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An animal's diet breadth is a central aspect of its life history, yet the factors determining why some species have narrow dietary breadths (specialists) and others have broad dietary breadths (generalists) remain poorly understood. This challenge is pronounced in herbivorous insects due to incomplete host plant data across many taxa and regions. Here, we develop and validate machine learning models to predict pollen diet breadth in bees, using a bee phylogeny and occurrence data for 682 bee species native to the United States, aiming to better understand key drivers. We found that pollen specialist bees made an average of 72.9% of their visits to host plants and could be predicted with high accuracy (mean 94%). Our models predicted generalist bee species, which made up a minority of the species in our dataset, with lower accuracy (mean 70%). The models tested on spatially and phylogenetically blocked data revealed that the most informative predictors of diet breadth are plant phylogenetic diversity, bee species' geographic range, and regional abundance. Our findings also confirm that range size is predictive of diet breadth and that both male and female specialist bees mostly visit their host plants. Overall, our results suggest we can use visitation data to predict specialist bee species in regions and for taxonomic groups where diet breadth is unknown, though predicting generalists may be more challenging. These methods can thus enhance our understanding of plant-pollinator interactions, leading to improved conservation outcomes and a better understanding of the pollination services bees provide.

Bee body size affects their ability to forage, carry pollen, and adapt to environmental conditions. Body size is influenced by environmental factors such as food availability and temperature. As these conditions continue to change, it is crucial to accurately measure body size and determine whether particular body measurements can predict size variation across and within species. Measuring body size is challenging as bees are often small and may be missing body parts. In this study, we examined ten different bee species across three different families. We compared body and wing measurements as proxies for dry weight for both damaged and non-damaged bees. We used fitted vs. residuals and QQ plots to test for normality and linearity, along with performing simple linear regression using R version 4.4.0. We determined that intertegular distance (ITD) and head width to be the most significant predictors of dry weight across species and within most species, suggesting that these measurements may be used to accurately estimate dry weight. We also found that intertegular distance significantly predicted dry mass even in specimens missing body parts, suggesting that this measurement is somewhat robust to specimen damage. These findings contribute to the understanding of the relationship between dry weight and body measurements and provide guidelines for accurately estimating body size within and across species, for a range of applications in ecological research and conservation efforts. Presented at UC Santa Barbara CSEP Summer Colloquium

This study investigates the impact of anthropogenic land use on the body size of bees across 18 different species. Adult bee body size, primarily influenced by developmental nutrition, is significantly affected by the availability of floral resources. Developed land often has reduced floral diversity and density is hypothesized to produce smaller bees due to limited food resources. Specimens from the UCSB Invertebrate Zoology Collection were categorized based on their collection sites into three land use types: developed, agricultural, and forest using USGS National Land Cover Database. Measurements of head width, intertegular distance (ITD), and dry mass were taken to assess body size. A body size index was calculated as the average of these measurements. Analysis of Variance (ANOVA) were done in Python version 3.12.4. Results indicate that bees from agricultural habitats are significantly larger than those from developed and forest habitats across all metrics (head width, ITD, dry mass, and body size index). These findings highlight the influence of landscape changes on bee functional traits, providing essential insights into the ecological consequences of land use on bee health.This poster was presented at the UCSB CSEP summer colloquium 2024.

This project aims to mitigate the critical decline in bee populations, essential for crop pollination and food security. With a shortage of taxonomic specialists to identify the vast array of bee species, the project's goal is to enhance the monitoring of population changes through an automated classification system. Utilizing a dataset of bee wing images, the project aims to develop a computational pipeline to identify species based on their unique wing vein patterns. This approach not only supports bee conservation efforts but also expands our understanding of complex geometric variations in nature, offering wider applications in biological research. This poster was presented at the UCSB Data Science Capstone showcase in 2024.

The documentation, preservation and rescue of biological diversity increasingly uses living biological samples. Persistent associations between species, biosamples, such as tissues and cell lines, and the accompanying data are indispensable for using, exchanging and benefiting from these valuable materials. Explicit authentication of such biosamples by assigning unique and robust identifiers is therefore required to allow for unambiguous referencing, avoid identification conflicts and maintain reproducibility in research. A predefined nomenclature based on uniform rules would facilitate this process. However, such a nomenclature is currently lacking for animal biological material. We here present a first, standardized, human-readable nomenclature design, which is sufficient to generate unique and stable identifying names for animal cellular material with a focus on wildlife species. A species-specific human- and machine-readable syntax is included in the proposed standard naming scheme, allowing for the traceability of donated material and cultured cells, as well as data FAIRification. Only when it is consistently applied in the public domain, as publications and inter-institutional samples and data are exchanged, distributed and stored centrally, can the risks of misidentification and loss of traceability be mitigated. This innovative globally applicable identification system provides a standard for a sustainable structure for the long-term storage of animal bio-samples in cryobanks and hence facilitates current as well as future species conservation and biomedical research.

Natural history collections serve as unique and vital records of life on Earth. At the Cheadle Center, the Collections & Biodiversity program investigates Earth's variety of life, encompassing species, ecosystems, and genetic differences, aiming to safeguard this diversity for the future. By integrating multiple disciplines like evolution, ecology, the study of living organisms, genetics, and the science of conserving species and habitats, we work toward understanding biodiversity on our changing planet.

North Campus Open Space (NCOS) is a large-scale restored habitat in the historic Devereaux slough in Goleta, California. Forty-four plywood cover boards measuring 3’ x 4’ were dispersed across NCOS in four distinct habitats (grassland, woodland, coast sage scrubland, and marshland). For over 4 years, researchers have tracked the presence of animals under these coverboards, mostly small rodents and herpetofauna, following the area’s habitat restoration. Patterns of their distribution are increasingly understood as surveys continue; however, little is known about the patterns of invertebrate distribution and the factors influencing them. Invertebrates provide essential ecosystem functions as they break down organic matter, recycling nutrients back into the ecosystem, and they can serve as insightful bioindicators of ecosystem health. Previous studies documented that in agricultural systems, mean annual precipitation and soil pH are the largest predictors of soil invertebrate richness. In this study, we test whether these variables accurately predict patterns of invertebrate richness in a restored ecosystem. We focused on two diverse macroinvertebrate taxa, Coleoptera (beetles) and Arachnida (spiders). By collecting, curating, and identifying beetles and spiders under each coverboard we were able to establish what species reside in the various habitats. We took measurements of the soil pH under each board as well as the humidity in the interspace between the soil and the board as a proxy for mean annual precipitation. Our preliminary data suggest that soil pH and relative humidity partially contribute to the distribution of macroinvertebrates within habitats. However, other factors such as cohabitation, soil type, adjacent vegetation, or other factors involved in nutrient cycling, could have larger effects on the distribution patterns of these taxa.This poster was presented at the UCSB undergraduate research colloquium 2024.

Wing venation characteristics are fundamental for defining and classifying insects. In bees these characteristics are relatively conserved, but the patterns between groups remain poorly understood. We employed geometric morphometrics to assess variation in wing venation across bees taxa. Geometric morphometrics allows for detailed shape analysis of wing structure, which may provide insights into evolutionary relationships. By digitally landmarking homologous wing vein characters of a diverse sample of bees, we quantified and compared phenotypic variation in order to assess whether the resulting morphological clusters reflect evolutionary divergence and align with established phylogeny. This study assesses the potential of geometric morphometrics to infer the phylogenetic placement of indeterminate bee species based solely on wing vein patterns and provides an effective pathway for species identification.Presented at the UCSB EEMB Undergraduate Research Symposium 2024

This research examines the effect of disruptive camouflage by studying the coloration of bees. Using the OpenCV and scikit-learn libraries in Python and incorporating the image theory, I developed a procedure that quantifies and analyzes the visual hotspots on bees, which is mainly applied to identify camouflage effects of different abdominal colors, namely, black and white stripes, black and yellow stripes, and iridescent hues. Patterns that contribute to disruptive camouflage are highlighted by K-mean clustering method. Through the application of the CV methods, we may obtain a quantitative perspective when assessing the qualitative coloration of the bees. Based on image theory-driven clustering, our method provides a foundation that could be extended to further hypothesize the camouflage functionality of coloration patterns.This poster was presented at the UCSB Undergraduate Research Colloquium in 2024.

Understanding the relationship between physiological factors on social behavior in bees is critical to understanding their ecological dynamics. This study investigates the relationship between body mass, ovary size, Dufour’s gland size, and chill coma recovery time in female Xylocopa tabaniformis bees. Analyses reveal a significant predictive relationship between bee body mass and both ovariole length and Dufour’s gland length. Body mass does not emerge as a significant predictor of CCRT, suggesting other factors as better predictors for thermal tolerance. Conflicting conclusions arise regarding CCRT as a function of physical trauma or age, emphasizing a need for further investigation. Future research should expand the scope of species studied, and the timing of the study to include pre-reproductive female bees. The findings of this study provide insight into the physiological underpinnings of bee sociality and highlight the complexity of bee ecological dynamics.This poster was presented at the UCSB URCA colloquium in Spring 2024.

Recovering biodiversity across trophic levels is a major challenge in restoration ecology. Specifically, predator population recovery depends on the timely re-establishment of their preferred prey species in restored habitats. Here, we evaluate potential dietary factors contributing to the loss of western burrowing owls (Athene cunicularia hypugaea (Bonaparte, 1825)) from a newly-restored coastal grassland. We examined owl pellets and found that burrowing owl diets were relatively low in vertebrate prey during their brief occupation of the restoration site (2.6% of prey items; found in 61.8% of sampled pellets). We suggest that preferred food limitation may have been one contributor to the loss of owls from the restoration site. These findings suggest the need to prioritise re-establishment of prey communities for effective long-term recovery of burrowing owls in restored landscapes.

The purpose of this research project is to investigate how rising temperatures, for instance climate change, can affect bees of various body sizes given their essential role in the global food supply through pollination of agricultural crops. To achieve this I utilized 3D imaging and 3D modeling techniques to calculate surface area-to-volume (SA/V) ratios of the bees that otherwise cannot be obtained using conventional methods. SA/V ratios were calculated for 4 different families (Halictidae, Colletidae, Apidae, and Megachilidae) in the order Hymenoptera and were analyzed alongside the bee’s Critical Thermal Maximum (CT Max) data, the maximum heat a bee can withstand before losing mobility, to gain insight on the bee's ability to survive in extreme hot temperatures. It is evident from the data that larger bees, characterized by smaller SA/V ratios, presented a higher CT Max suggesting their greater chance of survival in higher temperatures than smaller bees due to less heat exchange relative to their body size. This data implies that with the earth’s rising global temperatures larger bees will likely perform better than smaller bees. This poster was presented at the UCSB Center for Science & Engineering Partnerships Undergraduate Research Symposium, on August 9, 2023.

Humans are characterised as cooperative breeders, as not only the parents but also other members of the social group take part in raising offspring. The individuals who invest most in childrearing are usually the more closely related individuals. However, most studies have concentrated on close kin and the effects of more distant kin remain unknown. Here, we investigated the associations of child mortality (

Pollinators are crucial to agriculture and natural ecosystems. As such, it is important to consider how they may be affected by current and future changes in climate. In this study, we focus on the pilosity, or hairiness, of the long-horned bee Melissodes tepidus timberlakei Cockerell, 1929 and how that may inform us on how this species tolerates colder temperatures, and whether pilosity can inform us of body size and age. We hypothesized that (1) Hairier bees are more tolerant to cold temperature (2) hairier bees are heavier (3) younger bees are hairier than older bees (4) bees collected earlier in the year are hairier. Presented at UC Santa Barbara CSEP Summer Research Symposium August 15th, 2023.

ABSTRACT Functional traits offer an informative framework for understanding ecosystem functioning and responses to global change. Trait data are abundant in the literature, yet many communities of practice lack data standards for trait measurement and data sharing, hindering data reuse that could reveal large‐scale patterns in functional and evolutionary ecology. Here, we present a roadmap toward community data standards for trait‐based research on bees, including a protocol for effective trait data sharing. We also review the state of bee functional trait research, highlighting common measurement approaches and knowledge gaps. These studies were overwhelmingly situated in agroecosystems and focused predominantly on morphological and behavioral traits, while phenological and physiological traits were infrequently measured. Studies investigating climate change effects were also uncommon. Along with our review, we present an aggregated morphological trait dataset compiled from our focal studies, representing more than 1600 bee species globally and serving as a template for standardized bee trait data presentation. We highlight obstacles to harmonizing this trait data, especially ambiguity in trait classes, methodology, and sampling metadata. Our framework for trait data sharing leverages common data standards to resolve these ambiguities and ensure interoperability between datasets, promoting accessibility and usability of trait data to advance bee ecological research.

Habitat loss is a primary driver of global biodiversity decline, negatively impacting many species, including native bees. One approach to counteract the consequences of habitat loss is through restoration, which includes the transformation of degraded or damaged habitats to increase biodiversity. In this review, we survey bee habitat restoration literature over the last 14 years to provide insights into how best to promote bee diversity and abundance through the restoration of natural landscapes in North America. We highlight relevant questions and concepts to consider throughout the various stages of habitat restoration projects, categorizing them into pre-, during-, and post-restoration stages. We emphasize the importance of planning species- and site-specific strategies to support bees, including providing floral and non-floral resources and increasing nest site availability. Lastly, we underscore the significance of conducting evaluations and long-term monitoring following restoration efforts. By identifying effective restoration methods, success indicators, and areas for future research, our review presents a comprehensive framework that can guide land managers during this urgent time for bee habitat restoration.

Bees are important pollinators responsible for creating and maintaining the ecosystems that many animals and humans rely on. With many of its species on the decline, the Cheadle Center for Biodiversity and Ecological Restoration is leading the Big-Bee Project Network (http://big-bee.net) in an effort to digitize bee traits for further research in bee ecology. Since researchers lack data on anatomical traits that may make bees either vulnerable or resilient to human-induced environmental changes, it is important to find new methods for efficiently collecting these data.One bee trait identified to prioritize was a bee’s hairiness because they’re important for pollination, sensing nearby danger, and staying warm in cooler temperatures and cooler in hotter climates. Through the use of deep learning and computer vision, a new pipeline was created to measure the surface density of hair from bee images. By optimizing two pre-trained U-Nets on lateral-view images of bees, the model became capable of pixel-based classification of where the bee is in a photo and where the hair is. These models were trained and evaluated on hand-crafted datasets of 550 images, which were remasked overtime for better performance.While the main challenges came from differentiating texture of hair and texture of eyes/wings, another deep learning model succeeded at removing this noise before being fed into the hair detection pipeline. This multi-step pipeline improves previous findings by further preprocessing input images and is able to target the important signals of the image (hair). It also opens up the opportunity of using traditional edge detection methods for optimizing this problem.This research is sponsored by the McNair/Edison STEM Summer Research Program and the National Science Foundation (#2102006).

All winged insects have wing veins that form unique patterns. A strong correlation between wing size and body size has been established in the insect order Hymenoptera, including bees. Vein pattern in bees is also an important trait for identification at the genus level. However, how this pattern relates to the density of veins observed in the wing has not previously been studied and the overall relationship between wing vein number and body size has not been evaluated in bees in the absence of other hymenopteran taxa. Bee forewing vein density as it relates to body size and taxonomic group was investigated. Using dorsal and slide-plated wing images, wing vein density and intertegular span were measured for bee species within different genera and families. The study found that both taxonomic level and body size influence wing vein density, of which the taxonomic level of genus has the most significant effect, regardless of body size. This poster was presented at the UC Santa Barbara Undergraduate Research and Creative Activities Poster Colloquium on May 3rd, 2022. Github link: https://github.com/LeoEisner/Body-Size-and-Taxonomic-Influence-on-Bee-Wing-Vein-Density

Bees are important pollinators. However, their numbers and diversity are in decline which poses a serious threat to plant pollination and affects food production. The Big-Bee project (http://big-bee.net) launched in 2021 to research bee ecology and taxonomy through the digitalization of bee specimens. Bees’ ocelli are important navigational organs that receive and evaluate light intensities. The project aims at exploring the impact of bees’ sociality on potential variation in ocelli morphology. By measuring median ocellar width between sexes, I firstly tested whether sexual size dimorphism occurs in ocelli between sexes in the same bee species. Then, I evaluated whether the sexual size dimorphism exists based on social versus solitary bees. The results indicated the existence of a different median ocellar size between males and females within one species. However, such existence does not occur in males and females based on sociality categorization, indicating the sociality might not be a driver in causing the dimorphism. This poster was presented at UC Santa Barbara Undergraduate Research and Creative Activities Poster Colloquim on May 3rd, 2022.

The distribution of bees can be estimated, in part, by enviromental variables associated with geography. Using bee occurrence records, we quantified the estimated species richness of the EPA level 3 defined ecoregions using coverage-based rarefaction. Our study reveals that bee species richness is highest in ecoregions with warm dry summers. Furthermore, increased species richness was found to be correlated with maximum mean temperature.Presented at the iDigBio 6th Annual Digital Data in Biodiversity Research Conference on 05/24/2022 and the University of California Santa Barbara EEMB Undergraduate Research Symposium on 04/23/2022

The Cheadle Center for Biodiversity and Ecological Restoration (CCBER) is working with the UCSB Data Science capstone team to continue the research and understanding of bee ecology through image and trait digitalization.In efforts to help researchers involved in the Big-Bee project, we want to automate the process it takes to handle and process the digital images of bees. Given images of bees with a QR/Datamatrix code, we sought to develop scripts that can rename the image files based on the decoded box and extract EXIF metadata. In the latter half of our project, we shift our focus toward the hairy characteristics of bees. Given data of high definition lateral bee images, we want to create models that can quantify how hairy a bee is.  ResultsTwo scripts, the QR Code Scanner & MetaData Extractors, are published on GitHub and are currently used by two academic institutions. More trial testing is required to discover bugs and further improvements for the scripts. Using density as our metric, our U-net and transfer learning method yields an accuracy of 97.68% for bee masking and 87.2% for hair masking. Using entropy as our metric, we were able to compare the average entropy values for different bees binned in the following manner: low, middle, and high hairiness. The box-plot distribution of the bins and t-tests show that there was a positive correlation between bee hairiness and average entropy value. Furthermore, we discover that entropy analysis works best for specimens that have low reflectivity and minimal skin texture. QR Scanner: https://github.com/booleank/Bee-ScannerMetadataExtractor: https://github.com/harperklauke/Metadata-Extractor

Tens of millions of images from biological collections have become available online over 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. While image analysis has become mainstream in consumer applications, it is still used only 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 images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

Abstract This contribution summarizes the current state of research regarding so‐called sensor‐integrating machine elements as an enabler of digitalization in mechanical engineering and——if available—their application in industry. The focus is on the methodical aspects of the development of these machine elements in general as well as specific sensor‐integrating machine elements that are either already in use or currently under development. Developmental aspects include the robust design of initially evaluated concepts for sensor‐integrating machine elements as well as their modularization. Smart materials with sensory functions are included in the analysis as well as the differentiation with regard to add‐on sensors. The aim of the authors interlinked by a special research program funded by the German Research Foundation (DFG) is to facilitate the exchange with other researchers with the help of the comprehensive overview given in this contribution. The contribution concludes with a brief discussion of open challenges, such as the energy supply and data transfer in rotating systems and also data security.

Abstract Chloride channel accessory 2 (CLCA2) is a transmembrane protein, which promotes adhesion of keratinocytes and their survival in response to hyperosmotic stress. Here we show that CLCA2 is transported to the nucleus of keratinocytes via extracellular vesicles. The nuclear localization is functionally relevant, since wild‐type CLCA2, but not a mutant lacking the nuclear localization signal, suppressed migration of keratinocytes and protected them from hyperosmotic stress‐induced cell death. In the nucleus, CLCA2 bound to and activated β‐catenin, resulting in enhanced expression of Wnt target genes. Mass‐spectrometry‐based interaction screening and functional rescue studies identified RNA binding protein 3 as a key effector of nuclear CLCA2. This is of likely relevance in vivo because both proteins co‐localize in the human epidermis. Together, these results identify an unexpected nuclear function of CLCA2 in keratinocytes under homeostatic and stress conditions and suggest a role of extracellular vesicles and their nuclear transport in the control of key cellular activities.

A quarter of Asian elephants are captive, with greater than 90% of these tamed and cared for by handlers (mahouts) in Asia. Although taming is a much-discussed welfare issue, no studies to our knowledge have empirically assessed its impact on calves, and dialogue surrounding taming often lacks perspectives of those involved. Here, we interviewed mahouts involved in taming and monitored five physiological measures (faecal glucocorticoid metabolites (FGMs), serum cortisol, glucose, creatine kinase (CK) and heterophil:lymphocyte (H:L)) over the first 10 days of taming and following six months in 41 calves undergoing taming and 16 control individuals. These measures assess the duration and intensity of stress during taming. Interviews suggested mahouts had major concerns for their safety when discussing changing taming practices, an important consideration for future management. Calf physiological measures were elevated by 50–70% (FGMs/cortisol/glucose), 135% (H:L) and greater than 500% (CK) over the first few days of taming, indicative of elevated stress, not seen to the same extent in control adults. Some measures stabilized sooner (glucose/cortisol/CK/FGM: 7–10 days) than others (H:L: one–two months), indicating mostly acute stress. Our findings inform the welfare of approximately 15 000 captive elephants around the world. Future studies should compare taming in different populations and consider calf and mahout welfare.