The Status of the Western Bumble Bee (Bombus Occidentalis

To the University of Wyoming:

The members of the Committee approve the thesis of Christine Bell presented on 8 May

2019.

Dr. Michael E. Dillon, Chairperson

Dr. Lusha Tronstad, Co-Chairperson

Dr. Timothy Collier, External Department Member

Zach Wallace

APPROVED:

Dr. Merav Ben-David, Department Chair, Zoology and Physiology

Dr. Paula Lutz, Dean of the College of Arts and Sciences

Bell, Christine, Sampling methods and distribution modeling of bees: the status of the western

bumble bee (Bombus occidentalis) in Wyoming, M.S., Zoology and Physiology, August

2019.

Native bees provide crucial pollination services in both agricultural and natural settings. Several native bee populations have experienced declines in the last few decades, particularly bumble bees (genus Bombus). The western bumble bee (B. occidentalis) has been petitioned for listing under the Endangered Species Act, and this bee has historically occurred in Wyoming. However, the western bumble bee has not been monitored or surveyed in Wyoming for the last decade.

Monitoring declining species is essential to evaluate their conservation status and to inform future management practices as well as policy. Here, we first examine how sampling methods

(both passive and active) affect the abundance and number of taxa of bees collected. We then fit species distribution models for B. occidentalis using both historical and new data. We found that sampling method does affect the number and species of bees we collected, and we recommend that surveyors tailor their protocols to the taxa of interest. Our models suggest a decline in predicted suitable habitat for the western bumble bee in Wyoming.

SAMPLING METHODS AND DISTRIBUTION MODELING OF BEES: THE STATUS OF

THE WESTERN BUMBLE BEE (BOMBUS OCCIDENTALIS) IN WYOMING

Christine Bell

A thesis submitted to the Department of Zoology and Physiology

and the University of Wyoming

in partial fulfillment of the requirements

for the degree of

MASTER OF SCIENCE

in ZOOLOGY

Laramie, WY

August 2019

ACKNOWLEGDEMENTS

I would like to thank my advisors, Dr. Lusha Tronstad and Dr. Michael Dillon, as well as the members of my committee, Dr. Timothy Collier and Zach Wallace. I am very grateful to US

National Parks Service, US National Forest Service, Wyoming State Parks, and private landowners for providing us access to sample sites. I would also like to thank technicians

Madison Crawford, Charles Anderson, and Tighe Jones for their help in the field, Matthew

Green for his help in the lab and the field, and Dr. Scott Hotaling and Mark Andersen for their assistance. This project was funded by the Wyoming Bureau of Land Management, US Forest

Service, Wyoming Landscape Conservation Initiative, Wyoming NASA Space Grant

Consortium, and the University of Wyoming Biodiversity Institute. (Permit numbers GRTE-

2017-SCI-0035, YELL-2017-SCI-8002, FOLA-2018-SCI-0005, DETO-2018-SCI-0007)

iii

TABLE OF CONTENTS

Chapter One: Evaluating the best monitoring protocols for bees: vane traps, bee cups, and netting are not equal………………………………………………………………………………………. 1

Abstract……………………………………………………………………………………1

Introduction………………………………………………………………………………..2

Methods……………………………………………………………………………………4

Results……………………………………………………………………………………..6

Discussion…………………………………………………………………………………9

Chapter Two: Using species distribution models to assess the status of the declining western bumble bee (Hymenoptera: Apidae: Bombus occidentalis)………………………………………13

Abstract…………………………………………………………………………………..13

Introduction………………………………………………………………………………14

Methods…………………………………………………………………………………..17

Results……………………………………………………………………………………18

Discussion………………………………………………………………………………..20

References………………………………………………………………………………………..23

Tables…………………………………………………………………………………………….29

Figure Captions…………………………………………………………………………………..38

Figures……………………………………………………………………………………………41

CHAPTER 1

Evaluating the best monitoring protocols for bees: vane traps, bee cups, and

netting are not equal

Abstract

Bees are prolific and vital pollinators in both agricultural and natural settings, but some populations are declining, including the western bumble bee (Bombus occidentalis). Monitoring declining species is crucial to understand their status and conservation needs; however, a lack of standardized sampling methods can make range-wide monitoring difficult. Monitoring bees is usually done by three common sampling methods: blue vane traps, bee cups, and aerial netting.

Here we examine the difference in abundance and assemblages of bees sampled with these methods in Wyoming, USA, with a particular focus on bumble bees (genus Bombus). We sampled in Wyoming across 5 Level III Ecoregions. At each site we deployed the three most common methods as described above. We compared catch rate (insects/hour) and assemblage

(total number of taxa represented) for all bee genera and for species of Bombus. We collected both a greater abundance and assemblage of bees in vane traps than bee cups, with the exception of smaller sweat bees. The abundance of Bombus species collected did not vary between the vane traps and aerial netting; however, the assemblages detected were different between these two sampling methods. Our analysis suggests that sampling methods can affect both the abundance and assemblage of bees, and that sampling protocols should be tailored to the taxa of interest.

Introduction

Pollinators are vital to most terrestrial ecosystems for plant reproduction in both wild and agricultural settings. As many as 80% of native plant species depend on insect pollination to reproduce (Potts et al., 2010), including 35% of crops grown for human consumption. (Klein et al., 2007) Pollination by native bees often increases fruit set (Garibaldi et al., 2013). However, some native bee populations have been experiencing declines (Cameron et al., 2011; Jacobson et al., 2018; Meiners et al., 2019). Potential drivers of native bee declines include climate change, habitat loss and fragmentation, pathogens, invasive species, and pesticides (Potts et al., 2010).

Declines in abundance of pollinators could negatively affect native plant populations (Potts et al.,

2010), which could cause cascading effects for other herbivorous wildlife that feed on insect- pollinated plants like forbs and shrubs. Declining pollinators would likely have detrimental effects on crops requiring insect pollination, as many crops are sufficiently pollinated by native bees regardless of the presence of managed honey bees (Garibaldi et al., 2013; Mallinger and

Gratton, 2014). Pollinator declines in the United States have been so precipitous that one species, the rusty-patched bumble bee (Bombus affinis), has been listed as Endangered in the United

States under the Endangered Species Act (ESA; FR 50 CFR 17 3816 January 11, 2017), and three other species have been petitioned for ESA listing: the yellow-banded bumble bee (B. terricola), the western bumble bee (B. occidentalis), and Franklin’s bumble bee (B. franklini)

(Defenders of Wildlife, 2015).

Monitoring the decline of pollinator populations is crucial to understand their status and to identify potential causes for their declines (Joshi et al., 2015; Meiners et al., 2019; Rhoades et al., 2017). The lack of standardized sampling protocols in pollinator research makes comparison of studies and range-wide monitoring difficult (Lebuhn et al., 2013). Several methods are

commonly used to sample pollinators: colored pan traps/bee cups, vane traps, and aerial netting

(Rhoades et al., 2017; Roulston et al., 2007; Wilson et al., 2008). Pan traps are colored bowls or cups usually filled with soapy water that sample bees passively, and are perhaps the most widely used method (Droege et al., 2010). Vane traps, a more novel passive sampling method, are plastic jars fitted with a lid with two intersecting vanes (Stephen and Rao, 2005). Netting involves active sampling either by sweep netting vegetation or visually targeting bees and capturing them in an aerial insect net. These three methods can vary in both the abundance and diversity of bees sampled (Grundel et al., 2011; Rhoades et al., 2017). For example, cups tend to catch a higher abundance of small bees while vanes primarily caught large bees (Joshi et al.,

2015; McCravy et al., 2018). Sampling effort (e.g. netting duration and size of survey area, how long traps are deployed) will also change estimates of bee abundance and assemblage (Roulston et al., 2007; Wilson et al., 2008). Additionally, understanding what bees each sampling method collects is useful to better evaluate the status of the bee of interest, like bumble bees.

Bumble bees are efficient generalist pollinators that provide invaluable ecosystem services to both forbs and insect-pollinated crops (Losey and Vaughan, 2006). In the last few decades, notable bumble bee declines have been observed in the United Kingdom (Goulson et al., 2008) and in the U.S. (e.g. Cameron et al., 2011; Colla and Packer, 2008; MacPhail et al.,

2019). Monitoring is essential to help inform management practices and policy, which includes sampling methods. Different sampling methods – particularly vane traps and aerial netting – are known to be more effective for collecting bumble bees (Stephen and Rao, 2005; Strange and

Tripodi, 2019); however, we are not aware of any studies that compare the abundance and assemblage of bumble bees collected using vane traps, bee cups, and netting.

Our objective was to investigate how different sampling methods altered the abundance and assemblage (total number of taxa collected) of bees collected. We sampled 96 sites with bee cups, vane traps, and netting in Wyoming, USA to compare how sampling method altered bee assemblage structure, abundance, and richness. We sampled several habitat types including shortgrass prairie, sagebrush steppe, conifer forests and alpine tree line. We deployed three vane traps and three sets of bee cups at each site for 24-48 hours, and netted bumble bees at each site for 30 minutes. Our specific questions were 1.) How does sampling method affect catch rates

(insects/hour) of bees? 2.) How does sampling method affect bee assemblages? and 3.) What is the best method to collect bumble bees of conservation concern? Our results will help managers decide on a sampling method to monitor their bee(s) of interest. We recommend methods to sample different groups of bees and protocols for passive traps to help scientists and managers collect their target species.

Methods

Study area and sampling

We sampled across the state of Wyoming, USA in locations with varying climatic and landscape characteristics. Wyoming has a high-elevation (945 m to 2040 m) and semi-arid climate with several main ecotypes including short grass prairie, sagebrush steppe, conifer forest, and tundra. Annual precipitation varies widely in the state, from 15 cm in the basins to 230 cm in the mountains (NOAA Cooperative Station Normals, 1990).

We sampled pollinators in western Wyoming (55 sites) in May – August 2017 and eastern Wyoming (41 sites) in May – September 2018 (Fig. 1; 96 sites total). At 87 of the sites, we set out three blue vane traps (vane traps hereafter; Fig. 2a; SpringStar©) and three sets of bee

cups (Fig. 2b; 5 oz. polystyrene vials; 40 dram, Thornton Plastic Co., Salt Lake City, UT; painted yellow, white, and blue) for 24-48 hours. We placed vane traps and bee cups (also called pan traps, bowl traps, or bee bowls) at least 15 m apart and considered them to be independent samples (Droege et al., 2010). We actively target-netted bumble bees for 30 minutes (Fig. 2c, d) at 86 sites. All three sampling methods were done at the majority of sites (n = 80), with a small number of the sites only netted (n = 9), or had only cups and vane traps (n = 7). We visited most sites twice over the summer. Specimens were brought back to the laboratory where they were processed and identified to genus (Michener et al., 1994) or species (bumble bees only; Williams et al., 2014). Due to cryptic speciation, all individuals in the B. fervidus species complex were recorded as one species (Koch et al., 2018).

Statistical analysis

We used R (R Development Core Team 2013) and the packages plyr (Wickham, 2011),

Matrix (Bates and Maechler, 2013), and vegan (Oksanen et al., 2013) to calculate bee catch rates

(insects/hour), taxonomic richness, and perform statistical analyses. We calculated bee catch rate by dividing bee abundance by the number of hours each sampling method was deployed

(including night hours). Seventeen sites were removed from catch rate analysis due to incomplete deployment data or severe inclement weather during the trapping event (e.g. rain or snow). We compared bee catch rates of all bees, families, abundant genera, and bumble bee species as well as bee richness among sampling methods (vanes and cup sets for bees, and vanes, cup sets, and netting for bumble bees) with analysis of variance (ANOVA). We also compared bee catch rates and richness of all bees among cup colors using ANOVA. If a method was significantly different

(α ≤ 0.05), we used Tukey’s honest significant difference (HSD) to verify which methods

differed from one another with pair-wise comparisons. We excluded bees in the families

Andrenidae and Colletidae from analysis due to low abundance.

We then analyzed the data using the R package vegan (Oksanen et al. 2013) and specifically the metaMDS function with k = 5 and maxit = 250. For both data sets (all bees and bumble bees only) we removed any site with only one or two species observations because they introduced too many zeros for the metaMDS function to converge. Because cup samples had so few specimens for bumble bees only, all cup samples were removed from that analysis. Our final

MDS data set included 281 samples for the “all bees” data set (bee cups = 57, vane traps = 224) and 147 samples for “bumble bee only” (net = 67, vane = 80).

We characterized statistical differences between sampling methods using ‘adonis’ analysis with 999 iterations. Next, we characterized multivariate dispersion (i.e. how different samples are from one another) for both methods in both data sets using the function ‘betadisper’.

We assessed differences in dispersion between methods using Tukey’s Honest Significant

Differences tests. For all analyses, we used Bray-Curtis calculated dissimilarities. To better visualize taxonomic differences in bee assemblages collected with each sampling method, we constructed a ternary plot using the R package ggtern (Hamilton, 2015).

Results

We collected bees from 34 genera representing 5 families in vane traps and bee cups (n =

7,449; Table 1). We captured 33 genera in vane traps (97% of all genera collected) including 8 genera that were not present in cups. Bee cups caught 25 genera (76.5% of all genera collected) and one genus that was not present in vane traps (Table 1). Bee catch rate was four times higher in vane traps than sets of bee cups (Fig. 3a; F = 56.84, df = 1, p = <0.001) and we captured twice

as many genera in vane traps than in bee cup sets (Fig. 3b; F = 123.7, df = 1, p = <0.001). Within bee cups, blue cups captured more bees than white or yellow (F = 12.16, df = 2, p = <0.001;

Tukey’s HSD, p < 0.001). Blue cups caught 1.5 times more genera than both white and yellow cups (F = 12.38, df = 2, p = <0.001; Tukey’s HSD, p = <0.001).

Bee assemblages collected with bee cups and vane trap approaches differed (p = <0.001) but these differences only explained ~4.2% of the variation (Fig. 7a). Samples collected with bee cups were also more similar to one another (mean distance, bee cups = 0.066; mean distance, vane traps = 0.077; p, Tukey’s HSD <0.001).

We captured three genera of Andrenidae (1.0% of total abundance, 2.2% of total cup abundance, and 0.9% of total vane abundance) and two genera of Colletidae (0.9% of total abundance, 1.2% of total cup abundance, and 0.8% of total vane abundance). Four of the genera were collected in both vane traps and cups, and one genus was only collected in cups (Table 1).

We collected 7 genera of Halictidae that represented 38.2% of total bee abundance,

77.4% of total cup abundance, and 27.6% of total vane abundance. Vane traps caught about 1.5 times more halictid bees than cups (Fig. 4a; F = 4.207, df = 1, p = 0.041). Six of the genera were caught in both vane traps and bee cups, and one genus was caught only in vane traps. Three genera of Halictidae were most abundant. About 2.4 times more Agapostemon were collected in vane traps compared to bee cups (Fig. 5a; 10.0% of total abundance; F = 4.832, df = 1, p =

0.0296); however, there was no difference in abundance across sampling methods for Halictus

(Fig. 5b; 4.5% of total abundance; F = 2.366, df = 1, p = 0.127) or Lasioglossum (Fig. 5c; 22.4% of total abundance; F = 0.039, df = 1, p = 0.844).

We collected 9 genera of Megachilidae that represented 9.4% of total bee abundance,

5.7% of total cup abundance, and 10.1% total vane abundance. Vane traps caught about 7 times

more megachilid bees than cups (Fig. 4b; F = 20.1, df = 1, p = <0.001). Seven genera were collected in both vane traps and bee cups, and 2 genera were collected only in vane traps. Osmia was abundant and we caught 8 times more individuals in vane traps compared to cups (Fig. 5d;

5.2% of total abundance; F = 6.03, df = 1, p = 0.0151).

We captured 13 genera of Apidae that represented 50.5% of total bee abundance, 13.5% of total cup abundance, and 60.6% total vane abundance. Vane traps caught ~19 times more apid bees than cups (Fig. 4c; F = 23.42, df = 1, p = <0.001). Eight genera were caught in both vane traps and bee cups, and five genera were unique to vane traps. Four of the genera of Apidae were common in Wyoming and we collected more Anthophora (Fig. 5e; 6.4% total abundance; F =

5.102, df = 1, p = 0.0253), Bombus (Fig. 5f; 24.4%; F = 6.507, df = 1, p = 0.0116), Eucera (Fig.

5g; 7%; F = 4.337, df = 1, p = 0.041), and Melissodes (Fig. 5h; 3.5% of total abundance; F =

3.615, df = 1, p = 0.0608) in vane traps than bee cups.

We collected 20 species of Bombus (n = 3,595) using vane traps (85% of all Bombus species collected), bee cups (60% of all Bombus species collected), and aerial netting (95% of all

Bombus species collected). Five species were collected only in vane traps and netting, one species was collected only in vanes and cups, and 3 species were only captured by nets (Table 2).

Taxonomic assemblage differed among sampling methods (Fig. 6b; F = 11.11, df = 2, p =

<0.001). Vane traps captured 2.3 times as many genera as sets of cups (Tukey’s HSD, p <

0.03).Trapping methods also differed for the catch rate of species of Bombus (Fig. 6a; F = 3.85, df = 2, p = 0.022). The catch rate of netting and vane traps did not differ (p, Tukey’s HSD =

0.5785); however, both of these methods captured ~35 times more bumble bees than cups (p,

Tukey’s HSD ≤ 0.06).

For Bombus species collected, the net and vane trap methods differed in the recovered assemblages (p < 0.001) but these differences only explained ~3.1% of the variation (Fig. 7b).

The average difference among samples within groups was also highly similar (mean distance, net

= 0.069; mean distance, vane = 0.071; p, Tukey’s HSD = 0.23). Visualization by the ternary plot of the Bombus assemblage collected with each method showed a strong bias towards vane traps and netting, though interspecies variation occurred (Fig. 8).

Discussion

Trapping methods are widely known to differ in both abundance and assemblage of pollinators captured. We found that vane traps caught a greater abundance of bee genera and

Bombus species, as well as higher assemblages. However, our NMDS analysis suggests that trapping method alone is not the sole determinant of bee assemblage. In addition to trap type, floral abundance, sampling effort, timing of sampling, and ecosystem type can affect the species and abundance of bees captured. Trapping methods to monitor species of conservation concern should be tailored to increase detection probability and minimize bycatch. The results of our comparison of all bees collected in bee cups to blue vane traps were generally consistent with other similar studies, but each method had benefits and caveats. The abundance and assemblage of Bombus, including a rare species (B. occidentalis), collected by different trapping methods revealed that vane traps and netting are more effective at collecting bumble bees than bee cups.

Bee cups are a well-known and effective way to sample pollinators (Droege et al., 2010;

Wilson et al., 2016) and they are consistently effective in catching bees, regardless of sampling effort (Campbell et al., 2007; Westphal et al., 2008; Wilson et al., 2008). In most studies reviewed, blue cups were more effective at sampling bees than white and yellow cups, consistent

with our results. Bee cups tend to collect smaller bees like halictids (Joshi et al., 2015; McCravy et al., 2018), and are less successful when floral resources are abundant (Baum and Wallen,

2011). The type of container used for these samples varied widely, from small 3.25 oz. bowls

(Droege et al., 2010; Rhoades et al., 2017; Wilson et al., 2008) to 18-20 oz. bowls (Campbell et al., 2007; Westphal et al., 2008; Wilson et al., 2016). We used cups because they are robust and deep enough to retain water after 48 hours in the dry and windy conditions of Wyoming.

Vane traps routinely perform better than cups in both abundance and species richness of bees collected (Joshi et al., 2015; Rhoades et al., 2017; but see McCravey and Ruholl, 2017).

Vane traps are known to capture more large bees like Bombus (Gibbs et al., 2017; Joshi et al.,

2015; McCravy et al., 2018; Rhoades et al., 2017), and can catch a very high volume of bees

(Gibbs et al,. 2017; personal observations), suggesting they be used with caution when sampling for rare species. Joshi et al. (2015) found that vane traps had a higher ratio of common bees to rare bees when compared to other trapping methods, suggesting that vane traps may oversample common species. However, our data indicate that vanes collected a higher assemblage than netting (Fig. 7a).

Netting is a type of active sampling rather than passive traps like vane traps and bee cups, thus the effectiveness of netting is more dependent on sampling effort. Shorter netting times yielded fewer and less diverse bees (10 minutes; Rhoades et al., 2017) than much longer netting times (13.5 hours; Roulston et al., 2007). Instead of timed netting events, collecting a pre- determined number of bees can yield a more accurate representation of the community (Strange and Tripodi, 2019). Netting can also have extensive collector bias (Westphal et al., 2008), and can miss certain species if they are only active for part of the day (Roulston et al., 2007). Netting is also limited to areas with flowers in bloom.

Floral abundance affects pan trap sampling (Baum and Wallen, 2011; Wilson et al.,

2008), but to our knowledge, the effect of floral abundance on capture success has not been analyzed for other sampling methods. Netting is a favorite among bumble bee researchers

(Cameron et al., 2011; Strange and Tripodi, 2019), but is only possible in locations with blooming flowers. Our personal observations and unpublished data affirm that vane traps tended to collect more bees in areas without flowers and vice versa.

Trapping duration and the timing of sampling events can also affect bee abundance and assemblage. Our study, along with most others, left traps out for at least 24 hours to ensure time of day did not bias the bees collected. Bee abundance was calculated in many different ways in other studies, which makes comparisons difficult. We suggest calculating catch rate

(insects/hour) to strengthen comparison and range-wide monitoring despite sampling discrepancies between studies. Frequent sampling events (four times per month) at regular intervals catch a greater portion of all species present and account for the phenology of different species in a community (Banaszak et al., 2014), though fewer sampling events are recommended so bee populations are not over-sampled. Gezon et al. (2015) found that sampling twice a month did not have detrimental effects on native bee populations.

Certain trapping methods could vary depending on ecosystem type and targeted bee taxa or functional groups. Droege et al. (2010) examined sampling in four broad ecoregions, but only for pan trapping. Because we trapped all over the state in varying finer-scale (compared to

Droege et al., 2010) ecoregions with multiple sampling methods (Fig. 1), our data could be ideal for this comparison, particularly for Bombus species of concern

Our analyses lead us to recommend using both vane traps and aerial netting to monitor bumble bee populations, and conversely, to use bee cups to collect smaller bees. Our data

indicate that bee cups are not an effective way to catch bumble bees. Netting yielded high abundance and richness of bumble bees, but netting is limited by the presence of blooming flowers. Vane traps caught the largest assemblages of bumble bee species (Fig. 7b), and allowed us to sample areas we could not sample with nets alone. We still advise using vane traps with caution, but argue that they are a useful tool to monitor bumble bee populations. For example, mortality of rare species in vane traps could be avoided by leaving the traps dry and checking them often. Though we did not collect enough B. occidentalis to run analyses, we did observe some trends. We caught no B. occidentalis in bee cups (Table 2), so we do not recommend using these traps to detect this species. We netted more B. occidentalis than we caught in vane traps

(Table 2), but there were a few sites where they were captured only in vanes. Due to time and geographic constraints, we were not able to sample all locations at regular intervals, which we believe affected the detection of B. occidentalis.

Based on our results, we recommend selecting trapping methods tailored for the taxa of interest. Tailoring a trapping method for a species or group of conservation concern should increase detection rates while minimizing bycatch. As population declines are being detected in increasingly more species of bees, maximizing the effectiveness of techniques used to monitor these essential pollinators is necessary to understand drivers of the declines and inform conservation and management planning.

CHAPTER 2 Using species distribution models to assess the status of the declining western bumble bee (Hymenoptera: Apidae: Bombus occidentalis)

Abstract Monitoring populations of declining species is crucial to advance their conservation, but may be difficult if the species is rare or understudied. The western bumble bee (Bombus occidentalis) was once common in the western United States; however, this bee has declined drastically across its range over the last 30 years and is petitioned for listing under the United

States Endangered Species Act. Although several populations have been documented in the

Intermountain West, many areas remain under-sampled. Species distribution models (SDMs) can help guide sampling efforts to assess the conservation status of species of concern. Here, we used

SDMs developed from historical location data (1910-2010) to select sampling sites and investigate the distribution of B. occidentalis in Wyoming, USA. We sampled areas in Wyoming with varying probabilities of suitable habitat, revisited historical locations, and produced new

SDMs with the resulting data. Potentially suitable habitat for B. occidentalis declined by approximately 55% throughout the state, and we failed to detect the bee at 64% of historical locations. Our comparison of historical and current SDMs suggest that the variables that best predict suitable habitat have changed over time, and that regional- or local-scale surveys can provide a more comprehensive assessment of the conservation status of a declining species.

Introduction

Monitoring declining species is crucial for evaluating their conservation status, abundance, and distribution; however, sampling can be difficult if the species is thought to be rare (Thompson, 2004). Though invertebrates represent ~80% of named and described animals, they only account for 11% of the animals listed under the Endangered Species Act (Nature

Serve, 2015). Insects are the most diverse group of animals worldwide, but most are immensely under-studied. We are likely unaware of many insect species at risk of extinction simply because we are not looking (Dunn, 2005). Assessing the status of insect populations can be challenging, as many have patchy distributions across large ranges, exhibit highly variable phenology, and are represented poorly in historical records from under-sampling (Koch and Strange, 2009).

Historical data, usually in the form of museum specimens, are infrequent and likely temporally and spatially biased (Newbold, 2010; Rhoades et al., 2016). A few studies have documented the loss of insect biomass or biodiversity, which is concerning considering the vast ecosystem services they provide (Conrad et al., 2006; Hallmann et al., 2017), including detritivory, herbivory, parasitism (e.g. biological control), and pollination.

Pollination is essential for the reproduction of plants and most pollination is achieved through wind or bees (Michener, 2007). In particular, bumble bees (Hymenoptera: Apidae:

Bombus sp.) are highly efficient generalist pollinators that provide essential ecosystem services in both agricultural and natural settings (Losey and Vaughan, 2006). Data suggest that several bumble bee species have experienced notable declines in the last few decades (Williams et al.,

2014); however, the status of these species is uncertain as large areas of their historical ranges have not been surveyed. Causes of declines are largely unknown, but effects from pesticides, climate change, habitat degradation and fragmentation, and pathogen spillover from commercial

colonies are all suspected (Goulson et al., 2015). In North America, the decline of the rusty- patched bumble bee (B. affinis) warranted the species being listed as Endangered under the U.S.

Endangered Species Act in March 2017 (FR 50 CFR 17 3816 January 11, 2017). Three additional species, the yellow-banded bumble bee (B. terricola), Franklin’s bumble bee (B. franklini), and the western bumble bee (B. occidentalis) were petitioned for listing because of recently observed population declines (Defenders of Wildlife, 2015).

The western bumble bee was once one of the most common bumble bees in western

North America, spanning from central California to Alaska, east to South Dakota, and south into

New Mexico (Williams et al., 2014). Over the last 20 years, the western bumble bee has all but disappeared from much of its historical range, particularly on the west coast (Evans et al., 2008).

The status of populations in the intermountain west is largely unknown, and we are not aware of any surveys in the Rocky Mountain states conducted since 2009 (Cameron et al., 2011). To assess the current status of B. occidentalis, we must obtain up-to-date information on their distribution, as the remaining populations may be crucial to the survival of the species.

Species distribution models (SDMs) can predict suitable habitat of B. occidentalis and can inform future sampling and conservation efforts throughout their range. SDMs relate locations to environmental/landscape characteristics to predict the potential distribution of a species in areas where it has not been surveyed. These models are often used in conservation biology to estimate the current status of the species and aid management decisions (Crawford and Hoagland, 2010; Davis and Cipollini, 2016; Pogue et al., 2016; Rodríguez et al., 2007; Silva et al., 2016; Yost et al., 2008). Developing and exploring SDMs can also aid future research for an under-studied species or for communities by establishing baseline distribution information

(Pelletier et al., 2015). Augmenting historical collection data with new (current) occurrence data

can improve the performance of SDMs by presenting a more accurate representation of the current suitable habitat and can add insight into potential range shifts when compared with historical distributions (Rodríguez et al., 2007). Many techniques for modelling species distribution – such as generalized linear models, generalized additive models, random forest, etc.

– use presence and absence points within the spatial extent of the model. Absence points are often randomly generated and can lead to false absences, particularly when using historical or museum data, which could lead to erroneous predictions (Elith et al., 2011). One method – maximum entropy (MaxEnt) – uses presence-only data and reliably produces high performing models (Elith et al., 2011). MaxEnt models can be especially useful in predicting the distribution of rare or under-sampled organisms using small data sets (Silva et al., 2016), or in areas where little or no sampling has occurred (Newbold, 2010).

Though few historical records exist, range-wide B. occidentalis SDMs using only historical data and limited environmental factors indicate large swaths of suitable habitat in

Wyoming, USA (Sheffield et al., 2016). Local- or regional-scale models can identify population declines and provide a more comprehensive understanding of the status of a declining species by identifying unique environmental drivers affecting populations in particular parts of their range

(Rhoades et al., 2016). For example, bumble bee declines in North America were first detected in regional surveys (Cameron et al., 2011; Colla and Packer, 2008; Evans et al., 2008; Tripoldi and

Szalanski, 2014). Widespread species like B. occidentalis occur in many different types of ecosystems across their range, thus estimating changes in their predicted habitat in Wyoming may help calculate the status of the species in the eastern portion of their range.

Our objective was to investigate the status and distribution of B. occidentalis in

Wyoming, USA. We produced an SDM for B. occidentalis in Wyoming using available

historical occurrence data, and a variety of landscape and environmental variables to estimate the historical distribution and current status of this bee in the state. We then used the historical model to guide our sampling efforts, revisited historical locations (n = 28), and used these data to create an updated SDM. Specifically, we asked 1.) at what proportion of historical sites is B. occidentalis still present? 2.) Can SDMs reliably predict B. occidentalis suitable habitat in

Wyoming? and 3.) What variables best predict suitable habitat for B. occidentalis? Establishing baseline data will elicit new questions and research directions for this declining bee and provide information that will aid managers in their listing decision.

Methods

We obtained 90 unique historical location points from 1910-2010 from the Global

Biodiversity Information Facility (GBIF), the University of Wyoming Insect Museum, and personal observations. We produced a MaxEnt (version 3.4.1) model with historical occurrence data using ten-fold cross-validation with 25 potential climatic and landscape predictors (Phillips et al., 2006). We narrowed to the top 11 predictors by reviewing the scores for overall contribution and jackknife contribution (Table 3).

To generate new sampling locations, we selected sections in public land that varied in predicted suitable habitat in different ecoregions across a wide geographic range within the state.

We binned the probability levels (p) into 3 equal intervals and selected sections with low (0.0 < p

≤ 0.33, n = 17), medium (0.33 < p ≤ 0.66, n = 22), and high (0.66 < p ≤ 1.0, n = 36) predicted suitable habitat spread across 5 Level III ecoregions in the state (Chapman et al., 2004; Fig. S1).

Additionally, we revisited 28 historical sampling locations for a total of 103 sites.

We sampled pollinators in western Wyoming (62 sites) in May – August 2017 and eastern Wyoming (41 sites) in May – September 2018. We used 3 methods to detect the presence of the western bumble bee including 2 passive traps – blue vane traps and bee cup sets – and 1 active method – target netting. At 98 of the sites, we set out three vane traps (Fig. 2a) and three sets of bee cups (Fig. 2b; yellow, white and blue) for 24-48 hours. Vane traps and bee cups were placed at least 15 m apart to remain independent samples (Droege et al., 2010). We actively target netted bumble bees for 30 minutes (Fig. 2c, d) at 97 sites. Seven of the sites were only netted. We visited most sites twice over the summer. Specimens were brought back to the lab where they were processed and identified (Williams et al., 2014).

We fit an updated MaxEnt model with our new B. occidentalis locations using the same methods as our historical model, narrowed down to 7 predictor variables (Table 4). To compare the historical and current models, we calculated percent change of low, medium, and high probability areas.

Results

Field sampling and historical sites

We captured B. occidentalis at 27% (n = 28) of the sites we visited, and at 36% (n = 10) of the historical sites (Fig. 9). B. occidentalis was detected at 16 sites in western Wyoming in

2017 and at 12 sites in eastern Wyoming in 2018. All but two of the locations where we found B.

occidentalis were in areas with a high probability of suitable habitat as predicted by the SDM based on historical data.

Species distribution model

The fit of the historical SDM was satisfactory (AUC = 0.87; Fig. 10a). Suitable habitat for B. occidentalis was most affected by mean forest cover, precipitation of coldest quarter

(snowpack), isothermality (temperature evenness), mean herbaceous cover, mean temperature of wettest quarter, precipitation of wettest month, and precipitation seasonality (Table 5). The model predicted a relatively even proportion of areas of low (34.3%), medium (32.8%), and high

(32.9%) probability of suitable habitat.

The fit for our current model was improved (AUC = 0.90; Fig. 10b). The model indicated suitable habitat for B. occidentalis was closely associated with mean forest cover, mean temperature of warmest quarter, topographic position index, precipitation of the wettest month, and annual mean temperature (Table 6). The model predicted less suitable habitat for B. occidentalis currently compared to the historical model (low = 74.9%, medium = 10.3%, high =

14.8%).

Comparison of the historical and current models indicated a 55.0% decrease in areas with a high probability of suitable habitat, a 68.6% decrease in areas of medium probability, and a

118.4% increase in areas with a low probability (Fig. 11). Interestingly, B. occidentalis was found in low (n = 4), medium (n = 8), and high (n = 16) probability areas in the current model.

Discussion

Our SDMs suggested a loss in suitable habitat in Wyoming for B. occidentalis, an important generalist pollinator in western North America (Evans et al., 2008). Our regional survey indicated B. occidentalis populations may be affected by different environmental factors in certain parts of their range. Additionally, environmental factors driving predicted suitable habitat for the bee have changed over time. Our data provide a baseline of information on the distribution of the western bumble bee in Wyoming.

Range-wide SDMs for B. occidentalis suggest that temperature variables contributed the most to the model predicting the distribution of the bee (Sheffield et al., 2016). Our historical regional model for Wyoming indicated that precipitation variables play a greater role in predicting suitable habitat for B. occidentalis. Wyoming has a high elevation, relatively arid environment that is primarily water-limited (Chapman et al., 2004), which contrasts with other areas of B. occidentalis range such as the temperate rainforests of the Pacific Northwest.

However, temperature-related variables were some of the main contributors in our current model.

Climate change is known to affect insects (Kingsolver et al., 2011), and bumble bees in particular (Kerr et al., 2015), and may be responsible for this shift in environmental factors. We also found that suitable habitat is increasing at the edges of mountain ranges in Wyoming (Fig.

11), possibly indicating an elevational shift which has been observed in other bumble bee communities (Fourcade et al., 2019).

Previous models for the western bumble bee only used climatic variables (WorldClim

BioClim data; Cameron et al., 2011; Sheffield et al., 2016), a common practice with most SDMs

(Bucklin et al., 2015). We added multiple landscape variables along with climatic variables, with the former being some of the largest contributors (Tables 5 and 6), consistent with SDMs of

other taxa (Crawford and Hoagland, 2010; Pogue et al., 2016; Yost et al., 2008). Our historical model suggested higher probabilities of suitable habitat in areas with higher forest cover, more snowpack, an intermediate amount of temperature fluctuation, and lower herbaceous cover. Our current model indicated that B. occidentalis prefers habitats with little forest cover, areas with flatter ground or midslopes, and locations with higher precipitation averages (e.g., mountains).

Specifically, we found B. occidentalis in mountain meadows, forest edges, and in urban environments (Bell, personal observations).

Most SDMs are fit with historical location data, but are often improved with the addition of current data (Rodríguez et al., 2007). The comparison of our historical and current models illustrates the value of up-to-date data. We observed a loss in suitable habitat for B. occidentalis and potentially a range shift to higher elevations. Our current model predicting suitable habitat of

B. occidentalis provides a robust foundation for monitoring this declining species in Wyoming and other areas in the Intermountain Rockies. The observations of B. occidentalis in areas that our current SDM deemed a low or medium probability of suitable habitat may be an indication to interpret the models with caution. Maxent models often over-predict suitable habitat for a species

(Goljani Amirkhiz et al., 2018; Marcer et al., 2013); therefore, our results should be considered conservatively. Although SDMs are a useful tool to help guide survey efforts and identify variables that might be limiting the distribution of a species, they predict areas of suitable habitat, not the actual range of the species. Incorporating habitat characteristics at a finer scale – such as floral surveys, parasite and genetic analyses, etc. – will help us understand other ecological processes driving spatial patterns of B. occidentalis populations.

B. occidentalis was observed at 27% of the sites we visited in 2017 and 2018, consistent with other survey efforts in Wyoming and other areas of B. occidentalis range. In Wyoming,

Cameron et al. (2011) detected the bee at 25% of sites (n = 20) sampled in 2008 and 2009. In eastern Washington state, B. occidentalis was found at only 9% of the surveyed sites and the current abundance of B. occidentalis was drastically lower than the historical abundance, though the cause of these declines remains unclear (Rhoades et al., 2016). Low detection rates may be in part due to sampling in areas with low probability of suitable habitat, but also likely indicate a decline in B. occidentalis range.

Though our models indicate that suitable habitat for B. occidentalis is declining in

Wyoming, we still observed multiple populations. Their distribution appears to be patchy, though the species seems locally abundant within patches. Comparing our region-specific survey to areas where the bee is nearly extirpated could help identify potential causes for population declines. Wyoming provides a novel environment lacking some of the factors thought to drive population declines, including crops that tend to be pollinated by managed bumble bee colonies and widespread urban development. As B. occidentalis is nearly extirpated in large portions of its former range, Wyoming populations may become a stronghold for the species as a whole.

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Tables Table 1. Bee taxa collected in bee cups and vane traps in Wyoming, USA. Taxa sufficiently abundant for analyses denoted by (*). Taxa in bold were statistically significant.

Taxa Bee Cups Vane Traps

Andrenidae 30 53 Andrena 15 37 Calliopsis 3 - Perdita 12 16

Colletidae 18 46 Colletes 3 16 Hylaeus 15 30

Halictidae* 1115 1658 Agapostemon* 210 514 Augochlorella 4 10 Augochloropsis - 15 Dufourea 3 37 Halictus* 87 246 Lasioglossum* 805 819 Sphecodes 6 17

Megachilidae* 83 610 Anthidium 4 29 Ashmeadiella 2 1 Coelioxys 2 1

Taxa Bee Cups Vane Traps

Dianthidium 11 34 Heriades - 2 Hoplitis 13 118 Megachile 8 84 Osmia* 43 332 Stelis - 9

Apidae* 194 3640 Anthophora* 41 422 Apis mellifera 1 25 Bombus* 60 1811 Ceratina 4 71 Diadasia 18 162 Epeolus - 1 Eucera* 53 792 Habropoda - 8 Melecta - 8 Melissodes* 15 310 Nomada 2 7 Svastra - 17 Tetraloniella - 6

TOTAL BEES 1440 6007

Table 2. Number of Bombus species caught in bee cups, vane traps, and aerial netting in

Wyoming, USA.

Species Bee Cups Vane Traps Aerial Netting

B. appositus 4 247 58 B. auricomus - - 1 B. balteatus - 11 9 B. bifarius 4 322 406 B. bimaculatus - - 4 B. centralis 5 109 51 B. fervidus 13 222 80 B. flavidus 1 16 23 B. flavifrons 5 170 164 B. frigidus - - 1 B. griseocollis 1 15 55 B. huntii 5 120 184 B. insularis 1 154 150 B. melanopygus - 2 11 B. mixtus - 29 61 B. nevadensis - 78 66 B. occidentalis - 30 55 B. pensylvanicus 1 39 - B. rufocinctus 15 74 208 B. sylvicola 2 149 92

TOTAL BOMBUS 57 1787 1679

Table 3. Environmental variables used for historical species distribution models for the western bumble bee (Bombus occidentalis) in Wyoming. Variables included after model selection (n =

11; see Methods) indicated in bold.

Variable name Variable description bioclim1 Annual mean temperature bioclim2 Mean diurnal range bioclim3 Isothermality (temperature evenness) bioclim4 Temperature seasonality bioclim5 Maximum temperature of warmest month bioclim6 Minimum temperature of coldest month bioclim7 Temperature annual range bioclim8 Mean temperature of wettest quarter bioclim9 Mean temperature of driest quarter bioclim10 Mean temperature of warmest quarter bioclim11 Mean temperature of coldest quarter bioclim12 Annual precipitation bioclim13 Precipitation of wettest month bioclim14 Precipitation of driest month

Variable name Variable description bioclim15 Precipitation seasonality bioclim16 Precipitation of wettest quarter bioclim17 Precipitation of driest quarter bioclim18 Precipitation of warmest quarter bioclim19 Precipitation of coldest quarter elev Elevation forest Mean forest cover frestcc Percent forest canopy cover growdd Growing degree days (heat accumulation) herb Mean herbaceous cover tpi_11 Topographic position index, 11-cell focal

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Table 4. Environmental variables used for current species distribution models for the western bumble bee (Bombus occidentalis) in Wyoming. Variables included after model selection (n = 7; see Methods) indicated in bold.

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Table 5. Percent contribution of the top six variables in the historical species distribution model for Bombus occidentalis in Wyoming, USA, which account for over 90% of the total contribution.

Variable Variable description Percent contribution forest Mean forest cover 26.6 bioclim19 Precipitation of coldest 25.7

quarter (snowpack) bioclim3 Temperature evenness 10 herb Mean herbaceous cover 8.8 bioclim8 Mean temperature of wettest 8.5

quarter bioclim13 Precipitation of wettest month 5.8 bioclim15 Precipitation seasonality 5.2

Table 6. Percent contribution of the top six variables in the current species distribution model for

Bombus occidentalis in Wyoming, USA, which account for over 90% of the total contribution.

Variable Variable description Percent contribution forest Mean forest cover 68.9 bioclim10 Mean temperature of warmest 11.6

quarter tpi_11 Topographic position index 7.8 bioclim13 Precipitation of wettest month 5.5 bioclim1 Annual mean temperature 3.4

Figure Captions Figure 1. 2017-2018 sample site locations (red triangles) for bees in five Level III Ecoregions in

Wyoming separated by county boundaries (thick black lines), including Wyoming Basin (n =

26), Southern Rockies (n = 13), Middle Rockies (n = 47), Northwest Great Plains (n = 5), and

High Plains (n = 8). Two of the seven ecoregions, Snake River Plain and Wasatch and Unita

Mountains (white areas), were not sampled due to their small area.

Figure 2. Photos of a.) a vane trap, b.) a set of bee cups, and c.) using aerial nets to catch bumble bees (research conducted under Yellowstone Research Permit 2017-SCI-8002). d.) A netted

Bombus occidentalis.

Figure 3. Abundance (A, as insects/hour) and richness (B) of all bee genera sampled in bee cups and vane traps in Wyoming, USA. Black circles are mean values, bold lines are median values, lower and upper limits are the 25th and 75th percentiles and whiskers indicate the lower and upper limits of the data.

Figure 4. Abundance (as insects/hour) of bee families (A, Halictidae; B, Megachilidae; and C,

Apidae) sampled in bee cups and vane traps in Wyoming, USA. Black circles are mean values, bold lines are median values, lower and upper limits are the 25th and 75th percentiles and whiskers indicate the lower and upper limits of the data. We did not analyze Andrenidae or

Colletidae due to low abundance.

Figure 5. Abundance (as insects/hour) of dominant bee genera (A, Agapostemon; B, Halictus; C,

Lasioglossum; D, Osmia; E, Anthophora; F, Bombus, G, Eucera; and H, Melissodes) sampled in bee cups and vane traps in Wyoming, USA. Black circles are mean values, bold lines are median 38

values, lower and upper limits are the 25th and 75th percentiles and whiskers indicate the lower and upper limits of the data.

Figure 6. Abundance (A, as insects/hour) and richness (B) of Bombus species sampled in bee cups, vane traps, and aerial netting in Wyoming, USA. Black circles are mean values, bold lines are median values, lower and upper limits are the 25th and 75th percentiles and whiskers indicate the lower and upper limits of the data.

Figure 7. Comparisons of bee genera (A) collected in vane traps (white polygon) and bee cups

(grey polygon) and Bombus species (B) collected in vane traps (white polygon), and aerial netting (grey polygon) with non-metric multidimensional scaling (NMDS). Bee cups for Bombus species were removed from analysis due to low abundance.

Figure 8. Distribution of Bombus species collected in bee cups, vane traps, and aerial netting in

Wyoming, USA. The position of each point indicates the percentage of the associated species with each sampling method. The size of the circle represents total abundance of that species.

Figure 9. Sample site locations for bumble bees (Bombus sp.) in Wyoming separated by county boundaries (black lines). We visited both historical sites that we re-sampled in 2017-2018

(squares) and newly sampled sites (triangles). Blue points denote where B. occidentalis was captured, and yellow points denote where we failed to detect B. occidentalis.

Figure 10. Historical (a) and current (b) species distribution models for Bombus occidentalis in

Wyoming, USA with points representing historic (yellow circles) and current (2017-2018, blue triangles) locations of B. occidentalis. The darker color indicates a higher probability of occurrence.

Figure 11. The change in suitable habitat from the historical (Fig. 3a) and current (Fig. 3b) species distribution models for Bombus occidentalis in Wyoming, USA. Blue indicates a loss in suitable habitat over time and red indicates a gain in suitable habitat over time.

Figure 1.

a b d

Figure 2. 42

Figure 3.

A) Halictidae

B) Megachilidae

C) Apidae

Figure 4.

A) B) Halictus Agapostemon

D) Osmia C) Lasioglossum

E) Anthophora F) Bombus

G) Eucera H) Melissodes

Figure 5. 45

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10. 50

Figure 11. 51