Biology subjects

Koger, B.

Publications and source records attributed to Koger, B..

5 recordsLinked to original sources

bioRxiv2026

Solving Paleobotany's 200-year problem with fluorescence and artificial intelligence

Paleobotany has a 200-year problem: leaf fossils are often too poorly preserved to make proper taxonomic identification. Though they are the most abundant, and well-studied macroscopic fossil plant organ, many fossil leaves-- including specimens held in museum and university collections-- appear to lack the detailed features needed to take crucial next steps, like placement into phylogenetic context, generation of ecological interpretations, or reconstruction of paleoclimate. However, we have discovered that under UV and laser light [~]50% of leaves across a diverse survey of fossil deposits fluoresce in a way that reveals spectacularly detailed leaf venation and gross morphology that is invisible under white light. These 'Fossil Ciphers' bring new life into each step of the paleobotanical method, unlocking previously unattainable taxonomic classifications, quantitative morphometrics, and promising new avenues for the incorporation of AI into fossil leaf analyses. With the aid of fluorescence techniques, we can help solve paleobotany's 200-year problem.

paleontology↗

bioRxiv2025

Combining artificial intelligence and local ecological knowledge to document the largest ever-counted Cape buffalo mega-herd

Large aggregations of wild mammals are declining globally, sometimes before they can be scientifically documented and their ecological value understood, although non-scientists may be aware of these phenomena. Video recording of wildlife by non-academics is becoming more frequent with increasing activities of humans in natural habitats. This opportunistically-collected material can document rare or ecologically-important events, such as large aggregations, and thus provide potentially valuable data for ecologists and conservationists. We used footage of a large herd of Cape buffalo recorded by a wildlife film producer in the Mababe Depression, northern Botswana, and applied automated detection and tracking techniques to count individuals as they moved across the video frame. To complement this demographic snapshot, we accessed local ecological knowledge from stakeholders to provide contextual information for a more complete understanding of the ecosystem processes potentially driving population movement and trends. Manual review of the automated count resulted in a minimum group size of 3676 buffalo and a total estimated herd size of 4131 (range 3828-4446), which matched herd size estimates provided by local stakeholders. To our knowledge, this is the largest documented group of Cape buffalo, which we identify as a mega-herd and recommend research into the costs, benefits and ecological consequences of forming such large groups. This study represents an opportunistic collaboration among wildlife filmmakers, computer scientists, lay experts and ecologists, and highlights the value of combining contributions from different fields to generate information that can be used in conservation practice.

ecology↗

bioRxiv2025

FERAL: A Video-Understanding System for Direct Video-to-Behavior Mapping

Quantifying animal behavior often requires segmenting continuous actions into discrete, interpretable states, yet most automated pipelines infer actions from keypoint dynamics and are limited by keypoint tracking quality. Here we present FERAL (Feature Extraction for Recognition of Animal Locomotion), a supervised video-understanding toolkit that maps raw video directly to frame-level behavioral labels, bypassing the keypoint-extraction and pose-classification stages of conventional pipelines. Across benchmarks, FERAL matches or exceeds state-of-the-art pose- and video-based baselines. On the CalMS21 mouse social-interaction benchmark, it exceeds Googles VideoPrism using only a quarter of the training data. FERAL generalizes across species (apes, zebras, mice, ants, flies, and nematodes), recording conditions, and levels of organization, from single animals to social interactions and colony-scale collective behavior. Released as a user-friendly, open-source package, it integrates with existing analysis pipelines. By enabling scalable, species-agnostic behavioral quantification directly from raw video, FERAL broadens the experimental paradigms available for studying animal behavior.

animal behavior and cognition↗

bioRxiv2022

Multi-animal behavioral tracking and environmental reconstruction using drones and computer vision in the wild

O_LIMethods for collecting animal behavior data in natural environments, such as direct observation and bio-logging, are typically limited in spatiotemporal resolution, the number of animals that can be observed, and information about animals social and physical environments. C_LIO_LIVideo imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals locations, into geographic coordinates. C_LIO_LIWe demonstrate a new system for studying behavior in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. C_LIO_LIWe provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals body postures (poses), and extract environmental features, including topography of the landscape and animal trails. C_LIO_LIBy quantifying animal movement and posture, while simultaneously reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments. C_LI

animal behavior and cognition↗

bioRxiv2019

Fast and robust animal pose estimation

Quantitative behavioral measurements are important for answering questions across scientific disciplines--from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animals body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an eZcient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings--including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

animal behavior and cognition↗