Biology subjects

Orr, M. C.

Publications and source records attributed to Orr, M. C..

8 recordsLinked to original sources

bioRxiv2026

Cavity-nesting bees combine forest nesting habitats with surrounding floral resources in a subtropical forest diversity experiment

Wild bees face declines, and forests may serve as critical habitats for pollinators. However, how forest composition and the associated floral environment shape pollen provisioning and resource partitioning among cavity-nesting bees remains poorly understood. Here, we leveraged BEF-China, a large-scale subtropical forest biodiversity experiment with experimentally controlled plant (tree and shrub) communities, to investigate how forest composition and spatial context shape pollen provisioning, resource partitioning, and reproductive success of cavity-nesting bees. We used DNA metabarcoding to analyze floral composition of pollen provisioned by five cavity-nesting bee species, with samples collected from BEF-China across three years (2022- 2024). By comparing pollen taxonomic composition from whole-nest pooled samples and individual brood-cell samples with the experimentally planted species pool, we characterized dietary patterns and temporal dynamics of five bee species. Bees primarily relied on floral resources from the surrounding landscape, with planted trees providing essential but temporally restricted pollen supplements during specific phenological stages. Co-occurring bee species exhibited staggered nesting phenology and distinct dietary preferences for different plant families, with fine-scale resource differentiation even during periods of phenological overlap. Our results suggest that managed forests support cavity-nesting bees by providing critical woody floral resources during specific phenological gaps and offering stable nesting environments. To mitigate pollinator declines, forest management should prioritize maintaining diverse, phenologically complementary flowering vegetation within and surrounding forest stands. This ensures temporal continuity of pollen availability throughout the nesting season, which is particularly crucial for restoring pollinator services in simplified forest landscapes.

ecology↗

bioRxiv2026

Global Patterns and Drivers of Bee Diversity and Endemism on Islands

Islands harbor a disproportionate share of global biodiversity1, yet insects, even invaluable pollinators such as bees2, remain underrepresented in island biogeography research3. Here, we present the first global checklist of island bees, recording 4,140 species across 306 islands. Although islands comprise only [~]5% of Earths land area, they support [~]20% of global bee diversity, and 43% of insular species are endemic, making up [~]8% of all known bee species. Island bee species richness, mirroring continental trends4, peaks at mid-latitudes. Native richness increases with island area and declines with isolation, consistent with patterns in other taxa5. The strength of species-area relationships varies among biomes and is steepest in mediterranean-type systems, which also support disproportionately high bee richness relative to flowering plant diversity. Endemism is highest on large tropical islands, reflecting extensive in situ diversification. Major centers of bee endemism include Madagascar, Malesia (e.g. the Greater Sundas and New Guinea), and Hawaii, where a single large radiation of Hylaeus (Nesoprosopis) dominates6. Among islands capable of supporting endemic species, endemism scales strongly with total richness. These findings highlight the need to integrate island bee diversity into global conservation planning and position bees as a model for understanding insect evolution and conservation on islands.

ecology↗

bioRxiv2025

Sociality and nesting strategy shape the bimodal diversity gradient in bees

Bees are dominant pollinators across native and agricultural plant communities, yet the drivers of their patterns of geographic distribution and functional diversity remain poorly understood. Notably, bees exhibit a bimodal latitudinal diversity gradient, peaking in species diversity in temperate and arid regions rather than the tropics despite their close ecological and evolutionary ties to flowering plants, which show the opposite pattern. Here, we investigated whether two key life history traits thought to shape where bees can live--sociality and nesting strategy--may influence this pattern. We compiled data on sociality and nesting strategy for 4,293 bee species and combined it with a comprehensive phylogeny, curated global occurrence records, and newly-developed models of trait evolution to test 1) whether sociality and nesting biology are evolutionarily correlated, 2) how these life history traits shape bees climatic niches and niche breadths, and 3) whether the evolutionary dynamics of these traits may explain the bimodal latitudinal gradient in bee diversity. We find that the evolution of above-ground nesting is closely tied to the evolution of social behavior, but this trait combination has evolved rarely across the evolutionary history of bees. Where this combination does arise, species that are both social and above-ground nesters tend to have narrower climatic niches and are associated with environments that are warmer, wetter, and experience little seasonal temperature variation, making them more prevalent in tropical bee communities. Together, our results indicate that the bimodal diversity gradient in bees is driven by phylogenetic niche conservatism, where most bee lineages retain ancestral traits suited to arid environments and the derived syndrome necessary for thriving in the tropics evolves rarely.

evolutionary biology↗

bioRxiv2024

Multi-dimensionality of tree communities structure host-parasitoid networks and their phylogenetic composition

Environmental factors can influence ecological networks, but these effects are poorly understood in the realm of the phylogeny of host-parasitoid interactions. Especially, we lack a comprehensive understanding of the ways that biotic factors, including plant species richness, overall community phylogenetic and functional composition of consumers, and abiotic factors such as microclimate, determining host-parasitoid network structure and host-parasitoid community dynamics. To address this, we leveraged a five-year dataset of trap-nesting bees and wasps and their parasitoids collected in a highly-controlled, large-scale subtropical tree biodiversity experiment. We tested for effects of tree species richness, tree phylogenetic and functional diversity, and species and phylogenetic composition on species and phylogenetic diversity of both host and parasitoid communities and the composition of their interaction networks. We show that multiple components of tree diversity and canopy cover impacted both, species and phylogenetic composition of hosts and parasitoids. Generally, phylogenetic associations between hosts and parasitoids reflected non-randomly structured interactions between phylogenetic trees of hosts and parasitoids. Further, host-parasitoid network structure was influenced by tree species richness, tree phylogenetic diversity, and canopy cover. Our study indicates that the composition of higher trophic levels and corresponding interaction networks are determined by plant diversity and canopy cover especially via trophic links in species-rich ecosystems.

ecology↗

bioRxiv2024

Buzz-world: Global patterns and drivers of buzzing bees and poricidal plants

Foraging behavior frequently plays a major role in driving the geographic distribution of animals. Buzzing to extract protein-rich pollen from flowers is a key foraging behavior used by bee species across at least 83 genera (these genera comprise [~]58% of all bee species). Although buzzing is widely recognized to affect the ecology and evolution of bees and flowering plants (e.g., buzz-pollinated flowers), global patterns and drivers of buzzing bee biogeography remain unexplored. Here, we investigate the global species distribution patterns within each bee family and how patterns and drivers differ with respect to buzzing bee species. We found that both distributional patterns and drivers of richness typically differed for buzzing species compared to hotspots for all bee species and when grouped by family. A major predictor of the distribution, but not species richness overall for buzzing members of four of the five major bee families included in analyses (Andrenidae, Halictidae, Colletidae and to a lesser extent, Apidae) was the richness of poricidal flowering plant species, which depend on buzzing bees for pollination. As poricidal plant richness was highest in areas with low wind and high aridity, we discuss how global hotspots of buzzing bee biodiversity are likely driven by both biogeographic factors and plant host availability. Whilst we explored global patterns with State-level data, higher resolution work is needed to explore local level drivers of patterns, but from a global perspective, buzz-pollinated plants clearly play a greater role in the ecology and evolution of buzzing bees than previously predicted.

ecology↗

bioRxiv2023

BeeDC: An R package and globally synthesised and flagged bee occurrence dataset

Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We present BeeBDC, a new R package, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducible BeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and we added record-level flags for a series of potential quality issues. These data are provided in two formats, "cleaned" and "flagged-but-uncleaned". The BeeBDC package with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducible R workflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.

ecology↗

bioRxiv2020

The influence of data type and functional traits on native bee phenology metrics: Opportunistic versus inventory records

Efforts to understand activity patterns of bees, our most important pollinators, often rely on opportunistically collected museum records to model temporal shifts or declines. This type of data, however, may not be suitable for this purpose given high spatiotemporal variability of native bee activity. By comparing phenological metrics calculated from intensive systematic inventory data with those from opportunistic museum records for bee species spanning a range of functional traits, we explored biases and limitations of data types to determine best practices for bee monitoring and assessment. We compiled half a million records of wild bee occurrence from opportunistic museum collections and six systematic inventory efforts, focusing analyses on 45 well-represented species that spanned five functional traits: sociality, nesting habits, floral specialization, voltinism, and body size. We then used permutation tests to evaluate differences between data types in estimating three phenology metrics: flight duration, number of annual abundance peaks, and date of the highest peak. We used GLMs to test for patterns of data type significance across traits. All 45 species differed significantly in the value of at least one phenology metric depending on the data type used. The date of the highest abundance peak differed for 40 species, flight duration for 34 species, and the number of peaks for 15 species. The number of peaks was more likely to differ between data types for larger bees, and flight duration was more likely to differ for larger bees and specialist bees. Our results reveal a strong influence of data type on phenology metrics that necessitates consideration of data source when evaluating changes in phenological activity, possibly applicable to many taxa. Accurately assessing phenological change may require expanding wild bee monitoring and data sharing.

ecology↗

bioRxiv2018

Comparison of large-scale citizen science data and long-term study data for phenology modeling

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and inter-annual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science National Phenology Network (NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built process based phenology models for species common to each dataset. From these models we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two datasets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for NPN data had the lowest error when using models built from the NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. Accordingly, the choice of dataset depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale NPN data, which has high spatial representation and a large regional species pool. LTER datasets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models which integrate the strengths of both datasets.

ecology↗