A document by Helen Payne. Click on the document to view its contents.

IntroductionInferring generalizable patterns in species dynamics, distributions, and functional variation are central aims of ecology and evolutionary biology (MacArthur, 1972). Trait-based approaches, which quantify phenotypic characteristics that impact organisms’ fitness and/or functional role, provide a tractable comparative framework for understanding communities, ecosystems, and evolutionary processes (Mcgill et al., 2006; Violle et al., 2007). Functional trait studies have proliferated over the past two decades, addressing foundational questions in community ecology (Cadotte et al., 2015; Mcgill et al., 2006; Violle and Jiang, 2009), biogeography (Violle et al., 2014), and conservation biology (Cadotte et al., 2011; Wellnitz and Poff, 2001) across taxonomic groups. These works emphasize the promise of trait-based research for generating novel insights into central ecological concepts and theories.Increasingly, bee researchers are recognizing the utility of trait-based approaches for a wide variety of applications in ecological research. Bees (Hymenoptera: Apoidea: Anthophila) represent more than 20,000 species worldwide and display dramatic interspecific variation in morphology and behavior (Figure 1), including traits that mediate pollination services and responses to global environmental change (Supplementary Table 1). Exploration of functional traits has long been a cornerstone of bee research, yet only recently have these traits been systematically applied in bee ecological studies as a comparative framework for understanding community-level processes. Given their major functional role as the primary animal pollinators of terrestrial ecosystems (Ollerton et al., 2011), the bees represent a group ripe for exploration through a functional ecological lens.Here, we review an emerging body of literature that quantifies functional traits across bee communities to address questions in bee ecology. In doing so, we address the following questions: How have functional traits been used to study bee ecology? What have been the major outcomes and limitations in bee functional trait research? How might this framework be leveraged to address urgent questions in the study of global bee declines? We review the variety of methods used to quantify bee trait variation, highlight common methodological problems and inconsistencies, and recommend best practices. Additionally, we describe geographic, taxonomic, and trait biases across the body of bee functional trait work, and highlight research areas that merit particular attention in future studies. Finally, we emphasize the value of open trait data sharing, and propose a roadmap toward a global bee functional trait database, including an initial aggregated dataset of 3369 morphological measurements from 1209 bee species.

Madeleine M. Ostwald*, Valentina A. Venegas, and Katja C. Seltmann

Abstract1.     Climatic factors are known to mediate the expression of social behaviors. Likewise, variation in social behavior can dictate climate responses. Understanding interactions between climate and sociality is crucial for predicting impacts of climate change on animal populations.

Location-specific machine learning models trained on synthetic images represent a scalable paradigm for quantifying biodiversity. Digital images of specimens or samples can be manipulated and combined with computer-generated scene elements to produce nearly infinite synthetic images for training machine learning models. Application of this method for the automatic cataloging of wildlife from camera trap images is of urgent need during this time of high extinction rate and environmental change.

Phenotypic divergence is an important consequence of restricted gene flow in insular populations. This divergence can be challenging to detect when it occurs through subtle shifts in morphological traits, particularly in traits with complex geometries, like insect wing venation. Here, we employed geometric morphometrics to assess the extent of variation in wing venation patterns across reproductively isolated populations of the social sweat bee, Halictus tripartitus. We examined wing morphology of specimens sampled from a reproductively isolated population of H. tripartitus on Santa Cruz Island (Channel Islands, Southern California). Our analysis revealed significant differentiation in wing venation in this island population relative to conspecific mainland populations. We additionally found that this population-level variation was less pronounced than the species-level variation in wing venation among three sympatric congeners native to the region, Halictus tripartitus, Halictus ligatus, and Halictus farinosus. Together, these results provide evidence for subtle phenotypic divergence in an island bee population. More broadly, these results emphasize the utility and potential of wing morphometrics for large-scale assessment of insect population structure.

AbstractTens of millions of images from biological collections have become available online in the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. Whilst image analysis has become mainstream in consumer applications, it is still only used on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if the images of collection objects could be made accessible as a single corpus. In this paper, we make the case for building infrastructure that could support image analysis of collection objects. We show that such an infrastructure is entirely feasible and well worth the investment.IntroductionOwing to their central role in cataloguing the world’s biodiversity, global biological collections likely hold samples of most known macro-biodiversity. As such, they are an irreplaceable asset for research of all kinds, including ecology, conservation, natural history and epidemiology  \cite{Bradley_2014,Cook_2014,Davis_2019,Antonelli_2020}. They are also seen as an important and underused resource to address numerous questions in the context of biodiversity under global change \cite{Soltis_2017,Meineke_2018,Hussein_2022}. Thus ensuring access to, and integrating data from these collections is globally important for the future. Conservation and sustainable use of biodiversity are fundamental to the 2030 Agenda of the \cite{secretariat_of_the_convention_on_biological_diversity_biodiversity_2016} and achieving its sustainable development goals is only realistic with the collections that underpin accurate naming and knowledge of biodiversity.To keep pace with the demand for access to collections, digital imaging of biological collections has progressed at pace (Fig. 1). As of September 2022, the Global Biodiversity Information Facility (GBIF) has more than 49 million preserved or fossil specimens with an image. For just the nearly 400 million specimens of plants held in collections globally \cite{thiers_worlds_2020}, there are almost 38 million (9%) occurrences with images on GBIF. This number is expected to grow substantially. For example, the digitisation of the Kew herbarium, which holds over 7 million specimens will add to already major digitization programs in Australia, China, Europe and the United States among others \cite{willis_science_2018,Nelson_2018,Borsch_2020,chinese_virtual_herbarium_299000_2021}.\ref{240550}\ref{205447}

The reintroduction of endangered plant species is an essential conservation tool. Reintroductions can fail to create resilient, self-sustaining populations due to a poor understanding of environmental factors that limit or promote plant success. Biotic factors, specifically plant-arthropod interactions, have been shown to affect the establishment of endangered plant populations. Lupinus nipomensis (Nipomo Mesa lupine) is a state of California (California Rare Plant Rank: 1B.1) and federally (65 FR 14888) endangered endemic plant with only one extant population located along the central California coast. How arthropods positively or negatively interact with L. nipomensis is not well known and more information could aid conservation efforts. We conducted arthropod surveys of the entire L. nipomensis extant population in spring 2017. Observed arthropods present on L. nipomensis included 17 families, with a majority of individuals belonging to Thripidae. We did not detect any obvious pollinators of L. nipomensis, providing support for previous studies suggesting this lupine is capable of self-pollinating, and observed several arthropod genera that could potentially impact the reproductive success of L. nipomensis via incidental pollination or plant predation.