Predicting bee community responses to land-use changes : Effects of geographic and taxonomic biases

TLDR: Analysis of a global dataset of bee diversity at sites facing land-use change and intensification suggests that global extrapolation of models based on geographically and taxonomic restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.

Abstract: Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.

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1
Scientific RepoRts | 6:31153 | DOI: 10.1038/srep31153
www.nature.com/scientificreports
Predicting bee community
responses to land-use changes:
Eects of geographic and
taxonomic biases
Adriana De Palma
1,2
, Stefan Abrahamczyk
3
, Marcelo A. Aizen
4
, Matthias Albrecht
5
,
Yves Basset
6
, Adam Bates
7
, Robin J. Blake
8
, Céline Boutin
9
, Rob Bugter
10
, Stuart Connop
11
,
Leopoldo Cruz-López
12
, Saul A. Cunningham
13
, Ben Darvill
14
, Tim Diekötter
15,16,17
,
Silvia Dorn
18
, Nicola Downing
19
, Martin H. Entling
20
, Nina Farwig
21
, Antonio Felicioli
22
,
Steven J. Fonte
23
, Robert Fowler
24
, Markus Franzén
25
, Dave Goulson
24
, Ingo Grass
26
,
Mick E. Hanley
27
, Stephen D. Hendrix
28
, Farina Herrmann
26
, Felix Herzog
29
,
Andrea Holzschuh
30
, Birgit Jauker
31
, Michael Kessler
32
, M. E. Knight
27
, Andreas Kruess
33
,
Patrick Lavelle
34,35
, Violette Le Féon
36
, Pia Lentini
37
, Louise A. Malone
38
, Jon Marshall
39
,
Eliana Martínez Pachón
40
, Quinn S. McFrederick
41
, Carolina L. Morales
4
, Sonja Mudri-Stojnic
42
,
Guiomar Nates-Parra
40
, Sven G. Nilsson
43
, Erik Öckinger
44
, Lynne Osgathorpe
45
,
Alejandro Parra-H
46,47
, Carlos A. Peres
48
, Anna S. Persson
43
, Theodora Petanidou
49
, Katja Poveda
50
,
Eileen F. Power
51
, Marino Quaranta
52
, Carolina Quintero
4
, Romina Rader
53
, Miriam H. Richards
54
,
T’ai Roulston
55,56
, Laurent Rousseau
57
, Jonathan P. Sadler
58
, Ulrika Samnegård
59
,
Nancy A. Schellhorn
60
, Christof Schüepp
61
, Oliver Schweiger
25
, Allan H. Smith-Pardo
62,63
,
Ingolf Stean-Dewenter
30
, Jane C. Stout
51
, Rebecca K. Tonietto
64,65,66
, Teja Tscharntke
26
,
Jason M. Tylianakis
1,67
, Hans A. F. Verboven
68
, Carlos H. Vergara
69
, Jort Verhulst
70
, Catrin Westphal
26
,
Hyung Joo Yoon
71
& Andy Purvis
1,2
Land-use change and intensication threaten bee populations worldwide, imperilling pollination
services. Global models are needed to better characterise, project, and mitigate bees' responses
to these human impacts. The available data are, however, geographically and taxonomically
unrepresentative; most data are from North America and Western Europe, overrepresenting
bumblebees and raising concerns that model results may not be generalizable to other regions and
taxa. To assess whether the geographic and taxonomic biases of data could undermine eectiveness of
models for conservation policy, we have collated from the published literature a global dataset of bee
diversity at sites facing land-use change and intensication, and assess whether bee responses to these
pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central
and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and
between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total
abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity
of species and potentially in the nature of threats. These results suggest that global extrapolation
of models based on geographically and taxonomically restricted data may underestimate the true
uncertainty, increasing the risk of ecological surprises.
OPEN
1
Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Rd, Ascot, Berkshire SL5
7PY, UK.
2
Department of Life Sciences, Natural History Museum, Cromwell Road, London SW7 5BD, UK.
3
Nees
Institute for Plant Biodiversity, University of Bonn, Meckenheimer Allee 170, 53115 Bonn, Germany.
4
Laboratorio
Ecotono, INIBIOMA (CONICET - Universidad Nacional del Comahue), Quintral 1250, 8400 Bariloche, Río Negro,
Argentina.
5
Institute for Sustainability Sciences, Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland.
6
Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancon, Panama City, Republic of Panama.
Received: 29 February 2016
Accepted: 13 July 2016
Published: 11 August 2016

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Scientific RepoRts | 6:31153 | DOI: 10.1038/srep31153
Bees are one of the most important groups of pollinators of economic crops
1–3
, with both larvae and adults rely-
ing on oral products such as pollen and nectar
3
. Human impacts can reduce the diversity of pollinator assem-
blages
4,5
and therefore can impact pollination eciency and provision. is is a particular concern in agricultural
settings, as over 35% of the volume of human food crops produced globally depend upon animal pollination to
some extent
6
. Pollinator shortages can lead to reduced crop quality and yield
7,8
, with potentially large economic
7
Biosciences, Nottingham Trent University, Nottingham, NG11 8NS, UK.
8
Centre for Agri-Environmental Research,
School of Agriculture, Policy and Development, University of Reading, Earley Gate, Reading, RG6 6AR, UK.
9
Science
& Technology Branch, Environment and Climate Change Canada, 1125 Colonel By Drive, Carleton University, Ottawa,
Ontario K1A 0H3, Canada.
10
Alterra, Part of Wageningen University and Research, P.O. Box 47, 6700 AA
WageningenI, Netherlands.
11
Sustainability Research Institute, University of East London, 4-6 University Way,
Docklands, London E16 2RD, UK.
12
Grupo de Ecología y Manejo de Artrópodos, El Colegio de la Frontera Sur
(ECOSUR), Carretera Antiguo Aeropuerto km 2.5. Tapachula, 30700 Chiapas, Mexico.
13
CSIRO Land and Water,
Canberra, ACT 2601, Australia.
14
British Trust for Ornithology (Scotland), Biological and Environmental Sciences,
University of Stirling, FK9 4LA, UK.
15
Department of Landscape Ecology, Institute for Natural Resource Conservation,
Kiel University, Olshausenstrasse 75, 24118 Kiel, Germany.
16
Department of Biology, Nature Conservation, University
Marburg, Marburg, Germany.
17
Institute of Integrative Biology, ETH Zurich, Switzerland.
18
Applied Entomology, ETH
Zurich, Schmelzbergstr. 7/LFO, 8092 Zurich, Switzerland.
19
RSPB, Scottish Headquarters 2 Lochside View, Edinburgh
Park, Edinburgh, EH12 9DH, UK.
20
Institute for Environmental Sciences, University of Koblenz-Landau, Fortstr. 7,
76829 Landau, Germany.
21
Conservation Ecology, Faculty of Biology, Philipps-Universität Marburg, Karl-von-Frisch-
Str. 8, 35032 Marburg, Germany.
22
Dipartimento di Scienze Veterinarie, Viale delle Piagge 2, 56100, Pisa, Universitá
di Pisa, Italia.
23
Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA.
24
School of Life Sciences, University of Sussex, BN19QG, UK.
25
Helmholtz Centre for Environmental Research - UFZ,
Department of Community Ecology, Theodor-Lieser-Straβ e 4, 06120 Halle, Germany.
26
Agroecology, Department of
Crop Sciences, Georg-August-University Göttingen, D-37077 Göttingen, Germany.
27
School of Biological Sciences,
Plymouth University, Plymouth PL4 8AA, UK.
28
Department of Biology, University of Iowa, Iowa, USA.
29
Agroscope,
Institut for Sustainability Sciences, CH-8046 Zurich, Switzerland.
30
Department of Animal Ecology and Tropical
Biology, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
31
Justus-Liebig University,
Department of Animal Ecology, Heinrich-Bu-Ring 26-32, 35392 Giessen, Germany.
32
Institut für Systematische und
Evolutionäre Botanik, Switzerland.
33
Dept. for Ecology and Conservation of Fauna and Flora, Federal Agency for
Nature Conservation (Bundesamt für Naturschutz, BfN), Konstantinstrasse 110, D-53179 Bonn, Germany.
34
Institut
de Recherche pour le Développement (IRD), 93143 Bondy Cedex, France.
35
Centro Internacional de Agricultura
Tropical (CIAT), Tropical Soil Biology and Fertility Program, Latin American and Caribbean Region, Cali, Colombia.
36
INRA, UR 406 Abeilles et Environnement, CS 40509, F-84914 Avignon, France.
37
School of BioSciences, University
of Melbourne, Parkville VIC 3010, Australia.
38
New Zealand Institute for Plant and Food Research Ltd, Private Bag
92169, Auckland Mail Centre, Auckland 1142, New Zealand.
39
Marshall Agroecology Ltd, 2 Nut Tree Cottages,
Barton, Winscombe BS25 1DU, UK.
40
Departamento de Biología, Facultad de Ciencias, Universidad Nacional de
Colombia, Sede Bogotá, Colombia.
41
University of California, Riverside Department of Entomology, 900 University
Avenue, Riverside, CA 92521, USA.
42
Department of Biology and Ecology, Faculty of Science, University of Novi Sad,
21000 Novi Sad, Serbia.
43
Department of Biology, Lund University, SE-223 62 Lund, Sweden.
44
Swedish University of
Agricultural Sciences, Department of Ecology, Box 7044, SE-750 07 Uppsala, Sweden.
45
RSPB, UK Headquarters The
Lodge, Sandy, Bedfordshire, UK.
46
Laboratorio de Investigaciones en Abejas, LABUN, Departamento de Biología,
Facultad de Ciencias, Universidad Nacional de Colombia, Carrera 45 No. 26-85, Edif. Uriel Gutiérrez, Bogotá D.C.,
Colombia.
47
Corporación para la Gestión de Servicios Ecosistémicos, Polinización y Abejas - SEPyA, Bogotá D.C.,
Colombia.
48
School of Environmental Sciences, University of East Anglia, Norwich NR47TJ, UK.
49
Laboratory of
Biogeography & Ecology, Department of Geography, University of the Aegean, 81100 Mytilene, Greece.
50
Entomology Department, Cornell University, Ithaca, NY 14850, USA.
51
Botany, School of Natural Sciences, Trinity
College Dublin, Dublin 2, Ireland.
52
CREA-ABP, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria,
Centro di ricerca per l’agrobiologia e la pedologia, Via di Lanciola 12/A, I-50125 - Cascine del Riccio, Firenze, Italy.
53
School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia.
54
Department of Biological Sciences, Brock University, St. Catharines, Ontario, L2S 3A1, Canada.
55
Department of
Environmental Sciences, University of Virginia, Charlottesville, Virginia 22904-4123, USA.
56
Blandy Experimental
Farm, 400 Blandy Farm Lane, Boyce, Virginia 22620, USA.
57
Département des Sciences Biologiques, Université du
Québec à Montreál, C.P. 8888, succursale Centre-ville, Montreál, Québec H3C 3P8, Canada.
58
GEES (School of
Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
59
Department
of Ecology, Environment and Plant Sciences, Stockholm University, SE-106 91 Stockholm, Sweden.
60
CSIRO, Dutton
Park, QLD 4102, Australia.
61
University of Bern, Institute of Ecology and Evolution, Community Ecology,
Baltzerstrasse 6, 3012 Bern, Switzerland.
62
Animal and Plant Health Inspection Service, Plant Protection and
Quarantine, United States Department of Agriculture (USDA), South San Francisco, CA 94080, USA.
63
Faculty of
Sciences, National University of Colombia, Medellín (UNALMED), Columbia.
64
Plant Biology and Conservation,
Northwestern University, 2205 Tech Drive, O.T. Hogan Hall Rm 2-1444, Evanston, IL 60208, USA.
65
Chicago Botanic
Garden, 1000 Lake Cook Rd, Glencoe, IL 60011, USA.
66
Department of Biology, Saint Louis University, 3507 Laclede
Avenue, Macelwane Hall, St. Louis, MO 63103-2010, USA.
67
Centre for Integrative Ecology, School of Biological
Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
68
Division Forest, Nature,
and Landscape, Department of Earth & Environmental Sciences, KU Leuven, Celestijnenlaan 200E, B-3001 Leuven,
Belgium.
69
Departamento de Ciencias Químico-Biológicas, Universidad de las Américas Puebla, Mexico.
70
Spotvogellaan 68, 2566 PN, Den Haag, The Netherlands.
71
Department of Agricultural Biology, National Institute
of Agricultural Science, RDA, Wanju-gun, Jellabuk-do, 55365, Korea. Correspondence and requests for materials
should be addressed to A.D.P. (email: adrid@nhm.ac.uk)

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Scientific RepoRts | 6:31153 | DOI: 10.1038/srep31153
impacts
9
. ere has therefore been much research into responses of bee communities to human impacts such as
land-use change and intensication.
A number of syntheses have attempted to identify general trends in the response of bees to human impacts
5,10
.
However, their datasets have oen been geographically limited, with the majority of data arising from North
America and Western Europe
11
. e geographic patterns of bee decline and diversity are not understood suf-
ciently well to ensure that such generalisations are valid
11,12
. If species’ responses to disturbance vary among
regions, geographically-restricted models will be inadequate to support broad conclusions. e consequences
of basing management strategies on extrapolations from such models could be severe, as many under-studied
regions have a high economic dependency upon animal-pollinated crops
11,13
and may generally have limited
governmental capacity to adapt to environmental changes
14
.
Geographic variation in bee community responses could arise because dierences in land-use history and
practices mean that the threats facing assemblages dier across regions. Species subject to very recent disturbance
may be more vulnerable, whereas extinction lters
15–17
may have already removed many susceptible species from
landscapes where the intensication of farming started already decades ago, such as in temperate European agri-
cultural landscapes. Extinction debt may make matters worse still, if the full impact of land-use changes is not
yet evident
18,19
. In addition, dierences in landscape context across regions can inuence species’ responses. For
instance, Winfree et al.
5
found that habitat loss and fragmentation signicantly aected bee communities, but
only in areas where little natural habitat still remained.
Bee community responses may also vary regionally because community composition varies geographically.
Taxa can dier in their intrinsic susceptibility to land-use change and intensication, through having dierent
functional response traits
20–22
, the distribution of which within a community can aect resilience to pressures
23
. A
geographic bias towards North America and Western Europe has also resulted in a taxonomic bias; for instance,
bumblebees (Apidae: Bombus) are particularly diverse in these areas, whereas large areas of the world have no
native bumblebee species (e.g., most of Africa and Australasia). In addition, bumblebees are large, oen abundant
species with long ight seasons and relatively slow ight, making them fairly easy to sample and, in many cases,
to identify. Bumblebees may be more or less sensitive than other bees due to their ecological traits and habitat
requirements
24
, which have been shown to inuence responses to human impacts and vulnerability to decline
25,26
.
In addition, bumblebees have shown clearer declines than other bees in North America
25
and some European
countries
27
, so they may be atypical of broader bee diversity.
We compiled a global dataset of bee diversity from published sources of bee assemblages in sites diering in
pressures such as land use, and used this to explore whether models of responses to human impacts are robust
against geographic and taxonomic biases. Specically, we hypothesized that bee responses to land-use pressures
should vary signicantly with region and with taxonomic group (i.e., bumblebees or other bees) and so models
and projections will not be transferable across regions and taxa. Improved understanding in this area will help
to clarify whether knowledge based on a few regions and taxa is sucient to underpin policy decisions as well as
highlight systems for future study.
Methods
Data Collation. Data were sought from the literature where bee species abundance and/or occurrence were
reported for multiple sites. Suitable papers were identied by searching Web of Science at various times from
2011 to 2015, as well as searching journal alerts and assessing references cited in reviews. Papers were further
considered if more than one site was sampled for bee diversity using the same sampling method in the same
season and geographic coordinates of each site were available. Papers were prioritised if their data were col-
lected from February 2000 onwards, so that biodiversity data could be matched with remote-sensed data from
NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Data were supplemented with sources found
through the PREDICTS project (www.predicts.org.uk), which aims to develop global statistical models of how
local biodiversity responds to human impacts
28
. e database presented here is not a comprehensive compilation
of published sources on occurrence and abundance of bee species across sites diering in land use or intensity,
because of regional dierences in the ability to retrieve information about potential sources and because most
researchers we contacted did not make their data available. e dataset will, however, still be useful for research-
ers wishing to study land-use impacts on this important taxonomic group.
Where possible we extracted site-level records of bee species (Hymenoptera: Apoidea) occurrence and abun-
dance from suitable papers, along with data for other taxonomic groups if available. Raw data were usually not
included within the papers or supplementary les, so the papers’ corresponding authors were asked for these data.
Relevant data were available from 69 papers, hereaer referred to as ‘sources’ (Table1). Each source contains one
or more studies, where a study is dened as the set of samples within the same country that were taken using the
same methodology. By dening studies in this way, we reduce the impact of broad-scale biogeographic dierences
in diversity and avoid the confounding eects of methodological dierences: within, but not between, studies,
diversity data can be compared among sites in a straightforward fashion. Dierences in sampling eort within
a study were corrected for when necessary by dividing abundance by the sampling eort unit. is assumes
a linear relationship between abundance and sampling eort; generalised additive models suggested that this
assumption was appropriate (gamm4 package
29
, see Supplementary Data S1 for details). Within each study, we
recorded any blocked or split-plot design. e major land-use class and use intensity at each site were assessed
based on information in the associated paper, using the scheme described in Hudson et al.
28
(reproduced in
Supplementary Table S1). Briey, land use was classied as primary vegetation (native vegetation not known to
have ever been completely destroyed), secondary vegetation (where the primary vegetation has been completely
destroyed; this can include naturally recovering, actively restored, or semi-natural sites), cropland (planted with
herbaceous crops), plantation forest (planted with crop trees or shrubs), pasture (regularly or permanently grazed
by livestock) or urban (areas with human habitation, where vegetation is predominantly managed for civic or

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Scientific RepoRts | 6:31153 | DOI: 10.1038/srep31153
Reference Country
Sampling
years Studies
Within-
study sites
Bee taxa
(% binomial)
Other
taxa mMLE
Afrotropic 3 39 77 2304
Basset et al.
67
+†
Gabon 2001–2002 1 12 51 (19.61%) 1806 70
Gaigher & Samways
68
+†
South Africa 2006 1 10 6 (0%) 383 nr
Grass et al.
69
+†‡
South Africa 2011 1 17 21 (9.52%) 115 100
Australasia 8 200 135 497
Blanche et al.
70
+†
Australia 2005 2 11 8 (89.36%) 17 nr
Cunningham et al.
71
+†
Australia 2007–2008 1 24 69 (100%) 0 nr
Lentini et al.
72
+†
Australia 2009–2010 1 104 36 (100%) 0 nr
Kessler et al.
73
+†
Indonesia 2004–2005 1 15 9 (0%) 24 nr
Malone et al.
74
†‡
New Zealand 2006–2007 1 2 9 (100%) 0 nr
Todd et al.
75
+†
New Zealand 2007–2008 1 20 9 (100%) 442 27.3
Rader et al.
21
+†
New Zealand 2008–2009 1 24 5 (100%) 20 nr
Indo-Malay 4 16 1 0
Liow et al.
76
+†‡
Singapore, Malaysia 1999 4 16 1 (0%) 0 3000
Nearctic 16 399 242 117
Boutin et al.
77
+†
Canada 2000 3 60 3 (0%) 116 nr
Richards et al.
78
+†
Canada 2003 3 18 127 (95.04%) 0 nr
Hateld & Lebuhn
79
†
United States 2002–2003 1 120 13 (100%) 0 nr
McFrederick & LeBuhn
80
†‡
United States 2003–2004 2 40 5 (100%) 0 nr
Shuler et al.
81
+†
United States 2003 1 25 5 (60%) 0 nr
Winfree et al.
82
+†
United States 2003 2 80 1 (0%) 0 nr
Kwaiser & Hendrix
83
+
United States 2004 2 18 53 (97.22%) 1 nr
Julier & Roulston
84
+†
United States 2006 1 20 3 (100%) 0 250
Tonietto et al.
85
+†
United States 2006 1 18 67 (89.55%) 0 nr
Neotropic 16 286 436 775
Vázquez & Simberlo
86
+
Argentina 1999, 2001 1 8 25 (52%) 104 nr
Quintero et al.
87
†
Argentina 2000–2001 1 4 14 (35.71%) 38 1280
Schüepp et al.
88
+†
Belize 2009–2010 1 15 43 (100%) 65 nr
Tonhasca et al.
89
+†‡
Brazil 1997, 1999 1 9 21 (100%) 0 10
Barlow et al.
90
+†
Brazil 2005 1 3 22 (75%) 0 3500
Smith-Pardo & Gonzalez
91
+†
Colombia 1997 4 48 300 (46.2%) 0 nr
Parra-H & Nates-Parra
92
+†
Colombia 2003 1 26 21 (100%) 0 nr
Poveda et al.
93
+†
Colombia 2006–2007 2 34 4 (0%) 468 23
Tylianakis et al.
94
+†
Ecuador 2003–2004 1 48 16 (0%) 16 71
Vergara & Badano
64
+†
Mexico 2004 1 16 7 (71.43%) 8 nr
Fierro et al.
95
†‡
Mexico 2009–2010 1 3 4 (100%) 0 346.41
Rousseau et al.
96
+†
Nicaragua 2011 1 72 2 (100%) 81 30
Palearctic 64 2271 601 788
Verboven et al.
97
†
Belgium 2009 1 9 6 (66.67%) 0 11.34
Billeter et al.
98
+†
, Diekötter
et al.
99
+†
and Le Féon et al.
100
+†
Belgium, Czech Republic,
Estonia, France, Germany,
Netherlands, Switzerland
2001–2002 14 873 276 (98.46%) 7 nr
Kruess & Tscharntke
101
+
Germany 1996 2 34 17 (100%) 18 nr
Meyer et al.
102
+†
Germany 2000, 2005 2 30 14 (75%) 8 34.51
Diekötter et al.
103
†
Germany 2001 1 124 2 (100%) 0 353.55
Meyer et al.
104,105
+†
Germany 2004 1 32 109 (100%) 75 nr
Herrmann et al.
106
†‡
Germany 2005 2 26 1 (100%) 0 800
Holzschuh et al.
107
+
Germany 2007 2 134 3 (33.33%) 1 100
Weiner et al.
108
+
Germany 2007 1 29 59 (100%) 460 333
Nielsen et al.
109
+†‡
Greece 2004 4 32 1 (0%) 0 nr
Power & Stout
110
+†
Ireland 2009 1 20 9 (88.89%) 24 1200.24
Davis et al.
111
†‡
Ireland, United Kingdom
2005, 2007,
2008, 2009
1 12 1 (100%) 0 nr
Quaranta et al.
112
+†
Italy 2000 1 2 31 (100%) 0 200
Yoon et al.
113
Korea, Republic of 2000–2012 1 215 6 (100%) 1 nr
Kohler et al.
114
+†
Netherlands 2004–2005 4 19 26 (95.48%) 56 1500
Continued

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Scientific RepoRts | 6:31153 | DOI: 10.1038/srep31153
personal amenity). Use intensity was classied according to a three point scale: low, medium and high intensity.
For instance, high-intensity cropland would be monocultures with many signs of intensication such as large
elds with high levels of external inputs, irrigation and mechanisation; medium intensity cropland would only
show some, but not all, features of higher intensity cropland; low-intensity would refer to small elds with mixed
crops and little to no external inputs, irrigation or mechanisation. In one data source, information on the use
intensity was unavailable at the site-level, so information at the landscape level was used.
e dataset contained 111 studies from 69 sources and 3211 within-study sites (Table1). is amounted to
195,357 species diversity measurements (i.e., bee taxa and other taxa, Table1), including 107,176 measurements
of bee diversity (a single measurement being, for example, the abundance of a given species at a given site; see
Supplementary Data S2 for species list).
Analysis. For this analysis, we did not include studies that recorded only particular target species (for
instance, studies that were only interested in the abundance of a single species across sites), so that site-level
diversity measures would be meaningful. e nal dataset for the analysis included 101,524 diversity records
from 837 bee species at 2421 sites from across the globe (North America: 239 sites; Central America: 103; South
America: 176; Western Europe: 1211; Northern Europe: 325; Eastern Europe: 64; Southern Europe: 50; Middle
and Southern Africa: 39; South Eastern Asia: 31; Australia and New Zealand: 183). In this reduced dataset, many
combinations of land use and use intensity had too few sites to permit robust modelling. e data were there-
fore aggregated to give a variable of combined Land Use and Intensity (LUI) with the following levels: primary
vegetation, secondary vegetation, low-intensity cropland, medium-intensity cropland, high-intensity cropland,
pasture, plantation forest and urban. All LUI levels had at least 170 sites, except for plantation forest and urban
areas, which were scarce in the dataset with only 105 and 94 sites respectively. Sites were also classied by region
and subregion (according to United Nations classications), with Middle and Southern Africa combined into a
single category to increase the sample size.
For each site, we calculated three measures of bee community diversity as our response variables: total abun-
dance, within-sample species richness and Simpson’s diversity. Simpson’s diversity was calculated as:
Reference Country
Sampling
years Studies
Within-
study sites
Bee taxa
(% binomial)
Other
taxa mMLE
Goulson et al.
115
†
Poland 2006 1 32 22 (100%) 0 200
Mudri-Stojnic et al.
116
+†‡
Serbia 2011 1 16 55 (100%) 8 nr
Öckinger & Smith
117
Sweden 2004 1 36 11 (100%) 64 800
Franzén & Nilsson
118
+†
Sweden 2005 1 16 83 (100%) 43 nr
Samnegård et al.
119
+†
Sweden 2009 1 9 31 (100%) 0 90
Oertli et al.
120
+†
Switzerland 2001–2002 1 7 237 (100%) 0 2000
Albrecht et al.
121
+
Switzerland 2003–2004 2 202 75 (100%) 0 nr
Farwig et al.
122
+†
Switzerland 2008 1 30 1 (0%) 0 nr
Schüepp et al.
123
+†
Switzerland 2008 1 30 11 (72.73%) 69 0.2
Darvill et al.
124
†
United Kingdom 2001 1 17 3 (66.67%) 0 100
Marshall et al.
125
+†
United Kingdom 2003 2 84 25 (100%) 0 nr
Hanley (2005, unpublished
data)
†
United Kingdom 2004–2005 1 6 11 (100%) 0 1000
Knight et al.
126
†‡
United Kingdom 2004 1 12 1 (100%) 0 3.16
Connop et al.
127
†‡
United Kingdom 2005 1 5 2 (100%) 0 nr
Goulson et al.
128
†
United Kingdom 2007 1 14 2 (100%) 0 200.25
Hanley et al.
129
†
United Kingdom 2007–2010 1 34 6 (100%) 0 200.04
Blake et al.
130
†
United Kingdom 2008–2010 2 6 8 (75%) 2 90
Redpath et al.
131
†
United Kingdom 2008 1 11 7 (85.71%) 0 nr
Bates et al.
132
+†
United Kingdom 2009–2010 1 24 58 (100%) 50 56.6
Osgathorpe et al.
133
†
United Kingdom 2009–2010 2 45 11 (90.91%) 1 nr
R. E. Fowler (PhD thesis,
2014)
+†
United Kingdom 2011–2012 1 36 75 (100%) 0 nr
Hanley (unpublished data,
2011)
+†
United Kingdom 2011 1 8 23 (82.61%) 110 nr
Table 1. Data sources and sample sizes. mMLE = largest Maximum Linear Extent (in meters) of any site in
the source. MLE is the maximum distance between sampling points within a site, e.g. the length of a transect or
the distance between pan traps. nr = not reported. Numbers of taxa are the numbers of unique taxa for which
diversity measurements are given (so, if diversity measurements are available only for all bees combined, this
would count as one taxon). e percentage of bee species with a known binomial name is also given (% binomial).
Note that the gures here represent available data as curated by the PREDICTS team; these will not necessarily
match gures in the original papers.
+
Data were used in the presented analysis.
†
Data will be incorporated into
the PREDICTS database (which will be made openly available).
‡
Data are available from the referenced paper. For
all other datasets, please contact the corresponding author of that paper directly.