Biology subjects

Buckner, M. A.

Publications and source records attributed to Buckner, M. A..

3 recordsLinked to original sources

bioRxiv2025

UAV-based Remote Sensing of Bee Nesting Aggregations with Computer Vision for Object Detection

O_LIPollinating insects are in decline globally, threatening pollination services and driving a growing interest in pollinator monitoring and conservation. However, the implementation of conservation programs for these insects is often hindered by labor-intensive monitoring methods and in turn insufficient data to assess population trends. C_LIO_LIWe detail a method for surveying and censusing ground nesting bee aggregations, pairing automated UAV image capture with a custom trained computer vision-based object detection workflow using the YOLOv5m architecture. To highlight the ease of application and accuracy of the workflow, we surveyed a roughly 65m2 portion of a large Colletes inaequalis nesting aggregation. We compared the efficiency and performance of our model to manual counts of a technician. C_LIO_LIOur model detected the location of 1,094 nests, representing 88% of the nests present in our test dataset, and a true positive rate of 97%. Adjusting for error, our model estimated a total of 1,250 nests across the study site, comparable to the total estimated from a manual count of 1,259 nests. Our model detected nests 20 times faster than the manual counts while mapping the aggregation with millimeter accuracy. Spatial analyses show that bee nest density was heterogenous, with dense spatially clustered regions comprised of upwards of 60 nests per m2. C_LIO_LISynthesis and applications: Our novel application of UAV imagery and object detection models for mapping and censusing a ground nesting bee aggregation represents a rapid, cost-effective solution for overcoming limitations in traditional manual methods. Our workflow generates essential data with the high throughput required to help inform the conservation decisions needed to stem global bee declines. C_LI

ecology↗

bioRxiv2024

Fine-Scale Models of Bee Species Diversity and Habitat in New York State

Anthropogenic drivers of global change threaten bee diversity and the ecosystem services bees provide. Despite their importance, the conservation of bee pollinators is complicated by limited and often heavily biased occurrence data. A recent state-wide survey of insect pollinators across New York, United States generated a large spatial dataset of bee species occurrence records from community scientists, historical collections, and survey efforts. Using a combination of the state survey records with occurrence data from across the contiguous United States, we applied an ensemble modeling approach using balanced random forest and small bivariate generalized linear models to predict the distributions of most of the states bee species. We predicted the spatial distribution of bee species richness using a stacked species distribution model with climate, land cover, and soil covariates. To inform bee diversity conservation, we predicted spatial variation for each species and groups of species sharing similar life history traits. We also estimated statewide distribution of range-size rarity, ecological uniqueness, and climate exposure. We found that the richness of modeled species is high across the state, with the greatest richness in regions with low soil clay content and intermediate forest cover. The fine spatial scale and extent of our gridded data layers match the scale of conservation action in the state, providing an opportunity to incorporate wild bee diversity into broader statewide conservation planning. Conserving New York States bee pollinators is not straightforward, and decisions should be based on broader conservation priorities that incorporate bee biodiversity indicators into decision-making. Here, we encourage the inclusion of these vital pollinators in conservation decisions by leveraging the best available data and methods robust to small sample sizes to provide spatially explicit data products representing the distribution of bee diversity across the state of New York.

ecology↗

bioRxiv2024

Forecasting the Effects of Global Change on a Bee Biodiversity Hotspot

The Mojave and Sonoran Deserts, recognized as a global hotspot for bee biodiversity, are experiencing habitat degradation from urbanization, utility-scale solar energy (USSE) development, and climate change. In this study, we evaluated the current and future distribution of bee diversity in the region, assessed how protected areas safeguard bee species richness, and predicted how global change may affect bees across the region. Using Joint Species Distribution Models (JSDMs) of 148 bee species, we project changes in species distributions, occurrence area, and richness across the region under four global change scenarios between 1971 and 2050. We evaluated the threat posed by USSE development and predicted how climate change will affect the suitability of protected areas for conservation. Our findings indicate that changes in temperature and precipitation do not uniformly affect bee richness across the region. Protected areas in the Sonoran and Mojave Deserts are projected to experience mean losses of up to 5.8 species, whereas protected areas at higher elevations and transition zones may gain up to 7.8 species. Outside protected areas, bee diversity is threatened by urbanization and USSE development. Areas prioritized for future USSE development have an average species richness of 4.2 species higher than the study area average, and lower priority areas have 8.2 more species. USSE zones are expected to experience declines of 2.7 to 8.0 species by 2050 due to climate change alone. Despite the importance of solitary bees for pollination, their diversity is often overlooked in land management decisions. Our results show the utility of JSDMs for extending the usability of existing data-limited bee species records, easing the inclusion of these species in conservation and land management decision-making. The multiple threats from global change drivers underscore the importance of including ecologically vital, though often data-limited, species in land-use decisions.

ecology↗