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Using historical data to estimate bumble bee occurrence: Variable trends across species provide little support for community-level declines
Abstract
Bumble bees are globally important pollinators, especially in temperate regions, and evidence suggests that many species are declining. One recent high profile study by Soroye et al. (2020) applied occupancy models to dated historical collection data to quantify declines across North America and Europe. The authors modelled 66 species across a set of sites spanning both North America and Europe, rather than confining species to sites where they might be expected to occur. In addition, they inferred non-detections for time intervals where there is no evidence that the site was visited (by forcing every site to have exactly 3 visits in each era). We use simulated data to (i) investigate the validity of methods used in that study and (ii) test whether a multi-species framework that incorporates species' ranges and site visitation histories produces better estimates. We show that the method used by Soroye et al. (2020) yields biased estimates of declines, whereas our framework does not. We use such a model to provide revised and appreciably lower estimates for bumble bee community declines, with species-specific trends more closely matching classifications from IUCN. The species level trends we provide can help inform future species-at-risk assessments. Well-parameterized occupancy models may be a powerful tool for assessing species-wide trends using curated historical collection data.
... Typically, when applied to a community of species, occupancy models require a list of species recorded during each sampling event (i.e., "detections," or, species that must be present), as well as a list of species that were not observed despite a sampling effort (i.e., "non-detections," or, species that could either be absent from the site or present but not observed). Recent studies show that, in combination with appropriate data-and model-level structural decisions, occupancy models can be used to infer patterns of species occurrence using opportunistic, detection-only records such as those collected by museums or through community science initiatives (Guzman et al., 2021;Kery et al., 2010;Shirey et al., 2023). In this study, we extend the community occupancy modeling approach with data integration techniques, simultaneously modeling the separate processes generating two categorically different types of detection data (community science data and NHCs) that both emerge from a shared underlying species occurrence pattern (Davis et al., 2022;Doser et al., 2022;Schaub & Abadi, 2011). ...
... We then blocked the temporal span of our study into 3-year "intervals" (Guzman et al., 2021;Jackson et al., 2022;Shirey et al., 2023). We treated occurrence (Z i,j,k ) for a species i at site j during interval k as the outcome of a Bernoulli distributed random variable: ...
... We only modeled species occurrence at sites within their geographic range, allowing us to control for regional differences in species diversity (Shirey et al., 2023). Ranges were defined by drawing a convex hull around all detections from 2000 to present, and then intersecting the convex hull with the sites (Guzman et al., 2021;Shirey et al., 2023) (Appendix S1: Figure S11). We manually trimmed the ranges for two bumble bee species whose distributions are documented to be rapidly expanding (Bombus impatiens) or contracting (B. ...
As cities around the world expand, we urgently need to better understand the drivers of urban biodiversity, especially for functionally important groups such as insect pollinators. In this study, we gathered hoverfly and bumble bee pollinator observations from natural history collections and community science initiatives from 462 urban landscapes across 85 US metropolitan areas. We tested whether urban greenspace functions as pollinator habitat by examining whether the total area of greenspace in an urban landscape predicted pollinator occurrence, that is, the presence or absence of species in a landscape. Our study was designed to determine whether there were differences between natural greenspace area (i.e., urban greenbelts, nature reserves and forest/grassland fragments) and developed greenspace area (i.e., managed parks, cemeteries and golf courses) in their ability to support a diversity of pollinator species. After accounting for sampling biases using an integrated occupancy modeling approach, we found a positive association between native hoverfly occurrence and natural greenspace area. This implies that urban landscapes with more natural greenspace support higher native hoverfly diversity. On average, bumble bee occurrence was not associated with natural greenspace area; however, the response varied among species, with several at‐risk bumble bees showing a positive association. In contrast to natural greenspace area, we found no association between pollinator occurrence and the area of developed greenspace. In addition, we found that the proportion of racial minority households in an urban landscape was negatively associated with pollinator occurrence. This is consistent with the hypothesis that a history of systematic, unjust policies in neighborhoods with more racial minority households has lasting negative impacts on urban biodiversity. In conclusion, our results support the hypothesis that natural greenspace functions as vital habitat for urban pollinators. We recommend that cities preservation of remnant natural greenspace and improve developed greenspaces in order to promote urban pollinator conservation. These efforts should be prioritized in urban landscapes with a higher proportion of racial minority households to improve equal access to nature and pollinator ecosystem services.
... To confront the challenge that the most highly available distribution data could not be used in typical statistical models, new presence-only modeling frameworks that rely on "background" or "pseudo-absences" (Elith et al., 2006) have been widely used and have helped expand our knowledge about large-scale distributions (Elith & Leathwick, 2009). The use of presence-only data in large-scale ecological analysis have also expanded and continuing effort has been put towards modeling frameworks for using such data to estimate abundances Wepprich, 2019), phenological patterns (Larsen & Shirey, 2021), and range dynamics (Ascher et al., 2020;Guzman et al., 2021;Yackulic et al., 2013). ...
... For example, Ries et al. (2019) and Wepprich (2019) found that approaches used to estimate abundance declines from presence-only data in the Monarch butterfly, Danaus plexippus (Lepidoptera: Nymphalidae), were severely biased Wepprich, 2019). Finally, decisions made during the processing of presence-only data (including in imputation, censoring, and accounting for heterogeneous detection probability, etc.) can produce biased estimates of occupancy trends with a particular tendency to find declines (Ascher et al., 2020;Guzman et al., 2021). Underscoring all of these examples is, given the current biodiversity crisis, the need to develop new tools for inference. ...
... Yet, occupancy models demand non-detection data (specifically, zeros for all species in the community when not observed) and so their use with presence-only data has traditionally been considered inappropriate. Proposals for the use of occupancy models with presence-only data center on explicit additions of zeros, or "non-detections," in the data set by leveraging records of other species (Guzman et al., 2021;Jackson et al., 2022). A recent simulation study has confirmed that, when multiple species records in the same location are used as a proxy for an effort to have sampled the whole community, the inference of zeros in an occupancy modeling framework provides robust ecological signal from presence-only data (Shirey, Khelifa, et al., 2022). ...
Global climate change has been identified as a potential driver of observed insect declines, yet in many regions, there are critical data gaps that make it difficult to assess how communities are responding to climate change. Poleward regions are of particular interest because warming is most rapid while biodiversity data are most sparse. Building on recent advances in occupancy modeling of presence‐only data, we reconstructed 50 years (1970–2019) of butterfly occupancy trends in response to rising minimum temperatures in one of the most under‐sampled regions of North America. Among 90 modeled species, we found that cold‐adapted species are far more often in decline compared with their warm‐adapted, more southernly distributed counterparts. Furthermore, in a post hoc analysis using species' traits, we find that species' range‐wide average annual temperature is the only consistent predictor of occupancy changes. Species with warmer ranges were most likely to be increasing in occupancy. This trend results in the majority of butterflies increasing in occupancy probability over the last 50 years. Our results provide the first look at macroscale butterfly biodiversity shifts in high‐latitude North America. These results highlight the potential of leveraging the wealth of presence‐only data, the most abundant source of biodiversity data, for inferring changes in species distributions.
... Consequently, large-scale analyses must quantify how modelling assumptions may bias parameter estimates. For example, Soroye et al. [9] used occupancy models to assess species' declines and potential links to climate change in North America and Europe, however, subsequent analyses identified modelling assumptions that led to greatly overestimated declines in that work [10]. Identifying drivers of species-specific declines or increases remains an important and open question. ...
... While occupancy models are typically applied to the presence/absence data, recent studies have shown that their application to presence-only data is possible [10,[26][27][28][29]. In addition, multi-species occupancy models that account for species' expected ranges have been shown to be relatively effective at estimating species-specific trends over large timescales [10,30]. ...
... While occupancy models are typically applied to the presence/absence data, recent studies have shown that their application to presence-only data is possible [10,[26][27][28][29]. In addition, multi-species occupancy models that account for species' expected ranges have been shown to be relatively effective at estimating species-specific trends over large timescales [10,30]. Here, we apply occupancy models to a large bumblebee dataset to identify temporal drivers of change over the last century in North America. ...
Mounting evidence suggests that climate change, agricultural intensification and disease are impacting bumblebee health and contributing to species’ declines. Identifying how these factors impact insect communities at large spatial and temporal scales is difficult, partly because species may respond in different ways. Further, the necessary data must span large spatial and temporal scales, which usually means they comprise aggregated, presence-only records collected using numerous methods (e.g. diversity surveys, educational collections, citizen-science projects, standardized ecological surveys). Here, we use occupancy models, which explicitly correct for biases in the species observation process, to quantify the effect of changes in temperature, precipitation and floral resources on bumblebee site occupancy over the past 12 decades in North America. We find no evidence of genus-wide declines in site occupancy, but do find that occupancy is strongly related to temperature, and is only weakly related to precipitation or floral resources. We also find that more species are likely to be climate change ‘losers’ than ‘winners’ and that this effect is primarily associated with changing temperature. Importantly, all trends were highly species-specific, highlighting that genus or community-wide measures may not reflect diverse species-specific patterns that are critical in guiding allocation of conservation resources.
... In some groups, such as North American butterflies, undersampling is prolific in regions that are forecasted to experience the most dramatic changes in climate . Given these biases, improper treatment of unstructured data can lead to misleading inferences (Guzman et al., 2021;Larsen & Shirey, 2021). Thus, it is imperative that we develop statistical frameworks for analysing unstructured data to afford researchers the ability to test hypotheses related to how global change is impacting species over long time-periods and large spatial extents. ...
... For instance, it might be appropriate to infer non-detections of one (or more) species at a given site based on known detections of other species at that same site (Kamp et al., 2016;Kéry, 2010;van Strien et al., 2013). Constraining detection/non-detection records only to those sites that are plausibly within the range of a species' geographical distribution has also been shown to improve model performance (Guzman et al., 2021). Combined, these steps not only improve estimates of occupancy and detection, but can also reduce the computational size of analyses. ...
... The odonate dataset was downloaded from GBIF and can be found by the following DOI: https://doi.org/10.15468/ dl.cabqrc (GBIF, 2021). ...
Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed to enable robust inferences. Occupancy–detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time.
We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non‐detections in both space and time.
We find that not all datasets are suitable for occupancy–detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community‐wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case study on eastern North American odonates where we calculate long‐term trends of occupancy using our most robust workflow.
These results indicate that occupancy–detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable.
... Therefore, methods to account for spatial biases or observer variability in detection-nondetection modeling are commonly used (Van Strien et al., 2013). When biases are appropriately accounted for, integrating community science data with expert surveys can improve the accuracy of species distribution models and fill important spatial or temporal gaps when making inferences on distributional dynamics (Guzman et al., 2021;Robinson et al., 2020;Van Strien et al., 2013). This additional data can be particularly informative for rare or declining species when assessing their distributions across broad spatial regions (e.g., Ellis et al., 2023;Lin et al., 2022;Whitenack et al., 2023). ...
... By incorporating nondetection data, our framework has the benefit of estimating occupancy across a large landscape where sampling effort may be highly variable, and the actual distribution of the species may be obscured by nonrandom and unequal sampling effort. Validating nondetection assumptions and modeling decisions can be useful for identifying biased estimates of species declines that may mislead conservation applications (Guzman et al., 2021). We modeled variation in detection probabilities inherent in community science data by incorporating counts of common Bombus species. ...
There is growing interest in integrating community science data with structured monitoring data to estimate changes in distribution patterns of imperiled species, including pollinators. However, significant challenges remain in determining how unstructured community science data should be incorporated into formal analyses of species distributions. We developed a dynamic framework for combining community science and structured monitoring data of bumble bees to estimate changes in occupancy of rusty‐patched bumble bees (Bombus affinis), a federally endangered species in the United States. We applied traditional metapopulation theory and accounted for imperfect detection to estimate site‐specific extirpation risk and colonization rates across the known distribution of B. affinis in the Upper Midwest (USA). Despite a 144% increase in presence‐only detections from 2017 to 2022, occupancy probabilities and the estimated number of occupied sites remained static or declined slightly across a 4‐state region during this period. Our results provide preliminary evidence that the probability of local extirpation risk of B. affinis increased in response to drought, but that effect was tempered with a high number of neighboring patches occupied by B. affinis (i.e., rescue effect). Our framework can be used by managers to track population recovery goals for B. affinis and other bumble bees of conservation concern. In addition, our study highlights the importance of accounting for imperfect detection and addressing spatial sampling biases in bumble bee monitoring efforts, particularly those for which a portion of the monitoring data are generated from community science projects.
... auricomus). Bumblebees are ecologically and economically important pollinators of wild and agricultural plants [36], yet some species are currently experiencing dramatic declines in North America [37][38][39]. Some declines have been associated with climate change, but the responses between species are highly variable [40,41]. ...
... We chose to study a widely distributed and abundant species (B. impatiens) that is native to Iowa and has become increasingly common, while also recently expanding its geographical range [39,42]. In the present study, we examined locally collected B. impatiens, but we note the species is also commercially reared for pollination, which has contributed to their range expansion in the Pacific Northwest [43]. ...
Indices of climate vulnerability are used to predict species’ vulnerability to climate change based on intrinsic physiological traits, such as thermal tolerance, thermal sensitivity and thermal acclimation, but rarely is the consistency among indices evaluated simultaneously. We compared the thermal physiology of queen bumblebees between a species experiencing local declines (Bombus auricomus) and a species exhibiting continent-wide increases (B. impatiens). We conducted a multi-week acclimation experiment under simulated climate warming to measure critical thermal maximum (CTmax), critical thermal minimum (CTmin), the thermal sensitivity of metabolic rate and water loss rate and acclimation in each of these traits. We also measured survival throughout the experiment and after the thermal tolerance trials. Neither species acclimated to the temperature treatments by adjusting any physiological trait. We found conflicting patterns among indices of vulnerability within and between species. We also found that individuals with the highest CTmax exhibited the lowest survival following the thermal tolerance trial. Our study highlights inconsistent patterns across multiple indices of climate vulnerability within and between species, indicating that physiological studies measuring only one index of climate vulnerability may be limited in their ability to inform species’ responses to environmental change.
... Our work complements these studies by extending the focus to the quality of these data with respect to reporting, reproducible data collection, and the interests of conservation decision-makers. Based on the recognition Absence data are defined as a record with species, date, location, protocol, and effort, but with a count (number of individuals) of zero. 4 Occupancy models consider multiple visits (at least two) to calculate detection probabilities and probability of species occurrence (Graves et al., 2020;Guzman et al., 2021;Janousek et al., 2023;Boone et al., 2023b). This is different from calculating the geographic range using "area of occupancy", i.e., the number of grid cells with presence data. ...
... Occupancy models, when completed using best practices, provide a more accurate assessment of a species distribution because they consider imperfect detection (Graves et al., 2020;Guzman et al., 2021;Janousek et al., 2023;Otto et al., 2023;Boone et al., 2023a;Boone et al., 2023b). Their creation requires information about surveys where species were detected or not detected (i.e., absence data where the individual count is zero), and multiple visits at the same site. ...
Introduction
Bee conservation in the US is currently hindered by challenges associated with assessing the status and trends of a diverse group of >3000 species, many of which are rare, endemic to small areas, and/or exhibit high inter-annual variationin population size. Fundamental information about the distribution of most species across space and time, thus, is lacking yet urgently needed to assess population status, guide conservation plans, and prioritize actions among species and geographies.
Methods
Using wild bee data from two public data repositories representing the contiguous US, we evaluated the availability and sufficiency of data for use in species assessments of wild bees. We also examined the number of bee species recorded in each US state and the proportion of species with recent records (2012–2021).
Results
Although efforts to monitor bees continue to grow, there remains a massive paucity of data. Exceedingly few records (0.04%)reported both sampling protocol and effort, greatly limiting the usefulness of the data. Few species or locations have adequate publicly available data to support analyses of population status or trends, and fewer than half of species have sufficient data to delineate geographic range. Despite an exponential increase in data submissions since the 2000s, only 47% of species were reported within the last decade, which may be driven by how data are collected, reported, and shared, or may reflect troubling patterns of local or large-scale declines and extirpations.
Discussion
Based on our analysis, we provide recommendations to improve the quality and quantity of data that can be used to detect, understand, and respond to changes in wild bee populations.
... Our results indicate substantial effects on pollinator species, and this is in line with the expected high susceptibility of Mediterranean mountains biodiversity due to global warming (Bravo et al., 2008;Kougioumoutzis et al., 2020;Pauli et al., 2012). However, our projections did not indicate extinction of any pollinator species, in line with Guzman et al. (2021), who provided evidence for an overestimation of bumblebee declines by Soroye et al. (2020). Recent studies revealed resilience and even expansion in potential area of suitable habitat for some hoverfly pollinator species in eastern Mediterranean basin (Kaloveloni et al., 2015;Miličić et al., 2018) which was attributed to the species' ecological requirements and adaptive capacity. ...
... Indeed, even when a pollinator group was projected to shift upwards, some species within the group were found to move downslope and this may be the result of either both minimal and maximal altitudes decrease or because one of them decreases more than the other increases. These findings support the view that conservation applications must simultaneously consider taxon-specific forecasts along with focusing on a higher group (Ghisbain et al., 2020;Guzman et al., 2021). ...
Climate change is predicted to dramatically affect mountain biodiversity and especially mountain pollination systems due to the mutual dependence between plants and pollinators. In this work, we investigate climate change effects on pollinator distribution and diversity along the altitudinal gradient of Mt. Olympus, a biodiversity hotspot.
We used a species distribution modelling framework and predicted species richness hotspots, potential distribution and altitude change for 114 pollinator species, comprising bees, butterflies, and hoverflies along the altitudinal gradient (327–2596 m a.s.l.).
We projected potential loss of suitable habitat and upward shift for most pollinator groups, with the exception of bumblebees and hoverflies which were predicted to descend. Pollinator extinctions were not forecasted; instead, we observed a pronounced species-specific response to climate change. Species richness hotspots will be relocated to higher altitudes and to the north-eastern mountain side.
Projections for substantial but not detrimental climate change effects on pollinator fauna may be due to species differential resilience to climate change along with the existence of microrefugia on Mt. Olympus. Divergent response to global warming by bumblebees and hoverflies is probably due to species distribution modelling limitations, resulting in exclusion of the rarest species. We conclude that the predicted climate change impact stresses for the need of urgent conservation measures, including the expansion of the protection status over the whole mountain.
... To compare historical and recent periods, we considered two 30-year time intervals: 1910-1939 (hereafter P1) and 1990-2019 (hereafter P2). We considered equal interval durations to reduce biases related to fluctuations in bee populations (Guzman et al. 2021). Data recorded before 1910 and between 1940 and 1989 were too sparse and scattered to allow comparisons with P1 or P2 at any grid cell, and were therefore excluded from the analysis ( Figure S1); 9.2% and 80.1% of records occurred in P1 and P2, respectively. ...
Anthropic-related pollinator decline is a major challenge. To ensure that we characterize the underlying ecological processes and implement appropriate conservation measures, it is important to consider multiple dimensions of diversity. Here, we use a rare dataset of bee communities spanning over 100 years (1910–2019) in northern France, an area subject to severe land use alteration. By analyzing species-level data together with functional traits, we demonstrate a significant decline and homogenization of taxonomic diversity associated with a functional restructuring of bee communities. By combining extinction probabilities based on Red List categories with functional characteristics, we identified species critical for maintaining functional diversity and in need of urgent conservation actions. Present-day communities exhibited functional shifts favoring larger, eusocial generalist species with a long phenology, likely reflecting greater adaptability to reduced resource availability in space and time. Species with higher thermal resistance and warmer climatic niches were favored over time, indicating functional filtering of species adapted to climatic warming. In addition, recent bee communities were characterized by species with larger habitat breadth and stronger affinity for artificial habitats. These changes resulted in decreased functional evenness and increased dominance of species with extreme trait combinations, reducing functional redundancy and potentially destabilizing ecosystem processes. Suitable habitats and host plants are identified and recommended for supporting the most functionally threatened bee species. Our findings underscore the importance of considering functional traits in conservation prioritization efforts, and advocate a more integrated approach that incorporates both taxonomic and functional perspectives to effectively mitigate bee biodiversity loss.
... Our method likely provides a conservative estimate of individual species decline, as it assumes the net community of lady beetles, inclusive of alien species, has remained roughly stable, and changing rates of captures across the community through time are associated with sampling effort. Although occupancy models have been used to attempt to account for wide variation in sampling intensity in similar museum records (Erickson & Smith, 2021), these methods are generally dependent on large amounts of metadata to properly parameterize the models to give authentic estimates of absolute abundance (Guzman et al., 2021). Likewise, comprehensive land cover data at the spatial and temporal resolution required for other modeling techniques are not readily available for studies focused on time periods before contemporary remote sensing technology. ...
Understanding causes of insect population declines is essential for the development of successful conservation plans, but data limitations restrict assessment across spatial and temporal scales. Museum records represent a source of historical data that can be leveraged to investigate temporal trends in insect communities. Native lady beetle decline has been attributed to competition with established alien species and landscape change, but the relative importance of these drivers is difficult to measure with short‐term field‐based studies. We assessed distribution patterns for native lady beetles over 12 decades using museum records, and evaluated the relative importance of alien species and landscape change as factors contributing to changes in communities. We compiled occurrence records for 28 lady beetle species collected in Ohio, USA, from 1900 to 2018. Taxonomic beta‐diversity was used to evaluate changes in lady beetle community composition over time. To evaluate the relative influence of temporal, spatial, landscape, and community factors on the captures of native species, we constructed negative binomial generalized additive models. We report evidence of declines in captures for several native species. Importantly, the timing, severity, and drivers of these documented declines were species‐specific. Land cover change was associated with declines in captures, particularly for Coccinella novemnotata which declined prior to the arrival of alien species. Following the establishment and spread of alien lady beetles, processes of species loss/gain and turnover shifted communities toward the dominance of a few alien species beginning in the 1980s. Because factors associated with declines in captures were highly species‐specific, this emphasizes that mechanisms driving population losses cannot be generalized even among closely related native species. These findings also indicate the importance of museum holdings and the analysis of species‐level data when studying temporal trends in insect populations.
... Many biological control methods are compliant with organic farming standards. They support organic certification by aligning with principles of natural and sustainable agriculture practices [90]. 6. Cost-Effectiveness: While initial setup costs may be comparable to chemical pest control, biological control can lead to cost savings over time. ...
The future of farming holds great promise with the advancement of biological control techniques aimed at enhancing crop health and sustainability. Biological control involves harnessing natural enemies of pests, such as predators, parasitoids, and pathogens, to manage pest populations in agricultural ecosystems. This approach contrasts with conventional pesticide use, offering more environmentally friendly and sustainable solutions to pest management challenges. In recent years, biological control has seen significant technological advancements that promise to revolutionize crop protection practices. One such innovation is the development and application of microbial biopesticides, which utilize naturally occurring microorganisms like bacteria, fungi, and viruses to suppress pests and diseases. These biopesticides are often specific to target pests, minimizing harm to beneficial organisms and reducing chemical residues in crops and the environment. Moreover, the integration of precision agriculture technologies and data analytics has enhanced the efficacy and deployment of biological control strategies. Farmers can now monitor pest populations in real time, making informed decisions on when and where to apply biological agents. This precision not only optimizes pest control efforts but also minimizes input costs and environmental impact. Looking ahead, the future of farming with biological control techniques lies in further refining these methods through ongoing research and innovation. Advances in genetic technologies, such as CRISPR-based gene editing, offer the potential to engineer crops with inherent resistance to pests and diseases, reducing reliance on external control measures altogether. Furthermore, the promotion of ecological approaches like habitat manipulation and conservation biological control will enhance biodiversity and ecosystem services within agricultural landscapes, fostering resilient farming systems capable of adapting to future challenges posed by climate change and evolving pest pressures.
... An increasing number of wild bee species are declining worldwide, likely from a combination of stressors, including habitat loss, pesticides, malnutrition, climate change, invasive species, an increasing prevalence of wild bee diseases and pathogen spillover from managed bees (Cameron et al. 2011;Colla et al. 2012;Graystock et al. 2013;Goulson et al. 2015; Baron et al. 2017;Cameron & Sadd 2020;Botías et al. 2021;Burnham et al. 2021;Aldercotte et al. 2022;Jackson et al. 2022). The impacts of environmental changes on bumble bees (Apidae: Bombus), an important group of wild bee pollinators, are increasingly documented (Cameron et al. 2011;Botías et al. 2021;Guzman et al. 2021;Siviter et al. 2021;Jackson et al. 2022;Su et al. 2022). A growing number of investigations are assessing health of wild bumble bee individuals, colonies, and populations (Giacomini et al. 2018;Cameron & Sadd 2020;McNeil et al. 2020;Pislak Ocepek et al. 2021;Trillo et al. 2021;Tsvetkov et al. 2021;Garlin et al. 2022) and more than 50% of studies examining causes of bumble bee decline considered parasitic infections (Cameron & Sadd 2020). ...
An increasing number of wild bee species are declining or threatened with extinction worldwide. Decline has been proposed to be caused by a combination of threats, including increasing wild bee disease prevalence and pathogen spillover from managed bees that can reduce health of wild bees. Most approaches aiming at characterizing bee health, however, require sacrificing tens to hundreds of individual bees per site or species, with reports of several thousand individuals collected per study. Considering the widespread need to assess bee health, this sampling approach is not sustainable, especially for endangered populations or species. Here, we present a non-destructive protocol to collect bumble bee faeces and assess parasite loads of wild-caught individuals. The standard protocol consists of net-capturing individual bumble bees and placing them in a 10 cm (diameter) petri dish to collect faeces. This fecal screening approach is frequently used in laboratory settings, but much less in the field, which can impair conservation research. When placing bumble bees in a previously refrigerated cooler, we successfully collected faeces for 86% individuals, while the standard protocol, as used in laboratory settings, yielded 76% success in collecting faeces. We also identified cells and spores of two common gut parasites Crithidia spp. and Vairimorpha spp. in faecal samples. The faecal sampling presented here opens future avenues to assess bee pathogen loads using molecular techniques, while collected faeces could also be used to assess bee health more broadly, including bee microbiota and bee diet.
... However, these insects face multiple abiotic and biotic threats and stressors which have already led to losses in species richness, diversity (Biesmeijer et al. 2006;Zattara & Aizen 2021) and local extinctions (Ollerton et al. 2014). Serious declines in some bumblebee species have also been found (Graves et al. 2020;Guzman et al. 2021). Across Europe, at least 9% of bee species are considered threatened, whereas in Ireland almost one third of bee species are already placed in this category (Fitzpatrick et al. 2006;Nieto-Romero et al. 2014). ...
Globally, pollinating insects face significant pressure, largely due to intensively managed agricultural systems. There has been considerable focus on the provision of resources for pollinators in agricultural landscapes, but without understanding how existing farmland habitats affect pollinators there is a risk these conservation actions could fail. The aim of this study was to explore the relationships between the quantity, diversity, and quality of on-farm habitats with pollinator communities. To meet this aim, pollinator, floral and habitat features were assessed at twenty-nine sites, encompassing both livestock and crop systems, at a range of farming intensities, in two regions of Ireland. Results showed that the three main taxonomic pollinator groups (hoverflies, social bees, and solitary bees) were inconsistent in their responses to habitat and environmental variables. Hoverflies were negatively associated with farms with increasing amounts of linear feature and fewer drainage ditches, whereas bumblebees were positively associated with crop farms and the number of grassy margins, drainage ditches and hedgerows at a site. Solitary bees were negatively associated with crop farms and positively associated with high floral species richness. At a species level, community analysis showed that within taxonomic groups, individual species responded differently to environmental variables. This study demonstrates that different farm types and habitat features impact pollinator groups differently. One-size does not fit all, thus on-farm conservation actions should be designed with knowledge of taxon-specific responses to maximise benefits. The quantity and diversity of essential habitats are important along with the quality of those features in terms of their capacity to provide sufficient resources for pollinators.
... Worldwide, bumble bees face increasing threats associated with climate and anthropogenic landscape change, which have resulted in numerous population declines and species range restrictions (Szabo et al. 2012, Sanchez-Bayo and Goka 2014, Kerr et al. 2015, Biella et al. 2017, Guzman et al. 2021. In western North America, Bombus occidentalis Greene is a well-established case of species' decline, with declines reported throughout some southern portions of its range over the last several decades (Colla and Ratti 2010, Committee on the Status of Endangered Wildlife in Canada [COSEWIC] 2014). ...
We present evidence for historical change in a bumble bee community on Galiano Island, British Columbia, Canada, including the probable extirpation of three bumble bee species—Bombus insularis Smith, B. occidentalis Greene, and
B. suckleyi Greene—as well as the disappearance of two species represented by singletons in the historical record:
B. fervidus Fabricius and B. flavidus Eversmann. Evidence is based on a comparison of historical and contemporary species occurrence data, including recent data from intensive sampling targeting bumble bees using blue vane traps. The decline of B. occidentalis in southern portions of its range has long been observed, yet to our knowledge this is the first established case of its probable extirpation within an extensively surveyed part of its range. Results indicate that an additional species, B. vosnesenskii Radoszkowski, is a recent arrival on Galiano Island and has been expanding its range concurrently with the decline of B. occidentalis. Elsewhere in the region B. vosnesenskii has become a dominant species, particularly in urban environments. However, our data show it to be the least abundant species on this largely forested island. We also report patterns in the occurrence of B. sitkensis Nylander and B. vosnesenskii, suggesting that niche segregation may confound the effect of competitive exclusion previously reported for these species. Potential factors contributing to this likely case of bumble bee extirpation and subsequent colonization are discussed in the context of Galiano Island’s historical land use and ecology. In conclusion, we assess the potential for community science to aid in the detection of ecological change via comparison of historical baseline and contemporary crowd-sourced biodiversity data.
... Effects of climate change in bumble bees have been well documented using large-scale geographic and climate modeling approaches. In Europe and North America, climate has been found to be a better predictor than habitat in explaining declines, and regions with greater increase in temperature have experienced greater declines in bumble bee diversity (Soroye et al., 2020;though see Guzman et al., 2021). Bumble bee species have also experienced range contraction in response to climate warming, with southern ranges receding northward without a corresponding shift at the northern edges of their ranges (Kerr et al., 2015). ...
Globally, insects have been impacted by climate change, with bumble bees in particular showing range shifts and declining species diversity with global warming. This suggests heat tolerance is a likely factor limiting the distribution and success of these bees. Studies have shown high intraspecific variance in bumble bee thermal tolerance, suggesting biological and environmental factors may be impacting heat resilience. Understanding these factors is important for assessing vulnerability and finding environmental solutions to mitigate effects of climate change. In this study, we assess whether geographic range variation in bumble bees in the eastern United States is associated with heat tolerance and further dissect which other biological and environmental factors explain variation in heat sensitivity in these bees. We examine heat tolerance by caste, sex, and rearing condition (wild/lab) across six eastern US bumble bee species, and assess the role of age, reproductive status, body size, and interactive effects of humidity and temperature on thermal tolerance in Bombus impatiens . We found marked differences in heat tolerance by species that correlate with each species' latitudinal range, habitat, and climatic niche, and we found significant variation in thermal sensitivity by caste and sex. Queens had considerably lower heat tolerance than workers and males, with greater tolerance when queens would first be leaving their natal nest, and lower tolerance after ovary activation. Wild bees tended to have higher heat tolerance than lab reared bees, and body size was associated with heat tolerance only in wild‐caught foragers. Humidity showed a strong interaction with heat effects, pointing to the need to regulate relative humidity in thermal assays and consider its role in nature. Altogether, we found most tested biological conditions impact thermal tolerance and highlight the stages of these bees that will be most sensitive to future climate change.
... A complementary approach is to include only those visits that occur within the active period of the focal species. Such refinement can also be applied spatially, where visits to sites far beyond the likely range of the focal species can be removed (Guzman et al., 2021). ...
Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined 'regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the 'ideal' and 'minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.
... We, too, are sceptical about the models of Soroye et al. [12], but suggest that the most pernicious issue is likely to be the lack of representativeness in their data, not the precise way that nondetections were inferred (cf. [13]). We should remember that any model-based estimates, whether of a data selection mechanism (e.g., an observation process) or of an ecological state variable, can suffer from bias. ...
In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of “big data”, however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts.
... Insect pollinators, including wild bees, are declining worldwide (Klein et al., 2017;Didham et al., 2020;Wagner, 2020). This is exemplified by bumble bees, which provide important natural and crop-related pollination services, but have declined in Northern America and Europe, with important inter-specific variations in the observed trends (Cameron et al., 2011;Guzman et al., 2021;Jackson et al., 2022). Causes of such declines are multiple and include habitat loss, climate change, pesticides and disease spillover from managed bees (Goulson et al., 2015;Cameron and Sadd, 2020). ...
Increasing evidence highlights the importance of diet content in nine essential amino acids for bee physiological and behavioural performance. However, the 10th essential amino acid, tryptophan, has been overlooked as its experimental measurement requires a specific hydrolysis. Tryptophan is the precursor of serotonin and vitamin B3, which together modulate cognitive and metabolic functions in most animals. Here, we investigated how tryptophan deficiencies influence the behaviour and survival of bumble bees (Bombus terrestris). Tryptophan-deficient diets led to a moderate increase in food intake, aggressiveness and mortality compared with the control diet. Vitamin B3 supplementation in tryptophan-deficient diets tended to buffer these effects by significantly improving survival and reducing aggressiveness. Considering that the pollens of major crops and common plants, such as corn and dandelion, are deficient in tryptophan, these effects could have a strong impact on bumble bee populations and their pollination service. Our results suggest planting tryptophan and B3 rich species next to tryptophan-deficient crops could support wild bee populations.
... Several recent studies have suggested that terrestrial invertebrates may be suffering drastic population and diversity losses (Dirzo et al., 2014;Welti et al., 2020;Wepprich et al., 2019). However, these losses are not distributed equally across the planet nor across taxonomic diversity (Guzman et al., 2021;van Klink et al., 2020). Generalizations about trends in global invertebrate diversity and abundance require a solid data foundation, yet invertebrates remain significantly poorlysampled relative to their diversity and abundance (Høye et al., 2021;van Klink et al., 2020). ...
Despite growing concerns over the health of global invertebrate diversity, terrestrial invertebrate monitoring efforts remain poorly geographically distributed. Machine-assisted classification has been proposed as a potential solution to quickly gather large amounts of data; however, previous studies have often used unrealistic or idealized datasets to train and test their models.
In this study, we describe a practical methodology for including machine learning in ecological data acquisition pipelines. Here we train and test machine learning algorithms to classify over 72,000 terrestrial invertebrate specimens from morphometric data and contextual metadata. All vouchered specimens were collected in pitfall traps by the National Ecological Observatory Network (NEON) at 45 locations across the United States from 2016 to 2019. Specimens were photographed, and two separate machine learning paradigms were used to classify them. In the first, we used a convolutional neural network (ResNet-50), and in the second, we extracted morphometric data as feature vectors using ImageJ and used traditional machine learning methods to classify specimens. Issues stemming from inconsistent taxonomic label specificity were resolved by making classifications at the lowest identified taxonomic level (LITL). Taxa with too few specimens to be included in the training dataset were classified by the model using zero-shot classification.
When classifying specimens that were known and seen by our models, we reached a maximum accuracy of 72.7% using eXtreme Gradient Boosting (XGBoost) at the LITL. This nearly matched the maximum accuracy achieved by the CNN of 72.8% at the LITL. Models that were trained without contextual metadata underperformed models with contextual metadata. We also classified invertebrate taxa that were unknown to the model using zero-shot classification, reaching a maximum accuracy of 65.5% when using the ResNet-50, compared to 39.4% when using XGBoost.
The general methodology outlined here represents a realistic application of machine learning as a tool for ecological studies. We found that more advanced and complex machine learning methods such as convolutional neural networks are not necessarily more accurate than traditional machine learning methods. Hierarchical and LITL classifications allow for flexible taxonomic specificity at the input and output layers. These methods also help address the ‘long tail’ problem of underrepresented taxa missed by machine learning models. Finally, we encourage researchers to consider more than just morphometric data when training their models, as we have shown that the inclusion of contextual metadata can provide significant improvements to accuracy.
... FA was consistently higher in warmer and wetter years. Importantly, this finding seems to support other studies finding a strong signal of climate in driving insect population trends (Forister et al., 2018;Halsch et al., 2021;Kerr et al., 2015;Román-Palacios & Wiens, 2020; but see Guzman et al., 2021). It is also consistent with most bumblebee populations being better adapted to colder conditions, with different populations-regardless of whether they are from warm or cold environments-having similar tolerances to high temperatures (Martinet et al., 2021;Pimsler et al., 2020). ...
Determining when animal populations have experienced stress in the past is fundamental to understanding how risk factors drive contemporary and future species' responses to environmental change. For insects, quantifying stress and associating it with environmental factors has been challenging due to a paucity of time‐series data and because detectable population‐level responses can show varying lag effects. One solution is to leverage historic entomological specimens to detect morphological proxies of stress experienced at the time stressors emerged, allowing us to more accurately determine population responses.
Here we studied specimens of four bumblebee species, an invaluable group of insect pollinators, from five museums collected across Britain over the 20th century. We calculated the degree of fluctuating asymmetry (FA; random deviations from bilateral symmetry) between the right and left forewings as a potential proxy of developmental stress.
We: (a) investigated whether baseline FA levels vary between species, and how this compares between the first and second half of the century; (b) determined the extent of FA change over the century in the four bumblebee species, and whether this followed a linear or nonlinear trend; (c) tested which annual climatic conditions correlated with increased FA in bumblebees.
Species differed in their baseline FA, with FA being higher in the two species that have recently expanded their ranges in Britain. Overall, FA significantly increased over the century but followed a nonlinear trend, with the increase starting c. 1925. We found relatively warm and wet years were associated with higher FA.
Collectively our findings show that FA in bumblebees increased over the 20th century and under weather conditions that will likely increase in frequency with climate change. By plotting FA trends and quantifying the contribution of annual climate conditions on past populations, we provide an important step towards improving our understanding of how environmental factors could impact future populations of wild beneficial insects.
... The International Union for Conservation of Nature (IUCN) Red List has assessed that 16.5% of total vertebrate pollinators, which increases to 30% for island species, 9% of bees and butterfly species are either on the verge of extinction or at risk in Europe (IUCN, 2019;M€ unsch et al., 2019;Poniatowski et al., 2018). Soroye et al. (2020) and Guzman et al. (2021) have reported that more than 40% and 15% of bumble bee species (Bombus confuses, (Schenck, 1859), B. cullumanus, (Kirby, 1802), B. subterraneus (Linnaeus, 1758)) occupancy has declined over the 15 years in North America and Europe due to the consequences of the climate change event. Major categories of crops i.e. fruit and vegetables (fi50 billion) and edible oilseed crops (fi39 billion) would be drastically affected by the deterioration of the numbers of pollinators (M€ unsch et al., 2019;Qi et al., 2020). ...
ABSTRACT
Insect pollination establishes an ecosystem service around the globe, providing compelling
budgetary and creative profits along with developmental values to humans and vital eco�friendly measures for the environment. It is, therefore, essential to understand how insect
pollinator populations and communities respond to rapidly changing environments if we are
to maintain healthy and effective pollinator services. Although insect pollinators are known
to provide ecosystem services to more than 80% of the world’s flowering plants (including
cultivated crops), a steep decline (�20–40%) in their population has created an alarming
situation for global biodiversity. Threats to bee populations in recent years have increased
awareness about the critical role of pollinators for life on earth, as pollinators are predicted
to persist only when all animal-pollinated plant species persist. Additionally, increased usage
of chemical pesticides may result in the collapse of pollinators which leads to a decrease in
food resource density and also facilitates the increasing isolation of natural habitats. So, to
overcome pollinators’ decline, joint efforts of all stakeholders are needed to increase their
numbers on the planet. We have to cut down the use of synthetic pesticides, ban highly
toxic pesticides, tackle problems related to colony collapse disorder (CCD), climate change,
habitat loss and provide much-needed help to the native pollinator species to revive their
natural habitats. So, this paper aims to focus on appreciating the services of insect pollina�tors and rescuing them from the threats leading to their extinctions which in turn will help
in enhancing global food production
... Wild pollinators are essential for plant reproduction and the maintenance of ecosystem function Ollerton et al., 2011). Literature over the last decades have described a global pollinator decline (Burkle et al., 2013;Potts et al., 2010); however, recent reviews suggest a less alarming situation (Guzman et al., 2021;Saunders et al., 2020), in which the decline mostly occurs in anthropogenic ecosystems (Herrera, 2019). Such decline is thought to be mainly driven by land-use changes that lead to the homogenization of landscapes (Holzschuh et al., 2007) and communities (Gossner et al., 2016), affecting the availability of habitats and resources that pollinators need . ...
Loss of habitats and native species, introduction of invasive species, and changing climate regimes lead to the homogenization of landscapes and communities, affecting the availability of habitats and resources for economically important guilds, such as pollinators. Understanding how pollinators and their interactions vary along resource diversity gradients at different scales may help to determine their adaptability to the current diversity loss related to global change. We used data on 20 plant–pollinator communities along gradients of flower richness (local diversity) and landscape heterogeneity (landscape diversity) to understand how the diversity of resources at local and landscape scales affected (1) wild pollinator abundance and richness (accounting also for honey bee abundance), (2) the structure of plant–pollinator networks, (3) the proportion of actively selected interactions (those not occurring by neutral processes), and (4) pollinator diet breadth and species' specialization in networks. Wild pollinator abundance was higher overall in flower‐rich and heterogeneous habitats, while wild pollinator richness increased with flower richness (more strongly for beetles and wild bees) and decreased with honeybee abundance. Network specialization (H2′), modularity, and functional complementarity were all positively related to floral richness and landscape heterogeneity, indicating niche segregation as the diversity of resources increases at both scales. Flower richness also increased the proportion of actively selected interactions (especially for wild bees and flies), whereas landscape heterogeneity had a weak negative effect on this variable. Overall, network‐level metrics responded to larger landscape scales than pollinator‐level metrics did. Higher floral richness resulted in a wider taxonomic and functional diet for all the study guilds, while functional diet increased mainly for beetles. Despite this, specialization in networks (d′) increased with flower richness for all the study guilds, because pollinator species fed on a narrower subset of plants as communities became richer in species. Our study indicates that pollinators are able to adapt their diet to resource changes at local and landscape scales. However, resource homogenization might lead to poor and generalist pollinator communities, where functionally specialized interactions are lost. This study highlights the importance of including different scales to understand the effects of global change on pollination service through changes in resource diversity.
... and Soroye et al. (2020) have been criticised in this regard(Anon., 2020;Guzman et al., 2021;Willig et al., 2019). This brief overview of some recent disagreements highlights a fundamental problem: potential biases are rarely communicated to the reader in sufficient detail; instead, they are often addressed with a passing comment, if at all. ...
Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete ‘risk‐of‐bias’ assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.
We introduce ROBITT, a structured tool for assessing the ‘Risk‐Of‐Bias In studies of Temporal Trends in ecology’. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.
Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus‐forming process across experts working in relevant areas of ecology and evidence synthesis.
We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence‐based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.
... While there is significant debate over insect population abundance changes (Thomas et al., 2019;Beck and McCain, 2020;Bell et al., 2020;Guzman et al., 2021;Welti et al., 2021), we argue that understanding the mechanisms that could lead to population decline is still is a critical step toward mitigating the pressures imposed on entomofauna. In this special issue, we highlight research on factors affecting insect fertility-a characteristic that is clearly necessary to maintain viable populations. ...
Fragmented surveys and limited monitoring exclude most invertebrate species from conservation policy. We present a framework that generates annual occupancy predictions using species distribution models (SDMs) to reconstruct missing trends—not to extrapolate trends, but to fill them in (the fill-in approach). Instead of filtering poor-data regions and years or relying on static environmental variables, we use co-occurrence patterns (COP) to capture year-to-year shifts in species assemblages, to enable temporal prediction across all recorded habitats using sparse, presence-only datasets from multiple sources. Applied to four rare native ladybugs across North America (2007–2021), COP models exceeded reliability benchmarks (Accuracy > 0.70, AUC > 0.70, Kappa > 0.40, Brier < 0.25) across standard test splits, structurally distinct sources, and temporally divided periods. This indicates that annual predictions were robust to temporal bias arising from varying data volume and source composition, as supported by negligible effects in multiple regression. Predicted 10-year declines (9–31%) closely aligned with independent long-term regional monitoring data, operationalizing IUCN Red List classifications (from “Least Concern” to “Vulnerable”) in the absence of standardized monitoring. By translating fragmented observations—primarily from citizen science—into standardized annual trend estimates, the fill-in approach extends extinction risk assessment to data-deficient taxa long excluded from conservation frameworks.
Changes in climate may cause changes in the ranges, phenology and interactions of insects with other species and lead parasites to switch host species. A study of louse (flat) flies in the United Kingdom, Republic of Ireland and Isle of Man, in which licensed bird ringers acting as citizen scientists collected ectoparasites that left birds during ringing, showed recent range shifts of several species. The Common or Bird Louse Fly Ornithomya avicularia (Linnaeus, 1758), a vector of Haemoproteus sp. and trypanosomes, has undergone a major northwards range expansion of over 300 km in the United Kingdom (UK) since the 1960s. The Finch Louse Fly Ornithomya fringillina (Curtis, 1836) has also expanded its range over 300 km northwards and 400 km westwards into the Island of Ireland, and the Swallow Louse Fly Ornithomya biloba (Dufour, 1827) is now established in Wales and Southern England. The Grouse Louse Fly Ornithomya chloropus (Bergroth, 1901) has undergone a range contraction at lower altitudes and on the southern edge of its range. Other species of louse fly were detected: Crataerina pallida (Latreille, 1812), Stenepteryx hirundinis (Linnaeus, 1758), Pseudolynchia garzettae (Rondani, 1879) and Icosta minor (Bigot, 1858). Some generalist species have shifted their phenology, whereas the more specialist nest parasites of migrant birds have not, as the arrival and breeding dates of their hosts have not changed. The range changes of the generalist species of these ectoparasites may have implications for bird health, especially if they switch to new host species as their ranges shift.
Odonates (dragonflies and damselflies) have become popular study organisms for insect-based climate studies, due to the taxon’s strong sensitivity to environmental conditions, and an enthusiastic following by community scientists due to their charismatic appearance and size. Where formal records of this taxon can be limited, public efforts have provided nearly 1,500,000 open-sourced odonate records through online databases, making real-time spatio-temporal monitoring more feasible. While these databases can be extensive, concerns regarding these public endeavors have arisen from a variety of sources: records may be biased by human factors (ex: density, technological access) which may cause erroneous interpretations. Indeed, records of odonates in the east-central US documented in the popular database iNaturalist bear striking patterns corresponding to political boundaries and other human activities. We conducted a ‘ground-truthing’ study using a structured sampling method to examine these patterns in an area where community science reports indicated variable abundance, richness, and diversity which appeared to be linked to observation biases. Our observations were largely consistent with patterns recorded by community scientists, suggesting these databases were indeed capturing representative biological trends and raising further questions about environmental drivers in the observed data gaps.
The decline of many wild bee species has major consequences for pollination in natural and agro-ecosystems. One hypothesized cause of the declines is pesticide use; neonicotinoids and pyrethroids in particular have been shown to have pernicious effects in laboratory and field experiments, and have been linked to population declines in a few focal species. We used aggregated museum records, ecological surveys and community science data from across the contiguous United States, including 178,589 unique observations from 1,081 bee species (33% of species with records in the United States) across six families, to model species occupancy from 1995 to 2015 with linked land use data. While there are numerous causes of bee declines, we discovered that the negative effects of pesticides are widespread; the increase in neonicotinoid and pyrethroid use is a major driver of changes in occupancy across hundreds of wild bee species. In some groups, high pesticide use contributes to a 43.3% decrease in the probability that a species occurs at a site. These results suggest that mechanisms that reduce pesticide use (such as integrative pest management) can potentially facilitate pollination conservation.
A rapidly warming climate is driving changes in biodiversity worldwide, and its impact on insect communities is critical given their outsized role in ecosystem function and services. We use a long‐term dataset of North American bumble bee species occurrences to determine whether the community temperature index (CTI), a measure of the balance of warm‐ and cool‐adapted species in a community, has increased given warming temperatures. CTI has increased by an average of 0.99°C in strong association with warming maximum summer temperatures over the last 30 years with the areas exhibiting the largest increases including mid‐ to high latitudes as well as low and high elevations—areas relatively shielded from other intensive global changes. CTI shifts have been driven by the decline of cold‐adapted species and increases in warm‐adapted species within bumble bee communities. Our results show the pervasive impacts and ecological implications warming temperatures pose to insects.
Compared to non‐urban environments, cities host ecological communities with altered taxonomic diversity and functional trait composition. However, we know little about how these urban changes take shape over time. Using historical bee (Apoidea: Anthophila) museum specimens supplemented with online repositories and researcher collections, we investigated whether bee species richness tracked urban and human population growth over the past 118 years. We also determined which species were no longer collected, whether those species shared certain traits, and if collector behavior changed over time. We focused on Wake County, North Carolina, United States where human population size has increased over 16 times over the last century along with the urban area within its largest city, Raleigh, which has increased over four times. We estimated bee species richness with occupancy models, and rarefaction and extrapolation curves to account for imperfect detection and sample coverage. To determine if bee traits correlated with when species were collected, we compiled information on native status, nesting habits, diet breadth, and sociality. We used non‐metric multidimensional scaling to determine if individual collectors contributed different bee assemblages over time. In total, there were 328 species collected in Wake County. We found that although bee species richness varied, there was no clear trend in bee species richness over time. However, recent collections (since 2003) were missing 195 species, and there was a shift in trait composition, particularly lost species were below‐ground nesters. The top collectors in the dataset differed in how often they collected bee species, but this was not consistent between historic and contemporary time periods; some contemporary collectors grouped closer together than others, potentially due to focusing on urban habitats. Use of historical collections and complimentary analyses can fill knowledge gaps to help understand temporal patterns of species richness in taxonomic groups that may not have planned long‐term data.
Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise which limits the masking of the ranges of responses while still offering insight. Regardless of modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.
Mounting evidence of bumble bee declines and the listing of the rusty patched bumble bee (Bombus affinis Cresson) as federally endangered in the United States in 2017 and Canada in 2012 has stimulated an interest in monitoring and conservation. Understanding the influence of land use on occupancy patterns of imperiled species is crucial to successful recovery planning. Using detection data from community surveys, we assessed land use associations for 7 bumble bee species in Minnesota, USA, including B. affinis. We used multispecies occupancy models to assess the effect of 3 major land use types (developed, agricultural, and natural) within 0.5 and 1.5 km on occupancy of 7 Bombus (Hymenoptera: Apidae) species, while accounting for detection uncertainty. We found that B. affinis occupancy and detection were highest in developed landscapes and lowest in agricultural landscapes, representing an inverse relationship with the relative landcover ratios of these landscapes in Minnesota. Occupancy of 2 bumble bee species had strong positive associations with natural landscapes within 1.5 km and 2 species had strong negative associations with agricultural landscapes within 1.5 km. Our results suggest that best practices for imperiled Bombus monitoring and recovery planning depends upon the surrounding major land use patterns. We provide recommendations for urban planning to support B. affinis based on conservation success in the metropolitan areas of Minneapolis-St. Paul. We also encourage substantial survey effort be employed in agricultural and natural regions of the state historically occupied by B. affinis to determine the current occupancy state.
A rapidly warming climate has become one of the primary forces driving changes in biodiversity worldwide. The impact of warming temperatures on insect communities is of particular interest given their importance for ecosystem function and service provision and the uncertainty around whether insect communities can keep pace with the rate of increasing temperatures. We use a long-term dataset on bumble bee species occurrence and data on summer maximum temperature trends across North America to characterize community-level responses to recent climate warming. Bumble bees are relatively well recorded historically and are sensitive to warming temperatures. We examined responses using the community temperature index (CTI) – a measure of the balance of cool- and warm-adapted species within local communities. Starting in 2010, bumble bee average CTI across North America has rapidly increased after a period of slight increase from 1989 to the late 2000s. This increase is strongly associated with recent increases in maximum summer temperatures. The increase in CTI is spatially extensive, occurring throughout North America, but the areas of greatest concern include mid to high latitudes as well as low and high elevations - areas relatively shielded from other intensive global changes (e.g., land-use). On average, bumble bee CTI has increased 0.99°C from 1989 to 2018, a change of similar magnitude to the increase in maximum summer temperatures. The rapid shift in bumble bee communities appears to be at pace with shifting summer temperatures, with an approximate, equivalent northward shift of ~104 km from 1989-2018 for both. This indicates an adaptive capacity among some bumble bee species. However, warming temperatures are also likely reducing the occurrence and local abundance of cool-adapted species that may serve important ecological roles within their range. Our results provide strong evidence of the pervasive impacts posed to insect communities by temperature increases in the past few decades.
Research studies and conservation actions aimed at improving conditions for bees require a basic understanding of which species are present in a given region. The US state of Minnesota occupies a unique geographic position at the confluence of eastern deciduous forests, northern boreal forests, and western tallgrass prairie, which has led to a diverse and unique bee fauna. In recent years there have been multiple ongoing bee-focused inventory and research projects in Minnesota. Combined with the historic specimens housed in the University of Minnesota Insect Collection and other regional collections, these furnished a wealth of specimens available to form the basis of a statewide checklist. Here, we present the first comprehensive checklist of Minnesota bee species, documenting a total of 508 species in 45 genera. County-level occurrence data is included for each species, and further information on distribution and rarity is included for species of regional or national interest. Some species have their taxonomy clarified, with Perdita citrinella Graenicher, 1910 syn. nov. recognized as a junior synonym of Perdita perpallida Cockerell, 1901, P. bequaerti syn. nov. recognized as a junior synonym of P. pallidipennis Graenicher, 1910 stat. nov., Anthidiellum boreale (Robertson, 1902) stat. nov. recognized as a full species, and Anthidiellium beijingense Portman & Ascher nom. nov. is proposed for A. boreale Wu to resolve the homonymy with A. boreale (Robertson). We further include a list of species that may occur in Minnesota and highlight 11 species occurring in the state that are considered non-native. Recent collecting efforts, as well as increased taxonomic attention paid to Minnesota bees, have resulted in 66 species that have only been documented in the last 10 years. As a first step in determining native bees of conservation concern, we document 38 species that have not been detected in the state during the last 50 years and discuss their conservation status, along with other species for which evidence of decline exists. The checklist of Minnesota bees will continue to grow and change with additional surveys and research studies. In particular, recent surveys have continued to detect new bee species, and many bee groups are in need of taxonomic revision, with the most recent revisions for many genera occurring decades ago. Overall, this checklist strengthens our understanding of the bees of Minnesota and the broader region, informs conservation assessments, and establishes a baseline for faunal change.
Wild bees are declining worldwide, and many species are now threatened with extinction. Decline is caused by a combination of threats, including disease spillover from managed bees that reduces health of wild bees. An increasing number of studies thus aim to characterize bee health. The common approaches, however, require sacrificing tens to hundreds of individual bees per site or species, with reports of several thousand individuals collected per study. Considering the widespread need to assess bee health, this sampling approach is not sustainable, especially for endangered populations or species. Here, we propose a non-destructive method to assess parasite loads of wild-caught bumble bees. The standard protocol consists of net-capturing individual bumble bees and placing them in a 10 cm (diameter) petri dish to collect faeces. Although this approach is frequently used in laboratory settings, it is not in the field, because of the low success in collecting faeces. Placing bumble bees in a previously refrigerated cooler, we significantly improved faecal collection in the field from 76% with the standard protocol to 86% with the cooler protocol. We also successfully identified spores and cells of two common gut parasites Crithidia spp. and Vairimorpha spp. in faecal samples. The efficacy of the cooler protocol, combined to the low-cost and widespread availability of the equipment should promote its use in field studies.
Implication for insect conservation: As there are calls to reduce destructive sampling methods in bee research, using the updated cooler protocol will contribute to achieving this goal. This opens future avenues of combining this non-destructive approach to assess bee health with molecular tools.
Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood‐based approaches (e.g. packages lme4) and machine learning methods.
These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution, and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods.
Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement.
Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.
In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of “big data”, however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts.
Flowering plants provide critical resources for wild bees. Consequently, enhancing flowering plant communities through hedgerows or flower strips has become a common recommendation to support native pollinator populations. Meta-analysis finds that enhancements are associated with higher local bee diversity. However, many studies have also examined the impacts of unmanipulated (i.e., existing, naturally occurring) plant communities on the local bee community. These studies have typically been excluded from meta-analysis, leaving a partial picture of how local plant communities influence bees. We compiled studies of either approach that looked for an association between bee abundance and/or diversity and local floral resources, measured as floral abundance and/or diversity. From literature surveys, we discovered 60 relevant studies, from which we extracted 133 different effect sizes. Then, using meta-regression, we examined four relationships between floral community traits (i.e., floral abundance and diversity) and bee community traits (i.e., abundance and diversity). Overall, we found strong associations between the plant community and the bee community, regardless of study design, or ecosystem type. To ensure the support of abundant and diverse wild bee populations, our results indicate plant enhancements should be designed to optimize both floral diversity and abundance.
Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5,000 species in each of several defined “regions” (e.g., countries, protected areas, etc.) of the United Kingdom from 1970-2019. This data product corresponds closely to the notion of a species distribution “Essential Biodiversity Variable” (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the “ideal” and “minimal” criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper can act as a template for research groups around the world seeking to develop similar data products.
Environmental gradients generate and maintain biodiversity on Earth. Mountain slopes are among the most pronounced terrestrial environmental gradients, and the elevational structure of species and their interactions can provide unique insight into the processes that govern community assembly and function in mountain ecosystems. We recorded bumble bee–flower interactions over 3 years along a 1400‐m elevational gradient in the German Alps. Using nonlinear modeling techniques, we analyzed elevational patterns at the levels of abundance, species richness, species β‐diversity, and interaction β‐diversity. Though floral richness exhibited a midelevation peak, bumble bee richness increased with elevation before leveling off at the highest sites, demonstrating the exceptional adaptation of these bees to cold temperatures and short growing seasons. In terms of abundance, though, bumble bees exhibited divergent species‐level responses to elevation, with a clear separation between species preferring low versus high elevations. Overall interaction β‐diversity was mainly caused by strong turnover in the floral community, which exhibited a well‐defined threshold of β‐diversity rate at the tree line ecotone. Interaction β‐diversity increased sharply at the upper extreme of the elevation gradient (1800–2000 m), an interval over which we also saw steep decline in floral richness and abundance. Turnover of bumble bees along the elevation gradient was modest, with the highest rate of β‐diversity occurring over the interval from low‐ to mid‐elevation sites. The contrast between the relative robustness bumble bee communities and sensitivity of plant communities to the elevational gradient in our study suggests that the strongest effects of climate change on mountain bumble bees may be indirect effects mediated by the responses of their floral hosts, though bumble bee species that specialize in high‐elevation habitats may also experience significant direct effects of warming.
Annual report on activities of the regional groups of the IUCN Bumblebee Sub Group of the Wild Bee Specialist Group for 2021
ContextHabitat loss threatens to exacerbate climate change impacts on pollinator communities, particularly in Mediterranean-type ecosystems where late season floral resource availability is limited by seasonal drought. While gardens have been found to supplement floral resources in water-limited urban landscapes, less is known about the role of natural habitat diversity in sustaining late season floral resources in more intact landscapes. Objectives
We investigated the importance of habitat integrity and diversity for bumble bees in a water-limited ecosystem, observing bumble bee community response to seasonal drought across gradients of disturbance and soil moisture.Methods
We applied hierarchical models to estimate the effects of local site conditions versus landscape scale estimates of matrix habitat on bumble bee abundance. Floral resources, soil moisture, and other environmental variables were sampled along randomly distributed belt transects. Geospatial estimates of matrix habitat were derived from terrestrial ecosystem data. Bumble bees were sampled with blue vane traps.ResultsIn the late season we found that modified wet areas supported more floral resources and bumble bee workers as compared to dry semi-natural environments. Wetlands also supported more late season floral resources and bumble bee workers, though the latter effect was not significant. Despite higher levels of late season floral resources in modified wet environments, modified matrix habitat was negatively associated, and natural matrix positively associated, with workers in June and late-flying queens in July and August. We also detected differences in bumble bee community composition in disturbed versus undisturbed environments.Conclusions
Though wet modified habitats sustained the highest levels of late season floral resource availability and worker abundances in our study, bumble bee diversity and abundance were limited primarily by the availability of natural matrix habitat at the landscape scale. The conservation of natural habitat integrity and diversity can help support critical nesting and foraging habitat, and should be prioritized in efforts to foster the resilience of pollinator communities.
Farmland species face many threats, including habitat loss and malnutrition during key periods of their life cycle. This is aggravated in conventionally managed monocultures, leading to nutrient deficiencies that impair the survival and reproduction of farmland wildlife. For instance, protein deficiencies in wheat or vitamin B3 deficiency in maize reduce by up to 87% the reproductive success of the critically endangered common hamster (Cricetus cricetus), a flagship species of European farmlands. It is urgent to identify and implement agricultural practices that can overcome these deficiencies and help restoring hamsters’ reproductive success. As part of a conservation program to diversify farming habitats in collaboration with farmers, we tested whether associations between wheat or maize and three supplemental crops (soybean, sunflower and fodder radish) supported hamsters’ performance during hibernation and reproduction. We observed that maize–sunflower, maize–radish and wheat–soybean associations minimized hamsters’ body mass loss during hibernation. The wheat–soybean association led to the highest reproductive success (N = 2 litters of 4.5 ± 0.7 pups with a 100% survival rate to weaning), followed by maize–sunflower and maize–radish. These crop associations offer promising opportunities to overcome nutritional deficiencies caused by cereal monocultures. Their agronomic potential should promote their implementation on a large scale and benefit farmland biodiversity beyond the common hamster.
1. Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this has the potential to undermine statistical inference. In other disciplines, but particularly medicine, researchers are frequently required to complete "risk-of-bias" assessments to expose and document the potential for biases to undermine inference. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar tools are urgently needed in ecology. 2. We introduce ROBITT, a structured tool for assessing the "Risk-Of-Bias In studies of Temporal Trends in ecology". ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define their inferential goal(s) and relevant statistical population. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate likely sampling biases, then the user must explain what mitigating action will be taken. 3. Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document, and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis. 4. We propose that researchers should be strongly encouraged to include a ROBITT assessment as supplementary information when publishing studies of biodiversity trends. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, and provides an opportunity to describe data checks that might otherwise not be reported. ROBITT will also enable reviewers, editors, and readers to establish whether research conclusions are supported given a particular dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data, and provide a clearer picture of the uncertainties associated with our understanding of ecological reality.
Biological control is considered as a promising alternative to pesticide and plant resistance to manage plant diseases, but a better understanding of the interaction of its natural and societal functions is necessary for its endorsement. The introduction of biological control agents (BCAs) alters the interaction among plants, pathogens, and environments, leading to biological and physical cascades that influence pathogen fitness, plant health, and ecological function. These interrelationships generate a landscape of tradeoffs among natural and social functions of biological control, and a comprehensive evaluation of its benefits and costs across social and farmer perspectives is required to ensure the sustainable development and deployment of the approach. Consequently, there should be a shift of disease control philosophy from a single concept that only concerns crop productivity to a multifaceted concept concerning crop productivity, ecological function, social acceptability, and economical accessibility. To achieve these goals, attempts should make to develop “green” BCAs used dynamically and synthetically with other disease control approaches in an integrated disease management scheme, and evolutionary biologists should play an increasing role in formulating the strategies. Governments and the public should also play a role in the development and implementation of biological control strategies supporting positive externality.
• The current dearth of long‐term insect population trends is a major obstacle to conservation. Occupancy models have been proposed as a solution, but it remains unclear whether they can yield long‐term trends from natural history collections, since specimen records are normally very sparse. A common approach for sparse data is to coarsen its spatial and/or temporal resolution, although coarsening risks violating model assumptions.
• We (i) test whether occupancy trends of three social wasp (Hymenoptera: Vespidae: Vespinae) species – the common wasp (Vespula vulgaris), the German wasp (Vespula germanica) and the European hornet (Vespa crabro) – have changed in England between 1900 and 2016, and (ii) test the effect of spatiotemporal resolution on the performance of occupancy models using very sparse data. All models are based on an integrated dataset of occurrence records and natural history collection specimen records.
• We show that occupancy models can yield long‐term species‐specific trends from very sparse natural history collection specimens. We present the first quantitative trends for three Vespinae species in England over 116 years. Vespula vulgaris and V. germanica show stable trends over the time series, whilst V. crabro's occupancy decreased from 1950 to 1970 and increased since 1970. Moreover, we show that spatiotemporal resolution has little effect on model performance, although coarsening the spatial grain is an appropriate method for achieving enough records to estimate long‐term changes.
• With the increasing availability of biological records, the model formulation used here has the potential to provide novel insights by making use of natural history collections' unique specimen assemblages.
In their article entitled “Climate change contributes to widespread declines among bumblebees across continents” recently published in Science and reported upon worldwide, Soroye, Newbold & Kerr (1) used extensive specimen records to explore patterns of geographic range loss and expansion of bumble bees in Europe and North America, and, in line with their previous findings, invoked climate change as the key contributor to their declines (see 2-3). Here, we question the reliability of these findings, considering the flawed interpretation of extensive data sources used in this and other recent studies, particularly related to notable spatio-temporal heterogeneity in methods of collecting, curation, and identification. We are concerned that the attendance to only some of these relevant biases led the authors to reach over-extrapolated and imprecisely characterized conclusions that will be amplified by the media to produce a misinformed public.
In recent decades, many bumble bee species have declined due to changes in habitat, climate, and pressures from pathogens, pesticides, and introduced species. The western bumble bee (Bombus occidentalis), once common throughout western North America, is a species of concern and will be considered for listing by the U.S. Fish and Wildlife Service (USFWS) under the Endangered Species Act (ESA). We attempt to improve alignment of data collection and research with USFWS needs to consider redundancy, resiliency, and representation in the upcoming species status assessment. We reviewed existing data and literature on B. occidentalis, highlighting information gaps and priority topics for research. Priorities include increased knowledge of trends, basic information on several life-history stages, and improved understanding of the relative and interacting effects of stressors on population trends, especially the effects of pathogens, pesticides, climate change, and habitat loss. An understanding of how and where geographic range extent has changed for the two subspecies of B. occidentalis is also needed. We outline data that could be easily collected in other research projects that would increase their utility for understanding range-wide trends of bumble bees. We modeled the overall trend in occupancy from 1998 to 2018 of Bombus occidentalis within the continental United States using existing data. The probability of local occupancy declined by 93% over 21 yr from 0.81 (95% CRI = 0.43, 0.98) in 1998 to 0.06 (95% CRI = 0.02, 0.16) in 2018. The decline in occupancy varied spatially by landcover and other environmental factors. Detection rates vary in both space and time, but peak detection across the continental United States occurs in mid-July. We found considerable spatial gaps in recent sampling, with limited sampling in many regions, including most of ❖ www.esajournals.org 1 June 2020 ❖ Volume 11(6) ❖ Article e03141 Alaska, northwestern Canada, and the southwestern United States. We therefore propose a sampling design to address these gaps to best inform the ESA species status assessment through improved assessment of how the spatial distribution of stressors influences occupancy changes. Finally, we request involvement via data sharing, participation in occupancy sampling with repeated visits to distributed survey sites, and complementary research to address priorities outlined in this paper.
Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.
Large-scale biodiversity changes are measured mainly through the responses of a few taxonomic groups. Much less is known about the trends affecting most invertebrates and other neglected taxa, and it is unclear whether well-studied taxa, such as vertebrates, reflect changes in wider biodiversity. Here, we present and analyse trends in the UK distributions of over 5,000 species of invertebrates, bryophytes and lichens, measured as changes in occupancy. Our results reveal substantial variation in the magnitude, direction and timing of changes over the last 45 years. Just one of the four major groups analysed, terrestrial non-insect invertebrates, exhibits the declining trend reported among vertebrates and butterflies. Both terrestrial insects and the bryophytes and lichens group increased in average occupancy. A striking pattern is found among freshwater species, which have undergone a strong recovery since the mid-1990s after two decades of decline. We show that, while average occupancy among most groups appears to have been stable or increasing, there has been substantial change in the relative commonness and rarity of individual species, indicating considerable turnover in community composition. Additionally, large numbers of species have experienced substantial declines. Our results suggest a more complex pattern of biodiversity change in the United Kingdom than previously reported. By analysing changes in occupancy among >5,000 species of invertebrates, bryophytes and lichens in the United Kingdom over the past 45 years, the authors find substantial turnover in community composition among all groups, although average declines are evident only among terrestrial non-insect invertebrates.
Increasing temperatures and declines
One aspect of climate change is an increasing number of days with extreme heat. Soroye et al. analyzed a large dataset of bumble bee occurrences across North America and Europe and found that an increasing frequency of unusually hot days is increasing local extinction rates, reducing colonization and site occupancy, and decreasing species richness within a region, independent of land-use change or condition (see the Perspective by Bridle and van Rensburg). As average temperatures continue to rise, bumble bees may be faced with an untenable increase in frequency of extreme temperatures.
Science , this issue p. 685 ; see also p. 626
Here, we determine annual estimates of occupancy and species trends for 5,293 UK bryophytes, lichens, and invertebrates, providing national scale information on UK biodiversity change for 31 taxonomic groups for the time period 1970 to 2015. The dataset was produced through the application of a Bayesian occupancy modelling framework to species occurrence records supplied by 29 national recording schemes or societies (n = 24,118,549 records). In the UK, annual measures of species status from fine scale data (e.g. 1 × 1 km) had previously been limited to a few taxa for which structured monitoring data are available, mainly birds, butterflies, bats and a subset of moth species. By using an occupancy modelling framework designed for use with relatively low recording intensity data, we have been able to estimate species trends and generate annual estimates of occupancy for taxa where annual trend estimates and status were previously limited or unknown at this scale. These data broaden our knowledge of UK biodiversity and can be used to investigate variation in and drivers of biodiversity change.
Spatiotemporal patterns in biological communities are typically driven by environmental factors and species interactions. Spatial data from communities are naturally described by stacking models for all species in the community. Two important considerations in such multispecies or joint species distribution models (JSDMs) are measurement errors and correlations between species. Up to now, virtually all JSDMs have included either one or the other, but not both features simultaneously, even though both measurement errors and species correlations may be essential for achieving unbiased inferences about the distribution of communities and species co‐occurrence patterns. We developed two presence–absence JSDMs for modeling pairwise species correlations while accommodating imperfect detection: one using a latent variable and the other using a multivariate probit approach. We conducted three simulation studies to assess the performance of our new models and to compare them to earlier latent variable JSDMs that did not consider imperfect detection. We illustrate our models with a large Atlas data set of 62 passerine bird species in Switzerland. Under a wide range of conditions, our new latent variable JSDM with imperfect detection and species correlations yielded estimates with little or no bias for occupancy, occupancy regression coefficients, and the species correlation matrix. In contrast, with the multivariate probit model we saw convergence issues with large data sets (many species and sites) resulting in very long run times and larger errors. A latent variable model that ignores imperfect detection produced correlation estimates that were consistently negatively biased, that is, underestimated. We found that the number of latent variables required to represent the species correlation matrix adequately may be much greater than previously suggested, namely around n/2, where n is community size. The analysis of the Swiss passerine data set exemplifies how not accounting for imperfect detection will lead to negative bias in occupancy estimates and to attenuation in the estimated covariate coefficients in a JSDM. Furthermore, spatial heterogeneity in detection may cause spurious patterns in the estimated species correlation matrix if not accounted for. Our new JSDMs represent an important extension of current approaches to community modeling to the common case where species presence–absence cannot be detected with certainty.
Bombus impatiens, the common eastern bumble bee, is the first bumble bee established outside of its native range in North America. Native to the eastern portion of the continent, the species was imported to British Columbia in the early 2000s for greenhouse pollination and subsequently became established in the wild. Here we report on the continuing expansion of its range in the Pacific Northwest, including the detection of gynes and workers in Washington State. Sightings of B. impatiens in the region have become increasingly common based on various Internet mapping and reporting sites. The species has been observed about 30 km south of the British Columbia border, or 60 km from the first British Columbia detections. Species distribution models indicate that the Puget Sound and Willamette Valley are suitable habitat, and the bee will likely continue to expand its range southward towards California. The potential impacts of B. impatiens in the region are unknown and will be monitored in future research.
Pollination is a critical ecosystem service underpinning the productivity of agricultural systems across the world. Wild insect populations provide a substantial contribution to the productivity of many crops and seed set of wild flowers. However, large-scale evidence on species-specific trends among wild pollinators are lacking. Here we show substantial inter-specific variation in pollinator trends, based on occupancy models for 353 wild bee and hoverfly species in Great Britain between 1980 and 2013. Furthermore, we estimate a net loss of over 2.7 million occupied 1 km² grid cells across all species. Declines in pollinator evenness suggest that losses were concentrated in rare species. In addition, losses linked to specific habitats were identified, with a 55% decline among species associated with uplands. This contrasts with dominant crop pollinators, which increased by 12%, potentially in response agri-environment measures. The general declines highlight a fundamental deterioration in both wider biodiversity and non-crop pollination services.
In Canada, the Common Eastern Bumble Bee ( Bombusimpatiens Cresson) is native to southern Ontario and Quebec, but since being developed as a managed commercial pollinator, it has been exported to several other provinces for use in greenhouse and field crop settings. This has enabled this species to become established outside its natural range and it is now established in eastern Canada (New Brunswick, Nova Scotia, Prince Edward Island) and British Columbia. To date, the species has not been detected via field capture in the prairie provinces.
Here we report on recent captures of B.impatiens workers and males from south-eastern Alberta and suggest that these specimens escaped from nearby commercial greenhouses. The risk that the presence and looming establishment of this species has on native bumble bees in the Canadian prairies is discussed.
Molecular methods have greatly increased our understanding of the previously cryptic spatial ecology of bumble bees (Bombus spp.), with knowledge of the spatial ecology of these bees being central to conserving their essential pollination services. Bombus hypnorum, the Tree Bumble Bee, is unusual in that it has recently rapidly expanded its range, having colonized much of the UK mainland since 2001. However, the spatial ecology of B. hypnorum has not previously been investigated. To address this issue, and to investigate whether specific features of the spatial ecology of B. hypnorum are associated with its rapid range expansion, we used 14 microsatellite markers to estimate worker foraging distance, nest density, between‐year lineage survival rate and isolation by distance in a representative UK B. hypnorum population. After assigning workers to colonies based on full or half sibship, we estimated the mean colony‐specific worker foraging distance as 103.6 m, considerably less than values reported from most other bumble bee populations. Estimated nest density was notably high (2.56 and 0.72 colonies ha⁻¹ in 2014 and 2015, respectively), estimated between‐year lineage survival rate was 0.07, and there was no evidence of fine‐scale isolation by distance. In addition, genotyping stored sperm dissected from sampled queens confirmed polyandry in this population (mean minimum mating frequency of 1.7 males per queen). Overall, our findings establish critical spatial ecological parameters and the mating system of this unusual bumble bee population and suggest that short worker foraging distances and high nest densities are associated with its rapid range expansion.
Multi-species biodiversity indicators are increasingly used to assess progress towards the 2020 ‘Aichi’ targets of the Convention on Biological Diversity. However, most multi-species indicators are biased towards a few well-studied taxa for which suitable abundance data are available. Consequently, many taxonomic groups are poorly represented in current measures of biodiversity change, particularly invertebrates. Alternative data sources, including opportunistic occurrence data, when analysed appropriately, can provide robust estimates of occurrence over time and increase the taxonomic coverage of such measures of population change. Occupancy modelling has been shown to produce robust estimates of species occurrence and trends through time. So far, this approach has concentrated on well-recorded taxa and performs poorly where recording intensity is low. Here, we show that the use of weakly informative priors in a Bayesian occupancy model framework greatly improves the precision of occurrence estimates associated with current model formulations when analysing low-intensity occurrence data, although estimated trends can be sensitive to the choice of prior when data are extremely sparse at either end of the recording period. Specifically, three variations of a Bayesian occupancy model, each with a different focus on information sharing among years, were compared using British ant data from the Bees, Wasps and Ants Recording Society and tested in a simulation experiment. Overall, the random walk model, which allows the sharing of information between the current and previous year, showed improved precision and low bias when estimating species occurrence and trends. The use of the model formulation described here will enable a greater range of datasets to be analysed, covering more taxa, which will significantly increase taxonomic representation of measures of biodiversity change.
Significance
Deserts, already defined by climatic extremes, have warmed and dried more than other regions in the contiguous United States due to climate change. Our resurveys of sites originally visited in the early 20th century found Mojave Desert birds strongly declined in occupancy and sites lost nearly half of their species. Declines were associated with climate change, particularly decreased precipitation. The magnitude of the decline in the avian community and the absence of species that were local climatological “winners” are exceptional. Our results provide evidence that bird communities in the Mojave Desert have collapsed to a new, lower baseline. Declines could accelerate with future climate change, as this region is predicted to become drier and hotter by the end of the century.
Occupancy-abundance (OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal-, vs. point-sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e. cell size). In contrast, when using areal-sampling (e.g. species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant. Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy-abundance relationship. This article is protected by copyright. All rights reserved.
Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.
Wild bee declines have been ascribed in part to neonicotinoid insecticides. While short-term laboratory studies on commercially bred species (principally honeybees and bumblebees) have identified sub-lethal effects, there is no strong evidence linking these insecticides to losses of the majority of wild bee species. We relate 18 years of UK national wild bee distribution data for 62 species to amounts of neonicotinoid use in oilseed rape. Using a multi-species dynamic Bayesian occupancy analysis, we find evidence of increased population extinction rates in response to neonicotinoid seed treatment use on oilseed rape. Species foraging on oilseed rape benefit from the cover of this crop, but were on average three times more negatively affected by exposure to neonicotinoids than non-crop foragers. Our results suggest that sub-lethal effects of neonicotinoids could scale up to cause losses of bee biodiversity. Restrictions on neonicotinoid use may reduce population declines.
Miller-Struttmann et al. (2015) suggest that, in a North American alpine ecosystem, reduced flower abundance due to climate change has driven the evolution of shorter tongues in two bumble bee species. We accept the evidence that tongue length has decreased, but are unconvinced by the adaptive explanation offered. It posits foraging responses and competitive relationships not seen in other studies and interprets phenotypic change as evidence of evolutionary adaptation. By oversimplifying a complex phenomenon, it may exaggerate the potential for bees to quickly adapt to environmental changes.
Climate change decoupling mutualism
Many coevolved species have precisely matched traits. For example, long-tongued bumblebees are well adapted for obtaining nectar from flowers with long petal tubes. Working at high altitude in Colorado, Miller-Struttmann et al. found that long-tongued bumblebees have decreased in number significantly over the past 40 years. Short-tongued species, which are able to feed on many types of flowers, are replacing them. This shift seems to be a direct result of warming summers reducing flower availability, making generalist bumblebees more successful than specialists and resulting in the disruption of long-held mutualisms.
Science , this issue p. 1541
Habitat conversion is the primary driver of biodiversity loss, yet little is known about how it is restructuring the tree
of life by favoring some lineages over others. We combined a complete avian phylogeny with 12 years of Costa Rican bird surveys
(118,127 detections across 487 species) sampled in three land uses: forest reserves, diversified agricultural systems, and
intensive monocultures. Diversified agricultural systems supported 600 million more years of evolutionary history than intensive
monocultures but 300 million fewer years than forests. Compared with species with many extant relatives, evolutionarily distinct
species were extirpated at higher rates in both diversified and intensive agricultural systems. Forests are therefore essential
for maintaining diversity across the tree of life, but diversified agricultural systems may help buffer against extreme loss
of phylogenetic diversity.
A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models ( SDM s) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co‐occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model ( JSDM ) that integrates these distinct observational approaches by incorporating species co‐occurrence data into a SDM .
JSDM s estimate distributions of multiple species simultaneously and allow decomposition of species co‐occurrence patterns into components describing shared environmental responses and residual patterns of co‐occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia.
Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM . Eucalypt species that interbreed had similar environmental responses but had negative residual co‐occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities.
Environmental and residual correlations estimated from JSDM s can help indicate whether co‐occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDM s take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.
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Bumble bees are an important group of wild pollinators in North America and considerable concern has been expressed over declines in their populations. However, before causes for declines can be assessed, it is essential that the geographical and chronological patterns of decline be discovered. Hitherto a lack of assessment of historical data has hindered our efforts to determine which species are most at risk. Here, the status of 21 North American bumble bee species (Hymenoptera: Apidae) occurring in the eastern nearctic biogeographic region is assessed using a specimen-level database from compiled museum and survey records dating back to the late nineteenth century from various institutional collections. Using a combination of measures, bumble bee declines were assessed over their entire native ranges. We report here that half of the selected fauna is in varying levels of decline (especially Bombus ashtoni, B. fervidus, and B. variabilis), with the remaining species exhibiting stable or increasing trends (e.g., B. bimaculatus, B. impatiens, and B. rufocinctus). Suggestions for prioritizing conservation efforts for this important group of pollinators are given.
Bombus moderatus Cresson, 1863 occurs in the northern and western regions of North America and reaches its southern limit in Alberta. In 1915, the southernmost record was Banff; by 1987, it had appeared in Kananaskis Country, 40 km southeast of Banff, and by 2010, it had spread 80 km farther east to become one of the more common bumble bee species in Calgary, where it had never been previously recorded. This represents a rate of spread over the last 20 years of about 4 km/year. The simplest hypothesis that can account for this change is that it is just a continuation of the natural expansion of its range since the end of the last ice age. An alternative hypothesis is that it is filling the niche vacated as a result of the decline in another species, Bombus occidentalis Greene, 1858.
There is limited information available on changes in biodiversity at the European scale, because there is a lack of data from standardised monitoring for most species groups. However, a great number of observations made without a standardised field protocol is available in many countries for many species. Such opportunistic data offer an alternative source of information, but unfortunately such data suffer from non-standardised observation effort and geographical bias. Here we describe a new approach to compiling supranational trends using opportunistic data which adjusts for these two major imperfections. The non-standardised observation effort is dealt with by occupancy modelling, and the unequal geographical distribution of sites by a weighting procedure. The damselfly Calopteryx splendens was chosen as our test species. The data were collected from five countries (Ireland, Great Britain, the Netherlands, Belgium and France), covering the period 1990–2008. We used occupancy models to estimate the annual number of occupied 1 × 1 km sites per country. Occupancy models use presence-absence data, account for imperfect detection of species, and thereby correct for between-year variability in observation effort. The occupancy models were run per country in a Bayesian mode of inference using JAGS. The occupancy estimates per country were then aggregated to assess the supranational trend in the number of occupied 1 × 1 km2. To adjust for the unequal geographical distribution of surveyed sites, we weighted the countries according to the number of sites surveyed and the range of the species per country. The distribution of C.
splendens has increased significantly in the combined five countries. Our trial demonstrated that a supranational trend in distribution can be derived from opportunistic data, while adjusting for observation effort and geographical bias. This opens new perspectives for international monitoring of biodiversity.
A recent (March 2006) field survey of plant-pollinator interactions in North Patagonia, Argentina, revealed the presence of Bombus terrestris as flower visitors to several plant species in a natural environment. This is the first record of this invasive bumblebee in Argentina. The available evidence suggests that B. terrestris entered Argentina from Chile, where it was introduced in 1998, through low-altitude passes across the Andes.
The pollination effectiveness (floral visitation rate, percentage of flowers pollinated, and pollen deposition) of indigenous and introduced bees visiting lowbush blueberry (Vaccinium angustifolium Aiton) was studied in Nova Scotia from 1992 to 1994. Floral visitation rate alone was not a good indicator of pollination effectiveness, as not all floral visits resulted in successful pollination events. As a group, pollen-harvesting taxa pollinated >85% of flowers visited as compared with under 25% for nectar foragers. Equivalencies derived from floral visitation rates and pollination percentages show that the most effective pollen-harvesters, Bombus spp. queens and Andrena spp., would pollinate 6.5 and 3.6 flowers, respectively, in the time it would take a nectar-foraging honey bee, Apis mellifera L., to pollinate a single flower. Average pollen deposition for nectar-foragers (A. mellifera and Megachile rotundata F.) did not exceed 13 tetrads per visit, which was significantly less than all pollen-harvesters. Among pollen-harvesters, Bombus spp. workers, M. rotundata and Halictus spp. deposited moderate stigmatic loads (34, 28, and 26 tetrads, respectively), whereas Bombus spp. queens and Andrena spp. deposited >45 tetrads per single visit. Pollination equivalencies show A. mellifera would have to visit a flower four times to deposit the same amount of pollen as single visits by Bombus spp. queens or Andrena spp.
Bumblebees of the subgenus Bombus s. str. dominate (or used to dominate) many north temperate pollinator assemblages and include most of the commercial bumblebee pollinator species. Several species are now in serious decline, so conservationists need to know precisely which ones are involved. The problem is that many Bombus s. str. species are cryptic, so that species identification from morphology may be impossible for some individuals and is frequently misleading according to recent molecular studies. This is the first review of the entire subgenus to: (1) avoid fixed a priori assumptions concerning the limits of the problematic species; and (2) sample multiple sites from across the entire geographic ranges of all of the principal named taxa worldwide; and (3) fit an explicit model for how characters change within an evolutionary framework; and (4) apply explicit and consistent criteria within this evolutionary framework for recognising species. We analyse easily-obtained DNA (COI-barcode) data for 559 sequences from 279 localities in 33 countries using general mixed Yule-coalescent (GMYC) models, assuming only the morphologically distinctive species B. affinis Cresson, B. franklini (Frison), B. ignitus Smith and B. tunicatus Smith, and then recognise other comparable COI-barcode groups as putative species. These species correspond to modified concepts of the taxa B. cryptarum (Fabricius), B. hypocrita Perez, B. jacobsoni Skorikov, B. lantschouensis Vogt n. stat., B. longipennis Friese, B. lucorum (Linnaeus), B. magnus Vogt, B. minshanensis Bischoff n. stat., B. occidentalis Greene, B. patagiatus Nylander, B. sporadicus Nylander, B. terrestris (Linnaeus) and B. terricola Kirby (a total of 17 species). Seven lectotypes are designated. Our results allow us for the first time to diagnose all of the putative species throughout their global ranges and to map the extent of these geographic ranges.
1. Species richness is often used as a tool for prioritizing conservation action. One method for predicting richness and other summaries of community structure is to develop species-specific models of occurrence probability based on habitat or landscape characteristics. However, this approach can be challenging for rare or elusive species for which survey data are often sparse.
2. Recent developments have allowed for improved inference about community structure based on species-specific models of occurrence probability, integrated within a hierarchical modelling framework. This framework offers advantages to inference about species richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision in estimates of species richness by improving occupancy estimates for all species, including those that were observed infrequently.
3. We developed a hierarchical model to assess the community response of breeding birds in the Hudson River Valley, New York, to habitat fragmentation and analysed the model using a Bayesian approach.
4. The model was designed to estimate species-specific occurrence and the effects of fragment area and edge (as measured through the perimeter and the perimeter/area ratio, P/A), while accounting for imperfect detection of species.
5. We used the fitted model to make predictions of species richness within forest fragments of variable morphology. The model revealed that species richness of the observed bird community was maximized in small forest fragments with a high P/A. However, the number of forest interior species, a subset of the community with high conservation value, was maximized in large fragments with low P/A.
6. Synthesis and applications. Our results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness for conservation. This framework can be used to investigate the impacts of land-use change and fragmentation on species or assemblage richness, and to further understand trade-offs in species-specific occupancy probabilities associated with landscape variability.
Nondetection of a species at a site does not imply that the species is absent unless the probability of detection is 1. We propose a model and likelihood-based method for estimating site occupancy rates when detection probabilities are 1. The model provides a flexible framework enabling covariate information to be included and allowing for missing observations. Via computer simulation, we found that the model provides good estimates of the occupancy rates, generally unbiased for moderate detection probabilities (0.3). We estimated site occupancy rates for two anuran species at 32 wetland sites in Maryland, USA, from data collected during 2000 as part of an amphibian monitoring program, Frog-watch USA. Site occupancy rates were estimated as 0.49 for American toads (Bufo amer-icanus), a 44% increase over the proportion of sites at which they were actually observed, and as 0.85 for spring peepers (Pseudacris crucifer), slightly above the observed proportion of 0.83.
The introduced Bombusterrestris has recently been naturalized in Japan and become dominant in some local communities. We investigated potential niche overlaps
between introduced and native bumblebees in terms of morphological characteristics, seasonal flight activity, foraging and
nesting habitat use, and plant species visited. There were considerable niche overlaps in flower resource use between B.terrestris and B.hypocrita sapporoensis/B.pseudobaicalensis. Bombusterrestris also potentially competes for nest sites with B.hypocrita sapporoensis. During 3-year monitoring, B.pseudobaicalensis showed no noticeable change, but B.hypocrita sapporoensis decreased while B.terrestris increased. Abundant flower resources provided by exotic plants may buffer native bumblebees from competition for food with
introduced species. By contrast, the number of nest usurpers found in B.terrestris nests increased between 2003 and 2005, indicating that availability of nest sites was limiting and queens strongly competed
for nest sites. Our findings suggest that competition for nest sites rather than flower resources is the major ecological
mechanism for displacement of native bees. The large reduction of B.hypocrita sapporoensis queen indicates that B.terrestris may cause local extinction of native bumblebees. Control of established B.terrestris populations and prevention of further range expansion are urgently needed.
Human activities are not random in their negative and positive impacts on biotas. Emerging evidence shows that most species are declining as a result of human activities (‘losers’) and are being replaced by a much smaller number of expanding species that thrive in human-altered environments (‘winners’). The result will be a more homogenized biosphere with lower diversity at regional and global scales. Recent data also indicate that the many losers and few winners tend to be non-randomly distributed among higher taxa and ecological groups, enhancing homogenization.
Bumble bees (Bombus) are vitally important pollinators of wild plants and agricultural crops worldwide. Fragmentary observations, however, have suggested population declines in several North American species. Despite rising concern over these observations in the United States, highlighted in a recent National Academy of Sciences report, a national assessment of the geographic scope and possible causal factors of bumble bee decline is lacking. Here, we report results of a 3-y interdisciplinary study of changing distributions, population genetic structure, and levels of pathogen infection in bumble bee populations across the United States. We compare current and historical distributions of eight species, compiling a database of >73,000 museum records for comparison with data from intensive nationwide surveys of >16,000 specimens. We show that the relative abundances of four species have declined by up to 96% and that their surveyed geographic ranges have contracted by 23– 87%, some within the last 20 y. We also show that declining populations have significantly higher infection levels of the microsporidian pathogen Nosemabombi and lower genetic diversity compared with co-occurring populations of the stable (nondeclining) species. Higher pathogen prevalence and reduced genetic diversity are, thus, realistic predictors of these alarming patterns of decline in North America, although cause and effect remain uncertain.
Includes Supporting Information.
Bumble bees ( Bombus) are unusually important pollinators, with approximately 260 wild species native to all biogeographic regions except Africa, Australia, and New Zealand. As they are vitally important in natural ecosystems and to agricultural food production globally, the increase in reports of declining distribution and abundance over the past decade has led to an explosion of interest in bumble bee population decline. We summarize data on the threat status of wild bumble bee species across biogeographic regions, underscoring regions lacking assessment data. Focusing on data-rich studies, we also synthesize recent research on potential causes of population declines. There is evidence that habitat loss, changing climate, pathogen transmission, invasion of nonnative species, and pesticides, operating individually and in combination, negatively impact bumble bee health, and that effects may depend on species and locality. We distinguish between correlational and causal results, underscoring the importance of expanding experimental research beyond the study of two commercially available species to identify causal factors affecting the diversity of wild species.
Expected final online publication date for the Annual Review of Entomology, Volume 65 is January 7, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Large‐scale citizen‐science projects, such as atlases of species distribution, are an important source of data for macroecological research, for understanding the effects of climate change and other drivers on biodiversity, and for more applied conservation tasks, such as early‐warning systems for biodiversity loss.
However, citizen‐science data are challenging to analyse because the observation process has to be taken into account. Typically, the observation process leads to heterogeneous and non‐random sampling, false absences, false detections, and spatial correlations in the data. Increasingly, occupancy models are being used to analyse atlas data.
We advocate a dual approach to strengthen inference from citizen science data for the questions the programme is intended to address: (a) the survey design should be chosen with a particular set of questions and associated analysis strategy in mind and (b) the statistical methods should be tailored not only to those questions but also to the specific characteristics of the data.
We review the consequences of particular survey design choices that typically need to be made in atlas‐style citizen‐science projects. These include spatial resolution of the sampling units, allocation of effort in space, and collection of information about the observation process. On the analysis side, we review extensions of the basic occupancy models that are frequently necessary with atlas data, including methods for dealing with heterogeneity, non‐independent detections, false detections, and violation of the closure assumption.
New technologies, such as cell‐phone apps and fixed remote detection devices, are revolutionizing citizen‐science projects. There is an opportunity to maximize the usefulness of the resulting datasets if the protocols are rooted in robust statistical designs and data analysis issues are being considered. Our review provides guidelines for designing new projects and an overview of the current methods that can be used to analyse data from such projects.
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling.
Three species of bumble bee (Bombus spp.) are known to have become extinct in the British Isles. The first of these, Bombus pomorum was last collected (presumed extinct) in 1864. Here, I report the first direct evidence of the foraging behavior of Bombus pomorum from the analysis of pollen preserved on the hairs of the three surviving British museum specimens. The pollen removed from the bees belongs to 11 different plant families including Amaranthaceae, Apiaceae, Araliaceae, Asteraceae, Campanulaceae, Caprifoliaceae, Fabaceae, Geraniaceae, Lamiaceae, Onagraceae and Pinaceae. The diversity of the pollen taxa indicates that when present in the British Isles, Bombus pomorum adopted a generalized rather than narrow foraging strategy.
Aim
To understand how the integration of contextual spatial data on land cover and human infrastructure can help reduce spatial bias in sampling effort, and improve the utilization of citizen science‐based species recording schemes. By comparing four different citizen science projects, we explore how the sampling design's complexity affects the role of these spatial biases.
Location
Denmark, Europe.
Methods
We used a point process model to estimate the effect of land cover and human infrastructure on the intensity of observations from four different citizen science species recording schemes. We then use these results to predict areas of under‐ and oversampling as well as relative biodiversity ‘hotspots’ and ‘deserts’, accounting for common spatial biases introduced in unstructured sampling designs.
Results
We demonstrate that the explanatory power of spatial biases such as infrastructure and human population density increased as the complexity of the sampling schemes decreased. Despite a low absolute sampling effort in agricultural landscapes, these areas still appeared oversampled compared to the observed species richness. Conversely, forests and grassland appeared undersampled despite higher absolute sampling efforts. We also present a novel and effective analytical approach to address spatial biases in unstructured sampling schemes and a new way to address such biases, when more structured sampling is not an option.
Main conclusions
We show that citizen science datasets, which rely on untrained amateurs, are more heavily prone to spatial biases from infrastructure and human population density. Objectives and protocols of mass‐participating projects should thus be designed with this in mind. Our results suggest that, where contextual data is available, modelling the intensity of individual observation can help understand and quantify how spatial biases affect the observed biological patterns.
Aim
Long‐term monitoring of biodiversity is necessary to identify population declines and to develop conservation management. Because long‐term monitoring is labour‐intensive, resources to implement robust monitoring programmes are lacking in many countries. The increasing availability of citizen science data in online public databases can potentially fill gaps in structured monitoring programmes, but only if trends estimated from unstructured citizen science data match those estimated from structured monitoring programmes. We therefore aimed to assess the correlation between trends estimated from structured and unstructured data.
Location
Denmark.
Methods
We compared population trends for 103 bird species estimated over 28 years from a structured monitoring programme and from unstructured citizen science data to assess whether trends estimated from the two data sources were correlated.
Results
Trends estimated from the two data sources were generally positively correlated, but less than half the population declines identified from the structured monitoring data were recovered from the unstructured citizen science data. The mismatch persisted when we reduced the structured monitoring data from count data to occurrence data to mimic the information content of unstructured citizen science data and when we filtered the unstructured data to reduce the number of incomplete lists reported. Mismatching trends were especially prevalent for the most common species. Worryingly, more than half the species showing significant declines in the structured monitoring showed significant positive trends in the citizen science data.
Main conclusions
We caution that unstructured citizen science databases cannot replace structured monitoring data because the former are less sensitive to population changes. Thus, unstructured data may not fulfil one of the most critical functions of structured monitoring programmes, namely to act as an early warning system that detects population declines.
Occupancy modeling has been applied to a wide variety of taxa and sampling methods, including bird point counts. A critical assumption of basic occupancy models is that sites are occupied throughout the duration of the study, which is unlikely to be true for typical bird point-count studies. As such, we evaluated the implications of mobile animals on parameter estimates. We simulated the movement and detection of individual birds using an individual-based simulation model. We fit the basic occupancy model to data that represented a range of animal mobility, and determined the bias relative to known parameters used in the simulation. Occupancy depends on the size of the site selected, with smaller sites leading to lower occupancy for a given area and number of individuals present. At low animal density, occupancy scales approximately linearly with the area of sites, but at very high density, occupancy asymptotes at 1.0 across all site sizes. Even small amounts of movement lead to bias in estimates of occupancy and detectability, and the typical size of bird home ranges can lead to highly biased parameters. Moreover, variation in home range size over time or across habitats can lead to varying degrees of bias. Because of the potential for large bias in occupancy estimates, and their sensitivity to behaviors of birds (e.g., home range size), we recommend against applying current occupancy models to bird point-count data. © 2015 The Wildlife Society.
Policy‐makers increasingly demand robust measures of biodiversity change over short time periods. Long‐term monitoring schemes provide high‐quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present, we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy‐relevant time‐scales.
We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method–scenario combination, as well as the power to detect genuine trends.
We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition, all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful.
We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably F rescalo and occupancy–detection models.
The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection.
Many publications documenting large‐scale trends in the distribution of species make use of opportunistic citizen data, that is, observations of species collected without standardized field protocol and without explicit sampling design. It is a challenge to achieve reliable estimates of distribution trends from them, because opportunistic citizen science data may suffer from changes in field efforts over time (observation bias), from incomplete and selective recording by observers (reporting bias) and from geographical bias. These, in addition to detection bias, may lead to spurious trends.
We investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data. Occupancy models use detection/nondetection data and yield estimates of the percentage of occupied sites (occupancy) per year. These models take the imperfect detection of species into account. By correcting for detection bias, they may simultaneously correct for observation and reporting bias as well. We compared trends in occupancy (or distribution) of butterfly and dragonfly species derived from opportunistic data with those derived from standardized monitoring data. All data came from the same grid squares and years, in order to avoid any geographical bias in this comparison.
Distribution trends in opportunistic and monitoring data were well‐matched. Strong trends observed in monitoring data were rarely missed in opportunistic data.
Synthesis and applications . Opportunistic data can be used for monitoring purposes if occupancy models are used for analysis. Occupancy models are able to control for the common biases encountered with opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale. Opportunistic data may thus become an important source of information to track distribution trends in many groups of species.
Estimates of species richness and diversity are central to community and macroecology and are frequently used in conservation planning. Commonly used diversity metrics account for undetected species primarily by controlling for sampling effort. Yet the probability of detecting an individual can vary among species, observers, survey methods, and sites. We review emerging methods to estimate alpha, beta, gamma, and metacommunity diversity through hierarchical multispecies occupancy models (MSOMs) and multispecies abundance models (MSAMs) that explicitly incorporate observation error in the detection process for species or individuals. We examine advantages, limitations, and assumptions of these detection-based hierarchical models for estimating species diversity. Accounting for imperfect detection using these approaches has influenced conclusions of comparative community studies and creates new opportunities for testing theory.
The earliest deliberate introductions of bumblebees to areas outside of their native range occurred over 100 years ago. Transportation of bumblebees accelerated in the late 1980s following the development of techniques for mass rearing them, and their widespread adoption as the preferred pollinator for a range of glasshouse crops, primarily tomatoes. There is now a worldwide trade in one species, Bombus terrestris dalmatinus, originating from south east Europe. Within North America, which does not allow the importation of B. terrestris, the trade is primarily in Bombus impatiens. Trade in B. t. dalmatinus threatens the integrity of other subspecies within Europe, such as B. t. audax which is endemic to Britain and Ireland. However, there is a conspicuous absence of data as to whether B. t. dalmatinus has established in the wild outside its native range, and whether it interbreeds with native subspecies. Perhaps a more significant risk associated with trade in bumblebees is the accidental spread of parasites, and the subsequent risk that native bumblebee species may be exposed to parasites for which they have little resistance. There is circumstantial evidence that catastrophic declines of several North American bumblebee species may have been triggered by the accidental introduction of pathogens from Europe. Even if commercial bumblebee colonies are reared locally, the high densities at which they are kept mean that glasshouse nests are likely to act as reservoirs for spread of disease to wild bumblebee populations nearby. There is clearly the need for tight quarantining of bee colonies before transportation, and a moratorium should be placed on the transport of bumblebees in cases where native species suitable for commercial rearing are readily available.
The relationship between the North American bumble bee species Bombus moderatus Cresson and the European and Asian species in the subgenus Bombus sensu stricto has long been in question. Bombus moderatus has either been regarded as a distinct species or has been synonymized with B. lucorum (L.). We surveyed 10 Bombus s.str. species at 26 enzyme loci, using horizontal and vertical starch-gel electrophoresis. We found that B. moderatus differs from B. lucorum at three loci but is identical in these and all other loci surveyed with B. cryptarum (Fabricius) and B. magnus Vogt. The present distribution of B. moderatus, together with the observation that its closest relatives are the European B. cryptarum and B. magnus and the Asian B. hypocrita Pérez, suggests that differentiation occurred after dispersal to North America via Beringia.
Although there are numerous examples of individual species moving up in elevation and poleward in latitude in response to 20th century climate change, how communities have responded is less well understood and requires fully accounting for changes in species-specific detectability over time, which has been neglected in past studies. We use a hierarchical Bayesian occupancy model to examine bird species richness change and turnover along three elevation gradients surveyed 80-100 years apart in the Sierra Nevada of California, USA. Richness declined over the 20th century across all elevations. Turnover was greatest at the highest and the lowest elevations. These findings were only apparent, however, after species' detectability was incorporated into measures of species richness. Further partitioning of species richness changes by elevational life zone showed that numbers of low- and high-elevation species declined, without a concurrent expansion by mid-elevation species. Our results provide empirical evidence for biodiversity loss in protected montane areas during the 20th century and highlight the importance of accounting for detectability in comparisons of species richness over time.
Pollinators such as bees are essential to the functioning of terrestrial ecosystems. However, despite concerns about a global pollinator crisis, long-term data on the status of bee species are limited. We present a long-term study of relative rates of change for an entire regional bee fauna in the northeastern United States, based on >30,000 museum records representing 438 species. Over a 140-y period, aggregate native species richness weakly decreased, but richness declines were significant only for the genus Bombus. Of 187 native species analyzed individually, only three declined steeply, all of these in the genus Bombus. However, there were large shifts in community composition, as indicated by 56% of species showing significant changes in relative abundance over time. Traits associated with a declining relative abundance include small dietary and phenological breadth and large body size. In addition, species with lower latitudinal range boundaries are increasing in relative abundance, a finding that may represent a response to climate change. We show that despite marked increases in human population density and large changes in anthropogenic land use, aggregate native species richness declines were modest outside of the genus Bombus. At the same time, we find that certain ecological traits are associated with declines in relative abundance. These results should help target conservation efforts focused on maintaining native bee abundance and diversity and therefore the important ecosystems services that they provide.
Nontarget species such as pollinators may be of great importance to the restoration process and the long-term functioning of restored habitats, but little is known about how such groups respond to habitat restoration. I surveyed bee communities at five equal-aged restored sites, paired with five reference sites (riparian remnants) along the Sacramento River, California, United States. Flower availability and bee visitation patterns were also measured to examine the restoration of pollination function. Restoration of structural vegetation allowed diverse and abundant native bee communities to establish at the restoration sites; however, the composition of these important pollinator communities was distinct from that in the remnant riparian sites. Differences did not arise primarily from differences in the composition of the flowering-plant community; rather there must be other physical characteristics of the restored sites or differences in nesting site availability that led to the different pollinator communities. Because sites were spatially paired, the differences are unlikely to be driven by landscape context. Bee life-history and other biological traits may partially explain the differences between bee communities at restored and remnant sites. Patterns of visitation to native plant species suggest that pollination function is restored along with pollinator abundance and richness; however, function may be less robust in restored habitats. An examination of interaction networks between bees and plant species found at both restored and remnant riparian sites showed less redundancy of pollinators visiting some plants at restored habitats.
Long‐term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the “list length,” can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes.
List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization.