Biology subjects

Grozinger, C.

Publications and source records attributed to Grozinger, C..

3 recordsLinked to original sources

bioRxiv2026

BeeMonitor: Automated IoT video surveillance and an AI-powered video processing system for monitoring the foraging and nesting behavior of cavity-nesting solitary bees

O_LISolitary bee species that use artificial trap nests are important for agricultural crop production and as indicators of habitat quality. Quantifying cavity-nesting solitary bee foraging and nesting behavior is essential for real-time analysis of population numbers and pollination activity, as well as understanding how environmental conditions shape reproductive success and population dynamics. However, manual observation is labor-intensive, prone to observer bias, and unable to deliver continuous data. Existing automated systems either require individual bee marking or detect presence without resolving nest-tube-level entry and exit events. C_LIO_LIWe developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video, using Osmia cornifrons (the horn-faced mason bee) as a model system. A low-cost Raspberry Pi handles solar-powered field recording, while the software combines object detection (YOLOv26), a custom multiple-object tracker (BeeTrack), and a Random Forest classifier trained on trajectory-derived features to distinguish genuine events from incidental detections. C_LIO_LIOver a 29-day deployment, hardware reliability averaged 97.5% recording coverage. The pipeline achieved 91.3% precision and 87.3% recall (F1 = 0.893), generalizing robustly under leave-one-video-out cross-validation (mean F1 = 0.904). Detected foraging trips correlated strongly with brood cell counts (R2 = 0.849, p < 0.001, n = 19), and a Random Forest model (AUC = 0.820) identified solar radiation as the dominant driver of foraging activity, followed by temperature. C_LIO_LIBeeMonitor demonstrates that automated computer vision can reliably extract ecologically relevant behavioral data from continuous video, enabling real-time analysis of pollinator behavior and abundance at a temporal and spatial resolution unattainable through manual observation. Its modular design supports adaptation to other species and monitoring contexts. C_LI

animal behavior and cognition↗

bioRxiv2026

EcoMorph: Universal morphological trait quantification from natural language prompts for ecological research

0.O_LIMorphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator-plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. C_LIO_LIWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. C_LIO_LIWe validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R2 = 0.935, n = 74) under simple-background conditions and (R2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica. Object counts matched manual counts almost exactly for well-separated insects in an insect box (R2 = 0.9997, n = 12). C_LIO_LIBy combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation. C_LI

bioinformatics↗

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↗