DOI: 10.5061/dryad.31zcrjf0b
title: Data from: Separating biological signal from methodological noise in home range estimates

description: 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) turtles to assess the relative impacts on home
 range estimates due to (i) tracking accuracy and (ii) analytical methods.
 For both simulated data and empirical values of space use from the
 literature (n = 90 studies), relatively large home range estimates tended
 to be generated from lower-resolution Argos tracking compared to
 higher-resolution Fastloc-GPS tracking. These findings reflect
 inaccuracies in location data, providing spuriously large movements. For
 example, Argos and Fastloc-GPS home range size estimates for adult green
 turtles averaged 393 km2 and 53 km2, respectively (n = 64 and 39
 individuals). For simulated data, biases introduced by tracking accuracy
 had a far greater impact on home range size estimation than the analytical
 method used, apart from when using Autocorrelated Kernel Density
 Estimation (AKDE), which compensated for positional error very well. Our
 results suggest that in many cases, hawksbill and green turtles have
 relatively small home ranges (&lt; 10 km2 and in some cases, &lt;
 1 km2), with this picture of their limited space use only emerging through
 high-accuracy tracking. These general conclusions likely apply broadly
 across taxa and will impact attempts to assess patterns of home range
 sizes recorded for individuals across studies in different regions.
description: # 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
 Fastloc-GPS and Argos high- and low-accuracy data.  We calculated the 50%
 and 95% utilisation distribution (UD) for each mock tracking dataset using
 five analytical methods: 1) Minimum Convex Polygon (MCP), 2) fixed Kernel
 Density Estimation (KDE), 3) Autocorrelated Kernel Density Estimation
 (AKDE), 4) Biased Random Bridge (BRB), and 5) dynamic Brownian Bridge
 Movement Model (dBBMM). Additionally, we searched for peer-reviewed
 literature on foraging home ranges of hawksbill (*Eretemochelys
 imbricata*) and green (*Chelonia mydas*) turtles using the Web of Science
 and Google Scholar. We examined available studies for information on
 methods and metrics of home range sizes. ### Files and variables ####
 File: Data_code_Methodological_noise.zip **Description:** The ZIP folder
 contains two data files (.csv) and one code file (.R) described below.
 **Data_Simulated_animal_locations.csv**: This file contains location data
 generated for 10 simulated animals using 'simulate()' function
 in R package *ctmm.* Number of variables: 4 Number of header rows: 1
 Number of rows: 14410 Variable list: id: (character) simulated animal
 identifier date: (numeric) time and date stamps in the ‘dd/mm/yy hh:mm:ss’
 format lon: (numeric) longitude lat: (numeric) latitude Data type:
 character, numeric **Note**: Location data in this file was fully
 simulated and does not contain any real animal location data. As the data
 are simulated, there is no risk to any species listed as endangered on the
 IUCN Red List. **Data_lit-review_Methodological noise.csv**: This file
 contains information on foraging home ranges of hawksbill and green
 turtles extracted from published studies. Number of variables: 23 Number
 of header rows: 1 Number of rows: 130 Variable list: author: (character)
 Authors of the published study country: (character) Country where the
 study was conducted study.location: (character) Study location ocean:
 (character) Oceanic region foraging.ground: (character) Hawksbill/green
 turtle foraging ground identified by the published study year: (numeric)
 Years when the study was conducted season: (character) Season, if home
 range sizes were estimated for different seasons day.night: (character)
 Diel period, if home range sizes were estimated based on time of day
 method: (character) Tracking method (including acoustic, Argos,
 Fastloc-GPS, radio and citizen-based observations) method2: (character)
 Secondary equipment used (including depth sounders and tags, time-depth
 recorders and radio transmitters) analysis: (character) Primary analytical
 method namely MCP, KDE, Localised Convex Hull (LoCoH), AKDE, BRB, and
 dBBMM. analysis2: (character) Secondary analytical method or parameters
 used for primary analytical method loc.class: (character) Combination of
 Argos location classes used to filter locations sample.size: (numeric)
 Number of individuals that were tracked (n) species: (character) Sea
 turtle species tracked, either hawksbill or green turtles sex: (character)
 Sex of sampled sea turtles (f: female, m: male, u: unknown) dev.stage:
 (character) Life stage of sampled sea turtles ud95: (numeric) Mean home
 range size estimate as 95% UD (sq.km.), also called full home range size
 sd.95: (numeric) Standard deviation of 95% UD (sq.km.) ud50: (numeric)
 Mean home range size estimate as 50% UD (sq.km.), also called core area
 sd.50: (numeric) Standard deviation of 50% UD (sq.km.) mcp100: (numeric)
 Mean home range size estimate as 100% MCP (sq.km.) sd.mcp100: (numeric)
 Standard deviation of 100% MCP (sq.km.) **Note:** NAs indicate where data
 were not applicable or missing. Data type: character, numeric
 **Rcode_Methodological Noise**: This code includes steps to generate
 simulated animal location data, Argos and Fastloc-GPS mock tracking
 datasets, and estimate home range sizes using five analytical methods for
 the mock tracking datasets.  ## Code/software All analyses were conducted
 in R version 4.2.3. The code includes steps to generate simulated animal
 location data, create mock tracking datasets from the simulated movement
 data, and estimate space use (as home ranges) using five analytical
 methods. Required R packages: 1) *ctmm*, 2) *lubridate*, 3)
 *adehabitatHR*, 4) *move*, 5) *sf*, 6) *tidyverse*, and 7) *tictoc*.