Influences of Diet, Spatial Scale, and Sociality On

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

Rachael Ann Eaton

A DISSERTATION

Submitted to Michigan State University in partial fulfillment of the requirements for the degree of

Integrative Biology – Doctor of Philosophy Ecology, Evolutionary Biology and Behavior – Dual Major

2016 ABSTRACT 1 2 INFLUENCES OF DIET, SPATIAL SCALE, AND SOCIALITY ON AVIAN FORAGING 3 BEHAVIOR AND HABITAT USE IN CULTIVATED SWEET CHERRY ORCHARDS AND 4 THE RESULTING IMPLICATIONS FOR MANAGEMENT 5 6 By 7

Rachael Ann Eaton 8

Agricultural crops provide foods that attract a variety of foragers. In particular, fruit crops 9 attract many fruit eating bird species because fruits are densely available, energy-rich, and 10 readily accessible. Agricultural systems have important implications for avian foraging because 11 they manipulate the availability and quality of food. Avian fruit orchard use and crop 12 consumption represent a major conflict between humans and wild birds. Despite nearly a century 13 of attention paid to understanding the nature of this conflict, many inconsistencies and avenues 14 for research remain. In this dissertation, I utilized a variety of techniques including radio 15 tracking, focal observation, and bioenergetic modeling to understand more fully where, how, and 16 to what extent wild fruit eating birds use cultivated fruit resources in northwest Michigan, as well 17 resulting implications for crop producers. Species-specific information on crop damage and 18 habitat use is essential for better-informed pest management programs and damage mitigation. 19

Bird damage to fruit crops amounts to tens of millions of dollars in losses annually. Yet, 20 the development of successful damage-mitigation strategies for fruits is hindered by a lack of 21 species-specific damage information. In chapter 2, I used bioenergetic modeling that integrated 22 species-specific data on energetic demands and diet to estimate sweet cherry (Prunus avium) 23 consumption by American robins (Turdus migratorius) and cedar waxwings (Bombycilla 24 cedrorum). I then developed economic models to quantify species-specific financial loss due to 25 bird damage. Individual waxwings consumed significantly more sweet cherry and caused seven 26

times the financial loss than robins. I estimated economic losses at $US1.8 million and 27

$US147,000 from the waxwing and robin populations, respectively. 28

Species-specific variation in diet preferences could result in varying use of orchards and 29 impacts on the fruit-producing industry by different bird species. However, species-specific 30 studies of avian orchard use are lacking, particularly throughout the fruit-growing season. 31

Cultivated sweet cherries are high in sugar and low in proteins and lipids; American robins 32 typically prefer lipid-rich fruits, while cedar waxwings choose sugary fruits. Differences in diet 33 preferences may translate into species-specific patterns of habitat use for birds in fruit crops. In 34 chapter three, I used radio telemetry to quantify frequency of daily bird visits to orchards and the 35 amount of time birds spent visiting orchards each day over the fruit-ripening season. I found that 36 waxwings visited orchards a greater percentage of days than robins and spent more time in 37 orchards each day. 38

Birds forage in habitats where food abundance varies at multiple spatial scales; relative 39 resource abundance between hierarchical spatial scales likely influences within-patch foraging. 40

For frugivorous birds, fruit-growing agricultural regions provide a system of readily available 41 food resources heterogeneously distributed at increasingly broad hierarchical scales. In chapter 42 four, I conducted foraging observations and quantified fruit abundance at three spatial scales to 43 evaluate influences of fruit abundance at multiple spatial scales, and influences of sociality, on 44 avian behavior in sweet cherry orchards. Fruit abundance across multiple scales interacted to 45 influence patch residence time and proportion time spent feeding at sweet cherry trees; these 46 patterns differed between species. In addition, fruit abundance at large spatial scales influenced 47 patch residence time in robins and proportion time feeding by waxwings more strongly for birds 48 in large foraging groups than for those in small groups. 49

This thesis is dedicated to all the kind people in my life who were willing to put up with me 50 while I completed it. Thank you for everything. 51

iv ACKNOWLEDGEMENTS 52

Completing this dissertation has been a long road. Thankfully, innumerable colleagues, 54 friends, and family walked this road with me and played essential roles in helping me undertake 55 and complete my dissertation work. First, I must thank my graduate advisor, Catherine Lindell. 56

As a mentor, Catherine offered me the perfect balance of guidance and independence. As a 57 mentor she guided me and taught me how to be a scientist. Most importantly, she encouraged me 58 through many challenges and worked with me over the many years to help me succeed. Thank 59 you also to the lab group of fellow “bird nerds” that Catherine has assembled. Thank you to Sean 60

Williams, Megan Shave, Steve Roels, and Melissa Brady Hannay for the many years of 61 encouragement, feedback, and commiseration (when necessary). I would like to acknowledge my 62

Michigan State graduate advising committee of Kay Holekamp, Fred Dyer, and Brian Mauer. 63

Special thanks as well to George Linz of the USDA National Wildlife Research Center in North 64

Dakota. From hundreds of miles away, George served as an additional member of my advising 65 committee, provided valuable guidance on studying birds in agricultural systems, and lent me 66 both equipment and personnel to conduct my research. 67

I could not have completed this dissertation without the invaluable in-field assistance of 68

Megan Shave, Shayna Weiferich, Kate Howard, Ben Hawes, Bryant Dossman, and Emily Oja. 69

Completing this project also hinged upon generous partnerships with sweet cherry growers of 70 northwest Michigan. In particular, thanks to Francis Otto of Cherry Bay Orchards for allowing 71 me access to cherry orchards. Thank you to the staff of the Northwest Michigan Horticultural 72

Research Station for use of orchards, equipment, and administrative support. I also thank Lars 73

Brudvig and Jason Gallant of Michigan State University for use of laboratory space and 74 equipment. 75

I would also like to acknowledge the many sources that funded my dissertation research: 76

The Specialty Crop Research Initiative of the U.S. Department of Agriculture, The Wilson 77

Ornithological Society, the George J. and Martha C. Wallace Endowed Scholarship, the 78

Michigan State University College of Natural Science, the Michigan State University Ecology, 79

Evolutionary Biology, and Behavior program. 80

Finally to Béné Bachelot and all of my family and friends, I would really like to write 81 something nice for you here to express how valuable each of you has been to me, but this is my 82 final section to complete and after writing 31,000 words all I can manage is a brief but sincere 83

“thank you”…for everything. 84

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108

TABLE OF CONTENTS 109

110

LIST OF TABLES ix 111

LIST OF FIGURES x 112

CHAPTER 1: A REVIEW OF THE NEGATIVE AND POSITIVE 113 IMPLICATIONS OF BIRDS IN FRUIT AGRICULTURE 1 114 Introduction 2 115 Methods 3 116 Negative Implications of Avian Fruit Orchard Use 4 117 Bird damage to fruit by direct consumption 4 118 Species causing damage 5 119 Types of fruit damaged and field-based damage estimates 7 120 Spatial patterns of field-based damage estimates 12 121 Economic damage estimates 14 122 Indirect damage to fruit crops 15 123 Bird damage to fruit increases susceptibility to other pests 15 124 Birds consume invertebrate crop pollinators 17 125 Birds consume fruit tree flower buds 18 126 Positive Implications of Avian Fruit Orchard Use 19 127 Birds consume orchard pests 19 128 Invertebrate orchard pests 19 129 Mammalian orchard pests 22 130 Addressing Gaps in Avian Use of Cultivated Fruit Orchards 24 131 LITERATURE CITED 28 132 133 CHAPTER 2: ESTIMATING FRUIT DAMAGE AND ECONOMIC LOSS DUE TO BIRDS 134 WITH A BIOENERGETIC APPROACH 35 135 Abstract 36 136 Introduction 36 137 Methods 39 138 Bioenergetic models of avian sweet cherry consumption 40 139 Estimating proportion of sweet cherries in diets 42 140 Population sizes 44 141 Economic models of avian damage to sweet cherries 45 142 Results 47 143 Bioenergetic models of avian sweet cherry consumption 47 144 Economic models of avian damage to sweet cherries 47 145 Discussion 48 146 LITERATURE CITED 54 147 148 149

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CHAPTER 3: AMERICAN ROBINS AND CEDAR WAXWINGS VARY IN USE OF 150 CULTIVATED CHERRY ORCHARDS 60 151 Abstract 61 152 Introduction 61 153 Methods 64 154 Study Area & Species 64 155 Capture & Radio Deployment 66 156 Data Collection 67 157 Data Preparation 68 158 Day-to-day & Within-day Orchard Use 69 159 Statistical Analyses 69 160 Results 71 161 Study Demographics 71 162 Day-to-day & Within-day Orchard Use 72 163 Discussion 75 164 Day-to-day Orchard Use 75 165 Within-day Orchard Use 77 166 LITERATURE CITED 81 167 168 CHAPTER 4: FOOD ABUNDANCE AT MULTIPLE SPATIAL SCALES INFLUENCES 169 FORAGING BEHAVIORS 86 170 Abstract 87 171 Introduction 88 172 Methods 92 173 Behavioral Observations 93 174 Fruit Abundance 94 175 Tree-scale fruit abundance 95 176 Orchard-scale fruit abundance 95 177 Landscape-scale fruit abundance 96 178 Statistical Analyses 97 179 Results 98 180 Fruit abundance at the tree scale and orchard scale interacted to affect 181 proportion time feeding but not patch residence time 99 182 Fruit abundance at the orchard scale and landscape scale interacted to 183 affect proportion time feeding, but not patch residence time 99 184 Sociality altered the influence of fruit abundance at large spatial scales on 185 foraging behavior 101 186 Discussion 104 187 Fruit abundance at the tree scale and orchard scale interacted to affect 188 proportion time feeding but not patch residence time 105 189 Fruit abundance at the orchard scale and landscape scale interacted to 190 affect proportion time feeding, but not patch residence time 107 191 Sociality altered the influence of fruit abundance at large spatial scales on 192 foraging behavior 108 193 Conclusion 109 194 LITERATURE CITED 110 195

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LIST OF TABLES 196

197

Table 2.1 Types and sources of AMC values used to calculate proportion of sweet cherries 198 in diet 44 199 200 Table 2.2 Estimated sweet cherry consumption by American robins and cedar waxwings in 201 northwest Michigan orchards and resulting economic losses 47 202 203 Table 3.1 Study orchard area, 2013 harvest date, and land cover types adjacent to study 204 orchards 71 205 206 Table 3.2 Generalized liner mixed models exploring the relationships between within-day 207 orchard use of American robins and cedar waxwings, relative to species, 208 orchard, and days-to-harvest 73 209 210 Table 4.1 Group sizes, patch residence times, and proportions time feeding for American 211 robins and cedar waxwings 102 212 213 Table 4.2 Outcomes of species-specific generalized linear mixed models of patch residence 214 time and proportion time feeding 103 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239

LIST OF FIGURES 240

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Figure 2.1 Map of Leelanau County study region with mist netting and point count locations 242 identified 42 243 244 Figure 3.1 Map of Leelanau County study region and focal sweet cherry orchards 65 245 246 Figure 3.2 Percent of days American robins and cedar waxwings visited cherry orchards 247 relative to their respective tracking periods 72 248 249 Figure 3.3 Percent of the daylight period that American robins and cedar waxwings visited 250 cherry orchards on a given day 74 251 252 Figure 3.4 Percent of the daylight period that American robins and cedar waxwings visited 253 cherry orchards on a given day relative to days-before-harvest 75 254 255 Figure 4.1 Schematic of the tree, orchard, and landscape spatial scales 91 256 257 Figure 4.2 The effects of fruit abundance across multiple spatial scales on American robin 258 foraging behavior 100 259 260 Figure 4.3 The effects of fruit abundance between spatial scales on cedar waxwing 261 foraging behavior 101 262

CHAPTER 1 263 264 NEGATIVE AND POSITIVE IMPLICATIONS OF BIRDS IN FRUIT AGRICULTURE 265 266 267 Rachael A. Eaton 268

1 Introduction 269 270 Bird use of agricultural habitat is long-documented (Beal 1915, McDowell and Pillsbury 271

1959). Indeed “economic ornithology”, the study of bird-agriculture interactions and conflict, 272 began officially within the USDA in 1885 (Henderson and Preble 1935). Many bird species 273 utilize agricultural habitats for food resources, with disparate consequences for crop producers 274

(Drake and Grande 2002, Retamosa et al. 2008). In one regard, avian use of agriculture can be 275 beneficial. Birds can provide ecosystem services, natural processes that provide some benefit to 276 humans (Whelan et al. 2008), in the form of biological control and pest reduction, including both 277 invertebrate and vertebrate crop pests (e.g. Mols and Visser 2002). In contrast, avian use of 278 agriculture can have negative consequences, such as depredation or damage to the crops 279 themselves, that result in financial losses for crop producers (Retamosa et al. 2008, Lindell et al. 280

2012, Anderson et al. 2013). Thus, avian use of agricultural systems represents a complex 281 interaction between humans and wildlife with both positive and negative implications (Messmer 282

2009). The negative and positive implications of birds in agriculture have been studied in 283 numerous crops and in growing regions around the world (Dolbeer et al. 1994, Peisley et al. 284

2015). Yet, much of the research addressing costs or benefits of birds in agriculture has focused 285 on major crops (e.g. corn, grains; Dolbeer et al. 1994), while bird use of less abundant but high- 286 value crops such as fruits has historically received less attention. In this introductory chapter, my 287 objective was to explore the complex interaction between birds and fruit agriculture by 288 synthesizing existing literature on the negative and positive implications of wild birds in 289 cultivated fruit crops, evaluating our current understanding, and suggesting future directions for 290 fruit crop management and research. 291

Cultivated fruit crops provide abundant, dense, and readily accessible food resources for 292 many birds (Sallabanks 1993). However, the manner in which wild birds utilize fruit agriculture 293 for food can result in either damage to fruit crops and costs for crop growers, or protection 294 against other crop pests, benefiting crop producers. Historically, attempts to characterize the 295 relationship between birds and fruit agriculture have emphasized the negative consequences of 296 avian use of fruit orchards and potential for crop damage by birds (DeHaven 1974, Gebhardt et 297 al. 2011). Birds consume a wide variety of cultivated fruits (Johnson et al. 1989, Nelms et al. 298

1990, Somers and Morris 2002, Lindell et al. 2016). In addition to direct fruit consumption, birds 299 cause indirect damage to fruiting plants, such as flower bud damage (Summers and Pollack 300

1978), affecting fruit production (Wright and Summers 1960). The positive implications of avian 301 fruit orchard use are primarily viewed in the context of ecosystem services birds may provide. 302

The use of non-chemical biological agents to regulate or control pest species abundance, known 303 as biological control, is utilized in a variety of fruit crops both in the U.S. and Europe. In 304 agricultural settings, some birds provide regulating services by consuming other orchards pests 305 such as invertebrate herbivores or crop-damaging small mammals (Whelen et al. 2008, Wenny et 306 al. 2011). The majority of research in this area has assessed the role of birds in controlling 307 herbivorous insects. Considerably less information is available about predatory birds and the 308 beneficial consumption of avian and mammalian pests. 309

310

Methods 311

I searched the Web of Science database with relevant combinations of the keywords: 312 bird, orchard, avian, crop, damage, fruit, and vineyard to generate an initial list of papers for this 313 review. To this list I added any papers I knew of but that had not been produced by the database 314

search. Last, I added relevant references cited within or by any of these papers. Database 315 searches were conducted between January 28, 2016 and February 8, 2016. 316

From the database results, I included relevant studies that took place in fruit agriculture in 317 temperate regions of North America and Eurasia. I focused my review on these regions because 318 of their high contribution to global fruit production and their long-standing, but sporadic, history 319 of research into avian use of fruit agriculture. I included only studies that evaluated the role of 320 birds in fruit agriculture and excluded studies that were interested in agriculture’s effects on birds 321 or bird communities. I also excluded studies that took place exclusively in laboratory settings 322 and studies that reported on the efficacy of different bird deterrent techniques but provided no 323 data for bird damage in the absence of such techniques. 324

325

Negative Implications Of Avian Fruit Orchard Use 326

Bird damage to fruit by direct consumption 327

Studies reporting bird damage to fruit via direct consumption of the fruit itself occurred 328 in North America but not Europe, while European studies reported alternative mechanisms of 329 damage such as bud eating (Summers and Pollack 1978) or pollinator consumption (Galeotti and 330

Inglisa 2001). Most of the work in this area has been conducted in the United States, but some 331 studies have taken place in fruit growing regions of Ontario, Quebec, and British Columbia in 332

Canada. This geographic difference in foci could be attributed to long-standing infusion of 333 funding and research effort by national agencies such as the United States Department of 334

Agriculture into studies of bird damage to crops, economic losses, and loss mitigation. Within 335 the United States, research has occurred in major fruit-growing regions like Michigan, 336

California, New York, Washington, and Florida. 337

Species causing damage 338

In fruit-growing regions, orchards offer birds rich patches of foraging habitat with 339 numerous, perennial fruit resources (Dolbeer et al. 1994, Simon 2008, Lindell et al. 2012). 340

Cultivated fruits are attractive food resources for many species of birds because fruits are 341 abundant, energy-rich, and easily accessible. A variety of bird species consume and damage 342 cultivated fruit. Such damaging species range from predominantly frugivorous species such as 343 cedar waxwings (Bombycilla cedrorum; Stone 1974, Lindell et al. 2012) to omnivorous species 344 such as American robins (Turdus migratorius; Lindell et al. 2012) and common starlings 345

(Sturnus vulgarius; Stone 1973, Guarino et al. 1974). To a lesser extent, some studies identify 346 birds that are typically insectivores like woodpeckers and flycatchers (Boudreau 1972, DeHaven 347 and Hothem 1981) and granivores like house finches (DeHaven 1974, DeHaven and Hothem 348

1981, Tobin et al. 1989) as fruit damaging birds. 349

Among this diversity of avian fruit crop consumers, the most frequently cited fruit crop 350 pests across an array of fruit types are the omnivorous American robin and common starling 351

(Stevenson and Virgo 1971, Stone 1973, Guarino et al. 1974, Anderson et al. 2013) and the 352 frugivorous cedar waxwing. Birds of these species are frequent orchard visitors (Guarino et al. 353

1974, Lindell et al. 2012, Eaton et al. 2016) and often consume substantial amounts of fruit 354

(Boudreau 1972, Guarino et al. 1974, Lindell et al. 2012). These three species are considered 355 significant fruit pests in wine grapes (Vitis spp.; Stevenson and Virgo 1971, Bourdeau 1972), 356 sweet and tart cherries (Prunus avium and Prunus cerasus; Stone 1973, Guarino et al. 1974, 357

Lindell et al. 2012), and blueberries (Vaccinium corybosum; Lareau and Vincent 1985, Nelms et 358 al. 1990, Avery et al. 1991). American robins and common starlings, along with American crows 359

(Corvus brachyrhynchos), are significant pests in apples (NASS 1999, Anderson et al. 2013). A 360

survey of apple growers across the U.S. in 1998 estimated that common starlings and American 361 robins cause 16% and 9% of the damage to apple crops, respectively (NASS 1999). In Ontario, 362 starlings comprised 42% of all birds identified in sweet cherry orchards and caused 60% of 363 observed damaged (Virgo 1971). In Michigan cherry orchards, cedar waxwings were responsible 364 for >60% of all avian sweet cherry consumption, while American robins were responsible for 365

>40% of tart cherry consumption (Lindell et al. 2012). Most recently, apple, grape, blueberry, 366 and cherry growers from New York, Michigan, and the Pacific Northwest identified starlings and 367 robins as two of the most damaging bird species in fruit crops (Anderson et al. 2013). 368

Several features of American robins, common starlings and cedar waxwings contribute to 369 their role as principle fruit-damaging bird species. All three species frequently live and forage in 370 human-modified environments, including agricultural environments (Homan et al. 2010, Lindell 371 et al. 2012). These species take advantage of readily available fruit food resources; for example, 372 when foraging in high-fruit areas American robins increase the proportion of fruit in their diets 373

(Wheelwright 1986). Cedar waxwings are also highly frugivorous; 84% of their annual diet 374 consists of fruit (Witmer 1996). In addition, cedar waxwings show preferences for sugar-rich 375 fruits, like many cultivated crops, over lipid-rich fruits, like many wild fruits (Witmer and Van 376

Soest 1998). In addition, cedar waxwings and common starlings forage in large groups in 377 orchards and vineyards, which can contribute to the heavy degree of fruit consumption and 378 damage, making them particularly troublesome pests (Stone 1974, Nelms et al. 1990, Lindell et 379 al. 2012). 380

Much of the data on the identity of avian fruit crop pests comes from fruit grower reports 381 and species surveys of birds in fruit crops. For instance, a survey of >1500 fruit growers in New 382

York, Michigan, and the Pacific Northwest identified American robins and common starlings as 383

the top species responsible for bird damage to blueberry, wine grape, and cherry crops (Anderson 384 et al. 2013). However, the association between bird species frequently observed in orchards and 385 those that actually cause crop damage is not always clear. Virgo (1971) noted a high density of 386 robins present in Ontario sweet cherry orchards (28% of all birds observed); however, robins 387 were responsible for a relatively small proportion (~5%) of the damage. Critical components of 388 wildlife damage-control programs include understanding the identity and ecology of species 389 causing the damage (Somers and Morris 2002, Tracey et al. 2007). Recent work has emphasized 390 feeding observations of birds actually consuming fruit crops over survey-based studies or 391 grower-identified assessments to evaluate the role of different species in fruit damage (Lindell et 392 al. 2012). In order to understand better the extent of bird damage to fruit crops, more targeted 393 research studies are needed to identify the birds causing crop damage and robustly quantify 394 species-specific damage. Additional research that identifies problem species in particular regions 395 or crops of interest will be important to generate efficient and effective deterrent techniques. 396

Such an approach will be more efficient and economical than attempting to deter all birds since 397 not all birds cause problematic levels of damage. In the third chapter of my dissertation, I 398 explored this by quantifying and comparing the species-specific crop damage of two common 399 fruit-consuming species, American robins and cedar waxwings. 400

401

Types of fruit damaged and field-based damage estimates 402

The majority of research into estimating avian fruit crop consumption took place between 403 the 1970s and early 1990s (e.g. Virgo 1971, Stone 1974, Guarino et al. 1974, DeHaven and 404

Hothem 1981, Nelms et al. 1990, Avery et al. 1993, Vincent and Lareau 1993). Prior to this time, 405 bird consumption of cultivated fruit was noted as a growing concern but relatively little research 406

had investigated the problem (Virgo 1971). The current body of literature on the extent of bird 407 consumption of fruit crops comes from in-field damage assessments, as well as some direct 408 surveys of fruit growers. These studies reveal that a variety of cultivated tree, vine, and shrub 409 fruits are damaged by birds (Lindell et al. 2016), with most studies focused on North American 410 wine grapes (DeHaven and Hotherm 1981), sweet and tart cherries (e.g. Virgo 1971), and 411 blueberries (e.g. Avery et al. 1992). These fruits are economically valuable and thus there is 412 strong economic motivation to evaluate the extent of avian crop damage. For example, in the 413

United States wine grape production has a value of >2.5 billion dollars annually (NASS 2016), 414 and Canada’s wine industry generates 1.1 billion dollars annually (Agriculture and Agri-Food 415

Canada 2016). 416

Wine grapes, cherries, and blueberries also share several characteristics that make them 417 attractive food resources for fruit-consuming birds (Avery 2002). Grapes, cherries, and 418 blueberries are high in sugar and energy rich. They also have thin skins and soft pulp, enabling 419 easy consumption by birds (Avery 2002). In terms of quantifying bird damage and consumption, 420 other fruit crops have received considerably less attention in the literature than wine grapes, 421 cherries, and blueberries. A limited number of studies reporting bird damage exist for apples 422

(Tobin et al. 1989) and citrus fruit (Johnson et al. 1989). Several major patterns emerge from the 423 studies of avian fruit crop consumption. 424

The first major patterns is that most studies report low bird damage levels (e.g. <10%), 425 and this is evident across multiple crop types. Bird damage affects a relatively small proportion 426 of the overall fruit crop in apples (Malus pumila; Tobin et al. 1989), grapefruit (Citrus x 427 paradise; Johnson et al. 1989), wine grapes (DeHaven 1974), and cherries (Stone 1974, Lindell 428 et al. 2016). A recent multi-year field study of damage to Honeycrisp apples in Michigan, New 429

York, and Washington estimated damage at 2% (Lindell et al. 2016). My literature search 430 revealed one study of grapefruit (Citrus paradisi) damage in Texas (Johnson et al. 1989); 431 estimated damage across 30 grapefruit groves was 8%. In Ontario vineyards Stevenson and 432

Virgo (1971) found that 85% of 108 vineyards had some degree of bird damage, but most (69%) 433 showed damage levels of 10% or less. In Michigan tart cherries, Stone (1974) estimated damage 434 at 7.4% and Lindell et al. (2016) recently found a three-year average for damage was 2.6% 435

(Lindell et al. 2016). Sweet cherry crops tend to incur greater damage than those of tart cherries; 436 however, overall bird damage to sweet cherry is still relatively low (Virgo 1971, Lindell et al. 437

2016). Virgo (1971) estimated damage to sweet cherries in Ontario as 3%. Lindell et al. (Lindell 438 et al. 2016) generated three-year average estimate of 9% across multiple orchards in New York, 439

Michigan, and Washington. Notably, one study found higher bird damage in tart cherries than in 440 sweet cherries, in contrast to most reports (Guarino et al. 1974). However, this study was limited 441 to four trees in two orchards and may thus reflect local trends rather than large-scale patterns. 442

One important caveat regarding these reports of relatively low levels of bird damage is the 443 existence of considerable year-to-year variation among in-field damage estimates (Lindell et al. 444

2016). For example, damage is much higher in years of low overall crop yield (i.e. due to poor 445 weather or growing conditions) compared to years when crop yield is high (Lindell et al. 2016). 446

The relatively low damage in apples and citrus fruits may be explained in part by the 447 presence of a thick, tough outer skin of these fruits. A thicker fruit skin is likely more difficult to 448 break for birds with smaller bills, thus relatively few bird species may be capable of damaging 449 apple and grapefruit crops. Indeed, apple growers report that larger-billed birds like American 450 crows and wild turkeys (Meleagris gallopavo) are among the most significant apple pests (Tobin 451 et al. 1989, Anderson et al. 2013), while great-tailed grackles (Quiscalus mexicanus) are major 452

grapefruit consumers (Johnson et al. 1989). With fewer possible apple and grapefruit problem 453 species, overall damage estimates in these crops tend to be low. The reports of low bird damage 454 in wine grapes and cherries are somewhat surprising given the soft-skinned texture and high- 455 energy content that make these fruits attractive to wild foraging birds (Avery 2002). Estimated 456 bird damage could also be low across many studies and crop types because the high density of 457 available fruit in cultivated systems exceeds avian resource demands. In many fruit-growing 458 regions, orchards and vineyards occur in close proximity to other fruit agriculture. Thus, the 459 availability of fruit on a broad scale may be high enough that overall damage is low because fruit 460 resources are broadly available. This phenomenon is already reflected in instances of temporal 461 variation in fruit abundance; bird damage is lower in years with high fruit yield than in years 462 with low fruit yield (Lindell et al. 2016). The effect of fruit resource abundance at broad spatial 463 scales and the interaction between fruit abundance across multiple scales (e.g. an orchard and the 464 surrounding landscape) on avian foraging behavior have not been well explored and I 465 investigated these ideas further in my fourth dissertation chapter. 466

The second notable pattern among published studies is that damage estimates are highest 467 in blueberries compared to other crops. Damage estimates in blueberries range from of 17% in 468

Florida (Nelms et al. 1990) to as high as 85% in Michigan (Avery et al. 1993). Bird preferences 469 for particular fruit characteristics may explain the substantially high level of damage in blueberry 470 crops. Abundant fruit-consuming birds like American robins have demonstrated preferences for 471 blue fruits over red, green or yellow options (Willson 1994). In addition, highly frugivorous 472 birds like cedar waxwings demonstrate preferences for relatively small fruits that were similar in 473 diameter to ripening blueberries (~7 mm), compared to larger fruits (McPherson 1988, Avery et 474

al. 1993). Given their small size, blueberries are accessible to a variety of fruit-consuming 475 species of many sizes. 476

The third major pattern in bird damage to fruit is that early ripening fruit varieties incur 477 greater damage than varieties that ripen later. Early-ripening varieties of sweet cherries (Virgo 478

1971), blueberries (Nelms et al. 1990), and apples (Tobin et al. 1989) incur more damage than 479 their later-ripening counterparts. Early-ripening blueberry crops in Florida incurred damage as 480 high as 75% (Nelms et al. 1990). Tobin et al. (1989) reported that apple varieties that displayed 481 red coloration early in the season suffered the most damage. Several factors may contribute to 482 high damage among early-ripening fruits. Fruit color in an important factor in the avian fruit 483 selection process (Sallabanks 1993). Early-ripening fruits that turn from colors indicating unripe 484 fruit like green and yellow to “ripe” colors like red and blue likely stand out among other fruit 485 options and catch the attention of foraging birds. This effect is also supported by differences in 486 damage among varieties of different colors. For example, susceptibility to damage is higher 487 among darker varieties of grapes than lighter varieties (Boudreau 1972, DeHaven 1974). 488

Fruit varieties that ripen before others also represent an attractive but sparse resource in 489 orchards and vineyards. Such varieties likely incur heavy damage because alternative fruit 490 options in the foraging area are low at the time of ripening. Heavy damage to early-ripening 491 fruits can have particularly negative consequences for growers because these fruits are first to hit 492 markets and can have high economic value (Nelms et al. 1990). Patterns of heavy damage in 493 early-ripening varieties suggest that fruit growers could mitigate potential bird damage by 494 avoiding planting some varieties that ripen or color much earlier other varieties in the field. 495

496

497

Spatial patterns of field-based damage estimates 498

Bird damage to fruit crops shows several interesting spatial patterns. First, damage can be 499 vertically stratified within plants; evidence for this comes from studies of grapes, cherries, and 500 grapefruit. For example, DeHaven (1974) and Somers and Morris (2002) observed greater levels 501 of damage on grape bunches growing high on a vine, further from the ground. Similarly, cherry- 502 consuming birds in Michigan were observed almost exclusively in the top half of trees, 503 suggesting that damage will be heavier in the higher parts of cherry trees than in lower parts 504

(Lindell et al. 2012). However, like many trends in bird damage, there are inconsistencies in the 505 pattern of vertical stratification of damage. Virgo (1971) found no evidence of such stratification 506 in Ontario sweet cherry orchards. The upper parts of plants may incur greater damage because 507 fruit density and nutritional quality is often higher in the upper parts of fruit trees, making those 508 parts more attractive to fruit-eating birds (Houle et al. 2014). Future field assessments of bird 509 damage to fruit should account for this vertical stratification to avoid biased estimates of 510 damage. For example, sampling only in plant areas that are easily accessible to humans could 511 lead to underestimated damage. 512

Second, habitat features adjacent to fruit crops influence the degree of damage; however, 513 such patterns are inconsistent among studies. Anecdotal reports from fruit producers suggests 514 that orchards that are spatially isolated from other fruit agriculture suffer greater damage than 515 those that are near to other cultivated fruit fields. My literature search generated only two studies 516 to corroborate this point; Johnson et al. (1989) found more damage in isolated grapefruit 517 orchards than in orchards surrounded largely by other orchards. Isolated orchards may be 518 especially attractive because they offer birds a high density of food resources in an area that may 519 otherwise be limited in high-energy fruit resources. Lindell et al. (2016) recently determined that 520

sweet cherry blocks in Michigan sustained less damage when surrounded to a greater degree (e.g. 521 on 1 or more sides) by other sweet cherry orchards compared to isolated blocks. In contrast, 522

Virgo (1971) found no indication that the habitat surrounding a cherry orchard influenced the 523 amount of bird damage. Some studies report that crops surrounded by a relatively high amount of 524 non-fruit crops, such as forest, tend to have less damage than those with little surrounding forest 525

(Lindell et al. 2016). This pattern may due in part because prominent avian fruit pests, like 526 common starlings are not forest-dwelling species and tend to avoid fruit crops surrounded by 527 forests (Boudreau 1972). Man-made habitat features like power lines can also affect the extent of 528 bird damage because they offer perching places for many birds and draw in large numbers of 529 crop-damaging birds to adjacent fruit crops (Bourdreau 1972). For example, plantings of 530

Honeycrisp apples suffered slightly greater damage if utility wires ran overhead compared to 531 those without overhead wires (Lindell et al. 2016). 532

Some studies also report the existence of edge effects, wherein plants nearer to the edge 533 of an orchard or vineyard have greater damage than plants nearer to the interior (Somers and 534

Morris 2002). Texas grapefruit orchard edges had higher damage than interior areas (Johnson et 535 al. 1989). However, support for edge effects is not consistent within or among crop types 536

(Lindell et al. 2016). Somers and Morris (2002) found that damage levels in vineyards in Ontario 537 were greater near edges and declined closer to the vineyard interior. In contrast, DeHaven and 538

Hothem (1981) found no evidence of edge effects in California vineyards. This variation among 539 studies regarding the presence of edge effects is consistent with the larger literature on the edge 540 effect phenomenon, which has been difficult to generalize (Ries and Sisk 2004). Given that these 541 few studies in fruit crops suggest higher vulnerability to bird damage along field edges, crop 542 producers may experience more efficient damage mitigation by targeting edge areas. However, it 543

is clear that additional studies are needed to determine the circumstances under which edge 544 effects occur, and to what extent these effects vary among pest bird species. 545

These spatial effects contribute to variation in damage incurred by different fruit growers. 546

Situations in which many growers have little or no damage, while few growers have substantial 547 damage, can lead to relatively low costs to the industry overall despite significant challenges to 548 some growers (Virgo 1971). Collectively, these spatial patterns in bird damage suggest that fruit 549 producers may benefit from targeting bird deterrent efforts toward particular edges or growing 550 plots that are more likely to suffer bird damage, how such foci are likely to vary among fruit 551 types and growing locations. Therefore, the variability among studies and the relatively low 552 amount of research into bird damage to fruit crops suggest that further work is needed to 553 elucidate generalizable patterns of bird damage; especially considering most of the published 554 literature is over 20 years old. 555

556

Economic damage estimates 557

Some studies have translated in-field damage estimates into economic consequences for 558 fruit producers; however, such estimates are sparse throughout the past 40 years. The most 559 prominent pattern regarding economic estimates is the high degree of variability in costs incurred 560 by different fruit growers. Despite the generally low levels of bird damage reported in the 561 literature, some individual growers can suffer high financial losses. Johnson et al. (1989) 562 quantified US$ 2.2 million in damage in Texas grapefruit groves. However, bird damage was 563 highly variable among groves; some growers experienced loss of as little as $10 per ha, others 564 incurred as much as $1900 per ha. 565

The high degree of variability in field-based damage estimates suggests that alternative 566 mechanisms to overcome such variability may be important to generate more broadly applicable 567 damage estimates and resulting values of economic loss. Accurate loss estimates are important 568 for growers to evaluate whether, and to what extent, money and effort should be spent on bird 569 damage mitigation. Bioenergetic-based crop damage estimates that incorporate data on bird diets 570 and energy needs, along with the energy content of crops have gained prominence in the 571 literature in recent decades (e.g. Wiens and Dyer 1975, Peer et al. 2003, and Homan et al. 2011). 572

A bioenergetics method is not based upon damage levels at different orchards or vineyards and is 573 thus subject to less variance. Such an approach has not yet been applied to fruit agriculture. In 574 the second chapter of my dissertation, I utilized a bioenergetics approach to estimate bird 575 damage to Michigan cherry crops. 576

577

Indirect damage to fruit crops 578

There are considerably fewer studies of non-consumptive bird damage to fruit crops and 579 fruit plants compared to studies of avian crop consumption. Beyond direct consumption, negative 580 implications of avian orchard use include increased susceptibility of damaged fruit to other 581 pathogens (e.g. Ioriatti et al. 2015), pollinator consumption (Golawski and Golawska 2013), and 582 bud consumption (Greig-Smith and Wilson 1984). 583

584

Bird damage to fruit increases susceptibility to other pests 585

Birds often damage fruits but do not consume them fully. These injuries to fruit skin 586 provide entry points for invertebrate pests such as fruit flies (Stevenson and Virgo 1971, Ioriatti 587 et al. 2015) or fungal pathogens (Xu et al. 2001, Holb and Sherm 2008). The earliest mention of 588

this damage type comes from Stevenson and Virgo (1971); they posited that bird-damaged 589 grapes attracted fruit flies in Ontario vineyards but provided no data to that effect. Iorriati et al. 590

(2015) tested the likelihood of bird damage to increase spotted wing Drosophila (Drosophila 591 suzukii) infestation of wine grapes in Oregon and Italy. D. suzukii is a common invertebrate pest 592 that feeds on and lays eggs in cultivated fruits. Iorriati et al. (2015) experimentally incised grapes 593 to mimic bird damage and found a higher incidence of D. suzukii in these grapes than in un- 594 manipulated grapes. 595

Bird damage also invites fungal infestation such as brown rot, Monilinia fructigena (van 596

Leeuwen et al. 2000, Xu et al. 2001, Holb and Sherm 2008); studies addressing this type of 597 damage take place primarily in apple crops. Brown rot is the most significant fungal pathogen of 598 stone fruits, fruits with a fleshy outer tissue that surrounds a hard “stone” around the seed, 599 throughout the warm climates of the world (Ritchie 2000). Brown rot caused an 11% loss in 600 yield in Polish apple orchards; bird damage was responsible for 29% of new brown rot 601 inoculations (Xu et al. 2001). In Hungary, brown rot occurred in 94% of all injured apples. 602

However, insects were responsible for much more damage than birds; bird damage comprised 603 only 25% of the fruit injury (Holb and Sherm 2008). In Poland, birds caused more damage to 604 pears than insects or typical growth cracking (Xu et al. 2001); approximately 70% of new M. 605 fructigena inoculations stemmed from bird damage (Xu et al. 2001). 606

Only a few studies have explored the capacity for birds to increase fruit’s susceptibility to 607 other pathogens and drawing general patterns is difficult. Overall, such research is recent and 608 agrees that birds contribute to higher incidences of insect and fungal pathogens by damaging 609 fruit crops. There is some disagreement as to whether bird-caused injury in fruits leads to higher 610 pathogen incidence than insect-caused injury. Additional research is necessary to elucidate more 611

clearly the relative roles of birds and fruit-damaging insects in increased pathogen susceptibility. 612

In addition, no study attempted to quantify the financial consequences of increased pathogens 613 due to bird damage. Doing so would provide fruit producers and managers a more holistic 614 picture of the economic costs of bird damage to fruit. 615

616

Birds consume invertebrate crop pollinators 617

Some insectivorous birds in orchards consume fruit tree pollinators like bees (Bosch and 618

Trostle 2006, Galeotti and Inglisa 2001, Golawski and Golawska 2013). This type of damage 619 could adversely affect crop growth and yield and would be especially problematic for crops that 620 require cross-pollination. However, I found no study that directly quantified the effect of 621 pollinator consumption on crop yield. Among studies, the extent to which birds consume 622 pollinator bees varies. For instance, bees (Apidae) are more important for pollination of fruit 623 trees than other hymenopterans (Galeotti and Inglisa 2001). Galeotti and Inglisa (2001) analyzed 624 stomach contents of European bee-eaters (Merops apiaster) foraging in orchard-rich regions of 625

Italy and found that honeybees comprised up to 63% of the diet. In contrast, bees (Family: 626

Apidae) comprised just 2% of the red-backed shrike diet in Polish apple orchards (Golawski and 627

Golawska 2013). Bosch and Trostle (2006) noted that bluejays and American robins consumed 628 orchard mason bees (Osmia lignaria) emerging from hives in Utah sweet cherry orchards, 629 negatively affecting bee population growth. These varied results and the limited research in this 630 area invite further exploration to understand the extent of avian pollinator consumption and 631 specifically quantify the potential yield losses due to pollinator consumption. If bird 632 consumption of pollinators adversely affects crop yield, then bird damage mitigation strategies 633 should be expanded to protect crops during the flowering period. 634

Birds consume fruit tree flower buds 635

A few studies from the 1960s through the 1980s explored the negative consequences of 636 avian consumption of fruit tree buds (Newton 1964, Summers and Pollack 1978, Greig-Smith 637 and Wilson 1984, Greig-Smith 1987). However, research in this area is limited in scope and has 638 looked exclusively at Eurasian bullfinches (Pyrrhula pyrrhula) feeding on dormant flower buds 639 in English pear orchards (Summers and Pollack 1978). Wild tree fruit buds are a common foods 640 for Eurasian bullfinches and these birds take advantage of the abundance of buds in cultivated 641 fruits (Newton 1964), which explains why research on bud consumption focuses on this species. 642

Other noted bud-consuming species are tit species (Parus spp.) and house sparrows (Passer 643 domesticus; Newton 1964). 644

Eurasian bullfinches consume the center of the bud itself, which is a highly nutritious 645 food item (Summers and Pollock 1978). The type of indirect damage can adversely affect fruit 646 production because when birds consume flower buds, fewer flowers are available for pollination 647 and fruit production, which can lower yields. Yield loss is proportional to the severity of bud loss 648

(Summers and Pollock 1978). Summers and Pollock (1978) experimentally removed >50% of 649 buds from trees in a pear orchard to mimic bird damage and recorded yield losses between 50 650 and 85%. Bud damage, like direct fruit consumption, displays spatial and temporal patterns 651

(Wright and Summers 1960, Newton 1964, Summers and Pollack 1978). For example, 652 bullfinches removed 100% of buds from one pear variety, but only 12% of another in 1961 653

(Newton 1964). In contrast, bud removal the following year was less than 2% in both varieties 654

(Newton 1964). The exterior areas of orchards incur greater damage via bud loss than interior 655 areas (Summers and Pollack 1978, Greig-Smith and Wilson 1984). Greig-Smith and Wilson 656

(1984) documented up to 80% bud removal in pear trees near wooded edges. This is likely 657

because woodland areas are the preferred breeding habitat of Eurasian bullfinches (Newton 658

1964), making woodland-adjacent orchards particularly vulnerable to bud damage. 659

Birds consume dormant flower buds in the winter, outside the typical vulnerable period 660 for ripening fruit (Greig-Smith and Wilson 1984). These studies suggest that damage mitigation 661 efforts should not be limited to protecting fruits. Based on the available literature, it is not known 662 whether bud feeding and subsequent yield loss occur outside this system of bullfinches and 663

English fruit orchards. There is no evidence of research in this area after the 1980s. Given the 664 potential for high yield loss due to bud consumption, additional work is needed to determine 665 whether bud consumption is a concern in other crops and growing regions and whether other bird 666 species are causing winter season crop damage via bud consumption. 667

A significant gap in the literature remains regarding these indirect, non-consumptive 668 mechanisms of bird damage to fruit. Given the relatively limited and situation-specific 669 information in these areas, it is not yet clear how badly these alternative bird damage 670 mechanisms affect crop production. More research is necessary in these areas to determine 671 whether these negative implications of birds in fruit crops 1) are substantial enough to warrant 672 mitigation by crop producers and 2) are occurring on a broad scale across crop types and 673 growing regions. 674

675

Positive Implications Of Avian Fruit Orchard Use 676

Birds consume orchard pests 677

Invertebrate orchard pests 678

Insects are becoming increasingly resistant to pesticides and public concern about the use 679 of pesticides due to health concerns have become more widespread. This environment supports 680

the need for increasing research to understand the potential for biological control of persistent 681 and widespread invertebrate pests. Studies of avian consumption of crop pests are plentiful, but 682 few studies explore the potential for avian predation on crop pests to result in yield benefits due 683 to increased yield with the reduction in pest species (Mooney et al. 2010). Similarly to uneven 684 distribution among crop types in studies quantifying crop damage by birds, studies of potential 685 avian benefits to fruit crops are clustered among a relatively small number of crop types. 686

Research has been conducted primarily in apples (e.g. Mols and Visser 2002), and grapes (e.g. 687

Jedlicka et al. 2011). 688

A series of studies in apple orchards in the Netherlands have focused caterpillar 689 consumption by on great tits (Parus major) and the reduction in leaf damage caused by 690 caterpillars (Mols and Visser 2002, Mols et al. 2005, Mols and Visser 2007). Mols and Visser 691

(2002) demonstrated that bird predation on leaf-damaging caterpillars increases apple yield. 692

When birds were permitted to consume caterpillars, leaf damage decreased from 13.8% to11.2% 693 compared to trees from which birds were excluded (Mols and Visser 2002). Bird consumption of 694 caterpillars resulted in a 3.1 kg increase in crop yield per tree, specifically the production of more 695 apples (Mols and Visser 2002). In contrast, Pinol et al. (2010) found a relatively weak effect of 696 birds on arthropod abundance in citrus groves in Spain. Most of the research in this area comes 697 from apple orchards in the Netherlands. The contrast between those studies and that of Pinol et 698 al. suggests that the potential benefits of avian pest consumption are variable among crop types, 699 farms, or growing regions. This variability illustrates the need for future studies to assess the 700 relationships not only between birds and invertebrate pests but also between avian pest 701 consumption and resultant crop production to understand how crop growers can benefit from and 702 take advantage of avian orchard use. 703

By installing nest boxes in orchards, fruit growers can exploit bird predation on pest 704 arthropods (Mols et al. 2005, Mols and Visser 2007, Jedlicka et al. 2011). For example, apple 705 orchards with great tit nest boxes installed had half as much caterpillar damage as orchards 706 without nest boxes, resulting in an increased yield of 1200 kilograms per hectare (Mols and 707

Visser 2007). Jedlicka et al. (2011) saw insectivorous bird density increase by nearly four times 708 after installing nest boxes in California vineyards. In addition, Jedlicka et al. (2011) used sentinel 709 prey to determine that prey removal in vineyards with nest boxes was 2.4 times greater than in 710 vineyards without added nest. These studies illustrate not only that insectivorous birds can 711 benefit fruit growers by consuming herbivorous insects and contributing to increased yield, but 712 also that this benefit can be enhanced relatively easily and inexpensively by adding nest sites 713

(Mols and Visser 2002, Jedlicka et al. 2011). However, given that these patterns have been 714 explored in a select few studies and in only two types of fruit crops, more work in this area is 715 needed before making generalized recommendations to growers regarding the value of installing 716 structures to attract insectivores. 717

Invertebrate pests do not just consume tree leaves; species such as the codling moth 718

(Cydia pomonella) and winter moth (Operophtera brumata) attach to and build cocoons in bark 719 to overwinter. During the fruiting season, moth larvae are common fruit pests, damaging and 720 consuming the fruit itself. Bird species like great tits (Soloman et al. 1976), pine siskins (Roland 721 et al. 1986), and woodpecker species (MacLellen 1958) benefit fruit growers by uncovering and 722 feeding on these bark pests. The role of birds in controlling moth larval density in fruit orchards 723 was explored several decades ago in both North America (e.g. MacLellan 1958, Roland et al. 724

1986) and Europe (e.g. Soloman and Glen 1979). Bird consumption of codling moth larvae can 725 be substantial. In Nova Scotia, Canada downy woodpeckers (Picoides pubescens) and hairy 726

woodpeckers (Leuconotopicus villosus) took 52% of overwintering codling moth larvae 727 experimentally placed on apple trees (MacLellan 1958). Roland et al. (1986) found that when 728 birds were excluded from foraging in certain trees, codling moth larvae density was three times 729 greater than in trees where birds could freely consume larvae. Soloman and colleagues 730 experimentally added codling moth larvae to apple orchards in the UK to assess moth mortality 731 due to great tits (Soloman et al. 1976, Soloman and Glen 1979). In two successive years, birds 732 depredated approximately 95% of codling moth larvae. Consumption declined when larval 733 density declined, suggesting a density-dependent pattern to codling moth regulation by birds 734

(Soloman et al. 1976, Soloman and Glen 1979). 735

The extent to which bird predation on overwintering moths benefits crop production has 736 not been explored quantitatively. More research could better elucidate the conditions under 737 which birds regulate moth density most effectively. However, despite substantial avian predation 738 on moth larvae (MacLellan 1956, Roland et al. 1986), moth density often remains high during 739 the fruiting season and considerable moth damage to crop leaves and fruit remains (Roland et al. 740

1986). This lack of sufficient pest control benefit by birds may explain why research in this area 741 apparently subsided thirty years ago. Therefore, devoting additional energy to such studies may 742 not result in valuable information for fruit producers if avian moth larvae consumption is 743 consistently unable to mitigate moth abundance and subsequent crop damage. 744

745

Mammalian orchard pests 746

Bird predation on mammalian crop pests is another positive implication of avian use of 747 fruit orchards. Mammalian pests like voles (e.g. Microtus pennsylvanicus), moles, and gophers 748

(Family: Geomyidae) damage orchards and vineyards by digging burrows throughout fields, 749

damaging equipment like irrigation systems, or chewing the bark around fruit trees (Ashkam 750

1990, Moore et al. 1998). Use of rodenticides to regulate these pests is costly and concerns about 751 consequences for food safety have fueled study of biological control mechanisms. Relatively 752 recently, birds of prey have been investigated as means for controlling vertebrate damage in fruit 753 crops (Ashkam 1990, Moore et al. 1998, Paz et al. 2013). However, there is considerably less 754 published research in this area than studies of birds mitigating invertebrate damage. This 755 discrepancy may be due to the considerable challenges of systematically locating and tracking 756 sufficient numbers of wide-ranging predatory birds, quantifying consumption of mammalian 757 pests, and simultaneously assessing effects on pest mammal populations. 758

Studies of predatory birds and mammalian fruit pests typically explore ways in which 759 crop producers can attract predatory birds and reduce mammalian pest abundance (e.g. Askham 760

1990, Moore et al. 1998, Merwin et al. 1999, Paz et al. 2013). Askham (1990) installed artificial 761 perches and nest boxes to attract predatory birds to Washington apple orchards. However, no 762 owls occupied owl nest boxes and American kestrels (Falco sparverius) occupied only 13% of 763 boxes erected for them. Askham (1990) provided some summary data to suggest that vole 764 activity in the orchards with bird attracting structures was generally lower than in control plots, 765 but did not support these conclusions with robust analyses. Moore et al. (1998) surveyed fruit 766 growers in California to assess the perceived efficacy of owl nest boxes in controlling gophers. 767

While fruit growers saw 40% box occupancy, only 23% believed that barn owls (Tyto alba) 768 effectively controlled gophers (Moore et al. 1998); overall this survey revealed a high level of 769 variation among growers in the perceived efficacy of barn owls on gopher pests (Moore et al. 770

1998). In addition, Merwin et al. (1999), observed raptor species like red-tailed hawk (Buteo 771 jamaicensis) and American kestrel hunting around apple orchards with high vole density, but 772

these predators did not control vole abundance or reduce pest damage. The on-going problem of 773 mammalian pests, increasing interest in biological control methods, and dearth of robust data on 774 the capacity of predatory birds to regulate pest mammals suggest that such studies should be an 775 area of active research. 776

An on-going question about the positive implications of birds in fruit agriculture and the 777 of vertebrates as biological control agents is whether they can demonstrate a sufficient numerical 778 response to abundance of pest organisms to offer some kind of damage-limiting benefit and 779 subsequent increase in crop yield. The literature addressing the positive roles of birds offers 780 mixed messages regarding the capacity for insectivores and birds of prey to control crop pests 781 and positively affect crop production. Studies of insectivorous birds have begun exploring this, 782 but the number of studies remains limited, as does the number of crops in which these studies are 783 conducted. Overall, research energy could be expended on studies of tri-trophic interactions 784 involving predators, prey, and crops are necessary to quantify actual consequences of bird 785 predation of pest organisms on crop yield. Furthermore, I was unable to find published data from 786

North America or Eurasia on whether birds of prey affect fruit consumption by pest bird species; 787 however, such studies have demonstrated this relationship elsewhere in places like New Zealand 788

(e.g. Kross et al. 2012). Given the considerable evidence of birds as fruit pests, the potential for 789 biological control of fruit-consuming birds by raptors could be a valuable area of future research. 790

791

Addressing Gaps In Avian Use Of Cultivated Fruit Orchards 792

My review of the positive and negative implications of avian use of fruit crops revealed 793 several areas that warranted further exploration. First, bird damage to fruit exhibits variation over 794 time and space, but little has been done to evaluate avian orchard use over extended temporal 795

scales such as throughout the fruit-growing season. Second, estimates of bird damage based upon 796 in-field studies are highly variable and do not typically quantify damage by individual bird 797 species, despite indications that birds vary in fruit consumption within orchards. Third, the 798 availability of fruit at broad spatial scales and the habitat context within and around fruit 799 orchards can influence fruit damage. However, it remains unclear how fruit consumption of 800 individual birds is affected by resource availability at broad spatial scales and across multiple 801 scales. 802

I investigated these problems in the second, third, and fourth chapters of my dissertation 803 by studying two common fruit consuming species in Michigan sweet cherry orchards. American 804 robins and cedar waxwings are frequently identified crop pests and are responsible for a 805 relatively high proportion of cultivated sweet cherry consumption (Lindell et al. 2012). Cherry 806 orchards are attractive for fruit-eating birds because cherries are brightly colored, densely 807 available, and have easily accessible pulp (Sallabanks 1993). Cherries are also an economically 808 valuable fruit crop; Michigan growers estimate annual losses to birds of over $US2 million 809 statewide (Anderson et al. 2013). Therefore, sweet cherry orchards provide an ideal system in 810 which to explore questions of avian use of cultivated fruit crops. Below, I introduce and explain 811 the objectives for my second, third, and fourth dissertation chapters. 812

In my second dissertation chapter, I addressed the problem that, despite numerous studies 813 investigating avian consumption of cultivated sweet cherries (Virgo 1971, Curtis et al. 1994, 814

Lindell et al. 2012), the extent of avian orchard use over the growing season has not been 815 established. Information on the species-specific foraging patterns of fruit-eating birds throughout 816 the fruit growing season orchards can help growers target particular bird species and times when 817 fruit loss is greatest (Dolbeer et al. 1994, Somers and Morris 2002, Tracey et al. 2007). 818

Omnivorous American robins consume some fruit, as well as a large amount of invertebrates; 819 while cedar waxwings are heavily frugivorous (Witmer 1996). In addition these species differ in 820 fruit preference, which likely influences orchard use. In the second chapter of my dissertation, 821 my objective was to determine whether cedar waxwings exhibited more intense use of sweet 822 cherry orchards than robins based on their differences in diet. To achieve this objective I 823 captured wild birds and used radio-telemetry techniques to assess the frequency of visits to 824 cherry orchards throughout the cherry-growing season, as well as the time birds spent visiting 825 orchards each day. 826

In my third dissertation chapter, I addressed the problem that estimates of bird damage 827 and resulting financial loss lack a species-specific approach and can be subject to high variability 828 from one site to another. Bioenergetic models of bird damage provide species-specific estimates 829 that are based on avian energy needs, diet composition, and crop energy content (Wiens and 830

Dyer 1975, Peer et al. 2003). A species-specific approach is important because recent work has 831 shown that the degree of avian cherry consumption varies among species (Lindell et al. 2012). In 832 the third chapter of my dissertation, my objective was to provide the first species-specific, 833 region-wide estimates of bird damage and financial loss in sweet cherry crops. To achieve this 834 objective I collected data on avian energy needs, diet composition, and local population sizes to 835 construct bioenergetic and economic models of bird damage. 836

In my fourth dissertation chapter, I addressed the gap in the literature regarding the 837 influence of resource availability at broad spatial scales and across multiple scales on avian 838 foraging behavior in fruit orchards. Traditional foraging models are difficult to apply to highly 839 mobile animals foraging on broadly distributed resources because resource heterogeneity occurs 840 at multiple spatial scales. The influence of multi-scale resource availability on behavioral 841

responses is understudied, despite the hierarchical nature of foraging decisions. Fruit-eating birds 842 make foraging decisions at multiple scales (Sallabanks 1993). Thus, fruit resource availability at 843 the scale of the foraging patch, as well as across broader habitat and landscape scales likely 844 affect the behavior of frugivorous birds. In addition, research has not examined how multi-scale 845 resource availability could affect foraging within the context of sociality. In the fourth chapter of 846 my dissertation, my objectives were to 1) evaluate whether avian foraging behaviors depend on 847 relative fruit abundance at hierarchical spatial scales, and 2) determine if fruit abundance at large 848 spatial scales constrains social foragers more than solitary foragers. 849

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Agriculture and Agri-Food Canada. 2016. Canada’s Wine Industry. Accessed 24 July 2016. 860

Anderson, A., C. A. Lindell, K. M. Moxcey, W. F. Siemer, G. M. Linz, P. D. Curtis, J. E. 861 Carroll, C. L. Burrows, J. R. Boulanger, K. M. M. Steensma, and S. A. Shwiff. 2013. Bird 862 damage to select fruit crops: The cost of damage and the benefits of control in five states. 863 Crop Protection 52:103–109. 864

Ashkam, L.R. 2000. Efficacy of the aerial application of methyl anthranilate in reducing bird 865 damage to sweet corn, sunflowers, and cherries. Proceedings of the Nineteenth Vertebrate 866 Damage Pest Conference 22-25. 867

Avery, M.L., J.W. Nelson, and M.A. Cone. 1992. Survey of bird damage to blueberries in North 868 America. Proceedings of the Eastern Wildlife Damage Control Conference 5:105–110. 869

Avery, M.L. 2002. Behavioral and ecological considerations for managing bird damage to fruit. 870 United States Department of Agriculture National Wildlife Research Center – Staff 871 Publications: Paper 460. 872 873 Avery, M. L., J. L. Cummings, D. G. Decker, J. W. Johnson, J. C. Wise, and J. I. Howard. 1993. 874 Field and aviary evaluation of low-level application rates of methiocarb for reducing bird 875 damage to blueberries. Crop Protection 12:95–100. 876 877 Beal, F.E.L. 1915. Food habits of robins and bluebirds in the United States. United States 878 Department of Agriculture Bulletin 171:1-31. 879

Bosch, J., W. P. Kemp, and G. E. Trostle. 2006. Bee population returns and cherry yields in an 880 orchard pollinated with Osmia lignaria (Hymenoptera : Megachilidae). Journal of 881 Economic Entymology 99:408–413. 882

Boudreau, G. W. 1972. Factors related to bird depredations in vineyards. American Journal of 883 Enology and Viticulture 23:50–53. 884

DeHaven, R. W. 1974. Bird damage to wine grapes in central California, 1973. Proceedings of 885 the Sixth Vertebrate Pest Conference: Paper 9. 886

DeHaven, R. W., and R. L. Hothem. 1981. Estimating bird damage from damage incidence in 887 wine grape vineyards. American Journal of Enology and Viticulture 32:1–4. 888

Dolbeer, R.A., N.R. Holler, and D. W. Hawthorne.1994. Identification and assessment of 889 wildlife damage: An overview. The Handbook: Prevention and Control of Wildlife 890 Damage. University of Nebraska-Lincoln: Lincoln, Nebraska. 891

Drake, D., and J. Grande. 2002. Assessment of wildlife damage to agricultural crops in New 892 Jersey. Journal of Extension 40: 1IRB4. 893

Eaton, R.A., C.A. Lindell, H.J. Homan, G.M. Linz, and B.A. Maurer. 2016. American Robins 894 (Turdus migratorius) and Cedar Waxwings (Bombycilla cedrorum) vary in use of cultivated 895 cherry orchards 128:97–107. 896

Galeotti, P., and M. Inglisa. 2001. Estimating predation impact on honeybees Apis mellifera L. 897 by European bee-eaters Merops apiaster L. Revue d’Ecologie (La Terre et la Vie) 898 56:373–388. 899

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34 CHAPTER 2 1076 1077 ESTIMATING FRUIT DAMAGE AND ECONOMIC LOSS DUE TO BIRDS WITH A 1078 BIOENERGETIC APPROACH 1079 1080 1081 Rachael A. Eaton and Catherine A. Lindell 1082

35 Abstract 1083 1084 Bird damage and consumption of fruit crops amounts to tens of millions of dollars in 1085 losses annually across the United States. Yet, the development of successful damage-mitigation 1086 strategies for fruits is often hindered by a lack of species-specific damage information. A 1087 bioenergetic model of crop damage integrates species-specific data on energetic demands and 1088 diet to estimate crop consumption. Compared to traditional damage surveys, a bioenergetic 1089 approach permits a regional quantification of damage. In conjunction with local population sizes 1090 and crop economic value, bioenergetic models can be used to develop economic models that 1091 quantify financial loss due to bird damage on a species-specific level. We used a bioenergetic 1092 approach to quantify damage to sweet cherry crops (Prunus avium) by American robins (Turdus 1093 migratorius) and cedar waxwings (Bombycilla cedrorum) in an important sweet cherry 1094 production region of Michigan. Individual waxwings consumed three times as much sweet 1095 cherry and caused seven times the financial loss as robins, in large part because of larger local 1096 population sizes and greater reliance on fruit in trees by waxwings; robins take over half their 1097 fruit from the ground. We estimated that economic losses from this damage in our study area, 1098 over 28 days from preharvest to postharvest, were $US1.8 million and $US147,000 from the 1099 waxwing and robin populations, respectively. Bioenergetic model estimates allow us to contrast 1100 damage from multiple pest species and understand pest species ecology, which is essential for 1101 better-informed management programs. 1102

1103

Introduction 1104

Human demand for cultivated fruits has increased in recent decades (ERS 2015), due in 1105 part to increased attention on the associated health benefits of fruit consumption (e.g., World 1106

Health Organization 2010, Lock et al. 2005, Hjartåker et al. 2014). Fruit production in the United 1107

States is substantial; 28.4 million tons in 2014 with a value of more than $US20 billion (ERS 1108

2015). High-value fruit crops are often subject to extensive depredation by fruit-eating birds 1109

(Beal 1915, Dolbeer et al. 1994, Simon 2008); resulting financial losses are of major concern to 1110 fruit producers. Growers in Michigan, New York, and the Pacific Northwest recently estimated 1111 that bird damage to wine grapes, blueberries, apples, and cherries costs over $US180 million 1112 annually (Anderson et al. 2013). Successful strategies to mitigate bird damage have been 1113 hindered by the lack of information regarding 1) how much of the crop different bird species 1114 consume and 2) the size of local populations of crop-damaging species (Dolbeer et al. 1994, 1115

Somers and Morris 2002). Here, we used bioenergetic modeling to quantify avian consumption 1116 of a high-value crop, sweet cherry (Prunus avium), by two prominent fruit-consuming species. 1117

We provide the first species-specific, region-wide estimates of bird damage for this crop. We 1118 then integrated these bioenergetic-based consumption estimates with avian population data and 1119 the economic value of sweet cherry to generate models of monetary losses due to bird damage. 1120

A bioenergetic approach provides species-specific quantifications of bird damage that are 1121 based on avian energetic needs, diet composition, and energy content of the focal crop (Wiens 1122 and Dyer 1975, Peer et al. 2003). This approach overcomes a common challenge in 1123 understanding avian crop damage: quantifying the impact of individual pest species (Somers and 1124

Morris 2002, Simon 2008). Field-based estimates typically quantify the extent of pest damage by 1125 identifying damaged and missing fruits (e.g., Tracey and Saunders 2010, Lindell et al. 2016). 1126

Yet, such approaches cannot attribute damage to particular species because doing so would 1127 require near-continuous, wide-scale observation of birds feeding in crops. Such efforts are time- 1128 and cost-prohibitive. Previous attempts to quantify fruit damage by individual species have relied 1129

upon surveys of birds seen in orchards and attributed crop damage to the most frequently 1130 detected species (e.g., Johnson et al. 1989). However, frequently detected species are not 1131 necessarily the most important fruit consumers (Virgo 1971, Lindell et al. 2012). 1132

Economic models are typically used to estimate financial loss resulting from avian fruit 1133 damage and consumption. Usually economic models are based upon estimates of damage 1134 quantified by lost yield estimated by producers or from in-field damage surveys (Drake and 1135

Grande 2002). However, these methods can be subject to a high degree of variance and are time- 1136 intensive and personnel-intensive (Peer et al. 2003, Saxton 2006, Lindell et al. 2016). 1137

Additionally, previous economic models have typically assessed damage at the taxonomic level 1138 of class (e.g., Aves) rather than species (Drake and Grande 2002), yet Lindell et al. (2012) have 1139 shown that the degree of avian fruit consumption of sweet cherries is species specific. Accurate 1140 and species-specific assessments of financial losses due to bird depredation are crucial for the 1141 targeted and cost-effective implementation of loss-prevention strategies (Somers and Morris 1142

2002, Tracey et al. 2007). 1143

A bioenergetic approach has been undertaken to quantify damage to crops like corn 1144

(Weatherhead et al. 1982) and sunflower (Peer et al. 2003), but not in other valuable crops such 1145 as fruits. On a per-block annual basis cherry growers lose 2 – 13% of their crop to bird damage 1146 or consumption (Lindell et al. 2016). A block is defined as a contiguous area of crop with 1147 boundaries to adjacent land cover types of at least wide meters in width. Michigan growers 1148 estimate that bird damage to sweet cherries amounts to annual losses of over $US2 million 1149 statewide (Anderson et al. 2013). American robins (Turdus migratorius) and cedar waxwings 1150

(Bombycilla cedrorum) are frequent visitors to sweet cherry orchards throughout the growing 1151 season (Eaton et al. 2016) and consume more sweet cherries than other fruit-consuming birds 1152

(Lindell et al. 2012). Additionally, fruit producers perceive these species to be problematic, 1153 especially robins (Anderson et al. 2013). However, the extent of sweet cherry consumption and 1154 resulting economic cost of damage by these particular species over the fruit-ripening season in 1155 northwestern Michigan, an important sweet cherry production region, is not known. For this 1156 study, we developed species-specific bioenergetic models of American robin and cedar waxwing 1157 cherry consumption over the cherry-ripening season. We then incorporated these consumption 1158 data, along with population estimates, into economics models to quantify economic losses 1159 sustained by growers due to robin and waxwing crop damage. 1160

1161

Methods 1162

We conducted this study in Leelanau County, Michigan, a peninsula (land area = 900 1163 km2, water area = 5659 km2) on Lake Michigan and an agricultural region containing many 1164 orchards and vineyards (Figure 2.1). We selected as our study area a region of eastern Leelanau 1165

County (208 km2) that contains a majority of the county’s sweet cherry orchards. We used the 1166

USDA NASS CropScape program and the 2014 National Cropland Data Layer (NASS 2014) to 1167 calculate how much of this area comprised each of the three habitat types used in our study. Our 1168 study area consists of 70.7 km2 (33.9%) woodland, 51.8 km2 cherry orchard (sweet and tart 1169 varieties; 24.9%), and 20.0 km2 urban/built space (9.6%). Other land use types in the area include 1170 grassland/pasture, fallow or barren land, herbaceous land, and crops like corn and alfalfa (NASS 1171

2014). Sweet cherries make up 43% of the total cherry acreage in Leelanau County and 22% of 1172 total cherry acreage in Michigan (NASS 2015a). Given that the majority of cherry orchards in 1173 the county are included in our study area, we assumed this ratio of sweet to tart cherries held true 1174

in our study area. Thus we used this ratio to calculate the area of sweet cherries (22.3 km2, 32%) 1175 in our study area from total cherry area. 1176

Mean monthly rainfall and air temperature during the 2015 study period (May – July) 1177 were 5.8 cm and 17.4 °C, respectively (NWMHRS 2015). Both robins and waxwings are 1178 common in the study region during the summer cherry-growing season. Waxwings arrive in the 1179 region by late May and nest between mid-June and August (McPeek 2011a). Robins typically 1180 arrive in March, begin nesting in April, and often rear two broods (McPeek 2011b). 1181

1182

Bioenergetic models of avian sweet cherry consumption 1183

We generated species-specific bioenergetic models to estimate daily sweet cherry 1184 consumption by robins and waxwings. We first quantified robin and waxwing field metabolic 1185 rates (FMR) using a formula for free-living passerines (Nagy et al. 1999): 1186

y = 10.4x0.68 1187 where y is FMR (kJ/day) and x is mean bird body mass (grams). This formula is appropriate for 1188 estimating metabolic rate of wild birds because it incorporates basal metabolic rate, as well as 1189 activities like foraging and digestion, flying, reproduction, growth, and anti-predator behavior 1190

(Nagy et al. 1999). We calculated mean body mass from birds captured in mist nets (methods 1191 described below) from 30 June – 19 July 2015. 1192

Our species-specific bioenergetic models of cherry consumption during the fruit-ripening 1193 period took the following form: 1194

Per-bird consumption = !"# × moisture × diet × days 1195 !"#$$% !"!#$% !"#$%&'

1196

where DER is daily energy requirements (kJ/day), cherry energy density is the energy content of 1197 sweet cherries (14.85 kJ/g dry mass), moisture is a correction for water content of consumed 1198 cherry (1.82; USDA Agricultural Research Service 2016), diet is the proportion of sweet cherries 1199 in the diet, and days is the number of days in the study region over which cherries are most 1200 vulnerable to depredation (28 days). 1201

For both species we quantified daily energy requirements (DER) in kJ/day by dividing 1202

FMR by the apparent metabolizable energy coefficient (MEC*) of birds feeding on fruit (Smith 1203 et al. 2007). MEC* is defined as: (energy content of ingested food – energy content of excreta) / 1204 energy content of ingested food) (Karasov 1990). We used an MEC* value of 0.64 for passerines 1205 feeding on fruit pulp and skin (Karasov 1990, Smith et al. 2007). We calculated species-specific 1206 daily required dry food intake if birds were feeding solely on sweet cherries (g/ day) by dividing 1207

DER by the energy density (kJ/g dry mass) of sweet cherries (USDA Agricultural Research 1208

Service 2016). As these values are based on dry mass, we corrected for moisture content of sweet 1209 cherries (Peer et al. 2003, Smith et al. 2007) by multiplying by 1.82 (one plus the moisture 1210 proportion of raw sweet cherries: 0.82). To estimate the actual daily mass of sweet cherries 1211 consumed per bird, we multiplied this daily intake value by the proportion of sweet cherries in 1212 the diet of robins and waxwings (Peer et al. 2003, Smith et al. 2007). 1213

We quantified cherry consumption and subsequent economic loss for the period in our 1214 study area when sweet cherries are vulnerable to robin and waxwing depredation (Alkio et al. 1215

2014). We defined this as the twenty-eight days between the date on which cherries were first 1216 detected in fecal samples (method described in the following section) and the last date of cherry 1217 harvest in 2015. We excluded samples (n=11) collected prior to this period because cherries were 1218 not yet ripening. We did not distinguish males and females for analyses because sexes were of 1219

similar size, several waxwings could not be reliably sexed, and males and females did not differ 1220 in the proportion of cherries in the diet for either species. 1221

1222

Estimating proportion of sweet cherries in diets 1223

We captured birds in mist nets (38-mm and 30-mm) in four sweet cherry orchards from 1224

30 May – 19 July 2015 (Figure 2.1); nets were open for a total of 1255 net-hours. 1225

1226

1227

Figure 2.1 Map of Leelanau County study region with mist netting and point count 1228 locations identified. Map of Leelanau County, Michigan with Traverse City, Michigan 1229 identified. Shaded region identifies the 208 km2 study area. Open triangles identify mist-netting 1230 sites (n = 4) for fecal sample collection. Black circles identify point count (n =22) survey 1231 locations. 1232 1233

We extracted and placed each captured robin or waxwing in a paper bag to collect fecal samples. 1234

We air-dried samples in bags for at least 24 hours, transferred samples to vials, and froze them 1235 until processing. Fecal samples are a non-invasive technique for avian diet analysis and can 1236 provide accurate estimates of diet composition without the risks associated with use of emetics or 1237

harvest of stomach contents (Ralph et al. 1985, Rosenberg and Cooper 1990, Carlisle and 1238

Holberton 2006). Differential digestibility of diet items (e.g., invertebrates versus plant material) 1239 can complicate the use of fecal samples to quantify proportions of foods in bird diets. To 1240 overcome this, we applied correction factors based on the assimilability of food types (see 1241 below). 1242

Under a dissecting microscope, we sorted fecal sample contents into major food types: 1243 animal matter, fruit skin, seeds, and fruit pulp. We identified sweet cherries among samples of 1244 fruit skin by viewing samples under a compound microscope and comparing these to reference 1245 samples and images from the literature (Jordano and Herrera 1981, Martella et al. 1996, Oliveira 1246 et al. 2002). For each sample that contained sweet cherry skin, we dried fecal material to 1247 constant mass at 60 °C and weighed it to the nearest 0.001 g. We calculated the proportion by 1248 dry mass of each food type in these samples (n = 89). To account for differential digestibility of 1249 food types, we applied correction factors based on the apparent assimilable mass coefficients 1250

(AMC*) of particular food types (Lane et al. 1999). AMC* = (Dry mass of food consumed – Dry 1251 mass of excreta) / Dry mass of food consumed (Karasov 1990, Fassbinder-Orth and Karasov 1252

2006). Following suggestions of Castro et al. (1989) we used published species-specific AMC* 1253 values for a given food type, when available, and mean AMC* values for passerines for a given 1254 food type when species-specific information was unavailable (Table 2.1). We divided the dry 1255 mass of each food type in a sample by (1- AMC*) for that food (Lane et al. 1999). We assumed 1256 any pulp and fruit skin in a sample to be from the same fruit type because in no instance did a 1257 sample contain more than one type of fruit skin as well as pulp. We then calculated mean 1258 corrected proportions of sweet cherries, animal matter, and other fruit in samples (Lane et al. 1259

1999). The final mean proportions of sweet cherries in robin and waxwing diets included 1260

samples with no cherries present. Non-cherry fruit occurred only in waxwing fecal samples, 1261 therefore we used an AMC* value from waxwings feeding on a diet of mixed lipid-rich and 1262 sugar-rich fruits to correct the proportion of non-cherry fruit in the waxwing diet (Table 2.1). 1263

1264

Table 2.1 Types and sources of AMC values used to calculate proportion of sweet cherries 1265 in diet. 1266 1267

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 – 1268

0.03 (Karasov 1990). 1269

1270

Population sizes 1271

To quantify the size of robin and waxwing populations in the study region, we conducted 1272 fixed-radius (25 m) point count surveys at 22 randomly selected sites throughout the study area. 1273

Sixteen of these sites were at sweet cherry orchards, 3 were in urban/built areas and 3 were in 1274 woodland areas. All point count sites were > 1.8 km from one another. We initiated surveys 1275 between 0700 and 0930 and between 1700 and 1930 eastern daylight time; each survey was eight 1276 minutes in duration. We recorded all visual and aural observations of robins and waxwings 1277 detected within the survey radius, including birds flying within 25 m above the survey point. The 1278 first author (RAE) conducted surveys between 9 June and 29 July 2015 and surveyed each 1279 location five times. Temporal replicates allow for accommodation of imperfect detection of 1280 unmarked individuals (Fiske and Chandler 2011). We used counts of robins and waxwings from 1281

each survey to fit N-mixture models of population abundance (Royle 2004), using the R package 1282

“unmarked” (Chandler and Fisk 2011). We modeled population abundance as a negative 1283 binomial process and extracted the mode and 95% credible intervals for the abundance values at 1284 each survey site. We then used these estimates to quantify bird densities in each of the three 1285 surveyed land cover types (sweet cherry orchards, developed land, woodland). We multiplied 1286 these densities by the total area of each of the three surveyed land cover types of our study area 1287 to estimate habitat-specific population sizes. Finally, we summed habitat-specific estimates to 1288 quantify the total population sizes of robins and waxwings in our 208-km2 study area; we 1289 incorporated these population sizes into economic models of avian damage to sweet cherries (see 1290 below). We performed analyses in R statistical software (Version 3.0.3; R Core Team 2012). 1291

1292

Economic models of avian damage to sweet cherries 1293

Our bioenergetic models generated a per-bird total mass of cherry skin and pulp 1294 consumed because robins and waxwings do not digest and obtain energy from the cherry pit. 1295

However, the market price that fruit growers are paid includes the cherry pit mass, and economic 1296 models based on per-bird cherry consumption alone would underestimate loss. The proportion of 1297 cherry pit mass to total fruit mass comprises a range of 0.07 to 0.11 (Bandi et al. 2010). To 1298 account for the mass of the unconsumed pit when calculating financial loss, we multiplied our 1299 bioenergetic estimate of consumption by a correction factor of one plus the midpoint of this 1300 range (1.09). 1301

We used the average market price for sweet cherries in Michigan over the five-year 1302 period of 2010-2014 ($US0.0011/g; NASS 2015b) to calculate per bird economic loss over the 1303 study period. In addition, we multiplied this monetary value by population abundance estimates 1304

for each species to quantify the population-scale economic losses from robin and waxwing 1305 damage to sweet cherries. We calculated a range of economic loss estimates based on the 95% 1306 credible intervals of the population abundance estimates. 1307

As part of a related study, we conducted 150 man-hours of focal animal observations 1308

(Altmann 1974) on robins and waxwings consuming sweet cherries in Michigan orchards over 1309 the cherry-ripening period from 4 June 4 – 2 July, 2012 and 1 July – 24 July, 2014. All 1310 waxwings observed (n = 43) were consuming cherries directly from the tree, while robins ate 1311 cherries both in the tree and on the ground (n = 62; RAE, unpublished data). During 41% of 1312 these observations, robins ate cherries only in the tree. Given that growers do not harvest cherries 1313 that have fallen to the ground, only fruit consumption within trees should result in economic loss 1314 for growers. Therefore, in the economic model of loss due to robins we used an additional 1315 correction factor of 0.41 to represent the proportion of robin sweet cherry consumption that 1316 occurs in trees. 1317

Our species-specific models of economic loss from damage to sweet cherries took the 1318 following form: 1319

Per-bird economic losses = per-bird consumption × pit correction × market price 1320

1321 where per-bird consumption is the total per-bird cherry consumption over the study period from 1322 our bioenergetics model, pit correction is a correction for the mass of cherry pit that birds do not 1323 utilize for energy but for which growers are paid (1.09), and price is the market price of sweet 1324 cherry ($US0.011/g). Robin models included an additional correction (0.41) for birds feeding in 1325 cherry trees. 1326

1327

Results 1328

Bioenergetic models of avian sweet cherry consumption 1329

We analyzed 47 American robin fecal samples and 31 cedar waxwing samples. We 1330 estimated daily energy requirements of robins and waxwings at 318.8 and 181.3 kJ/day, 1331 respectively. After correcting for assimilation efficiency, the proportion of sweet cherries in the 1332 robin diet was 10.8% (± 24); the waxwing diet contained 51.4% (±46) sweet cherry. For 1333 comparison, the uncorrected proportions of sweet cherry in the diet were 13.6% (±27) for robins 1334 and 51.3% (±46) for waxwings. Based on these results, individual robins consumed 4.0 g sweet 1335 cherry each day, while waxwings consumed 12.0 g sweet cherry each day (Table 2.2). 1336

1337

Economic models of avian damage to sweet cherries 1338

Robins and waxwings have a combined estimated population size of 772,956 individuals 1339 in the cherry orchard, woodland, and developed areas of our study region (Table 2.2). These two 1340 cherry-eating species caused an estimated $US1,986,948 in losses to local cherry producers 1341 during the 2015 cherry-ripening season. When considering the 95% CI of population estimates, 1342 this loss may range from $US991,609 - $US3,952,449. 1343

1344

Table 2.2 Estimated sweet cherry consumption by American robins and cedar waxwings in 1345 northwest Michigan orchards and resulting economic losses. 1346 1347

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

Table 2.2 (cont’d) 1348

267,563 455,393 Population size (95% CI) (151,227 – 618,994) (224,860 – 894,060) Total economic loss ($US) 0.55 4.04 Per bird^ 147,160 1,839,788 Per species (95% CI)^ (83,175 – 340,447) (908,434 – 3,612,002) *Means and standard deviations of field-collected body masses. ^Values for the entire 28- 1349

day pre-harvest study period. 1350

1351

Discussion 1352

In these first bioenergetic and economic models of avian damage to fruit crops we 1353 demonstrate that species differences in proportion of fruit in the diet and local population sizes 1354 result in substantial between-species variation in fruit consumption and resulting financial loss. 1355

Our bioenergetic models generated damage estimates based upon species-specific diet 1356 and physiology data. Recent field studies of birds in cherry orchards demonstrated that bird 1357 species vary in both their reliance on orchard habitat (Eaton et al. 2016) and their cherry 1358 consumption (Lindell et al. 2012). Building upon this work, our models showed that a species- 1359 specific approach uncovers striking differences in the damage attributable to different species. 1360

We estimated that an individual cedar waxwing caused a financial loss that was seven times that 1361 of an individual robin. This substantial difference between species stems from 1) a five-fold 1362 greater proportion of cherries in the waxwing diet, than to robins, and 2) the fact that waxwings 1363 consume only saleable cherries that are still on the trees, while robins often forage on the ground. 1364

Despite robins’ greater daily energy needs, waxwings had a substantially larger 1365 proportion of sweet cherries in the diet. We found that sweet cherries comprised 11% of the 1366 robin diet; this is similar to Virgo’s (1971) calculation of 12% for robins caught in Ontario sweet 1367

cherry orchards. We estimated that sweet cherries made up 51% of the waxwing diet, which 1368 corresponds reasonably with Witmer’s (1996) estimate that fruit, including non-cherry species, 1369 comprises 72% of the waxwing summer diet. Our results suggest that class-based (i.e., all birds) 1370 or non-specific quantification of damage misses important patterns that could influence bird 1371 management strategies. Assuming that all species detected in orchards have equal potential as 1372 fruit consumers could incorrectly characterize the problem. For example, robins and waxwings 1373 are both frequently observed in orchards, but our species-specific approach demonstrated that 1374 these species vary widely in the extent of damage inflicted. This is consistent with previous 1375 studies of robins in sweet cherry orchards that show relatively low cherry consumption by 1376 robins, despite their frequent detection (Virgo 1971, Lindell et al. 2012). 1377

Where birds feed in an orchard affects their role as a crop pest. In our system, waxwings 1378 eat cherries in trees while robins feed both in trees and on the ground (RAE, unpublished data). 1379

Virgo (1971) also noted extensive ground foraging by robins in Ontario sweet cherry orchards. 1380

Cherry growers only harvest and sell fruit from trees, and our models indicated that robins 1381 feeding in trees caused relatively little damage compared to waxwings. However, robins 1382 occasionally knock fruit to the ground or descend from a tree carrying a fruit and consume it on 1383 the ground (RAE, personal observation). We cannot assume all instances of robin ground 1384 feeding are completely free of cost to fruit growers. However, waxwings are clearly more 1385 problematic to growers than robins, whose ground consumption of fruit may actually be 1386 beneficial in removing resources for arthropod or fungal species that damage fruit. Despite 1387 growers’ frequent reports that robins are pest species in sweet cherry orchards (Anderson et al. 1388

2013), our current study and previous work suggest that bird damage mitigation efforts in 1389

northwest Michigan sweet cherry orchards could be more targeted and efficient if focused on 1390 waxwings (Lindell et al. 2012). 1391

Our study is the first bioenergetic approach to quantifying bird damage to fruit. This 1392 technique has been used successfully in field crops (e. g., Peer et al. 2003) to provide regional, 1393 species-specific estimates of avian crop damage. Previous attempts to attribute damage to 1394 various bird species have often relied on surveys of birds flying in orchards or observations of 1395 birds consuming crops (e.g., Virgo 1971, Johnson et al. 1989). However, bird species vary in 1396 their conspicuousness, and survey or observation-based data alone may provide inaccurate or 1397 incomplete information on the level of the damage (Dolbeer et al. 1994). In addition, such 1398 approaches typically have not considered population size variation among potential pest species. 1399

Our study demonstrated the added importance of population data to understanding the 1400 capacity of certain species for crop damage. For example, individual waxwings caused seven 1401 times greater financial loss than robins over the study period. However, when extrapolated to the 1402 population scale, waxwings caused 12.5 times greater financial loss to the region than robins. 1403

This substantial difference in robin and waxwing estimated population sizes is a largely a result 1404 of more widespread waxwing sightings and larger waxwing groups, than robins. We detected 1405 waxwings more frequently, at more locations, and in larger groups than robins, leading to our 1406 larger population estimates for waxwings than robins. 1407

Our population estimates are reasonable for robins and waxwings in a fruit-growing 1408 region. Our estimate of 1878 robins per km2 in sweet cherry habitat is similar to Virgo’s (1971) 1409 average density of 18.53 robins per hectare (or 1853 per km2) of sweet cherry orchards in 1410

Ontario, Canada. We estimated that waxwings have a larger population than robins, which is 1411 expected given waxwings’ smaller size, the great amount of cultivated fruit in the region, and the 1412

waxwings’ greater reliance on fruit (Witmer 1996). Fruit comprises the majority of waxwings’ 1413 diet throughout the year (Witmer 1996). In addition, waxwings are semi-colonial breeders and 1414 relatively non-territorial (Lea 1942). Rothstein (1971) reported a density of 14.1 nests per hectare 1415

(1410 nests per km2) in a forested area near a field of wild cherry trees in northern Michigan. 1416

Waxwings also forage in large groups in orchards away from the nesting site (Lea 1942, Nelms 1417 et al. 1990, Lindell et al. 2012). For example, a group of > 500 waxwings consumed blueberries 1418 on a 0.024 km2 farm in Florida (Nelms et al. 1990). Our own work has shown that waxwings in 1419 cherry orchards forage in groups four times larger than groups of robins, a territorial species 1420

(Lindell et al. 2012). The gregarious and frugivorous nature of waxwings could allow for a high 1421 number of birds during the breeding season, particularly in resource-rich fruit-production 1422 regions. 1423

We conducted this study in a major sweet cherry growing area of Michigan. 1424

Approximately half of Michigan’s total sweet cherry acreage is in Leelanau County (NASS 1425

2015a). We estimated that our 208 km2 study area within Leelanau County contained 22.6 km2 of 1426 sweet cherry (~29% of total Michigan sweet cherry acreage; NASS 2014). Our bioenergetic 1427 models of damage suggested robins and waxwings caused at least $US991,000 in damage in this 1428 area and may cause as much as $US3.9 million. Anderson et al. (2013) used grower-reported 1429 damage estimates from 2011 to generate a statewide damage value for Michigan sweet cherries. 1430

They calculated that all birds collectively caused $US2,090,723 in damage to sweet cherries 1431 annually (Anderson et al. 2013). Given that robins and waxwings are two significant cherry- 1432 consuming species in Michigan (Lindell et al. 2012), our estimate of damage by these two 1433 species is reasonable for an area that contains a substantial proportion of all sweet cherry 1434 orchards in Michigan. 1435

Provided model inputs are available or obtainable (e.g. proportion of crop in the diet and 1436 avian population size) for the area of interest, bioenergetic models of damage are applicable to a 1437 variety of crops and to production areas of various sizes. Bioenergetic and economic models of 1438 avian crop damage are important prerequisites to comprehensive damage management programs. 1439

Sweet cherry growers implement bird damage management strategies in an effort to reduce 1440 financial losses. Economic models indicate that these efforts save the United States sweet cherry 1441 industry up to $US238 million in the short run and up to $US29 million in the long run (Elser et 1442 al. 2016). However, evidence suggests that many commonly used management techniques like 1443 scare-eye balloons, raptor-resembling kites, and loud audio deterrents have limited or 1444 inconsistent success in mitigating bird damage to fruit crops (Eaton 2016). These may be 1445 particularly ineffective for species like waxwings that are relatively tolerant of people and have a 1446 short fright distance (Eaton 2016). Bird damage mitigation techniques that specifically target 1447 problem-species, like waxwings, may permit more efficient management. For example, planting 1448 a more-preferred fruit source adjacent to orchards, a decoy crop, could draw waxwings away 1449 from cherry crops and reduce damage. Waxwings show size preference when selecting berries 1450 and choose fruits that are much smaller (~7.5 mm diameter; Avery et al. 1993) than ripening 1451 sweet cherries (~12 – 23 mm; NWMHRC 2016). Therefore, luring waxwings away from cherry 1452 crops could be an additional management strategy that would increase success more than solely 1453 attempting to frighten them away. 1454

Bioenergetic model estimates add to the suite of damage quantification tools including 1455 block-based field estimates and crop producer surveys. Our bioenergetic approach to quantifying 1456 bird damage to fruit can be used to assess the impact of other potential problem species. For 1457 example, cherry, apple, blueberry and grape growers surveyed across the U.S. identified 1458

common starlings (Sturnus vulgaris) as important fruit pests (Anderson et al. 2013). A 1459 bioenergetic approach would provide a tool to quantify the impact of this and other suspected 1460 avian crop pests. In turn, model estimates allow us to contrast damage from multiple pest species 1461 and understand pest species ecology, which is essential for more informed pest management 1462 programs and damage management (Dolbeer et al. 1994, Somers and Morris 2002). 1463

1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490

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59 CHAPTER 3 1691 1692 AMERICAN ROBINS AND CEDAR WAXWINGS VARY IN USE OF CULTIVATED 1693 CHERRY ORCHARDS 1694 1695 1696 1697 Rachael A. Eaton, Catherine A. Lindell, H. Jeffrey Homan, George M. Linz, and Brian A. Mauer 1698 (Wilson Journal of Ornithology, 2016,128: 97-107). 1699

60 Abstract 1700

Some fruit-eating bird species commonly consume cultivated fruit. Species-specific 1701 variation in diet preferences could result in varying use of orchards and impacts on the fruit- 1702 producing industry by different bird species. However, species-specific studies of avian orchard 1703 use are lacking, particularly throughout the fruit-growing season. Our objectives were to quantify 1704 the frequency of daily bird visits to orchards and the amount of time birds spent visiting orchards 1705 each day over the fruit-ripening season. Birds are well-documented consumers of cultivated 1706 sweet cherries (Prunus avium), which are relatively high in sugar and low in proteins and lipids. 1707

American Robins (Turdus migratorius) and Cedar Waxwings (Bombycilla cedrorum) are 1708 common fruit-consumers in sweet cherry orchards. Robins often consume larger proportions of 1709 invertebrates and prefer lipid-rich fruits, while waxwings choose sugary fruits. Given these 1710 species-specific diet differences, we hypothesized waxwings would spend a greater proportion of 1711 days and more time each day in cherry orchards than robins. We used radio telemetry to track the 1712 habitat use of 25 American Robins and 17 Cedar Waxwings in Michigan sweet cherry orchards. 1713

Over their respective radio-tracking periods, waxwings visited orchards a marginally greater 1714 percentage of days than robins (waxwings: mean = 21%, SD = 22; robins: mean = 6%, SD = 4). 1715

In addition, waxwings visited orchards for significantly more time each day. Differences in diet 1716 preferences and nutritional physiology may translate into species-specific patterns of habitat use 1717 for birds in fruit-rich environments. 1718

1719

Introduction 1720

Animals are expected to forage where and when they can obtain sufficient accessible and 1721 nutritious foods (Hengeveld et al. 2009). Orchards offer rich patches of foraging habitat with 1722

abundant food resources for birds, which lead to conflicts with orchard growers (Simon 2008). 1723

Growers experience lost yields and often implement costly techniques to mitigate bird 1724 consumption of crops (USDA 1998, Anderson et al. 2013). Fruit growers in New York, 1725

Michigan, and the Pacific Northwest have reported yield losses due to birds of up to 31% in 1726 cherries, 18% in blueberries, and 9% in wine grapes; these losses translate to tens of millions of 1727 dollars (Anderson et al. 2013). Knowledge of foraging patterns of avian frugivores in and around 1728 orchards can thus offer crop producers valuable information for mitigating bird consumption of 1729 crops through targeting species and times when fruit loss is greatest (Dolbeer et al. 1994, Somers 1730 and Morris 2002, Tracey et al. 2007). For example, bird consumption of blueberries is greater in 1731 early ripening varieties; such information could allow growers to identify fruit varieties that 1732 ripen later in the season and reduce the potential for avian crop consumption (Tobin et al. 1991). 1733

Birds are well-documented consumers of cultivated sweet cherries (Prunus avium) 1734

(Curtis et al. 1994, Lindell et al. 2012), but neither the frequency nor length of bird visits to 1735 commercial cherry orchards over the growing season have been established. A limited 1736 understanding of the orchard use and behavior of cherry-consuming species has hindered our 1737 ability to develop effective management programs that minimize costs of bird activity in 1738 orchards (Tracey et al. 2007). Cherry orchards are attractive for fruit-eating birds because 1739 cherries are brightly colored, densely available, and have easily accessible pulp (Sallabanks 1740

1993). Cherries are relatively sugar-rich fruits; >50% of dry pulp mass comprises sugars (Witmer 1741 and Van Soest 1998, USDA 2013b). However, the lipid and protein contents among Prunus 1742 fruits (including cherries) are relatively low (<2%) compared to the protein content of other fruits 1743

(5%) or insects (Stiles 1993, Witmer and Van Soest 1998). Fruit consumption by birds is 1744 behaviorally and physiologically complex (Sallabanks 1993, Levey and Martinez del Rio 2001, 1745

Corlett 2011). Birds can discern nutritional differences among food types and make foraging 1746 decisions to meet energetic and nutritional needs (Lepczyk et al. 2000, Schaefer et al. 2003, Alan 1747 et al. 2013). Therefore, cherries may not appeal equally to all birds. 1748

American Robins (Turdus migratorius) and Cedar Waxwings (Bombycilla cedrorum) 1749

[hereafter robins and waxwings] are highly frugivorous (Wheelwright 1986, Witmer 1996). 1750

These species are also responsible for a relatively high proportion of observed avian cherry 1751 consumption compared to other species, e.g. American Crow (Corvus brachyrhynchos), 1752

Common Grackle (Quiscalus quiscula), European Starling (Sturnus vulgaris; Lindell et al. 1753

2012). Fruit comprises ~57% of the annual diet of robins, in addition to large proportions of 1754 invertebrates, while waxwings’ diet contains ~84% fruit (Witmer 1996). In addition, robins more 1755 efficiently assimilate and prefer fruits that are relatively high in proteins and lipids, and low in 1756 sugars (Stiles 1993, Willson 1994, Witmer and Van Soest 1998, but see Lepcyzyk et al. 2000). 1757

Waxwings more efficiently assimilate and show a preference for high-sugar fruits (Witmer and 1758

Van Soest 1998, Witmer 1998). Furthermore, waxwings maintain body mass on fruit alone for 1759 extended periods, for example up to 27 days (Holthuijzen and Adkisson 1984), despite fruit’s 1760 relatively low protein content. In contrast, Levey and Karasov (1989) attempted a 10-day fruit- 1761 only feeding trial on captive robins but shortened the trial to four days as birds had already lost 1762

10-14% of initial body mass. In addition, waxwings feed nestlings insects for approximately 1763 three days (Putnam 1949), after which fruit comprises 87% of food deliveries (Lea 1942), while 1764 robins provision almost exclusively with animal matter (Hamilton 1935), but older nestling 1765 robins may also receive fruit (Eaton 1914). Our previous work has revealed relatively little use of 1766 cherry orchards by robins and waxwings for invertebrate consumption and nesting activities, 1767 therefore we focused this study on fruit consumption. 1768

These species-specific patterns likely influence the frequency and length of foraging 1769 visits in orchards during the cherry-growing season. For certain frugivorous birds, orchards may 1770 become increasingly attractive as fruits ripen because sugar content increases and bird energy 1771 needs may be met more efficiently (Serrano et al. 2005). Furthermore, after harvest, orchards 1772 may no longer be viable foraging habitat for avian frugivores, given the near-complete removal 1773 of fruit from trees (<10% of cherries remaining, M. Whiting, Personal communication). 1774

Here, we used radio-telemetry to evaluate the use of cultivated sweet cherry orchards in 1775

Michigan by robins and waxwings. We hypothesized that waxwings would exhibit more intense 1776 use of cherry orchards than robins based on their diets. We predicted that waxwings would 1) 1777 visit focal orchards on more days throughout the cherry season, and 2) spend more time each day 1778 visiting orchards than robins. Further, given that sugar content increases as cherries ripen, we 1779 expected that 1) robins and waxwings would increase their use of orchards as harvest 1780 approached, and 2) that orchard use by both species would decline abruptly after cherries were 1781 harvested and fruit availability declined. For both of these expectations, we predicted a stronger 1782 effect for the more fruit-specialized waxwings. 1783

1784

Methods 1785

Study Area & Species 1786

We conducted the study in four sweet cherry orchards in Leelanau County, near Traverse City 1787

(44° 46’ N, 85° 37’ W), in northwest Michigan from June - September 2013 (Figure 3.1). 1788

Leelanau County is a peninsula (land area = 900 km2, water area = 5659 km2 ) extending into 1789

Lake Michigan and an agricultural region comprising many orchards (e.g. sweet and tart 1790 cherries, wine grapes, apples). As of 2012, orchards comprised 6% of the county land area, with 1791

sweet cherry orchards accounting for 2% of the county land area (USDA 2012). Other major 1792 crops and land cover types include alfalfa, mixed forests, and residential or developed areas 1793

(Lindell et al. 2012). The average rainfall during the 2013 fruit-growing season (April – October) 1794 was 54.9 cm (Northwest Michigan Horticultural Research Station 2013). 1795

1796

Figure 3.1 Map of Leelanau County study region and focal sweet cherry orchards. Map of 1797 Michigan with Leelanau County identified (left). Leelanau County with Traverse City and the 1798 four sweet cherry study orchards identified (right). 1799 1800

The mean distance between study sites was 5.1 km (range = 1.4 – 10.4 km). One site was 1801 located at the Northwest Michigan Horticultural Research Station (STA) and Cherry Bay (CB) 1802

Orchards, Inc. managed three sites (Table 3.1). In the study region, sweet cherry trees typically 1803 reach full bloom in early May; small green fruits are evident 20 days later, and cherries begin 1804 ripening 50 - 60 days after full bloom. Growers apply a variety of insecticides depending upon 1805 the target pest species, fruit growth stage, and product availability (W. Klein, Personal 1806 communication). Products vary in required application frequency (e.g. from three days up to two 1807 weeks). Michigan State University Extension provides recommendations for insecticide use to 1808 commercial fruit producers in the region. Orchard managers prune trees and mow grass 1809 occasionally throughout the growing period. 1810

In Michigan, robins are abundant during the breeding season and typically arrive in 1811

March; most robins do not overwinter (McPeek 2011a). Robins begin nesting in April and May 1812 and commonly rear two broods (Howell 1942, McPeek 2011a). Waxwings are common in the 1813 study region where they travel and forage in small flocks year-round (McPeek 2011b). 1814

Waxwings generally arrive in Michigan by late May (although some overwinter). They are 1815 among the latest nesting birds in North America and nest in colonies in trees of various species 1816 including maple (Acer spp.), oak (Quercus spp.), and pine (Pinus spp.; Lea 1942, Putnam 1949, 1817

Rothstein 1971). The majority of nesting occurs between mid-June and August (McPeek 2011b). 1818

Orchard growers in the study region do not remove nests from cherry trees during the growing 1819 season (growers may remove old nests during the winter; W. Klein, Personal communication). 1820

1821

Capture & Radio Deployment 1822

We captured birds via mist nets in each study orchard and radio-tagged adult robins and 1823 waxwings between 1 June and 15 July. We typically opened nets by 0700 and closed them by 1824

1600 EDT. We aged and weighed birds and sexed them using external breeding characteristics 1825

(i.e. presence of brood patch or cloacal protuberance; Pyle 1997). Waxwings did not exhibit 1826 breeding characteristics and could not be reliably sexed. We fitted a metal band, plastic colored 1827 bands, and an A1055 radio transmitter from Advanced Telemetry Systems (Isanti, Minnesota, 1828

USA) on 25 robins and 17 waxwings that appeared in good condition and were sufficiently 1829 massive to wear the 0.9 g radio transmitter and harness (<3% of bird body mass). We used 1-mm 1830 elastic cord and the figure-eight leg-harness method to attach transmitters (Rappole and Tipton 1831

1991). Transmitters broadcasted at a pulse rate of 30-34 pulses per min within a frequency range 1832 of 164.00–165.66 MHz. Expected battery life was 50 - 60 days. After radio deployment, we gave 1833

birds a two-day acclimation period to permit a return to normal behavior prior to data collection. 1834

Radio-tags from two waxwings were recovered during the study; we suspected both birds were 1835 depredated. We had valid orchard use data from both individuals and included these data in 1836 analyses. 1837

1838

Data Collection 1839

To track orchard use, we placed one stationary data receiving system in each study 1840 orchard, away from objects that could dampen or block incoming signals. We installed receiving 1841 systems between 15 June and 20 June 2013 and retrieved them on 11 September 2013. To 1842 assemble stationary systems, we encased a programmable, R4550 data-logging signal receiver 1843 from Advanced Telemetry Systems (Isanti, Minnesota, USA) powered by a deep-cycle battery in 1844 a large plastic container and cabled the receiver to a six-element Yagi antenna bolted to an 1845 elevated mount of 3-m height (Homan et al. 2013). Prior to data collection, we time and date 1846 synchronized all receivers. 1847

The receivers scanned through a list of all radio frequencies associated with birds, 1848 remaining on each frequency for 6 sec, for 24 hr per day throughout the study. If a frequency was 1849 detected during these 6 sec, the receiver monitored that frequency for 50 sec and recorded the 1850 date, time, and strength of the strongest signal (a function of distance between the receiving 1851 antennae and a bird’s transmitting antenna) detected for that bird during the 50 sec. The receiver 1852 then continued to scan through the list of frequencies. To promote independence among data for 1853 a given bird, receiving systems ultimately stored only the data record (if one was made) with the 1854 strongest signal detected over every 10-min period throughout the day. This record also included 1855 the number of radio pulses recorded for that bird during the 10-min period. The number of radio 1856

pulses reflected the number of times (i.e. for how long) a bird’s transmitter emitted the signal 1857 during the 10-min period. The strength of a bird’s signal did not affect the receiver’s ability to 1858 detect other birds in the area. If a particular frequency was not detected, the receiver scanned for 1859 the next frequency. To ensure that radio-tagged birds were still in the region during the study, we 1860 searched the area 5 -7 days per week using a pickup truck with a roof-mounted, rotatable set of 1861 dual 6-element Yagi antennae. The tracking periods of individual birds could include days on 1862 which birds were not detected using orchards, but were located in the study region during mobile 1863 searches. 1864

1865

Data Preparation 1866

Telemetry data receiving systems can detect false signals from objects (e.g. solar flares, 1867 power lines, garage door openers) with frequencies similar to those in our study. We used Visual 1868

Basic for Applications with Excel to cull false records and extract valid data for analysis. Valid 1869 data were those with associated pulse rates of 28–34 pulses per min; this range accounted for 1870 fully functioning radio transmitters, as well as slower pulsing radios whose batteries had 1871 weakened. We determined a bird was using an orchard if the receiver recorded a signal strength 1872

≥140 (maximum radio signal strength was 155). We conducted calibrations at each study site 1873 prior to deployment and determined that a signal of ≥140 would only register if a bird were in a 1874 study orchard. To calibrate orchard use, we affixed a radio transmitter to a 2 m-long pole, stood 1875 with the transmitter extended into a tree to simulate a bird at 4–6 locations in each orchard, and 1876 recorded the signal strength detected by the receiving system at each location. The line of sight 1877 receiving distance of the stationary systems was ≤0.05 km (Homan et al. 2103). 1878

1879

Day-to-day & Within-day Orchard Use 1880

We evaluated bird use of cherry orchards in two ways. First, we quantified day-to-day 1881 orchard use by calculating the proportion of days a bird was within a focal orchard out of the 1882 total number of days in the bird’s tracking period (defined as the first day after the bird’s 1883 acclimation period through the last day a bird was detected in the study region; Equation 1). We 1884 defined a day as only the daylight period (one hr before sunrise through one hr after sunset). We 1885 calculated one day-to-day orchard use value for each bird. Second, we quantified within-day 1886 orchard use to determine the amount of time birds visited focal orchards on a given day 1887

(Equation 2). To quantify within-day orchard use, we first identified the length of the daylight 1888 period for each day of the study and divided this period into 10-min time blocks. We then 1889 quantified the number of 10-min time blocks in which a bird was in an orchard on a given day 1890 and divided this by the total number of 10-min blocks of that day (Equation 2). An individual 1891 could have multiple within-day orchard use values. We report values for day-to-day and within- 1892 day orchard use as percentages. 1893

Equation (1) �� − �� − �� ℎ�� = !"#$%& !" !"#$ !"#$ !"#"$#"! !" !"#!!"# 1894 !"#$%& !" !"#!!! !"!#$ !"#$%&'( !"#$

Equation (2) ��ℎ�� − �� ℎ�� = !"#$%& !" !"!!"# !"#$ !"#$%& !" !"#!!"# !" !"# !"#$ 1895 !"#$%& !" !"#$ !"#$%& !" !"#!!! !"#$%&!! !"#$%&

1896

Statistical Analyses 1897

The proportion values for day-to-day orchard use were right-skewed so we applied a 1898 logarithmic transformation. We approximated the log-transformed day-to-day orchard use data 1899 using a normal distribution with equal variances, which satisfied assumptions for a two-sample 1900

Student’s t-test. We determined if day-to-day orchard use data from male and female robins 1901 could be pooled. To account for sample size differences in males (9) and females (3), we used 1902

boot-strapping techniques to select three samples from males at random, with replacement, to 1903 compare with females. We ran 1000 iterations of the sampling and t-test procedures and applied 1904 the false discovery rate approach to correct α for multiple statistical comparisons (Benjamini and 1905

Hochberg 1995). We used a two-sample Student’s t-test to evaluate differences in the day-to-day 1906 orchard use of robins and waxwings. 1907

We constructed generalized linear mixed models (GLMM) with a binomial distribution, a 1908 logit link function, and bird as a random effect to analyze within-day orchard use. We first used a 1909

GLMM with sex as a fixed effect and individual bird as a random effect (to account for some 1910 birds having multiple within-day orchard use values) to determine whether male and female 1911 within-day orchard use data could be pooled. A binomial distribution was appropriate for the 1912 within-day use data because we calculated these values as the proportion of 10-min time blocks 1913 in a given day in which a bird was in an orchard. The total number of time blocks in a day served 1914 as the number of trials for these models. We constructed 10 GLMMs to assess the effects of 1915 species and days-to-harvest on within-day orchard use (Table 3.2). We calculated the variable 1916 days-to-harvest for each date by subtracting this date from the harvest date of the relevant 1917 orchard. We included the orchard in which a bird was detected by a receiving system as a 1918 covariate for which we had no a priori expectation but which might have confounded the 1919 influence of days-to harvest. We used the Akaike Information Criterion corrected for small 1920 sample sizes (AICc) for model selection; we identified the best-fit model as that with ΔAIC 1921 value < 2 (Burnham and Anderson 2002). We performed analyses in R statistical software (R 1922

Core Team 2012), using ‘lme4’ (Bates et al. 2014) and ‘AICcmodavg’ (Mazerolle 2013) 1923 packages. 1924

1925

Results 1926

Study Demographics 1927

We outfitted 25 robins (16 males, 9 females) and 17 waxwings (sexes unknown) with 1928 radio transmitters (Table 3.1). 1929

Table 3.1 Study orchard area, 2013 harvest date, and land cover types adjacent to study 1930 orchards. The number and sexes (M: male, F: female, U: unknown) of American Robins and 1931 Cedar Waxwings caught at each sweet cherry orchard. Adjacent land covers (within 25 m of 1932 orchard edge) were assessed visually at each site as part of a related study in 2013. 1933

Orchard Site Harvest Adjacent land cover types Robins Waxwings Area

STA 3.84 ha 19 July Tart cherry, mowed grass, non-fruit 5 M, 3 F 8 U crops, herbaceous (< 1 m tall)

CB1 2.6 ha 11 July Tart cherry, mowed grass, 4 M, 3 F 3 U herbaceous (< 1 m tall)

10 July Mowed grass, paved road, sweet 2 M CB2 11.6 ha cherry, non-fruit crops 5 U 9 July Tart cherry, paved road, herbaceous 5 M, 3 F CB3 0.4 ha (< 1 m tall) 1934 Among all radio-tagged birds, we did not detect 19 individuals (six male and seven female robins 1935 and six waxwings) in any of our study orchards after initial capture. Our sample population for 1936 analyses was comprised of 12 robins and 11 waxwings that used orchards STA, CB1, and CB3— 1937 no birds used CB2. Student’s t-tests with bootstrapping showed no difference between male and 1938 female robins for day-to-day orchard use (t = -0.50, P = 0.94). The GLMM with sex as the fixed 1939 effect and individual bird as a random effect suggested that within-day orchard use did not differ 1940 between male and female robins (z = 0.32, P = 0.75). Therefore, we pooled data for the two 1941 sexes. 1942

Day-to-day and Within-day Orchard Use 1943

Among robins and waxwings who used cherry orchards (n = 23), we detected individuals 1944 somewhere in the study region for a mean of 40.8 d (SD = 19.5), while birds visited focal 1945 orchards for a mean of 3.3 d (SD = 3.0), or 13% (SD = 17). Waxwings visited orchards on a 1946 marginally greater percent of days throughout the season (mean = 21%, SD = 22) than robins 1947

(mean = 6%, SD = 4; t = - 1.97 on log-transformed data, df = 21, P = 0.063; Figure 3.2). 1948

1949

Figure 3.2 Percent of days American robins and cedar waxwings visited cherry orchards 1950 relative to their respective tracking periods. Waxwings visited orchards for a greater 1951 proportion of their total tracking days than robins. Data represent day-to-day use values (n = 23). 1952 Black squares represent sample means. Data are untransformed and are jittered for visual clarity. 1953 1954 The best-fit model of within-day orchard use, according to AICc selection criteria, 1955 included species as the fixed effect and individual bird as a random effect (R2 = 0.46; Table 3.2). 1956

Waxwings visited orchards a mean of 5% (SD = 6) of the daylight period in orchards, while 1957 robins visited a mean of 2% (SD = 2). Waxwings spent significantly more time visiting orchards 1958

per day than robins (species = 0.88, SE = 0.31, P = 0.005; Figure 3.3). Individual variation 1959 among birds explained 34% (SD = 0.58) of the variance in within-day orchard use. 1960

Table 3.2 Generalized liner mixed models exploring the relationships between within-day 1961 orchard use of American robins and cedar waxwings relative to species, orchard, and days- 1962 to-harvest. Parameter numbers (K), deviance (Dev), AICc, ΔAICc, and model weight (wi) 1963 values are also included. All models also included individual bird as a random effect. The star 1964 symbol denotes a two-way interaction term between covariates. 1965 1966

Model K Dev AICc ΔAICc 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 + 8 374.3 400.5 10.6 0.00 Species*Orchard

Days-to-harvest + Orchard + Species + 7 380.1 401.5 11.6 0.00 Species*Days-to-harvest

Days-to-harvest + Orchard + Species + 9 374.0 405.9 15.9 0.00 Species*Orchard + Species*Days-to-harvest

1967

1968

1969

1970

1971

Figure 3.3 Percent of the daylight period that American robins and cedar waxwings visited 1972 cherry orchards on a given day. Waxwings visited orchards for more time each day than 1973 robins. Data represent within-day use values (n = 77) from a study population of 23 individual 1974 birds. Black squares represent sample means. Data are jittered for visual clarity. 1975 1976

The days-to-harvest and orchard covariates did not appear in the best-fit model of within-day 1977 orchard use (Table 3.2). The days-to-harvest covariate was retained in the second best model of 1978 within-day orchard use (ΔAIC = 2.6). This model suggested a decreasing trend in within-day 1979 orchard use as harvest approached, however this was not significant (days-to-harvest = -0.004, z 1980

= -0.57, P = 0.57). Orchard use at the STA orchard declined slightly up to 20 days before 1981 harvest; orchard use then remained constant until after harvest (Figure 3.4). Orchard use at CB1 1982 and CB3 was seemingly constant across the entire study period (Figure 3.4). 1983

1984

Figure 3.4 Percent of the daylight period that American robins and cedar waxwings visited 1985 cherry orchards on a given day relative to days-before-harvest. Data are separated by 1986 orchard (STA, CB1 and CB3). Data represent within-day use values (n = 77) from a study 1987 population of 23 birds. Dashed lines represent the first possible date of valid data collection for 1988 birds radio-tagged at each orchard. On the x-axis, zero represents the date of harvest for a given 1989 site. 1990 1991

Discussion 1992

Robins and waxwings differ in fruit preference, nutritional requirements, and physiology 1993

(Levey and Karasov 1989, Witmer and Van Soest 1998). These differences translated into 1994 species-specific patterns of orchard use. 1995

1996

Day-to-day Orchard Use 1997

Our metric of day-to-day orchard use showed a marginally significant trend (P = 0.063) 1998 suggesting that waxwings visited orchards a higher percentage of days throughout their tracking 1999 periods than robins. Fruit comprises a larger proportion of waxwing annual diets (Witmer 1996), 2000

and, thus, waxwings take greater advantage of the abundant supply of cherries over the growing 2001 season than robins. Robins, although predominantly frugivorous, typically consume and 2002 provision nestlings with large proportions of animal matter during the summer, while fruit 2003 consumption is higher in fall and winter (Wheelwright 1986). Therefore, a sweet cherry orchard 2004 may not be as valuable of a foraging habitat for robins seeking proteinaceous foods like insects 2005 and annelids, compared to waxwings. While it is possible that robins forage for non-cherry foods 2006 while visiting cherry orchards, 165 hrs of foraging observations yielded only six instances of 2007 robins consuming invertebrates but dozens of instances of cherry consumption (R. Eaton, 2008

Personal observation). Additionally, growers in focal orchards used insecticides regularly to 2009 diminish insect populations, potentially further limiting the value of cherry orchards for foraging 2010 robins. However, more study is needed to determine the relative proportions of fruit and 2011 invertebrates in the diet of robins in fruit orchards. Individual variation may also be a contributor 2012 to the patterns of day-to-day orchard use, as three individual waxwings appear to drive the 2013 relatively high day-to-day orchard use of waxwings. 2014

Our hypothesized difference in day-to-day orchard use between robins and waxwings 2015 was marginally significant, which could arise if robins used orchards more than expected while 2016 waxwings used them less. Robins may have used orchards more than predicted if orchards were 2017 near nesting sites, e.g. in windbreaks around orchards (Yahner 1982). In comparison, waxwing 2018 use of orchards may not have been particularly high if late-nesting waxwings were not yet tied to 2019 a breeding territory (Putnam 1949), and freer to travel among foraging patches than breeding 2020 robins. We conducted a preliminary, systematic search for nests in the study region that revealed 2021 very few, thus it is unlikely that robins and waxwings used cherry orchards for nesting. 2022

2023

Within-day Orchard Use 2024

Waxwings spent significantly more time visiting focal orchards than robins on a given 2025 day. Outside of the cultivated-fruit growing season, robins and waxwings also show differential 2026 timing of within-day fruit-foraging behavior (Chavez-Ramirez and Slack 1994). Wintering 2027 robins and waxwings in Texas spent 5 h and 8 h per day, respectively, feeding on Juniper berries 2028

(Juniperus ashei; Chavez-Ramirez and Slack 1994). Furthermore, once nesting is underway, 2029 waxwings may spend more time visiting orchards than robins on a given day to gather fruit for 2030 nestlings. Waxwings provision chicks primarily with fruit and begin doing so as early as day 2031 three after hatching (Putnam 1949), whereas robins do not provision with fruit until chicks are 2032 older (Howell 1942 and references therein). Future studies could address the potential influence 2033 of nesting phenology on frugivore orchard use by tracking breeding status, nesting, and brood 2034 rearing throughout the fruit-growing season. 2035

Unexpectedly, we did not detect an influence of days-to-harvest on the amount of time 2036 birds spent visiting orchards. As cherries ripened, we expected birds to spend more time visiting 2037 orchards each day. In contrast to expectations, a temporal decline in within-day orchard use as 2038 harvest approached was evident for the STA orchard only. STA is a research orchard with 2039 dozens of sweet cherry varieties, including some early ripening. Unlike other orchards in the 2040 region, multiple trees at STA had red and ripening fruit when our study began. Therefore, STA 2041 may have attracted birds early in the fruiting season (Nelms et al. 1990, Tobin et al. 1991). 2042

Cultivated orchards may provide fruit-eating birds with the majority of—or only—fruit options 2043 during this time. For example, waxwings consumed substantial proportions of early-ripening 2044 varieties of cultivated blueberries compared to later-ripening blueberries (Nelms et al. 1990). 2045

Contrary to our expectations, birds used orchards after harvest. After harvest, some fruits 2046 remain on the trees (<10% of the pre-harvest amount; M. Whiting, Personal communication) and 2047 ground. Remaining fruits are still numerous, accessible, and visually appealing, and could attract 2048 frugivorous birds (Sallabanks 1993, USDA 2013a). The first author observed robins consuming 2049 cherries in trees and on the ground in post-harvest orchards in the study region (R. Eaton, 2050 personal observation). In addition, as late-season nesters waxwings are likely provisioning 2051 offspring around or after cherry harvest. Remaining fruit in post-harvest orchards may continue 2052 to serve as important waxwing foraging habitat. If focal orchards were near nesting sites and 2053 within the regular foraging ranges of radio-tagged birds, post-harvest orchard use may reflect 2054 birds’ tendencies to forage close to nests (Swihart and Johnson 1986). Birds may also have 2055 continued to use orchards after cherry harvest to forage for food unrelated to harvest (e.g. 2056 invertebrates), however most cherry growers apply a post-harvest insecticide spray which may 2057 limit insect availability (W. Klein, Personal communication). 2058

Nearly half of our radio-tagged birds were detected in the area throughout the study but 2059 never used study orchards after the initial capture and acclimation period. Birds may have been 2060 captured as they traveled through an otherwise unused orchard. The home range sizes of robins 2061 are not well documented (Vanderhoff et al. 2014), although breeding robins have been known to 2062 forage up to 300 m from their nests (Knupp et al. 1977). Given the distance among our focal 2063 orchards (≥1.4 km) and their typical area (mean = 4.6 ha; SD = 4.9), robins captured at one 2064 orchard would not likely be detected using another, and robin foraging ranges in our study region 2065 do not likely contain more than a couple cherry orchards. To our knowledge, the home range 2066 sizes of waxwings are unknown. We did not find any radio-tagged birds using multiple focal 2067 orchards. Alternatively, captured birds may have avoided orchards if the capture experience itself 2068

served as a deterrent. For instance, we captured two male robins at CB2, but neither returned to 2069 that orchard. However, we later detected both individuals using the nearby STA orchard. 2070

While we documented the frequency and length of visits to focal orchards, the extent to 2071 which robins and waxwings used other cherry orchards in our study region is unknown. Given 2072 that cherry orchards are widespread in the study region, it is probable that robins and waxwings 2073 used non-focal orchards during the study period. However, the number of orchards visited is 2074 difficult to estimate given limited of knowledge of robin and waxwing home ranges. If home 2075 ranges of robins and waxwings are large relative to orchard size, our study provides a 2076 conservative picture of avian use of cherry orchards in an orchard-rich landscape. If home ranges 2077 are relatively small, it is possible that birds used non-focal orchards very little. Thus, our results 2078 suggest that orchards might not be predominant foraging habitat for frugivorous birds in this 2079 region. These uncertainties invite further study, particularly to track and evaluate avian habitat 2080 use in orchard landscapes with a more detailed resolution (e.g. GPS-tracking) to determine 2081 frequency of use of non-orchard habitat and home ranges of prominent avian frugivores. 2082

Bird use and consumption of agricultural crops is often viewed as problematic 2083

(Weatherhead et al. 1982, Anderson et al. 2013). However, avian use of these habitats may also 2084 provide ecosystem services to growers (Whelan et al. 2008) both before and after harvest. Before 2085 harvest, avian consumption of crop-damaging invertebrates can increase the yield of cultivated 2086 crops (Mols and Visser 2002). For example, great tits (Parus major) reduced caterpillar 2087 consumption on cultivated apples and increased fruit yield (Mols and Visser 2002). After 2088 harvest, fruit often remains on the ground and our study showed that birds continue to visit post- 2089 harvest orchards. Fruit remaining after harvest can serve as vectors for infections such as 2090

American brown rot (Monilinia fructicola). This fungus can over-winter in fruits that have fallen 2091

to the ground and inoculate infections during the following spring (Bush et al. 2009). Avian post- 2092 harvest consumption of cherries on the ground could reduce remaining fruits and limit the spread 2093 of infection. This and other potential benefits of avian fruit consumption deserve further study. 2094

This work demonstrates that two prominent avian fruit-eating species differ in how intensely 2095 they use orchards over the fruit-growing season, likely as a result of the differences in their food 2096 preferences and reliance on fruit. While waxwings visit orchards on a greater proportion of days 2097 and spend more time within orchards each day than robins, waxwings also consume a relatively 2098 high proportion of fruit than robins and other fruit eating species e.g. Common Grackles 2099

(Quiscalus quiscula) and European Starlings (Sturnus vulgaris; Lindell et al. 2012). In addition, 2100 waxwings are more likely to forage in groups than robins (Lindell et al. 2012) and have faster 2101 sugary-fruit assimilation rates than thrushes (Witmer and Van Soest 1998). Robins typically eat 2102 relatively little fruit during the breeding season than other times of year, yet showed higher fruit 2103 consumption when foraging around orchards than in less-fruiting habitats like meadows 2104

(Wheelwright 1986). Therefore, cherry orchards may serve as more important foraging habitat 2105 for more-frugivorous birds like waxwings than birds requiring proteinaceous resources, like 2106 robins. More work is needed to evaluate the extent to which birds foraging in orchards consume 2107 fruit versus other food sources like invertebrates. 2108

2109

2110

2111

2112

2113

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81 LITERATURE CITED 2116

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85 CHAPTER 4 2290

FOOD ABUNDANCE AT MULTIPLE SPATIAL SCALES INFLUENCES 2291 FORAGING BEHAVIORS 2292 2293

Rachael A. Eaton and Catherine A. Lindell 2294

86 Abstract 2295

Animals forage in habitats where food abundance varies at multiple spatial scales; 2296 relative resource abundance between hierarchical spatial scales likely influences within-patch 2297 foraging. We evaluated influences of fruit abundance at multiple spatial scales and sociality on 2298 avian behavior in sweet cherry orchards (Prunus avium). We observed omnivorous American 2299 robins (Turdus migratorius) and frugivorous cedar waxwings (Bombycilla cedrorum) and 2300 quantified cherry abundance at three spatial scales. Fruit abundance across multiple scales 2301 interacted to influence patch residence time and proportion time feeding at sweet cherry trees; 2302 these patterns differed between species. Fruit abundance at the tree scale relative to that of the 2303 orchard did not affect patch residence time; however, waxwings at trees with little food had a 2304 greater proportion time feeding within low-fruit orchards than within high-fruit orchards. As fruit 2305 abundance increased in an orchard, robins decreased proportion time feeding regardless of fruit 2306 abundance at the landscape scale; waxwings decreased proportion time feeding only in high-fruit 2307 landscapes. Fruit abundance at large spatial scales influenced robin patch residence time and 2308 waxwing proportion time feeding more strongly for birds in large foraging groups than for those 2309 in small groups. Differences in robin and waxwing behaviors may be explained, in part, by 2310 species differences in the degrees of frugivory and sociality; waxwings are more frugivorous and 2311 typically forage in larger groups than robins. Our study is among the first to demonstrate that 2312 avian behaviors are influenced by food abundance across multiple spatial scales, including large 2313 scales. 2314

2315

2316

2317

Introduction 2318

Animals forage in heterogeneous and spatially complex environments where food 2319 abundance and distribution can vary spatially and at multiple spatial scales (MacArthur and 2320

Pianka 1966, Levin 1992, Johnson et al. 2001, Garcia and Ortiz-Pulido 2004, Butler et al. 2005). 2321

Thirty years ago in their seminal review, Senft et al. (1987) proposed that traditional applications 2322 of foraging models prove difficult for animals foraging on broadly distributed resources because 2323 heterogeneity in food abundance can occur at scales beyond just the patch itself, defined as a 2324 discrete aggregation of resources. For highly mobile species able to forage over large areas, the 2325 relative availability of resources at hierarchical spatial scales likely influences foraging behavior 2326

(Sallabanks 1993, Ritchie 1998, Searle et al. 2006, Searle et al. 2008). For instance, behavioral 2327 decisions may be affected by food abundance at the scale of a tree (patch), a forest of many trees 2328

(a habitat area comprised of multiple patches), or a landscape (a region of non-contiguous 2329 foraging habitats). Understanding and accounting for this spatial hierarchy in resource 2330 distribution when investigating foraging behaviors remains a key challenge in ecology (Senft et 2331 al. 1987, Kotliar and Wiens 1990, Thompson et al. 2001, Searle et al. 2005, Searle et al. 2006). 2332

Although ecologists are aware of the influence of resource availability at multiple spatial 2333 scales on animals (Wiens 1989, Kotliar and Wiens 1990, Caro and Sherman 2011), research has 2334 typically focused on demography (Richmond et al. 2012), reproduction (Cornell and Donovan 2335

2010), and population-level responses such as abundance and distribution (Lima and Zollner 2336

1996, Olsson et al. 2000, Moegenburg and Levey 2003, Withey and Marzluff 2008, Pickett and 2337

Sirwardena 2011). The influence of multi-scale resource availability on behavioral responses is 2338 much less studied. Searle et al. (2006) studied captive mammalian herbivores in an experimental 2339 system with heterogeneous resource distribution at hierarchical scales. They demonstrated that 2340

patch residence time was influenced by heterogeneity in resource abundance at multiple spatial 2341 scales. However, the effect of food availability across spatial scales on the behavior of other 2342 highly mobile foragers in a non-captive setting has not been assessed. In order to understand the 2343 effects of resource heterogeneity across multiple spatial scales on wild foragers, we studied the 2344 behavior of fruit-eating birds foraging in an agricultural environment. 2345

Our current understanding of the relationship between wild foraging birds and food 2346 availability is generally limited to the positive correlations between bird abundance and food 2347 availability measured at different spatial scales (Withey and Marzluff 2008, Moorman and 2348

Bowen 2012). These relationships can vary with spatial scale (Garcia and Ortiz-Pulido 2004). 2349

Yet, studies of the relationships between food abundance and avian foraging behaviors are 2350 typically approached from a single spatial scale (e.g. Goss-Custard et al. 2006), despite the 2351 hierarchical nature of foraging decisions (Stephens 2008). Fruit-eating birds forage in particular 2352 patches (e.g. a tree) but travel widely throughout foraging habitats and regions (Price 2006). 2353

Compared to birds in other foraging guilds (e.g. insectivores), fruit-eating birds have relatively 2354 high movement activity (Neuschulz et al. 2012) and make foraging decisions at multiple scales 2355

(Sallabanks 1993). Thus, fruit resource availability at the scale of the foraging patch, as well as 2356 across broader habitat and landscape scales likely affect the behavior of frugivorous birds. 2357

For frugivorous birds, fruit-growing agricultural regions provide a system of readily 2358 available food resources heterogeneously distributed at increasingly broad hierarchical spatial 2359 scales. Birds forage at individual fruit trees (a patch), within an orchard containing many fruit 2360 trees (a habitat), and within a broader landscape that may contain multiple fruit orchard habitats. 2361

Trees and orchards vary in fruit abundance because heterogeneity in tree health, age, and 2362 pollination success influence a tree’s fruit yield. This heterogeneity leads to variation in fruit 2363

abundance between the tree and orchard spatial scales. In addition, fruit orchards vary in the 2364 extent to which they are isolated from other orchards of the same type in the surrounding 2365 landscape. Thus, fruit abundance at a landscape scale (i.e. a particular orchard and the 2366 surrounding matrix of land covers) varies among orchards. Sweet cherry (Prunus avium) 2367 production regions of Michigan display this hierarchical nature of fruit abundance and permit us 2368 to assess the extent to which variation in food abundance among spatial scales (e.g. between tree 2369 and orchard and between orchard and landscape) influences avian foraging behaviors. Fruit 2370 orchards are an ideal system in which to study multi-scale influences of resource availability on 2371 foraging behavior because 1) orchards provide abundant resources for avian frugivores (Pimentel 2372 et al. 1992), 2) fruit consumption is easily observed, and 3) fruit availability can be quantified at 2373 the tree, orchard, and landscape spatial scales. 2374

In addition, we wanted to explore the idea that variation in resource abundance across 2375 spatial scales has different effects on the decisions made by social and solitary foragers (Galef 2376 and Giraldeau 2001, Sernland et al. 2003, Fernandez-Juricic et al. 2004, Jedlicka et al. 2006). 2377

Unlike solitary feeders, social foragers may be more sensitive to variation in resource availability 2378 because of potential intra-group food competition (Rieucau and Giraldeau 2009). The potential 2379 for resource competition can influence a forager’s use of a particular foraging patch or habitat 2380

(Symington 1988, Giraldeau and Dubois 2008, Rieucau and Giraldeau 2009). For instance, larger 2381 foraging groups require more resources in a given patch than smaller foraging groups; foraging 2382 patches with sufficient food to support multiple foragers are likely more limited than patches for 2383 solitary individuals (Livoreil and Giraldeau 1997). Thus, food abundance and distribution at 2384 larger spatial scales may exert a stronger influence on the behavior of socially foraging birds 2385 than solitary birds. However, research has not examined how multi-scale resource availability 2386

affects foraging within the context of sociality. This knowledge gap limits our ability to predict 2387 and analyze consequences of environmental heterogeneity on behavior. 2388

In this study, we evaluated the effects of hierarchical variation in resource abundance 2389 and foraging sociality on the amount of time fruit-eating birds spent in and feeding at sweet 2390 cherry trees. The amount of time animals spend at a particular foraging patch or site is influenced 2391 by food abundance (Charnov 1976) and can be used to assess how resource abundance across 2392 spatial scales influences the likelihood of staying in the patch (Searle et al. 2006). While 2393 spending time in a patch, birds forage but also engage in behaviors such as grooming, vigilance, 2394 calling, and perching. Therefore, a measure of the time animals spend actually feeding at a patch 2395

(proportion time feeding) is an important behavioral variable for evaluating the effects of food 2396 abundance on foraging behavior (Searle et al. 2006). For this study, we define the tree-scale 2397

(patch) as focal cherry trees in or under which a bird is observed foraging (Figure 4.1). We 2398 define the orchard-scale as areas of multiple contiguous rows of evenly spaced cherry trees. We 2399 define the landscape-scale as a circular area (500-m radius) extending from the center of each 2400 orchard. 2401

2402

Figure 4.1 Schematic of the tree, orchard, and landscape spatial scales. Scales increase in 2403 size from left to right. Dark green parcels in the landscape represent other potential sweet cherry 2404 orchards in the landscape surrounding a study orchard. 2405 2406

Within the context of this framework we developed the following hypotheses: 2407

Hypothesis 1: Highly mobile fruit-eating birds use their environments at multiple spatial scales; 2408 therefore patch residence time and proportion time feeding behaviors should depend on relative 2409 fruit abundance at hierarchical spatial scales. We predicted that the interactions between fruit 2410 abundance of 1) trees within orchards and 2) orchards within landscapes would significantly 2411 affect avian patch residence time and proportion time feeding. 2412

2413

Hypothesis 2: Birds foraging in groups are more constrained by fruit abundance at relatively 2414 large spatial scales (e.g. orchard and landscape) than solitary foragers. We predicted that fruit 2415 abundance at the relatively large scales of orchard and landscape would exert a stronger 2416 influence on birds foraging in groups than on solitary birds. 2417

2418

Methods 2419

We conducted this study in northwest Michigan sweet cherry orchards in eastern 2420

Leelanau County, near Traverse City (44° 46’ N, 85° 37’ W) from June - July in 2012 and 2014. 2421

Leelanau County is primarily a peninsula (area = 804 km2) surrounded by Lake Michigan and an 2422 agricultural area with many orchards (e.g. sweet and tart cherries and apples) and vineyards. As 2423 of 2011, fruit orchards comprised 7% of the peninsula land area; sweet cherry orchards 2424 accounted for 26% of the total orchard acreage (USDA 2012). Other prominent land cover types 2425 include non-fruit agriculture (e.g. corn), forests, and open fields. We conducted this study in 12 2426 sweet cherry orchards in 2012 and 10 in 2014, totaling 14 distinct orchards. 2427

To address our objectives, we observed American robins (Turdus migratorius) and cedar 2428 waxwings (Bombycilla cedrorum), two key cherry-consuming species (Lindell et al. 2012). 2429

Robins and waxwings are common in the study region and breed during the summer cherry- 2430

growing season. Waxwings arrive by late May (although some overwinter) and nest semi- 2431 colonially, primarily between mid-June and August (McPeek 2011a). Robins typically arrive in 2432

March, begin nesting in April, and rear two broods (McPeek 2011b). Robins and waxwings vary 2433 in their degree of foraging sociality (Lindell et al. 2012). In cherry orchards, robins typically 2434 forage alone or in small groups with a mean group size of 1.2 ± 0.5 SD (Lindell et al. 2012). 2435

Waxwings travel and forage in flocks throughout the year (McPeek 2011b) and the mean group 2436 size of waxwings feeding in cherry orchards during summer is 4.2 ± 3.8 SD (Lindell et al. 2012). 2437

2438

Behavioral Observations 2439

We conducted observations over the cherry-ripening period from 04-Jun-2012 – 02-Jul- 2440

2012 and 01-Jul-2014 – 24-Jul-2014. We walked systematically through orchards and conducted 2441 focal-animal sampling (Altmann 1974). When we detected a bird, we followed it until the bird 2442 left the orchard or was lost from sight and recorded all behaviors (e.g. eating, perched, calling, 2443 grooming, walking) into a digital voice recorder (Sony ICD-BX800). In some instances, it was 2444 possible to follow a bird from the initial tree to another. If birds traveled to subsequent trees but 2445 no additional eating was observed, for the purpose of analyses, we considered the observation to 2446 end when a bird left the initial tree. If birds were observed in an initial tree but only ate cherries 2447 in the second tree, we considered the observation to begin when the bird flew to the tree in which 2448 it was actually observed eating. If a bird was observed eating in multiple trees, we randomly 2449 selected one tree from the observation and used the corresponding behavioral data from that tree 2450 in analyses. We considered these periods in which a bird was seen foraging and consuming 2451 cherries to be “observed foraging bouts.” We set a minimum observation length of 20 seconds 2452 for inclusion in analyses (Morrison et al. 2010), and in doing so omitted three observations. After 2453

observations ended we flagged focal trees for quantification of tree-scale cherry abundance. 2454

Upon preparation of data for analysis, we excluded any observations for which corresponding 2455 tree data or group sizes were missing. Four observers performed foraging observations, with the 2456 first author (RAE) conducting >78% of the observations. 2457

We quantified two foraging response variables for each observation: 1) “patch residence 2458 time” and 2) “proportion time feeding”. We defined patch residence time as the total amount of 2459 time (in seconds) that a bird spent in or under the focal tree. We defined proportion time feeding 2460 as the proportion of the patch residence time in which a bird was actually consuming cherries. 2461

This was calculated as the total duration of all feeding activity divided by the patch residence 2462 time. We adjusted the denominator in the proportion time feeding calculation to omit any time 2463 where the bird was out of the observer’s sight and thus feeding could not be confirmed. We 2464 defined group size as the number of conspecific birds in or under the same tree as the focal bird 2465 during the observation (Chavez-Ramirez and Slack 1994). To insure independence among 2466 observations, we did not observe additional individuals from a foraging group after observing 2467 one member. 2468

2469

Fruit Abundance 2470

We calculated cherry abundance at three spatial scales: individual cherry trees, cherry 2471 orchards comprised of multiple trees, and landscapes around cherry orchards (500-m-radius 2472 buffer area surrounding orchards; Fig. 1). 2473

2474

2475

2476

Tree-scale fruit abundance 2477

We sampled two branches on each focal tree. We randomly selected a horizontal sector 2478 of the tree (north-northeast, east-northeast, etc.) for each branch to be sampled. Next, we 2479 measured the tree’s height with a laser range finder (Nikon Forester model) and randomly 2480 selected a height, in 0.5-m intervals, from the base of a tree’s foliage (roughly 0.5 m above the 2481 ground) to the tree’s height. We identified the closest branch to the chosen sector at the chosen 2482 height and counted all cherries on the branch from the terminal end of woody growth on the tip 2483 of the branch inward, up to 1 m. If the branch was less than 1 m, we measured the branch length. 2484

We calculated the number of cherries per meter of branch length for each sample and averaged 2485 the two samples to generate our metric of tree-scale cherry abundance. Cherries were ripening 2486 and no longer green during the observation and cherry sampling periods. 2487

2488

Orchard-scale fruit abundance 2489

To conduct orchard-wide cherry sampling, we first divided each orchard spatially into 2490 five strata (north, east, south, west, and interior). The two outermost rows of trees on any edge 2491 were assigned to the respective edge stratum; all other trees were considered part of the interior 2492

(after Tracey and Saunders 2010). To identify sample trees in each stratum, we randomly 2493 selected a starting tree and systematically identified up to 11 additional trees for sampling, for a 2494 maximum of 60 trees per orchard. If a stratum had fewer than 12 trees, we sampled as many as 2495 trees as possible. Branch selection procedures were the same as for tree-scale cherry 2496 quantification; however, we used only one branch per tree during orchard-scale sampling 2497

(Lindell et al. 2016). 2498

To quantify orchard-scale fruit abundance, we calculated a value of cherries per meter of 2499 branch for each sampled tree and averaged these values across all trees sampled in an orchard. 2500

Because orchards varied in tree number, we incorporated the total number of trees in an orchard 2501

(which included both sampled and un-sampled trees) into our variable of orchard-scale fruit 2502 abundance. To estimate the number of trees in each study orchard, we counted the number of 2503 rows of trees within an orchard and the number of trees in the outermost rows. We multiplied 2504 these two numbers to estimate the maximum number of trees in the orchard. This method may 2505 slightly overestimate the number of trees because orchards sometimes have gaps where a tree 2506 would otherwise be planted within a row. We then multiplied the orchard-level average of 2507 cherries per meter of branch length by the number of trees in the orchard. Thus, our final metric 2508 of orchard-scale fruit abundance is presented as cherries per meter of branch length, scaled to the 2509 size of each orchard. One study orchard was harvested in 2014 before orchard-wide fruit 2510 sampling could be conducted. For the two observations that occurred there, we assigned the 2014 2511 mean orchard abundance value among all study orchards as the orchard abundance value for that 2512 site. Preliminary analyses indicated no difference in cherries per meter between trees in edge or 2513 interior strata within an orchard, and we made no distinction between these in further analyses. 2514

2515

Landscape-scale fruit abundance 2516

We conducted geographic analyses in ArcGIS 10.0 (ESRI). We digitized study orchards 2517 using 2012 National Agricultural Imagery Program (NAIP) orthoimages. We then calculated the 2518 geographic centroid of each study orchard and delineated a 500-m-radius buffer around this 2519 point. We used NAIP orthoimagery to digitize and classify land cover parcels within each buffer. 2520

Using NAIP land cover classifications, we could not distinguish between orchards of different 2521

tree fruits; therefore, we conducted ground-truthing surveys in 2013 to verify which parcels were 2522 sweet cherry orchards. We calculated the area of each polygon comprising sweet cherry orchards 2523 and summed these areas (Figure 4.1). We defined the landscape scale metric of cherry abundance 2524 for each study orchard as the percent of the total buffer area consisting of sweet cherry orchards. 2525

This technique included the area of the study orchard itself in the landscape-scale metric of 2526 cherry abundance. Therefore, for parsimony and to prevent collinearity between orchard- and 2527 landscape-scale fruit abundance, we did not include orchard area as a separate covariate during 2528 analyses. 2529

2530

Statistical Analyses 2531

We performed all analyses in R statistical software (Version 3.0.3; R Core Team 2012), 2532 using the ‘lme4’ (Bates et al. 2014) and ‘lmerTest’ (Kuznetsova et al. 2015) packages. We 2533 square root transformed fruit abundance data at all three scales to reduce skew. We then Z- 2534 transformed these values to allow for comparisons of effect sizes among variables with differing 2535 units of measure (Gelman et al. 2013). We Z-transformed group size within each species, in 2536 order to correct for differences in foraging sociality (Gelman et al. 2013). We Z-transformed, 2537 without centering, patch residence time in order to improve model convergence. We constructed 2538 species-specific generalized linear mixed models (GLMMs) to compare influences of cherry 2539 abundance at the three scales and group size on patch residence time and proportion time 2540 feeding. To address Hypothesis 1, we included the interactions between tree-scale and orchard- 2541 scale fruit abundance and between orchard-scale and landscape-scale fruit abundance. To address 2542

Hypothesis 2, we included three additional interaction terms: foraging group size and fruit 2543 abundance at each of the three scales. We treated patch residence time as a gamma distributed 2544

variable with an inverse link function and proportion time feeding as a binomial distributed 2545 variable with a logit link function. The total number of seconds a bird was observed (excluding 2546 any time the bird was out of sight) served as the number of trials for the binomial distribution in 2547 this model. Orchard identity and study year were included as random effects. “Time of day” 2548

(before or after 1200) and “bird position” covariates (e.g. in tree, on ground, both) were initially 2549 included in models, but we removed them after preliminary analyses indicated no influence on 2550 response variables. We make the simplifying assumption that model predictor variables are 2551 measured without error, which means that all effect size estimates are optimistic. We conducted 2552 variance inflation factor (VIF) tests to assess collinearity among predictor variables (VIFs < 8.8; 2553

Neter et al. 1996). We calculated R2 values as one minus the ratio of the residual sum of squares 2554 and the total sum of squares. 2555

2556

Results 2557

In total, we have data from 105 observations of birds consuming cherries (62 robins, 43 2558 waxwings). Patch residence time was explained, in part, by interactions among cherry abundance 2559 at multiple spatial scales and group size (R2 = 38% and 16% for robins and waxwings, 2560 respectively). These factors also explained some of the variation in proportion time feeding (R2 2561

=19% and 30% for robins and waxwings, respectively). The random effects of “orchard identity” 2562 and “study year” explained low amounts of variation in patch residence time (R2 = 0% for both 2563 effects for waxwings; R2 = 5% and 0%, for “orchard identity” and “study year”, respectively for 2564 robins). Random effects explained <4% of the variance in proportion time feeding for both 2565 species. 2566

2567

Fruit abundance at the tree scale and orchard scale interacted to affect proportion time 2568 feeding but not patch residence time 2569

Fruit abundance at the tree scale relative to that of the entire orchard did not affect robin 2570

(Figure 4.2A) or waxwing (Figure 4.3A) patch residence time. Fruit abundance at the tree scale 2571 relative to that of the entire orchard significantly affected proportion time feeding for waxwings 2572

(Figure 4.3C), but not for robins (Figure 4.2C). In particular, robin proportion time feeding 2573 declined with increasing fruit abundance at the tree scale regardless of orchard-scale fruit 2574 abundance (Figure 4.2C). Waxwings at a tree with low fruit abundance had a greater proportion 2575 time feeding when the tree was within an orchard also with low fruit abundance, than when the 2576 tree was within an orchard with high fruit abundance; the opposite pattern was observed for 2577 waxwings in trees with high fruit abundance (Figure 4.3C). 2578

2579

Fruit abundance at the orchard scale and landscape scale interacted to affect proportion time 2580 feeding, but not patch residence time 2581

Fruit abundance at the orchard scale relative to that of the landscape scale did not affect 2582 waxwing patch residence time (Figure 4.3B). Neither fruit abundance at the orchard nor 2583 landscape scale affected robin patch residence time (Table 4.2), despite a small but significant 2584 interaction effect between these two predictor variables (Table 4.2, Figure 4.3B). As cherry 2585 orchards increased in fruit abundance, robins stayed longer at a cherry tree (patch) when an 2586 orchard was within a high-fruit-abundance landscape. In contrast, robins left a tree sooner when 2587 in a high-fruit-abundance orchard within a low-fruit landscape (Figure 4.2B). In addition, fruit 2588 abundance at the orchard scale relative to that of the landscape scale significantly affected 2589 proportion time feeding of both robins (Figure 4.2D) and waxwings (Figure 4.3D). As fruit 2590

abundance increased at the orchard scale, robins decreased proportion time feeding, with a 2591 stronger effect for robins in fruit-rich landscapes than robins in fruit-poor landscapes (Figure 2592

4.2D). As fruit abundance increased at the orchard scale, waxwings in high-fruit landscapes 2593 similarly decreased proportion time feeding (Figure 4.3D). 2594

2595

Figure 4.2 The effects of fruit abundance across multiple spatial scales on American robin 2596 foraging behavior. The interaction between fruit abundance at the tree scale and orchard scale 2597 on American robin patch residence time (A) and proportion time feeding (C). The interaction 2598 between fruit abundance at the orchard scale and three levels of landscape-scale fruit abundance 2599 on robin patch residence time (B) and proportion time feeding (D). Solid line represents low fruit 2600 abundance value (1st quantile), Dashed line represents median abundance value, Dotted line 2601 represents high fruit abundance value (3rd quantile). Cherry abundance data are square root and 2602 Z-transformed. 2603 2604

100

2605

Figure 4.3 The effects of fruit abundance between spatial scales on cedar waxwing foraging 2606 behavior. The interaction between fruit abundance at the tree scale and orchard scale on cedar 2607 waxwing patch residence time (A) and proportion time feeding (C). The interaction between fruit 2608 abundance at the orchard scale and three levels of landscape-scale fruit abundance on robin patch 2609 residence time (B) and proportion time feeding (D). Solid line = low fruit abundance value (1st 2610 quantile), Dashed line = median abundance value, Dotted line = high fruit abundance value (3rd 2611 quantile). Cherry abundance data are square root and Z-transformed. 2612

Sociality altered the influence of fruit abundance at large spatial scales on foraging behavior 2613

Waxwing foraging group size ranged from one to six birds with a mean of 2.2; no 2614 waxwing groups of four were observed (Table 4.1). Robin foraging group size ranged from one 2615 to four with a mean of 1.5 (Table 4.1). To clarify, we did not assess between-scale interactions in 2616 the context of foraging sociality in these analyses, but rather the interaction between fruit 2617 abundance at a single scale and foraging group size (Table 4.2). For both robins and waxwings, 2618

101

fruit abundance at large spatial scales (e.g. orchard and landscape) interacted with foraging group 2619 size to affect behavior (Table 4.2). Fruit abundance at large spatial scales did not affect patch 2620 residence time differently for solitary waxwings than group-foraging waxwings (Table 4.2). Fruit 2621 abundance at large spatial scales scale affected patch residence time of robins in groups 2622 differently and more strongly than solitary robins (Table 4.2). 2623

Foraging group size interacted significantly with fruit abundance at large spatial scales to 2624 affect waxwing proportion time feeding (Table 4.2). The effect of fruit abundance at large spatial 2625 scales on proportion time feeding was stronger for waxwings in relatively large foraging groups 2626 than solitary or paired waxwings (Table 4.2). In contrast, fruit abundance at large spatial scales 2627 did not affect proportion time feeding differently for solitary robins compared to group-foraging 2628 robins (Table 4.2). 2629

Table 4.1 Group sizes, patch residence times, and proportions time feeding for American 2630 robins and cedar waxwings. Mean, standard deviation, and maximum values of group size. 2631 Mean and standard deviation for response variables: patch residence time and proportion time 2632 feeding. Mean, standard deviation, and range of tree-, orchard-, and landscape-scale cherry 2633 abundance. 2634 2635 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 – 194.6 cherries/m of branch length Orchard 17,665 ± 51,172; range: 642 – 166,509 (cherries/m * # trees in orchard) Landscape 11% ± 6.5; range: 1.1 – 24.1% of 500-m radius buffer 2636 2637 2638 2639 2640 2641 2642 2643 2644

102

Table 4.2 Outcomes of species-specific generalized linear mixed models of patch residence 2645 time and proportion time feeding. Model-generated parameter estimates, standard errors (SE), 2646 t- z-, and P-values of covariates and interaction terms from cedar waxwing-specific (unshaded 2647 rows) and American robin-specific (shaded rows) GLMMs for dependent variables patch 2648 residence time and proportion time feeding. Statistically significant values are bolded. 2649 2650 Covariate Estimate SE t P Response Variable: Patch residence time 1.05 0.199 5.27 <0.001 Intercept 1.93 0.34 5.68 <0.001 -0.122 0.357 -0.492 0.62 Tree-scale cherry abundance -0.048 0.233 -0.204 0.84

-0.236 0.357 0.662 0.51 Orchard-scale cherry abundance 0.167 0.281 0.593 0.55 0.385 0.259 1.49 0.14 Landscape-scale cherry abundance -0.100 0.281 -0.356 0.72 -0.044 0.136 -0.323 0.75 Group size 0.161 0.182 0.889 0.37

-0.252 0.323 -0.781 0.44 Tree x orchard 0.365 0.208 1.76 0.08 0.35 0.28 1.26 0.21 Orchard x landscape -0.377 0.189 -2.00 0.045 -0.076 0.165 -0.458 0.65 Tree x group size 0.592 0.285 2.08 0.038 0.186 0.222 0.838 0.40 Orchard x group size -0.180 0.262 -0.69 0.49 -0.002 0.138 -0.016 0.99 Landscape x group size -0.605 0.302 -2.00 0.045 2651 2652 2653 2654 2655 2656 2657

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Table 4.2 (cont’d) 2658 2659 Response variable: Proportion time feeding Estimate SE z P -0.187 0.449 -0.416 0.68 Intercept -0.727 1.36 -0.53 0.59 0.603 0.068 8.848 <0.001 Tree-scale cherry abundance -0.373 0.06 -6.61 <0.001 0.015 0.098 0.1550 0.88 Orchard-scale cherry abundance -2.32 0.193 -12.1 <0.001 -0.993 0.403 -2.47 0.014 Landscape-scale cherry abundance 1.16 0.33 3.49 <0.001 0.411 0.048 8.53 <0.001 Group size 0.292 0.036 7.99 <0.001 0.184 0.089 2.07 0.038 Tree x orchard 0.082 0.060 1.37 0.17 -0.589 0.099 -5.94 <0.001 Orchard x landscape 0.198 0.077 2.55 0.011 -0.261 0.051 -5.17 <0.001 Tree x group size 0.114 0.062 1.86 0.06 -0.674 0.081 -8.35 <0.001 Orchard x group size -0.048 0.065 -0.741 0.46 0.834 0.075 11.06 <0.001 Landscape x group size -0.053 0.065 0.813 0.42 2660 2661

Discussion 2662

We hypothesized that fruit abundance at multiple spatial scales in a fruit-growing region 2663 would interact to affect the foraging behavior of fruit-eating birds. We found support for 2664

Hypothesis 1 in that relative fruit abundance between the tree and orchard scales and between the 2665 orchard and landscape spatial scales interacted to influence American robin and cedar waxwing 2666 proportion time feeding. However, we found no indication that fruit abundance at multiple 2667

104

spatial scales affected patch residence time. In addition, we found that fruit abundance at 2668 multiple spatial scales interacted in a complex manner with foraging group size to influence 2669 proportion time feeding and patch residence time. We discuss these principle findings in detail 2670 below. 2671

2672

Fruit abundance at the tree scale and orchard scale interacted to affect proportion time 2673 feeding but not patch residence time 2674

We expected that the heterogeneity in fruit abundance between cherry trees and cherry 2675 orchards would interact to influence robin and waxwing patch residence time and proportion 2676 time feeding. Unexpectedly, the relative fruit abundance between the tree and orchard spatial 2677 scales did not affect patch residence time of either species. Similarly, Palacio et al. (2015) found 2678 no correlation between fruit crop size at a single spatial scale and the amount of time wild fruit- 2679 eating birds spent at trees. Searle et al. (2006) considered multi-scale effects using a controlled 2680 experiment and found that the food availability and spatial arrangement of patches within larger- 2681 scale areas affected patch residence time in captive grizzly bears and mule deer, in contrast to 2682 our findings. Unlike Searle et al. (2006), we conducted our study with wild birds in a non-captive 2683 setting. The amount of time pulp-consuming birds like robins and waxwings spend at fruit trees 2684 may be affected by fruit handling time, rather than fruit abundance alone (Palacio et al. 2015). 2685

Fruit handling time, such as the time required to bite, consume, and digest fruit pulp, could 2686 require a minimum amount of patch residence time (Foster 1987). 2687

In addition, robins and waxwings nest during the cherry-growing season in Michigan 2688

(McPeek 2011a, McPeek 2011b). The need to leave a particular tree and return to feed offspring 2689 at necessary time intervals likely influences robin and waxwing patch residence time 2690

105

(Kacelnik 1984). Furthermore, while at cherry trees, many birds displayed non-feeding behaviors 2691 in addition to actual cherry consumption. For example, birds spent time perching, calling, or 2692 grooming. Our findings suggest that patch residence time of fruit-eating birds is not influenced 2693 simply by heterogeneity in fruit abundance between hierarchical spatial scales, but is likely 2694 affected by additional complexities for animals in the wild. 2695

Heterogeneity in fruit abundance between a cherry tree and the orchard did influence 2696 proportion time feeding, however this effect was small and evident only in waxwings. This result 2697 likely arises because waxwings exhibit greater frugivory during summer than robins (Witmer 2698

1996), and thus variation in fruit abundance affected our response variable that specifically 2699 measured fruit consumption behavior, proportion time feeding, for the highly frugivorous focal 2700 species but not the more omnivorous species. The effect of fruit abundance at a particular cherry 2701 tree on waxwing proportion time feeding varied with fruit abundance at the orchard scale. When 2702 fruit was locally sparse at a tree, waxwings in low-fruit orchards devoted more time to feeding 2703 compared to waxwings in high-fruit orchards. The opposite pattern was observed for birds in 2704 high-fruit trees. When fruit is sparse at both the tree and orchard scales, birds may need to devote 2705 a lot of time to consuming cherries in order to meet fruit resources needs, since these resources 2706 are relatively hard to come by at a particular tree and throughout the orchard (Olsson et al. 2000). 2707

These findings indicate that the influence of resource abundance at a small spatial scale (e.g. a 2708 particular tree) on avian foraging behavior can be contingent upon fruit abundance across a 2709 somewhat broad foraging area, such as an orchard. 2710

2711

2712

2713

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Fruit abundance at the orchard scale and landscape scale interact to affect proportion time 2714 feeding but not patch residence time 2715

We found no clear evidence that relative fruit abundance between the orchard and 2716 landscape scales affected robins and waxwing patch residence time. Similar to our discussion of 2717 the tree-scale and orchard-scale interaction above, this result is likely due to fact that while at 2718 cherry trees, robins and waxwings engaged in behaviors unrelated to fruit abundance such as 2719 grooming and communicating. 2720

Cherry abundance at the orchard and landscape scales interacted to affect proportion time 2721 feeding of both robins and waxwings. Robins decreased proportion time feeding as fruit became 2722 more abundant across the orchard, and this effect was slightly greater for robins in high-fruit 2723 landscapes than those in low-fruit landscapes. Waxwings in fruit-rich landscapes showed a 2724 similar pattern. Birds make fruit selection decisions hierarchically, with abundance being one of 2725 the initial determinants of selection (Sallabanks 1993). If birds are feeding in fruit-rich orchards 2726

(habitats) and landscapes, they may be able to spend a smaller proportion of time eating and 2727 more time searching for, or selecting among, fruit options and still meet their energy needs. 2728

Indeed, theoretical foraging models suggest selectivity among food options should increase with 2729 food abundance (Emlen 1966), and selectivity in birds foraging among fruits of a single type 2730 may also increase with abundance, especially given the multi-step nature of avian fruit selection 2731

(Sallabanks 1993). 2732

In contrast, waxwings in relatively low-fruit landscapes actually devoted more time to 2733 consuming cherries, as orchards became more fruit rich. Waxwings feed preferentially on 2734 abundant fruits over rare fruits (McPherson 1987) and likely seek out foraging areas (e.g. 2735 landscapes with an abundance of fruiting cherry orchards) with widely available fruit. Therefore, 2736

107

foraging decisions for waxwings and other fruit-specialized species may occur at scales larger 2737 than that of a particular fruit orchard. These species should devote more time to feeding at a 2738 given tree if alternative fruit-rich orchards are sparse in the landscape and will require 2739 considerable search time and energy to locate (Charnov 1976). These interactions between fruit 2740 abundance at the orchard and landscape scales suggest that small-scale, observable foraging 2741 behaviors are affected by the relative abundance of fruit across multiple, relatively large spatial 2742 scales, such as the landscape surrounding a particular foraging habitat (Searle et al. 2006). 2743

Furthermore, these multi-scale interactions can significantly influence not only highly 2744 frugivorous birds (e.g. waxwings), but more omnivorous species (e.g. robins) as well. 2745

2746

Sociality altered the influence of fruit abundance at large spatial scales on foraging behavior 2747

We hypothesized that fruit abundance at the relatively large orchard and landscape scales 2748 would have a greater effect on birds foraging in groups than on solitary foragers. As expected, 2749 the degree of foraging sociality interacted with fruit abundance at the orchard and landscape 2750 scales and affected the foraging behaviors of robins and waxwings differently (Table 4.2). 2751

Interactions between waxwing foraging group size and fruit abundance affected only proportion 2752 time feeding, while these interactions affected only the patch residence time for robins. 2753

For the highly frugivorous and group-foraging waxwings, birds in small groups (i.e. 2754 solitary or paired birds) exhibited weaker behavioral responses to increases in fruit abundance at 2755 large spatial scales, compared to waxwings in large groups (e.g. three or more birds). In addition, 2756 waxwings in small groups showed opposite responses to changes in fruit abundance at large 2757 spatial scales compared to waxwings in large groups. These results supported our expectation 2758 that fruit abundance at scales larger than the patch (e.g. the orchard or surrounding landscape) 2759

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would exert a stronger influence on birds in large groups compared to solitary birds or those in 2760 small groups. As group size increases, the potential for resource competition and intra-group 2761 aggression also increases, which can reduce resource intake (Rutten et al. 2010). The effects of 2762 resource competition on the behavior of group-foraging birds can be mitigated if groups feed in 2763 areas with more widely available resources (Caraco 1979). Thus, resource availability at the 2764 relatively large orchard and landscape scales and the availability of suitable foraging areas to 2765 support the needs of a foraging group are important considerations for group-foraging birds (as 2766 reviewed in Marshall et al. 2012). 2767

2768

Conclusion 2769

It is important to consider multiple spatial scales in evaluating relatively large-scale 2770 ecological patterns (e.g. population abundance, distribution, and species diversity; Wiens 1989, 2771

Levin 1992, Cornell and Donovan 2010, Caro and Sherman 2011). The hierarchical nature of 2772 resource availability for animals in natural and semi-natural systems affects foraging behavior in 2773 a manner that cannot be fully understood via traditional single-scale approaches. Our study is 2774 among the first to demonstrate that avian behaviors are influenced by food abundance across 2775 between spatial scales, including relatively large scales. In order to understand avian behavior in 2776 complex and heterogeneous resource environments more accurately and completely, we must 2777 take into account multiple scales (Senft et al. 1987, Pinaud and Weimerskirch 2006, Searle et al. 2778

2006). Furthermore, cross-scale influences of resource abundance should be evaluated within the 2779 context of other important influences like foraging sociality and species-specific feeding 2780 preferences. 2781

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