Are your data too coarse for speed estimation? Diffusion rates as an alternative measure of animal movement
DeNicola, V.; Mezzini, S.; Cagnacci, F.; Fleming, C.
Show abstract
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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting 96%
- Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data 96%
- Multistate Ornstein-Uhlenbeck approach for practical estimation of movement and resource selection around central places 96%
Similar papers in this journal
Similar papers in this journal
- Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds 96%
- A model-based hypothesis framework to define and estimate the diel niche via the 'Diel.Niche' R package 94%
- Songbird parents coordinate offspring provisioning at fine spatio-temporal scales. 93%
Similar papers in this journal
- Estimating abundance with interruptions in data collection using open population spatial capture-recapture models 94%
- Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution 93%
- Ungulate personality and the human shield contribute to long-distance migration loss 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.