Yu, Q.-Y. | bioRxiv Science

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↗

bioRxiv2019

Association Tests Using Copy Number Profile Curves (CONCUR) Enhances Power in Rare Copy Number Variant Analysis

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV association, kernel-based methods have been shown to be a powerful tool because their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their ability to accommodate effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., Copy Number profile Curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype association by comparing individuals copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of \"copy number profile curves\" to describe the CNV profile of an individual, and the \"common area under the curve (cAUC) kernel\" to model the multi-feature CNV effects. Compared to existing methods, CONCUR captures the effects of CNV dosage and length, accounts for the continuous nature of copy number values, and accommodates between- and within-locus etiological heterogeneities without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable and improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in gene pathways associated with phenotypes using data from the Swedish Schizophrenia Study and the Taiwan Biobank.\n\nAuthor summaryCopy number variants comprise a large proportion of variation in human genomes. Large rare CNVs, especially those disrupting genes or changing the dosages of genes, can carry relatively strong risks for neurodevelopmental and neuropsychiatric disorders. Kernel-based association methods have been developed for the analysis of rare CNVs and shown to be a valuable tool. Kernel methods model the collective effect of rare CNVs using flexible kernel functions that capture the characteristics of CNVs and measure CNV similarity of individual pairs. Typically kernels are created by summarizing similarity within an artificially defined \"CNV locus\" and then collapsing across all loci. In this work, we propose a new kernel-based test, CONCUR, that is based on the CNV location information contained in standard processing of the variants and removes the need for any arbitrarily defined CNV loci. CONCUR quantifies similarity between individual pairs as the common area under their copy number profile curves and is designed to detect CNV dosage, length and dosage-length interaction effects. In simulation studies and real data analysis, we demonstrate the ability of CONCUR test to detect CNV effects under diverse CNV architectures with power and robustness over existing methods.

bioinformatics↗