Biology subjects

Williams, H. J.

Publications and source records attributed to Williams, H. J..

3 recordsLinked to original sources

bioRxiv2025

Energetic benefits of social information for movement in patchy landscapes

Movement is costly, and animals are under strong selective pressure to move efficiently, yet, in patchy, dynamic landscapes, decision-making is inherently uncertain. We quantify the energetic savings achieved by using up-to-date information presented within social cues for reducing movement costs. We use an agent-based model, founded on realistic aeronautical rules and parametrised on the Andean condor (Vultur gryphus), to study movement in patchy landscapes. By explicitly considering altitude, flight results in a sequence of soaring and gliding in the 3D space. We investigate how the cost of movement to an overall goal varies when birds use social information from others that are either fixed in space or moving collectively to the common goal, and under different risk-taking speed strategies, from slow and cautious to fast and risky. The value of social information is operationalised as energetic savings in units of basal metabolic rate. Under low predictability, agents with intermediate risk and high social-information use exhibit lowest movement costs, with up to 41% energy savings over asocial movement. By extending classical aeronautical theory to social and variable environments we demonstrate the adaptive value of social information for efficient movement in patchy, unpredictable landscapes.

animal behavior and cognition↗

bioRxiv2025

Fine-scale animal proximity detection and localization via multi-sensor biologgers

O_LIAccurately quantifying spatial interactions is central to understanding social behavior, information flow, predator-prey dynamics, and disease transmission. Proximity loggers that record received signal strength indicator (RSSI) offer a promising approach for estimating pairwise distances, particularly in environments where GPS is unavailable or imprecise. However, RSSI is often dismissed as too noisy for fine-scale inference, with performance that depends on environmental conditions, tag orientation, and between-device variability. Incorporating additional tag-measured data may improve RSSI performance and enable its use as a continuous measure of distance in variable environments. C_LIO_LIHere, we assess the utility of continuous RSSI as a fine-scale distance estimator and localization tool using a novel multi-sensor WiFi biologger (WildFi). We conducted four experiments: (1) testing how tag orientation affects RSSI-distance relationships; (2) evaluating whether environmental covariates measured by onboard sensors improve proximity estimates; (3) assessing the accuracy of trilateration-based tag localization using fixed gateway arrays; and (4) comparing RSSI- and GPS-inferred proximity in free-ranging Egyptian fruit bats (Rousettus aegyptiacus). C_LIO_LIWhile RSSI alone could predict distance with reasonable accuracy, incorporating additional tag-sensed information (e.g., temperature, humidity, barometric pressure) and accounting for tag-level heterogeneity significantly improved predictive accuracy. Based on RSSI predictions, we could estimate tag location with a median error of 2.6 meters, accurate enough to indirectly estimate proximity networks without tag-to-tag communication. In deployments on free-flying bats, we found that RSSI and GPS were only weakly concordant, with GPS unreliable for detecting fine-scale interactions (<50 m). In contrast, RSSI could capture both fine-scale and some long-range interactions up to [~]250m. C_LIO_LIThese findings highlight RSSIs potential as a robust metric for proximity logging, particularly when combined with multi-sensor data and pre-deployment validations. Integrating multi-sensor data streams further enhances RSSI interpretability. Future biologger designs should prioritize synergy among data streams for integrated insights into proximity and animal behavior. C_LI Data and code for peer review statementData and code to reproduce the results of the paper are provided in a zip folder for peer review. We also provided our compiled code.

ecology↗

bioRxiv2018

Rapid Paediatric Sequencing (RaPS): Comprehensive real-life workflow for rapid diagnosis of critically ill children

BackgroundRare genetic conditions are frequent risk factors for, or direct causes of, organ failure requiring paediatric intensive care unit (PICU) support. Such conditions are frequently suspected but unidentified at PICU admission. Compassionate and effective care is greatly assisted by definitive diagnostic information. There is therefore a need to provide a rapid genetic diagnosis to inform clinical management.\n\nTo date, Whole Genome Sequencing (WGS) approaches have proved successful in diagnosing a proportion of children with rare diseases, but results may take months to report or require the use of equipment and practices not compatible with a clinical diagnostic setting. We describe an end-to-end workflow for the use of rapid WGS for diagnosis in critically ill children in a UK National Health Service (NHS) diagnostic setting.\n\nMethodsWe sought to establish a multidisciplinary Rapid Paediatric Sequencing (RaPS) team for case selection, trio WGS, a rapid bioinformatics pipeline for sequence analysis and a phased analysis and reporting system to prioritise genes with a high likelihood of being causal. Our workflow was iteratively developed prospectively during the analysis of the first 10 children and applied to the following 14 to assess its utility.\n\nFindingsTrio WGS in 24 critically ill children led to a molecular diagnosis in ten (42%) through the identification of causative genetic variants. In three of these ten individuals (30%) the diagnostic result had an immediate impact on the individuals clinical management. For the last 14 trios, the shortest time taken to reach a provisional diagnosis was four days (median 7 days).\n\nInterpretationRapid WGS can be used to diagnose and inform management of critically ill children using widely available off the shelf products within the constraints of an NHS clinical diagnostic setting. We provide a robust workflow that will inform and facilitate the rollout of rapid genome sequencing in the NHS and other healthcare systems globally.\n\nFundingThe study was funded by NIHR GOSH/UCL BRC: ormbrc-2012-1

genomics↗