DeNicola, V. | Search bioRxiv

bioRxiv2025

Are your data too coarse for speed estimation? Diffusion rates as an alternative measure of animal movement

Estimates of speed and distance traveled are routine in ecological research to provide a link between behavior and energetics. Conventional straight-line displacement (SLD) methods return severely and differentially biased estimates with no ability to evaluate the accuracy of the estimate. Recent methodological advances have improved our ability to estimate these parameters using continuous-time speed and distance (CTSD) estimation. However, even with CTSD estimation, many datasets are too coarse, or the location error is too great to reliably measure speed or distance traveled. To address these limitations, we investigated the relationship between CTSD-estimated mean speed and diffusion rate, where diffusion rate is defined as the variance in displacements per time interval. We calculated CTSD mean speed and diffusion rate estimates using telemetry data (i.e., trajectory data) from over 100 white-tailed deer and simulated telemetry data generated from a known movement model. We examined the relationship between the two measures in both datasets and the effect of sampling frequency on the effective sample size and the estimation of the two parameters. We found that mean speed and diffusion rate were strongly and nonlinearly correlated, with a 1% increase in diffusion rate predicting a 0.40% increase in mean speed (99% CI: 0.38-0.42%) for our focal species. Diffusion rate estimates remained substantially more accurate and precise than speed across sampling interval regimes, even when speed estimation was not possible. Our findings demonstrate that diffusion rate outperforms mean speed as a measure of movement activity under marginal data conditions. Diffusion rate is a reliable measure of movement activity and can link behavior to energetics across a wider range of datasets while maintaining accuracy even when data quality is low. By using speed and diffusion together, researchers can rely on more robust insights into space use across a range of ecological contexts.

ecology↗

bioRxiv2020

A comprehensive framework for handling location error in animal tracking data

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While GPS location error can often be ignored in coarsely sampled data, fine-scale data require more care, and tools to do this have not kept pace. Current approaches to dealing with location error largely fall into two categories--either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. In some cases these approaches can provide a level of correction, but they have known limitations, and in some cases they can be worse than doing nothing. Here, we provide a general framework to account for location error in the analysis of triangulated and trilatcralizcd animal tracking data, which includes GPS, Argos Doppler-shift, triangulated VHF, trilatcralized acoustic and cellular location data. We apply our error-modelselection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices were used to track a wide range of taxa comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. In almost half of the tested device models, error-model selection was necessary to obtain the best performing error model, and in almost a quarter of tested device models, the reported DOP values were actually misinformative. Then, using empirical tracking data from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed, and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. Because error-induced biases depend on many factors--sampling schedule, movement characteristics, tracking device, habitat, etc.--differential bias can easily confound biological inference and lead researchers to draw false conclusions. We demonstrate how error-informed analyses on calibrated tracking data can provide more accurate estimates are that are insensitive to location error, and allow researchers to use all of their data.

ecology↗