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

Abstract

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.