Separating biological signal from methodological noise in home range estimates

Space use is commonly estimated in animal ecology. It has become a cornerstone of evidence-based conservation planning, with animal tracking increasingly used to underpin the designation of protected areas with high conservation value. However, tracking technologies and analytical methods may introduce biases in home range size estimates. We assessed these potential biases using simulated tracking data and published home range size estimates from empirical studies of animal tracking. We first simulated animal movement data and added published location error estimates for different technologies used for tracking sea turtles. Location data were analysed using common space use estimation methods (Minimum Convex Polygon, fixed and Autocorrelated Kernel Density Estimation, Biased Random Bridge, and dynamic Brownian Bridge Movement Model). Second, we reviewed home range size estimates obtained using different technologies to track hawksbill (Eretmochelys imbricata) and green (Chelonia mydas) ..., , # Separating biological signal from methodological noise in home range estimates Dataset DOI: [10.5061/dryad.31zcrjf0b](10.5061/dryad.31zcrjf0b) ## Description of the data and file structure This repository contains the data and code used in Kale et al. (2025) to investigate methodological approaches for analysing animal movement data. The files include simulated animal movement data used in the study and home range size data extracted from published literature. **Study overview** We simulated animal movement data based on the Ornstein-Uhlenbeck Foraging (OUF) Gaussian process using the 'simulate()' function from package *ctmm* in R version 4.2.3. Data were generated for 10 distinct individuals to ensure computational feasibility while obtaining reasonable estimates of variability. To create mock tracking datasets from these simulated data, we added randomly generated, independent location errors for each location fix, drawn from bivariate (x and y) normal distributions typical of ...,