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A new kernel density estimator for accurate home‐range and species‐range area estimation
Author(s) -
Fleming Christen H.,
Calabrese Justin M.
Publication year - 2017
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12673
Subject(s) - kernel density estimation , estimator , statistics , range (aeronautics) , nonparametric statistics , small area estimation , kernel (algebra) , multivariate kernel density estimation , confidence interval , variable kernel density estimation , mathematics , survey data collection , econometrics , computer science , kernel method , artificial intelligence , engineering , combinatorics , support vector machine , aerospace engineering
SummaryKernel density estimators are widely applied to area‐related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, area estimates are obtained indirectly, by first estimating the location distribution from tracking (home range) or survey (geographic range) data and then estimating areas from that distribution. This indirect approach leads to biased area estimates and difficulty in deriving reasonable confidence intervals. We introduce a new kernel density estimator (and associated confidence intervals) focused specifically on area estimation that applies to both independently sampled survey data and autocorrelated tracking data. We test our methods against simulated movement data and demonstrate its use with African buffalo data. The area‐corrected kernel density estimator produces much more accurate area estimates, particularly at small sample sizes, and the newly derived confidence intervals are more reliable than existing alternatives. This new method is the most efficient nonparametric home‐range estimator for animal tracking data and should also be considered when calculating nonparametric range estimates from survey data. This estimator is now the default method in the ctmm r package.