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Biological Invasions ISSN 1387-3547Volume 13Number 9 Biol Invasions (2011) 13:2115-2133DOI 10.1007/s10530-011-0030-y

Global invasion by Anthidium manicatum(Linnaeus) (Hymenoptera: Megachilidae):assessing potential distribution in NorthAmerica and beyond

James P. Strange, Jonathan B. Koch,Victor H. Gonzalez, Lindsay Nemelka &Terry Griswold

ORIGINAL PAPER

Global invasion by Anthidium manicatum (Linnaeus)(Hymenoptera: Megachilidae): assessing potentialdistribution in North America and beyond

James P. Strange • Jonathan B. Koch •

Victor H. Gonzalez • Lindsay Nemelka •

Terry Griswold

Received: 8 July 2010 / Accepted: 20 May 2011 / Published online: 22 June 2011

� Springer Science+Business Media B.V. (outside the USA) 2011

Abstract The wool carder bee, Anthidium manica-

tum, is the most widely distributed unmanaged bee in

the world. It was unintentionally introduced to North

America in the late 1960s from Europe, and subse-

quently, into South America, New Zealand and the

Canary Islands. We provide information on the local

distribution, seasonal abundance and sex ratio of

A.manicatum from samples collected in an intensive

two-year survey across Utah, USA. Anthidium man-

icatum was detected in 10 of the 29 Utah counties,

largely in urban and suburban settings. Combining

presence-only and MaxEnt background data from

literature, museum databases and new records from

Utah, we constructed three species distribution mod-

els to examine the potential distribution of A. manic-

atum in its native Eurasian range as well as invaded

ranges of North and South America. The A. manic-

atum model based on locality and background data

from the species’ native range predicted 50% of the

invasive records associated with high habitat suit-

ability (HS C 0.90). The invasive North American

model predicted a much broader distribution of A.

manicatum (214% increase); whereas, the South

American model predicted a narrower distribution

(88% decrease). The poor predictive power of the

latter model in estimating suitable habitats in the

invasive South American range of A. manicatum

suggests that the bee may still be limited by the

bioclimatic constraints associated with a novel envi-

ronment. Estimates of niche similarity (D) between

the native and invasive models find that the North

America bioclimatic niche is more similar to the

bioclimatic niche of the native model (D = 0.78),

whereas the bioclimatic niche of the South America

invasion is relatively dissimilar (D = 0.69). We

discuss the naturalization of A. manicatum in North

America, possibly through punctuated dispersal, the

probability of suitable habitats across the globe and

the synanthropy exhibited by this invasive species.

Keywords Anthidium manicatum � Invasion

dynamics � Species distribution modeling �Synanthropy � MaxEnt background data

Introduction

Bees (Hymenoptera: Apoidea) are among the most

important pollinators of many flowering plants

including agricultural crops through which they

contribute an estimated one in three bites of food

J. P. Strange (&) � J. B. Koch � V. H. Gonzalez �T. Griswold

USDA-ARS Pollinating Insects - Biology, Management

and Systematics Laboratory, Utah State University,

261 BNR, Logan, UT 84322-5310, USA

e-mail: james.strange@ars.usda.gov

J. B. Koch � L. Nemelka

Biology Department, Utah State University,

5305 Old Main Hill, Logan, UT 84322-5305, USA

123

Biol Invasions (2011) 13:2115–2133

DOI 10.1007/s10530-011-0030-y

Author's personal copy

consumed by humans (Buchmann and Nabhan 1996).

While several bee species have been intentionally

transported around the globe for pollination services

[e.g., A. mellifera L., B. terrestris L., M. rotundata

(Fabricius)], many other species across multiple

families are finding their ways into novel environ-

ments through accidental and indirect introductions

[e.g., M. sculpturalis Smith, A. oblongatum (Illiger),

H. hyalinatus (Smith)]. There is growing concern that

the shuffling of bee pollinators across habitats may

facilitate the spread of novel bee pathogens (Goka

et al. 2001; Goulson 2004; Colla et al. 2006) and

provoke competitive interactions with native bees for

floral resources and habitat (Roubik 1980; Goulson

2003; Schmid-Hempel et al. 2007). While the

Africanized honey bee invasion of the New World

may be the most dramatic example of the unintended

consequences of bee movement (Roubik 1980), focus

has not been placed on the impacts of invasions by

unintentionally introduced bees, nor have the under-

lying factors that facilitate their range expansion been

studied.

To date, most research has focused on the impacts

of managed bees on native bee communities

(reviewed in Goulson 2003), while virtually nothing

is known about the ecological ramifications of unin-

tentionally introduced bees (but see Severinghaus

et al. 1981). The wool carder bee, Anthidium

manicatum (Linnaeus), is a solitary bee in the family

Megachilidae that has been unintentionally introduced

into several regions of the world, and has recently

shown rapid expansion in its non-native geographic

distribution (Smith 1991; Miller et al. 2002; Hoebeke

and Wheeler 2005; Maier 2005; Zavortink and Shanks

2008; Gibbs and Sheffield 2009). Females and males

of A. manicatum, like many other anthidiines, are

conspicuous because of their black and yellow-striped

abdomen and robust body form. In its invasive range

males can be distinguished from native anthidiines by

the distinctive series of protruding spines on the

posterior segments of their abdomen (Fig. 1). Males

are territorial and aggressively defend mating sites

from intruders by employing the abdominal spines to

break or disable the intruders’ wings during aerial

battery (Pechuman 1967; Wirtz et al. 1992). Male A.

manicatum do not discriminate between conspecific

and heterospecific intruders and are often seen

patrolling flowers and attacking other bees that enter

into their territory (Kurtak 1973; Severinghaus et al.

1981). This behavior is of particular interest because

some bees, particularly bumble bees, have been

documented to be deterred from foraging by A.

manicatum (Pechuman 1967; Comba et al. 1999).

Because of their distinctive body form and aggressive

behavior A. manicatum are easily recognizable inhab-

itants of residential gardens and are widely reported

Fig. 1 Anthidium manicatum (#). Dorsal view of abdominal spines (right) used in aerial battery

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on internet identification sites such as ‘‘Bug Guide’’

and ‘‘Discover Life’’, where pictures of the bees are

posted along with location and date of sighting.

The native distribution of A. manicatum spans

most of Europe, western Asia and coastal North

Africa. Anthidium manicatum was first observed in

North America in 1963 in Ithaca, New York (Jaycox

1967). Initial range expansion by A. manicatum was

apparently not rapid; it was subsequently documented

from Ontario, Canada in 1991 (Smith 1991), and

Pennsylvania in 1996 (Miller et al. 2002; Hoebeke

and Wheeler 2005). However, since 1996 A. manic-

atum detection has increased dramatically; it is now

well documented throughout the eastern USA (e.g.,

Miller et al. 2002; Matteson et al. 2008; Tonietto and

Ascher 2008), and most recently in California

(Zavortink and Shanks 2008), Colorado, Idaho and

western Canada (Gibbs and Sheffield 2009). In less

than 50 years A. manicatum has dispersed across

North America, primarily documented in major urban

areas. Concurrently, it has colonized large portions of

southern Brazil, Peru, Suriname, Argentina, Para-

guay, Uruguay, New Zealand, and the Canary Islands

(Hoebeke and Wheeler 2005; Gibbs and Sheffield

2009), making it the most widely distributed Anthi-

dium in the world and the most widespread unman-

aged bee species. Records of A. manicatum from

central Asia are known from the 1960s (Wu 2005),

but it is unclear if these records are due to a recent

range expansion, or are from poorly documented

areas of their native range.

Despite the widespread distribution of A. manic-

atum, little is known about the invasive potential of

this bee to colonize new areas. As recently as 2009

A. manicatum was called ‘‘adventive’’ (Gibbs and

Sheffield 2009), indicating that it was newly colo-

nizing and not yet well established in North America.

While the bee occupies a large and diverse habitat in

its native range including Europe, western Asia and

North Africa, the species initially seemed to be

restricted in North America to northeastern USA and

eastern Canada. As such, it is arguable that the

bioclimatic profiles of both the native and invasive

range are relatively similar. However, the more

recent records of A. manicatum in South America

and western North America argue for a broader range

of habitat than previously suspected. Given that

A. manicatum is found in a diverse range of habitats

worldwide, we hypothesize that it is not limited by

the bioclimatic profile associated with its native

distribution.

Alternatively, the distribution of A. manicatum

may be related to nesting behavior or foraging diet

(Comba et al. 1999; Corbet et al. 2001). Anthidium

manicatum often nests in holes and cavities in wood or

hollowed stems of plants, thus, facilitating dispersal

(Kurtak 1973). Female A. manicatum card fibers off of

the leaves and stems of plants such as wooly hedge

nettle (Stachys byzantina K. Koch) and use the

material to line nest cells in cavities (Muller et al.

1996). The host-plants associated with the carding

behavior of A. manicatum seem to be restricted to the

family Lamiaceae, whereas pollen and nectar foraging

occurs on a greater array of plants, primarily plants in

the family’s Lamiaceae, Fabaceae and Scrophularia-

ceae (Kurtak 1973, reviewed in Zavortink and Shanks

2008). However, it is still unknown to what extent

A. manicatum will utilize novel plants for nest-

building or larval provisions in invaded environments,

or whether the species range is limited by the presence

of introduced European plant species.

During 2007 and 2008 the Utah Department of

Agriculture and Food (UDAF) conducted a statewide

monitoring and eradication program of the invasive

Japanese beetle (Popillia japonica Newman Coleop-

tera: Scarabaeidae) in response to beetle detections in

Orem, Utah, USA. The network of baited beetle traps

deployed throughout the state, particularly concen-

trating on populated areas, resulted in large captures

of non-target insects. These unintentionally trapped

insects, termed ‘‘by-catch’’, were primarily composed

of Coleoptera, Lepidoptera and Hymenoptera (espe-

cially bees) (the authors, pers. obs.). Anthidium

manicatum was found among the by-catch, providing

an opportunity to survey from large portions of Utah.

Here we report on the distribution, seasonal

abundance and sex ratio of A. manicatum in Utah.

We also construct three models of A. manicatum

potential distribution based on a suite of proximal

bioclimatic variables. The first model reflects the

locality records associated with the native Eurasian

range of A. manicatum, whereas the latter two models

reflect the invasive locality records associated with

North and South America. We then assess several of

the bioclimatic variables used in these models and

compare them with the bioclimatic profile associated

with locality records compiled from the Utah study,

the largest regional survey of A. manicatum outside

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its native range. Finally, we compare the model of

A. manicatum based on native locality records to the

models based on their invasive ranges using niche

similarity indices and discuss this species’ ability to

colonize novel environments.

Methods

Trap protocol and seasonal abundance data

Trece Japanese Beetle Traps� (JBT) were set

throughout the state of Utah (Fig. 2a) from April to

November, 2007 (147 trap days) and May through

September, 2008 (127 trap days) as part of the UDAF

Japanese beetle monitoring and eradication program.

Approximately 3,250 JBT locations were distributed

throughout the state of Utah, with high trap density in

areas known to be infested with Japanese beetles. The

highest concentration of JBT was within the popu-

lated corridor of Utah along Interstate 15, known as

the Wasatch Front (Fig. 2b); however, lower

densities of JBT were located throughout Utah in

the rest of the 29 counties.

JBT were emptied biweekly by state inspectors, by-

catch was collected into labeled plastic bags (Whirl–

pak�) and sent to the United States Department of

Agriculture- Agricultural Research Service- Pollinat-

ing Insects Research Unit (PIRU) in Logan, Utah. Each

two-week period from a JBT at a site is hereafter

termed a trapping period. The trapping season in 2008

was shortened by 20 days to reduce the amount of

bumble bee (Bombus) queens that were being killed as

by-catch. Traps were placed approximately two weeks

later in the spring and retrieved a week earlier in the fall

of 2008, thus, fewer samples were taken in June and no

data are available for November 2008. The JBT

contents were sorted at PIRU and all bees in the genus

Anthidium were pinned, labeled and identified to

species. In addition to JBT specimens, net collections

of A. manicatum were conducted in 2009 in Cache

County, to verify the presence of the bee in an area with

low JBT density. JBT locations and collection dates

provided by UDAF personnel were entered into the US

Fig. 2 Anthidium manicatum distribution in Utah, USA.

Background collection effort summary (a) of all bees reported

in the US National Pollinating Insect Database (NPID) since

1999 and (b) map inset of A. manicatum detected in Trece

Japanese Beetle Traps� (JBT) during 2007 and 2008 along the

Wasatch Front in Utah, USA. White crosses represent

A. manicatum detection using JBT and white triangles

represent JBT with no A. manicatum detected. Gray circles

indicate bee surveys associated with the NPID, with size of

circle proportional to survey effort. Underlying gray-scale in

(b) represents human population density, where darker colors

indicate highly urban areas (ESRI 2008). The cross-hatched

polygon in both figures represents the Great Salt Lake

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National Pollinating Insect Database (NPID) (US

NPID 2011) housed at PIRU and the number of female

and male A. manicatum were recorded for each

trapping event. These data were used to determine

the seasonal abundance and sex ratio of A. manicatum

in Utah.

Locality records and covariate selection

Using locality records from literature, museum dat-

abases [i.e., Global Biodiversity Information Facility

(GBIF; http://www.gbif.org/)] and the specimens

from the JBT in Utah, we compiled a total of 281

unique locality records of A. manicatum from its

native range and 119 locality records outside of its

native range (Table 1; ‘‘Appendix’’). In addition, we

used records from Discover Life (Ascher and

Pickering 2011) and Iowa State University’s online

insect identification repository Bug Guide (Bartlett

2011), but only if specimens could be unambiguously

identified from the pictures posted online. Collection

locations of specimen records extracted from NPID

were georeferenced using Google Earth (http://www.

earth.google.com/). We excluded specimens with

questionable identifications and all records used in

this study were verified by published works or

through personal communication. To reduce geo-

graphic bias from the Utah occurrence records of

A. manicatum, only one randomly selected occur-

rence record from each county in Utah was utilized in

the final model.

Nineteen bioclimatic variables (Hijmans et al.

2005) were initially considered in modeling the

distribution of A. manicatum. These biologically

meaningful variables have proven useful in predicting

the distribution of many different organisms, includ-

ing bees (e.g., Gonzalez et al. 2010). Bioclimatic

variables were downloaded from the WorldClim

database (http://www.worldclim.org/) at a spatial

resolution of 1 km2 and processed using Arc-GIS 9.3

(ESRI 2008).

To reduce multicollinearity in the variables we

calculated multiple pair wise correlation coefficient

values (Pearson correlation coefficient, r) using

occurrence records from both the native and invasive

range of A. manicatum. From each set of highly

correlated variables (|r| [ 0.75) we retained only one

variable for the final model. In the native data set we

could not find any significant correlations across the

bioclimatic variables. However, when combining all

occurrence records of A. manicatum in both the

native and invasive range we were able to reduce the

initial set of 19 variables to 10. We ran models on

both the reduced variable set and on the full set of 19

variables. We did not detect any qualitative differ-

ences in model performance or prediction and

therefore present the models based on the reduced

variable set. The bioclimatic variables retained in the

final models are as follows: mean temperature diurnal

range, temperature annual range, mean temperature

Table 1 General information about A. manicatum caught in

Japanese Beetle Traps in Utah, USA in 2007 and 2008

2007 2008

Trap days 147 127

Total bees 194 197

Bees/day 1.32 1.55

Number males 76 114

Number females 118 83

Month

June 92 4a

July 42 86

Aug 26 53

Sept 5 36

Oct 27 16

Nov 2 0a

County

Box Elder 1 0

Cache* 0 Obs.

Davisb 24 35

Juab 0 2

Salt Lake 42 57

Sanpete 1 0

Summit 1 0

Utah 111 80

Wasatch 2 5

Weber 13 17

* Anthidium manicatum observed and net collected in 2008

and 2009 at multiple locations in Cache County including

several in Logan, the most populous city in the countya Two male A. manicatum collected in Davis County had

unspecified dates and thus, are omitted from the monthly

summary, but included in the total and County summaryb Trapping period in 2008 was mid-June to mid-October thus,

fewer bees were collected in June 2008 and no bees were

collected in November 2008 because of the truncated trapping

period

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of wettest quarter, mean temperature of driest quarter,

mean temperature of warmest quarter, precipitation

seasonality, precipitation of wettest quarter, precipi-

tation of driest quarter, precipitation of warmest

quarter and precipitation of coldest quarter. Like

many other bee species, these are proximal variables

related to the bioclimatic profile associated their

foraging resources (i.e., nectar and pollen availability

from flowering plants) and nesting habitats. For a full

discussion of the bioclimatic variables see Hijmans

et al. (2005).

Modeling procedure and validation

To estimate the distribution of A. manicatum across

its native and invasive ranges, we employed the

species distribution modeling program MaxEnt v 3.3

(Phillips et al. 2006). MaxEnt estimates relative

habitat suitability (HS), between 0 and 1, where

values closer to 0 suggest low HS and values closer to

1 suggest high HS. The estimation of HS is based on

the conditional density of covariates associated with

presence sites and the unconditional density of

covariates associated with the study area (Elith

et al. 2011). Unlike traditional SDM methods that

require both presence and absence data, MaxEnt only

requires presence data (Phillips et al. 2006). In lieu of

absence, MaxEnt relies on background data across

the study area to make the estimation of HS. This

feature of MaxEnt is advantageous when true absence

is unknown or confounded by a low detection

frequency or species phenology (Elith et al. 2006).

A major property of the MaxEnt modeling program

is the assumption of an unbiased distribution of locality

records (Phillips et al. 2006). However, as museum

records of specimens are usually biased towards roads

and urban areas, it is critical to account for collection

bias when assigning background data for model

construction. While models with spatially biased

locality records may perform well, it is likely that the

model is estimating collection bias, rather than HS

(Elith et al. 2011). In the MaxEnt framework, several

approaches have been suggested to reduce spatial bias

in models, including spatial filtering and biased grid

files of presence data (Phillips et al. 2009; Veloz 2009).

An alternative method includes the assignment of

equally biased background data in model construction

(Elith et al. 2011), and will be used in our modeling

approach. To account for both the spatial bias and

geographic extent of museum and publication locality

records of A. manicatum, we utilized GBIF to collect

spatially biased background data (see ‘‘Appendix’’ for

a summary of data sources). We searched GBIF for

georeferenced localities of confamilial bees to A. man-

icatum (Megachilidae) in Europe, North America and

South America regardless of genus or species. Back-

ground locality records retrieved from GBIF were

limited to a maximum convex polygon (MCP) con-

structed with A. manicatum locality records across all

three continents and were calculated using the open-

source software program Geospatial Modeling Envi-

ronment (Spatial Ecology LLC 2010). These MCPs

also limited the geographic extent of the models

constructed using MaxEnt.

Three A. manicatum models were constructed: a

native model, a North American invasive model and a

South American invasive model. The native model was

trained on the bioclimatic variables associated with

locality (l = 281) and background (b = 4,016) data

within the MCP native distribution of A. manicatum.

The native model was subsequently projected onto the

full geographic extent of the Europe, North America

and South America, the latter two representing

A. manicatum’s invasive range. The North American

invasive model estimated HS based on North Amer-

ican locality (l = 72) and background (b = 3,415)

data, whereas the South American invasive model

estimates HS based on South American locality

(l = 47) and background (b = 267) data. The limited

background data set associated with the latter continent

is a relic of the poor collection and digitization effort of

South American Megachilidae (Appendix). This nar-

row approach was also performed to accommodate for

the bioclimatic dissimilarity between the two invaded

ranges of A. manicatum. Both invasive models were

then projected onto the respective continent to estimate

HS associated with the bioclimatic profile A. manic-

atum in novel environments. As the spatial resolution

of the bioclimatic variables utilized in the MaxEnt

models represent 1 km2, multiple locality records that

fell within a grid pixel were removed from both

presence and background locality records for all

models constructed.

MaxEnt models were fitted using default settings of

prevalence, feature type, logistic output (constrains HS

estimates between 0 and 1) and regularization (Phillips

et al. 2006) across 100 replicates, with the exception of

the South American invasive model (40 replicates).

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The reduced replicate set of the South American

invasive model reflects the limited locality and back-

ground data set associated with South American

A. manicatum and Megachilidae data, respectively.

Each replicate was assessed using tenfold cross-valida-

tion to estimate predictive performance of held out data.

The built in area under the curve (AUC) statistic in

MaxEnt is reported to assess the overall accuracy of a

model replicate, where values above 0.5 suggest that the

models performed better than random (Fielding and Bell

1997). The final models and respective AUC test

statistic represent the average of the model replicates.

Bioclimatic niche similarity metrics

To estimate the similarity of HS associated with the

bioclimatic profiles of native and invasive models,

Schoener’s metric for niche overlap (D) was calcu-

lated (reviewed in Warren et al. 2008). This metric

constrains estimates of niche overlap between 0 and

1, where values closer to 1 suggest high niche overlap

and values closer to 0 suggest low niche overlap. To

assess niche similarity between projections of the

native and invasive models in North and South

America, 10,000 random points were drawn from the

continental extents and aggregated with the respec-

tive raster grids using ArcGIS 9.3. The open-source

statistical software package R v2.9.2 (R Development

Core Team 2009) was utilized for the Pearson’s

correlation coefficient analysis, to construct box plots

to visualize the bioclimatic variables and for the

estimation of bioclimatic niche similarity.

Results

Distribution and seasonal abundance of A.

manicatum in Utah

The present study surveyed by-catch from 3,250 JBT

throughout Utah (Fig. 2a) over a 2 year period. Utah,

Salt Lake and Davis Counties, in descending order,

had the highest number of records; however, those

counties also had the highest density of traps in the

state and the number of trapped individuals is not

adjusted for trap density. Over the 2 year period,

A. manicatum was detected in nine counties using

JBT and in a tenth county (Cache) by net collection in

2008 and 2009 (Fig. 2a; Table 1). In 2007, 194

individuals of A. manicatum were recorded (1.32

bees/trap-day) from 106 JBT trapping events

(Table 1). In 2008, 197 individuals (1.55 bees/trap-

day) were detected in 156 JBT trapping events.

Females were more frequently collected than males

in 2007, whereas males were more common in 2008

in contrast with observations in the native range

which show a distinct female biased sex ratio (Wirtz

et al. 1988). However, when pooled across both years

the sex ratio was about 1:1.

The seasonal abundance of A. manicatum adults in

Utah is at least 5 months long, lasting from June to

October (Table 1). Anthidium manicatum was detected

as late as November 2007 indicating that individuals

were active within the 2 weeks period prior to that date

based on JBT deployment and collection times.

Females and males appear in the earliest sample period

and both were detected in traps throughout the season.

In 2007, June had the highest capture of A. manicatum

(n = 92) and in 2008 July had the highest capture

(n = 86). The JBT were not located at all of the same

sites in both years, but many cities have multiple

specimen records from both years (e.g., Brigham City,

Ogden, Orem, Provo, Salt Lake City) indicating

established populations along the Wasatch Front

(Fig. 2b). Furthermore, despite the numerous sampling

efforts across the state of Utah within the past 10 years,

A. manicatum was never detected in intensive bee

surveys in wild lands (Fig. 2a).

Potential distribution

The A. manicatum native model performed well

(AUCtrain = 0.76, AUCtest = 0.69 ± 0.16), predict-

ing 71 and 19% of the known A. manicatum localities

associated with HS C 0.90 in North (Fig. 3a) and

South America (Fig. 4a), respectively. As expected,

the North American invasive model predicted a much

wider range of HS (Fig. 3b), whereas the South

American invasive model surprisingly predicted a

much narrower range of HS (Fig. 4b). Based on the

AUC statistic of model performance, the North

American invasive model predicted the distribution

of A. manicatum exceptionally well (AUCTrain =

0.96; AUCTest = 0.94 ± 0.07), estimating HS C

0.90 for 85% of the A. manicatum locality records.

However, the South American invasive model did not

perform as well (AUCTrain = 0.81; AUCTest =

0.67 ± 0.25), but is arguably better than a random

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model (AUC C 0.50). It did not estimate any invasive

records to be associated with HS C 0.90, but rather

estimated 34% of the records to be associated with HS

between 0.70 and 0.80. At a threshold of HS C 0.90,

the native A. manicatum model estimates distributions

of 6.7 9 106 km2 in North America whereas the

invasive models estimates distributions of 2.1 9 107

km2, a 214% increase. In South America the native A.

manicatum model estimates a distribution of

8.0 9 106 km2, whereas the invasive model estimate

a distribution of 9.4 9 105 km2, an 88% decrease. The

invasive models estimate the potential distribution in

North America to be much broader in scope, encom-

passing most of the contiguous USA and southern

Canada (Fig. 3b), whereas the South American

distribution is much more limited to coastal regions

(Fig. 4b). Finally, based on Schoener’s D of niche

similarity, a comparison of the A. manicatum

bioclimatic niche of the native and invasive models

of North America are more similar (D = 0.78) than

the bioclimatic niche on South America (D = 0.69).

In the latter scenario, the lack of niche similarity is not

surprising considering its subtropical bioclimatic

profile and prediction capabilities.

A comparison of the bioclimatic profile associated

with the native and invasive distributions of A. man-

icatum reveals interesting departures and patterns in

both precipitation (Fig. 5) and temperature tolerances

of the species (Fig. 6). For example, the bioclimatic

variable precipitation seasonality (Fig. 5a) shows

greater variability when associated to locality records

of A. manicatum in the invasive distribution relative

to the native distribution. Also, the precipitation of

the warmest quarter associated with the invasive

distribution of A. manicatum (Fig. 5b) is more varied

than in the native distribution, particularly where the

bee has invaded regions that receive higher precip-

itation than the native distribution. The greater range

of precipitation values observed in both precipitation

seasonality and precipitation of warmest quarter

associated with individuals captured in the Southern

Hemisphere (i.e., Brazil, Argentina and Chile) sug-

gest that A. manicatum is not deterred by environ-

ments with heavy precipitation or in regions where

the seasonality of precipitation differs from the native

distribution. The pattern of less variability and

generally drier conditions is observed in the three

other precipitation variables, precipitation of driest

quarter (Fig. 5c), precipitation of wettest quarter

(Fig. 5d) and precipitation of coldest quarter

(Fig. 5e). While we observe a narrower range of

precipitation values associated with locality records

in the Utah portion of its distribution, these values fall

well within the variability observed in both A. man-

icatum’s native and invasive distributions.

Although locality records representing invasive

A. manicatum show dramatic variability in their

precipitation bioclimatic profile, it seems that tem-

perature is less variable across both its native and

invasive distributions. For example in both the

temperature annual range (Fig. 6a) and mean tem-

perature of driest quarter (Fig. 6b) we detect no

qualitative differences among the locality records

associated with the native and invasive distributions

of A. manicatum. In fact, mean temperature of driest

quarter has more variance in the native distribution

than in the invasive distribution. However, when

Fig. 3 North American A. manicatum probable distribution

and historic collection effort. The probable distribution

(underlying gray-scale) of A. manicatum is estimated by

aggregating bioclimatic variables associated with (a) locality

records exclusive to its native distribution and (b) pooled

locality records from its native and invasive range. White

crosses represent A. manicatum detection. Underlying gray-

scale for both models represents habitat suitability (HS) with

darker colors indicating higher HS

2122 J. P. Strange et al.

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comparing the temperature variables associated with

its bioclimatic profile in Utah, it appears that

A. manicatum is able to tolerate more temperature

extremes than would be suggested by its native

distribution (Fig. 6c–e), and there seems to be

qualitative differences when comparing locality

records associated with mean temperature of wettest

quarter (Fig. 6d). From the assessment of these two

temperature-related bioclimatic variables it seems

that A. manicatum may not be limited by temperature

in its invasive distribution. Moreover, it is also

possible that A. manicatum has not been detected in

areas where temperature related variables depart from

the currently known bioclimatic profile of the spec-

imens compiled in this study.

Discussion

The present Utah survey not only explores the

breadth of the A. manicatum invasion into more arid

regions of the USA, but also the depth of the pattern.

Since accidental introduction to New York prior to

1963 (Kurtak 1973), the species has colonized a large

territory in North America. However, the full inva-

sive potential of this species is only now being

realized as new records are documented throughout

the USA and Canada (Matteson et al. 2008; Tonietto

and Ascher 2008; Zavortink and Shanks 2008; Gibbs

and Sheffield 2009). The number of A. manicatum

specimens detected in the survey accounted for about

1/3 of the total number of anthidiine specimens

collected from JBT, far exceeding the combined

number of specimens of the other eight native

Anthidium. In Utah County, where JBT were most

dense, 78 A. manicatum individuals were detected at

dozens of sites, primarily in urban areas, indicating a

well established population. The presence of the bee

in less urban areas, where limited numbers of traps

were deployed, is not well known. However, multiple

individuals at several locations have been observed

and collected in Cache County where traps failed to

detect the bee.

Not surprisingly, the North American invasive

model estimates broad HS in the invaded range

(Fig. 3b), possibly over predicting invasive distribu-

tion. Conversely, the South American invasive model

was unable to predict high HS for known occurrence

records (Fig. 4b), further suggesting that the model is

not effective at predicting invasive distribution.

Unlike similar studies of invasiveness (e.g.,

Broennimann and Guisan 2008; Steiner et al. 2008),

pooling native and invasive A. manicatum locality

data did not improve model predictions. Thus, our

analysis represents a narrow geographic approach in

modeling the invasive potential of A. manicatum. By

Fig. 4 South American

A. manicatum probable

distribution and historic

collection effort. The

probable distribution of

A. manicatum is estimated

by aggregating bioclimatic

variables associated with

(a) locality records

exclusive to its native

distribution and (b) pooled

locality records from its

native and invasive range.

White crosses represent

A. manicatum detection.

Underlying gray-scale for

both models represents

habitat suitability (HS) with

darker colors indicating

higher HS

Global invasion by Anthidium manicatum (Linnaeus) 2123

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limiting the geographic extent of the invasive models

to separate continents, we were able to better estimate

known occurrences. This may be due to the nature of

the background data utilized (Elith et al. 2011), which

is narrower in distribution, or may be a relic of the

bioclimatic variability associated with the invasive

distributions (Figs. 5 and 6).

Anthidium manicatum is naturalized in environ-

ments not predicted by the native model, such as the

Intermountain West in the USA (Fig. 3a). Further-

more, the poor predictive capability of the South

American invasive model suggests that the distribu-

tion of A. manicatum may not be necessarily guided

by bioclimatic variables, but rather by the availability

of floral resources found in urban gardens (Kurtak

1973), or by microclimates generated from changes

in land use. While we think that the present invasion

models are a conservative estimate for future

Fig. 5 Boxplotcomparisons of

precipitation related

bioclimatic space inhabited

by A. manicatum in their

invasive (I), native (N) and

Utah (U) distributions.

Precipitation amounts are

reported in mm

precipitation

Fig. 6 Boxplot

comparisons of temperature

related bioclimatic space

inhabited by A. manicatumin their invasive (I), native

(N) and Utah

(U) distributions.

Temperatures are reported

in �C

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A. manicatum colonization, it provides a theoretical

structure on which to base future surveys. Our study

contributes to the growing body of literature utilizing

a SDM approach to predict the invasive potential of a

species, facilitating discussion on the mechanism

behind bioclimatic niche flexibility or conservatism

(Fitzpatrick et al. 2007; Warren et al. 2008; Rodder

and Lotters 2009).

The naturalization of A. manicatum in apparently

non-suitable environments poses interesting biological

questions. For example, are there physiological or

behavioral adaptations that allow this species to thrive

in new environments? In the invaded regions of Utah,

temperatures fall below -20�C in the winter, and on

average can be colder than the temperatures associated

with the native range of A. manicatum (Fig. 6d).

Additionally, this region is characterized by lower

precipitation than the native range (Fig. 6a–e). These

drastic changes in temperature and moisture could

directly affect the metabolic processes of the insect.

However, our data indicate that this species tends to

occur in urban settlements (Fig. 3b), where tempera-

tures are usually warmer than rural areas, and irrigation

provides moisture during dry summers. We suspect

that human modified landscapes might facilitate their

survival in otherwise marginal or unsuitable habitats

and that the most predictive bioclimatic variables in the

models, temperature and precipitation, may not reflect

the actual values. Interestingly, the seasonal activity of

A. manicatum adults in Utah, lasting from June to

October, seems longer than that reported in Germany

by Wirtz et al. (1992) where bees survived only to

August, perhaps a factor of the warmer summer climate

in Utah (Fig. 6b,c).

Anthidium manicatum may exploit new plant

resources for nectar, pollen or nest materials that

enable it to inhabit novel environments. In France and

Germany, the diet of A. manicatum is restricted to

about 25 plants, nearly all in Fabaceae, Lamiaceae

and Scrophulariaceae (Wirtz et al. 1992). In New

York, Kurtak (1973) found that 37 plant species, most

of which are non-native ornamentals, were used as

food, and only five species were used for nesting

material. Notably, while the primary plant utilized for

nesting substrate was the introduced S. byzantina,

females also used the pubescence of Populus delto-

ides Bartram ex Marsh (Salicaceae) seeds, indicating

that they will use at least some native plant materials

for nesting purposes. However, the survival success

of offspring reared in P. deltoides cotton is not

known.

Recent surveys in natural and agricultural land-

scapes conducted by the authors have not yielded

specimens of A. manicatum. To date, all records in

western North America are restricted to residential

and commercial gardens, further suggesting synan-

thropy (Jaycox 1966; Severinghaus et al. 1981;

Matteson et al. 2008; Zavortink and Shanks 2008).

Several species of hedge nettles (Stachys) are native

to the USA, but none of them have been documented

as hosts for A. manicatum. In New York urban

gardens, Matteson et al. (2008) found A. manicatum

in 44% of the gardens surveyed whereas A. oblong-

atum (Illiger), another adventive anthidiine bee, was

only present in 5.6%. Synanthropy of A. manicatum

may be dependent on non-native ornamental plants

for forage and/or nesting substrate (especially

S. byzantina), thus, limiting expansion to areas with

gardens containing appropriate floral mixes.

The exact mechanism of A. manicatum transport is

not yet known. However, it appears to be rapidly

colonizing distant environments through punctuated

dispersal (Davis and Thompson 2000). As docu-

mented with many other organisms (e.g., Davis and

Thompson 2000 and references therein), A. manica-

tum could be transported by humans across long

distances via nursery stock of ornamental plants. This

would not be surprising considering their wood

nesting behavior (Kurtak 1973; Severinghaus et al.

1981; Wirtz et al. 1992). Further research on the

nesting biology and dietary requirements of this

species is warranted to determine if invasion of wild

lands is possible. Based on its rapid colonization,

especially in major urban settlements, and male

aggressive behavior, we propose that A. manicatum

should no longer be considered adventive, but should

be referred to as invasive (Colautti and MacIsaac

2004). Beyond the semantic change, the invasion of

A. manicatum may have repercussions in western

North America where 32 native Anthidium bees exist

and may be adversely affected by the presence of this

territorial and aggressive species.

Acknowledgments We thank Daniel Downey, Clint Burfitt

and the staff at the Utah Department of Agriculture and Food

for providing us access to the trap by-catch and critical

information on trap locations and trapping protocols. We thank

Leah Lewis and Joyce Knoblett for assistance in sorting by-

catch from JBT. We thank Harold Ikerd and Daniel Young for

Global invasion by Anthidium manicatum (Linnaeus) 2125

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specimen preparation and curation. Molly Rightmyer and

Samuel Rivera provided critical reviews on an earlier version

of this manuscript. We thank Matthew Fitzpatrick for editorial

assistance, Janani Kalidasan and an anonymous reviewer for

providing invaluable suggestions that greatly improved our

analysis and discussion.

Appendix

See Table 2.

Table 2 Background and locality online data source summary of A. manicatum

Source Institution URL Data Type

GBIF Agricultural research council’s

biosystematics division

http://data.gbif.org/datasets/resource/11946 Megachilidae background

GBIF Okostation (Freiburg) http://data.gbif.org/datasets/resource/2750 Megachilidae background

GBIF Animals BioFokus http://data.gbif.org/datasets/resource/8066 Megachilidae background

GBIF Artenvielfalt am Eich-

Gimbsheimer Altrhein

http://data.gbif.org/datasets/resource/11328 Megachilidae background

GBIF Artenvielfalt am Schlern http://data.gbif.org/datasets/resource/8055 Megachilidae background

GBIF Artenvielfalt auf der Weide—

GEO-Hauptveranstaltung in

Crawinkel

http://data.gbif.org/datasets/resource/2697 Megachilidae background

GBIF Australian museum provider for

OZCAM

http://data.gbif.org/datasets/resource/9105 Megachilidae background

GBIF Balkon (Norderstedt) http://data.gbif.org/datasets/resource/3048 Megachilidae background

GBIF Bee specimens http://data.gbif.org/datasets/resource/1941 Megachilidae background

GBIF Bees of Canada/Gschwendtner

property collection

http://data.gbif.org/datasets/resource/1942 Megachilidae background

GBIF Bees of Canada/Joker’s hill

collection

http://data.gbif.org/datasets/resource/1947 Megachilidae background

GBIF Bees of Ireland http://data.gbif.org/datasets/resource/10809 Megachilidae background

GBIF Bees, wasps and ants recording

society—Bees, wasps and ants

recording society—Trial dataset

http://data.gbif.org/datasets/resource/12037 Megachilidae background

GBIF Biodiversidad de Costa Rica http://data.gbif.org/datasets/resource/333 Megachilidae background

GBIF Biological and palaeontological

collection and observation data

MNHNL

http://data.gbif.org/datasets/resource/8107 Megachilidae background

GBIF Biologiezentrum Linz http://data.gbif.org/datasets/resource/1104 Megachilidae background

GBIF Biologische station im Kreis

Wesel

http://data.gbif.org/datasets/resource/2703 Megachilidae background

GBIF Biospharenpark Wienerwald—

Pfaffsatten

http://data.gbif.org/datasets/resource/8971 Megachilidae background

GBIF Biospharenpark Wienerwald—

Wiener Steinhofgrunde

http://data.gbif.org/datasets/resource/3392 Megachilidae background

GBIF Biotope in Rheine—Aktion 350 http://data.gbif.org/datasets/resource/9001 Megachilidae background

GBIF Bristol regional environmental

records centre—BRERC October

2009

http://data.gbif.org/datasets/resource/11926 Megachilidae background

GBIF Bugs (GBIF-SE: Artdatabanken) http://data.gbif.org/datasets/resource/1154 Megachilidae background

GBIF BUND—Dassower see (Lubeck/

Dassow)

http://data.gbif.org/datasets/resource/2707 Megachilidae background

GBIF CNIN/Abejas de Mexico/Apoidea http://data.gbif.org/datasets/resource/8394 Megachilidae background

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Table 2 continued

Source Institution URL Data Type

GBIF Colecao de Abelhas do Museu de

Ciencias e Tecnologia da

PUCRS

http://data.gbif.org/datasets/resource/2003 Megachilidae background

GBIF Colecao de Entomologia do

Laboratorio de Biologia Vegetal

http://data.gbif.org/datasets/resource/2004 Megachilidae background

GBIF Colecao de Entomologia do

Laboratorio de Biologia Vegetal

http://data.gbif.org/datasets/resource/12105 Megachilidae background

GBIF Colecao Entomologica do Depto.

de Sistematica e Ecologia

http://data.gbif.org/datasets/resource/2001 Megachilidae background

GBIF Colecao Entomologica Moure e

Costa

http://data.gbif.org/datasets/resource/2000 Megachilidae background

GBIF ColecaoEntomologica Paulo

Nogueira-Neto—IB/USP

http://data.gbif.org/datasets/resource/1997 Megachilidae background

GBIF Colecao Entomologica Pe. Jesus

Santiago Moure (Hymenoptera)

http://data.gbif.org/datasets/resource/1998 Megachilidae background

GBIF Coleccion del Departamento de

Biologıa Animal (Zoologıa) de la

Universidad de La Laguna

http://data.gbif.org/datasets/resource/8081 Megachilidae background

GBIF Corantioquia http://data.gbif.org/datasets/resource/8101 Megachilidae background

GBIF Countryside council for Wales—

UK biodiversity action plan

invertebrate data for Wales

http://data.gbif.org/datasets/resource/932 Megachilidae background

GBIF Countryside council for wales—

welsh invertebrate database

(WID)

http://data.gbif.org/datasets/resource/11890 Megachilidae background

GBIF Department of freswater

invertebrates, makana

biodiversity centre, Albany

museum, Grahamstown

http://data.gbif.org/datasets/resource/11947 Megachilidae background

GBIF Dorset environmental records

centre–Dorset SSSI species

records 1952–2004 (Natural

England)

http://data.gbif.org/datasets/resource/11862 Megachilidae background

GBIF EcoRecord—Natural England’s

scientific files

http://data.gbif.org/datasets/resource/11819 Megachilidae background

GBIF EcoRecord—wildlife trust for

Birmingham and the Black

Country surveys

http://data.gbif.org/datasets/resource/11888 Megachilidae background

GBIF EDIT—ATBI in Mercantour/Alpi

Marittime (France/Italy)

http://data.gbif.org/datasets/resource/7949 Megachilidae background

GBIF Entomology collection http://data.gbif.org/datasets/resource/7911 Megachilidae background

GBIF Fohrenried (Fronreute und Baindt) http://data.gbif.org/datasets/resource/2970 Megachilidae background

GBIF FFH-Gebiet ‘‘Calwer Heckengau’’ http://data.gbif.org/datasets/resource/3373 Megachilidae background

GBIF Finnish entomological database:

Hymenoptera

http://data.gbif.org/datasets/resource/8239 Megachilidae background

GBIF Freiburger GEO-Tag der

Artenvielfalt

http://data.gbif.org/datasets/resource/8969 Megachilidae background

GBIF Freigelande Naturschutzscheune

Reinheimer Teich (Kreis

Darmstadt-Dieburg)

http://data.gbif.org/datasets/resource/2845 Megachilidae background

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Table 2 continued

Source Institution URL Data Type

GBIF GEO biodiversity day http://data.gbif.org/datasets/resource/1094 Megachilidae background

GBIF GEO Hauptveranstaltung Tirol

(Innsbruck)

http://data.gbif.org/datasets/resource/2662 Megachilidae background

GBIF GEO-Hauptveranstaltung (Insel

Vilm)

http://data.gbif.org/datasets/resource/2704 Megachilidae background

GBIF GEO-Hauptveranstaltung (NLP

Harz/Hochharz)

http://data.gbif.org/datasets/resource/2643 Megachilidae background

GBIF GEO-Hauptveranstaltung in

‘‘wildtierland’’

http://data.gbif.org/datasets/resource/8974 Megachilidae background

GBIF GEO-Tag der Artenvielfalt

Hornwiesen-Grundschule

http://data.gbif.org/datasets/resource/2783 Megachilidae background

GBIF Gieselbachtal Fulda-Harmerz http://data.gbif.org/datasets/resource/3100 Megachilidae background

GBIF Gravel master thesis/Chubut

Argentina

http://data.gbif.org/datasets/resource/1943 Megachilidae background

GBIF Gunma museum of natural history

insect specimen

http://data.gbif.org/datasets/resource/8020 Megachilidae background

GBIF Gurgltal (Tarrenz) http://data.gbif.org/datasets/resource/2727 Megachilidae background

GBIF Highland biological recording

group—HBRG insects dataset

http://data.gbif.org/datasets/resource/11867 Megachilidae background

GBIF Hymenoptera collection of the

finnish museum of natural

history

http://data.gbif.org/datasets/resource/8080 Megachilidae background

GBIF Hymenopteran specimen database

of Osaka museum of natural

history

http://data.gbif.org/datasets/resource/611 Megachilidae background

GBIF Ibaraki nature museum, arthropoda

collection

http://data.gbif.org/datasets/resource/1814 Megachilidae background

GBIF Illinois natural history survey http://data.gbif.org/datasets/resource/225 Megachilidae background

GBIF Innenstadt Gottingen—Natur

Zuhause

http://data.gbif.org/datasets/resource/2851 Megachilidae background

GBIF Insect (MNHM-IN) http://data.gbif.org/datasets/resource/11470 Megachilidae background

GBIF Insects http://data.gbif.org/datasets/resource/625 Megachilidae background

GBIF Insekten http://data.gbif.org/datasets/resource/3292 Megachilidae background

GBIF Instituto de Ciencias Naturales http://data.gbif.org/datasets/resource/2559 Megachilidae background

GBIF Inventaire national du Patrimoine

naturel (INPN)

http://data.gbif.org/datasets/resource/2620 Megachilidae background

GBIF Isartal Dingolfing http://data.gbif.org/datasets/resource/3117 Megachilidae background

GBIF Knerer collection/Gschwendtner

property

http://data.gbif.org/datasets/resource/1945 Megachilidae background

GBIF LaBoOb02 http://data.gbif.org/datasets/resource/2629 Megachilidae background

GBIF Laboratorio de Ecologia e

Biogeografia de Insetos da

Caatinga

http://data.gbif.org/datasets/resource/2002 Megachilidae background

GBIF Landschaftspflegehof (Berlin) http://data.gbif.org/datasets/resource/2656 Megachilidae background

GBIF Langes Tannen http://data.gbif.org/datasets/resource/2682 Megachilidae background

GBIF Lillachtal mit Kalktuffquelle bei

Weissenohe

http://data.gbif.org/datasets/resource/3002 Megachilidae background

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Table 2 continued

Source Institution URL Data Type

GBIF Lothian wildlife information

centre—Lothian wildlife

information centre secret garden

survey

http://data.gbif.org/datasets/resource/856 Megachilidae background

GBIF Merseyside BioBank—Merseyside

environmental advisory service

dataset

http://data.gbif.org/datasets/resource/11893 Megachilidae background

GBIF Missouri botanical garden http://data.gbif.org/datasets/resource/12084 Megachilidae background

GBIF Mokpo museum of natural history

insect

http://data.gbif.org/datasets/resource/568 Megachilidae background

GBIF Morandin PhD thesis/La Crete

Alberta

http://data.gbif.org/datasets/resource/1946 Megachilidae background

GBIF Museum Victoria provider for

OZCAM

http://data.gbif.org/datasets/resource/9107 Megachilidae background

GBIF NABU Naturschutzhof Netttetal

(Sassenfeld) e. V.

http://data.gbif.org/datasets/resource/2759 Megachilidae background

GBIF National system of protected areas

in poland—animals

http://data.gbif.org/datasets/resource/8248 Megachilidae background

GBIF National trust—Anglesey Abbey

wildlife species data held by the

national trust

http://data.gbif.org/datasets/resource/11780 Megachilidae background

GBIF National trust—hatfield forest

species data held by the national

trust

http://data.gbif.org/datasets/resource/11874 Megachilidae background

GBIF National trust—Ickworth species

data held by the national trust

http://data.gbif.org/datasets/resource/11821 Megachilidae background

GBIF National trust—Wicken Fen nature

reserve species data held by the

national trust

http://data.gbif.org/datasets/resource/11873 Megachilidae background

GBIF National trust for Scotland

(staff)—NE Scotland NTS

properties species records

http://data.gbif.org/datasets/resource/11841 Megachilidae background

GBIF Natural England—invertebrate site

register—England

http://data.gbif.org/datasets/resource/944 Megachilidae background

GBIF Natural history museum rotterdam http://data.gbif.org/datasets/resource/693 Megachilidae background

GBIF Naturhistorisches Museum Mainz

Zoological Collection

http://data.gbif.org/datasets/resource/12678 Megachilidae background

GBIF Naturschutzgebiet Bausenberg http://data.gbif.org/datasets/resource/2657 Megachilidae background

GBIF Naturschutzgebiet Bausenberg

(Niederzissen)

http://data.gbif.org/datasets/resource/2674 Megachilidae background

GBIF Neckartalsudhang (Horb) http://data.gbif.org/datasets/resource/2680 Megachilidae background

GBIF Packer collection/Madagascar http://data.gbif.org/datasets/resource/1948 Megachilidae background

GBIF Paleobiology database http://data.gbif.org/datasets/resource/563 Megachilidae background

GBIF Peabody entomology DiGIR

service

http://data.gbif.org/datasets/resource/8138 Megachilidae background

GBIF Philosophenwald und Wieseckaue

in GieAYen

http://data.gbif.org/datasets/resource/2690 Megachilidae background

GBIF Rapid assessment program (RAP)

biodiversity survey database

http://data.gbif.org/datasets/resource/8076 Megachilidae background

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Table 2 continued

Source Institution URL Data Type

GBIF Ratti master thesis/Fraser Valley,

British Columbia

http://data.gbif.org/datasets/resource/1944 Megachilidae background

GBIF Rohrmeistereiplateau und

angrenzendes Gebiet

http://data.gbif.org/datasets/resource/3382 Megachilidae background

GBIF Royal horticultural society http://data.gbif.org/datasets/resource/11879 Megachilidae background

GBIF Rund um das LUGY http://data.gbif.org/datasets/resource/3022 Megachilidae background

GBIF Schlern—(Bozen) http://data.gbif.org/datasets/resource/2661 Megachilidae background

GBIF Schulgelande Hans-Carossa-

gymnasium (Berlin)

http://data.gbif.org/datasets/resource/3235 Megachilidae background

GBIF Schulhof Sandhofenschule

(Mannheim)

http://data.gbif.org/datasets/resource/3045 Megachilidae background

GBIF Scottish wildlife trust—

commissioned surveys and staff

surveys and reports for SWT

reserves

http://data.gbif.org/datasets/resource/11903 Megachilidae background

GBIF Shropshire ecological data

network—Shropshire ecological

data network database

http://data.gbif.org/datasets/resource/11906 Megachilidae background

GBIF South African museum collection http://data.gbif.org/datasets/resource/11952 Megachilidae background

GBIF South East Wales biodiversity

records centre—CCW regional

data: South East Wales non-

sensitive species records

http://data.gbif.org/datasets/resource/12702 Megachilidae background

GBIF Stadtpark Sulzbach-Rosenberg http://data.gbif.org/datasets/resource/2800 Megachilidae background

GBIF Staffordshire ecological record—

SER Site-based surveys

http://data.gbif.org/datasets/resource/11913 Megachilidae background

GBIF Staffordshire ecological record—

SER species-based surveys

http://data.gbif.org/datasets/resource/11912 Megachilidae background

GBIF Steinbruch Mainz-Weisenau, 3.

Jahr

http://data.gbif.org/datasets/resource/3135 Megachilidae background

GBIF Streuobstwiese RSG (Cham) http://data.gbif.org/datasets/resource/2637 Megachilidae background

GBIF Suffolk biological records

centre—Suffolk biological

records centre (SBRC) dataset

http://data.gbif.org/datasets/resource/11927 Megachilidae background

GBIF Teich Berlin Wuhlheide http://data.gbif.org/datasets/resource/2853 Megachilidae background

GBIF Thames valley environmental

records centre—local wildlife

site surveys Oxfordshire

http://data.gbif.org/datasets/resource/11895 Megachilidae background

GBIF The Norwegian species

observation service—

invertebrates

http://data.gbif.org/datasets/resource/11833 Megachilidae background

GBIF Trockenrasen und Buchenwald in

der Umgebung der

Jugendherberge Bad

Blankenburg

http://data.gbif.org/datasets/resource/2723 Megachilidae background

GBIF Truppenubungsplatz

Panzerkaserne Boblingen

http://data.gbif.org/datasets/resource/2965 Megachilidae background

GBIF Tullie house museum—Tullie

house museum natural history

collections

http://data.gbif.org/datasets/resource/11900 Megachilidae background

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Table 2 continued

Source Institution URL Data Type

GBIF Tullie house museum—Tullie

house museum. Invertebrate

records other than Lepidoptera.

Pre-2009 for Cumbria

http://data.gbif.org/datasets/resource/11917 Megachilidae background

GBIF University of Alberta museums

entomology collection

http://data.gbif.org/datasets/resource/2618 Megachilidae background

GBIF Unna-Muhlhausen Wiesen http://data.gbif.org/datasets/resource/2865 Megachilidae background

GBIF Unser Schulgelande http://data.gbif.org/datasets/resource/2714 Megachilidae background

GBIF USDA-ARS bee biology and

systematics laboratory

http://data.gbif.org/datasets/resource/1904 Megachilidae background

GBIF Wald und Wiese am Buchwald http://data.gbif.org/datasets/resource/2676 Megachilidae background

GBIF Wiesen am Treffpunkt Freizeit http://data.gbif.org/datasets/resource/3487 Megachilidae background

GBIF Wiesen-Walder-Wasser um

Dansenberg, Biospharenreservat

Pfalzerwald

http://data.gbif.org/datasets/resource/3500 Megachilidae background

GBIF ZFMK Hymenoptera collection http://data.gbif.org/datasets/resource/1840 Megachilidae background

GBIF Biologiezentrum Linz

Oberoesterreich

http://data.gbif.org/datasets/resource/1104 A. manicatum

GBIF Entomology collection http://data.gbif.org/datasets/resource/7911 A. manicatum

GBIF EUNIS http://data.gbif.org/datasets/resource/198 A. manicatum

GBIF Colecao de Abelhas do Museu de

Ciencias e Tecnologia da

PUCRS

http://data.gbif.org/datasets/resource/2003 A. manicatum

GBIF ZFMK Hymenoptera collection http://data.gbif.org/datasets/resource/1840 A. manicatum

GBIF Insects collection of the Ghent

University zoology museum

http://data.gbif.org/datasets/resource/1938 A. manicatum

GBIF Colecao Entomologica Pe. Jesus

Santiago Moure (Hymenoptera)

http://data.gbif.org/datasets/resource/1998 A. manicatum

GBIF University of Ghent—zoology

museum—invertebratacollectie

http://data.gbif.org/datasets/resource/2625 A. manicatum

GBIF Hymenoptera collection of the

finnish museum of natural

history

http://data.gbif.org/datasets/resource/8080 A. manicatum

GBIF Colecao Entomologica Paulo

Nogueira-Neto—IB/USP

http://data.gbif.org/datasets/resource/1997 A. manicatum

GBIF USDA-ARS Bee biology and

systematics laboratory

http://data.gbif.org/datasets/resource/1904 A. manicatum

GBIF Coleccion del Departamento de

Biologia Animal (Zoologia) de la

Universidad de La Laguna

http://data.gbif.org/datasets/resource/8081 A. manicatum

GBIF Bees of Canada/Gschwendtner

property collection

http://data.gbif.org/datasets/resource/1942 A. manicatum

GBIF Biologiezentrum Linz http://data.gbif.org/datasets/resource/1104 A. manicatum

GBIF Inventaire national du Patrimoine

naturel (INPN)

http://data.gbif.org/datasets/resource/2620 A. manicatum

GBIF Okostation (Freiburg) http://data.gbif.org/datasets/resource/2750 A. manicatum

GBIF Ratti master thesis/Fraser Valley,

British Columbia

http://data.gbif.org/datasets/resource/1944 A. manicatum

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Table 2 continued

Source Institution URL Data Type

GBIF Bees, wasps and ants recording

society—bees, wasps and ants

recording society—trial dataset

http://data.gbif.org/datasets/resource/934 A. manicatum

GBIF Highland biological recording

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GBIF Naturschutzgebiet Bausenberg http://data.gbif.org/datasets/resource/2657 A. manicatum

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http://data.gbif.org/datasets/resource/2697 A. manicatum

GBIF Rund um das LUGY http://data.gbif.org/datasets/resource/3022 A. manicatum

GBIF NABU Naturschutzhof Netttetal

(Sassenfeld) e. V.

http://data.gbif.org/datasets/resource/2759 A. manicatum

GBIF Unna-Muhlhausen Wiesen http://data.gbif.org/datasets/resource/2865 A. manicatum

GBIF Innenstadt Gottingen—Natur

Zuhause

http://data.gbif.org/datasets/resource/2851 A. manicatum

GBIF Bugs (GBIF-SE: Artdatabanken) http://data.gbif.org/datasets/resource/1154 A. manicatum

GBIF Finnish entomological database:

hymenoptera

http://data.gbif.org/datasets/resource/8239 A. manicatum

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A. manicatum

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20m?kind=Anthidium?manicatum

A. manicatum

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