% bud removal in pear trees near wooded edges. This is likely '&(!!!19 because woodland areas are the preferred breeding habitat of Eurasian bullfinches (Newton '&)!1964), making woodland-adjacent orchards particularly vulnerable to bud damage.  '&*! Birds consume dormant flower buds in the winter, outside the typical vulnerable period ''+!for ripening fruit (Greig-Smith and Wilson 1984). These studies suggest that damage mitigation ''"!efforts should not be limited to protecting fruits. Based on the available literature, it is not known ''#!whether bud feeding and subsequent yield loss occur outside this system of bullfinches and ''$!English fruit orchards. There is no evidence of research in this area after the 1980s. Given the ''%!potential for high yield loss due to bud consumption, additional work is needed to determine ''&!whether bud consumption is a concern in other crops and growing regions and whether other bird '''!species are causing winter season crop damage via bud consumption.   ''(! A significant gap in the literature remains regarding these indirect, non-consumptive '')!mechanisms of bird damage to fruit. Given the relatively limited and situation-specific ''*!information in these areas, it is not yet clear how badly these alternative bird damage '(+!mechanisms affect crop production. More research is necessary in these areas to determine '("!whether these negative implications of birds in fruit crops 1) are substantial enough to warrant '(#!mitigation by crop producers and 2) are occurring on a broad scale across crop types and '($!growing regions.  '(%! '(&!Positive Implications Of Avian Fruit Orchard Use '('!Birds consume orchard pests '((!Invertebrate orchard pests '()! Insects are becoming increasingly resistant to pesticides and public concern about the use '(*!of pesticides due to health concerns have become more widespread. This environment supports ')+!!!20 the need for increasing research to understand the potential for biological control of persistent ')"!and widespread invertebrate pests. Studies of avian consumption of crop pests are plentiful, but ')#!few studies explore the potential for avian predation on crop pests to result in yield benefits due ')$!to increased yield with the reduction in pest species (Mooney et al. 2010). Similarly to uneven ')%!distribution among crop types in studies quantifying crop damage by birds, studies of potential ')&!avian benefits to fruit crops are clustered among a relatively small number of crop types. ')'!Research has been conducted primarily in apples (e.g. Mols and Visser 2002), and grapes (e.g. ')(!Jedlicka et al. 2011).   '))! A series of studies in apple orchards in the Netherlands have focused caterpillar ')*!consumption by on great tits (Parus major) and the reduction in leaf damage caused by '*+!caterpillars (Mols and Visser 2002, Mols et al. 2005, Mols and Visser 2007). Mols and Visser '*"!(2002) demonstrated that bird predation on leaf-damaging caterpillars increases apple yield. '*#!When birds were permitted to consume caterpillars, leaf damage decreased from 13.8% to11.2% '*$!compared to trees from which birds were excluded (Mols and Visser 2002). Bird consumption of '*%!caterpillars resulted in a 3.1 kg increase in crop yield per tree, specifically the production of more '*&!apples (Mols and Visser 2002). In contrast, Pinol et al. (2010) found a relatively weak effect of '*'!birds on arthropod abundance in citrus groves in Spain. Most of the research in this area comes '*(!from apple orchards in the Netherlands. The contrast between those studies and that of Pinol et '*)!al. suggests that the potential benefits of avian pest consumption are variable among crop types, '**!farms, or growing regions. This variability illustrates the need for future studies to assess the (++!relationships not only between birds and invertebrate pests but also between avian pest (+"!consumption and resultant crop production to understand how crop growers can benefit from and (+#!take advantage of avian orchard use.  (+$!!!21  By installing nest boxes in orchards, fruit growers can exploit bird predation on pest (+%!arthropods (Mols et al. 2005, Mols and Visser 2007, Jedlicka et al. 2011). For example, apple (+&!orchards with great tit nest boxes installed had half as much caterpillar damage as orchards (+'!without nest boxes, resulting in an increased yield of 1200 kilograms per hectare (Mols and (+(!Visser 2007). Jedlicka et al. (2011) saw insectivorous bird density increase by nearly four times (+)!after installing nest boxes in California vineyards. In addition, Jedlicka et al. (2011) used sentinel (+*!prey to determine that prey removal in vineyards with nest boxes was 2.4 times greater than in ("+!vineyards without added nest. These studies illustrate not only that insectivorous birds can (""!benefit fruit growers by consuming herbivorous insects and contributing to increased yield, but ("#!also that this benefit can be enhanced relatively easily and inexpensively by adding nest sites ("$!(Mols and Visser 2002, Jedlicka et al. 2011). However, given that these patterns have been ("%!explored in a select few studies and in only two types of fruit crops, more work in this area is ("&!needed before making generalized recommendations to growers regarding the value of installing ("'!structures to attract insectivores.  ("(! Invertebrate pests do not just consume tree leaves; species such as the codling moth (")!(Cydia pomonella) and winter moth (Operophtera brumata) attach to and build cocoons in bark ("*!to overwinter. During the fruiting season, moth larvae are common fruit pests, damaging and (#+!consuming the fruit itself. Bird species like great tits (Soloman et al. 1976), pine siskins (Roland (#"!et al. 1986), and woodpecker species (MacLellen 1958) benefit fruit growers by uncovering and (##!feeding on these bark pests. The role of birds in controlling moth larval density in fruit orchards (#$!was explored several decades ago in both North America (e.g. MacLellan 1958, Roland et al. (#%!1986) and Europe (e.g. Soloman and Glen 1979). Bird consumption of codling moth larvae can (#&!be substantial. In Nova Scotia, Canada downy woodpeckers (Picoides pubescens) and hairy (#'!!!22 woodpeckers (Leuconotopicus villosus) took 52% of overwintering codling moth larvae (#(!experimentally placed on apple trees (MacLellan 1958). Roland et al. (1986) found that when (#)!birds were excluded from foraging in certain trees, codling moth larvae density was three times (#*!greater than in trees where birds could freely consume larvae. Soloman and colleagues ($+!experimentally added codling moth larvae to apple orchards in the UK to assess moth mortality ($"!due to great tits (Soloman et al. 1976, Soloman and Glen 1979). In two successive years, birds ($#!depredated approximately 95% of codling moth larvae. Consumption declined when larval ($$!density declined, suggesting a density-dependent pattern to codling moth regulation by birds ($%!(Soloman et al. 1976, Soloman and Glen 1979).  ($&! The extent to which bird predation on overwintering moths benefits crop production has ($'!not been explored quantitatively. More research could better elucidate the conditions under ($(!which birds regulate moth density most effectively. However, despite substantial avian predation ($)!on moth larvae (MacLellan 1956, Roland et al. 1986), moth density often remains high during ($*!the fruiting season and considerable moth damage to crop leaves and fruit remains (Roland et al. (%+!1986). This lack of sufficient pest control benefit by birds may explain why research in this area (%"!apparently subsided thirty years ago. Therefore, devoting additional energy to such studies may (%#!not result in valuable information for fruit producers if avian moth larvae consumption is (%$!consistently unable to mitigate moth abundance and subsequent crop damage.  (%%!  (%&!Mammalian orchard pests (%'! Bird predation on mammalian crop pests is another positive implication of avian use of (%(!fruit orchards. Mammalian pests like voles (e.g. Microtus pennsylvanicus), moles, and gophers (%)!(Family: Geomyidae) damage orchards and vineyards by digging burrows throughout fields, (%*!!!23 damaging equipment like irrigation systems, or chewing the bark around fruit trees (Ashkam (&+!1990, Moore et al. 1998). Use of rodenticides to regulate these pests is costly and concerns about (&"!consequences for food safety have fueled study of biological control mechanisms. Relatively (&#!recently, birds of prey have been investigated as means for controlling vertebrate damage in fruit (&$!crops (Ashkam 1990, Moore et al. 1998, Paz et al. 2013). However, there is considerably less (&%!published research in this area than studies of birds mitigating invertebrate damage. This (&&!discrepancy may be due to the considerable challenges of systematically locating and tracking (&'!sufficient numbers of wide-ranging predatory birds, quantifying consumption of mammalian (&(!pests, and simultaneously assessing effects on pest mammal populations.  (&)! Studies of predatory birds and mammalian fruit pests typically explore ways in which (&*!crop producers can attract predatory birds and reduce mammalian pest abundance (e.g. Askham ('+!1990, Moore et al. 1998, Merwin et al. 1999, Paz et al. 2013). Askham (1990) installed artificial ('"!perches and nest boxes to attract predatory birds to Washington apple orchards. However, no ('#!owls occupied owl nest boxes and American kestrels (Falco sparverius) occupied only 13% of ('$!boxes erected for them. Askham (1990) provided some summary data to suggest that vole ('%!activity in the orchards with bird attracting structures was generally lower than in control plots, ('&!but did not support these conclusions with robust analyses. Moore et al. (1998) surveyed fruit (''!growers in California to assess the perceived efficacy of owl nest boxes in controlling gophers. ('(!While fruit growers saw 40% box occupancy, only 23% believed that barn owls (Tyto alba) (')!effectively controlled gophers (Moore et al. 1998); overall this survey revealed a high level of ('*!variation among growers in the perceived efficacy of barn owls on gopher pests (Moore et al. ((+!1998). In addition, Merwin et al. (1999), observed raptor species like red-tailed hawk (Buteo (("!jamaicensis) and American kestrel hunting around apple orchards with high vole density, but ((#!!!24 these predators did not control vole abundance or reduce pest damage. The on-going problem of (($!mammalian pests, increasing interest in biological control methods, and dearth of robust data on ((%!the capacity of predatory birds to regulate pest mammals suggest that such studies should be an ((&!area of active research.  (('! An on-going question about the positive implications of birds in fruit agriculture and the (((!of vertebrates as biological control agents is whether they can demonstrate a sufficient numerical (()!response to abundance of pest organisms to offer some kind of damage-limiting benefit and ((*!subsequent increase in crop yield. The literature addressing the positive roles of birds offers ()+!mixed messages regarding the capacity for insectivores and birds of prey to control crop pests ()"!and positively affect crop production. Studies of insectivorous birds have begun exploring this, ()#!but the number of studies remains limited, as does the number of crops in which these studies are ()$!conducted. Overall, research energy could be expended on studies of tri-trophic interactions ()%!involving predators, prey, and crops are necessary to quantify actual consequences of bird ()&!predation of pest organisms on crop yield. Furthermore, I was unable to find published data from ()'!North America or Eurasia on whether birds of prey affect fruit consumption by pest bird species; ()(!however, such studies have demonstrated this relationship elsewhere in places like New Zealand ())!(e.g. Kross et al. 2012). Given the considerable evidence of birds as fruit pests, the potential for ()*!biological control of fruit-consuming birds by raptors could be a valuable area of future research.  (*+! (*"!Addressing Gaps In Avian Use Of Cultivated Fruit Orchards (*#! My review of the positive and negative implications of avian use of fruit crops revealed (*$!several areas that warranted further exploration. First, bird damage to fruit exhibits variation over (*%!time and space, but little has been done to evaluate avian orchard use over extended temporal (*&!!!25 scales such as throughout the fruit-growing season. Second, estimates of bird damage based upon (*'!in-field studies are highly variable and do not typically quantify damage by individual bird (*(!species, despite indications that birds vary in fruit consumption within orchards. Third, the (*)!availability of fruit at broad spatial scales and the habitat context within and around fruit (**!orchards can influence fruit damage. However, it remains unclear how fruit consumption of )++!individual birds is affected by resource availability at broad spatial scales and across multiple )+"!scales. )+#! I investigated these problems in the second, third, and fourth chapters of my dissertation )+$!by studying two common fruit consuming species in Michigan sweet cherry orchards. American )+%!robins and cedar waxwings are frequently identified crop pests and are responsible for a )+&!relatively high proportion of cultivated sweet cherry consumption (Lindell et al. 2012). Cherry )+'!orchards are attractive for fruit-eating birds because cherries are brightly colored, densely )+(!available, and have easily accessible pulp (Sallabanks 1993). Cherries are also an economically )+)!valuable fruit crop; Michigan growers estimate annual losses to birds of over $US2 million )+*!statewide (Anderson et al. 2013). Therefore, sweet cherry orchards provide an ideal system in )"+!which to explore questions of avian use of cultivated fruit crops. Below, I introduce and explain )""!the objectives for my second, third, and fourth dissertation chapters.  )"#!  In my second dissertation chapter, I addressed the problem that, despite numerous studies )"$!investigating avian consumption of cultivated sweet cherries (Virgo 1971, Curtis et al. 1994, )"%!Lindell et al. 2012), the extent of avian orchard use over the growing season has not been )"&!established. Information on the species-specific foraging patterns of fruit-eating birds throughout )"'!the fruit growing season orchards can help growers target particular bird species and times when )"(!fruit loss is greatest (Dolbeer et al. 1994, Somers and Morris 2002, Tracey et al. 2007). )")!!!26 Omnivorous American robins consume some fruit, as well as a large amount of invertebrates; )"*!while cedar waxwings are heavily frugivorous (Witmer 1996). In addition these species differ in )#+!fruit preference, which likely influences orchard use. In the second chapter of my dissertation, )#"!my objective was to determine whether cedar waxwings exhibited more intense use of sweet )##!cherry orchards than robins based on their differences in diet. To achieve this objective I )#$!captured wild birds and used radio-telemetry techniques to assess the frequency of visits to )#%!cherry orchards throughout the cherry-growing season, as well as the time birds spent visiting )#&!orchards each day. )#'!In my third dissertation chapter, I addressed the problem that estimates of bird damage )#(!and resulting financial loss lack a species-specific approach and can be subject to high variability )#)!from one site to another. Bioenergetic models of bird damage provide species-specific estimates )#*!that are based on avian energy needs, diet composition, and crop energy content (Wiens and )$+!Dyer 1975, Peer et al. 2003). A species-specific approach is important because recent work has )$"!shown that the degree of avian cherry consumption varies among species (Lindell et al. 2012). In )$#!the third chapter of my dissertation, my objective was to provide the first species-specific, )$$!region-wide estimates of bird damage and financial loss in sweet cherry crops. To achieve this )$%!objective I collected data on avian energy needs, diet composition, and local population sizes to )$&!construct bioenergetic and economic models of bird damage.  )$'! In my fourth dissertation chapter, I addressed the gap in the literature regarding the )$(!influence of resource availability at broad spatial scales and across multiple scales on avian )$)!foraging behavior in fruit orchards. Traditional foraging models are difficult to apply to highly )$*!mobile animals foraging on broadly distributed resources because resource heterogeneity occurs )%+!at multiple spatial scales. The influence of multi-scale resource availability on behavioral )%"!!!27 responses is understudied, despite the hierarchical nature of foraging decisions. Fruit-eating birds )%#!make foraging decisions at multiple scales (Sallabanks 1993). Thus, fruit resource availability at )%$!the scale of the foraging patch, as well as across broader habitat and landscape scales likely )%%!affect the behavior of frugivorous birds. In addition, research has not examined how multi-scale )%&!resource availability could affect foraging within the context of sociality. In the fourth chapter of )%'!my dissertation, my objectives were to 1) evaluate whether avian foraging behaviors depend on )%(!relative fruit abundance at hierarchical spatial scales, and 2) determine if fruit abundance at large )%)!spatial scales constrains social foragers more than solitary foragers.  )%*! )&+! )&"! )&#! )&$! )&%! )&&! )&'! )&(!!!28 LITERATURE CITED)&)!29 LITERATURE CITED )&*!Agriculture and Agri-Food Canada. 2016. CanadaÕs Wine Industry. Accessed 24 July 2016. )'+!Anderson, A., C. A. Lindell, K. M. Moxcey, W. F. Siemer, G. M. Linz, P. D. Curtis, J. E. )'"!Carroll, C. L. Burrows, J. R. Boulanger, K. M. M. Steensma, and S. A. Shwiff. 2013. Bird )'#!damage to select fruit crops: The cost of damage and the benefits of control in five states. )'$!Crop Protection 52:103Ð109. )'%!Ashkam, L.R. 2000. Efficacy of the aerial application of methyl anthranilate in reducing bird )'&! damage to sweet corn, sunflowers, and cherries. Proceedings of the Nineteenth Vertebrate )''! Damage Pest Conference 22-25.  )'(!Avery, M.L., J.W. Nelson, and M.A. Cone. 1992. Survey of bird damage to blueberries in North )')! America. Proceedings of the Eastern Wildlife Damage Control Conference 5:105Ð110. )'*!Avery, M.L. 2002. Behavioral and ecological considerations for managing bird damage to fruit. )(+! United States Department of Agriculture National Wildlife Research Center Ð Staff )("! Publications: Paper 460.   )(#! )($!Avery, M. L., J. L. Cummings, D. G. Decker, J. W. Johnson, J. C. Wise, and J. I. Howard. 1993. )(%! Field and aviary evaluation of low-level application rates of methiocarb for reducing bird )(&! damage to blueberries. Crop Protection 12:95Ð100. )('! )((!Beal, F.E.L. 1915. Food habits of robins and bluebirds in the United States. United States  )()!  Department of Agriculture Bulletin 171:1-31.  )(*!Bosch, J., W. P. Kemp, and G. E. Trostle. 2006. Bee population returns and cherry yields in an ))+! orchard pollinated with Osmia lignaria (Hymenoptera!: Megachilidae). Journal of ))"! Economic Entymology 99:408Ð413. ))#!Boudreau, G. W. 1972. Factors related to bird depredations in vineyards. American Journal of ))$! Enology and Viticulture 23:50Ð53. ))%!DeHaven, R. W. 1974. Bird damage to wine grapes in central California, 1973. Proceedings of ))&! the Sixth Vertebrate Pest Conference: Paper 9. ))'!DeHaven, R. W., and R. L. Hothem. 1981. Estimating bird damage from damage incidence in ))(! wine grape vineyards. American Journal of Enology and Viticulture 32:1Ð4. )))!Dolbeer, R.A., N.R. Holler, and D. W. Hawthorne.1994. Identification and assessment of ))*! wildlife damage: An overview. The Handbook: Prevention and Control of Wildlife )*+! Damage. University of Nebraska-Lincoln: Lincoln, Nebraska. )*"!!!30 Drake, D., and J. Grande. 2002. Assessment of wildlife damage to agricultural crops in New )*#! Jersey. Journal of Extension 40: 1IRB4. )*$!Eaton, R.A., C.A. Lindell, H.J. Homan, G.M. Linz, and B.A. Maurer. 2016. American Robins )*%!(Turdus migratorius) and Cedar Waxwings (Bombycilla cedrorum) vary in use of cultivated )*&!cherry orchards 128:97Ð107. )*'!Galeotti, P., and M. Inglisa. 2001. Estimating predation impact on honeybees Apis mellifera L. )*(! by European bee-eaters Merops apiaster L. Revue dÕEcologie (La Terre et la Vie) )*)! 56:373Ð388. )**!Gebhardt, K., A. M. Anderson, K. N. Kirkpatrick, and S. A. Shwiff. 2011. A review and *++! synthesis of bird and rodent damage estimates to select California crops. Crop Protection *+"! 30:1109Ð1116. *+#!Golawski A, and S. Golawska. 2013. Are the birds dangerous for insect pollinators? The *+$! relationship between hymenopterans and the red-backed shrike. Journal of Insect *+%! Conservation 17:1155Ð1160. *+&! *+'!Greig-Smith, P. W., and G. M. Wilson. 1984. Patterns of activity and habitat use by a *+(! population of bullfinches (Pyrrhula pyrrhula) in relation to bud-feeding in *+)! Oochards. Journal of Applied Ecology 21:401Ð422. *+*! *"+!Greig-Smith, P. W. 1987. Bud-feeding by bullfinches: Methods for spreading damage evenly *""! within orchards. Journal of Applied Ecology 24:49Ð62. *"#! *"$!Guarino, J. L., W. F. Shake, and E. W. Schafer. 1974. Reducing bird damage to ripening *"%! cherries with methiocarb. Journal of Wildlife Management 38:338Ð342. *"&! *"'!Henderson, W.C., and E.A. Preble. 1935. 1885 Ð Fiftieth anniversary notesÑ1935. The Survey *"(! 16: 59Ð65. *")! *"*!Holb, I. J., and H. Scherm. 2008. Quantitative relationships between different injury factors and *#+! development of brown rot caused by Monilinia fructigena in integrated and organic apple *#"! orchards. Phytopathology 98:79Ð86. *##! *#$!Homan, H. Jeffrey, A.A. Slowik, L.B. Penry, G.M. Linz, M. Bodenchuk, and R.L. Gilliland. *#%! 2010. Site use of European starlings captured and radio-tagged at Texas feedlots during *#&! Winter. USDA National Wildlife Research Center - Staff Publications: Paper 1267. *#'!  *#(!Houle, A., N.L. Conklin-Brittain, and R.W. Wrangham. 2014. Vertical stratification of the *#)! nutritional value of fruit: Macronutrients and condensed tannins. American Journal of *#*! Primatology 76:1207-1232. *$+! *$"!Ioriatti, C., V. Walton, D. Dalton, G. Anfora, A. Grassi, S. Maistri, and V. Mazzoni. 2015. *$#! Drosophila suzukii (Diptera: Drosophilidae) and its potential impact towine grapes during *$$!!!31  harvest in two cool climate wine grape production regions. Journal of Economic *$%! Entomology 108:1148Ð1155. *$&! *$'!Johnson, D., F. Guthery, and N. E. Koerth. 1989. Grackle damage to grapefruit in the lower Rio *$(! Grande Valley. Wildlife Society Bulletin 17:46Ð50. *$)! *$*!Kross, S.M., J.M. Tylianakis, and X.J. Nelson. 2012. Effects of introducing threatened falcons *%+! into vineyards on abundance of Passeriformes and bird damage to grapes. Conservation *%"! Biology 26:142Ð149. *%#! *%$!Lindell, C. A., R. A. Eaton, E. M. Lizotte, and N. L. Rothwell. 2012. Bird consumption of  *%%!  sweet and tart cherries. Human-Wildlife Interactions 6:283Ð290. *%&! *%'!Lindell, C.A., K.S. Steensma, P.D. Curtis, J.R. Boulanger, J.E. Carroll, C. Burrows, D.P.  Lusch,  *%(!  N.L. Rothwell, S.L. Wieferich, H.M. Henrichs, D.K. Leigh, R.A. Eaton, and G.M. Linz.  *%)!  2016. Proportions of bird damage in tree fruits are higher in low-fruit-abundance   *%*!  contexts. Crop Protection 90:40-48. *&+!MacLellan, B. C. R. 1958. Role of woodpeckers in control of the codling moth in Nova Scotia. *&"! The Canadian Entomologist 90:18Ð22. *&#!McPherson, J. 1988. Preferences of cedar waxwings in the laboratory for fruit species, colour *&$! and size: a comparison with field observations. Animal Behaviour 36: 961Ð969. *&%!Messmer, T.A. 2009. Human-wildlife conflicts: emerging challenges and opportunities. Human-*&&! Wildlife Conflicts 3:10-17. *&'!Mols, C. M. M., and M. E. Visser. 2002. Great tits can reduce caterpillar damage in apple *&(! orchards. Journal of Applied Ecology 39:888Ð899. *&)!Mols, C. M. M., and M. E. Visser. 2007. Great tits (Parus major) reduce caterpillar damage in *&*! commercial apple orchards. PloS one 2:e202. *'+!Mols, C. M. M., A. J. van Noordwijk, and M. E. Visser. 2005. Assessing the reduction of *'"!caterpillar numbers by Great Tits Parus major breeding in apple orchards. Ardea 93:259Ð*'#! 269. *'$!Mooney, K. A., D. S. Gruner, N. A. Barber, S. A. Van Bael, S. M. Philpott, and R. Greenberg. *'%! 2010. Interactions among predators and the cascading effects of vertebrate insectivores *'&! on arthropod communities and plants. Proceedings of the National Academy of Sciences *''! of the United States of America 107:7335Ð40. *'(!Moore, T., D. Van Vuren, and C. Ingels. 1998. Are barn owls a biological control for gophers? *')! Evaluating effectiveness in vineyards and orchards. Proceedings of the Eighteenth *'*! Vertebrate Pest Conference: Paper 61.  *(+!!!32 Merwin, I. A., J. A. Ray, and P. D. Curtis. 1999. Orchard groundcover management systems *("! affect meadow vole populations and damage to apple trees. HortScience 34:271Ð274. *(#!McDowell, R. D., and H. W. Pillsbury. 1959. Wildlife damage to crops in the United States. *($! Journal of Wildlife Management 23:240Ð241. *(%!Nelms, C. O., M. L. Avery, and D. G. Decker. 1990. Assessment of bird damage to early-*(&! ripening blueberries in Florida. Proceedings of the Fourteenth Vertebrate Pest *('! Conference: Paper 62.  *((!Newton, I. 1964. Bud-eating by bullfinches in relation to the natural food-supply. Journal of *()! Applied Ecology 1:265Ð279. *(*!Paz, A., D. Jareno, L. Arroyo, J. Vinuela, B. Arroyo, F. Mougeot, J. J. Luque-Larena, and J. A. *)+! Fargallo. 2013. Avian predators as a biological control system of common vole (Microtus *)"! arvalis) populations in north-western Spain: Experimental set-up and preliminary results. *)#! Pest Management Science 69:444Ð450. *)$!Peisley, R.K., M.E. Saunders, and G.W. Luck. 2015. A systematic review of the benefits and *)%! costs of bird and insect activity in agroecosystems. Springer Science Reviews 3:113Ð*)&! 125. *)'!Retamosa, M., L. Humberg, and J. Beasley. 2008. Modeling wildlife damage to crops in northern *)(! Indiana. Human-Wildlife Interactions 2:225Ð239. *))!Ries, L., and T.D. Sisk. 2004. A predictive model of edge effects. Ecology 85:2917Ð2926. *)*!Ritchie, D.F. 2000. Brown rot of stone fruits. The Plant Health Instructor, doi: 10.1094/PHI-I-**+! 2000-1025-01. **"!Roland, J., S. J. Hannon, and M. A. Smith. 1986. Foraging pattern of pine siskins and its **#! influence on winter moth survival in an apple orchard. Oecologia 69:47Ð52. **$!Sallabanks, R. 1993. Hierarchical mechanisms of fruit selection by an avian frugivore. Ecology **%! 74:1326Ð1336. **&!Simon, G. 2008. A short overview of bird control in sweet and sour cherry orchards Ð **'! Possibilities of protection of bird damage and its effectiveness. International Journal of **(! Horticultural Science 14:107Ð111. **)!Solomon, M. E., and D. M. Glen. 1979. Prey density and rates of predation by tits (Parus Spp.) ***! on larvae of codling moth (Cydia pomonella) under bark. Journal of Applied Ecology "+++! 16:49Ð59. "++"!Solomon, M., D. Glen, D. Kendall, and N. Milsom. 1976. Predation of overwintering larvae of "++#! codling moth (Cydia pomonella L.) by birds. Journal of Applied Ecology 13:341Ð352. "++$!!!33 Somers, C. M., and R. D. Morris. 2002. Birds and wine grapes: Foraging activity causes small-"++%! scale damage patterns in single vineyards. Journal of Applied Ecology 39:511Ð523. "++&!Stevenson, A. B., and B. B. Virgo. 1971. Damage by robins and starlings to grapes in Ontario. "++'! Canadian Journal of Plant Science 51:201Ð210. "++(!Stone, C. P. 1973. Bird damage to tart cherries in Michigan, 1972. Bird Control Seminars "++)! Proceedings: Paper 95. "++*!Summers, D. D., and M. R. Pollock. 1978. The effects of bullfinch damage on the yield of pear "+"+! trees. Annals of Applied Biology 88:448Ð450. "+""!Tobin, M. E., R. A. Dolbeer, and P. P. Woronecki. 1989. Bird damage to apples in the mid-"+"#! Hudson Valley of New York. HortScience 24:859. "+"$!Tracey J., M. Bomford, Q. Hart, G. Saunders, and R. Sinclair. 2007. Managing bird damage to "+"%! fruit and other horticultural crops. Bureau of Rural Sciences, Canberra, Australia. "+"&!United States Department of Agriculture Ð National Agriculture Statistics Service (NASS). 1999. "+"'! Fruit wildlife damage. Accessed 20 July 2016.   "+"(!United States Department of Agriculture - National Agricultural Statistics Service   "+")!  (NASS). 2016. Crop Values 2015 Summary. Accessed 28 July 2016.   "+"*! "+#+!van Leeuwen, G., A. Stein, I. Holb, and M. J. Jeger. 2000. Yield loss in apple caused by "+#"! Monilinia fructugena (Aderh. & Ruhl.) Honey, and spatio-temporal dynamics of disease "+##! development. European Journal of Plant Pathology 106:519Ð528. "+#$! "+#%!Vincent, C., and M. J. Lareau. 1993. Effectiveness of methiocarb and netting for bird control in a "+#&! highbush blueberry plantation in Quebec, Canada. Crop Protection 12:397Ð399. "+#'! "+#(!Virgo, B. B. 1971. Bird damage to sweet cherries in the Niagara Peninsula, Ontario. "+#)! Canadian Journal of Plant Science 51:415Ð423. "+#*! "+$+!Wenny, D. G., T. L. DeVault, M. D. Johnson, D. Kelly, C. H. Sekercioglu, D. F. Tomback, and "+$"! C. J. Whelan. 2011. The need to quantify ecosystem services provided by birds. Auk "+$#! 128:1Ð14. "+$$!Wheelwright, N. 1986. The diet of American robins: an analysis of U.S. Biological Survey "+$%! records. Auk 103:710Ð725. "+$&!Whelan, C.J., D.G. Wenny, and R.J. Marquis. 2008. Ecosystem services provided by birds. "+$'! Annals of the New York Academy of Sciences 1134:25Ð60. "+$(!Witmer, M.C., and P. J. Van Soest. 1998. Contrasting digestive strategies of fruit-eating birds. "+$)!Functional Ecology 12:728Ð741. "+$*!!!34 Wright, E. N., and D. D. B. Summers. 1960. The biology and economic importance of the "+%+! bullfinch. Annals of Applied Biology 48:415Ð418. "+%"!Xu, X. M., J. D. Robinson, A. M. Berrie, and D. C. Harris. 2001. Spatio-temporal dynamics of "+%#!brown rot (Monilinia fructigena) on apple and pear. Plant Pathology 50:569Ð578.  "+%$! "+%%! "+%&! "+%'! "+%(! "+%)! "+%*! "+&+! "+&"! "+&#! "+&$! "+&%! "+&&! "+&'! "+&(! "+&)! "+&*! "+'+! "+'"! "+'#! "+'$! "+'%! "+'&! "+''! "+'(! "+')! "+'*! "+(+! "+("! "+(#! "+($! "+(%! "+(&!35 CHAPTER 2 "+('! "+((!ESTIMATING FRUIT DAMAGE AND ECONOMIC LOSS DUE TO BIRDS WITH A "+()!BIOENERGETIC APPROACH "+(*! "+)+! "+)"!Rachael A. Eaton and Catherine A. Lindell"+)#!36 Abstract "+)$! "+)%! Bird damage and consumption of fruit crops amounts to tens of millions of dollars in "+)&!losses annually across the United States. Yet, the development of successful damage-mitigation "+)'!strategies for fruits is often hindered by a lack of species-specific damage information. A "+)(!bioenergetic model of crop damage integrates species-specific data on energetic demands and "+))!diet to estimate crop consumption. Compared to traditional damage surveys, a bioenergetic "+)*!approach permits a regional quantification of damage. In conjunction with local population sizes "+*+!and crop economic value, bioenergetic models can be used to develop economic models that "+*"!quantify financial loss due to bird damage on a species-specific level. We used a bioenergetic "+*#!approach to quantify damage to sweet cherry crops (Prunus avium) by American robins (Turdus "+*$!migratorius) and cedar waxwings (Bombycilla cedrorum) in an important sweet cherry "+*%!production region of Michigan. Individual waxwings consumed three times as much sweet "+*&!cherry and caused seven times the financial loss as robins, in large part because of larger local "+*'!population sizes and greater reliance on fruit in trees by waxwings; robins take over half their "+*(!fruit from the ground. We estimated that economic losses from this damage in our study area, "+*)!over 28 days from preharvest to postharvest, were $US1.8 million and $US147,000 from the "+**!waxwing and robin populations, respectively. Bioenergetic model estimates allow us to contrast ""++!damage from multiple pest species and understand pest species ecology, which is essential for ""+"!better-informed management programs. ""+#! ""+$!Introduction ""+%! Human demand for cultivated fruits has increased in recent decades (ERS 2015), due in ""+&!part to increased attention on the associated health benefits of fruit consumption (e.g., World ""+'!!!37 Health Organization 2010, Lock et al. 2005, Hjart„ker et al. 2014). Fruit production in the United ""+(!States is substantial; 28.4 million tons in 2014 with a value of more than $US20 billion (ERS ""+)!2015). High-value fruit crops are often subject to extensive depredation by fruit-eating birds ""+*!(Beal 1915, Dolbeer et al. 1994, Simon 2008); resulting financial losses are of major concern to """+!fruit producers. Growers in Michigan, New York, and the Pacific Northwest recently estimated """"!that bird damage to wine grapes, blueberries, apples, and cherries costs over $US180 million """#!annually (Anderson et al. 2013). Successful strategies to mitigate bird damage have been """$!hindered by the lack of information regarding 1) how much of the crop different bird species """%!consume and 2) the size of local populations of crop-damaging species (Dolbeer et al. 1994, """&!Somers and Morris 2002). Here, we used bioenergetic modeling to quantify avian consumption """'!of a high-value crop, sweet cherry (Prunus avium), by two prominent fruit-consuming species. """(!We provide the first species-specific, region-wide estimates of bird damage for this crop. We """)!then integrated these bioenergetic-based consumption estimates with avian population data and """*!the economic value of sweet cherry to generate models of monetary losses due to bird damage.  ""#+!A bioenergetic approach provides species-specific quantifications of bird damage that are ""#"!based on avian energetic needs, diet composition, and energy content of the focal crop (Wiens ""##!and Dyer 1975, Peer et al. 2003). This approach overcomes a common challenge in ""#$!understanding avian crop damage: quantifying the impact of individual pest species (Somers and ""#%!Morris 2002, Simon 2008). Field-based estimates typically quantify the extent of pest damage by ""#&!identifying damaged and missing fruits (e.g., Tracey and Saunders 2010, Lindell et al. 2016). ""#'!Yet, such approaches cannot attribute damage to particular species because doing so would ""#(!require near-continuous, wide-scale observation of birds feeding in crops. Such efforts are time- ""#)!and cost-prohibitive. Previous attempts to quantify fruit damage by individual species have relied ""#*!!!38 upon surveys of birds seen in orchards and attributed crop damage to the most frequently ""$+!detected species (e.g., Johnson et al. 1989). However, frequently detected species are not ""$"!necessarily the most important fruit consumers (Virgo 1971, Lindell et al. 2012).  ""$#!Economic models are typically used to estimate financial loss resulting from avian fruit ""$$!damage and consumption. Usually economic models are based upon estimates of damage ""$%!quantified by lost yield estimated by producers or from in-field damage surveys (Drake and ""$&!Grande 2002). However, these methods can be subject to a high degree of variance and are time-""$'!intensive and personnel-intensive (Peer et al. 2003, Saxton 2006, Lindell et al. 2016). ""$(!Additionally, previous economic models have typically assessed damage at the taxonomic level ""$)!of class (e.g., Aves) rather than species (Drake and Grande 2002), yet Lindell et al. (2012) have ""$*!shown that the degree of avian fruit consumption of sweet cherries is species specific. Accurate ""%+!and species-specific assessments of financial losses due to bird depredation are crucial for the ""%"!targeted and cost-effective implementation of loss-prevention strategies (Somers and Morris ""%#!2002, Tracey et al. 2007). ""%$!A bioenergetic approach has been undertaken to quantify damage to crops like corn ""%%!(Weatherhead et al. 1982) and sunflower (Peer et al. 2003), but not in other valuable crops such ""%&!as fruits. On a per-block annual basis cherry growers lose 2 Ð 13% of their crop to bird damage ""%'!or consumption (Lindell et al. 2016). A block is defined as a contiguous area of crop with ""%(!boundaries to adjacent land cover types of at least wide meters in width. Michigan growers ""%)!estimate that bird damage to sweet cherries amounts to annual losses of over $US2 million ""%*!statewide (Anderson et al. 2013). American robins (Turdus migratorius) and cedar waxwings ""&+!(Bombycilla cedrorum) are frequent visitors to sweet cherry orchards throughout the growing ""&"!season (Eaton et al. 2016) and consume more sweet cherries than other fruit-consuming birds ""&#!!!39 (Lindell et al. 2012). Additionally, fruit producers perceive these species to be problematic, ""&$!especially robins (Anderson et al. 2013). However, the extent of sweet cherry consumption and ""&%!resulting economic cost of damage by these particular species over the fruit-ripening season in ""&&!northwestern Michigan, an important sweet cherry production region, is not known. For this ""&'!study, we developed species-specific bioenergetic models of American robin and cedar waxwing ""&(!cherry consumption over the cherry-ripening season. We then incorporated these consumption ""&)!data, along with population estimates, into economics models to quantify economic losses ""&*!sustained by growers due to robin and waxwing crop damage.  ""'+! ""'"!Methods ""'#! We conducted this study in Leelanau County, Michigan, a peninsula (land area = 900 ""'$!km2, water area = 5659 km2) on Lake Michigan and an agricultural region containing many ""'%!orchards and vineyards (Figure 2.1). We selected as our study area a region of eastern Leelanau ""'&!County (208 km2) that contains a majority of the countyÕs sweet cherry orchards. We used the ""''!USDA NASS CropScape program and the 2014 National Cropland Data Layer (NASS 2014) to ""'(!calculate how much of this area comprised each of the three habitat types used in our study. Our ""')!study area consists of 70.7 km2 (33.9%) woodland, 51.8 km2 cherry orchard (sweet and tart ""'*!varieties; 24.9%), and 20.0 km2 urban/built space (9.6%). Other land use types in the area include ""(+!grassland/pasture, fallow or barren land, herbaceous land, and crops like corn and alfalfa (NASS ""("!2014). Sweet cherries make up 43% of the total cherry acreage in Leelanau County and 22% of ""(#!total cherry acreage in Michigan (NASS 2015a). Given that the majority of cherry orchards in ""($!the county are included in our study area, we assumed this ratio of sweet to tart cherries held true ""(%!!!40 in our study area. Thus we used this ratio to calculate the area of sweet cherries (22.3 km2, 32%) ""(&!in our study area from total cherry area.  ""('!Mean monthly rainfall and air temperature during the 2015 study period (May Ð July) ""((!were 5.8 cm and 17.4 ¡C, respectively (NWMHRS 2015). Both robins and waxwings are ""()!common in the study region during the summer cherry-growing season. Waxwings arrive in the ""(*!region by late May and nest between mid-June and August (McPeek 2011a). Robins typically "")+!arrive in March, begin nesting in April, and often rear two broods (McPeek 2011b).  "")"! "")#!Bioenergetic models of avian sweet cherry consumption "")$! We generated species-specific bioenergetic models to estimate daily sweet cherry "")%!consumption by robins and waxwings. We first quantified robin and waxwing field metabolic "")&!rates (FMR) using a formula for free-living passerines (Nagy et al. 1999): "")'!y = 10.4x0.68 "")(!where y is FMR (kJ/day) and x is mean bird body mass (grams). This formula is appropriate for ""))!estimating metabolic rate of wild birds because it incorporates basal metabolic rate, as well as "")*!activities like foraging and digestion, flying, reproduction, growth, and anti-predator behavior ""*+!(Nagy et al. 1999). We calculated mean body mass from birds captured in mist nets (methods ""*"!described below) from 30 June Ð 19 July 2015. ""*#! Our species-specific bioenergetic models of cherry consumption during the fruit-ripening ""*$!period took the following form: ""*%!Per-bird consumption = !"#!"#$$%!!"!#$%!!"#$%&' " moisture " diet " days ""*&! ""*'!!!41 where DER is daily energy requirements (kJ/day), cherry energy density is the energy content of ""*(!sweet cherries (14.85 kJ/g dry mass), moisture is a correction for water content of consumed ""*)!cherry (1.82; USDA Agricultural Research Service 2016), diet is the proportion of sweet cherries ""**!in the diet, and days is the number of days in the study region over which cherries are most "#++!vulnerable to depredation (28 days). "#+"! For both species we quantified daily energy requirements (DER) in kJ/day by dividing "#+#!FMR by the apparent metabolizable energy coefficient (MEC*) of birds feeding on fruit (Smith "#+$!et al. 2007). MEC* is defined as: (energy content of ingested food Ð energy content of excreta) / "#+%!energy content of ingested food) (Karasov 1990). We used an MEC* value of 0.64 for passerines "#+&!feeding on fruit pulp and skin (Karasov 1990, Smith et al. 2007). We calculated species-specific "#+'!daily required dry food intake if birds were feeding solely on sweet cherries (g/ day) by dividing "#+(!DER by the energy density (kJ/g dry mass) of sweet cherries (USDA Agricultural Research "#+)!Service 2016). As these values are based on dry mass, we corrected for moisture content of sweet "#+*!cherries (Peer et al. 2003, Smith et al. 2007) by multiplying by 1.82 (one plus the moisture "#"+!proportion of raw sweet cherries: 0.82). To estimate the actual daily mass of sweet cherries "#""!consumed per bird, we multiplied this daily intake value by the proportion of sweet cherries in "#"#!the diet of robins and waxwings (Peer et al. 2003, Smith et al. 2007). "#"$!We quantified cherry consumption and subsequent economic loss for the period in our "#"%!study area when sweet cherries are vulnerable to robin and waxwing depredation (Alkio et al. "#"&!2014). We defined this as the twenty-eight days between the date on which cherries were first "#"'!detected in fecal samples (method described in the following section) and the last date of cherry "#"(!harvest in 2015. We excluded samples (n=11) collected prior to this period because cherries were "#")!not yet ripening. We did not distinguish males and females for analyses because sexes were of "#"*!!!42 similar size, several waxwings could not be reliably sexed, and males and females did not differ "##+!in the proportion of cherries in the diet for either species. "##"! "###!Estimating proportion of sweet cherries in diets "##$! We captured birds in mist nets (38-mm and 30-mm) in four sweet cherry orchards from "##%!30 May Ð 19 July 2015 (Figure 2.1); nets were open for a total of 1255 net-hours.  "##&! "##'! "##(!Figure 2.1 Map of Leelanau County study region with mist netting and point count "##)!locations identified. Map of Leelanau County, Michigan with Traverse City, Michigan "##*!identified. Shaded region identifies the 208 km2 study area. Open triangles identify mist-netting "#$+!sites (n = 4) for fecal sample collection. Black circles identify point count (n =22) survey "#$"!locations.  "#$#! "#$$!We extracted and placed each captured robin or waxwing in a paper bag to collect fecal samples. "#$%!We air-dried samples in bags for at least 24 hours, transferred samples to vials, and froze them "#$&!until processing. Fecal samples are a non-invasive technique for avian diet analysis and can "#$'!provide accurate estimates of diet composition without the risks associated with use of emetics or "#$(!!!43 harvest of stomach contents (Ralph et al. 1985, Rosenberg and Cooper 1990, Carlisle and "#$)!Holberton 2006). Differential digestibility of diet items (e.g., invertebrates versus plant material) "#$*!can complicate the use of fecal samples to quantify proportions of foods in bird diets. To "#%+!overcome this, we applied correction factors based on the assimilability of food types (see "#%"!below).  "#%#! Under a dissecting microscope, we sorted fecal sample contents into major food types: "#%$!animal matter, fruit skin, seeds, and fruit pulp. We identified sweet cherries among samples of "#%%!fruit skin by viewing samples under a compound microscope and comparing these to reference "#%&!samples and images from the literature (Jordano and Herrera 1981, Martella et al. 1996, Oliveira "#%'!et al. 2002). For each sample that contained sweet cherry skin, we dried fecal material to "#%(!constant mass at 60 ¡C and weighed it to the nearest 0.001 g. We calculated the proportion by "#%)!dry mass of each food type in these samples (n = 89). To account for differential digestibility of "#%*!food types, we applied correction factors based on the apparent assimilable mass coefficients "#&+!(AMC*) of particular food types (Lane et al. 1999). AMC* = (Dry mass of food consumed Ð Dry "#&"!mass of excreta) / Dry mass of food consumed (Karasov 1990, Fassbinder-Orth and Karasov "#&#!2006). Following suggestions of Castro et al. (1989) we used published species-specific AMC* "#&$!values for a given food type, when available, and mean AMC* values for passerines for a given "#&%!food type when species-specific information was unavailable (Table 2.1). We divided the dry "#&&!mass of each food type in a sample by (1- AMC*) for that food (Lane et al. 1999). We assumed "#&'!any pulp and fruit skin in a sample to be from the same fruit type because in no instance did a "#&(!sample contain more than one type of fruit skin as well as pulp. We then calculated mean "#&)!corrected proportions of sweet cherries, animal matter, and other fruit in samples (Lane et al. "#&*!1999). The final mean proportions of sweet cherries in robin and waxwing diets included "#'+!!!44 samples with no cherries present. Non-cherry fruit occurred only in waxwing fecal samples, "#'"!therefore we used an AMC* value from waxwings feeding on a diet of mixed lipid-rich and "#'#!sugar-rich fruits to correct the proportion of non-cherry fruit in the waxwing diet (Table 2.1).  "#'$! "#'%!Table 2.1 Types and sources of AMC values used to calculate proportion of sweet cherries "#'&!in diet.  "#''! "#'(! Food type AMC* Value based on Source Arthropods  0.74a Published studies  Karasov 1990 Sweet cherry 0.61a Published studies; fruit pulp and skin Karasov 1990 Other fruit 0.34 Waxwings on mixed whole fruits Holthuijzen & Adkisson 1984 aCalculated from reported metabolizable energy content (MEC) values using AMC = MEC Ð "#')!0.03 (Karasov 1990).  "#'*! "#(+!Population sizes  "#("! To quantify the size of robin and waxwing populations in the study region, we conducted "#(#!fixed-radius (25 m) point count surveys at 22 randomly selected sites throughout the study area. "#($!Sixteen of these sites were at sweet cherry orchards, 3 were in urban/built areas and 3 were in "#(%!woodland areas. All point count sites were > 1.8 km from one another. We initiated surveys "#(&!between 0700 and 0930 and between 1700 and 1930 eastern daylight time; each survey was eight "#('!minutes in duration. We recorded all visual and aural observations of robins and waxwings "#((!detected within the survey radius, including birds flying within 25 m above the survey point. The "#()!first author (RAE) conducted surveys between 9 June and 29 July 2015 and surveyed each "#(*!location five times. Temporal replicates allow for accommodation of imperfect detection of "#)+!unmarked individuals (Fiske and Chandler 2011). We used counts of robins and waxwings from "#)"!!!45 each survey to fit N-mixture models of population abundance (Royle 2004), using the R package "#)#!ÒunmarkedÓ (Chandler and Fisk 2011). We modeled population abundance as a negative "#)$!binomial process and extracted the mode and 95% credible intervals for the abundance values at "#)%!each survey site. We then used these estimates to quantify bird densities in each of the three "#)&!surveyed land cover types (sweet cherry orchards, developed land, woodland). We multiplied "#)'!these densities by the total area of each of the three surveyed land cover types of our study area "#)(!to estimate habitat-specific population sizes. Finally, we summed habitat-specific estimates to "#))!quantify the total population sizes of robins and waxwings in our 208-km2 study area; we "#)*!incorporated these population sizes into economic models of avian damage to sweet cherries (see "#*+!below). We performed analyses in R statistical software (Version 3.0.3; R Core Team 2012). "#*"! "#*#!Economic models of avian damage to sweet cherries  "#*$! Our bioenergetic models generated a per-bird total mass of cherry skin and pulp "#*%!consumed because robins and waxwings do not digest and obtain energy from the cherry pit. "#*&!However, the market price that fruit growers are paid includes the cherry pit mass, and economic "#*'!models based on per-bird cherry consumption alone would underestimate loss. The proportion of "#*(!cherry pit mass to total fruit mass comprises a range of 0.07 to 0.11 (Bandi et al. 2010). To "#*)!account for the mass of the unconsumed pit when calculating financial loss, we multiplied our "#**!bioenergetic estimate of consumption by a correction factor of one plus the midpoint of this "$++!range (1.09).  "$+"!  We used the average market price for sweet cherries in Michigan over the five-year "$+#!period of 2010-2014 ($US0.0011/g; NASS 2015b) to calculate per bird economic loss over the "$+$!study period. In addition, we multiplied this monetary value by population abundance estimates "$+%!!!46 for each species to quantify the population-scale economic losses from robin and waxwing "$+&!damage to sweet cherries. We calculated a range of economic loss estimates based on the 95% "$+'!credible intervals of the population abundance estimates.   "$+(! As part of a related study, we conducted 150 man-hours of focal animal observations "$+)!(Altmann 1974) on robins and waxwings consuming sweet cherries in Michigan orchards over "$+*!the cherry-ripening period from 4 June 4 Ð 2 July, 2012 and 1 July Ð 24 July, 2014. All "$"+!waxwings observed (n = 43) were consuming cherries directly from the tree, while robins ate "$""!cherries both in the tree and on the ground (n = 62; RAE, unpublished data). During 41% of "$"#!these observations, robins ate cherries only in the tree. Given that growers do not harvest cherries "$"$!that have fallen to the ground, only fruit consumption within trees should result in economic loss "$"%!for growers. Therefore, in the economic model of loss due to robins we used an additional "$"&!correction factor of 0.41 to represent the proportion of robin sweet cherry consumption that "$"'!occurs in trees. "$"(! Our species-specific models of economic loss from damage to sweet cherries took the "$")!following form: "$"*!Per-bird economic losses = per-bird consumption " pit correction " market price  "$#+! "$#"!where per-bird consumption is the total per-bird cherry consumption over the study period from "$##!our bioenergetics model, pit correction is a correction for the mass of cherry pit that birds do not "$#$!utilize for energy but for which growers are paid (1.09), and price is the market price of sweet "$#%!cherry ($US0.011/g). Robin models included an additional correction (0.41) for birds feeding in "$#&!cherry trees. "$#'! "$#(!!!47 Results "$#)!Bioenergetic models of avian sweet cherry consumption "$#*! We analyzed 47 American robin fecal samples and 31 cedar waxwing samples. We "$$+!estimated daily energy requirements of robins and waxwings at 318.8 and 181.3 kJ/day, "$$"!respectively. After correcting for assimilation efficiency, the proportion of sweet cherries in the "$$#!robin diet was 10.8% (± 24); the waxwing diet contained 51.4% (±46) sweet cherry. For "$$$!comparison, the uncorrected proportions of sweet cherry in the diet were 13.6% (±27) for robins "$$%!and 51.3% (±46) for waxwings. Based on these results, individual robins consumed 4.0 g sweet "$$&!cherry each day, while waxwings consumed 12.0 g sweet cherry each day (Table 2.2).  "$$'! "$$(!Economic models of avian damage to sweet cherries "$$)! Robins and waxwings have a combined estimated population size of 772,956 individuals "$$*!in the cherry orchard, woodland, and developed areas of our study region (Table 2.2). These two "$%+!cherry-eating species caused an estimated $US1,986,948 in losses to local cherry producers "$%"!during the 2015 cherry-ripening season. When considering the 95% CI of population estimates, "$%#!this loss may range from $US991,609 - $US3,952,449.  "$%$! "$%%!Table 2.2 Estimated sweet cherry consumption by American robins and cedar waxwings in "$%&!northwest Michigan orchards and resulting economic losses.  "$%'! "$%(!Model Elements American Robin Cedar Waxwing Body mass (g)* 79.3 (±3.9) 34.6 (±4.3) FMR (kJ/day) 204 116 Proportion of cherries in diet 10.8% (± 24) 51.4% (±46) Daily per-bird consumption (g) 4.0 12.0 Total per-bird consumption (g)^ 112.0 336.6 !!48 Table 2.2 (contÕd) "$%)!Population size (95% CI) 267,563 (151,227 Ð 618,994) 455,393 (224,860 Ð 894,060) Total economic loss ($US)  Per bird^ 0.55 4.04 Per species (95% CI)^ 147,160 (83,175 Ð 340,447) 1,839,788 (908,434 Ð 3,612,002)  *Means and standard deviations of field-collected body masses. ^Values for the entire 28-"$%*! day pre-harvest study period. "$&+! "$&"!Discussion "$&#! In these first bioenergetic and economic models of avian damage to fruit crops we "$&$!demonstrate that species differences in proportion of fruit in the diet and local population sizes "$&%!result in substantial between-species variation in fruit consumption and resulting financial loss. "$&&! Our bioenergetic models generated damage estimates based upon species-specific diet "$&'!and physiology data. Recent field studies of birds in cherry orchards demonstrated that bird "$&(!species vary in both their reliance on orchard habitat (Eaton et al. 2016) and their cherry "$&)!consumption (Lindell et al. 2012). Building upon this work, our models showed that a species-"$&*!specific approach uncovers striking differences in the damage attributable to different species. "$'+!We estimated that an individual cedar waxwing caused a financial loss that was seven times that "$'"!of an individual robin. This substantial difference between species stems from 1) a five-fold "$'#!greater proportion of cherries in the waxwing diet, than to robins, and 2) the fact that waxwings "$'$!consume only saleable cherries that are still on the trees, while robins often forage on the ground.  "$'%! Despite robinsÕ greater daily energy needs, waxwings had a substantially larger "$'&!proportion of sweet cherries in the diet. We found that sweet cherries comprised 11% of the "$''!robin diet; this is similar to VirgoÕs (1971) calculation of 12% for robins caught in Ontario sweet "$'(!!!49 cherry orchards. We estimated that sweet cherries made up 51% of the waxwing diet, which "$')!corresponds reasonably with WitmerÕs (1996) estimate that fruit, including non-cherry species, "$'*!comprises 72% of the waxwing summer diet. Our results suggest that class-based (i.e., all birds) "$(+!or non-specific quantification of damage misses important patterns that could influence bird "$("!management strategies. Assuming that all species detected in orchards have equal potential as "$(#!fruit consumers could incorrectly characterize the problem. For example, robins and waxwings "$($!are both frequently observed in orchards, but our species-specific approach demonstrated that "$(%!these species vary widely in the extent of damage inflicted. This is consistent with previous "$(&!studies of robins in sweet cherry orchards that show relatively low cherry consumption by "$('!robins, despite their frequent detection (Virgo 1971, Lindell et al. 2012).  "$((! Where birds feed in an orchard affects their role as a crop pest. In our system, waxwings "$()!eat cherries in trees while robins feed both in trees and on the ground (RAE, unpublished data). "$(*!Virgo (1971) also noted extensive ground foraging by robins in Ontario sweet cherry orchards. "$)+!Cherry growers only harvest and sell fruit from trees, and our models indicated that robins "$)"!feeding in trees caused relatively little damage compared to waxwings. However, robins "$)#!occasionally knock fruit to the ground or descend from a tree carrying a fruit and consume it on "$)$!the ground (RAE, personal observation). We cannot assume all instances of robin ground "$)%!feeding are completely free of cost to fruit growers. However, waxwings are clearly more "$)&!problematic to growers than robins, whose ground consumption of fruit may actually be "$)'!beneficial in removing resources for arthropod or fungal species that damage fruit. Despite "$)(!growersÕ frequent reports that robins are pest species in sweet cherry orchards (Anderson et al. "$))!2013), our current study and previous work suggest that bird damage mitigation efforts in "$)*!!!50 northwest Michigan sweet cherry orchards could be more targeted and efficient if focused on "$*+!waxwings (Lindell et al. 2012).  "$*"! Our study is the first bioenergetic approach to quantifying bird damage to fruit. This "$*#!technique has been used successfully in field crops (e. g., Peer et al. 2003) to provide regional, "$*$!species-specific estimates of avian crop damage. Previous attempts to attribute damage to "$*%!various bird species have often relied on surveys of birds flying in orchards or observations of "$*&!birds consuming crops (e.g., Virgo 1971, Johnson et al. 1989). However, bird species vary in "$*'!their conspicuousness, and survey or observation-based data alone may provide inaccurate or "$*(!incomplete information on the level of the damage (Dolbeer et al. 1994). In addition, such "$*)!approaches typically have not considered population size variation among potential pest species.  "$**! Our study demonstrated the added importance of population data to understanding the "%++!capacity of certain species for crop damage. For example, individual waxwings caused seven "%+"!times greater financial loss than robins over the study period. However, when extrapolated to the "%+#!population scale, waxwings caused 12.5 times greater financial loss to the region than robins. "%+$!This substantial difference in robin and waxwing estimated population sizes is a largely a result "%+%!of more widespread waxwing sightings and larger waxwing groups, than robins. We detected "%+&!waxwings more frequently, at more locations, and in larger groups than robins, leading to our "%+'!larger population estimates for waxwings than robins.  "%+(! Our population estimates are reasonable for robins and waxwings in a fruit-growing "%+)!region. Our estimate of 1878 robins per km2 in sweet cherry habitat is similar to VirgoÕs (1971) "%+*!average density of 18.53 robins per hectare (or 1853 per km2) of sweet cherry orchards in "%"+!Ontario, Canada. We estimated that waxwings have a larger population than robins, which is "%""!expected given waxwingsÕ smaller size, the great amount of cultivated fruit in the region, and the "%"#!!!51 waxwingsÕ greater reliance on fruit (Witmer 1996). Fruit comprises the majority of waxwingsÕ "%"$!diet throughout the year (Witmer 1996). In addition, waxwings are semi-colonial breeders and "%"%!relatively non-territorial (Lea 1942). Rothstein (1971) reported a density of 14.1 nests per hectare "%"&!(1410 nests per km2) in a forested area near a field of wild cherry trees in northern Michigan. "%"'!Waxwings also forage in large groups in orchards away from the nesting site (Lea 1942, Nelms "%"(!et al. 1990, Lindell et al. 2012). For example, a group of > 500 waxwings consumed blueberries "%")!on a 0.024 km2 farm in Florida (Nelms et al. 1990). Our own work has shown that waxwings in "%"*!cherry orchards forage in groups four times larger than groups of robins, a territorial species "%#+!(Lindell et al. 2012). The gregarious and frugivorous nature of waxwings could allow for a high "%#"!number of birds during the breeding season, particularly in resource-rich fruit-production "%##!regions.  "%#$! We conducted this study in a major sweet cherry growing area of Michigan. "%#%!Approximately half of MichiganÕs total sweet cherry acreage is in Leelanau County (NASS "%#&!2015a). We estimated that our 208 km2 study area within Leelanau County contained 22.6 km2 of "%#'!sweet cherry (~29% of total Michigan sweet cherry acreage; NASS 2014). Our bioenergetic "%#(!models of damage suggested robins and waxwings caused at least $US991,000 in damage in this "%#)!area and may cause as much as $US3.9 million. Anderson et al. (2013) used grower-reported "%#*!damage estimates from 2011 to generate a statewide damage value for Michigan sweet cherries. "%$+!They calculated that all birds collectively caused $US2,090,723 in damage to sweet cherries "%$"!annually (Anderson et al. 2013). Given that robins and waxwings are two significant cherry-"%$#!consuming species in Michigan (Lindell et al. 2012), our estimate of damage by these two "%$$!species is reasonable for an area that contains a substantial proportion of all sweet cherry "%$%!orchards in Michigan. "%$&!!!52  Provided model inputs are available or obtainable (e.g. proportion of crop in the diet and "%$'!avian population size) for the area of interest, bioenergetic models of damage are applicable to a "%$(!variety of crops and to production areas of various sizes. Bioenergetic and economic models of "%$)!avian crop damage are important prerequisites to comprehensive damage management programs. "%$*!Sweet cherry growers implement bird damage management strategies in an effort to reduce "%%+!financial losses. Economic models indicate that these efforts save the United States sweet cherry "%%"!industry up to $US238 million in the short run and up to $US29 million in the long run (Elser et "%%#!al. 2016). However, evidence suggests that many commonly used management techniques like "%%$!scare-eye balloons, raptor-resembling kites, and loud audio deterrents have limited or "%%%!inconsistent success in mitigating bird damage to fruit crops (Eaton 2016). These may be "%%&!particularly ineffective for species like waxwings that are relatively tolerant of people and have a "%%'!short fright distance (Eaton 2016). Bird damage mitigation techniques that specifically target "%%(!problem-species, like waxwings, may permit more efficient management. For example, planting "%%)!a more-preferred fruit source adjacent to orchards, a decoy crop, could draw waxwings away "%%*!from cherry crops and reduce damage. Waxwings show size preference when selecting berries "%&+!and choose fruits that are much smaller (~7.5 mm diameter; Avery et al. 1993) than ripening "%&"!sweet cherries (~12 Ð 23 mm; NWMHRC 2016). Therefore, luring waxwings away from cherry "%&#!crops could be an additional management strategy that would increase success more than solely "%&$!attempting to frighten them away.  "%&%!Bioenergetic model estimates add to the suite of damage quantification tools including "%&&!block-based field estimates and crop producer surveys. Our bioenergetic approach to quantifying "%&'!bird damage to fruit can be used to assess the impact of other potential problem species. For "%&(!example, cherry, apple, blueberry and grape growers surveyed across the U.S. identified "%&)!!!53 common starlings (Sturnus vulgaris) as important fruit pests (Anderson et al. 2013). A "%&*!bioenergetic approach would provide a tool to quantify the impact of this and other suspected "%'+!avian crop pests. In turn, model estimates allow us to contrast damage from multiple pest species "%'"!and understand pest species ecology, which is essential for more informed pest management "%'#!programs and damage management (Dolbeer et al. 1994, Somers and Morris 2002).  "%'$! "%'%! "%'&! "%''! "%'(! "%')! "%'*! "%(+! "%("! "%(#! "%($! "%(%! "%(&! "%('! "%((! "%()! "%(*! "%)+! "%)"! "%)#! "%)$! "%)%! "%)&! "%)'! "%)(! "%))! "%)*! "%*+!54 LITERATURE CITED "%*"!55 LITERATURE CITED "%*#!Alkio, M., U. Jonas, M. Declercq, S. Van Nocker, S., and M. Knoche. 2014. 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Studies in Avian Biology 13: 391Ð415. "&%&!Lane, S.J., J.C. Alonso, J.A. Alonso, and M.A. Naveso. 1999. Seasonal changes in diet and diet "&%'!selection of great bustards (Otis t. tarda) in north-west Spain. Journal of Zoology 247: 201Ð"&%(!214. "&%)!Lea, R. 1942. A study of the nesting habits of the Cedar Waxwing. The Wilson Bulletin 54:224Ð"&%*!237. "&&+!Lindell, C.A., R.A. Eaton, E.M. Lizotte, and N.L. Rothwell. 2012. Bird consumption of sweet "&&"!and tart cherries. Human-Wildlife Interactions 6:283Ð290. "&&#!Lindell, C. A., K. S. Steensma, P. D. Curtis, J. R. Boulanger, J. E. Carroll, C. Burrows, D. P. "&&$! Lusch,  N. L. Rothwell, S. L. Wieferich, H. M. Henrichs, D. K. Leigh, R. A. Eaton, and "&&%! G. M. Linz. 2016. Proportions of bird damage in tree fruits are higher in low-fruit-"&&&! abundance contexts. Crop Protection 90:40-48. "&&'! "&&(!!!57 Lock, K., J. Pomerleau, L. Causer, D. R. Altmann, and M. McKee. 2005. The global burden of "&&)! disease attributable to low consumption of fruit and vegetables: implications for the global "&&*! strategy on diet. Bulletin of the World Health Organization 83:100-108. "&'+! "&'"!Martella M. B., J. L. Navarro, J. M. Gonnet, and S. A. Monge. 1996. Diet of Rheas in an "&'#! agroecosystem of central Argentina. Journal of Wildlife Management 60:586Ð592. "&'$! "&'%!McPeek G. 2011a. Cedar Waxwing (Bombycilla cedrorum). The Second Michigan Breeding "&'&! Bird Atlas, 2002-2008. Version 1.0. Kalamazoo Nature Center: Kalamazoo, Michigan.. "&''! "&'(!McPeek G. 2011b. American Robin (Turdus migratorius). The Second Michigan Breeding Bird "&')! Atlas, 2002-2008. Version 1.0. Kalamazoo Nature Center: Kalamazoo, Michigan. "&'*! "&(+!McPherson J. 1988. Preferences of cedar waxwings in the laboratory for fruit species, colour and "&("! size: a comparison with field observations. Animal Behavior 36:961Ð969.  "&(#! "&($!Nagy, K. A., I. A. Girard, and T. K. Brown. 1999. Energetics of free-ranging mammals, reptiles, "&(%! and birds. Annual Review of Nutrition 19:247Ð77. "&(&! "&('!Northwest Michigan Horticultural Research Center (NWMHRC). 2015. NWMHRC Temperature "&((! and Rainfall Summary. Enviro-weather. Accessed 9 May 2016.  "&()! "&(*!Northwest Michigan Horticultural Research Center (NWMHRC). 2016. 2016 Growth stages Ð "&)+! NW Michigan Horticultural Research Center. Accessed 9 May 2016.  "&)"! "&)#!Oliveira P., P. Marrero, and M. Nogales. 2002. Diet of the endemic Maeira Laurel Pigeon and "&)$! fruit resource availability: A study using microhistological analyses. Condor 104:811Ð"&)%! 822. "&)&! "&)'!Peer, B. D., H. J. Homan, G. M. Linz, and W. J. Bleier. 2003. Impact of blackbird damage to "&)(! sunflower: bioenergetic and economic models. Ecological Applications 13:248-256. "&))! "&)*!Ralph, C., S. Nagata, and C. Ralph. 1985. Analysis of droppings to describe diets of small birds. "&*+! Journal of Field Ornithology 56:165Ð174. "&*"! "&*#!Rothstein, S. I. 1971. High nest density and non-random nest placement in the Cedar Waxwing. "&*$! Condor 73:483Ð485. "&*%! "&*&!Rosenberg, K.V., and R. J. Cooper. 1990. Approaches to avian diet analysis. Studies in Avian "&*'! Biology 13: 80-90. "&*(! "&*)!Royle J. A. 2004. N-mixture models for estimating population size from spatially replicated "&**! counts. Biometrics 60:108Ð115. "'++! "'+"!Saxton, V. P. 2006. To develop a robust statistical method for assessing bird damage to crops, "'+#! particularly fruit. Report to the Sustainable Farming Fund.  "'+$!!!58 Simon, G. 2008. A short overview of bird control in sweet and sour cherry orchards "'+%! possibilities of protection of bird damage and its effectiveness. International Journal  of "'+&! Horticultural Science 14:107!111. "'+'! "'+(!Smith S. B., K. H. McPherson, J. M. Backer, B. J. Pierce, D. W. Podlesak, and S. R. "'+)! McWilliams. 2007. Fruit quality and consumption by songbirds during autumn migration "'+*! fruit quality and consumption by songbirds during autumn migration. Wilson Journal of "'"+! Ornithology 119:419Ð428. "'""! "'"#!Somers, C. M., and R. D. Morris. 2002. Birds and wine grapes: foraging activity causes small"'"$! scale damage patterns in single vineyards. Journal of Applied Ecology 39:511!523. "'"%! "'"&!Tracey, J. P., M. Bomford, Q. Hart, G. Saunders, and R. Sinclair. 2007. Managing bird "'"'! damage to fruit and other horticultural crops. Bureau of Rural Sciences: Canberra, "'"(! Australia. "'")! "'"*!Tracey, J. P., and G. R. Saunders. 2010. A technique to estimate bird damage in wine grapes. "'#+! Crop Protection 29:435Ð439. "'#"! "'##!USDA National Agricultural Statistics Service: Cropland Data Layer. 2014. Published crop-"'#$! specific data layer. USDA-NASS, Washington, DC. "'#%! "'#&!USDA National Agricultural Statistics Service. 2015a. 2014-2015 Fruit Rotational Survey. "'#'! USDA-NASS, Washington, DC.  "'#(! "'#)!USDA National Agricultural Statistics Service. 2015b. Quick Stats: Sweet Cherry Price "'#*! Received Ð Michigan. USDA-NASS, Washington, DC. "'$+! "'$"!USDA Agricultural Research Service. 2016. Basic Report 09070, Cherries, sweet, raw. USDA "'$#! National Nutrient Database for Standard Reference. USDA-ARS, Washington, DC.   "'$$! "'$%!Virgo, B. B. 1971. Bird damage to sweet cherries in the Niagara Peninsula, Ontario. "'$&! Canadian Journal of Plant Science 51:415Ð423. "'$'! "'$(!Weatherhead, P. J., S. Tinker, and H. Greenwood. 1982. Indirect assessment of avian damage "'$)! to agriculture. Journal of Applied Ecology 19:773Ð782. "'$*! "'%+!Wiens, J. A., and M. I. Dyer. 1975. Simulation modelling of red-winged blackbird impact on "'%"! grain crops. Journal of Applied Ecology 12:63Ð82. "'%#! "'%$!Willson M. 1994. Fruit choices by captive American Robins. Condor 96:494Ð502. "'%%! "'%&!Witmer, M. C. 1996. Annual diet of Cedar Waxwings based on US Biological Survey records "'%'! (1885-1950) compared to diet of American Robins: Contrasts in dietary patterns and  natural "'%(! history. Auk 113:414Ð430. "'%)!!!59 World Health Organization. 2010. Global strategy on diet, physical activity and health. "'%*! Accessed 9 Sept 2016. "'&+! "'&"! "'&#! "'&$! "'&%! "'&&! "'&'! "'&(! "'&)! "'&*! "''+! "''"! "''#! "''$! "''%! "''&! "'''! "''(! "'')! "''*! "'(+! "'("! "'(#! "'($! "'(%! "'(&! "'('! "'((! "'()! "'(*! "')+! "')"! "')#! "')$! "')%! "')&! "')'! "')(! "'))! "')*! "'*+!60 CHAPTER 3 "'*"! "'*#!AMERICAN ROBINS AND CEDAR WAXWINGS VARY IN USE OF CULTIVATED "'*$!CHERRY ORCHARDS "'*%! "'*&! "'*'! "'*(!Rachael A. Eaton, Catherine A. Lindell, H. Jeffrey Homan, George M. Linz, and Brian A. Mauer "'*)!(Wilson Journal of Ornithology, 2016,128: 97-107)."'**!61 Abstract "(++! Some fruit-eating bird species commonly consume cultivated fruit. Species-specific "(+"!variation in diet preferences could result in varying use of orchards and impacts on the fruit-"(+#!producing industry by different bird species. However, species-specific studies of avian orchard "(+$!use are lacking, particularly throughout the fruit-growing season. Our objectives were to quantify "(+%!the frequency of daily bird visits to orchards and the amount of time birds spent visiting orchards "(+&!each day over the fruit-ripening season. Birds are well-documented consumers of cultivated "(+'!sweet cherries (Prunus avium), which are relatively high in sugar and low in proteins and lipids. "(+(!American Robins (Turdus migratorius) and Cedar Waxwings (Bombycilla cedrorum) are "(+)!common fruit-consumers in sweet cherry orchards. Robins often consume larger proportions of "(+*!invertebrates and prefer lipid-rich fruits, while waxwings choose sugary fruits. Given these "("+!species-specific diet differences, we hypothesized waxwings would spend a greater proportion of "(""!days and more time each day in cherry orchards than robins. We used radio telemetry to track the "("#!habitat use of 25 American Robins and 17 Cedar Waxwings in Michigan sweet cherry orchards. "("$!Over their respective radio-tracking periods, waxwings visited orchards a marginally greater "("%!percentage of days than robins (waxwings: mean = 21%, SD = 22; robins: mean = 6%, SD = 4). "("&!In addition, waxwings visited orchards for significantly more time each day. Differences in diet "("'!preferences and nutritional physiology may translate into species-specific patterns of habitat use "("(!for birds in fruit-rich environments.  "(")! "("*!Introduction "(#+! Animals are expected to forage where and when they can obtain sufficient accessible and "(#"!nutritious foods (Hengeveld et al. 2009). Orchards offer rich patches of foraging habitat with "(##!!!62 abundant food resources for birds, which lead to conflicts with orchard growers (Simon 2008). "(#$!Growers experience lost yields and often implement costly techniques to mitigate bird "(#%!consumption of crops (USDA 1998, Anderson et al. 2013). Fruit growers in New York, "(#&!Michigan, and the Pacific Northwest have reported yield losses due to birds of up to 31% in "(#'!cherries, 18% in blueberries, and 9% in wine grapes; these losses translate to tens of millions of "(#(!dollars (Anderson et al. 2013). Knowledge of foraging patterns of avian frugivores in and around "(#)!orchards can thus offer crop producers valuable information for mitigating bird consumption of "(#*!crops through targeting species and times when fruit loss is greatest (Dolbeer et al. 1994, Somers "($+!and Morris 2002, Tracey et al. 2007). For example, bird consumption of blueberries is greater in "($"!early ripening varieties; such information could allow growers to identify fruit varieties that "($#!ripen later in the season and reduce the potential for avian crop consumption (Tobin et al. 1991). "($$! Birds are well-documented consumers of cultivated sweet cherries (Prunus avium) "($%!(Curtis et al. 1994, Lindell et al. 2012), but neither the frequency nor length of bird visits to "($&!commercial cherry orchards over the growing season have been established. A limited "($'!understanding of the orchard use and behavior of cherry-consuming species has hindered our "($(!ability to develop effective management programs that minimize costs of bird activity in "($)!orchards (Tracey et al. 2007). Cherry orchards are attractive for fruit-eating birds because "($*!cherries are brightly colored, densely available, and have easily accessible pulp (Sallabanks "(%+!1993). Cherries are relatively sugar-rich fruits; >50% of dry pulp mass comprises sugars (Witmer "(%"!and Van Soest 1998, USDA 2013b). However, the lipid and protein contents among Prunus "(%#!fruits (including cherries) are relatively low (<2%) compared to the protein content of other fruits "(%$!(5%) or insects (Stiles 1993, Witmer and Van Soest 1998). Fruit consumption by birds is "(%%!behaviorally and physiologically complex (Sallabanks 1993, Levey and Martinez del Rio 2001, "(%&!!!63 Corlett 2011). Birds can discern nutritional differences among food types and make foraging "(%'!decisions to meet energetic and nutritional needs (Lepczyk et al. 2000, Schaefer et al. 2003, Alan "(%(!et al. 2013). Therefore, cherries may not appeal equally to all birds.  "(%)! American Robins (Turdus migratorius) and Cedar Waxwings (Bombycilla cedrorum) "(%*![hereafter robins and waxwings] are highly frugivorous (Wheelwright 1986, Witmer 1996). "(&+!These species are also responsible for a relatively high proportion of observed avian cherry "(&"!consumption compared to other species, e.g. American Crow (Corvus brachyrhynchos), "(&#!Common Grackle (Quiscalus quiscula), European Starling (Sturnus vulgaris; Lindell et al. "(&$!2012). Fruit comprises ~57% of the annual diet of robins, in addition to large proportions of "(&%!invertebrates, while waxwingsÕ diet contains ~84% fruit (Witmer 1996). In addition, robins more "(&&!efficiently assimilate and prefer fruits that are relatively high in proteins and lipids, and low in "(&'!sugars (Stiles 1993, Willson 1994, Witmer and Van Soest 1998, but see Lepcyzyk et al. 2000). "(&(!Waxwings more efficiently assimilate and show a preference for high-sugar fruits (Witmer and "(&)!Van Soest 1998, Witmer 1998). Furthermore, waxwings maintain body mass on fruit alone for "(&*!extended periods, for example up to 27 days (Holthuijzen and Adkisson 1984), despite fruitÕs "('+!relatively low protein content. In contrast, Levey and Karasov (1989) attempted a 10-day fruit-"('"!only feeding trial on captive robins but shortened the trial to four days as birds had already lost "('#!10-14% of initial body mass. In addition, waxwings feed nestlings insects for approximately "('$!three days (Putnam 1949), after which fruit comprises 87% of food deliveries (Lea 1942), while "('%!robins provision almost exclusively with animal matter (Hamilton 1935), but older nestling "('&!robins may also receive fruit (Eaton 1914). Our previous work has revealed relatively little use of "(''!cherry orchards by robins and waxwings for invertebrate consumption and nesting activities, "('(!therefore we focused this study on fruit consumption. "(')!!!64 These species-specific patterns likely influence the frequency and length of foraging "('*!visits in orchards during the cherry-growing season. For certain frugivorous birds, orchards may "((+!become increasingly attractive as fruits ripen because sugar content increases and bird energy "(("!needs may be met more efficiently (Serrano et al. 2005). Furthermore, after harvest, orchards "((#!may no longer be viable foraging habitat for avian frugivores, given the near-complete removal "(($!of fruit from trees (<10% of cherries remaining, M. Whiting, Personal communication).  "((%!Here, we used radio-telemetry to evaluate the use of cultivated sweet cherry orchards in "((&!Michigan by robins and waxwings. We hypothesized that waxwings would exhibit more intense "(('!use of cherry orchards than robins based on their diets. We predicted that waxwings would 1) "(((!visit focal orchards on more days throughout the cherry season, and 2) spend more time each day "(()!visiting orchards than robins. Further, given that sugar content increases as cherries ripen, we "((*!expected that 1) robins and waxwings would increase their use of orchards as harvest "()+!approached, and 2) that orchard use by both species would decline abruptly after cherries were "()"!harvested and fruit availability declined. For both of these expectations, we predicted a stronger "()#!effect for the more fruit-specialized waxwings. "()$! "()%!Methods "()&!Study Area & Species "()'!We conducted the study in four sweet cherry orchards in Leelanau County, near Traverse City "()(!(44¡ 46Õ N, 85¡ 37Õ W), in northwest Michigan from June - September 2013 (Figure 3.1). "())!Leelanau County is a peninsula (land area = 900 km2, water area = 5659 km2 ) extending into "()*!Lake Michigan and an agricultural region comprising many orchards (e.g. sweet and tart "(*+!cherries, wine grapes, apples). As of 2012, orchards comprised 6% of the county land area, with "(*"!!!65 sweet cherry orchards accounting for 2% of the county land area (USDA 2012). Other major "(*#!crops and land cover types include alfalfa, mixed forests, and residential or developed areas "(*$!(Lindell et al. 2012). The average rainfall during the 2013 fruit-growing season (April Ð October) "(*%!was 54.9 cm (Northwest Michigan Horticultural Research Station 2013).  "(*&! "(*'!Figure 3.1 Map of Leelanau County study region and focal sweet cherry orchards. Map of "(*(!Michigan with Leelanau County identified (left). Leelanau County with Traverse City and the "(*)!four sweet cherry study orchards identified (right). "(**! ")++! The mean distance between study sites was 5.1 km (range = 1.4 Ð 10.4 km). One site was ")+"!located at the Northwest Michigan Horticultural Research Station (STA) and Cherry Bay (CB) ")+#!Orchards, Inc. managed three sites (Table 3.1). In the study region, sweet cherry trees typically ")+$!reach full bloom in early May; small green fruits are evident 20 days later, and cherries begin ")+%!ripening 50 - 60 days after full bloom. Growers apply a variety of insecticides depending upon ")+&!the target pest species, fruit growth stage, and product availability (W. Klein, Personal ")+'!communication). Products vary in required application frequency (e.g. from three days up to two ")+(!weeks). Michigan State University Extension provides recommendations for insecticide use to ")+)!commercial fruit producers in the region. Orchard managers prune trees and mow grass ")+*!occasionally throughout the growing period. ")"+!!!66 In Michigan, robins are abundant during the breeding season and typically arrive in ")""!March; most robins do not overwinter (McPeek 2011a). Robins begin nesting in April and May ")"#!and commonly rear two broods (Howell 1942, McPeek 2011a). Waxwings are common in the ")"$!study region where they travel and forage in small flocks year-round (McPeek 2011b). ")"%!Waxwings generally arrive in Michigan by late May (although some overwinter). They are ")"&!among the latest nesting birds in North America and nest in colonies in trees of various species ")"'!including maple (Acer spp.), oak (Quercus spp.), and pine (Pinus spp.; Lea 1942, Putnam 1949, ")"(!Rothstein 1971). The majority of nesting occurs between mid-June and August (McPeek 2011b). ")")!Orchard growers in the study region do not remove nests from cherry trees during the growing ")"*!season (growers may remove old nests during the winter; W. Klein, Personal communication).  ")#+! ")#"!Capture & Radio Deployment ")##! We captured birds via mist nets in each study orchard and radio-tagged adult robins and ")#$!waxwings between 1 June and 15 July. We typically opened nets by 0700 and closed them by ")#%!1600 EDT. We aged and weighed birds and sexed them using external breeding characteristics ")#&!(i.e. presence of brood patch or cloacal protuberance; Pyle 1997). Waxwings did not exhibit ")#'!breeding characteristics and could not be reliably sexed. We fitted a metal band, plastic colored ")#(!bands, and an A1055 radio transmitter from Advanced Telemetry Systems (Isanti, Minnesota, ")#)!USA) on 25 robins and 17 waxwings that appeared in good condition and were sufficiently ")#*!massive to wear the 0.9 g radio transmitter and harness (<3% of bird body mass). We used 1-mm ")$+!elastic cord and the figure-eight leg-harness method to attach transmitters (Rappole and Tipton ")$"!1991). Transmitters broadcasted at a pulse rate of 30-34 pulses per min within a frequency range ")$#!of 164.00Ð165.66 MHz. Expected battery life was 50 - 60 days. After radio deployment, we gave ")$$!!!67 birds a two-day acclimation period to permit a return to normal behavior prior to data collection. ")$%!Radio-tags from two waxwings were recovered during the study; we suspected both birds were ")$&!depredated. We had valid orchard use data from both individuals and included these data in ")$'!analyses. ")$(! ")$)!Data Collection ")$*! To track orchard use, we placed one stationary data receiving system in each study ")%+!orchard, away from objects that could dampen or block incoming signals. We installed receiving ")%"!systems between 15 June and 20 June 2013 and retrieved them on 11 September 2013. To ")%#!assemble stationary systems, we encased a programmable, R4550 data-logging signal receiver ")%$!from Advanced Telemetry Systems (Isanti, Minnesota, USA) powered by a deep-cycle battery in ")%%!a large plastic container and cabled the receiver to a six-element Yagi antenna bolted to an ")%&!elevated mount of 3-m height (Homan et al. 2013). Prior to data collection, we time and date ")%'!synchronized all receivers. ")%(!The receivers scanned through a list of all radio frequencies associated with birds, ")%)!remaining on each frequency for 6 sec, for 24 hr per day throughout the study. If a frequency was ")%*!detected during these 6 sec, the receiver monitored that frequency for 50 sec and recorded the ")&+!date, time, and strength of the strongest signal (a function of distance between the receiving ")&"!antennae and a birdÕs transmitting antenna) detected for that bird during the 50 sec. The receiver ")&#!then continued to scan through the list of frequencies. To promote independence among data for ")&$!a given bird, receiving systems ultimately stored only the data record (if one was made) with the ")&%!strongest signal detected over every 10-min period throughout the day. This record also included ")&&!the number of radio pulses recorded for that bird during the 10-min period. The number of radio ")&'!!!68 pulses reflected the number of times (i.e. for how long) a birdÕs transmitter emitted the signal ")&(!during the 10-min period. The strength of a birdÕs signal did not affect the receiverÕs ability to ")&)!detect other birds in the area. If a particular frequency was not detected, the receiver scanned for ")&*!the next frequency. To ensure that radio-tagged birds were still in the region during the study, we ")'+!searched the area 5 -7 days per week using a pickup truck with a roof-mounted, rotatable set of ")'"!dual 6-element Yagi antennae. The tracking periods of individual birds could include days on ")'#!which birds were not detected using orchards, but were located in the study region during mobile ")'$!searches.  ")'%! ")'&!Data Preparation ")''! Telemetry data receiving systems can detect false signals from objects (e.g. solar flares, ")'(!power lines, garage door openers) with frequencies similar to those in our study. We used Visual ")')!Basic for Applications with Excel to cull false records and extract valid data for analysis. Valid ")'*!data were those with associated pulse rates of 28Ð34 pulses per min; this range accounted for ")(+!fully functioning radio transmitters, as well as slower pulsing radios whose batteries had ")("!weakened. We determined a bird was using an orchard if the receiver recorded a signal strength ")(#!#140 (maximum radio signal strength was 155). We conducted calibrations at each study site ")($!prior to deployment and determined that a signal of  #140 would only register if a bird were in a ")(%!study orchard. To calibrate orchard use, we affixed a radio transmitter to a 2 m-long pole, stood ")(&!with the transmitter extended into a tree to simulate a bird at 4Ð6 locations in each orchard, and ")('!recorded the signal strength detected by the receiving system at each location. The line of sight ")((!receiving distance of the stationary systems was $0.05 km (Homan et al. 2103).   ")()! ")(*!!!69 Day-to-day & Within-day Orchard Use "))+! We evaluated bird use of cherry orchards in two ways. First, we quantified day-to-day "))"!orchard use by calculating the proportion of days a bird was within a focal orchard out of the "))#!total number of days in the birdÕs tracking period (defined as the first day after the birdÕs "))$!acclimation period through the last day a bird was detected in the study region; Equation 1). We "))%!defined a day as only the daylight period (one hr before sunrise through one hr after sunset). We "))&!calculated one day-to-day orchard use value for each bird. Second, we quantified within-day "))'!orchard use to determine the amount of time birds visited focal orchards on a given day "))(!(Equation 2). To quantify within-day orchard use, we first identified the length of the daylight ")))!period for each day of the study and divided this period into 10-min time blocks. We then "))*!quantified the number of 10-min time blocks in which a bird was in an orchard on a given day ")*+!and divided this by the total number of 10-min blocks of that day (Equation 2). An individual ")*"!could have multiple within-day orchard use values. We report values for day-to-day and within-")*#!day orchard use as percentages.  ")*$!Equation (1) !"#!!"!!"#!!"#!!"#!!"#!!"#$%&!!"!!"#$!!"#$!!"#"$#"!!!"!!"#!!"#!"#$%&!!"!!"#!!!!!"!#$!!"#$%&'(!!"#$ ")*%!Equation (2) !"#!!"!!"#!!"#!!"#!!"#!!"#$%&!!"!!"!!"#!"#$!!"#$%&!!"!!"#!!"#!!"!!"#!!"#$!"#$%&!!"!!"#$!!"#$%&!!"!!"#!!!!!"#$%&!!!!"#$%& ")*&! ")*'!Statistical Analyses ")*(! The proportion values for day-to-day orchard use were right-skewed so we applied a ")*)!logarithmic transformation. We approximated the log-transformed day-to-day orchard use data ")**!using a normal distribution with equal variances, which satisfied assumptions for a two-sample "*++!StudentÕs t-test. We determined if day-to-day orchard use data from male and female robins "*+"!could be pooled. To account for sample size differences in males (9) and females (3), we used "*+#!!!70 boot-strapping techniques to select three samples from males at random, with replacement, to "*+$!compare with females. We ran 1000 iterations of the sampling and t-test procedures and applied "*+%!the false discovery rate approach to correct % for multiple statistical comparisons (Benjamini and "*+&!Hochberg 1995). We used a two-sample StudentÕs t-test to evaluate differences in the day-to-day "*+'!orchard use of robins and waxwings. "*+(! We constructed generalized linear mixed models (GLMM) with a binomial distribution, a "*+)!logit link function, and bird as a random effect to analyze within-day orchard use. We first used a "*+*!GLMM with sex as a fixed effect and individual bird as a random effect (to account for some "*"+!birds having multiple within-day orchard use values) to determine whether male and female "*""!within-day orchard use data could be pooled. A binomial distribution was appropriate for the "*"#!within-day use data because we calculated these values as the proportion of 10-min time blocks "*"$!in a given day in which a bird was in an orchard. The total number of time blocks in a day served "*"%!as the number of trials for these models. We constructed 10 GLMMs to assess the effects of "*"&!species and days-to-harvest on within-day orchard use (Table 3.2). We calculated the variable "*"'!days-to-harvest for each date by subtracting this date from the harvest date of the relevant "*"(!orchard. We included the orchard in which a bird was detected by a receiving system as a "*")!covariate for which we had no a priori expectation but which might have confounded the "*"*!influence of days-to harvest. We used the Akaike Information Criterion corrected for small "*#+!sample sizes (AICc) for model selection; we identified the best-fit model as that with &AIC "*#"!value < 2 (Burnham and Anderson 2002). We performed analyses in R statistical software (R "*##!Core Team 2012), using Ôlme4Õ (Bates et al. 2014) and ÔAICcmodavgÕ (Mazerolle 2013) "*#$!packages. "*#%! "*#&!!!71 Results "*#'!Study Demographics "*#(! We outfitted 25 robins (16 males, 9 females) and 17 waxwings (sexes unknown) with "*#)!radio transmitters (Table 3.1).  "*#*!Table 3.1 Study orchard area, 2013 harvest date, and land cover types adjacent to study "*$+!orchards. The number and sexes (M: male, F: female, U: unknown) of American Robins and "*$"!Cedar Waxwings caught at each sweet cherry orchard. Adjacent land covers (within 25 m of "*$#!orchard edge) were assessed visually at each site as part of a related study in 2013. "*$$! "*$%!Among all radio-tagged birds, we did not detect 19 individuals (six male and seven female robins "*$&!and six waxwings) in any of our study orchards after initial capture. Our sample population for "*$'!analyses was comprised of 12 robins and 11 waxwings that used orchards STA, CB1, and CB3Ñ"*$(!no birds used CB2. StudentÕs t-tests with bootstrapping showed no difference between male and "*$)!female robins for day-to-day orchard use (t = -0.50, P = 0.94). The GLMM with sex as the fixed "*$*!effect and individual bird as a random effect suggested that within-day orchard use did not differ "*%+!between male and female robins (z = 0.32, P = 0.75). Therefore, we pooled data for the two "*%"!sexes.  "*%#!Orchard Site Area Harvest Adjacent land cover types Robins Waxwings STA 3.84 ha 19 July Tart cherry, mowed grass, non-fruit crops, herbaceous (< 1 m tall) 5 M, 3 F 8 U CB1 2.6 ha 11 July Tart cherry, mowed grass, herbaceous (< 1 m tall) 4 M, 3 F 3 U CB2 11.6 ha 10 July Mowed grass, paved road, sweet cherry, non-fruit crops 2 M  CB3 0.4 ha 9 July Tart cherry, paved road, herbaceous (< 1 m tall) 5 M, 3 F 5 U !!72 Day-to-day and Within-day Orchard Use "*%$! Among robins and waxwings who used cherry orchards (n = 23), we detected individuals "*%%!somewhere in the study region for a mean of 40.8 d (SD = 19.5), while birds visited focal "*%&!orchards for a mean of 3.3 d (SD = 3.0), or 13% (SD = 17). Waxwings visited orchards on a "*%'!marginally greater percent of days throughout the season (mean = 21%, SD = 22) than robins "*%(!(mean = 6%, SD = 4; t = - 1.97 on log-transformed data, df = 21, P = 0.063; Figure 3.2).  "*%)! "*%*!Figure 3.2 Percent of days American robins and cedar waxwings visited cherry orchards "*&+!relative to their respective tracking periods. Waxwings visited orchards for a greater "*&"!proportion of their total tracking days than robins. Data represent day-to-day use values (n = 23). "*&#!Black squares represent sample means. Data are untransformed and are jittered for visual clarity.  "*&$! "*&%! The best-fit model of within-day orchard use, according to AICc selection criteria, "*&&!included species as the fixed effect and individual bird as a random effect (R2 = 0.46; Table 3.2). "*&'!Waxwings visited orchards a mean of 5% (SD = 6) of the daylight period in orchards, while "*&(!robins visited a mean of 2% (SD = 2). Waxwings spent significantly more time visiting orchards "*&)!!!73 per day than robins (species = 0.88, SE = 0.31, P = 0.005; Figure 3.3). Individual variation "*&*!among birds explained 34% (SD = 0.58) of the variance in within-day orchard use.  "*'+!Table 3.2 Generalized liner mixed models exploring the relationships between within-day "*'"!orchard use of American robins and cedar waxwings relative to species, orchard, and days-"*'#!to-harvest. Parameter numbers (K), deviance (Dev), AICc, &AICc, and model weight (wi) "*'$!values are also included. All models also included individual bird as a random effect. The star "*'%!symbol denotes a two-way interaction term between covariates.   "*'&! "*''!Model K Dev AICc &AICc wi Within-day orchard use~      Species 3 382.7 389.9 0.0 0.64 Days-to-harvest + Species 4 382.3 392.6 2.6 0.17 Orchard + Species 5 380.9 394.4 4.5 0.07 Days-to-harvest 3 387.3 394.6 4.7 0.03 Orchard 4 385.9 396.1 6.2 0.03 Days-to-harvest + Orchard + Species 6 380.8 398.1 8.2 0.01 Days-to-harvest + Orchard 5 385.5 399.1 9.1 0.01 Days-to-harvest + Orchard + Species + Species*Orchard 8 374.3 400.5 10.6 0.00 Days-to-harvest + Orchard + Species +  Species*Days-to-harvest 7 380.1 401.5 11.6 0.00 Days-to-harvest + Orchard + Species + Species*Orchard + Species*Days-to-harvest 9 374.0 405.9 15.9 0.00  "*'(! "*')! "*'*!!!74  "*(+! "*("!Figure 3.3 Percent of the daylight period that American robins and cedar waxwings visited "*(#!cherry orchards on a given day. Waxwings visited orchards for more time each day than "*($!robins. Data represent within-day use values (n = 77) from a study population of 23 individual "*(%!birds. Black squares represent sample means. Data are jittered for visual clarity.  "*(&!  "*('!The days-to-harvest and orchard covariates did not appear in the best-fit model of within-day "*((!orchard use (Table 3.2). The days-to-harvest covariate was retained in the second best model of "*()!within-day orchard use (&AIC = 2.6). This model suggested a decreasing trend in within-day "*(*!orchard use as harvest approached, however this was not significant (days-to-harvest = -0.004, z "*)+!= -0.57, P = 0.57). Orchard use at the STA orchard declined slightly up to 20 days before "*)"!harvest; orchard use then remained constant until after harvest (Figure 3.4). Orchard use at CB1 "*)#!and CB3 was seemingly constant across the entire study period (Figure 3.4).  "*)$!!!75  "*)%!Figure 3.4 Percent of the daylight period that American robins and cedar waxwings visited "*)&!cherry orchards on a given day relative to days-before-harvest. Data are separated by "*)'!orchard (STA, CB1 and CB3). Data represent within-day use values (n = 77) from a study "*)(!population of 23 birds. Dashed lines represent the first possible date of valid data collection for "*))!birds radio-tagged at each orchard. On the x-axis, zero represents the date of harvest for a given "*)*!site. "**+! "**"!Discussion "**#!Robins and waxwings differ in fruit preference, nutritional requirements, and physiology "**$!(Levey and Karasov 1989, Witmer and Van Soest 1998). These differences translated into "**%!species-specific patterns of orchard use. "**&! "**'!Day-to-day Orchard Use "**(! Our metric of day-to-day orchard use showed a marginally significant trend (P = 0.063) "**)!suggesting that waxwings visited orchards a higher percentage of days throughout their tracking "***!periods than robins. Fruit comprises a larger proportion of waxwing annual diets (Witmer 1996), #+++!!!76 and, thus, waxwings take greater advantage of the abundant supply of cherries over the growing #++"!season than robins. Robins, although predominantly frugivorous, typically consume and #++#!provision nestlings with large proportions of animal matter during the summer, while fruit #++$!consumption is higher in fall and winter (Wheelwright 1986). Therefore, a sweet cherry orchard #++%!may not be as valuable of a foraging habitat for robins seeking proteinaceous foods like insects #++&!and annelids, compared to waxwings. While it is possible that robins forage for non-cherry foods #++'!while visiting cherry orchards, 165 hrs of foraging observations yielded only six instances of #++(!robins consuming invertebrates but dozens of instances of cherry consumption (R. Eaton, #++)!Personal observation). Additionally, growers in focal orchards used insecticides regularly to #++*!diminish insect populations, potentially further limiting the value of cherry orchards for foraging #+"+!robins. However, more study is needed to determine the relative proportions of fruit and #+""!invertebrates in the diet of robins in fruit orchards. Individual variation may also be a contributor #+"#!to the patterns of day-to-day orchard use, as three individual waxwings appear to drive the #+"$!relatively high day-to-day orchard use of waxwings.  #+"%! Our hypothesized difference in day-to-day orchard use between robins and waxwings #+"&!was marginally significant, which could arise if robins used orchards more than expected while #+"'!waxwings used them less. Robins may have used orchards more than predicted if orchards were #+"(!near nesting sites, e.g. in windbreaks around orchards (Yahner 1982). In comparison, waxwing #+")!use of orchards may not have been particularly high if late-nesting waxwings were not yet tied to #+"*!a breeding territory (Putnam 1949), and freer to travel among foraging patches than breeding #+#+!robins. We conducted a preliminary, systematic search for nests in the study region that revealed #+#"!very few, thus it is unlikely that robins and waxwings used cherry orchards for nesting.  #+##! #+#$!!!77 Within-day Orchard Use #+#%! Waxwings spent significantly more time visiting focal orchards than robins on a given #+#&!day. Outside of the cultivated-fruit growing season, robins and waxwings also show differential #+#'!timing of within-day fruit-foraging behavior (Chavez-Ramirez and Slack 1994). Wintering #+#(!robins and waxwings in Texas spent 5 h and 8 h per day, respectively, feeding on Juniper berries #+#)!(Juniperus ashei; Chavez-Ramirez and Slack 1994). Furthermore, once nesting is underway, #+#*!waxwings may spend more time visiting orchards than robins on a given day to gather fruit for #+$+!nestlings. Waxwings provision chicks primarily with fruit and begin doing so as early as day #+$"!three after hatching (Putnam 1949), whereas robins do not provision with fruit until chicks are #+$#!older (Howell 1942 and references therein). Future studies could address the potential influence #+$$!of nesting phenology on frugivore orchard use by tracking breeding status, nesting, and brood #+$%!rearing throughout the fruit-growing season.  #+$&!Unexpectedly, we did not detect an influence of days-to-harvest on the amount of time #+$'!birds spent visiting orchards. As cherries ripened, we expected birds to spend more time visiting #+$(!orchards each day. In contrast to expectations, a temporal decline in within-day orchard use as #+$)!harvest approached was evident for the STA orchard only. STA is a research orchard with #+$*!dozens of sweet cherry varieties, including some early ripening. Unlike other orchards in the #+%+!region, multiple trees at STA had red and ripening fruit when our study began. Therefore, STA #+%"!may have attracted birds early in the fruiting season (Nelms et al. 1990, Tobin et al. 1991). #+%#!Cultivated orchards may provide fruit-eating birds with the majority ofÑor onlyÑfruit options #+%$!during this time. For example, waxwings consumed substantial proportions of early-ripening #+%%!varieties of cultivated blueberries compared to later-ripening blueberries (Nelms et al. 1990).  #+%&!!!78 Contrary to our expectations, birds used orchards after harvest. After harvest, some fruits #+%'!remain on the trees (<10% of the pre-harvest amount; M. Whiting, Personal communication) and #+%(!ground. Remaining fruits are still numerous, accessible, and visually appealing, and could attract #+%)!frugivorous birds (Sallabanks 1993, USDA 2013a). The first author observed robins consuming #+%*!cherries in trees and on the ground in post-harvest orchards in the study region (R. Eaton, #+&+!personal observation). In addition, as late-season nesters waxwings are likely provisioning #+&"!offspring around or after cherry harvest. Remaining fruit in post-harvest orchards may continue #+&#!to serve as important waxwing foraging habitat. If focal orchards were near nesting sites and #+&$!within the regular foraging ranges of radio-tagged birds, post-harvest orchard use may reflect #+&%!birdsÕ tendencies to forage close to nests (Swihart and Johnson 1986). Birds may also have #+&&!continued to use orchards after cherry harvest to forage for food unrelated to harvest (e.g. #+&'!invertebrates), however most cherry growers apply a post-harvest insecticide spray which may #+&(!limit insect availability (W. Klein, Personal communication).  #+&)!Nearly half of our radio-tagged birds were detected in the area throughout the study but #+&*!never used study orchards after the initial capture and acclimation period. Birds may have been #+'+!captured as they traveled through an otherwise unused orchard. The home range sizes of robins #+'"!are not well documented (Vanderhoff et al. 2014), although breeding robins have been known to #+'#!forage up to 300 m from their nests (Knupp et al. 1977). Given the distance among our focal #+'$!orchards (#1.4 km) and their typical area (mean = 4.6 ha; SD = 4.9), robins captured at one #+'%!orchard would not likely be detected using another, and robin foraging ranges in our study region #+'&!do not likely contain more than a couple cherry orchards. To our knowledge, the home range #+''!sizes of waxwings are unknown. We did not find any radio-tagged birds using multiple focal #+'(!orchards. Alternatively, captured birds may have avoided orchards if the capture experience itself #+')!!!79 served as a deterrent. For instance, we captured two male robins at CB2, but neither returned to #+'*!that orchard. However, we later detected both individuals using the nearby STA orchard. #+(+!While we documented the frequency and length of visits to focal orchards, the extent to #+("!which robins and waxwings used other cherry orchards in our study region is unknown. Given #+(#!that cherry orchards are widespread in the study region, it is probable that robins and waxwings #+($!used non-focal orchards during the study period. However, the number of orchards visited is #+(%!difficult to estimate given limited of knowledge of robin and waxwing home ranges. If home #+(&!ranges of robins and waxwings are large relative to orchard size, our study provides a #+('!conservative picture of avian use of cherry orchards in an orchard-rich landscape. If home ranges #+((!are relatively small, it is possible that birds used non-focal orchards very little. Thus, our results #+()!suggest that orchards might not be predominant foraging habitat for frugivorous birds in this #+(*!region. These uncertainties invite further study, particularly to track and evaluate avian habitat #+)+!use in orchard landscapes with a more detailed resolution (e.g. GPS-tracking) to determine #+)"!frequency of use of non-orchard habitat and home ranges of prominent avian frugivores.  #+)#!Bird use and consumption of agricultural crops is often viewed as problematic #+)$!(Weatherhead et al. 1982, Anderson et al. 2013). However, avian use of these habitats may also #+)%!provide ecosystem services to growers (Whelan et al. 2008) both before and after harvest. Before #+)&!harvest, avian consumption of crop-damaging invertebrates can increase the yield of cultivated #+)'!crops (Mols and Visser 2002). For example, great tits (Parus major) reduced caterpillar #+)(!consumption on cultivated apples and increased fruit yield (Mols and Visser 2002). After #+))!harvest, fruit often remains on the ground and our study showed that birds continue to visit post-#+)*!harvest orchards. Fruit remaining after harvest can serve as vectors for infections such as #+*+!American brown rot (Monilinia fructicola). This fungus can over-winter in fruits that have fallen #+*"!!!80 to the ground and inoculate infections during the following spring (Bush et al. 2009). Avian post-#+*#!harvest consumption of cherries on the ground could reduce remaining fruits and limit the spread #+*$!of infection. This and other potential benefits of avian fruit consumption deserve further study.  #+*%!This work demonstrates that two prominent avian fruit-eating species differ in how intensely #+*&!they use orchards over the fruit-growing season, likely as a result of the differences in their food #+*'!preferences and reliance on fruit. While waxwings visit orchards on a greater proportion of days #+*(!and spend more time within orchards each day than robins, waxwings also consume a relatively #+*)!high proportion of fruit than robins and other fruit eating species e.g. Common Grackles #+**!(Quiscalus quiscula) and European Starlings (Sturnus vulgaris; Lindell et al. 2012). In addition, #"++!waxwings are more likely to forage in groups than robins (Lindell et al. 2012) and have faster #"+"!sugary-fruit assimilation rates than thrushes (Witmer and Van Soest 1998). Robins typically eat #"+#!relatively little fruit during the breeding season than other times of year, yet showed higher fruit #"+$!consumption when foraging around orchards than in less-fruiting habitats like meadows #"+%!(Wheelwright 1986). Therefore, cherry orchards may serve as more important foraging habitat #"+&!for more-frugivorous birds like waxwings than birds requiring proteinaceous resources, like #"+'!robins. More work is needed to evaluate the extent to which birds foraging in orchards consume #"+(!fruit versus other food sources like invertebrates.  #"+)! #"+*! #""+! #"""! #""#! #""$! #""%!81 LITERATURE CITED#""&!82 LITERATURE CITED #""'! #""(!Alan R.R., S.R. McWilliams, K.J. McGraw. 2013. The importance of antioxidants for avian fruit #"")! selection during autumn migration. Wilson Journal of Ornithology 125:513-525. #""*! #"#+!Anderson A., C.A. Lindell, K.M. Moxcey, W.F. Siemer, G.M. Linz, P.D. Curtis, J.E. #"#"! Carroll, C.L. Burrows, J.R. Boulanger, K.M.M. 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The ##'"!birds of North America. Number 309. ##'#! ##'$!Weatherhead P.J., S. Tinker, H. Greenwood. 1982. Indirect assessment of avian damage to ##'%!agriculture. Journal of Applied Ecology 19:773Ð782. ##'&! ##''!Wheelwright N. 1986. The diet of American Robins: an analysis of U.S. Biological Survey ##'(!records. Auk 103:710Ð725. ##')! ##'*!Whelan C.J., D.G. Wenny, R.J. Marquis. 2008. Ecosystem services provided by birds. Annals of ##(+!the New York Academy of Sciences 1134:25Ð60. ##("! ##(#!Willson M. 1994. Fruit choices by captive American Robins. Condor 96:494Ð502. ##($! ##(%!Witmer M.C. 1996. Annual diet of Cedar Waxwings based on U.S. Biological Survey records ##(&!(1885-1950) compared to diet of American Robins: contrasts in dietary patterns and natural ##('!history. Auk 113:414Ð430.  ##((! ##()!Witmer M.C. 1998. Ecological and evolutionary implications of energy and protein ##(*! requirements of avian frugivores eating sugary diets. Physiological Zoology ##)+! 71:599Ð610. ##)"! ##)#!Witmer M.C. and P.J. Van Soest. 1998. Contrasting digestive strategies of fruit-eating birds. ##)$!Functional Ecology 12:728Ð741. ##)%!Yahner R.H. 1982. Avian nest densities and nest-site selection in farmstead shelterbelts.  ##)&! Wilson Bulletin 94:156Ð175. ##)'! ##)(! ##))! ##)*!86 CHAPTER 4 ##*+!FOOD ABUNDANCE AT MULTIPLE SPATIAL SCALES INFLUENCES ##*"!FORAGING BEHAVIORS ##*#! ##*$!Rachael A. Eaton and Catherine A. Lindell  ##*%!87 Abstract ##*&! Animals forage in habitats where food abundance varies at multiple spatial scales; ##*'!relative resource abundance between hierarchical spatial scales likely influences within-patch ##*(!foraging. We evaluated influences of fruit abundance at multiple spatial scales and sociality on ##*)!avian behavior in sweet cherry orchards (Prunus avium). We observed omnivorous American ##**!robins (Turdus migratorius) and frugivorous cedar waxwings (Bombycilla cedrorum) and #$++!quantified cherry abundance at three spatial scales. Fruit abundance across multiple scales #$+"!interacted to influence patch residence time and proportion time feeding at sweet cherry trees; #$+#!these patterns differed between species. Fruit abundance at the tree scale relative to that of the #$+$!orchard did not affect patch residence time; however, waxwings at trees with little food had a #$+%!greater proportion time feeding within low-fruit orchards than within high-fruit orchards. As fruit #$+&!abundance increased in an orchard, robins decreased proportion time feeding regardless of fruit #$+'!abundance at the landscape scale; waxwings decreased proportion time feeding only in high-fruit #$+(!landscapes. Fruit abundance at large spatial scales influenced robin patch residence time and #$+)!waxwing proportion time feeding more strongly for birds in large foraging groups than for those #$+*!in small groups. Differences in robin and waxwing behaviors may be explained, in part, by #$"+!species differences in the degrees of frugivory and sociality; waxwings are more frugivorous and #$""!typically forage in larger groups than robins. Our study is among the first to demonstrate that #$"#!avian behaviors are influenced by food abundance across multiple spatial scales, including large #$"$!scales.   #$"%! #$"&! #$"'! #$"(!!!88 Introduction #$")! Animals forage in heterogeneous and spatially complex environments where food #$"*!abundance and distribution can vary spatially and at multiple spatial scales (MacArthur and #$#+!Pianka 1966, Levin 1992, Johnson et al. 2001, Garcia and Ortiz-Pulido 2004, Butler et al. 2005). #$#"!Thirty years ago in their seminal review, Senft et al. (1987) proposed that traditional applications #$##!of foraging models prove difficult for animals foraging on broadly distributed resources because #$#$!heterogeneity in food abundance can occur at scales beyond just the patch itself, defined as a #$#%!discrete aggregation of resources. For highly mobile species able to forage over large areas, the #$#&!relative availability of resources at hierarchical spatial scales likely influences foraging behavior #$#'!(Sallabanks 1993, Ritchie 1998, Searle et al. 2006, Searle et al. 2008). For instance, behavioral #$#(!decisions may be affected by food abundance at the scale of a tree (patch), a forest of many trees #$#)!(a habitat area comprised of multiple patches), or a landscape (a region of non-contiguous #$#*!foraging habitats). Understanding and accounting for this spatial hierarchy in resource #$$+!distribution when investigating foraging behaviors remains a key challenge in ecology (Senft et #$$"!al. 1987, Kotliar and Wiens 1990, Thompson et al. 2001, Searle et al. 2005, Searle et al. 2006).  #$$#! Although ecologists are aware of the influence of resource availability at multiple spatial #$$$!scales on animals (Wiens 1989, Kotliar and Wiens 1990, Caro and Sherman 2011), research has #$$%!typically focused on demography (Richmond et al. 2012), reproduction (Cornell and Donovan #$$&!2010), and population-level responses such as abundance and distribution (Lima and Zollner #$$'!1996, Olsson et al. 2000, Moegenburg and Levey 2003, Withey and Marzluff 2008, Pickett and #$$(!Sirwardena 2011). The influence of multi-scale resource availability on behavioral responses is #$$)!much less studied. Searle et al. (2006) studied captive mammalian herbivores in an experimental #$$*!system with heterogeneous resource distribution at hierarchical scales. They demonstrated that #$%+!!!89 patch residence time was influenced by heterogeneity in resource abundance at multiple spatial #$%"!scales. However, the effect of food availability across spatial scales on the behavior of other #$%#!highly mobile foragers in a non-captive setting has not been assessed. In order to understand the #$%$!effects of resource heterogeneity across multiple spatial scales on wild foragers, we studied the #$%%!behavior of fruit-eating birds foraging in an agricultural environment.  #$%&! Our current understanding of the relationship between wild foraging birds and food #$%'!availability is generally limited to the positive correlations between bird abundance and food #$%(!availability measured at different spatial scales (Withey and Marzluff 2008, Moorman and #$%)!Bowen 2012). These relationships can vary with spatial scale (Garcia and Ortiz-Pulido 2004). #$%*!Yet, studies of the relationships between food abundance and avian foraging behaviors are #$&+!typically approached from a single spatial scale (e.g. Goss-Custard et al. 2006), despite the #$&"!hierarchical nature of foraging decisions (Stephens 2008). Fruit-eating birds forage in particular #$&#!patches (e.g. a tree) but travel widely throughout foraging habitats and regions (Price 2006). #$&$!Compared to birds in other foraging guilds (e.g. insectivores), fruit-eating birds have relatively #$&%!high movement activity (Neuschulz et al. 2012) and make foraging decisions at multiple scales #$&&!(Sallabanks 1993). Thus, fruit resource availability at the scale of the foraging patch, as well as #$&'!across broader habitat and landscape scales likely affect the behavior of frugivorous birds.    #$&(! For frugivorous birds, fruit-growing agricultural regions provide a system of readily #$&)!available food resources heterogeneously distributed at increasingly broad hierarchical spatial #$&*!scales. Birds forage at individual fruit trees (a patch), within an orchard containing many fruit #$'+!trees (a habitat), and within a broader landscape that may contain multiple fruit orchard habitats. #$'"!Trees and orchards vary in fruit abundance because heterogeneity in tree health, age, and #$'#!pollination success influence a treeÕs fruit yield. This heterogeneity leads to variation in fruit #$'$!!!90 abundance between the tree and orchard spatial scales. In addition, fruit orchards vary in the #$'%!extent to which they are isolated from other orchards of the same type in the surrounding #$'&!landscape. Thus, fruit abundance at a landscape scale (i.e. a particular orchard and the #$''!surrounding matrix of land covers) varies among orchards. Sweet cherry (Prunus avium) #$'(!production regions of Michigan display this hierarchical nature of fruit abundance and permit us #$')!to assess the extent to which variation in food abundance among spatial scales (e.g. between tree #$'*!and orchard and between orchard and landscape) influences avian foraging behaviors. Fruit #$(+!orchards are an ideal system in which to study multi-scale influences of resource availability on #$("!foraging behavior because 1) orchards provide abundant resources for avian frugivores (Pimentel #$(#!et al. 1992), 2) fruit consumption is easily observed, and 3) fruit availability can be quantified at #$($!the tree, orchard, and landscape spatial scales.  #$(%! In addition, we wanted to explore the idea that variation in resource abundance across #$(&!spatial scales has different effects on the decisions made by social and solitary foragers (Galef #$('!and Giraldeau 2001, Sernland et al. 2003, Fernandez-Juricic et al. 2004, Jedlicka et al. 2006). #$((!Unlike solitary feeders, social foragers may be more sensitive to variation in resource availability #$()!because of potential intra-group food competition (Rieucau and Giraldeau 2009). The potential #$(*!for resource competition can influence a foragerÕs use of a particular foraging patch or habitat #$)+!(Symington 1988, Giraldeau and Dubois 2008, Rieucau and Giraldeau 2009). For instance, larger #$)"!foraging groups require more resources in a given patch than smaller foraging groups; foraging #$)#!patches with sufficient food to support multiple foragers are likely more limited than patches for #$)$!solitary individuals (Livoreil and Giraldeau 1997). Thus, food abundance and distribution at #$)%!larger spatial scales may exert a stronger influence on the behavior of socially foraging birds #$)&!than solitary birds. However, research has not examined how multi-scale resource availability #$)'!!!91 affects foraging within the context of sociality. This knowledge gap limits our ability to predict #$)(!and analyze consequences of environmental heterogeneity on behavior.  #$))!  In this study, we evaluated the effects of hierarchical variation in resource abundance #$)*!and foraging sociality on the amount of time fruit-eating birds spent in and feeding at sweet #$*+!cherry trees. The amount of time animals spend at a particular foraging patch or site is influenced #$*"!by food abundance (Charnov 1976) and can be used to assess how resource abundance across #$*#!spatial scales influences the likelihood of staying in the patch (Searle et al. 2006). While #$*$!spending time in a patch, birds forage but also engage in behaviors such as grooming, vigilance, #$*%!calling, and perching. Therefore, a measure of the time animals spend actually feeding at a patch #$*&!(proportion time feeding) is an important behavioral variable for evaluating the effects of food #$*'!abundance on foraging behavior (Searle et al. 2006). For this study, we define the tree-scale #$*(!(patch) as focal cherry trees in or under which a bird is observed foraging (Figure 4.1). We #$*)!define the orchard-scale as areas of multiple contiguous rows of evenly spaced cherry trees. We #$**!define the landscape-scale as a circular area (500-m radius) extending from the center of each #%++!orchard.  #%+"! #%+#!Figure 4.1 Schematic of the tree, orchard, and landscape spatial scales. Scales increase in #%+$!size from left to right. Dark green parcels in the landscape represent other potential sweet cherry #%+%!orchards in the landscape surrounding a study orchard. #%+&! #%+'!Within the context of this framework we developed the following hypotheses: #%+(!!!92 Hypothesis 1: Highly mobile fruit-eating birds use their environments at multiple spatial scales; #%+)!therefore patch residence time and proportion time feeding behaviors should depend on relative #%+*!fruit abundance at hierarchical spatial scales. We predicted that the interactions between fruit #%"+!abundance of 1) trees within orchards and 2) orchards within landscapes would significantly #%""!affect avian patch residence time and proportion time feeding.  #%"#! #%"$! Hypothesis 2: Birds foraging in groups are more constrained by fruit abundance at relatively #%"%!large spatial scales (e.g. orchard and landscape) than solitary foragers. We predicted that fruit #%"&!abundance at the relatively large scales of orchard and landscape would exert a stronger #%"'!influence on birds foraging in groups than on solitary birds.  #%"(!  #%")!Methods #%"*! We conducted this study in northwest Michigan sweet cherry orchards in eastern #%#+!Leelanau County, near Traverse City (44¡ 46Õ N, 85¡ 37Õ W) from June - July in 2012 and 2014. #%#"!Leelanau County is primarily a peninsula (area = 804 km2) surrounded by Lake Michigan and an #%##!agricultural area with many orchards (e.g. sweet and tart cherries and apples) and vineyards. As #%#$!of 2011, fruit orchards comprised 7% of the peninsula land area; sweet cherry orchards #%#%!accounted for 26% of the total orchard acreage (USDA 2012). Other prominent land cover types #%#&!include non-fruit agriculture (e.g. corn), forests, and open fields. We conducted this study in 12 #%#'!sweet cherry orchards in 2012 and 10 in 2014, totaling 14 distinct orchards. #%#(! To address our objectives, we observed American robins (Turdus migratorius) and cedar #%#)!waxwings (Bombycilla cedrorum), two key cherry-consuming species (Lindell et al. 2012). #%#*!Robins and waxwings are common in the study region and breed during the summer cherry-#%$+!!!93 growing season. Waxwings arrive by late May (although some overwinter) and nest semi-#%$"!colonially, primarily between mid-June and August (McPeek 2011a). Robins typically arrive in #%$#!March, begin nesting in April, and rear two broods (McPeek 2011b). Robins and waxwings vary #%$$!in their degree of foraging sociality (Lindell et al. 2012). In cherry orchards, robins typically #%$%!forage alone or in small groups with a mean group size of 1.2 ± 0.5 SD (Lindell et al. 2012). #%$&!Waxwings travel and forage in flocks throughout the year (McPeek 2011b) and the mean group #%$'!size of waxwings feeding in cherry orchards during summer is 4.2 ± 3.8 SD (Lindell et al. 2012).   #%$(! #%$)!Behavioral Observations #%$*! We conducted observations over the cherry-ripening period from 04-Jun-2012 Ð 02-Jul-#%%+!2012 and 01-Jul-2014 Ð 24-Jul-2014. We walked systematically through orchards and conducted #%%"!focal-animal sampling (Altmann 1974). When we detected a bird, we followed it until the bird #%%#!left the orchard or was lost from sight and recorded all behaviors (e.g. eating, perched, calling, #%%$!grooming, walking) into a digital voice recorder (Sony ICD-BX800). In some instances, it was #%%%!possible to follow a bird from the initial tree to another. If birds traveled to subsequent trees but #%%&!no additional eating was observed, for the purpose of analyses, we considered the observation to #%%'!end when a bird left the initial tree. If birds were observed in an initial tree but only ate cherries #%%(!in the second tree, we considered the observation to begin when the bird flew to the tree in which #%%)!it was actually observed eating. If a bird was observed eating in multiple trees, we randomly #%%*!selected one tree from the observation and used the corresponding behavioral data from that tree #%&+!in analyses. We considered these periods in which a bird was seen foraging and consuming #%&"!cherries to be Òobserved foraging bouts.Ó We set a minimum observation length of 20 seconds #%&#!for inclusion in analyses (Morrison et al. 2010), and in doing so omitted three observations. After #%&$!!!94 observations ended we flagged focal trees for quantification of tree-scale cherry abundance. #%&%!Upon preparation of data for analysis, we excluded any observations for which corresponding #%&&!tree data or group sizes were missing. Four observers performed foraging observations, with the #%&'!first author (RAE) conducting >78% of the observations.  #%&(! We quantified two foraging response variables for each observation: 1) Òpatch residence #%&)!timeÓ and 2) Òproportion time feedingÓ. We defined patch residence time as the total amount of #%&*!time (in seconds) that a bird spent in or under the focal tree. We defined proportion time feeding #%'+!as the proportion of the patch residence time in which a bird was actually consuming cherries. #%'"!This was calculated as the total duration of all feeding activity divided by the patch residence #%'#!time. We adjusted the denominator in the proportion time feeding calculation to omit any time #%'$!where the bird was out of the observerÕs sight and thus feeding could not be confirmed. We #%'%!defined group size as the number of conspecific birds in or under the same tree as the focal bird #%'&!during the observation (Chavez-Ramirez and Slack 1994). To insure independence among #%''!observations, we did not observe additional individuals from a foraging group after observing #%'(!one member.  #%')! #%'*!Fruit Abundance #%(+! We calculated cherry abundance at three spatial scales: individual cherry trees, cherry #%("!orchards comprised of multiple trees, and landscapes around cherry orchards (500-m-radius #%(#!buffer area surrounding orchards; Fig. 1).  #%($! #%(%! #%(&! #%('!!!95 Tree-scale fruit abundance #%((! We sampled two branches on each focal tree. We randomly selected a horizontal sector #%()!of the tree (north-northeast, east-northeast, etc.) for each branch to be sampled. Next, we #%(*!measured the treeÕs height with a laser range finder (Nikon Forester model) and randomly #%)+!selected a height, in 0.5-m intervals, from the base of a treeÕs foliage (roughly 0.5 m above the #%)"!ground) to the treeÕs height. We identified the closest branch to the chosen sector at the chosen #%)#!height and counted all cherries on the branch from the terminal end of woody growth on the tip #%)$!of the branch inward, up to 1 m. If the branch was less than 1 m, we measured the branch length. #%)%!We calculated the number of cherries per meter of branch length for each sample and averaged #%)&!the two samples to generate our metric of tree-scale cherry abundance. Cherries were ripening #%)'!and no longer green during the observation and cherry sampling periods.  #%)(! #%))!Orchard-scale fruit abundance #%)*! To conduct orchard-wide cherry sampling, we first divided each orchard spatially into #%*+!five strata (north, east, south, west, and interior). The two outermost rows of trees on any edge #%*"!were assigned to the respective edge stratum; all other trees were considered part of the interior #%*#!(after Tracey and Saunders 2010). To identify sample trees in each stratum, we randomly #%*$!selected a starting tree and systematically identified up to 11 additional trees for sampling, for a #%*%!maximum of 60 trees per orchard. If a stratum had fewer than 12 trees, we sampled as many as #%*&!trees as possible. Branch selection procedures were the same as for tree-scale cherry #%*'!quantification; however, we used only one branch per tree during orchard-scale sampling #%*(!(Lindell et al. 2016).  #%*)!!!96  To quantify orchard-scale fruit abundance, we calculated a value of cherries per meter of #%**!branch for each sampled tree and averaged these values across all trees sampled in an orchard. #&++!Because orchards varied in tree number, we incorporated the total number of trees in an orchard #&+"!(which included both sampled and un-sampled trees) into our variable of orchard-scale fruit #&+#!abundance. To estimate the number of trees in each study orchard, we counted the number of #&+$!rows of trees within an orchard and the number of trees in the outermost rows. We multiplied #&+%!these two numbers to estimate the maximum number of trees in the orchard. This method may #&+&!slightly overestimate the number of trees because orchards sometimes have gaps where a tree #&+'!would otherwise be planted within a row. We then multiplied the orchard-level average of #&+(!cherries per meter of branch length by the number of trees in the orchard. Thus, our final metric #&+)!of orchard-scale fruit abundance is presented as cherries per meter of branch length, scaled to the #&+*!size of each orchard. One study orchard was harvested in 2014 before orchard-wide fruit #&"+!sampling could be conducted. For the two observations that occurred there, we assigned the 2014 #&""!mean orchard abundance value among all study orchards as the orchard abundance value for that #&"#!site. Preliminary analyses indicated no difference in cherries per meter between trees in edge or #&"$!interior strata within an orchard, and we made no distinction between these in further analyses.  #&"%! #&"&!Landscape-scale fruit abundance #&"'! We conducted geographic analyses in ArcGIS 10.0 (ESRI). We digitized study orchards #&"(!using 2012 National Agricultural Imagery Program (NAIP) orthoimages. We then calculated the #&")!geographic centroid of each study orchard and delineated a 500-m-radius buffer around this #&"*!point. We used NAIP orthoimagery to digitize and classify land cover parcels within each buffer. #&#+!Using NAIP land cover classifications, we could not distinguish between orchards of different #&#"!!!97 tree fruits; therefore, we conducted ground-truthing surveys in 2013 to verify which parcels were #&##!sweet cherry orchards. We calculated the area of each polygon comprising sweet cherry orchards #&#$!and summed these areas (Figure 4.1). We defined the landscape scale metric of cherry abundance #&#%!for each study orchard as the percent of the total buffer area consisting of sweet cherry orchards.  #&#&!This technique included the area of the study orchard itself in the landscape-scale metric of #&#'!cherry abundance. Therefore, for parsimony and to prevent collinearity between orchard- and #&#(!landscape-scale fruit abundance, we did not include orchard area as a separate covariate during #&#)!analyses.  #&#*! #&$+!Statistical Analyses #&$"!  We performed all analyses in R statistical software (Version 3.0.3; R Core Team 2012), #&$#!using the Ôlme4Õ (Bates et al. 2014) and ÔlmerTestÕ (Kuznetsova et al. 2015) packages. We #&$$!square root transformed fruit abundance data at all three scales to reduce skew. We then Z-#&$%!transformed these values to allow for comparisons of effect sizes among variables with differing #&$&!units of measure (Gelman et al. 2013). We Z-transformed group size within each species, in #&$'!order to correct for differences in foraging sociality (Gelman et al. 2013). We Z-transformed, #&$(!without centering, patch residence time in order to improve model convergence. We constructed #&$)!species-specific generalized linear mixed models (GLMMs) to compare influences of cherry #&$*!abundance at the three scales and group size on patch residence time and proportion time #&%+!feeding. To address Hypothesis 1, we included the interactions between tree-scale and orchard-#&%"!scale fruit abundance and between orchard-scale and landscape-scale fruit abundance. To address #&%#!Hypothesis 2, we included three additional interaction terms: foraging group size and fruit #&%$!abundance at each of the three scales. We treated patch residence time as a gamma distributed #&%%!!!98 variable with an inverse link function and proportion time feeding as a binomial distributed #&%&!variable with a logit link function. The total number of seconds a bird was observed (excluding #&%'!any time the bird was out of sight) served as the number of trials for the binomial distribution in #&%(!this model. Orchard identity and study year were included as random effects. ÒTime of dayÓ #&%)!(before or after 1200) and Òbird positionÓ covariates (e.g. in tree, on ground, both) were initially #&%*!included in models, but we removed them after preliminary analyses indicated no influence on #&&+!response variables. We make the simplifying assumption that model predictor variables are #&&"!measured without error, which means that all effect size estimates are optimistic. We conducted #&&#!variance inflation factor (VIF) tests to assess collinearity among predictor variables (VIFs < 8.8; #&&$!Neter et al. 1996). We calculated R2 values as one minus the ratio of the residual sum of squares #&&%!and the total sum of squares.    #&&&! #&&'!Results #&&(! In total, we have data from 105 observations of birds consuming cherries (62 robins, 43 #&&)!waxwings). Patch residence time was explained, in part, by interactions among cherry abundance #&&*!at multiple spatial scales and group size (R2 = 38% and 16% for robins and waxwings, #&'+!respectively). These factors also explained some of the variation in proportion time feeding (R2 #&'"!=19% and 30% for robins and waxwings, respectively). The random effects of Òorchard identityÓ #&'#!and Òstudy yearÓ explained low amounts of variation in patch residence time (R2 = 0% for both #&'$!effects for waxwings; R2 = 5% and 0%, for Òorchard identityÓ and Òstudy yearÓ, respectively for #&'%!robins). Random effects explained <4% of the variance in proportion time feeding for both #&'&!species.  #&''! #&'(!!!99 Fruit abundance at the tree scale and orchard scale interacted to affect proportion time #&')!feeding but not patch residence time  #&'*! Fruit abundance at the tree scale relative to that of the entire orchard did not affect robin #&(+!(Figure 4.2A) or waxwing (Figure 4.3A) patch residence time. Fruit abundance at the tree scale #&("!relative to that of the entire orchard significantly affected proportion time feeding for waxwings #&(#!(Figure 4.3C), but not for robins (Figure 4.2C). In particular, robin proportion time feeding #&($!declined with increasing fruit abundance at the tree scale regardless of orchard-scale fruit #&(%!abundance (Figure 4.2C). Waxwings at a tree with low fruit abundance had a greater proportion #&(&!time feeding when the tree was within an orchard also with low fruit abundance, than when the #&('!tree was within an orchard with high fruit abundance; the opposite pattern was observed for #&((!waxwings in trees with high fruit abundance (Figure 4.3C).  #&()! #&(*!Fruit abundance at the orchard scale and landscape scale interacted to affect proportion time #&)+!feeding, but not patch residence time #&)"! Fruit abundance at the orchard scale relative to that of the landscape scale did not affect #&)#!waxwing patch residence time (Figure 4.3B). Neither fruit abundance at the orchard nor #&)$!landscape scale affected robin patch residence time (Table 4.2), despite a small but significant #&)%!interaction effect between these two predictor variables (Table 4.2, Figure 4.3B). As cherry #&)&!orchards increased in fruit abundance, robins stayed longer at a cherry tree (patch) when an #&)'!orchard was within a high-fruit-abundance landscape. In contrast, robins left a tree sooner when #&)(!in a high-fruit-abundance orchard within a low-fruit landscape (Figure 4.2B). In addition, fruit #&))!abundance at the orchard scale relative to that of the landscape scale significantly affected #&)*!proportion time feeding of both robins (Figure 4.2D) and waxwings (Figure 4.3D). As fruit #&*+!!!100 abundance increased at the orchard scale, robins decreased proportion time feeding, with a #&*"!stronger effect for robins in fruit-rich landscapes than robins in fruit-poor landscapes (Figure #&*#!4.2D). As fruit abundance increased at the orchard scale, waxwings in high-fruit landscapes #&*$!similarly decreased proportion time feeding (Figure 4.3D).  #&*%! #&*&! Figure 4.2 The effects of fruit abundance across multiple spatial scales on American robin #&*'!foraging behavior. The interaction between fruit abundance at the tree scale and orchard scale #&*(!on American robin patch residence time (A) and proportion time feeding (C). The interaction #&*)!between fruit abundance at the orchard scale and three levels of landscape-scale fruit abundance #&**!on robin patch residence time (B) and proportion time feeding (D). Solid line represents low fruit #'++!abundance value (1st quantile), Dashed line represents median abundance value, Dotted line #'+"!represents high fruit abundance value (3rd quantile). Cherry abundance data are square root and #'+#!Z-transformed. #'+$! #'+%!!!101  #'+&!Figure 4.3 The effects of fruit abundance between spatial scales on cedar waxwing foraging #'+'!behavior. The interaction between fruit abundance at the tree scale and orchard scale on cedar #'+(!waxwing patch residence time (A) and proportion time feeding (C). The interaction between fruit #'+)!abundance at the orchard scale and three levels of landscape-scale fruit abundance on robin patch #'+*!residence time (B) and proportion time feeding (D). Solid line = low fruit abundance value (1st #'"+!quantile), Dashed line = median abundance value, Dotted line = high fruit abundance value (3rd #'""!quantile). Cherry abundance data are square root and Z-transformed. #'"#!Sociality altered the influence of fruit abundance at large spatial scales on foraging behavior #'"$! Waxwing foraging group size ranged from one to six birds with a mean of 2.2; no #'"%!waxwing groups of four were observed (Table 4.1). Robin foraging group size ranged from one #'"&!to four with a mean of 1.5 (Table 4.1). To clarify, we did not assess between-scale interactions in #'"'!the context of foraging sociality in these analyses, but rather the interaction between fruit #'"(!abundance at a single scale and foraging group size (Table 4.2). For both robins and waxwings, #'")!!!102 fruit abundance at large spatial scales (e.g. orchard and landscape) interacted with foraging group #'"*!size to affect behavior (Table 4.2). Fruit abundance at large spatial scales did not affect patch #'#+!residence time differently for solitary waxwings than group-foraging waxwings (Table 4.2). Fruit #'#"!abundance at large spatial scales scale affected patch residence time of robins in groups #'##!differently and more strongly than solitary robins (Table 4.2).  #'#$! Foraging group size interacted significantly with fruit abundance at large spatial scales to #'#%!affect waxwing proportion time feeding (Table 4.2). The effect of fruit abundance at large spatial #'#&!scales on proportion time feeding was stronger for waxwings in relatively large foraging groups #'#'!than solitary or paired waxwings (Table 4.2). In contrast, fruit abundance at large spatial scales #'#(!did not affect proportion time feeding differently for solitary robins compared to group-foraging #'#)!robins (Table 4.2).  #'#*!Table 4.1 Group sizes, patch residence times, and proportions time feeding for American #'$+!robins and cedar waxwings. Mean, standard deviation, and maximum values of group size. #'$"!Mean and standard deviation for response variables: patch residence time and proportion time #'$#!feeding. Mean, standard deviation, and range of tree-, orchard-, and landscape-scale cherry #'$$!abundance. #'$%! #'$&!Species Group size Patch residence time Proportion time feeding American robin 1.5 ± 0.9; max: 4 132 ± 115 seconds 48 % ± 28 % Cedar waxwing 2.2 ± 1.2; max: 6 183 ± 145 seconds 44 % ± 27% Spatial scale Cherry abundance Tree 44. 8 ± 46.1; range: 0 Ð 194.6 cherries/m of branch length Orchard 17,665 ± 51,172; range: 642 Ð 166,509 (cherries/m *  # trees in orchard) Landscape 11% ± 6.5; range: 1.1 Ð 24.1% of 500-m radius buffer  #'$'! #'$(! #'$)! #'$*! #'%+! #'%"! #'%#! #'%$! #'%%!!!103 Table 4.2 Outcomes of species-specific generalized linear mixed models of patch residence #'%&!time and proportion time feeding. Model-generated parameter estimates, standard errors (SE), #'%'!t- z-, and P-values of covariates and interaction terms from cedar waxwing-specific (unshaded #'%(!rows) and American robin-specific (shaded rows) GLMMs for dependent variables patch #'%)!residence time and proportion time feeding. Statistically significant values are bolded. #'%*!  #'&+! #'&"! #'&#! #'&$! #'&%! #'&&! #'&'! #'&(!Covariate Estimate SE t P Response Variable: Patch residence time     Intercept 1.05 0.199 5.27 <0.001 1.93 0.34 5.68 <0.001 Tree-scale cherry abundance -0.122 0.357 -0.492 0.62 -0.048 0.233 -0.204 0.84 Orchard-scale cherry abundance -0.236 0.357 0.662 0.51 0.167 0.281 0.593 0.55 Landscape-scale cherry abundance 0.385 0.259 1.49 0.14 -0.100 0.281 -0.356 0.72 Group size -0.044 0.136 -0.323 0.75 0.161 0.182 0.889 0.37 Tree x orchard -0.252 0.323 -0.781 0.44 0.365 0.208 1.76 0.08 Orchard x landscape 0.35 0.28 1.26 0.21 -0.377 0.189 -2.00 0.045 Tree x group size -0.076 0.165 -0.458 0.65 0.592 0.285 2.08 0.038 Orchard x group size 0.186 0.222 0.838 0.40 -0.180 0.262 -0.69 0.49 Landscape x group size -0.002 0.138 -0.016 0.99 -0.605 0.302 -2.00 0.045 !!104 Table 4.2 (contÕd) #'&)! #'&*! #''+! #''"!Discussion #''#! We hypothesized that fruit abundance at multiple spatial scales in a fruit-growing region #''$!would interact to affect the foraging behavior of fruit-eating birds. We found support for #''%!Hypothesis 1 in that relative fruit abundance between the tree and orchard scales and between the #''&!orchard and landscape spatial scales interacted to influence American robin and cedar waxwing #'''!proportion time feeding. However, we found no indication that fruit abundance at multiple #''(!Response variable: Proportion time feeding Estimate SE z P Intercept -0.187 0.449 -0.416 0.68 -0.727 1.36 -0.53 0.59 Tree-scale cherry abundance 0.603 0.068 8.848 <0.001 -0.373 0.06 -6.61 <0.001 Orchard-scale cherry abundance 0.015 0.098 0.1550 0.88 -2.32 0.193 -12.1 <0.001 Landscape-scale cherry abundance -0.993 0.403 -2.47 0.014 1.16 0.33 3.49 <0.001 Group size 0.411 0.048 8.53 <0.001 0.292 0.036 7.99 <0.001 Tree x orchard 0.184 0.089 2.07 0.038 0.082 0.060 1.37 0.17 Orchard x landscape -0.589 0.099 -5.94 <0.001 0.198 0.077 2.55 0.011 Tree x group size -0.261 0.051 -5.17 <0.001 0.114 0.062 1.86 0.06 Orchard x group size -0.674 0.081 -8.35 <0.001 -0.048 0.065 -0.741 0.46 Landscape x group size 0.834 0.075 11.06 <0.001 -0.053 0.065 0.813 0.42 !!105 spatial scales affected patch residence time. In addition, we found that fruit abundance at #'')!multiple spatial scales interacted in a complex manner with foraging group size to influence #''*!proportion time feeding and patch residence time. We discuss these principle findings in detail #'(+!below.  #'("! #'(#!Fruit abundance at the tree scale and orchard scale interacted to affect proportion time #'($!feeding but not patch residence time  #'(%! We expected that the heterogeneity in fruit abundance between cherry trees and cherry #'(&!orchards would interact to influence robin and waxwing patch residence time and proportion #'('!time feeding. Unexpectedly, the relative fruit abundance between the tree and orchard spatial #'((!scales did not affect patch residence time of either species. Similarly, Palacio et al. (2015) found #'()!no correlation between fruit crop size at a single spatial scale and the amount of time wild fruit-#'(*!eating birds spent at trees. Searle et al. (2006) considered multi-scale effects using a controlled #')+!experiment and found that the food availability and spatial arrangement of patches within larger-#')"!scale areas affected patch residence time in captive grizzly bears and mule deer, in contrast to #')#!our findings. Unlike Searle et al. (2006), we conducted our study with wild birds in a non-captive #')$!setting. The amount of time pulp-consuming birds like robins and waxwings spend at fruit trees #')%!may be affected by fruit handling time, rather than fruit abundance alone (Palacio et al. 2015). #')&!Fruit handling time, such as the time required to bite, consume, and digest fruit pulp, could #')'!require a minimum amount of patch residence time (Foster 1987).  #')(! In addition, robins and waxwings nest during the cherry-growing season in Michigan #'))!(McPeek 2011a, McPeek 2011b). The need to leave a particular tree and return to feed offspring #')*!at necessary time intervals likely influences robin and waxwing patch residence time  #'*+!!!106 (Kacelnik 1984). Furthermore, while at cherry trees, many birds displayed non-feeding behaviors #'*"!in addition to actual cherry consumption. For example, birds spent time perching, calling, or #'*#!grooming. Our findings suggest that patch residence time of fruit-eating birds is not influenced #'*$!simply by heterogeneity in fruit abundance between hierarchical spatial scales, but is likely #'*%!affected by additional complexities for animals in the wild.  #'*&! Heterogeneity in fruit abundance between a cherry tree and the orchard did influence #'*'!proportion time feeding, however this effect was small and evident only in waxwings. This result #'*(!likely arises because waxwings exhibit greater frugivory during summer than robins (Witmer #'*)!1996), and thus variation in fruit abundance affected our response variable that specifically #'**!measured fruit consumption behavior, proportion time feeding, for the highly frugivorous focal #(++!species but not the more omnivorous species. The effect of fruit abundance at a particular cherry #(+"!tree on waxwing proportion time feeding varied with fruit abundance at the orchard scale. When #(+#!fruit was locally sparse at a tree, waxwings in low-fruit orchards devoted more time to feeding #(+$!compared to waxwings in high-fruit orchards. The opposite pattern was observed for birds in #(+%!high-fruit trees. When fruit is sparse at both the tree and orchard scales, birds may need to devote #(+&!a lot of time to consuming cherries in order to meet fruit resources needs, since these resources #(+'!are relatively hard to come by at a particular tree and throughout the orchard (Olsson et al. 2000). #(+(!These findings indicate that the influence of resource abundance at a small spatial scale (e.g. a #(+)!particular tree) on avian foraging behavior can be contingent upon fruit abundance across a #(+*!somewhat broad foraging area, such as an orchard.  #("+! #(""! #("#! #("$!!!107 Fruit abundance at the orchard scale and landscape scale interact to affect proportion time #("%!feeding but not patch residence time #("&! We found no clear evidence that relative fruit abundance between the orchard and #("'!landscape scales affected robins and waxwing patch residence time. Similar to our discussion of #("(!the tree-scale and orchard-scale interaction above, this result is likely due to fact that while at #(")!cherry trees, robins and waxwings engaged in behaviors unrelated to fruit abundance such as #("*!grooming and communicating.   #(#+! Cherry abundance at the orchard and landscape scales interacted to affect proportion time #(#"!feeding of both robins and waxwings. Robins decreased proportion time feeding as fruit became #(##!more abundant across the orchard, and this effect was slightly greater for robins in high-fruit #(#$!landscapes than those in low-fruit landscapes. Waxwings in fruit-rich landscapes showed a #(#%!similar pattern. Birds make fruit selection decisions hierarchically, with abundance being one of #(#&!the initial determinants of selection (Sallabanks 1993). If birds are feeding in fruit-rich orchards #(#'!(habitats) and landscapes, they may be able to spend a smaller proportion of time eating and #(#(!more time searching for, or selecting among, fruit options and still meet their energy needs. #(#)!Indeed, theoretical foraging models suggest selectivity among food options should increase with #(#*!food abundance (Emlen 1966), and selectivity in birds foraging among fruits of a single type #($+!may also increase with abundance, especially given the multi-step nature of avian fruit selection #($"!(Sallabanks 1993).  #($#! In contrast, waxwings in relatively low-fruit landscapes actually devoted more time to #($$!consuming cherries, as orchards became more fruit rich. Waxwings feed preferentially on #($%!abundant fruits over rare fruits (McPherson 1987) and likely seek out foraging areas (e.g. #($&!landscapes with an abundance of fruiting cherry orchards) with widely available fruit. Therefore, #($'!!!108 foraging decisions for waxwings and other fruit-specialized species may occur at scales larger #($(!than that of a particular fruit orchard. These species should devote more time to feeding at a #($)!given tree if alternative fruit-rich orchards are sparse in the landscape and will require #($*!considerable search time and energy to locate (Charnov 1976). These interactions between fruit #(%+!abundance at the orchard and landscape scales suggest that small-scale, observable foraging #(%"!behaviors are affected by the relative abundance of fruit across multiple, relatively large spatial #(%#!scales, such as the landscape surrounding a particular foraging habitat (Searle et al. 2006). #(%$!Furthermore, these multi-scale interactions can significantly influence not only highly #(%%!frugivorous birds (e.g. waxwings), but more omnivorous species (e.g. robins) as well.  #(%&! #(%'!Sociality altered the influence of fruit abundance at large spatial scales on foraging behavior #(%(! We hypothesized that fruit abundance at the relatively large orchard and landscape scales #(%)!would have a greater effect on birds foraging in groups than on solitary foragers. As expected, #(%*!the degree of foraging sociality interacted with fruit abundance at the orchard and landscape #(&+!scales and affected the foraging behaviors of robins and waxwings differently (Table 4.2). #(&"!Interactions between waxwing foraging group size and fruit abundance affected only proportion #(&#!time feeding, while these interactions affected only the patch residence time for robins.  #(&$! For the highly frugivorous and group-foraging waxwings, birds in small groups (i.e. #(&%!solitary or paired birds) exhibited weaker behavioral responses to increases in fruit abundance at #(&&!large spatial scales, compared to waxwings in large groups (e.g. three or more birds). In addition, #(&'!waxwings in small groups showed opposite responses to changes in fruit abundance at large #(&(!spatial scales compared to waxwings in large groups. These results supported our expectation #(&)!that fruit abundance at scales larger than the patch (e.g. the orchard or surrounding landscape) #(&*!!!109 would exert a stronger influence on birds in large groups compared to solitary birds or those in #('+!small groups. As group size increases, the potential for resource competition and intra-group #('"!aggression also increases, which can reduce resource intake (Rutten et al. 2010). The effects of #('#!resource competition on the behavior of group-foraging birds can be mitigated if groups feed in #('$!areas with more widely available resources (Caraco 1979). Thus, resource availability at the #('%!relatively large orchard and landscape scales and the availability of suitable foraging areas to #('&!support the needs of a foraging group are important considerations for group-foraging birds (as #(''!reviewed in Marshall et al. 2012).  #('(! #(')!Conclusion  #('*! It is important to consider multiple spatial scales in evaluating relatively large-scale #((+!ecological patterns (e.g. population abundance, distribution, and species diversity; Wiens 1989, #(("!Levin 1992, Cornell and Donovan 2010, Caro and Sherman 2011). The hierarchical nature of #((#!resource availability for animals in natural and semi-natural systems affects foraging behavior in #(($!a manner that cannot be fully understood via traditional single-scale approaches. Our study is #((%!among the first to demonstrate that avian behaviors are influenced by food abundance across #((&!between spatial scales, including relatively large scales. In order to understand avian behavior in #(('!complex and heterogeneous resource environments more accurately and completely, we must #(((!take into account multiple scales (Senft et al. 1987, Pinaud and Weimerskirch 2006, Searle et al. #(()!2006). 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