A Global Quantitative Synthesis of Local and Landscape Effects on Wild Bee Pollinators in Agroecosystems

Home , Agapostemon texanus, Halictus farinosus, Halictus tripartitus, Lasioglossum vagans, Macropis, Megachile argentina

Ecology Letters, (2013) 16: 584–599 doi: 10.1111/ele.12082 LETTER A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems

Abstract Christina M. Kennedy,1*† Bees provide essential pollination services that are potentially affected both by local farm management and Eric Lonsdorf,1 Maile C. Neel,2 the surrounding landscape. To better understand these different factors, we modelled the relative effects of Neal M. Williams,3 Taylor H. landscape composition (nesting and floral resources within foraging distances), landscape configuration 4 5 Ricketts, Rachael Winfree, Riccardo (patch shape, interpatch connectivity and habitat aggregation) and farm management (organic vs. conven- 6 3,7 Bommarco, Claire Brittain, Alana L. tional and local-scale field diversity), and their interactions, on wild bee abundance and richness for 39 crop Burley,8 Daniel Cariveau,5 Luısa G. systems globally. Bee abundance and richness were higher in diversified and organic fields and in land- Carvalheiro,9,10,11 Natacha P. Chacoff,12 Saul A. Cunningham,13 scapes comprising more high-quality habitats; bee richness on conventional fields with low diversity bene- Bryan N. Danforth,14 Jan-Hendrik fited most from high-quality surrounding land cover. Landscape configuration effects were weak. Bee Dudenhoffer,€ 15 Elizabeth Elle,16 responses varied slightly by biome. Our synthesis reveals that pollinator persistence will depend on both Hannah R. Gaines,17 Lucas A. the maintenance of high-quality habitats around farms and on local management practices that may offset Garibaldi,18 Claudio Gratton,17 impacts of intensive monoculture agriculture. Andrea Holzschuh,15,19 Rufus Isaacs,20 Steven K. Javorek,21 Keywords 22 7 Shalene Jha, Alexandra M. Klein, Agri-environment schemes, diversified farming system, ecologically scaled landscape index, ecosystem ser- 15 23 Kristin Krewenka, Yael Mandelik, vices, farm management, habitat fragmentation, landscape structure, organic farming, pollinators. Margaret M. Mayfield,8 Lora Morandin,18 Lisa A. Neame,16 Mark 24 14 Ecology Letters (2013) 16: 584–599 Otieno, Mia Park, Simon G. Potts,24 Maj Rundlof,€ 6,25 Agustin Saez,26 Ingolf Steffan-Dewenter,19 Hisatomo Taki,27 Blandina Felipe Viana,28 Catrin Westphal,15 Julianna K. Wilson,20 Sarah S. Greenleaf29 and Claire Kremen29

1Urban Wildlife Institute, Lincoln Park Zoo, Chicago, IL, 60614, USA 18Sede Andina, Universidad Nacional de Rıo Negro (UNRN) and Consejo 2Department Plant Science and Landscape Architecture, University of Nacional de Investigaciones Cientıﬁcas y Tecnicas (CONICET), Mitre 630, CP Maryland, College Park, Maryland, 20742, USA 8400, San Carlos de Bariloche, Rıo Negro, Argentina 3Department of Entomology, University of California, One Shields Ave., Davis, 19Department of Animal Ecology and Tropical Biology, Biocenter, University CA, 95616, USA of Wurzburg,€ Am Hubland, 97074, Wurzburg,€ Germany 4Gund Institute for Ecological Economics, University of Vermont, Burlington, 20Department of Entomology, Michigan State University, East Lansing, MI, VT, 05401, USA 48824, USA 5Department of Entomology, Rutgers University, New Brunswick, NJ, 08901, 21Agriculture and Agri-Food Canada, Atlantic Food and Horticultural Research USA Centre, 32 Main Street, Kentville, NS, B4N 1J5, Canada 6Department of Ecology, Swedish University of Agricultural Sciences, 22Integrative Biology, 401 Biological Laboratories, University of Texas, Austin, SE-75007, Uppsala, Sweden TX, 78712, USA 7Section Ecosystem Functions, Institute of Ecology, Leuphana University of 23Department of Entomology, The Hebrew University of Jerusalem, P.O. Box Luneburg,€ Scharnhorststraße 1, 21335, Luneburg,€ Germany 12, Rehovot, 76100, Israel 8School of Biological Sciences, The University of Queensland, Goddard 24School of Agriculture, Policy and Development, University of Reading, Building, St Lucia Campus, Brisbane, QLD, 4072, Australia Reading, RG6 6AR, UK 9Institute of Integrative and Comparative Biology, University of Leeds, Leeds, 25Department of Biology, Lund University, SE-223 62, Lund, Sweden LS2 9JT, UK 26Laboratorio Ecotono-CRUB, Universidad Nacional del Comahue - INIBIOMA, 10NCB-Naturalis, postbus 9517, 2300 RA, Leiden, The Netherlands (8400) San Carlos de Bariloche, Rıo Negro, Argentina 11Department of Zoology and Entomology, University of Pretoria, Pretoria 27Department of Forest Entomology, Forestry and Forest Products Research 0002, South Africa Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan 12Instituto de Ecologıa Regional (IER), Facultad de Ciencias Naturales e IML, 28Biology Institute, Federal University of Bahia – UFBA, Rua Barao~ de Geremo- UNT. CC 34, 4107, Tucuman, Argentina abo, s/n Campus Universitario de Ondina, Salvador, BA, 40170-210, Brazil 13CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia 29Department of Environmental Science, Policy and Management, University 14Department of Entomology, Cornell University, Ithaca, NY, 14853, USA of California, Berkeley, CA, 94720-3114, USA 15Department of Crop Sciences, Agroecology, Georg August University †Current afﬁliation:Development by Design Program, The Nature Gottingen,€ Grisebachstr, 6 D-37077, Gottingen,€ Germany Conservancy, Fort Collins, CO, 80524, USA 16Department of Biological Sciences, Simon Fraser University, Burnaby, BC, *Correspondence: E-mail: [email protected] V5A 1S6, Canada 17Department of Entomology, University of Wisconsin, 1630 Linden Drive, Madison, WI, 53706, USA

not account for variation caused by different farm management INTRODUCTION practices; and it does not account explicitly for landscape configura- Wild bees are a critical component of ecosystems and provide tion (i.e. the spatial arrangement of habitat patches in a landscape), essential pollination services to wild plants (Kearns et al. 1998) and which can impact floral, nesting and overwintering resources for to crops (Klein et al. 2007) in agricultural landscapes. In some situa- bees (Kremen et al. 2007) and has been hypothesised to be an tions, wild bees alone can fully pollinate crops (Kremen et al. 2002; important, yet unaccounted for determinant of bee communities Winfree et al. 2007b), and bee richness can enhance the magnitude (Lonsdorf et al. 2009). and temporal stability of pollination (Kremen et al. 2002; Klein et al. Here, we performed an empirical synthesis to disentangle the 2009; Garibaldi et al. 2011). However, growers often rely on the independent and interactive effects of local management and land- managed honey bee (Apis mellifera) to provide crop pollination. Apis scape structure on wild bees, which is essential to inform ecosystem declines in regions of the United States and Europe (Potts et al. service-based land use recommendations in agroecosystems 2010b), concomitant with increases in pollination-dependent crop (Tscharntke et al. 2005, 2012). We apply the Lonsdorf et al. (2009) cultivation globally, have increased the potential for pollination model to 39 studies on 23 crops in 14 countries on 6 continents to shortfalls for farmers (Aizen et al. 2008). These factors in turn capture landscape composition effects on bee richness and abun- increase the importance of wild pollinators (Potts et al. 2010b). It is dance, accounting for the floral and nesting value of all habitat therefore vital to determine the environmental conditions, both at types in a landscape. We expand on previous analyses by determin- local and landscape scales, that support diverse and abundant wild ing the influence of landscape configuration (patch shape, interpatch bee assemblages in agroecosystems. connectivity and habitat aggregation) and local farm management Two drivers are proposed to influence wild bee abundance and (organic vs. conventional farming and local-scale field diversity). richness on farms: local management practices on the farm and the Using mixed model analysis in a model selection framework, we quality and structure of the surrounding landscape (Kremen et al. then test the relative importance of landscape composition (i.e. 2007). There is growing evidence for the importance of local field model output), landscape configuration, local farm management and management on wild pollinators, both separately and in interaction their potential interactions, as predictors of observed wild bee abun- with landscape effects, as revealed in regional studies (Williams & dance and richness in crop fields. Kremen 2007; Rundlof€ et al. 2008; Batary et al. 2011; Concepcion et al. 2012). Different management practices, such as organic farming or increasing within-field habitat heterogeneity, can improve bee METHODS abundance, richness and productivity even in landscapes with little Studies and measures of pollinators natural habitat (Williams & Kremen 2007; Holzschuh et al. 2008; Rundlof€ et al. 2008; Batary et al. 2011), as long as sufficient habitat We analysed pollinator and landscape data from 605 field sites from exists to maintain source populations (Tscharntke et al. 2005, 2012). 39 studies in different biomes (tropical and subtropical, n = 10; Whether these local-scale and interactive effects are consistent Mediterranean, n = 8; and other temperate, n = 21) and on 23 across global agriculture remains unknown. crops with varying degrees of dependency on pollinators (Table 1, Research on landscape-level effects on pollinators has focused see Appendix S1 for references of published studies and Appendix predominantly on the contribution of natural and semi-natural areas S2 for methods of unpublished studies in Supporting Information). surrounding farms, which may provide essential habitats and key Our analyses focused on bees because they are considered the most floral resources and nesting sites that contribute to the long-term important crop pollinators (Klein et al. 2007) and their biology is persistence of wild bees (Westrich 1996; Williams & Kremen 2007). relatively well known. We analysed only wild species, because the Syntheses of data across multiple taxa, crop species and biomes abundance of managed species depends more on human choice of reveal that bee visitation, richness and stability increase with placement than on landscape or local field site characteristics. We decreasing distance from these habitats (Ricketts et al. 2008; targeted studies that sampled bees at multiple independent fields Garibaldi et al. 2011). These studies offer insights into the impor- within an agricultural landscape (across a gradient in agricultural tance of natural areas in sustaining pollination services in human- intensity) based on author knowledge and previous synthetic work modified landscapes, but their use of binary landscape categories (Ricketts et al. 2008; Garibaldi et al. 2011). Author(s) of each study (e.g. natural and semi-natural habitat vs. cropland) fails to account provided site-specific data on (1) bee abundance and/or visitation for the complexity of different habitats known to provide partial and bee richness, (2) spatial locations of fields, (3) characterisation resources for bees (Westrich 1996; Winfree et al. 2007a). These of local management (organic vs. conventional and field diversity), recent syntheses also do not consider species’ responses to local- (4) GIS data on surrounding multi-class land cover and (5) esti- scale management practices or differential responses to habitat attri- mates of nesting and floral resource quality for different bee guilds butes. for each land-cover class. Within studies, all sites were separated by To develop a more robust understanding of how different land- distances of 350 m–160 km (mean Æ SD: 25 Æ 22 km), with only cover types influence wild (bee) pollinators in agricultural land- 0.02% site pairs located < 1 km apart (Appendix S3). scapes, a spatially explicit model has been developed to predict relative bee abundance based on the composition of habitats and their Bee abundance and richness floral and nesting resources (Lonsdorf et al. 2009). The Lonsdorf All 39 studies measured bee abundance on (n = 22) or number of et al. (2009) model produces an ecologically scaled landscape index visits to (n = 17) crop flowers, and all but one study measured spe- (sensu Vos et al. 2001) that captures the estimated quality and cies richness (Table 1). Abundance was quantified as the number of amounts (and potential seasonal shifts) of habitats in a landscape, individual bees collected from aerial netting, pan trapping or both; and is scaled based on species mobility. This model, however, does bee visitation was measured as the total number of times a bee

© 2013 Blackwell Publishing Ltd/CNRS © 586 03BakelPbihn Ltd/CNRS Publishing Blackwell 2013 Table 1 Studies included in the modelling of local and landscape effects on global wild bee assemblages .M Kennedy M. C.

Crop pollinator Honey bee: # Years Site distance Study Citation§ Crop species dependence* Bee ﬂower visitors modelled managed, feral$ sampled # Sites range (mean) (m) Location

† Tropical and subtropical biomes Coffee_A Jha & Vandermeer 2010 Coffea Medium 44 taxa: Augochlora spp., Yes, yes 1 7 >925–4030 Chiapas, tal. et arabica (10–40%) Augochlorella sp., Augochloropsis spp., (2470) Mexico Caenaugochlora sp., Ceratina spp., Dialictus spp., Euglossa sp., Halictus spp., Melitoma spp., Melissodes sp., Plebia sp., Trigona sp., Trigonisca sp., Xylocopa sp. Coffee_B Ricketts 2004; C. arabica Medium 11 taxa: Apis sp., Melipona sp., No, yes 1 8 >490–3100 San Isidro Ricketts et al. 2004 (10–40%) Nannotrigona sp., Partamona sp., (1400) del General, Plebeia sp., Plebia sp., Costa Rica Trigona spp., Trigonisca sp. Grapefruit Chacoff & Aizen 2006; Citrus Little 14 taxa: Apis mellifera, No, yes 3 12 >430–74 000 Yungas, Chacoff et al. 2008 paradisi (< 10%) Augochlorospis spp., (33 200) Argentina Bombus sp., Dialictus sp., Megachilidae sp., Plebeia spp., Psaenythia sp., Tetragonisca sp., Trigona spp. Longan Blanche et al. 2006 Dimocarpus Medium 3 taxa: A. mellifera, Homalictus No, yes 1 6 >2500–80 000 Queensland, longan (10–40%) dampieri, Trigona carbonaria (43 000) Australia Macadamia_A Blanche et al. 2006 Macadamia Essential 1 taxon: A. mellifera No, yes 1 5 >10 000–40 000 Queensland, integrifolia (>90%) (24 000) Australia Macadamia_B Mayfield (unpublished Macadamia Essential 1 taxon: Trigona carbonaria Yes, yes 1 10 >430–24 000 New South data) integrifolia (>90%) (13 300) Wales, Australia Mango Carvalheiro et al. 2010 Mangifera indica High 3 taxa: Ceratina spp., Yes, yes 1 12 >1700–13 600 Limpopo, (40–90%) Xylocopa sp. (6500) South Africa Passion flower Viana & Silva Passiflora edulis Essential 4 taxa: A. mellifera, Trigona spinipes, No, yes 1 16 >1000–9600 Bahia, Brazil (unpublished data) Sims f. flavicarpa (>90%) Xylocopa (Megaxylocopa) (4400) frontalis, Xylocopa (Neoxylocopa) grisescens Pigeon pea Otieno et al. Cajanus cajan Little 48 taxa: Amegilla spp., Anthidium sp., Yes, no 1 12 >2100–35 000 Kibwezi (unpublished data) (< 10%) Anthophora sp., Braunsapis sp., (16 300) District, Kenya Ceratina sp., Coelioxys sp., Dactylurina sp., Euaspis sp., Halictus sp., Heriades sp., Hypotrigona sp., Lasioglossum sp., Lipotriches sp., Lithurge sp., Macrogalea sp., Megachile spp., Meliponula sp., Melissodes sp., Nomia sp., Pachyanthidium sp., Pachymelus sp., Plebeina sp., Pseudapis sp., Pseudoanthidium sp., Pseudophilanthus sp., Systropha sp., Tetralonia sp., Tetraloniella sp., Thyreus sp.,

Xylocopa spp. Letter

(continued) Table 1. (continued) Letter

Crop pollinator Honey bee: # Years Site distance Study Citation§ Crop species dependence* Bee ﬂower visitors modelled managed, feral$ sampled # Sites range (mean) (m) Location

Sunflower_A Carvalheiro et al. 2011 Helianthus Medium 4 taxa: Lasioglossum sp., Megachile sp., Yes, yes 1 30 >350–24 000 Limpopo, annuus (10–40%) Tetraloniella sp., Xylocopa sp. (8400 m) South Africa Mediterranean biome Almond_A Klein et al. 2012; Klein, Prunus dulcis High 38 taxa: Agapostemon sp., Andrena spp., Yes, no 1 23 >1460–46 000 California, USA Brittain, & Kremen (40–90%) Bombus sp., Ceratina spp., (17 600) (unpublished data) Eucera spp., Habropoda sp., Halictus spp.; Hoplitis sp., Lasioglossum spp., Micralictoides sp., Osmia spp., Panurginus sp., Protosmia sp., Stelis sp. Almond_B Kremen (unpublished P. dulcis High 8 taxa: Andrena sp., Bombus sp., Yes, no 1 15 >1150–54 100 California, USA data) (40–90%) Dialictus sp., Halictus spp., (25 400) Lasioglossum sp. Almond_C Mandelik (unpublished P. dulcis High 27 taxa: Andrena spp., Ceratina spp., Yes, no 1 6 >1100–23 000 Judean data) (a) (40–90%) Eucera spp., Halictus sp., (13 100) Foothills, Lasioglossum spp., Nomada spp. Israel ¶ Sunflower_B Greenleaf & Kremen H. annuus Medium 13 taxa: Agapostemon sp., Yes, no 3 15 1400–55 000 California, USA 2006 (b) (10–40%) Anthophoridae spp., Bombus spp., (20 600) Halictus spp., Lasioglossum sp., Megachile spp., Svastra sp., Xylocopa sp. Sunflower_C Mandelik (unpublished H. annuus Medium 60 taxa: Andrena spp., Ceratina spp., Yes, no 1 13 1200–26 600 Judean data) (b) (10–40%) Ceylalictus sp., Colletes sp., (11 050) Foothills, Eucera spp., Halictus spp., Israel Hylaeus spp., Lasioglossum spp., Nomada spp., Nomioides sp., Osmia sp., Panurgus sp., Systropha sp. Tomato_A Greenleaf & Kremen Solanum Little 4 taxa: Anthophora urbana, Bombus Yes, no 1 10 2900–58 000 California, USA 2006 (a) lycopersicum (< 10%) vosnesenskii, Lasioglossum incompletus, (27 100) Small striped bee ¶ Watermelon_A Kremen et al. 2002, 2004 Citrullus Essential 17 taxa: Agapostemon sp., Anthophora sp., Yes, no 2 34 >410–69 500 California, USA lanatus (>90%) Bombus spp., Calliopsis sp., Halictus spp., (25 240) Hylaeus sp., Lasioglossum spp., Melissodes spp., Osmia sp., Peponapis sp., pollinators on effects landscape and Local Sphecodes sp., Triepeolus sp. Watermelon_B Mandelik (unpublished C. lanatus Essential 47 taxa: Ceratina spp., Ceylalictus sp., Yes, no 1 19 >935–30 100 Judean © data) (c) (>90%) Eucera spp., Halictus spp., Hylaeus spp., (14 000) Foothills, 03BakelPbihn Ltd/CNRS Publishing Blackwell 2013 Lasioglossum spp., Lithurgus sp., Israel Megachile spp., Nomada spp., Nomiapis spp., Ochreriades sp., Xylocopa sp.

(continued) 587 © Table 1. (continued) 588 03BakelPbihn Ltd/CNRS Publishing Blackwell 2013 .M Kennedy M. C. Crop pollinator Honey bee: # Years Site distance Study Citation§ Crop species dependence* Bee ﬂower visitors modelled managed, feral$ sampled # Sites range (mean) (m) Location

‡ Other temperate biomes ¶ Apple Park & Danforth Malus domestica Essential 58 taxa: Andrena spp., Augochlora sp., Yes, yes 2 14 >2500–110 000 New York,

(unpublished data) (>90%) Augochlorella sp., Augochloropsis sp., (52 200) USA al. et Bombus spp., Ceratina sp., Colletes sp., Halictus spp., Lasioglossum spp., Nomada spp., Osmia spp., Sphecodes sp., Xylocopa sp. Blueberry_A Isaacs & Kirk 2010 Vaccinium High 4 taxa: Andrena spp., Bombus spp., Yes, no 1 12 >1200–10 200 Michigan, USA corymbosum, (40–90%) Halictidae spp., Xylocopa sp. (36 000) cv. Jersey Blueberry_B Javorek (unpublished Vaccinium Essential 18 taxa: Andrena spp., Augochlorella sp., Yes, no 3 16 >2000–155 700 Prince data) angustifolium (>90%) Bombus spp., Colletes sp., Halictus spp., (66 000) Edward Lasioglossum spp., Osmia spp. Island, Canada Blueberry_C Tuell et al. 2009 Vaccinium High 101 taxa: Agapostemon spp., Andrena spp., Yes, no 3 15 >2800–80 400 Michigan, USA corymbosum (40–90%) Augochlora sp., Augochlorella sp., (31 600) Augochloropsis sp., Bombus spp., Ceratina spp., Colletes spp., Halictus spp., Hoplitis spp., Hylaeus spp., Lasioglossum spp., Megachile spp., Nomada spp., Osmia spp., Sphecodes spp., Xylocopa sp. Buckwheat Taki et al. 2010 Fagopyrum High 17 taxa: Apis cerana, Chalicodoma sp., Yes, no 2 17 450–9500 Ibaraki, Japan esculentum (40–90%) Coelioxys sp., Colletes spp., Epeolus sp., (3500) Halictus sp., Hylaeus spp., Lasioglossum spp., Lipotriches sp., Megachile spp., Sphecodes sp., Xylocopa sp. Canola_A** Arthur et al. 2010 Brassica napus Medium 2 taxa: A. mellifera, native bees No, yes 1 19 >375–27 497 Boorowa New and juncea (10–40%) (11 100) South Wales, Australia Canola_B Prache, MacFadyen, B. napus Medium 12 taxa: Amegilla sp., Lasioglossum spp., Yes, yes 1 10 >530–6400 Bethungra & Cunningham and juncea (10–40%) Leioproctus spp., Lipotriches sp. (4100) New South (unpublished data) Wales, Australia Canola_C Bommarco, Marini & Brassica napus Medium 8 taxa: Bombus spp. Yes, no 1 10 >3850–71 000 Uppland, Vaissiere 2012 (10–40%) (26 700) Sweden Canola_D Morandin & Winston Brassica rapa High 86 taxa: Andrena spp., Anthidium sp., No, no 2* 54 >480–67 700 Alberta, 2005 and napus (40–90%) Anthophora spp., Bombus spp., (24 600) Canada Coelioxys spp., Colletes spp., Diadasia sp., Eucera sp., Halictus spp., Heriades sp., Hoplitis spp., Hylaeus spp., Lasioglossum spp., Megachile spp., Melissodes sp., Nomada spp., Osmia spp., Panurginus sp., Protandrena spp., Sphecodes spp., Stelis sp.

(continued) Letter Table 1. (continued) Letter

Crop pollinator Honey bee: # Years Site distance Study Citation§ Crop species dependence* Bee ﬂower visitors modelled managed, feral$ sampled # Sites range (mean) (m) Location

Cantaloupe Winfree et al. 2008 Cucumis melo Essential 18 taxa: Agapostemon sp., Yes, no 1 14 >2200–72 300 New Jersey & (>90%) Andrena sp., (35 000) Pennsylvania, Augochlora sp., Augochlorella sp., USA Bombus spp., Ceratina sp., Halictus spp., Lasioglossum sp., Megachile sp., Melissodes sp., Peponapis sp., Triepeolus sp., Xylocopa sp. Cherry Holzschuh, Prunus avium High 25 taxa: Andrena spp., Bombus spp., Yes, no 1 8 >900À7600 Hesse, Germany Dudenhoffer,€ & (40–90%) Lasioglossum spp., Nomada sp., (4000) Tscharntke 2012 Osmia sp. Cranberry_A Cariveau (unpublished Vaccinium High 43 taxa: Andrena spp., Augochlora sp., Yes, no 1 16 >1000–33 000 New Jersey, data) macrocarpon (40–90%) Augochlorella sp., Augochloropsis spp., (15 700) USA Bombus spp., Ceratina sp., Coeloxys spp., Heriades sp., Hoplitis sp., Hylaeus sp., Lasioglossum spp., Megachile spp., Melitta sp., Nomada spp., Osmia spp., Panurginius sp., Sphecodes sp., Xylocopa sp. Cranberry_B Gaines (unpublished V. macrocarpon High 106 taxa: Agapostemon spp., Andrena spp., Yes, no 1 15 >3200–56 000 Wisconsin, USA data) (40–90%) Augochlora sp., Augochlorella sp., (27 000) Bombus spp., Calliopsis sp., Ceratina spp., Coelioxys sp., Colletes sp., Halictus spp., Hoplitis spp., Hylaeus spp., Lasioglossum spp., Macropis sp., Megachile spp. Melissodes sp., Nomada sp., Osmia spp., Sphecodes spp., Stelis sp. Field bean Carre et al. 2009 Vicia faba Little 44 taxa: Andrena spp., Bombus spp., Yes, no 1 10 3700–39 000 South East (< 10%) Coelioxys sp., Halictus sp., (23 900) England Lasioglossum spp., Nomada spp., Sphecodes sp. Pepper Winfree et al. 2008 Capsicum Little 15 taxa: Augochlora sp., Yes, no 1 21 >1100–72 200 New Jersey & annuum (< 10%) Augochlorella sp., Bombus spp., (34 700) Pennsylvania, Halictus sp., Lasioglossum spp. USA ¶ pollinators on effects landscape and Local Red clover Bommarco et al. 2012; Trifolium Essential 15 taxa: Bombus spp. Yes, no 2 25 >860–119 000 Skane, Sweden Rundlof€ & Bommarco pratense (>90%) (54 600) (unpublished data) © Squash Neame & Elle Curcurbita pepo, Essential 24 taxa: Agapostemon spp., Yes, no 1 9 >420–26 500 Okanagan- 03BakelPbihn Ltd/CNRS Publishing Blackwell 2013 (unpublished data) C. moschata, (>90%) Bombus spp., Ceratina spp., (9960) Similkameen C. maxima Dialictus sp., Halictus spp., Valley, BC, Lasioglossum spp., Melissodes spp. Canada Strawberry Carre et al. 2009; Fragaria sp. Medium 28 taxa: Andrena spp., Yes, no 1 10 >3870–49 300 Lower Saxony, Steffan-Dewenter, (10–40%) Bombus spp., Halictus spp., (24 000) Germany Krewenka, Vaissiere Lasioglossum spp., Nomada spp., & Westphal Osmia spp., Sphecodes sp. (unpublished data)

(continued) 589 © 590 03BakelPbihn Ltd/CNRS Publishing Blackwell 2013 .M Kennedy M. C. tal. et

Table 1. (continued)

Crop pollinator Honey bee: # Years Site distance Study Citation§ Crop species dependence* Bee ﬂower visitors modelled managed, feral$ sampled # Sites range (mean) (m) Location

Sunﬂower_D Saez, Sabatino, H. annuus Medium 9 taxa: Augochlora sp., Yes, yes 1 21 >370–68 100 SE Pampas, & Aizen 2012 (10–40%) Augochloropsis sp., Bombus sp., (22 900) Argentina Dialictus sp., Halictus spp., Megachile sp., Melissoptila sp., Xylocopa sp. Tomato_B Winfree et al. 2008 S. lycopersicum Little 16 taxa: Andrena sp., Augochlora sp., Yes, no 1 13 >1500–89 100 New Jersey & (< 10%) Augochlorella sp., Augochloropsis sp., (39 000) Pennsylvania, Bombus spp., Halictus sp., USA Lasioglossum spp. Watermelon_C Winfree et al. 2007b C. lanatus Essential 46 taxa: Agapostemon spp., Augochlora sp., Yes, no 1 23 >875–89 500 New Jersey & (>90%) Augochlorella sp., Augochloropsis sp., (36 800) Pennsylvania, Bombus spp., Calliopsis sp., Ceratina spp., USA Halictus spp., Hylaeus spp., Lasioglossum spp., Megachile spp., Melissodes sp., Peponapis sp., Ptilothrix sp., Triepeolus sp., Xylocopa sp.

*Dependence of crops on pollinators for reproduction based on Klein et al. (2007): low dependence (< 10% yield reduction without pollinators), modest (10–40%), high (40–90%) or essential (>90%). †Studies located in tropical (< 23.5° latitude in both hemispheres) and subtropical zones (between 20° and 40° latitude in both hemispheres), collectively referred to as tropical. ‡Studies located at >23.5° and < 66.5° north latitude, except those with Mediterranean climate (warm to hot, dry summers and mild to cold, wet winters). $A. mellifera modelled when only feral and non-managed: Canola_A, Coffee_B, Grapefruit, Longan, Macadamia_A and Passion flower studies. ¶Majority of sites only sampled in 1 year. **Richness not modelled because native bee species not resolved taxonomically. §See Appendix S1 for complete references for published studies; and Appendix S2 for methodology of unpublished studies. Letter Letter Local and landscape effects on pollinators 591 landed on, foraged from or touched a flower per plot or transect in across seasons (permitting coding of temporal variation in floral a given time interval (hereafter collectively referred to as abundance). resources). Highest overall habitat suitabilities (aggregated across When studies measured both visits and abundance, we used the lat- nesting and floral resources) were assigned to natural and semi-natu- ter estimate, which provided the finest taxonomic resolution. In ral areas (i.e. shrubland, grassland, forest and woody wetlands) and almost 75% of cases, richness was to species-level (n = 502 of 675 to a lesser extent certain croplands (i.e. orchards and vineyards, pas- taxa), but sometimes it was based on morphospecies (n = 6), spe- ture and fallow fields and perennial crops) and low density develop- cies-group (n = 15), subgenera (n = 34), genera (n = 113), genus- ment and open spaces (Table S4_2). Authors also coded each bee group (n = 3) or body size classes (n = 2) (sensu Michener 2000). As species or group by nesting guild and designated their flight period. social bees may be more sensitive than solitary bees to habitat isola- For all expert-derived parameters (i.e. floral and nesting values, nest- tion (Ricketts et al. 2008) and human disturbance (Williams et al. ing guild and seasonality), authors consulted independent data 2010), we characterised each species as social or solitary. Social spe- sources when available. We generated LLI for each bee species, and cies included highly eusocial (e.g. Melipona, Trigona, Apis) to primi- then aggregated into total abundance over all bee species by weight- tively eusocial or semi-social species (e.g. most bumble bees and ing indices by study-wide relative abundances of corresponding spe- many Halictinae such as Lasioglossum and Halictus) (Michener 2000). cies. The Lonsdorf model was implemented using ArcGIS, and is available through the Natural Capital Project (‘Crop Pollination’ tool Local and landscape variables within the InVEST Software, http://www.naturalcapitalproject.org/ For each study, we obtained (1) a characterisation of two aspects of InVEST.html) (Tallis et al. 2011). local farm management (organic vs. conventional farming and local- scale field diversity), (2) an ecologically scaled measure of landscape Landscape configuration composition using the Lonsdorf et al. (2009) model and (3) statistical measures of landscape configuration using the program FRAG- We quantified habitat configuration 3 km around field sites using STATS 3.3 (McGarigal et al. 2002). landscape-level metrics in the program FRAGSTATS 3.3 (McGari- gal et al. 2002), to coincide with the spatial extent of the Lonsdorf model and typical foraging ranges of bees (Greenleaf et al. 2007) Local farm management (Figure S5_1). We examined metrics that captured aspects of habitat To characterise farm management, fields were categorised by shape, connectivity, aggregation and heterogeneity that were inde- authors as organic (i.e. lacking or having highly reduced use of her- pendent of LLI, based on an analysis of artificial multi-class neutral bicides, fertilisers and pesticides, n = 91) or conventional (i.e. pri- landscapes (With & King 1997) using a modified version of SIM- marily using synthetic inputs to cultivate crops, n = 514), and as MAP 2.0 (Saura & Martınez-Millan 2000) (see Appendix S5 for fur- locally diverse (fields < 4 ha, with mixed crop types within or ther detail). Final landscape metrics were orthogonal to LLI scores across fields and/or presence of non-crop vegetation, such as as well as to one another and quantified three aspects of configura- hedgerows, flower strips, and/or weedy margins or agroforestry, tion independent of area: (1) perimeter-area ratio distribution n = 173) or locally simple (monocultural fields 4 ha, lacking (PARA_MN, mean patch shape and edge density), (2) Euclidean crop or other plant diversity, n = 432). Field type and field diversity nearest neighbour distance distribution (ENN_CV, variation in in- were not necessarily coupled, with 38% of fields being organic and terpatch connectivity) and (3) interspersion and juxtaposition index locally simple, whereas 21% of fields were conventional and locally (IJI, patch aggregation). diverse; therefore, we examined the independent and potentially interactive effects of these two management variables. Statistical analyses We analysed the influence of local and landscape factors on empirical wild bee abundance and richness using general linear mixed- Landscape composition effects models with Gaussian error distribution. Following Williams We characterised landscape composition around farm sites using the et al. (2010), we predicted each pollinator response variable (abun- Lonsdorf et al. (2009) model, which produces an ecologically scaled dance and richness) based on the general model structure: E = b0 bX ? = b + b index of habitat quality in a two-step process. First, using the GIS (a, r) e e ln[E(a,r)] 0 iXi, where E(a, r) is expected land cover it calculates pollinator ‘supply’ at each pixel wild bee abundance or richness, Xi are the covariates (local and 9 (30 m 30 m cell), based on the suitability of the surrounding landscape variables) and covariate interactions, bi are the partial b land cover for nesting and floral resources, assuming that nearby regression coefficients for each i covariate and interaction and 0 is resources contribute more than distant resources (based on an the expected value when covariates are null. As some sites had val- exponential function parameterised by the typical species’ foraging ues of abundance and richness equal to zero, we transformed distance). Second, using the pollinator supply values, the model pre- responses by ln [a + 1, r + 1]. Residuals of fitted models were dicts an expected abundance of pollinators arriving at any given approximately normally distributed with no strong pattern of over- pixel, again assuming that pollinator supply from nearby pixels con- dispersion or heteroscedasticity (see Appendix S6 for further infor- tributes more than that from pixels farther away. The model pro- mation). We modelled total, social and solitary bee abundance and duces a quality index (0–1) of total pollinator abundance at any site richness across all studies and total abundance and richness in tropi- in the landscape, which we refer to as the ‘Lonsdorf landscape cal and subtropical (collectively referred to as tropical), Mediterra- index’ (LLI) (see Appendix S4 for further detail). nean and temperate studies separately to assess potential differences We calculated the LLI for field sites within the 39 study regions. by biome. Authors assigned nesting and floral suitability values to land-cover To account for interstudy differences in methods and sampling classes, and overall floral values were calculated as a weighted sum units and for correlation of fields sampled across multiple years, we

© 2013 Blackwell Publishing Ltd/CNRS 592 C. M. Kennedy et al. Letter included additive random effects for the intercept with respect to with the highest Akaike weights (Table S7_2). Based on main both study and site-within-study. Our models estimated different effects, and holding other variables constant at their average value, intercepts per study to account for the hierarchical data structure total bee abundance and social bee abundance across all studies and differences among crop systems, which has been found to be increased on average by 36.6 and 33.8%, respectively, for each 0.1 effective for cross-study syntheses (Stram 1996; Gelman & Hill unit increase in LLI (or by an estimated factor of 22.6 and 18.4, 2007). By modelling an exponential relationship between bee respectively, with LLI increasing from 0 to 1) (Fig. 1a, c), whereas responses and covariates, coefficients estimated proportional solitary bee abundances were estimated to increase by 5.1% per 0.1 changes in responses as a function of covariates (see Ricketts et al. unit increase in LLI (or by a factor of 1.64 with LLI increasing 2008; Williams et al. 2010). Even though intercepts were allowed to from 0 to 1) (Fig. 1e). For local-scale effects, abundances of total vary for each study, we modelled a common slope (bi) given our bees, and of solitary and social species were on average higher when goal of quantifying a general relationship to local and landscape fields had a diversity of crops or non-crop vegetation (76.3, 73.5 variables across crop systems. To interpret the main effects in the and 61.6% respectively) and when managed organically (74.0, 72.8 presence of interactions, we mean-centred continuous covariates and 45.2%, respectively; 95% CIs > 0 in all cases) (Table 2, Fig. 1; (Gelman & Hill 2007; Schielzeth 2010). Figure S7_1). Effects of landscape configuration on bee abundance We developed a candidate model set to test fixed effects. Our were weak, with lower summed Akaike weights (total, w = 0.30– global model included all main effects and all two-way interactions 0.40; social, w = 0.67–0.97; solitary, w = 0.14–0.16), and model- between landscape composition (LLI), field type (FT) (conventional averaged partial slope coefficients near 0. Variation in interpatch vs. organic) and field-scale diversity (FD) (locally simple vs. locally distance (i.e. ENN_CV), however, was predicted to cause 3% diverse) and between LLI, FT, and FD with landscape configuration declines in social bee abundance per 10% increase in ENN_CV (PARA_MN, ENN_CV, IJI). Our candidate set included 135 mod- (w = 0.97, 95% CIs not overlap zero) (Table 2). els, and was balanced such that each of the six covariates appeared Similarly, wild bee richness was strongly determined by LLI and in 88 models (Table S6_1). organic vs. conventional management but to a lesser extent field- We ranked competing models based on AICc, identified top scale diversity for total, social and solitary bees (w 0.92) across models (i.e. ΔAICc from the best model < 2.0) for each response all studies (Table 2). Total bee richness and social bee richness variable, and calculated associated Akaike weights (w) (Burnham & increased significantly on average by 38.0 and 29.7% per 0.1 unit Anderson 2002). To assess local and landscape effects, we calcu- increase in LLI (or by a factor of 25.0 and 13.5, respectively, with lated model-averaged partial regression coefficients for each covari- LLI changing from 0 to 1) (Fig. 1b, d), and solitary bee richness ate based on the 95% confidence set (Burnham & Anderson 2002). increased by 8.7% per 0.1 increase in LLI (or a factor of 2.3 with a We determined the relative importance of each covariate based on change in LLI from 0 to 1) based on point estimates only (Fig. 1f). the sum of Akaike weights across the entire model set, with 1 being Average richness of total, solitary and social species was significantly the most important (present in all models with weight) and 0 the higher on organic than conventional fields by 49.9, 48.1 and 28.5% least important. Covariates were considered important if they respectively; however, only solitary bee richness was significantly appeared in top models (ΔAICc < 2.0) and had a relatively high (28.0%) higher in locally diversified fields (Table 2). Bee richness summed Akaike weight (w > 0.6). We report 95% confidence inter- did not respond strongly to landscape structure (low Akaike weights vals (CIs) around model-averaged partial slope coefficients (bi) for and 95% CIs including zero), but all three configuration metrics aggregated studies and 90% CIs for biome-specific analyses (due to (PARA_MN, ENN_CV and IJI) appeared in some of the top mod- reduced sample sizes) and deemed an effect significant if uncondi- els for social bee richness (Table S7_2). tional CIs did not include zero. Statistical analyses were performed When studies were analysed by biome, LLI had a positive effect using the R statistical system v 2.11.1 (R Development Core Team on both bee abundance and richness in tropical and Mediterranean 2008); model selection for mixed models was conducted using systems (w > 0.99), causing an average increase of 23.2 and 35.5% ‘lme4’ package (Bates et al. 2008) and ‘MuMIn’ package for model- in tropical and 128.9 and 41.1% in Mediterranean, respectively, for averaging of coefficients (Barton 2011). each 0.1 unit increase in LLI (Table 3, Fig. 2). LLI did not significantly affect bees in temperate studies, where field type was the dominant factor (w = 1.00) (Table 3). In both Mediterranean and RESULTS temperate systems, organic fields were estimated to harbour 67.7 A total of 675 bee taxa were modelled using the Lonsdorf et al. and 41.5% higher bee abundance and 56.1 and 43.8% higher bee (2009) model, with an average of 52 ( Æ 27 1 SD) taxa per study richness than in conventional fields (Fig. 3). Across all biomes, hab- (Table 1). Per field site, average total bee richness was ~7(Æ 61 itat aggregation (as measured by IJI) had the greatest influence of SD) and average total abundance was ~56 ( Æ 144 1 SD) (Appen- configuration metrics (w > 0.80 for all bee responses except tropical dix S7, Table S7_1). Social and solitary species were roughly equally richness, and appearing in all top models) (Table 3, Table S7_2). represented across studies (social bees represented 47% of total We found some evidence of interactions between local and land- abundance). scape factors, which were stronger and better supported for rich- Across all studies, abundances of wild bees were best predicted ness than for abundance (Table 2, Appendix S7). The average by field type (conventional vs. organic), field-scale diversity (locally influence of LLI on bee richness and abundance decreased when simple vs. locally diverse; both variables with w 0.99 for total, fields were diversified and managed organically; however, the only social and solitary bees) and Lonsdorf landscape index (an ecologi- significant interaction was between LLI and field-scale diversity for cally scaled index of landscape composition) (w = 1.00 for total and total bee richness across all studies (Table 2). For each 0.1 unit social bees, and 0.74 for solitary bees) (Table 2). These three covari- increase in LLI, total bee richness and abundance was estimated ates were included in the most supported models (ΔAICc < 2.0) to increase in locally simple (monocultural) fields by 32.0 and

Table 2 Model-averaged partial regression coefﬁcients and unconditional 95% CIs from models of total, social and solitary wild bee abundance and richness (n = 39 studies) in relation to local and landscape factors (model set in Appendix S5). Coefﬁcients are based on log-transformed data and in bold where CIs do not include 0. Akaike weights (wj) indicate relative importance of covariate j based on summing weights across models where covariate j occurs. LLI = Lonsdorf landscape index (an ecologically scaled index of landscape composition); FT = Field type (conventional vs. organic); FD = Field-scale diversity (locally simple vs. locally diverse); PARA_MN = perimeter-area ratio distribution; ENN_CV = Euclidean nearest neighbour distance distribution; and IJI = interspersion & juxtaposition index

Total bee abundance Social bee abundance Solitary bee abundance ^ ^ ^ Covariate w b Lower CI Upper CI w b Lower CI Upper CI w b Lower CI Upper CI

Lonsdorf landscape index (LLI) 1.00 3.1200 1.4600 4.7800 1.00 2.9100 1.3000 4.5100 0.74 0.4930 À1.0200 2.0100 Field type-organic (FT) 1.00 0.5540 0.2670 0.8410 0.99 0.3730 0.1260 0.6190 1.00 0.5470 0.2950 0.7990 Field diversity-complex (FD) 1.00 0.5670 0.2490 0.8850 0.99 0.4800 0.1630 0.7970 1.00 0.5510 0.2510 0.8520 PARA_MN 0.30 0.0000 À0.0004 0.0004 0.67 0.0000 À0.0007 0.0006 0.16 À0.0001 À0.0004 0.0003 ENN_CV 0.40 À0.0006 À0.0026 0.0014 0.97 À0.0030 À0.0055 À0.0005 0.14 0.0000 À0.0008 0.0008 IJI 0.33 0.0008 À0.0033 0.0048 0.73 0.0026 À0.0037 0.0089 0.14 À0.0002 À0.0025 0.0022 LLI:FT 0.21 À0.1840 À1.4900 1.1200 0.05 À0.0006 À0.5320 0.5310 0.59 À1.5700 À4.6000 1.4700 LLI:FD 0.25 À0.3840 À2.3000 1.5300 0.07 À0.1220 À1.2700 1.0300 0.23 À0.2700 À1.9100 1.3700 FT:FD 0.34 À0.1160 À0.5200 0.2880 0.05 À0.0098 À0.1450 0.1250 0.26 À0.0317 À0.3110 0.2480 LLI:PARA_MN 0.02 0.0000 À0.0008 0.0007 0.05 0.0000 À0.0012 0.0011 0.01 0.0000 À0.0005 0.0005 LLI:ENN_CV 0.02 0.0001 À0.0023 0.0025 0.12 À0.0013 À0.0098 0.0072 0.00 0.0000 À0.0012 0.0012 LLI:IJI 0.01 0.0001 À0.0081 0.0083 0.06 0.0019 À0.0211 0.0249 0.00 0.0000 À0.0047 0.0047 FT:PARA_MN 0.02 0.0000 À0.0001 0.0001 0.09 À0.0001 À0.0005 0.0004 0.00 0.0000 0.0000 0.0000 FT:ENN_CV 0.02 0.0000 À0.0007 0.0006 0.10 À0.0003 À0.0026 0.0020 0.00 0.0000 À0.0002 0.0002 FT:IJI 0.01 0.0001 À0.0021 0.0023 0.08 0.0010 À0.0070 0.0090 0.00 0.0000 À0.0009 0.0009 FD:PARA_MN 0.02 0.0000 À0.0002 0.0001 0.06 0.0000 À0.0003 0.0002 0.00 0.0000 0.0000 0.0000 FD:ENN_CV 0.02 0.0000 À0.0007 0.0008 0.08 À0.0001 À0.0018 0.0016 0.00 0.0000 À0.0003 0.0003 FD:IJI 0.02 À0.0001 À0.0020 0.0019 0.06 À0.0003 À0.0049 0.0043 0.00 0.0000 À0.0008 0.0008

Total bee richness Social bee richness Solitary bee richness

Covariate w b^ Lower CI Upper CI w b^ Lower CI Upper CI w b^ Lower CI Upper CI

Lonsdorf landscape index (LLI) 1.00 3.2200 2.0700 4.3600 1.00 2.6000 1.2400 3.9500 0.92 0.8370 À0.2960 1.9700 Field type-organic (FT) 1.00 0.4050 0.2180 0.5920 1.00 0.2510 0.1070 0.3950 1.00 0.3930 0.2220 0.5650 Field diversity-complex (FD) 0.99 0.0470 À0.1560 0.2500 0.93 À0.0585 À0.2350 0.1180 0.98 0.2470 0.0335 0.4600 PARA_MN 0.23 0.0000 À0.0003 0.0002 0.57 À0.0001 À0.0005 0.0003 0.20 0.0000 À0.0003 0.0002 ENN_CV 0.24 À0.0003 À0.0013 0.0008 0.58 À0.0005 À0.0018 0.0007 0.20 À0.0002 À0.0012 0.0008 IJI 0.23 À0.0001 À0.0019 0.0017 0.56 À0.0002 À0.0028 0.0024 0.19 À0.0001 À0.0019 0.0017 LLI:FT 0.41 À0.3400 À1.5700 0.8880 0.20 0.0579 À0.5830 0.6990 0.81 À1.5300 À3.4500 0.3800 LLI:FD 0.96 À2.6400 À4.5400 À0.7310 0.77 À1.9100 À4.3100 0.5010 0.36 À0.3720 À1.7900 1.0500 FT:FD 0.64 À0.1540 À0.4630 0.1540 0.31 À0.0487 À0.2430 0.1460 0.39 À0.0710 À0.3340 0.1920 LLI:PARA_MN 0.00 0.0000 À0.0001 0.0001 0.15 0.0004 À0.0016 0.0024 0.04 0.0000 À0.0007 0.0006 LLI:ENN_CV 0.00 0.0000 À0.0003 0.0003 0.01 0.0000 À0.0009 0.0009 0.04 0.0000 À0.0017 0.0016 LLI:IJI 0.00 0.0000 À0.0012 0.0012 0.01 0.0003 À0.0070 0.0077 0.04 À0.0017 À0.0200 0.0166 FT:PARA_MN 0.00 0.0000 0.0000 0.0000 0.12 À0.0001 À0.0004 0.0003 0.01 0.0000 0.0000 0.0000 FT:ENN_CV 0.00 0.0000 À0.0001 0.0001 0.01 0.0000 À0.0003 0.0003 0.00 0.0000 À0.0002 0.0002 FT:IJI 0.00 0.0000 À0.0002 0.0002 0.01 0.0000 À0.0012 0.0013 0.01 0.0000 À0.0007 0.0007 FD:PARA_MN 0.00 0.0000 0.0000 0.0000 0.24 À0.0001 À0.0006 0.0004 0.00 0.0000 0.0000 0.0000 FD:ENN_CV 0.00 0.0000 À0.0001 0.0001 0.12 À0.0001 À0.0010 0.0009 0.00 0.0000 À0.0001 0.0001 FD:IJI 0.00 0.0000 À0.0004 0.0004 0.12 0.0000 À0.0024 0.0025 0.00 0.0000 À0.0004 0.0004

5.2% on average, respectively, relative to locally diverse fields increase about twice as much when IJI = 10 as when IJI = 0 (Figure S7_2a). Similar increases caused by LLI were higher by 4.6 (Table 3, Figure S7_3). and 2.5% for bee richness and abundance, respectively, in conventional fields relative to organic (but in all cases, except for total DISCUSSION richness, 95% CIs included 0) (Figure S7_2b). These interactions predict that the marginal increase from higher habitat quality Although it is increasingly evident that pollinators can be influenced within a landscape is on average less when crop fields are diversi- by both local and landscape characteristics (e.g. Tscharntke et al. fied or organically managed. Local farming variables may also 2005; Kremen et al. 2007; Batary et al. 2011; Concepcion et al. interact. Effects of organic farming on bee richness and abundance 2012), this study is the first global, quantitative synthesis to test the were reduced by 21.4% (w = 0.64) and 19.1% (w = 0.34) on aver- relative and interactive effects of landscape composition and land- age when fields were locally diversified (Figure S7_2c) (but again scape configuration in combination with local farming practices CIs included 0). In tropical crop systems, landscape composition (conventional vs. organic farming, and field diversity). We found (LLI) and configuration (IJI) had a significant positive interaction, that both landscape- and local-scale factors influenced wild bee such that a 10% increase in LLI caused average bee abundance to assemblages in significant and sometimes interactive ways. At the

Org−Simple Org−Diverse Conv−Simple Conv−Diverse

(a) (b) 50 180 6.0 7.5 45 160 140 3.0 4.5 35 40 1.5 30 LN(Total bee richness) LN(Total bee abundance) 0.0 1.0 2.0 3.0 4.0 0.0 25 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Lonsdorf landscape index Lonsdorf landscape index 20 15 Total bee richness Total bee abundance 510 0 020406080100120

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Lonsdorf landscape index Lonsdorf landscape index

(c) (d) 20 6.0 7.5 3.0 4.0 4.5 2.0 15 LN(Social bee abundance) LN(Social bee richness) 0.0 1.5 3.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Lonsdorf landscape index Lonsdorf landscape index Social bee richness Social bee abundance 0 10203040506070 0510

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Lonsdorf landscape index Lonsdorf landscape index

(e) (f) 35 40 3.0 4.0 2.0 3.0 4.5 6.0 7.5 1.5 LN(Solitary bee richness) LN(Solitary bee abundance) 0.0 1.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Lonsdorf landscape index Lonsdorf landscape index Solitary bee richness Solitary bee abundance 0 5 10 15 20 25 30 0 5 10 15 20

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Lonsdorf landscape index Lonsdorf landscape index

Figure 1 Response to Lonsdorf landscape index of wild bee abundance (a) and richness (b), social bee abundance (c) and richness (d), and solitary bee abundance (e) and richness (f) in relation to field type (conventional vs. organic) and field diversity (locally simple vs. diverse). Estimates are based on model-averaged partial regression coefficients for all studies (n = 39) for important main effects [E (abundance, richness) = ƒ (LLI + FT + FD)] (Table 2). Predicted relationship based on back- transformed estimates on normal scale in the main graph (with 95% CIs in Figure S7_1) and modelled log-linear relationship with sites in the inset (based on mean values per site, varying intercepts by site and study not shown). y-axis scales vary by bee responses; predicted relationships between LLI = 0–0.60 graphed (although maximum LLI = 1.0) because 0.61 was maximum score derived for empirical landscapes. landscape scale, bee abundance and richness were higher if more diversity enhanced bee abundance, and organic management high-quality habitats surrounded fields (i.e. higher LLI scores). This enhanced richness (Table 2). When studies were analysed by biome, effect was most pronounced in Mediterranean and tropical systems organic farming was the driving management effect in Mediterra- (Fig. 2). At the local scale, both organic management and field-level nean and temperate crop systems (Table 3, Fig. 3). Divergent regio-

Table 3 Model-averaged partial regression coefficients and unconditional 90% CIs from models of wild bee abundance and richness by biome in relation to local and landscape factors. Coefficients are based on log-transformed data and in bold where CIs do not include 0. Akaike weights (wj) indicate relative importance of covariate j based on summing weights across models where covariate j occurs. (See Table 1 for biome definitions, Table 2 for covariate definitions, Appendix S6 for model set and Appendix S7 for summary statistics by biome)

Bee abundance – tropical/subtropical Bee abundance – Mediterranean Bee abundance – temperate ^ ^ ^ Covariate w b Lower CI Upper CI w b Lower CI Upper CI w b Lower CI Upper CI

Lonsdorf landscape index (LLI) 1.00 2.0900 0.5310 3.6600* 0.99 8.2800 3.1400 13.4000* 0.47 0.3980 À1.1000 1.8900 Farm type-organic (FT) 0.40 0.1820 À0.2950 0.6590 0.88 0.5170 0.0701 1.0989 0.99 0.4450 0.1530 0.7370* Field diversity-complex (FD) 0.32 0.1520 À0.3240 0.6280 0.94 1.0000 À0.4430 2.4500 0.86 0.1940 À0.1140 0.5020 PARA_MN 0.44 0.0001 À0.0005 0.0006 0.79 0.0000 À0.0021 0.0022 0.80 À0.0005 À0.0015 0.0004 ENN_CV 0.44 À0.0002 À0.0020 0.0015 0.81 À0.0022 À0.0067 0.0022 0.78 0.0003 À0.0020 0.0026 IJI 0.95 0.0122 0.0018 0.0226 0.82 0.0064 À0.0078 0.0205 0.83 0.0021 À0.0058 0.0100 LLI:FT 0.05 0.1870 À1.8700 2.2400 0.04 0.1420 À2.3000 2.5900 0.08 À0.2320 À1.7600 1.2900 LLI:FD 0.02 À0.0136 À0.8900 0.8630 0.13 À0.5300 À4.6300 3.5700 0.03 0.0063 À0.6280 0.6410 FT:FD 0.01 0.0011 À0.0911 0.0933 0.14 À0.3220 À1.8200 1.1800 0.11 À0.0508 À0.3780 0.2770 LLI:PARA_MN 0.04 À0.0001 À0.0013 0.0011 0.20 0.0059 À0.0266 0.0385 0.05 À0.0005 À0.0040 0.0031 LLI:ENN_CV 0.04 0.0005 À0.0043 0.0053 0.02 0.0024 À0.0320 0.0367 0.03 0.0002 À0.0055 0.0058 LLI:IJI 0.94 0.1410 0.0582 0.2250* 0.09 À0.0519 À0.3550 0.2510 0.11 À0.0011 À0.0379 0.0358 FT:PARA_MN 0.02 À0.0001 À0.0009 0.0008 0.29 À0.0012 À0.0052 0.0028 0.06 0.0000 À0.0004 0.0004 FT:ENN_CV 0.02 0.0001 À0.0020 0.0022 0.03 À0.0001 À0.0023 0.0021 0.04 À0.0002 À0.0030 0.0025 FT:IJI 0.23 0.0036 À0.0109 0.0180 0.05 0.0009 À0.0106 0.0124 0.70 À0.0231 À0.0550 0.0089 FD:PARA_MN 0.00 0.0000 À0.0002 0.0002 0.62 À0.0069 À0.0173 0.0034 0.04 0.0000 À0.0002 0.0002 FD:ENN_CV 0.00 0.0000 À0.0004 0.0004 0.12 À0.0016 À0.0104 0.0071 0.04 0.0002 À0.0017 0.0021 FD:IJI 0.09 0.0001 À0.0070 0.0072 0.19 0.0060 À0.0264 0.0383 0.68 À0.0188 À0.0438 0.0062

Bee richness – tropical/subtropical Bee richness – Mediterranean Bee richness – temperate

Covariate w b^ Lower CI Upper CI w b^ Lower CI Upper CI w b^ Lower CI Upper CI

Lonsdorf landscape index (LLI) 1.00 3.0400 1.6700 4.4200* 0.99 3.4400 1.2900 5.5900* 0.23 0.1630 À0.7530 1.0800 Farm type-organic (FT) 0.40 0.0837 À0.1520 0.3190 0.97 0.3470 0.1190 0.5760* 1.00 0.3630 0.1310 0.5950* Field diversity-complex (FD) 0.41 À0.0078 À0.2620 0.2460 0.91 0.2800 À0.3870 0.9460 0.32 À0.0358 À0.1870 0.1150 PARA_MN 0.28 0.0000 À0.0003 0.0003 0.78 0.0002 À0.0007 0.0011 0.37 À0.0001 À0.0005 0.0003 ENN_CV 0.31 À0.0003 À0.0016 0.0009 0.77 0.0007 À0.0010 0.0024 0.35 À0.0004 À0.0018 0.0010 IJI 0.50 0.0019 À0.0034 0.0072 0.80 0.0009 À0.0061 0.0079 0.81 À0.0018 À0.0069 0.0033 LLI:FT 0.07 0.0798 À0.8550 1.0200 0.07 0.1840 À1.5000 1.8700 0.06 À0.1030 À0.9880 0.7810 LLI:FD 0.25 À0.9180 À3.7600 1.9300 0.22 À0.6910 À3.5100 2.1300 0.05 À0.0872 À0.8970 0.7230 FT:FD 0.05 0.0074 À0.1260 0.1400 0.17 À0.1600 À0.8190 0.5000 0.13 À0.0663 À0.3770 0.2440 LLI:PARA_MN 0.10 0.0004 À0.0018 0.0026 0.05 0.0005 À0.0058 0.0068 0.02 À0.0001 À0.0014 0.0012 LLI:ENN_CV 0.02 0.0000 À0.0009 0.0009 0.01 0.0003 À0.0070 0.0076 0.01 0.0000 À0.0019 0.0020 LLI:IJI 0.36 0.0232 À0.0318 0.0782 0.41 À0.1160 À0.3690 0.1370 0.04 0.0002 À0.0151 0.0154 FT:PARA_MN 0.02 0.0000 À0.0002 0.0002 0.07 0.0001 À0.0006 0.0007 0.06 À0.0001 À0.0006 0.0004 FT:ENN_CV 0.00 0.0000 À0.0004 0.0004 0.03 À0.0001 À0.0011 0.0010 0.01 0.0000 À0.0009 0.0008 FT:IJI 0.02 0.0002 À0.0028 0.0032 0.14 0.0012 À0.0072 0.0096 0.73 À0.0256 À0.0548 0.0036 FD:PARA_MN 0.03 À0.0001 À0.0006 0.0005 0.31 À0.0015 À0.0053 0.0024 0.01 0.0000 À0.0001 0.0001 FD:ENN_CV 0.00 0.0000 À0.0002 0.0002 0.17 À0.0010 À0.0054 0.0033 0.00 0.0000 À0.0003 0.0003 FD:IJI 0.02 À0.0001 À0.0023 0.0021 0.55 0.0128 À0.0130 0.0386 0.11 À0.0012 À0.0080 0.0056

*Unconditional 95% CIs not overlap 0. nal patterns may have emerged in part due to sampling effects, and tats within bee foraging ranges than by their configuration is con- should be confirmed through analyses with additional data sets. sistent with habitat loss being among the key drivers of global Overall, in most cases, organic, diverse fields harboured the greatest pollinator declines (Potts et al. 2010a). Nonetheless, we also abundance and richness of wild bees, whereas conventional, simple expected this landscape aspect to influence pollinators given the fields harboured the lowest (Fig. 1, Figure S7_1). Regarding local- importance of habitat configuration on species persistence (e.g. landscape interactions, the beneficial effect of surrounding land- Tscharntke et al. 2002; Fahrig 2003). Configuration metrics were scape composition on average decreased when fields were multi- selected to be orthogonal to LLI scores, precisely to test unique cropped or with non-crop vegetation or were managed organically aspects of configuration independent of composition; however, (Table 2, Figure S7_2), but these trends did not necessarily hold on certain configuration effects may already be captured within LLI a per biome basis (Table 3), again possibly due to the smaller num- scores, which include spatial information by weighting the contri- ber of studies per biome. bution of habitat types by foraging distance (Lonsdorf et al. 2009). In contrast, configuration of habitats at a landscape scale had Of the three configuration metrics examined, we found greatest little impact on total bee richness and abundance. Our finding that support for the effects of variation in interpatch distance wild bees are more impacted by the amount of high-quality habi- (ENN_CV) on social bee abundance (Table 2), with slight declines

(a) 210% Tropical/Subtropical (a) Mediterranean Temperate 170%

130%

90% Total bee abundance 50% 0 50 100 150

0.0 0.1 0.2 0.3 0.4 0.5 0.6 10% Lonsdorf landscape index Tropical/Subtropical Mediterranean Temperate All biomes % change in bee abundance organic fields –30% (b) Tropical/Subtropical Mediterranean 85% Temperate (b)

65%

45% Total bee richness 25% 020406080

0.0 0.1 0.2 0.3 0.4 0.5 0.6 5% Lonsdorf landscape index Tropical/Subtropical Mediterranean Temperate All biomes

% change in bee richness organic fields –15% Figure 2 Response to Lonsdorf landscape index (LLI) of wild bee abundance (a) and richness (b) by biome, based on model-averaged partial regression Figure 3 Percent change in wild bee abundance (a) and wild bee richness (b) in coefficients and unconditional 90% CIs (in Table 3) for tropical and subtropical organic fields relative to conventional fields for tropical and subtropical studies studies (dashed line for mean) and Mediterranean studies (black line for mean) (n = 10), Mediterranean studies (n = 8), temperate studies (n = 21) and overall (grey shading for CIs with dark grey denoting overlapping CIs). Mean effect for (n = 39). Estimates based on model-averaged partial regression coefficients and temperate studies provided by grey line for reference (CIs not presented due to unconditional 90% CIs by biome and CIs 95% overall (asymmetric CIs due to = insignificance). LLI 0.61 was maximum score observed for tropical landscapes, exponential relationship) (in Tables 2 and 3). LLI = 0.19 for Mediterranean landscapes, and LLI = 0.40 for temperate landscapes. predicted as variation in distance(s) among similar habitat patches affected by farm management (Table 2, Fig. 1). Ricketts et al. (2008) increases. In addition, bees in tropical systems had greatest abun- proposed that specialised nesting requirements, longer flight seasons dance in landscapes with more interspersed high-quality habitats and foraging distances may predispose social bees to greater sensi- (i.e. both higher IJI and LLI scores) (Table 3, Figure S7_3). Over- tivity to habitat isolation. Nesting requirement explanations may not all, our results did not provide strong evidence for how bees hold in our study because social bees nested in both ground and respond to different aspects of landscape configuration (Table 2–3, tree cavities. Although social bees displayed a range of body sizes Table S7_2). Other studies have also found that some bee taxa do across studies, 64.7% of our crop systems had bee assemblages in not respond to landscape heterogeneity (Steffan-Dewenter 2003) which social species were larger bodied than solitary species, with or that they respond idiosyncratically (Carre et al. 2009), which correspondingly larger foraging distances (by 1.36 times, Greenleaf may suggest that bees are adequately mobile to tolerate habitat et al. 2007). As a result, social bees may perceive landscapes at larger fragmentation as long as the amount of total habitat is sufficient. spatial scales than solitary bees, and thus, be more sensitive to land- We note that our assessments of landscape composition and con- scape-level habitat structure. figuration relied in part on expert opinion of suitability of land- Empirical tests of the assertion that diversified farming systems cover types as habitat for bees (Appendix S4), with inherent (i.e. supporting vegetative diversity from plot to field to landscape uncertainties and limitations (Lonsdorf et al. 2009). Results from scales; sensu Kremen & Miles 2012) can provide access to different this study highlight the need for data on the foraging, nesting, floral and nesting resources over space and time are accumulating. and movement patterns of crop pollinators in different habitat Meta-analyses and multi-region studies on local farm management types and landscape contexts. practices and landscape effects support both scales as important for Increasing agricultural intensification and losses of high-quality pollinators. These effects have been found to be additive (Holz- habitats can shift pollinator communities to become dominated by schuh et al. 2008; Gabriel et al. 2010) or interactive (Rundlof€ et al. common, widespread taxa (e.g. Carre et al. 2009). Although we did 2008; Batary et al. 2011; Concepcion et al. 2012). In the latter case, not model individual bee taxa to discern this type of community management interventions – like agri-environment schemes that shift, we detected differences in responses of social vs. solitary wild promote low input, low disturbance farming and the maintenance bees. Social bees were affected more by landscape effects (LLI and of field diversity – may be most effective in landscapes with inter- to a lesser extent ENN_CV) than were solitary bees, but both were mediate-levels of heterogeneity (Tscharntke et al. 2012).

We found that local management factors have an effect across a carbon sequestration) in agricultural systems without necessarily wide range of available bee habitats in agroecosystems (Fig. 1), and diminishing crop yields (Pretty 2008; Kremen & Miles 2012). that both field-scale diversity and organic farming have distinct, positive impacts on wild bee abundance and richness (Tables 2–3). Most CONCLUSION striking is that higher vegetation diversity in conventional crop fields may increase pollinator abundance to the same extent as organically Our global synthesis expands the growing body of empirical managed fields with low vegetation diversity (see also Winfree et al. research addressing how changes in landscape structure through 2008). Local-scale field diversity also increases wild bee richness habitat loss, fragmentation or degradation affect pollinators and slightly, although not to the point that it is predicted to match the potentially pollination services. We found that the most important richness of organic fields (Fig. 1). In some regions, fields under factors enhancing wild bee communities in agroecosystems were the organic management are increasingly becoming large monocultures. amounts of high-quality habitats surrounding farms in combination Our results suggest that such a trend will ultimately be detrimental with organic management and local-scale field diversity. Our find- for wild bees and their pollination services. Finally, the interactions ings suggest that as fields become increasingly simplified (large between local and landscape factors suggest that the local benefits of monocultures), the amount and diversity of habitats for wild bees in a diversity of crops or natural vegetation and organic management the surrounding landscape become even more important. On the could transcend an individual field or farm because the improved other hand, if farms are locally diversified then the reliance on the quality of habitats on one field can provide benefits to adjacent or surrounding landscape to maintain pollinators may be less pro- nearby fields (see also Holzschuh et al. 2008). In this way, the distinc- nounced. Moreover, farms that reside within highly intensified and tion between local farm management and landscape effects blur. As a simplified agricultural landscapes will receive substantial benefits result, the agricultural landscape becomes more of a multifunctional from on-farm diversification and organic management. Safe-guard- matrix that sustains both crop productivity and natural capital rather ing pollinators and their services within an agricultural matrix will than being a single purpose landscape with limited biodiversity value therefore be achieved through improved on-farm management prac- (Perfecto & Vandermeer 2010). tices coupled with the maintenance of landscape-level high-quality Ultimately, our results suggest that there are several ways to miti- habitats around farms. gate the negative impacts of agricultural intensification on insect-pollinators, which is generally characterised in many parts of the world by ACKNOWLEDGEMENTS high usage of pesticides and other synthetic chemical inputs, large field size and low (generally monoculture) crop and vegetation diver- We thank Nasser Olwero (World Wildlife Fund) for the develop- sity (Tscharntke et al. 2005; Meehan et al. 2011). Reductions in the ment of ArcGIS pollinator research tool, J. Regetz (National Center abundance and richness of wild bees associated with intensive agricul- for Ecological Analysis and Synthesis, NCEAS) for guidance on ture are thought to result from a combination of lack of floral datasets/analyses, E.E. Crone (Harvard University) for statistical resources other than mass-flowering crops (Holzschuh et al. 2008; consultations and Sharon Baruch-Mordo (The Nature Conservancy) Rundlof€ et al. 2008), lack of nest sites (Williams et al. 2010) and high for R graphing code. This study was part of the NCEAS for use of pesticides (Brittain et al. 2010). In turn, such declines in wild Restoring Pollination Services Working Group (led by C. Kremen bee communities are expected to lead to reduced pollination services and N.M. Williams, supported by National Science Foundation to crops (Klein et al. 2009). One mechanism for enhancing pollinator (NSF) grant no. DEB-00–72909) and by NSF grant no. DEB- populations is to increase the amount of semi-natural habitat in the 0919128 (PIs: CK, EL, MN and NMW). R. Bommarco, M. landscape (Steffan-Dewenter et al. 2002; Kremen et al. 2004). Our Rundlof,€ I. Steffan-Dewenter, A. Holzschuh, L.G. Carvalheiro and results suggest that with each additional 10% increase in the amount S.G. Potts’ contributions were supported in part by ‘STEP – Status of high-quality bee habitats in a landscape, wild bee abundance and and Trends of European Pollinators’ (EC FP7 grant no. 244090). richness may increase on average by 37%. Such actions, however, are A.M. Klein’s project was supported by the Germany Science often beyond the capacities of individual producers and can poten- Foundation (DFG, KL 1849/4–1), D. Cariveau’s project was sup- tially lead to trade-offs between conservation and economic interests. ported by New Jersey Agricultural Experiment Station through Increasing habitat heterogeneity of agricultural landscapes within the Hatch Multistate Project #08204 to R.W. K. Krewenka and C. scale of bee foraging ranges is also expected to provide benefits for Westphal’s contributions by the EU FP6 project ALARM (GOCE- pollination-dependent crops. Specifically, switching from conven- CT-2003-506675, http://www.alarmproject.net), H. Gaines and C. tional to organic farming could lead to an average increase in wild bee Gratton’s contributions by University of Wisconsin Hatch Grant abundance and richness by 74 and 50%, respectively, and enhancing WIS01415 and H. Taki’s contribution was supported by Global field diversity could lead to an average 76% increase in bee abundance Environment Research Funds (S-9) of the Ministry of the Environ- (Table 2). Potential actions to benefit native bees within farms include ment, Japan. reduced use of bee-toxic pesticides, herbicides and other synthetic chemical inputs, planting small fields of different flowering crops, AUTHORSHIP increasing the use of mass-flowering crops in rotations and breaking up crop monocultures with uncultivated features, such as hedgerows, C.M.K. prepared, modelled and analysed the data and wrote the low-input meadows or semi-natural woodlands (Tscharntke et al. manuscript; E.L. and M.C.N. assisted with neutral landscape model- 2005; Brosi et al. 2008). These techniques can be accomplished within ling; C.K., E.L., M.C.N. and N.M.W. designed the study, guided fields by individual property owners or managers. The resulting multi- analyses and wrote the manuscript; T.H.R. and R.W. consulted on functional landscapes can enhance natural capital and the stocks and study development; L.A.G. and L.G.C. advised on analyses and flows of other of ecosystem services (e.g. pest regulation, soil fertility, revised the manuscript; R.B., C.B., A.L.B., D.C., L.G.C., N.P.C.,

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Appendix S1. References of published studies included in our synthesis.

Arthur, A.D., Li, J., Henry, S. & Cunningham, S.A. (2010). Influence of woody vegetation on pollinator densities in oilseed Brassica fields in an Australian temperate landscape. Basic and Applied Ecology, 11, 406-414.

Bommarco, R., Lundin, O., Smith, H.G. & Rundlöf, M. (2012). Drastic historic shifts in bumble- bee community composition in Sweden. Proceedings of the Royal Society B: Biological Sciences, 279, 309-315.

Bommarco, R., Marini, L. & Vaissière, B.E. (2012). Insect pollination enhances seed yield, quality and market value in oilseed rape. Oecologia, 169, 1025-1032.

Blanche, K.R., Ludwig, J.A. & Cunningham, S.A. (2006). Proximity to rainforest enhances pollination and fruit set in orchards. Journal of Applied Ecology, 43, 1182-1187.

Carré, G., Roche, P., Chifflet, R., Morison, N., Bommarco, R., Harrison-Crips, J., Krewenka, K., Potts, S.G., Roberts, S.P.M., Rodet, G., Settele, J., Steffan-Dewenter, I., Szentgyörgyi, H., Tscheulin, T., Westphal, C., Woyciechowski, M. & Vaissière, B.E. (2009). Landscape context and habitat type as drivers of bee diversity in European annual crops Agriculture, Ecosystems and Environment, 133, 40-47.

Carvalheiro, L.G., Seymour, C.L., Veldtman, R. & Nicolson, S.W. (2010). Pollination services decline with distance from natural habitat even in biodiversity-rich areas. Journal of Applied Ecology, 47, 810-820.

Carvalheiro, L.G., Veldtman, R., Shenkute, A.G., Tesfay, G.B., Pirk, C.W.W., Donaldson, J.S. & Nicolson, S.W. (2011). Natural and within-farmland biodiversity enhances crop productivity. Ecology Letters, 14, 251-259.

Chacoff, N.P., Aizen, M.A. & Aschero, V. (2008). Proximity to forest edge does not affect crop production despite pollen limitation. Proceedings of the Royal Society B-Biological Sciences, 275, 907-913.

Chacoff, N.P. & Aizen, M.A. (2006). Edge effects on flower-visiting insects in grapefruit plantations bordering premontane subtropical forest. Journal of Applied Ecology, 43, 18-27.

Greenleaf, S.S. & Kremen, C. (2006a). Wild bee species increase tomato production and respond differently to surrounding land use in Northern California. Biological Conservation, 133, 81-87.

Greenleaf, S.S. & Kremen, C. (2006b). Wild bees enhance honey bees' pollination of hybrid sunflower. Proceedings of the National Academy of Sciences - USA, 103, 13890-13895.

Holzschuh, A., Dudenhöffer, J.-H., Tscharntke, T. (2012). Landscapes with wild bee habitats enhance pollination, fruit set and yield of sweet cherry. Biological Conservation, 153, 101-107

Kennedy et al. Modeling local and landscape effects on pollinators Page 2 of 3

Isaacs, R. & Kirk, A.K. (2010). Pollination services provided to small and large highbush blueberry fields by wild and managed bees. Journal of Applied Ecology, 47, 841-849.

Jha, S. & Vandermeer, J.H. (2010). Impacts of coffee agroforestry management on tropical bee communities. Biological Conservation, 143, 1423-1431.

Klein, A.-M., Brittain, C., Hendrix, S.D., Thorp, R., Williams, N., & Kremen, C. (2012). Wild pollination services to California almond rely on semi-natural habitat. Journal of Applied Ecology, 49, 723-732.

Kremen, C., Williams, N.M. & Thorp, R.W. (2002). Crop pollination from native bees at risk from agricultural intensification. Proceedings of the National Academy of Sciences, 99, 16812- 16816.

Kremen, C., Williams, N.M., Bugg, R.L., Fay, J.P. & Thorp, R.W. (2004). The area requirements of an ecosystem service: crop pollination by native bee communities in California. Ecology Letters, 7, 1109-1119.

Morandin, L.A. & Winston, M.L. (2005). Wild bee abundance and seed production in conventional, organic, and genetically modified canola. Ecological Applications, 15, 871-881.

Morandin, L.A. & Winston, M.L. (2006). Pollinators provide economic incentive to preserve natural land in agroecosystems. Agriculture, Ecosystems & Environment, 116, 289-292.

Ricketts, T.H. (2004). Tropical forest fragments enhance pollinator activity in nearby coffee crops. Conservation Biology, 18, 1262-1271.

Ricketts, T.H., Daily, G.C., Ehrlich, P.R. & Michener, C.D. (2004). Economic value of tropical forest to coffee production. Procedings of the National Academy of Sciences - USA, 101, 12579- 12582.

Sáez, A., Sabatino, M., Aizen, M.A. (2012) Interactive Effects of Large- and Small-Scale Sources of Feral Honey-Bees for Sunflower in the Argentine Pampas. PLoS ONE, 7, e30968.

Taki, H., Okabe, K., Makino, S., Yamaura, Y. & Sueyoshi, M. (2009). Contribution of small insects to pollination of common buckwheat, a distylous crop. Annals of Applied Biology, 155, 121-129.

Taki, H., Okabe, K., Yamaura, Y., Matsuura, T., Sueyoshi, M., Makino, S.i. & Maeto, K. (2010). Effects of landscape metrics on Apis and non-Apis pollinators and seed set in common buckwheat. Basic and Applied Ecology, 11, 594-602.

Tuell, J.K., Ascher, J.S. & Isaacs, R. (2009). Wild bees (Hymenoptera: Apoidea: Anthophila) of the Michigan highbush blueberry agroecosystem. Annals of the Entomological Society of America, 102, 275-287.

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Winfree, R., Williams, N.M., Dushoff, J. & Kremen, C. (2007). Wild bees provide insurance against ongoing honey bee losses. Ecology Letters, 10, 1105-1113.

Winfree, R., Williams, N.M., Gaines, H., Ascher, J.S. & Kremen, C. (2008). Wild bee pollinators provide the majority of crop visitation across land-use gradients in New Jersey and Pennsylvania, USA. Journal of Applied Ecology, 45, 793-802.

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Appendix S2. Methodology of unpublished studies included in our synthesis.

Methodology for the 16 studies included in our synthesis with unpublished data is described below (see also Table 1).

For Cariveau (unpublished data), the pollination of the Stevens cultivar of Vaccinium macrocarpon Aiton (cranberry) was conducted at 16 farms in June 2009 in Burlington County of New Jersey, USA. Farms varied in the amount of surrounding land cover comprised of agriculture. GIS data were compiled by the New Jersey Department of Environmental Protection.

Land-cover polygons were delineated with hand-digitization using 2002 digital color infrared orthophotography at a scale of 1:2400 at a 0.31 m pixel resolution.

At each farm, sixty-meter transects were placed parallel with the edge of natural habitat.

Along each transect, the author recorded pollen deposition, visitation frequency, flower visitor abundance. To collect pollen depositions, receptive stigmas were collected from open cranberry flowers and placed in 70% EtOH. Pollen tetrads were stained using aniline blue and counted under a compound florescent scope. To assess visitation frequency and flower visitor abundance, each transect was sampled once in the morning and once in the afternoon during two different weeks. Data collection took place between 9:00 and 18:00 during non-inclement weather

(temperature > 15°C, wind speed <3.5m s-1). To record visitation frequency, every two meters, a

1x1 meter quadrat of flowers was observed for 45 seconds for a total of 1.55 hours of observation for each farm. Following each observation, flower visitors were collected using a hand-net. Each collection period lasted for 30 minutes and the timer was stopped while handling insects. The resulted in 2 hours of collection for each farm. Managed honey bees (Apis mellifera) were the dominant flower visitor (76%); the dominant native flower visitors were Bombus species (17%). While honey bees were recorded during flower observations, they were not

Kennedy et al. Modeling local and landscape effects on pollinators Page 2 of 19 collected with the hand net. Feral honey bees are not known to occur in this study system.

For Gaines (unpublished data), the abundance and diversity of bees was investigated in commercial cranberry bogs (Vaccinium macrocarpon) in Jackson, Juneau, Monroe, and Wood

Counties in central Wisconsin (USA) between May and July 2008. Bees were pan trapped four times during the growing season – once before, twice during, and once after cranberry bloom - using blue, yellow and white traps. Traps were left out for 6 hour intervals between 0830 and

1700 under consistent weather conditions (wind < 2.5m/s, sunny to bright overcast, temp >

14oC). Thirty-traps were deployed per site per sampling round and all traps were within 50 meters of a non-agricultural farm edge. This was done at 15 commercial cranberry bogs located at least 2km from each other. Sites were selected such that the landscape within one kilometer covered a gradient ranging from 15-82% woodland and 10-76% agriculture. Agriculture in this area is comprised mainly of cranberry, corn, soybean, alfalfa, and pasture. Landscape information was extracted using a geographic information system (ArcMap) from the United

States Department of Agriculture National Agricultural Statistics Services Cropland Data Layer

(USDA NASS CDL 2008) with a resolution of 56 meters. Agricultural land-cover categories was based on 2008 satellite imagery (collected between April 1 – Sept 30, 2008) and non-agricultural land-cover categories were based on 2001 satellite imagery (USDA National Land Cover

Dataset). Agapostemon texanus was the most common species collected out of 1282 total specimens representing 108 species of native bees.

In Javorek (unpublished data) study, bee abundance and diversity on lowbush blueberry (Vaccinium angustifolium Ait.) was investigated in Prince Edward Island, Canada during 2005, 2007 and 2009 to correspond to the biennial cropping pattern of the fields.

Lowbush blueberry fields were established by clear cutting woodland and allowing the

Kennedy et al. Modeling local and landscape effects on pollinators Page 3 of 19

Vaccinium angustifolium (that existed as an under story component) to spread forming a dense mosaic of low-growing “clones” (genotypes). Blueberry is grown in a heterogeneous landscape that includes forests, bogs, wetlands, meadows, abandoned farm fields, mixed agriculture, hayfields and pasture.

At each study site (N =16) , bees were sampled using a combination of aerial netting and pantraps on three days roughly corresponding with early, middle and late lowbush blueberry flowering (June). For aerial netting, the observer moved throughout the blueberry field for one hour capturing each bee encountered. Thirty pantraps were deployed at each study site alternating blue, white and yellow at three meter intervals. Bees collected during this study were identified (S.K. Javorek and J.S. Ascher) and are housed at Agriculture and Agri-Food Canada

Research Centre, Kentville Nova Scotia, Canada with select vouchers retained at the American

Museum of Natural History, New York, NY, USA. All collections were done between 10:00 and

3:00 on sunny/light overcast days with temperatures >16ºC.

During this study 53 bee species were collected visiting lowbush blueberry. The main wild pollinating species were Bombus (Pyrobombus) impatiens (Cresson), B. (Pyrobombus) ternarius Say, B. (Pyrobombus) vagans Smith, Andrena (Melandrena) carlini Cockerell, A.

(Melandrena) vicina Smith, A. (Andrena) rufosignata Cockerell, A. (Andrena) carolina Viereck,

Lasioglossum (Dialictus) spp. and Lasioglossum (Evylaeus) spp. Managed honey bees (Apis mellifera Linnaeus) or alfalfa leafcutting bees (Megachile (Eutricharaea) rotundata (Fabricius)) where introduced at most sites to bolster pollination.

Botanical surveys were conducted to determine the abundance, diversity and phenologies of flowering plants in cover types within a 2.5 km radius blueberry fields. From this a foraging resource value (0-10) was assigned to each cover type for April/May, June (blueberry bloom),

Kennedy et al. Modeling local and landscape effects on pollinators Page 4 of 19

July and August/September. Land-cover data were based visual interpretation and digitization of colour infrared aerial photography flown at 1: 7,500 (flown July –September 2000) (at 1-5 m resolution) and updated to reflect 2005 land cover (PEI Department of Environment 2000).

For Klein, Brittain and Kremen (unpublished data), bee abundance and species richness in almond orchards (Prunus dulcis L.) were investigated in Yolo and Colusa counties in northern California, USA, during 2008. Bee species richness and abundance were sampled using pantraps, before, during and after the bloom. This was done in eight organic and fifteen conventional almond orchards with different levels of isolation from semi-natural or natural habitats (chaparral shrub, oak savannah, riparian, and oak woodland). Insects in the 23 orchards were sampled by placing a cluster of three pantraps (yellow, white and blue) at five points 0 meters from the orchard edge and at five points 50/100 meters from the orchard edge. The pans were left out for one day and this was done three times (3 sampling rounds) during 2008: once shortly before almond bloom, once during bloom and once shortly after bloom. This meant that at each orchard there were 30 pans for one sampling round, totalling 90 pantraps per orchard over the season. Only bees were considered in the current analysis and the bees caught in pantraps were identified by Robbin Thorp (UC Davis) and Alexandra-Maria Klein. For information on the sampling of flower visitation and fruit set, see Klein et al. (2012).

Land cover was based on aerial imagery at 1 meter resolution from the National

Agriculture Imagery Program (NAIP) from 2009. The land cover surrounding the orchards within 1 km buffers was hand digitized using ArcGIS and assigned to 12 habitat categories.

Kremen (unpublished data) investigated bee visitation to almond (Prunus dulcis) in

Yolo County, California in 2004. The almond varieties studied were hermaphroditic but self- incompatible and were visited by a variety of wild bees (Andrena sp., Bombus vosenesnskii,

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Halictus tripartitus, Halictus farinosus, Lasioglossum (Evylaeus) sp., Lasioglossum (Dialictus) sp., Lasioglossum sp. and other unidentified native bee species). Managed honey bees had been placed by farmers at most sites and were abundant at all sites. Pollinator visitation rates and species richness data were obtained in 16 sites that varied in distance from 14 to 989 m from natural habitat including riparian, oak-woodland and chaparral shrub vegetation. In each site, the number and richness of social and solitary bees visiting almond flowers were estimated from 10 whole tree scans per site (circa. 1 min of observation per tree) on a single day between 10:00 and

15:00 during standardized weather conditions (sunny to light overcast skies with temperatures

>14.8°C and wind velocity <2.7 m s-1). Landcover data are described in Kremen et al. (2004) and are based on a supervised classification of Landsat TM imagery from year 2000.

In the studies coded as Mandelik (unpublished data) (a,b,c), flower-visitors to Prunus dulcis (almond), Helianthus annuus (sunflower) , and Citrullus lanatus Thunb. (watermelon), respectively, were investigated along a gradient of decreasing proportion of open land (not developed or cultivated) in 1500-5000 m radii around sampling points within crop fields. The open land included mainly native dwarf shrubland and chaparral and planted forests (pine and broadleaf). Satellite images and land-cover data were obtained from the GIS unit of the Hebrew

University of Jerusalem, updated to 2002 at a 1.3 m resolution. Land-cover types were reclassified into 10 categories: annual rotational crop fields including vegetables, cereals, legume& orchards, built-up area, roads, the area within military bases that is NOT defined as "open area" and includes mainly areas that are either paved or occupied by Acacia, barren land - area that was prepared for development and all natural vegetation removed and ground flattened, planted braodleaf forests, planted pine forests, planted eucalyptus forests, artificial reservoirs, natural habitat. This re-classification best describe differences in availability of foraging resources and

Kennedy et al. Modeling local and landscape effects on pollinators Page 6 of 19 nesting substrates along the landscape. Site tours were conducted to verify land-cover data at questionable locations (where a mis-match between different data layers was apparent). All three studies were conducted in the Judean Foothills, a Mediterranean ecosystem in central Israel during crop bloom in February-March 2009 for the almond, and in May-June 2009 for the sunflower and the watermelon. The almond study was conducted in 7 orchard margins, the sunflower study was conducted in 13 field margins, and the watermelon study was conducted in

19 field margins. Study plots (25 × 25 m) were separated by at least 1.2 km from each other. In all three studies field work was conducted under standardized weather conditions (sunny to light overcast skies, temperatures >18 ºC and mean wind velocity <3.5 m s-1, excluding three occasions). Each plot was sampled between one to three times (mostly twice), each time occurring on a separate day. In each sampling day two sampling sessions, 2-3 hours apart, were conducted. Each sampling session included 10-20 min of observations of Apis mellifera visits to crop flowers followed by 10 min of bee netting (the stopwatches were stopped when handling bees that were caught). Bee sampling was conducted between 8:00 and 15:00 in the almond study, between 8:00 and 16:00 in the sunflower study, and between 7:00 and 11:00 in the watermelon study. In addition, we used coloured pantraps (ca. 300 ml white, blue and yellow bowls filled with soapy water) to sample bees active in the fields and orchards. In the almond orchard we used 16 pantraps opened for 6 hours, in the sunflower we used 12 pantraps opened for 7 hours, and in the watermelon study we used 12 pantraps opened for 3.5 hours. In all three studies the main flower-visiting species was the managed honey bee Apis mellifera (accounting for 99%, 95% and 88% of recorded bee visits in the almond, sunflower, and watermelon studies respectively). All honey bees in the region are managed; there are no feral colonies in the region

Kennedy et al. Modeling local and landscape effects on pollinators Page 7 of 19 due to the Varoa mites. Dominant wild bee visitors in all three studies were small to medium sized bees of the genus Lasioglossum spp.

For Mayfield (unpublished data), the pollination of Macadamia integrifolia

(Macadamia nut trees) was investigated in the Northern Rivers region of New South Wales,

Australia (near the towns of Byron Bay and Lismore), during August and September of 2008.

For this study, insects visiting Macadamia flowers were observed on 5 farms and in 10 sampling areas (very large farms - multiple km in diameter - had one to four sampling regions within their boundaries). Farms varied in management approach but pesticides were not sprayed on any farm during our observation period. Observations in each sampling area were made on two or three non-consecutive days across the blooming season. All observations were made on sunny cool days between 0900 and 1730 corresponding to the warmest part of each day. The mean temperature at 0900 in this region was 15 ˚C in August 2008 and 20 ˚C in September 2008 with daily averages ranging from 20˚C in August to 23˚C in September. Macadamia flowers are clustered on pendent inflorescences and thus observations were made on multiple clearly visible inflorescences for each observation period. Each observation period was 5 minutes in length.

Concurrent observations were made by 2 – 4 people across three non-consecutive parallel transects running from 5 – 500m from field borders abutting forest vegetation. Observers alternated which end of transects they started at to ensure that near and far trees were observed at multiple times of day within a sampling area. During each observation period the identity of each flower visitor was noted as was the number of flowers it visited. Forest vegetation next to all farms was classified broadly as rehabilitated or remnant patches of subtropical rainforest. Apis mellifera were abundant on all farms, even those without kept hives. The largest farm (4 separate sampling regions) had feral and kept A. mellifera hives. This farm also had kept native Trigona

Kennedy et al. Modeling local and landscape effects on pollinators Page 8 of 19 sp. bees in hives positioned among the Macadamia trees in several sampling areas. The most abundant flower visitors in this system by far was A. mellifera, with beetles, flies, Lepidoptera and native Trigona bees representing a very small proportion of flower visits.

The GIS map used in this analysis was created using 2.5 m color imagery acquired by the

SPOT 5 satellite (SPOT Imaging Services) in October 2007. Land-cover data was sourced from the NSW Department of Environment, Climate Change and Water for the upper northern extent of New South Wales at 1:25000 resolution based on polygons developed using conventional interpretation of homogenous overstorey patterns discernible from 1997 aerial photography and created in 2001 (Upper North East CRAFTI Floristic Layer).

In the Neame and Elle (unpublished data) study, we assessed the contribution of wild bees and honeybees to squash pollination at nine farms in the Okanagan-Similkameen Regional

District, located in south-central British Columbia, Canada. All sampling took place in August,

2010. Natural habitat in this region is sage-scrub dominated in the valley bottoms and is the northernmost extension of the Great Basin Desert, with ponderosa pine forest at higher elevations. Conversion of land for agriculture, especially orchards and vineyards, is increasing in the region. Farms were both conventional and organic, but for this crop in this area, farming practices on conventional farms differed very little from the organic farms. All farms grew multiple squash varieties (4 to 15) and usually other ground crops on the same property. Squash varieties assessed were one of three species: Curcurbita pepo (summer squash and acorn squash varieties), C. moschata (butternut squash), or C. maxima (buttercup squash and pumpkin varieties). We assessed wild bee and honeybee visits to multiple varieties, as at any given farm there was substantial variation in the number of plants of each variety. Our observations focused on acorn and butternut squash varieties, but also included buttercup squash and summer squash

Kennedy et al. Modeling local and landscape effects on pollinators Page 9 of 19 at sites where those two varieties were not abundant. All honeybees in this area are managed; approximately half of the farms had hives located next to the squash field, but local honeybee keepers have hives located throughout the area so honeybees occur in all sites.

To assess the abundance and visit rate of bees to squash flowers, we conducted visit observation surveys and netting surveys. On each of two survey dates per field we conducted one

15-minute netting and two pollinator visit observation transect surveys. Two sites had fewer visit observation transects (sites CAL and KBF had only two and three visit observation transects respectively, rather than the usual 4) due to weather conditions that inhibited bee activity

(especially high winds in these valleys). Both surveys on a sampling date started from the same end of the squash field; on the next survey date we started on the opposite end of the field, in a different row.

Visit observation-transect surveys: We conducted ten visit observations per transect, at 5 m intervals from the edge of the field. For each observation period we chose several flowers that could be observed simultaneously and observed them for two minutes. The number of flowers observed during observation periods was typically 3 to 4 flowers, but ranged from 2 to 7. We recorded the number of pollinator visits, whether the flower visited was male or female, and the morphospecies identity of the visitor (typically to generic level).

Netting surveys: Each netting survey consisted of catching all bees observed visiting squash flowers for 15 minutes. The survey effort was focused on the main varieties in which we conducted visit observations. We pinned and identified all specimens to species, with assistance with Melissodes species ID from Terry Griswold (USDA-ARS Bee Biology and Systematics

Lab, Logan, Utah). Specimens are stored in the Simon Fraser University collection.

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GIS land cover: To obtain land use cover for the area we hand-digitized orthophoto imagery in Google Earth (GE version 6) within a three kilometer radius of each site. Orthophoto imagery in GE6 I this region is sourced from the Province of British Columbia (imagery date

August 15, 2010), with images to 1m resolution. We categorized land use into eleven categories that included agricultural (e.g. orchards, ground crops, pasture), developed (residential and commercial), and natural/semi-natural (e.g. sage-scrub, road embankments, riverside) land use types. Categorization of digitized polygons was also informed by personal knowledge of land use surrounding the sample sites. We typically did not differentiate land use at a spatial scale smaller than 5m.

For Otieno (unpublished data), bee diversity, functional traits and visitation to pigeonpea crop were investigated in Kibwezi District in Eastern Kenya. Six simple versus complex site pairs were chosen across a gradient of landscape contexts, each site buffered by a 1 km spatial landscape comprising of semi-native habitats and rain-fed agricultural fields. One site of each pair was locally complex (dominated by semi-native habitat patches) positioned within at most 200 m of these patches. The other site was locally simple (dominated by rain-fed arable fields) positioned within at least 500 m from semi-native patches maintaining a minimum distance of 2km between the site pairs as determined using digital elevation and land use maps in

ArcGIS 9.3. The Shuttle Radar Topography Mission (SRTM) data for elevation and a land- use/land-cover map derived from a Landsat 7 Enhanced Thematic Mapper image (2003) were also used to in selecting sites and ground-truthed in April 2009. In all cases, semi-native habitats were considered to be patches of vegetation that comprised predominantly of native plants and animals.

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Local management of each site was also assessed to determine whether it was conventional or organic through face to face interviews with farmers. Variations in levels of fertilizer application and pesticide usage were found to be the main management practices used in the study area. Key among these practices was insecticide usage, which emerged as the most consistent practice either used or not used by farmers. Insecticide treated fields were classified as conventional while insecticide free fields were categorized as organic.

To measure the abundance of bees visiting flowers, 100 m long transects were laid in a

North to South orientation, each separated by a minimum of 10 m from each other at each site.

Five of these transects were within the crop field, five in the semi-natural patches immediately next to the crop and one transect at the interface between the crop field and the semi-natural habitat measuring about 2 m wide. This habitat was consistent in all our study sites and was either a planted hedge or fence with wild plants to mark the boundary of crop fields. Each transect was walked for 10 minutes, twice a day (between 09h00 and 16h00) recording insect flower visitors, 2 m either side once weekly from April to 13th June 2009.

Park & Danforth (unpublished data) surveyed diversity and abundance of bees visiting apple, Malus domestica, in Tompkins, Wayne, and Schuyler counties in Western New York,

USA. The study landscape was heterogeneous, marked by fragmented deciduous woodlands and mixed agriculture. Apple was a dominant crop species in Wayne County. A total of 14 orchards

(10 in 2009, 6 in 2010), varying in size and amount of surrounding natural habitat, were surveyed once in May 2009 and 2010 during the apple bloom on days with temperature > 60°F between 10am and 3:30pm. Distance between sites was at least 1.9km. At each site, multiple trials of 15-minute timed, aerial netting were conducted along tree rows; only bees visiting apple blossoms or hovering around apple trees were collected. The number of timed net collections per

Kennedy et al. Modeling local and landscape effects on pollinators Page 12 of 19 site varied according to farm size. Given unequal sample size among orchards, an average estimate of timed netting trials was provided per site. Renting managed honey bees, Apis mellifera, for pollination is common practice among growers in this region; the presence of honey bee hives was recorded at each site. Landscape composition within a 3km radius of study orchards was characterized, using a geographic information system (ArcMap 9.3.1), from the

United States Department of Agriculture National Agricultural Statistics Service Cropland Data

Layer (USDA NASS CDL 2010; 30-m resolution), merged with a hand-digitized orchard layer.

The orchard layer was created from USDA Agriculture Service Center county-level, digital orthophotos (USDA ASC 2009; 1-m resolution). Land cover was consolidated into 18 classes.

Aside from Apis mellifera, the most abundant bees in this study included medium and large

Andrena, notably A. (Melandrena) vicina, A. (Melandrena) regularis, A. (Melandrena) crataegi, and A. (Simandrena) nasonii.

For Prache, MacFadyen, & Cunningham (unpublished data), the study was conducted in a landscape in southern New South Wales, Australia, defined by a circle of 5-km radius centered on S 34o42’50”, E 147o43’20”. Land use was mainly agricultural, with fields of canola (Brassica napus and juncea), cereals (wheat, barley), pasture, and remnant patches of native vegetation (Eucalyptus woodland).

To construct a land-cover map for this circular landscape we used a SPOT (Système

Probatoire d'Observation de la Terre) satellite image acquired in 2005 (2.5 m resolution). Fields

(crops and pasture) and patches of remnant vegetation were outlined by hand and then ground survey was used to assign current field type during the study period in 2009.

We sampled bee abundance using blue van traps (Stephen and Rao 2005), hung at 1.2 m above the ground. Trapping locations were at field edges or up to 50 m into the field. Traps were

Kennedy et al. Modeling local and landscape effects on pollinators Page 13 of 19 checked weekly over a 5 week period (22 Sept to 27 October 2009) but data were pooled over time. In total 11,674 bees were trapped.

Data were analyzed for 10 locations in the landscape: 4 of the locations represent single trapping points, whereas the other 6 combine two trapping points that were pooled for this study because they were separated by less than 500 m (in which case abundance was halved to make sampling intensity comparable). Although we trapped 29 different species, 16 of these were represented by 5 or fewer individuals so they were excluded from further analysis. The second most abundant species was Apis mellifera, which is common as a feral in this landscape, but was also present in managed hives during this study and therefore were also excluded from analysis.

This left 12 species, here listed from most to least abundant: Leioproctus maculatus,

Lasioglossum hemichaleum, Lasioglossum cambagei, Lasioglossum clelandi, Lasioglossum vetripene, Lasioglossum lanarium, Lipotriches sp., Lasioglossum litteri, Lasioglossum cognatum, Lasioglossum soroculum, Amegilla chlorocyanea, Leioproctus sp.

In Rundlöf & Bommarco (unpublished data), pollination in arable fields of flowering red clover (Trifolium pratense L.) intended for seed production was investigated in Scania, the southernmost part of Sweden, in 2008 (14 sites) and 2010 (17 sites) (Bommarco et al. 2012). The focal red clover seed fields ranged in size from 4-16 hectares in 2008 and from 5-18 hectares in

2010. The region and landscapes surrounding the clover fields are dominated by agriculture, but fields were selected to cover a range of landscapes (radius 1 km) differing in complexity and proportion of semi-natural habitats.

The land-use data in the study is based on the national version of the CORINE land cover, GSD Land Cover Data, which is based on computer classification of satellite imagery from the year 2000 and on a variety of national maps, provided by the Swedish mapping,

Kennedy et al. Modeling local and landscape effects on pollinators Page 14 of 19 cadastral and land registration authority (Lantmäteriet 2010). Land cover is divided into 58 classes, data resolution is 25 m, data accuracy is 75 % and the projection is SWEREF 99 TM

(SWEdish REference Frame 1999, Transverse Mercator) (Lantmäteriet 2010).

All insects visiting the red clover were recorded along 1 m wide and 50 m long transects in the red clover seed fields; four transects located 4 and 12 m from the field edge in 2008, and two transects located 8 and 100 m (or for smaller fields in the field centre) from the field edge in

2010 (Bommarco et al. 2012). Each site was in 2008 visited twice and in 2010 three to five times

(mean 4.0 visits per site), to cover the main flowering period of the red clover fields. Sampling was done between June 25th and July 29th 2008, and July 5th and August 10th 2010, on days with warm, sunny and calm weather. The visitors of the red clover were predominantly bumble bees and honeybees, with a few visits from day-flying butterflies. Bees were either determined to species in the field (honeybees and bumble bee queens) or collected (bumble bee workers and males) and put in individual tubes filled with 70% ethanol and brought to the lab for species determination. The density of bumble bees in the fields were more than three times as high in

2008 (29.3 ± 3.0 (mean± SE) bees per transect) compared to in 2010 (7.8 ± 0.8 bees per transect), while the densities of honeybees were more equal between years (8.1 ± 3.1 and 7.6 ±

1.4 bees per transect, respectively).

For Steffan-Dewenter, Krewenka, Vaissière & Westphal (unpublished data), the study region was located in the vicinity of Göttingen (51.63°n. latitude, 9.86° e. longitude, altitude: 171m above NN), southern Lower Saxony and Northern Hesse, Germany. Ten strawberry fields with a minimum distance between fields of 3.8 km were selected along a gradient of increasing land use intensity. For each field a circular landscape sector with radius of

1000m was mapped in July 2005. A mapping scale of 5m (Deutsche Grundkarte 1:5000, UTM

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ETR S89 32N, WGS 84) was used and percentages of land use types were calculated using the program ArcView 3.2 (ESRI Geoinformatik GmbH, Hannover, Germany). The landscape gradient was measured as amount of arable land (annual crops) in the landscape, which ranged from 13.6% (structurally complex) to 82.9% (structurally poor), (50.10 ± 6.77, Mean ± SEM).

Calcareous grasslands, hedges, old fallows, orchard meadows, embankments and bushes or small woods were mapped as semi-natural habitats, since they are assumed as sources of bee populations in the agricultural landscape (Garibaldi et al. 2011). Other mapped habitat types were flowering crops like oilseed rape, potato, field beans and peas, clover, phacelia, wild mustard and sunflowers and other land use types including intensively managed grasslands, intensively managed orchards and strawberry fields, forests, gardens, settlements, limestone quarries, roads and water bodies. Additionally, less detailed GIS data were extracted for a radius of 3km from CORINE land-cover maps (Carré et al. 2009).

The size of the studied strawberry fields was at least 80 x 55m and data were collected in an area of 50 x 25m in the centre of the fields in a homogeneous and representative zone, with a distance of at least 15 m to the field boundaries.

Pollinator surveys: During the flowering period from the 27th of April until the 16th of

June 2005 pollinator sampling was conducted under good weather conditions, with at least 15°C, no precipitation and dry vegetation and a wind speed below 40 kmh-1. Pollinator observations were done in a transect with a length of 150m, which was divided into six subunits of 25m each.

The subunits were walked in a slow speed taking five minutes for 25 m, and flower visiting bees were caught with an aerial net in a width of two meters to each side of the transect.

The study Viana & Silva (unpublished data) was carried out during 2005 in the

‘irrigated perimeter of Maniçoba’, in São Francisco Valley region, at the municipality of

Kennedy et al. Modeling local and landscape effects on pollinators Page 16 of 19

Juazeiro, State of Bahia (40o16”W e 9o17”S), in Northeast Brazil. The landscape in this area is locally complex composed by several private properties with conventional farm management, used for crop production of various plant species as mango, guava, coconut, passion fruit, sugar cane, among others, interspersed with areas covered by natural white dry forest called

“Caatinga”, deforested areas and areas in several stages of ecological succession. Despite the predominance of small farmers in that region (media of farm’s size = 25ha), most of them with polycultures, the land use is very intensive. We represented land cover in this region based on a

Supervised Classification (using Maxlike algorithm) of processed and georeferenced satellite imagery acquired from CBERS (China-Brazil Earth Resources Satellite) (www.inpe.br) with

15m spatial resolution (acquired on 17/11/2004).

In order to representatively sample the study area, we generated a random list of geographic coordinates for the landscape and selected the first 16 that felt inside blocks of yellow passion fruit, Passiflora edulis. This procedure was aided by the use of ArcView software

(version 3.3, ESRI, Redlands, California) and global positioning systems (GPS) (Garmin

International, Olathe, Kansas). We used as criterion for including a block in the sample a minimum distance of 1 km to blocks already chosen. We did so in order to ensure the spatial independence of samples. The landcover polygons were handling delineated using 2006 satellite imagery at a 0.30 m pixel resolution.

The relative abundance of bees was determined by measuring the number of bees visiting passion flowers in a transect of 50m long, laid within the crop field, with mean of 90 flowers observed for 15 minutes during three times on three different days. In total was summed twelve hours of observation. The main flower-visiting species was the feral honey bee Apis mellifera

Linnaeus 1758, wild social bee species Trigona spinipes Fabricius 1793 and wild solitary bees

Kennedy et al. Modeling local and landscape effects on pollinators Page 17 of 19 species, Xylocopa (Megaxylocopa) frontalis Olivier, 1789 and Xylocopa (Neoxylocopa) grisescens Lepeletier, 1841. The last two species mentioned above are the main pollinators of passion fruit in the study region. These bees have wide geographic distribution (Hurd & Moure

1963) and build their nests in dry or dead plant material. In general, they construct linear nests, either using pre-existing cavities or digging into dry dead trunks and branches. In the study area, these bees are strongly dependent on the presence of Commiphora leptophloeos (Mart.) J. B.

Gillett (Burseraceae), a plant species that is endemic of the Caatinga vegetation.

The nest abundance were indirectly evaluated, quantify the number of cavities used by

Xylocopa sp for nesting in the environment around the plantation sites. The surrounding area of

16 sites cultivated with Passiflora edulis were inventoried following the distance method described by Greig-Smith (1983) with modifications. Each sampling area comprised 1km radius measured from the center of P. edulis cultivar. Four sampling bases were marked at the edges of the cultivar. Three quadrats were delineated at each sampling base considering the imaginary line traced at 90º, totaling 12 quadrats/site. Thus, the nested Xylocopa substrates were located by walking along twelve directions, following quadrats. To estimate the abundance of nested substrates two samples were taken at each quadrat. The abundance of nests per site was determined by the sum of nests in each substrate.

Sources cited:

Bommarco, R., Lundin, O., Smith, H.G. & Rundlöf, M. (2012). Drastic historic shifts in bumble

bee community composition in Sweden. Proceedings of the Royal Society B: Biological

Sciences, 279, 309-315.

Kennedy et al. Modeling local and landscape effects on pollinators Page 18 of 19

Carré, G., Roche, P. Chifflet, R., Morison, N., Bommarco, R., Harrison-Cripps, J., Krewenka,

K., Potts, S.G., Roberts, S.P.M., Rodet, G., Settele, J., Steffan-Dewenter, I., Szentgyörgi,

H., Tscheulin, T., Westphal, C., Woyciechowski, M. & Vassière, B.E. (2009). Landscape

context and habitat type as drivers of bee biodiversity in European annual crops.

Agriculture, Ecosystems & Environment, 133, 40‒47.

Garibaldi, L.A., Steffan-Dewenter, I., Kremen, C., Morales, J.M., Bommarco, R., Cunningham,

S.A., Carvalheiro, L.G., Chacoff, N.P., Dudenhöffer, J.H., Greenleaf, S.S., Holzschuh,

A., Isaacs, R., Krewenka, K.M., Mandelik, Y, Mayfield, M.M., Morandin, L.A., Potts,

S.G., Ricketts, T.H., Szentgyörgyi, H., Westphal, C., Winfree, R., & Klein, A.M. (2011).

Stability of pollination services decreases with isolation from natural areas despite

honey bee visits. Ecology Letters, 14, 1062–1072

Klein, A.-M., Brittain, C., Hendrix, S.D., Thorp, R., Williams, N.M., & Kremen, C. (2012). Wild

pollination services to California almond rely on semi-natural habitat. Journal of Applied

Ecology, 49, 723-732.

Kremen, C., Williams, N. M., Bugg, R. L., Fay, J. P. & Thorp, R.W. (2004). The area

requirements of an ecosystem service: crop pollination by native bee communities in

California. Ecology Letters, 7, 1109-1119.

Lantmäteriet. (2010). Produktbeskrivning: GSD-Marktäckedata. [Product description: GSD Land

Cover Data (in Swedish)]. Updated: March 26, 2010. Downloaded: October 2010. URL:

www.lantmateriet.se/upload/filer/kartor/kartor_och_geografisk_info/GSD-

Produktbeskrivningar/md_prod.pdf.

PEI Department of Environment. (2000). Energy & Forestry, Resource Inventory, Corporate

Land Use Inventory 2000. URL: www.gov.pe.ca/gis/.

Kennedy et al. Modeling local and landscape effects on pollinators Page 19 of 19

Stephen, W. P. and Rao, S. (2005). Unscented Traps for Non-Apis Bees (Hymenoptera:

Apoidea). Journal of the Kansas Entomological Society, 78, 373-380.

Kennedy et al. Modeling local and landscape effects on pollinators Page 1 of 2

Appendix S3. Inter-site distances of farms included in our synthesis.

In our synthesis, all field sites sampled within studies were separated by distances of

>350-160,000 m (mean ± SD: 25,000 ± 22,000 m), with only 0.02% site pairs located <1 km apart (Figure S3_1). For multi-year studies, inter-site distances include fields sampled within the same year as well as across years. Samples among sites within a similar study region were also commonly separated temporally by different years and/or different crop cycles within years

(Table 1). This level of spatial and temporal separation should be sufficient to ensure independent sampling of pollinator communities among sites given known nesting and foraging distances for the majority of bee species (Gathmann & Tscharntke 2002; Greenleaf et al. 2007).

As further confirmation of independence, we found no evidence of spatial correlation based on visual inspection of semi-variograms for residuals of global models (i.e., models of all studies with all local and landscape variables and their interactions) by inter-site distance ranges (i.e., variance of the difference in residuals did not increase with increasing distance).

Sources cited:

Gathmann, A. & Tscharntke, T. (2002). Foraging ranges of solitary bees. Journal of Animal

Ecology, 71, 757-764.

Greenleaf, S., Williams, N., Winfree, R. & Kremen, C. (2007). Bee foraging ranges and their

relationships to body size. Oecologia, 153, 589-596.

Kennedy et al. Modeling local and landscape effects on pollinators Page 2 of 2

Figure S3_1. Distribution of inter-site field distances. 6073 inter-site distances were assessed

based on site pairs within each study, including farms sampled with the same year as well as

across years for multi-year studies. 10% of site pairs were separated by 5000 m or less, 50% by

20,000 m or less, and 90% by 52,000 m or less.

1000

900

800

700

600

500

Frequency 400

300

200

100

Inter-site Distances (m)

Kennedy et al. Modeling local and landscape effects on pollinators Page 1 of 5

Appendix S4. Determining landscape composition based on Lonsdorf et al. (2009) model

The Lonsdorf et al. (2009) model codes multi-class landscapes in terms of their contributions to bee floral and nesting resources, by assigning each land-cover type an estimated suitability of its resources to specific bee guilds. Thus, model scores reflect landscape composition – the proportional areas of different habitat types within a landscape – within bee foraging range(s). To do so, for each study, data holders generated a nesting suitability layer as a direct translation of the land-cover map for each study region. They first assigned each bee taxa to a nesting guild and in turn assigned nesting suitability values for each taxa to each land-cover type in their multi-class land-cover map based on expert opinion

(as informed by quantitative field estimates when available) (Lonsdorf et al 2009). Suitability was scaled from 0 to 1 (with 0 indicating land cover that provided no nesting resources and 1 indicating land cover that provided 100% suitable nesting habitat), which could differ by bee taxa found within each study system.

The amount of suitable foraging habitat available to pollinators at a nest location was then calculated as the distance-weighted sum of relativized suitability values for each location in the landscape (Lonsdorf et al. 2009). Distance decay functions in the model were determined by size-specific foraging capability of each bee species or taxa (Greenleaf et al.

2007), using measurements of inter-tegular span, body size or pre-existing databases

(Discover Life, Potts unpublished data, Williams et al. 2010). Like for nesting values, floral values were assigned by data holders. We allowed for floral resource production to vary among seasons. Expert opinion of authors (as informed by survey data when available) was used to assess flight periods for each bee taxa, thus accounting for variation among bee species in their flight seasons (e.g. some are present in summer only, while others are present

Kennedy et al. Modeling local and landscape effects on pollinators Page 2 of 5 in multiple seasons). The overall floral resources available were calculated as a weighted sum across seasons. To standardize across studies, we applied the Lonsdorf et al. (2009) model at a 30-m resolution; for land-cover maps with <30m resolution, we accounted for proportions of each land-cover class within a 30-m parcel (or cell) (see details on land-cover map resolutions in Appendix S5).

Expert-derived estimation of habitat suitability for land cover types

To characterize how data providers estimated habitat suitability across study regions, we classified empirical land cover classes into standardized cover types (Table S4_1) that were modified based on the National Land Cover Database (NLCD) (Vogelmann et al. 1998) and CORINE Land Cover nomenclature (European Environment Agency 2000), because the majority of land cover datasets followed these systems. (We note that this standardization was not applied in the pollinator model runs, as described above, and did not influence the

Lonsdorf landscape index for field sites; rather this characterization was done post-hoc to describe trends in how data providers valued land cover types for bees). After standardizing land cover types, we then quantified average floral and nesting values attributed by data providers to these generalized cover classes. To facilitate comparison among studies and cover types, we totaled nesting and floral values across different bee taxa and multiple seasons, respectively (when relevant) and then rescaled resource values from 0 to 1 within each study, such that a cover type with the highest overall nesting or floral resource value was assigned a value of 1 and the lowest a value of 0. Across all 39 studies, highest overall habitat suitabilities (aggregated across nesting and floral resources) were assigned to natural and semi-natural habitat types, in particular shrubland, forest (broadleaved forest and to a lesser extent mixed forest), natural grassland, and woody wetlands, which were estimated to

Kennedy et al. Modeling local and landscape effects on pollinators Page 3 of 5 have almost two times more resources than other cover types (Table S4_2). Of secondary importance were certain types of cropland (in particular orchards and vineyards, pasture and fallow fields, and to lesser extent perennial row crops) and low density development and open spaces. Cover classes estimated to provide the most nesting areas were shrubland, broadleaved and mixed forest, woody wetlands, and natural grassland, whereas shrubland, orchards and vineyards, and natural grassland were estimated to provide the greatest floral resources. Least suitable cover types were considered to be open water and barren areas, followed by cropland composed of annual row crops, high intensity developed areas, and herbaceous wetlands.

Sources cited:

European Environment Agency (2000). CORINE land cover technical guide - Addendum 2000.

Commission of the European Communities, Coppenhagen, 105 pp.

Greenleaf, S., Williams, N., Winfree, R. & Kremen, C. (2007). Bee foraging ranges and their

relationships to body size. Oecologia, 153, 589-596.

Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N. & Greenleaf, S. (2009).

Modelling pollination services across agricultural landscapes. Annals of Botany, 103,

1589-1600.

Vogelmann, J.E., Sohl, T.L., Campbell, P.V. & Shaw, D.M. (1998). Regional land cover

characterization using Landsat thematic mapper data and ancillary data sources.

Environmental Monitoring and Assessment, 51, 415-428.

Williams, N.M., Crone, E.E., Roulston, T.H., Minckley, R.L., Packer, L. & Potts, S.G. (2010).

Ecological and life-history traits predict bee species responses to environmental

disturbances. Biological Conservation, 143, 2280-2291.

Kennedy et al. Modeling local and landscape effects on pollinators Page 4 of 5

Table S4_1. Standardized cover types used to reclassify land cover maps for the 39 studies.

Class (Level I) Class (Level II) Class (Level III) Description Natural & Semi-Natural Grassland Grassland/Herbaceous Areas dominated by natural gramanoid or herbaceous vegetation that are not subject to intensive management such as tilling. Natural & Semi-Natural Forest Broadleaved Forest Areas dominated by trees (generally >5 m tall) where broad-leaved species predominate. Includes eucalyptus and deciduous tree plantations, oak woodlands, woodland/riparian areas.

Natural & Semi-Natural Forest Coniferous Forest Areas dominated by trees (generally >5 m tall) where coniferous species predominate. Includes pine plantations, non-evergreen coniferous woodlands (e.g., Larix), and Christmas tree plantations.

Natural & Semi-Natural Forest Mixed Forest Areas dominated by trees (generally >5 m tall) where neither broad-leaved nor coniferous species predominate. Includes mixed-forest woodlands. Natural & Semi-Natural Shrubland Shrubland Areas dominated by natural or semi-natural herbaceous and scattered woody vegetation (generally <6 m tall, not touching to interlocking). Both evergreen and deciduous trees or shrubs that are small or stunted because of environmental conditions are included. May occur naturally or be a result of human activity; includes chaparral, woodland, savanna, and transitional woodland-shrub. Natural & Semi-Natural Wetlands Herbaceous wetlands Areas dominated by perennial herbaceous vegetation and where the soil or substrate is periodically saturated with or covered with water. Natural & Semi-Natural Wetlands Woody Wetlands Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Natural & Semi-Natural Cultivated/Ruderal Cultivated/Ruderal Areas consisting of ruderal vegetation or non-agricultural plantings, including hedgerows, field Vegetation Vegetation margins (vegetated shrubs/flowers at edges of fields), and vegetation along roadways/ditches. Cultivated Cropland Orchards/Vineyards Permanent crops such as vineyards, fruit and nut orchards, olive groves, coffee farms, and agroforestry. Cultivated Cropland Perennial row crops Areas in production with perennial row crops, including perrennial herbs (e.g., alfalfa), fruits (e.g., berry plantations), and vegatables. Cultivated Cropland Annual row crops Areas in production with annual row crops, such as cereals, legumes, roots, and vegetables. Cultivated Grassland Pasture/Fallow Fields Areas of grasses planted or is intensively managed for livestock grazing or the production of seed or hay crops. Also, includes sugarcane, rice fields, fallow fields and set-asides. Developed Developed Developed-Low Areas with a mixture of constructed materials and vegetation, where impervious surfaces account intensity to open for <50% percent of total cover. These areas include discontinuous urban fabric, low density housing, spaces urban greenery, lawns, gardens, parks, golf courses, agricultural farms, military bases, and recreation areas. Developed Developed Developed-Medium to Areas with a mixture of constructed materials and vegetation, where impervious surfaces account high intensity for >50% of total cover. These areas include highly developed areas such as urban centres, commercial/industrial areas, cemeteries, transportation networks/roads, mines, dumps, and construction sites. Unsuitable Barren Barren or sparsely Open spaces with little or no vegetation, including bare rock, gravel pits, sand dune,, silt, clay, vegetated beaches, dunes, and burnt areas. Unsuitable Open water Open water Areas of open water or permanent ice/snow cover, including both inland and marine waters.

Kennedy et al. Modeling local and landscape effects on pollinators Page 5 of 5

Table S4_2. Average (± SD) nesting suitability and floral resource values for standardized land cover types across the 39 studies as determined by data providers. Prior to determining mean values, nesting and floral values were totaled across different bee taxa and multiple seasons, respectively, and then rescaled from 0 to 1 within each study.

Total Nesting + Floral Nesting Suitability Floral Resource Land cover type Count Mean SD Mean SD Mean SD Natural & Semi-Natural 145 0.60 0.33 0.62 0.33 0.47 0.34 Grassland/He rbace ous 18 0.64 0.27 0.64 0.28 0.64 0.28 Forest 62 0.60 0.35 0.67 0.33 0.37 0.36 Broadleaved forest 38 0.71 0.31 0.76 0.30 0.53 0.35 Coniferous forest 15 0.35 0.25 0.46 0.30 0.06 0.08 Mixed forest 9 0.53 0.38 0.64 0.37 0.25 0.26 Shrubland 34 0.80 0.24 0.77 0.24 0.69 0.27 Wetlands 25 0.38 0.30 0.33 0.30 0.36 0.28 Herbaceous wetlands 18 0.29 0.28 0.20 0.23 0.38 0.31 Woody wetlands 7 0.61 0.20 0.65 0.19 0.32 0.19 Cultivated/Ruderal vegetation 6 0.38 0.20 0.46 0.24 0.23 0.07 Cultivated 120 0.36 0.29 0.25 0.27 0.48 0.34 Cropland 84 0.33 0.26 0.20 0.22 0.50 0.35 Orchards/Vineyards 25 0.46 0.25 0.28 0.17 0.67 0.31 Perennial row crops 17 0.37 0.27 0.28 0.31 0.50 0.33 Annual row crops 42 0.24 0.23 0.12 0.18 0.40 0.33 Grassland 36 0.42 0.33 0.36 0.32 0.42 0.32 Pasture/Fallow fields 36 0.42 0.33 0.36 0.32 0.42 0.32 Developed 63 0.33 0.30 0.34 0.32 0.23 0.25 Developed-Low intensity to open spaces 29 0.42 0.31 0.41 0.31 0.31 0.29 Developed-Medium to high intensity 34 0.25 0.26 0.28 0.32 0.15 0.18 Unsuitable 43 0.09 0.18 0.10 0.22 0.05 0.14 Barren or sparsely vegetated 18 0.21 0.23 0.25 0.28 0.12 0.20 Open water 25 0.00 0.01 0.00 0.01 0.00 0.01

Kennedy et al. Modeling local and landscape effects on pollinators Page 1 of 13

Appendix S5. Using neutral modeling to select landscape-level metrics.

In addition to characterizing landscape composition across study regions, we also quantified landscape configuration. To do so, we used neutral landscapes, which are grid representations of maps in which ‘habitat’ distributions are generated by random or fractal algorithms in a way that explicitly controls two fundamental aspects of landscape pattern: composition and configuration (Gardner & Urban 2007). Neutral landscapes are effective tools in ecology and help to identify species’ perceptions to landscape structure (With &

King 1997). We applied neutral modeling to select three of the 36 landscape metrics offered by FRAGSTATS to incorporate into a full, mixed-model analysis that includes the Lonsdorf et al. (2009) landscape index (LLI). We wanted each chosen metric to be uncorrelated with the LLI, as well as uncorrelated with each other. To identify landscape metrics that captured aspects of landscape structure that were not accounted for by the Lonsdorf et al. (2009) model, we generated neutral landscapes that differed regularly along two gradients: proportion of each habitat type (%x) and aggregation of habitat types over the landscape (p, the degree of spatial autocorrelation among adjacent cells) using modified version of

SIMMAP 2.0 software (Saura & Martínez-Millán 2000). Each landscape included three habitat types (classes) that were separately assigned different suitability (x) for bee nesting

(Nsx) and foraging (Fsm) as x=0 for the poor habitat class, x=0.5 or 0.25 for the intermediate habitat class and x=1 for the good habitat class. Suitabilities were assigned under different assumptions of correlation between nesting and foraging habitat quality (as described below).

Rather than exploring landscapes along the entire gradients of % and p (cf. Neel et al. 2004), we limited the area of good quality habitat in our landscapes to the range that had potential to be fragmented; i.e., %1 < 0.5. Above this amount of habitat in a landscape there is little room Kennedy et al. Modeling local and landscape effects on pollinators Page 2 of 13 for variation in configuration, whereas below it, a small enough proportion of the total landscape is occupied that spatial configuration of habitat patches can vary (Gustafson &

Parker 1992). We investigated the 26 combinations of habitat amount in which the condition for %1 was met and in which %0 and %0.5, 0.25 take all possible values > 0 at 0.1 increments

(Figure S5_1a). Each of the 26 combinations was created using five values of p at equal increments from 10 to 50. We chose these values of p because they produced neutral landscapes similar in pattern to empirical landscapes, and p must be less than pc, the percolation threshold (pc ≈ 0.5928) to obtain the full range of landscape patterns possible

(Saura 2003). Each % by p combination was replicated 100 times yielding 13,000 neutral landscapes. Each landscape comprised 210 x 210 pixels to which we ascribed a pixel size of

30 m to simulate a 6 km x 6 km landscape that was similar to the scale of the empirical landscapes in this study (Figure S5_1b). Patches were defined using an eight neighbor rule for both SIMMAP and FRAGSTATS outputs.

For each of the 13,000 landscapes, we modeled total pollinator (bee) abundance

(Abundos) measured at the landscape centroid (i.e., field site) for four bee species with typical foraging distances of 180 m, 360 m, 750 m, and 1500 m and then calculated an average pollinator (bee) abundance score from each of the four species’ scores. Abundos depends on the amount and quality of nesting habitat within an estimated maximum foraging distance of 3 km from the centroid (Figure S5_1b, circle within dark grey “core” area). These pollinators in turn depend on the floral resources 3 km of their nesting site. Thus, Abundos measured at the centroid potentially depends on the amount and quality of nesting and floral resources within 3 − 6 km of the landscape centroid (Figure S5_1b, light grey circle). To test the effect of variation in habitat suitability among bees we simulated five different nesting and floral suitability patterns with Kennedy et al. Modeling local and landscape effects on pollinators Page 3 of 13 respect to the three different land-cover types from perfectly correlated to perfectly uncorrelated

(Table S5_3). Because our goal was to select landscape configuration metrics that were as robust to differences due to variation in suitability estimates as possible, the suitability patterns were designed to maximize differences among degree of correlations. In this way we could evaluate the sensitivity of the relationships between metrics and model scores of abundance to these correlations.

We then calculated landscape-level metrics (Table S5_4) for each of 13,000 neutral landscapes as well as for empirical landscapes. By using landscape-level metrics, we accounted for configuration of all identified habitat cover types in each study region and measured the aggregate properties of landscape heterogeneity rather than focusing on the individual contributions of each habitat type (McGarigal et al. 2002). Metrics were calculated for landscapes extending 3 km around each field site where possible, which coincided with the spatial extent calculated by the LLI and typical foraging ranges of bees (Gathmann & Tscharntke

2002; Greenleaf et al. 2007). In four studies land-cover data were restricted to 1-km or 1.5-km radii around fields. To capture biologically relevant habitat configuration, land-cover maps were first, reclassified into “habitat suitability” cover types that reflected nesting or foraging suitability (see Appendix S4). As such, different land-cover types designated within a map, such as different forms of development (e.g., urban areas, industrial areas, impervious surfaces) were classified as a single suitability type when they were attributed identical floral and nesting values by expert opinion. The number of habitat suitability cover classes varied from 3 to 27 among the different studies (mean ± 1 SD = 10.74 ± 5.08). It should be noted that landscape configuration metrics were derived from land-cover classifications that reflected unique “habitat suitability” cover types (i.e., classes differed in floral and nesting resources) as determined by expert Kennedy et al. Modeling local and landscape effects on pollinators Page 4 of 13 opinion. If expert-opinion regarding differential resource availability in the initial cover types within a region was faulty, then our ability to detect meaningful relationships would be limited.

However, the fact that we did see significant effects of landscape composition alone based on this classification (see Results section) suggests that expert-derived cover types were meaningful in predicting bee responses.

In addition to the number and type(s) of habitat suitability classes, the resolution of land- cover data could have varied by study. About half of the land-cover datasets had ≤10 m pixel sizes (22 of 46 maps). Most fine-scale maps were digitized by data providers from satellite imagery or aerial photography. The remaining studies relied on 25−30 m resolution (N = 18) or

56−100 m maps (N = 6). For the seven studies in which multiple land-cover maps were available, we relied on the map deemed most reliable by each author in terms of its spatial resolution, accuracy, and appropriateness of land-cover classes delineated in relation to the bee community. To allow for comparison across study regions, we standardized maps with resolutions <30 m by resampling and assigning the “majority” land-cover class within a 30-m squared area prior to calculating metrics.

For each of 13,000 neutral landscapes we determined the Pearson’s product-moment correlation coefficients (r) between each of the landscape metrics and the average LLI model abundance score of the four simulated bee species under the five habitat suitability scenarios. We averaged the model scores from the four bee species and determined the absolute value of the correlation for each of the five habitat suitability scenarios to the 36 landscape metrics. Thus each of the 36 metrics had five correlation values (Table S5_3).

Because the correlations varied across scenarios, we examined the results from the five scenarios in several ways to select final metrics. We computed the average, minimum and Kennedy et al. Modeling local and landscape effects on pollinators Page 5 of 13 maximum correlation value for each metric. We ranked the metrics, as well as ranked the average, minimum and maximum r values. We then averaged the ranks. Each of these analyses yielded slightly different results for the three metrics that showed the minimum correlation or rank. For simplicity we provide only the five correlations. Ultimately, we selected one metric that predominately characterized patch shape, another metric that characterized patch isolation, and finally one that characterized patch contagion or interspersion to capture different elements of landscape structure.

Landscape metrics found to be among the least correlated with model scores, and thus the most likely to explain deviations from model predictions and empirical observations in study landscapes were: (1) perimeter-area ratio distribution (PARA_MN), which measures mean shape and edge density of patches in a landscape (�̅ ± 1 SD = 0.02 ± 0.02); 2) Euclidean nearest neighbor distance distribution (ENN_CV) (�̅ ± 1 SD = 0.06 ± 0.04), which measures variation in inter-patch connectivity in a landscape; and (3) interspersion & juxtaposition index (IJI) (�̅ ± 1

SD = 0.04 ± 0.02), which measures patch aggregation or the extent to which habitat patches are clumped together versus interspersed among different habitat patches (Table S5_5). These metrics were also uncorrelated with model abundance scores based on our empirical modeling of bee assemblages and landscape metrics for the 39 studies (PARA_MN: r = 0.12; ENN_CV: r =

-0.09; IJI: r = 0.03) (Table S5_5). In addition to being selected because they were weakly correlated with pollinator (bee) model scores based on both neutral and empirical landscapes, these metrics were also not strongly correlated with one another, thus, captured independent aspects of landscape configuration (i.e., habitat shape, connectivity, and aggregation) (r < |0.60| based on neutral landscapes and r < |0.12| based on empirical landscapes) (McGarigal et al.

2000). Kennedy et al. Modeling local and landscape effects on pollinators Page 6 of 13

In addition to having desired statistical independence, selected configuration metrics have been widely applied and found important in relevant ecological contexts. Euclidean nearest neighbor measures (e.g., ENN_CV) are the most common metrics applied in ecology for structural connectivity (Calabrese & Fagan 2004), and have been found important for pollinators

(Ricketts et al. 2008). Characterizing patch shape and edges with metrics like PARA_MN is supported by findings that edge (or length of boundaries) of fields or semi-natural areas can strongly impact species richness in agricultural systems (Carré et al. 2009; Concepción et al.

2012). For example, boundaries with semi-natural vegetation can act as corridors for movement or provide additional food resources in agricultural landscapes, or can be detrimental if they fragment habitats or act as barriers or sinks (Gabriel et al. 2010; Concepción et al. 2012). Lastly, wild bees have been found to significantly respond to landscape heterogeneity, which has been measured by IJI (Carré et al. 2009). An intermixing of habitat types may contain diverse foraging and nesting resources that help support more diverse and abundant bee species (Winfree et al. 2007); this landscape aspect was previously predicted by co-authors to be a potential important driver of pollinator communities across diverse agricultural systems (Lonsdorf et al.

2009).

Sources cited:

Calabrese, J.M. & Fagan, W.F. (2004). A comparison-shopper's guide to connectivity metrics.

Frontiers in Ecology and the Environment, 2, 529-536.

Carré, G., Roche, P., Chifflet, R., Morison, N., Bommarco, R., Harrison-Crips, J., et al. (2009).

Landscape context and habitat type as drivers of bee diversity in European annual crops

Agriculture, Ecosystems and Environment, 133, 40-47. Kennedy et al. Modeling local and landscape effects on pollinators Page 7 of 13

Concepción, E.D., Diaz, M., Kleijn, D., Báldi, A., Batáry, P., Clough, Y., et al. (2012).

Interactive effects of landscape context constrain the effectiveness of local agri-

environmental management. Journal of Applied Ecology, 49, 695-705.

Gabriel, D., Sait, S.M., Hodgson, J.A., Schmutz, U., Kunin, W.E. & Benton, T.G. (2010). Scale

matters: the impact of organic farming on biodiversity at different spatial scales. Ecology

Letters, 13, 858–869.

Gardner, R.H. & Urban, D.L. (2007). Neutral models for testing landscape hypotheses.

Landscape Ecology, 22, 15–29.

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across gradients of class aggregation and area. Landscape Ecology, 19, 435-455. Kennedy et al. Modeling local and landscape effects on pollinators Page 8 of 13

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et al. (2008). Landscape effects on crop pollination services: Are there general patterns?

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Table S5_3. Five scenarios modeled in relation to nesting suitability at a location x for bee species s (Nsx) and foraging suitability in location m surrounding nesting location x for bee species s (Fsm), based on three habitat types or land-cover classes (1−3). Nesting and floral values suitability values of 0 indicate a poor habitat type, 0.5 or 0.25 indicate intermediate quality habitat types and 1 a good habitat type. We applied different assumptions of correlation between nesting and foraging habitat: perfectly correlated (scenario 1), intermediate correlation

(scenarios 2 and 3), and perfectly uncorrelated (scenarios 4 and 5). Each scenario was modeled for four different species (s) with foraging distances of 180, 360, 750, and 1500 m.

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Nsx Fsm Nsx Fsm Nsx Fsm Nsx Fsm Nsx Fsm Class 1 1 1 1 0.25 0.25 1 0 1 1 0 Class 2 0.5 0.5 0.25 1 1 0.25 1 0 0 1 Class 3 0 0 0 0 0 0 0 0 0 0

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Table S5_4. Landscape-level metrics calculated for both multi-class neutral landscapes and empirical landscapes for the 39 studies. Metrics were computed using FRAGSTATS 3.3 (using

30-m raster cell size, an eight-neighbor rule for patch delineation). Where relevant, we computed

(1) mean (MN), (2) area-weighted mean (AM) and (3) coefficient of variation (CV) for each target metric (as described by McGarigal et al. 2002).

Classification Landscape-level metric Code

Area/Density/Edge metrics Patch Area Distribution AREA Edge Density ED Radius of Gyration Distribution GYRATE Landscape Shape Index LSI Patch Density PD

Shape metrics Fractal Index Distribution FRAC Perimeter-Area Fractal Dimension PAFRAC Perimeter-Area Ratio Distribution PARA Shape Index Distribution SHAPE

Euclidean Nearest Neighbor Distance Isolation/proximity metrics Distribution ENN

Connectivity metrics Patch Cohesion Index COHESION Connectance Index CONNECT*

Contagion/Interspersion metrics Aggregation Index AI Contagion CONTAG Landscape Division Index DIVISION Interspersion & Juxtaposition Index IJI Effective Mesh Size MESH Percentage of Like Adjacencies PLADJ

Diversity Modified Simpson’s Diversity Index MSIDI Modified Simpson’s Evenness Index MSIEI Shannon’s Diversity Index SHDI Shannon’s Evenness Index SHEI Simpson’s Diversity Index SIDI Simpson’s Evenness Index SIEI * Based on 100 m threshold distance (i.e., search radius) Kennedy et al. Modeling local and landscape effects on pollinators Page 11 of 13

Table S5_5. Correlations between landscape metrics and Lonsdorf et al. (2009) modeled pollinator (bee) abundance scores for 1) empirical study landscapes, and 2) neutral landscapes based on community average score across four simulated species (with typical foraging distances of 180 m, 360 m, 750 m, and 1500 m) and under five different habitat suitability scenarios (as specified in Table S5_3). We report only Pearson’s product-moment correlation coefficients (r), because they were highly correlated (r > 0.90) with the Spearman’s rank correlation coefficients

(r). Landscape metrics selected for analyses appear in bold. Kennedy et al. Modeling local and landscape effects on pollinators Page 12 of 13

Empirical Neutral landscapes Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Metric r p-value r r r r r AI -0.26 0.00 0.04 0.02 0.01 0.16 0.16 AREA_AM -0.27 0.00 0.32 0.08 0.08 0.26 0.26 AREA_CV 0.03 0.50 0.30 0.09 0.09 0.20 0.20 AREA_MN -0.13 0.00 0.04 0.01 0.01 0.11 0.11 COHESION -0.02 0.69 0.19 0.07 0.07 0.21 0.21 CONNECT -0.18 0.00 0.18 0.04 0.04 0.03 0.03 CONTAG -0.45 0.00 0.14 0.02 0.02 0.23 0.22 DIVISION 0.3 0.00 0.32 0.08 0.08 0.26 0.26 ED 0.29 0.00 0.04 0.02 0.01 0.16 0.16 ENN_AM -0.11 0.01 0.00 0.02 0.02 0.12 0.13 ENN_ CV -0.09 0.05 0.04 0.03 0.03 0.10 0.10 ENN_MN -0.14 0.00 0.02 0.00 0.00 0.16 0.16 FRAC_AM 0.4 0.00 0.21 0.05 0.06 0.12 0.12 FRAC_CV 0.12 0.01 0.19 0.06 0.06 0.24 0.24 FRAC_MN 0.05 0.27 0.14 0.04 0.04 0.21 0.21 GYRATE_AM -0.18 0.00 0.29 0.08 0.08 0.26 0.26 GYRATE_CV -0.01 0.75 0.03 0.00 0.00 0.16 0.16 GYRATE_MN -0.14 0.00 0.20 0.05 0.05 0.10 0.10 IJI 0.03 0.49 0.00 0.06 0.06 0.05 0.05 LSI 0.36 0.00 0.04 0.02 0.01 0.16 0.16 MESH -0.27 0.00 0.32 0.08 0.08 0.26 0.26 MSIDI 0.19 0.00 0.25 0.04 0.04 0.23 0.23 MSIEI 0.39 0.00 0.25 0.04 0.04 0.23 0.23 PAFRAC 0.22 0.00 0.15 0.05 0.05 0.22 0.22 PARA_AM 0.28 0.00 0.04 0.02 0.01 0.16 0.16 PARA_CV -0.09 0.05 0.07 0.01 0.01 0.00 0.01 PARA_MN 0.03 0.43 0.05 0.01 0.01 0.02 0.01 PD 0.09 0.04 0.05 0.01 0.01 0.08 0.08 PLADJ -0.24 0.00 0.04 0.02 0.01 0.16 0.16 SHAPE_AM 0.39 0.00 0.25 0.08 0.08 0.13 0.13 SHAPE_CV 0.39 0.00 0.07 0.02 0.02 0.11 0.11 SHAPE_MN 0.03 0.54 0.23 0.07 0.07 0.26 0.26 SHDI 0.13 0.00 0.21 0.02 0.02 0.21 0.21 SHEI 0.37 0.00 0.21 0.02 0.02 0.21 0.21 SIDI 0.25 0.00 0.24 0.03 0.03 0.23 0.23 SIEI 0.33 0.00 0.24 0.03 0.03 0.23 0.23

Kennedy et al. Modeling local and landscape effects on pollinators Page 13 of 13

Figure S5_1. a. Dots represent combinations of %0 (bad), %0.5 (intermediate) and %1 (good) habitat of neutral landscapes that were generated. b. 6 km x 6 km landscape corresponding to bees with typical foraging ranges (arrow) of up to 3 km. Bees nesting in the grey (core) region can reach the centroid (field) of this landscape, but their abundances are influenced by availability of foraging resources within light grey (total) region.

Page 1 of 8

Appendix S6. Candidate model set.

We analyzed the influence of landscape and local factors on empirical wild bee

β0 βX abundance and richness based on the general model structure: E(a, r) = e e ® ln[E(a,r) = β0 +

βiXi, where E(a, r) is the expected wild bee abundance or richness, βi are the partial regression coefficients, and Xi are the covariates (local and landscape variables) and covariate interactions.

We log-transformed both abundance and richness by ln [a + 1, r + 1]. Residuals of fitted models were approximately normally distributed with no strong pattern of overdispersion or heteroscedasticity (based on plotting residuals vs. fitted values and vs. study identity). We applied Gaussian error distribution based on log-transformed response variables, rather than

Poisson or negative binomial error distribution based on counts, because of improved model fits

(i.e., lower AIC values and deviance scores). Different error distributions yielded similar strength and directional patterns for covariates. We also investigated transforming our observations using

y ji - yi z-scores ( ), which standardizes contrasting means (y i )and standard deviations (SDi ) SDi among systems, as applied in other meta-analyses (Garibaldi et al. 2011; Maestre et al. 2012).

Again, we found that the most supported covariates and their directional trends were generally consistent between z-score and ln-transformations. Log-linear models, however, were uniformly more strongly supported than those based on z-scores based on lower deviance scores and AIC values (i.e., ∆AIC > 175 for abundance and ∆AIC > 915 for richness) and lower model weights for richness. Given the lack of improvement based on z-score transformations, and reduced fit with our data, we present only log-linear relationships.

We analyzed 135 models (candidate model set). Our global model included all main effects and all two-way interactions between ecologically-scaled landscape composition Page 2 of 8

(Londsorf Landscape Index, LLI) and local farming variables (field type, FT, organic vs. conventional, and field-scale diversity, FD, locally simple vs. complex crop diversity) and between LLI, FT, or FD with landscape configuration covariates (perimeter-area ratio distribution , PARA_MN; Euclidean nearest neighbor distance distribution, ENN_CV; interspersion & juxtaposition index, IJI). These interactions reflect previous research that suggests that habitat configuration can mediate effects of habitat amount (Andren 1994; Fahrig

2002; Goodsell & Connell 2002) while local farming practices mediate effects of landscape composition (Holzschuh et al. 2007; Rundlöf et al. 2008; Batary et al. 2011; Concepción et al.

2012). We did not include interactions between the different landscape configuration covariates because of a lack of biological justification. The model set was balanced, with each of the six covariates (main effects) appearing in 88 different models and each of the two-way interactions appearing in 13 models. We calculated model-averaged estimates of partial slope coefficients based on the 95% confidence set (Burnham & Anderson 2002). Model averaging combines parameter estimates from each model using their associated Akaike weights to account for the fact that each model has some degree of validity and to provide a mean estimate and standard error that incorporates both within- and across-model uncertainty. This approach reduces model bias and allows for more robust inferences than those based on a single selected best model

(Burnham & Anderson 2002); and permits nuanced interpretation of the strength of evidence of the importance of each covariate.

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visiting bees in cereal fields: effects of farming system, landscape composition and

regional context. Journal of Applied Ecology, 44, 41-49. Page 4 of 8

Maestre, F.T., Quero, J.L., Gotelli, N.J., Escudero, A., Ochoa, V., Delgado-Baquerizo, M., et al.

(2012). Plant Species Richness and Ecosystem Multifunctionality in Global Drylands.

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Rundlöf, M., Nilsson, H. & Smith, H.G. (2008). Interacting effects of farming practice and

landscape context on bumble bees. Biological Conservation, 141, 417-426.

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Table S6_1. Candidate model structures testing relationships between pollinator measures (wild bee abundance and wild bee richness) and landscape composition (Lonsdorf landscape index,

LLI), local farm management (organic vs. conventional farming and field-scale diversity), and landscape configuration (PARA_MN, ENN_CV, IJI). Models #1-134 were special cases of global model #135. Lonsdorf landscape index (LLI) is the pollinator abundance score derived by the spatially-explicit Lonsdorf et al. (2009) model. Field type (FT) is whether fields were conventional or organic and Field diversity (FD) is whether fields were locally simple (large monocultural fields) or locally diverse (small fields with inter-mixed crops and/or non-crop plantings). PARA_MN is the perimeter-area ratio distribution, which measures patch shape complexity in a landscape. ENN_CV is the Euclidean nearest neighbor distance distribution, which measures the variation in inter-patch connectivity in a landscape. IJI is the interspersion & juxtaposition index, which measures habitat aggregation in a landscape. : denotes an interaction effect was modeled. Page 6 of 8

index (LLI) Lonsdorf landscapeFarm typeField (FT) diversityShape (FD)(PARA_MN)ConnectivityAggregation (ENN_CV)LLI:FT (IJI)LLI:FDFT:FD LLI:PARA_MNFT:PARA_MNFD:PARA_MNLLI:ENN_CVFT:ENN_CVFD:ENN_CVLLI:IJIFT:IJI FD:IJI 1 X 2 X 3 X 4 X X 5 X X 6 X X 7 X X X 8 X 9 X 10 X 11 X X 12 X X 13 X X 14 X X X 15 X X 16 X X 17 X X 18 X X X 19 X X X 20 X X X 21 X X X X 22 X X 23 X X 24 X X 25 X X X 26 X X X 27 X X X 28 X X X X 29 X X 30 X X 31 X X 32 X X X 33 X X X 34 X X X 35 X X X X 36 X X X 37 X X X 38 X X X 39 X X X X 40 X X X X 41 X X X X 42 X X X X X 43 X X X 44 X X X 45 X X X 46 X X X X 47 X X X X 48 X X X X 49 X X X X X Page 7 of 8

50 X X X 51 X X X 52 X X X 53 X X X X 54 X X X X 55 X X X X 56 X X X X X 57 X X X X 58 X X X X 59 X X X X 60 X X X X X 61 X X X X X 62 X X X X X 63 X X X X X X 64 X X X 65 X X X 66 X X X 67 X X X 68 X X X 69 X X X 70 X X X 71 X X X 72 X X X 73 X X X 74 X X X 75 X X X 76 X X X X 77 X X X X 78 X X X X 79 X X X X X 80 X X X X X 81 X X X X X 82 X X X X X X 83 X X X X X 84 X X X X X 85 X X X X X 86 X X X X X X X 87 X X X X X X X 88 X X X X X X X 89 X X X X X 90 X X X X X 91 X X X X X 92 X X X X X X X 93 X X X X X X X 94 X X X X X X X 95 X X X X X 96 X X X X X 97 X X X X X 98 X X X X X X X 99 X X X X X X X 100 X X X X X X X Page 8 of 8

101 X X X X X X X 102 X X X X X X X 103 X X X X X X X 104 X X X X X X X X 105 X X X X X X X X 106 X X X X X X X X 107 X X X X X X X X X 108 X X X X X X X 109 X X X X X X X 110 X X X X X X X 111 X X X X X X X X 112 X X X X X X X X 113 X X X X X X X X 114 X X X X X X X X X 115 X X X X X X X 116 X X X X X X X 117 X X X X X X X 118 X X X X X X X X 119 X X X X X X X X 120 X X X X X X X X 121 X X X X X X X X X 122 X X X X X X X 123 X X X X X X X 124 X X X X X X X 125 X X X X X X X X 126 X X X X X X X X 127 X X X X X X X X 128 X X X X X X X X X 129 X X X X X X X X X X 130 X X X X X X X X X X 131 X X X X X X X X X X X X X 132 X X X X X X X X X X X X X X 133 X X X X X X X X X X X X X X X X X 134 X X X X X X X X X X X X X X X X X 135 X X X X X X X X X X X X X X X X X X

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Appendix S7. Summary statistics for variables and model selection statistics

Table S7_1. Summary statistics for study variables based on total or mean (± 1SD) values per study (N = 39).

# Studies # Sites Wild Abundance*Wild Richness* Honey bees* # Sites per FT # Sites per FD LLI PARA_MN ENN_CV IJI Biome† Total Mean SD Mean SD Mean SD Mean SD Conv Organic Simple Diverse Mean SD Mean SD Mean SD Mean SD Tropical/Subtropical 10 11.80 6.85 72.13 120.72 5.00 6.53 57.13 73.41 108 10 88 30 0.12 0.16 554.42 275.42 105.71 44.98 63.33 10.19 Mediterranean 8 16.88 8.08 27.44 23.91 4.71 2.94 77.63 101.62 96 39 109 26 0.04 0.01 913.62 77.56 150.17 10.70 60.00 6.94 Temperate 21 16.76 9.55 58.26 128.41 9.43 6.56 57.68 63.95 310 42 235 117 0.11 0.10 666.33 279.91 110.23 34.31 64.85 9.72 All Biomes 39 15.51 8.90 55.49 113.88 7.27 6.39 61.21 75.11 514 91 432 173 0.10 0.11 688.36 279.54 117.26 38.20 63.46 9.53 †See Table 1 for biome definitions. *Based on mean estimates per site (see Table 1 for total bee taxa per crop system). Kennedy et al. Modeling local and landscape effects on pollinators Page 2 of 14

Table S7_2. Summary of model selection statistics for wild bee abundance and richness as a function of local and landscape variables. K is the number of parameters included in the model

(including fixed and random effects); Deviance is -2 times the logarithm of the probability of the data given the estimated model parameters and is a statistical summary of model fit; AIC is

Akaike’s Information Criterion and AICc is AIC adjusted for finite sample size, which judge a model by how close its fitted values are to true values and can be interpreted as the weight of evidence in favor of model i being the best model for the data with respect to the entire model set; !AICc is the difference in AICc value for model i when compared with the top ranked model; wi is the Akaike weight of model i, which is interpreted as the probability that model i is the best model of those considered in the entire model set. The sum of the Akaike weights for all models in the model set = 1. All models that had any weight within the candidate model set are displayed, but models denoted by " fell outside of the 95% confidence set (#w $ 0.95). Models in bold are within 2 !AIC units of the top model, and considered to have substantial and equal model support (‘top models’). The global model was bee abundance or richness = f (LLI*FT +

LLI*FD + FT*FD + LLI*PARA_MN + FT*PARA_MN + FD*PARA_MN + LLI*ENN_CV +

FT*ENN_CV + FD*ENN_CV + LLI*IJI + FT*IJI + FD*IJI), with study and site-within-study treated as random effects (1|Study/Site). * indicates main effects plus their interaction. Model # corresponds to the model specified in the candidate model set (Appendix S6). LLI = Lonsdorf landscape index (an ecologically-scaled index of landscape composition); FT = Field type

(conventional vs. organic); FD = Field-scale diversity (locally simple vs. locally diverse);

PARA_MN = perimeter-area ratio distribution (measure of patch shape); ENN_CV = Euclidean nearest neighbor distance distribution (measure of inter-patch connectivity); and IJI = Kennedy et al. Modeling local and landscape effects on pollinators Page 3 of 14 interspersion & juxtaposition index (measure of habitat aggregation).

Model # Model structure K Deviance AICc ∆AICc w Total bee abundance 78 FT*FD+LLI 8 1771.37 1787.57 0.00 0.12 58 LLI+FT+FD+ENN_CV 8 1771.89 1788.09 0.52 0.09 7 LLI+FT+FD 7 1774.21 1788.37 0.79 0.08 81 LLI*FD+FT*FD 9 1770.20 1788.45 0.88 0.08 77 LLI*FD+FT 8 1772.80 1789.00 1.43 0.06 76 LLI*FT+FD 8 1772.90 1789.10 1.52 0.06 80 LLI*FT+FT*FD 9 1771.26 1789.51 1.94 0.05 62 LLI+FT+FD+ENN_CV+IJI 9 1771.32 1789.57 2.00 0.05 59 LLI+FT+FD+IJI 8 1773.40 1789.60 2.03 0.04 79 LLI*FT+LLI*FD 9 1771.84 1790.09 2.52 0.03 60 LLI+FT+FD+PARA_MN+ENN_CV 9 1771.85 1790.10 2.53 0.03 57 LLI+FT+FD+PARA_MN 8 1774.14 1790.34 2.76 0.03 82 LLI*FT+LLI*FD+FT*FD 10 1770.17 1790.48 2.90 0.03 103 FT*FD+LLI+PARA_MN+ENN_CV+IJI 11 1768.14 1790.51 2.94 0.03 106 LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 12 1767.08 1791.52 3.94 0.02 61 LLI+FT+FD+PARA_MN+IJI 9 1773.30 1791.55 3.98 0.02 63 LLI+FT+FD+PARA_MN+ENN_CV+IJI 10 1771.26 1791.57 4.00 0.02 101 LLI*FT+FD+PARA_MN+ENN_CV+IJI 11 1769.25 1791.62 4.05 0.02 105 LLI*FT+FT*FD+PARA_MN+ENN_CV+IJI 12 1767.78 1792.22 4.65 0.01 102 LLI*FD+FT+PARA_MN+ENN_CV+IJI 11 1769.96 1792.33 4.76 0.01 104 LLI*FT+LLI*FD+PARA_MN+ENN_CV+IJI 12 1768.38 1792.82 5.25 0.01 110 FD*PARA_MN+LLI+FT+ENN_CV+IJI 11 1770.45 1792.82 5.25 0.01 115 LLI*ENN_CV+FT+FD+PARA_MN+IJI 11 1770.49 1792.86 5.29 0.01 109 FT*PARA_MN +LLI+FD+ENN_CV+IJI 11 1770.66 1793.03 5.46 0.01 108 LLI*PARA_MN+FT+FD+ENN_CV+IJI 11 1770.69 1793.07 5.49 0.01 117 FD*ENN_CV+LLI+FT+PARA_MN+IJI 11 1770.77 1793.14 5.56 0.01 116 FT*ENN_CV+LLI+FD+PARA_MN+IJI 11 1771.01 1793.38 5.80 0.01 107 LLI*FT+LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 13 1766.87 1793.38 5.81 0.01 ! 123 FT*IJI +LLI+FD+PARA_MN+ENN_CV 11 1771.07 1793.44 5.86 0.01 ! 124 FD*IJI+LLI+FT+PARA_MN+ENN_CV 11 1771.12 1793.50 5.92 0.01 ! 122 LLI*IJI+FT+FD+PARA_MN+ENN_CV 11 1771.24 1793.61 6.04 0.01 ! Social bee abundance 58 LLI+FT+FD+ENN_CV 8 1847.00 1863.21 0.00 0.17 62 LLI+FT+FD+ENN_CV+IJI 9 1845.75 1864.00 0.80 0.12 60 LLI+FT+FD+PARA_MN+ENN_CV 9 1847.00 1865.25 2.05 0.06 115 LLI*ENN_CV+FT+FD+PARA_MN+IJI 11 1843.54 1865.92 2.71 0.04 109 FT*PARA_MN+LLI+FD+ENN_CV+IJI 11 1843.59 1865.97 2.76 0.04 63 LLI+FT+FD+PARA_MN+ENN_CV+IJI 10 1845.73 1866.04 2.84 0.04 118 LLI*ENN_CV+FT*ENN_CV+FD+PARA_MN+IJI 12 1841.71 1866.15 2.95 0.04 123 FT*IJI +LLI+FD+PARA_MN+ENN_CV 11 1844.11 1866.48 3.28 0.03 116 FT*ENN_CV +LLI+FD+PARA_MN+IJI 11 1844.35 1866.72 3.52 0.03 102 LLI*FD+FT+PARA_MN+ENN_CV+IJI 11 1844.40 1866.77 3.56 0.03 117 FD*ENN_CV+LLI+FT+PARA_MN+IJI 11 1844.59 1866.96 3.76 0.03 Kennedy et al. Modeling local and landscape effects on pollinators Page 4 of 14

110 FD*PARA_MN+LLI+FT+ENN_CV+IJI 11 1844.86 1867.23 4.02 0.02 103 FT*FD+LLI+PARA_MN+ENN_CV+IJI 11 1845.14 1867.52 4.31 0.02 122 LLI*IJI+FT+FD+PARA_MN+ENN_CV 11 1845.16 1867.53 4.33 0.02 125 LLI*IJI+FT*IJI+FD+PARA_MN+ENN_CV 12 1843.35 1867.79 4.58 0.02 108 LLI*PARA_MN+FT+FD+ENN_CV+IJI 11 1845.43 1867.80 4.60 0.02 113 FT*PARA_MN+FD*PARA_MN+LLI+ENN_CV+IJI 12 1843.39 1867.83 4.63 0.02 119 LLI*ENN_CV+FD*ENN_CV+FT+PARA_MN+IJI 12 1843.42 1867.86 4.65 0.02 127 FT*IJI+FD*IJI+LLI+PARA_MN+ENN_CV 12 1843.42 1867.86 4.65 0.02 120 FT*ENN_CV+FD*ENN_CV+LLI+PARA_MN+IJI 12 1843.46 1867.90 4.70 0.02 111 LLI*PARA_MN+FT*PARA_MN+FD+ENN_CV+IJI 12 1843.53 1867.97 4.77 0.02 101 LLI*FT+FD+PARA_MN+ENN_CV+IJI 11 1845.66 1868.03 4.83 0.02 124 FD*IJI+LLI+FT+PARA_MN+ENN_CV 11 1845.73 1868.10 4.90 0.02 121 LLI*ENN_CV+FT*ENN_CV+FD*ENN_CV+PARA_MN+IJI 13 1841.71 1868.22 5.01 0.01 106 LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 12 1843.90 1868.34 5.13 0.01 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 1842.01 1868.53 5.32 0.01 104 LLI*FT+LLI*FD+PARA_MN+ENN_CV+IJI 12 1844.39 1868.83 5.62 0.01 112 LLI*PARA_MN+FD*PARA_MN+FT+ENN_CV+IJI 12 1844.69 1869.13 5.92 0.01 105 LLI*FT+FT*FD+PARA_MN+ENN_CV+IJI 12 1845.12 1869.56 6.36 0.01 126 LLI*IJI+FD*IJI+FT+PARA_MN+ENN_CV 12 1845.14 1869.58 6.37 0.01 114 LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN+ENN_CV+IJI 13 1843.35 1869.86 6.66 0.01 7 LLI+FT+FD 7 1855.78 1869.94 6.73 0.01 ! 59 LLI+FT+FD+IJI 8 1853.85 1870.05 6.84 0.01 ! Solitary bee abundance 76 LLI*FT+FD 8 1758.60 1774.80 0.00 0.27 79 LLI*FT+LLI*FD 9 1757.98 1776.23 1.43 0.13 80 LLI*FT+FT*FD 9 1758.60 1776.85 2.05 0.10 6 FT+FD 6 1765.47 1777.58 2.78 0.07 66 FT*FD 7 1763.60 1777.76 2.96 0.06 82 LLI*FT+LLI*FD+FT*FD 10 1757.97 1778.28 3.48 0.05 36 FT+FD+PARA_MN 7 1764.71 1778.87 4.06 0.04 38 FT+FD+IJI 7 1765.25 1779.41 4.60 0.03 7 LLI+FT+FD 7 1765.36 1779.51 4.71 0.03 37 FT+FD+ENN_CV 7 1765.43 1779.59 4.78 0.02 78 FT*FD+LLI 8 1763.45 1779.65 4.85 0.02 101 LLI*FT+FD+PARA_MN+ENN_CV+IJI 11 1757.53 1779.91 5.10 0.02 77 LLI*FD+FT 8 1764.00 1780.21 5.40 0.02 81 LLI*FD+FT*FD 9 1762.27 1780.52 5.71 0.02 40 FT+FD+PARA_MN+IJI 8 1764.54 1780.74 5.93 0.01 57 LLI+FT+FD+PARA_MN 8 1764.59 1780.79 5.98 0.01 39 FT+FD+PARA_MN+ENN_CV 8 1764.65 1780.85 6.05 0.01 104 LLI*FT+LLI*FD+PARA_MN+ENN_CV+IJI 12 1756.89 1781.33 6.52 0.01 59 LLI+FT+FD+IJI 8 1765.14 1781.34 6.53 0.01 41 FT+FD+ENN_CV+IJI 8 1765.23 1781.43 6.63 0.01 58 LLI+FT+FD+ENN_CV 8 1765.32 1781.52 6.72 0.01 105 LLI*FT+FT*FD+PARA_MN+ENN_CV+IJI 12 1757.53 1781.97 7.17 0.01 ! 61 LLI+FT+FD+PARA_MN+IJI 9 1764.41 1782.66 7.86 0.01 ! 42 FT+FD+PARA_MN+ENN_CV+IJI 9 1764.50 1782.75 7.94 0.01 !

Kennedy et al. Modeling local and landscape effects on pollinators Page 5 of 14

Total bee richness 81 LLI*FD+FT*FD 9 969.46 987.72 0.00 0.34 82 LLI*FT+LLI*FD+FT*FD 10 969.02 989.34 1.62 0.15 79 LLI*FT+LLI*FD 9 971.11 989.37 1.65 0.15 77 LLI*FD+FT 8 973.74 989.95 2.23 0.11 106 LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 12 965.85 990.30 2.58 0.09 104 LLI*FT+LLI*FD+PARA_MN+ENN_CV+IJI 12 966.98 991.43 3.71 0.05 107 LLI*FT+LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 13 965.01 991.54 3.82 0.05 102 LLI*FD+FT+PARA_MN+ENN_CV+IJI 11 970.53 992.91 5.19 0.03 ! 64 LLI*FT 7 981.56 995.72 8.00 0.01 ! Social bee richness 77 LLI*FD+FT 8 845.44 861.65 0.00 0.16 81 LLI*FD+FT*FD 9 843.97 862.23 0.58 0.12 130 LLI*FD+FD*PARA_MN+FD*ENN_CV+FD*IJI+FT 14 833.72 862.33 0.68 0.11 102 LLI*FD+FT+PARA_MN+ENN_CV+IJI 11 840.23 862.61 0.96 0.10 106 LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 12 838.53 862.98 1.33 0.08 82 LLI*FT+LLI*FD+FT*FD 10 843.12 863.44 1.79 0.06 79 LLI*FT+LLI*FD 9 845.41 863.67 2.02 0.06 114 LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN+ENN_CV+IJI 13 837.26 863.79 2.14 0.05 112 LLI*PARA_MN+FD*PARA_MN+FT+ENN_CV+IJI 12 839.73 864.19 2.54 0.04 107 LLI*FT+LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 13 838.11 864.64 2.99 0.04 104 LLI*FT+LLI*FD+PARA_MN+ENN_CV+IJI 12 840.22 864.67 3.02 0.04 86 LLI*PARA_MN+FT*PARA_MN+ENN_CV+IJI 11 842.67 865.06 3.41 0.03 110 FD*PARA_MN+LLI+FT+ENN_CV+IJI 11 844.13 866.52 4.87 0.01 111 LLI*PARA_MN+FT*PARA_MN+FD+ENN_CV+IJI 12 842.64 867.09 5.44 0.01 113 FT*PARA_MN+FD*PARA_MN+LLI+ENN_CV+IJI 12 842.71 867.16 5.52 0.01 44 LLI+FT+ENN_CV 7 853.40 867.56 5.91 0.01 46 LLI+FT+PARA_MN+ENN_CV 8 851.69 867.90 6.25 0.01 4 LLI+FT 6 856.64 868.76 7.12 0.01 Solitary bee richness 76 LLI*FT+FD 8 1058.39 1074.60 0.00 0.24 79 LLI*FT+LLI*FD 9 1057.10 1075.36 0.76 0.17 80 LLI*FT+FT*FD 9 1057.57 1075.83 1.24 0.13 82 LLI*FT+LLI*FD+FT*FD 10 1056.24 1076.56 1.97 0.09 101 LLI*FT+FD+PARA_MN+ENN_CV+IJI 11 1055.32 1077.70 3.11 0.05 66 FT*FD 7 1063.86 1078.02 3.42 0.04 81 LLI*FD+FT*FD 9 1060.29 1078.55 3.96 0.03 104 LLI*FT+LLI*FD+PARA_MN+ENN_CV+IJI 12 1054.13 1078.58 3.99 0.03 78 FT*FD+LLI 8 1062.39 1078.59 4.00 0.03 129 LLI*FT+LLI*PARA_MN+LLI*ENN_CV+LLI*IJI+FD 14 1050.02 1078.63 4.03 0.03 105 LLI*FT+FT*FD+PARA_MN+ENN_CV+IJI 12 1054.57 1079.02 4.43 0.03 64 LLI*FT 7 1065.60 1079.76 5.16 0.02 107 LLI*FT+LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 13 1053.34 1079.86 5.27 0.02 6 FT+FD 6 1069.03 1081.15 6.55 0.01 77 LLI*FD+FT 8 1065.34 1081.54 6.95 0.01 37 FT+FD+ENN_CV 7 1067.80 1081.96 7.37 0.01 7 LLI+FT+FD 7 1067.83 1081.99 7.40 0.01

Kennedy et al. Modeling local and landscape effects on pollinators Page 6 of 14

Model # Model structure K Deviance AICc ∆AICc w

Bee abundance - Tropical and subtropical biomes 73 LLI*IJI 7 305.63 320.47 0.00 0.51 98 LLI*IJI+FT*IJI+PARA_MN+ENN_CV 11 299.82 323.86 3.39 0.09 125 LLI*IJI+FT*IJI+FD+PARA_MN+ENN_CV 12 297.58 324.00 3.53 0.09 122 LLI*IJI+FT+FD+PARA_MN+ENN_CV 11 300.55 324.58 4.11 0.07 95 LLI*IJI+PARA_MN+ENN_CV 9 305.47 324.84 4.37 0.06 126 LLI*IJI+FD*IJI+FT+PARA_MN+ENN_CV 12 299.46 325.88 5.41 0.03 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 297.16 326.00 5.53 0.03 99 LLI*IJI+FD*IJI+PARA_MN+ENN_CV 11 302.61 326.64 6.17 0.02 129 LLI*FT+LLI*PARA_MN+LLI*ENN_CV+LLI*IJI+FD 14 295.39 326.69 6.22 0.02 LLI*FT+LLI*FD+LLI*PARA_MN+FT*PARA_MN+LLI*ENN_CV 132 +FT*ENN_CV+LLI*IJI+FT*IJI 18 286.12 327.68 7.21 0.01 7 LLI+FT+FD 7 314.45 329.29 8.82 0.01 4 LLI+FT 6 316.89 329.51 9.04 0.01 !

Bee abundance - Mediterranean biome 110 FD*PARA_MN+LLI+FT+ENN_CV+IJI 11 401.94 426.00 0.00 0.18 113 FT*PARA_MN+FD*PARA_MN+LLI+ENN_CV+IJI 12 399.85 426.31 0.31 0.15 130 LLI*FD+FD*PARA_MN+FD*ENN_CV+FD*IJI+FT 14 396.16 427.52 1.52 0.08 112 LLI*PARA_MN+FD*PARA_MN+FT+ENN_CV+IJI 12 401.34 427.79 1.79 0.07 114 LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN+ENN_CV+IJI 13 399.26 428.15 2.15 0.06 78 FT*FD+LLI 8 411.47 428.57 2.57 0.05 87 LLI*PARA_MN+FD*PARA_MN+ENN_CV+IJI 11 404.68 428.74 2.74 0.04 126 LLI*IJI+FD*IJI+FT+PARA_MN+ENN_CV 12 402.99 429.45 3.44 0.03 109 FT*PARA_MN +LLI+FD+ENN_CV+IJI 11 405.42 429.48 3.48 0.03 99 LLI*IJI+FD*IJI+PARA_MN+ENN_CV 11 405.96 430.03 4.02 0.02 FT*FD+FT*PARA_MN+FD*PARA_MN+FT*ENN_CV+FD*ENN_ 131 CV+ FT*IJI+FD*IJI+LLI 17 391.56 430.58 4.58 0.02 80 LLI*FT+FT*FD 9 411.26 430.65 4.64 0.02 81 LLI*FD+FT*FD 9 411.34 430.72 4.72 0.02 5 LLI+FD 6 418.94 431.57 5.57 0.01 7 LLI+FT+FD 7 416.73 431.58 5.58 0.01 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 402.84 431.73 5.73 0.01 111 LLI*PARA_MN+FT*PARA_MN+FD+ENN_CV+IJI 12 405.38 431.83 5.83 0.01 44 LLI+FT+ENN_CV 7 417.15 432.00 5.99 0.01 59 LLI+FT+FD+IJI 8 414.93 432.03 6.03 0.01 45 LLI+FT+IJI 7 417.29 432.14 6.14 0.01 4 LLI+FT 6 419.52 432.15 6.15 0.01 52 LLI+FD+IJI 7 417.38 432.23 6.22 0.01 82 LLI*FT+LLI*FD+FT*FD 10 410.56 432.27 6.27 0.01 58 LLI+FT+FD+ENN_CV 8 415.24 432.34 6.34 0.01 103 FT*FD+LLI+PARA_MN+ENN_CV+IJI 11 408.36 432.42 6.42 0.01 LLI*FD+FT*FD+LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN +LLI*ENN_CV+FT*ENN_CV+FD*ENN_CV+LLI*IJI+FT*IJI+FD* 134 IJI 21 382.76 432.59 6.59 0.01 48 LLI+FT+ENN_CV+IJI 8 415.78 432.88 6.88 0.01

Kennedy et al. Modeling local and landscape effects on pollinators Page 7 of 14

Bee abundance - Other temperate biomes 100 FT*IJI+FD*IJI+PARA_MN+ENN_CV 11 968.41 991.03 0.00 0.37 127 FT*IJI+FD*IJI+LLI+PARA_MN+ENN_CV 12 967.96 992.70 1.67 0.16 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 967.96 994.81 3.78 0.06 74 FT*IJI 7 981.23 995.49 4.46 0.04 124 FD*IJI+LLI+FT+PARA_MN+ENN_CV 11 973.29 995.91 4.88 0.03 66 FT*FD 7 981.76 996.02 4.99 0.03 64 LLI*FT 7 982.61 996.87 5.84 0.02 78 FT*FD+LLI 8 980.62 996.95 5.92 0.02 FT*FD+FT*PARA_MN+FD*PARA_MN+FT*ENN_CV+FD*ENN_ 131 CV+ FT*IJI+FD*IJI+LLI 17 962.00 997.45 6.42 0.01 76 LLI*FT+FD 8 981.22 997.55 6.52 0.01 96 FT*IJI +PARA_MN+ENN_CV 9 979.19 997.61 6.58 0.01 126 LLI*IJI+FD*IJI+FT+PARA_MN+ENN_CV 12 973.25 997.98 6.95 0.01 LLI*FT+FT*FD+LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN +LLI*ENN_CV+FT*ENN_CV+FD*ENN_CV+LLI*IJI+FT*IJI+FD* 133 IJI 21 953.95 998.16 7.13 0.01 129 LLI*FT+LLI*PARA_MN+LLI*ENN_CV+LLI*IJI+FD 14 969.43 998.42 7.39 0.01 80 LLI*FT+FT*FD 9 980.26 998.68 7.65 0.01 123 FT*IJI +LLI+FD+PARA_MN+ENN_CV 11 976.20 998.82 7.79 0.01 81 LLI*FD+FT*FD 9 980.46 998.88 7.85 0.01 68 FT*PARA_MN 7 984.62 998.88 7.85 0.01 2 FT 5 988.91 999.05 8.02 0.01 22 FT+PARA_MN 6 986.88 999.07 8.04 0.01 98 LLI*IJI+FT*IJI+PARA_MN+ENN_CV 11 976.63 999.25 8.22 0.01 125 LLI*IJI+FT*IJI+FD+PARA_MN+ENN_CV 12 974.67 999.40 8.37 0.01 36 FT+FD+PARA_MN 7 985.20 999.46 8.43 0.01 79 LLI*FT+LLI*FD 9 981.19 999.61 8.58 0.01 6 FT+FD 6 987.45 999.64 8.61 0.01

Bee richness - Tropical and subtropical biomes 73 LLI*IJI 7 136.35 151.19 0.00 0.26 77 LLI*FD+FT 8 136.27 153.35 2.16 0.09 65 LLI*FD 7 138.84 153.67 2.49 0.08 95 LLI*IJI+PARA_MN+ENN_CV 9 134.93 154.29 3.10 0.06 67 LLI*PARA_MN 7 140.01 154.84 3.66 0.04 79 LLI*FT+LLI*FD 9 135.89 155.25 4.06 0.03 81 LLI*FD+FT*FD 9 136.21 155.57 4.39 0.03 16 LLI+ENN_CV 6 143.08 155.71 4.52 0.03 4 LLI+FT 6 143.32 155.95 4.76 0.02 1 LLI 5 145.53 155.98 4.79 0.02 44 LLI+FT+ENN_CV 7 141.16 156.00 4.81 0.02 112 LLI*PARA_MN+FD*PARA_MN+FT+ENN_CV+IJI 12 130.16 156.58 5.39 0.02 82 LLI*FT+LLI*FD+FT*FD 10 135.19 156.87 5.68 0.02 98 LLI*IJI+FT*IJI+PARA_MN+ENN_CV 11 133.14 157.17 5.99 0.01 5 LLI+FD 6 144.64 157.26 6.07 0.01 7 LLI+FT+FD 7 142.69 157.52 6.34 0.01 51 LLI+FD+ENN_CV 7 142.70 157.54 6.35 0.01 70 LLI*ENN_CV 7 142.90 157.74 6.55 0.01 122 LLI*IJI+FT+FD+PARA_MN+ENN_CV 11 133.75 157.78 6.59 0.01 15 LLI+PARA_MN 6 145.17 157.79 6.61 0.01 18 LLI+PARA_MN+ENN_CV 7 142.96 157.79 6.61 0.01 20 LLI+ENN_CV+IJI 7 143.08 157.91 6.72 0.01 Kennedy et al. Modeling local and landscape effects on pollinators Page 8 of 14

43 LLI+FT+PARA_MN 7 143.10 157.93 6.75 0.01 58 LLI+FT+FD+ENN_CV 8 140.92 158.00 6.81 0.01 45 LLI+FT+IJI 7 143.27 158.10 6.92 0.01 64 LLI*FT 7 143.28 158.12 6.93 0.01 17 LLI+IJI 6 145.52 158.14 6.96 0.01 86 LLI*PARA_MN+FT*PARA_MN+ENN_CV+IJI 11 134.14 158.17 6.98 0.01 46 LLI+FT+PARA_MN+ENN_CV 8 141.10 158.18 7.00 0.01 48 LLI+FT+ENN_CV+IJI 8 141.12 158.20 7.02 0.01 83 LLI*PARA_MN+ENN_CV+IJI 9 138.93 158.30 7.11 0.01 99 LLI*IJI+FD*IJI+PARA_MN+ENN_CV 11 134.49 158.53 7.34 0.01 108 LLI*PARA_MN+FT+FD+ENN_CV+IJI 11 134.62 158.65 7.46 0.01 87 LLI*PARA_MN+FD*PARA_MN+ENN_CV+IJI 11 134.73 158.76 7.57 0.01 114 LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN+ENN_CV+IJI 13 130.01 158.85 7.67 0.01 102 LLI*FD+FT+PARA_MN+ENN_CV+IJI 11 134.82 158.86 7.67 0.01 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 130.15 159.00 7.81 0.01 !

Bee richness - Mediterranean biome 126 LLI*IJI+FD*IJI+FT+PARA_MN+ENN_CV 12 151.98 178.44 0.00 0.26 130 LLI*FD+FD*PARA_MN+FD*ENN_CV+FD*IJI+FT 14 148.34 179.70 1.26 0.14 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 151.85 180.74 2.31 0.08 110 FD*PARA_MN+LLI+FT+ENN_CV+IJI 11 156.89 180.96 2.52 0.07 78 FT*FD+LLI 8 164.70 181.80 3.37 0.05 112 LLI*PARA_MN+FD*PARA_MN+FT+ENN_CV+IJI 12 156.47 182.92 4.49 0.03 81 LLI*FD+FT*FD 9 163.69 183.08 4.64 0.03 99 LLI*IJI+FD*IJI+PARA_MN+ENN_CV 11 159.07 183.13 4.69 0.03 113 FT*PARA_MN+FD*PARA_MN+LLI+ENN_CV+IJI 12 156.75 183.21 4.77 0.02 82 LLI*FT+LLI*FD+FT*FD 10 161.54 183.25 4.81 0.02 FT*FD+FT*PARA_MN+FD*PARA_MN+FT*ENN_CV+FD*ENN_ 131 CV+ FT*IJI+FD*IJI+LLI 17 144.73 183.74 5.31 0.02 4 LLI+FT 6 171.15 183.78 5.35 0.02 80 LLI*FT+FT*FD 9 164.43 183.81 5.37 0.02 45 LLI+FT+IJI 7 169.04 183.89 5.46 0.02 98 LLI*IJI+FT*IJI+PARA_MN+ENN_CV 11 159.95 184.02 5.58 0.02 7 LLI+FT+FD 7 169.84 184.69 6.25 0.01 124 FD*IJI+LLI+FT+PARA_MN+ENN_CV 11 160.90 184.96 6.52 0.01 59 LLI+FT+FD+IJI 8 168.07 185.17 6.73 0.01 114 LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN+ENN_CV+IJI 13 156.32 185.21 6.77 0.01 43 LLI+FT+PARA_MN 7 170.54 185.39 6.96 0.01 77 LLI*FD+FT 8 168.64 185.74 7.30 0.01 125 LLI*IJI+FT*IJI+FD+PARA_MN+ENN_CV 12 159.28 185.74 7.30 0.01 64 LLI*FT 7 170.97 185.82 7.38 0.01 LLI*FD+FT*FD+LLI*PARA_MN+FT*PARA_MN+FD*PARA_MN +LLI*ENN_CV+FT*ENN_CV+FD*ENN_CV+LLI*IJI+FT*IJI+FD* 134 IJI 21 135.99 185.82 7.38 0.01 47 LLI+FT+PARA_MN+IJI 8 168.75 185.85 7.42 0.01 48 LLI+FT+ENN_CV+IJI 8 168.84 185.94 7.51 0.01 44 LLI+FT+ENN_CV 7 171.14 185.99 7.56 0.01 103 FT*FD+LLI+PARA_MN+ENN_CV+IJI 11 162.08 186.15 7.71 0.01 ! 107 LLI*FT+LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 13 157.41 186.30 7.86 0.01 ! 79 LLI*FT+LLI*FD 9 166.97 186.35 7.91 0.01 !

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Bee richness - Other temperate biomes 74 FT*IJI 7 615.63 629.90 0.00 0.46 96 FT*IJI +PARA_MN+ENN_CV 9 614.22 632.66 2.76 0.12 100 FT*IJI+FD*IJI+PARA_MN+ENN_CV 11 611.10 633.75 3.85 0.07 66 FT*FD 7 619.85 634.12 4.22 0.06 127 FT*IJI+FD*IJI+LLI+PARA_MN+ENN_CV 12 610.86 635.63 5.73 0.03 68 FT*PARA_MN 7 621.43 635.70 5.81 0.03 78 FT*FD+LLI 8 619.47 635.82 5.93 0.02 123 FT*IJI +LLI+FD+PARA_MN+ENN_CV 11 613.38 636.02 6.13 0.02 98 LLI*IJI+FT*IJI+PARA_MN+ENN_CV 11 613.84 636.49 6.59 0.02 81 LLI*FD+FT*FD 9 618.13 636.57 6.67 0.02 84 FT*PARA_MN +ENN_CV+IJI 9 618.58 637.02 7.12 0.01 64 LLI*FT 7 622.78 637.05 7.16 0.01 128 LLI*IJI+FT*IJI+FD*IJI+PARA_MN+ENN_CV 13 610.68 637.57 7.68 0.01 80 LLI*FT+FT*FD 9 619.36 637.80 7.91 0.01 76 LLI*FT+FD 8 621.54 637.89 8.00 0.01 86 LLI*PARA_MN+FT*PARA_MN+ENN_CV+IJI 11 615.28 637.93 8.03 0.01 79 LLI*FT+LLI*FD 9 619.56 638.00 8.10 0.01 125 LLI*IJI+FT*IJI+FD+PARA_MN+ENN_CV 12 613.34 638.10 8.21 0.01 103 FT*FD+LLI+PARA_MN+ENN_CV+IJI 11 615.71 638.35 8.46 0.01 82 LLI*FT+LLI*FD+FT*FD 10 617.94 638.48 8.58 0.01 106 LLI*FD+FT*FD+PARA_MN+ENN_CV+IJI 12 613.96 638.73 8.83 0.01 !

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Figure S7_1. Response to landscape composition (Lonsdorf landscape index, LLI) of total, social and solitary wild bee abundance and richness on organic, locally diverse fields versus conventional, locally simple fields. Estimates are based on model-averaged partial regression coefficients (and unconditional 95% CIs) for all studies (N = 39) for important main effects (E (abundance, richness) = ƒ

(LLI + FT + FD)) (see also Table 2). Organic, locally diverse: black circles and dashed line (CIs outlined by dashed line with light grey shading); Conventional, locally simple: triangles and grey solid line (CIs with dark grey shading). Note that y-axis scales vary by bee response measures; relationships between LLI = 0 up to 0.60 are graphed (even though LLI = 1.0 is the theoretical maximum) because 0.61 was the maximum score derived for empirical study landscapes.

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Figure S7_2. Percent change in wild bee abundance and richness per 0.1 incremental increase in the Lonsdorf landscape index (LLI) in relation to (a) field-scale diversity, FD (locally simple vs. locally diverse) and (b) field type, FT (conventional vs. organic) and (c) percent change in bee abundance and richness on locally simple and diverse fields on organic relative to conventional fields. Estimates based on model-averaged partial regression coefficients (and unconditional

95% CIs) for important main effects plus each individual target interaction (E(abundance, richness) = ƒ (LLI + FT + FD) + (LLI:FD or LLI:FT or FT:FD, respectively); * denotes two-way interaction with unconditional 95% CIs around model-averaged partial slope coefficient that did not include 0 (asymmetric CIs due to exponential relationship) (see Table 2).

(a)

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(b)

(c)

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Figure S7_3. Percent change in wild bee abundance in tropical and subtropical studies (N = 10) per 0.1 increase in the Lonsdorf landscape index (LLI) in relation to landscape configuration

(interspersion & juxtaposition index, IJI). Across studies, IJI ranged from 0 to 95.91 (mean =

63.33) (theoretical IJI range: 0-100) (Table S7_1). Estimates based on model-averaged partial regression coefficients (and unconditional 90% CIs) for important main effects plus target interaction (E(abundance) = ƒ (LLI + IJI+ LLI:IJI). 90% CIs around model-averaged partial slope coefficient did not include 0 (asymmetric CIs due to exponential relationship) (see Table

3). Significant interaction between LLI:IJI indicates that maximum bee abundance is achieved with high LLI and IJI values, and effect of LLI is greater with increasing IJI values.

350%

300%

250% Tropical 0

200%

150%

per"0.1"Increase"in"LLI 100%

%"Change"in"Bee"Abundance 50%

0% IJI)=)0 IJI)=)10 IJI)=)50