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A comprehensive analysis of autocorrelation and bias in home range estimation
Author(s) -
Noonan Michael J.,
Tucker Marlee A.,
Fleming Christen H.,
Akre Thomas S.,
Alberts Susan C.,
Ali Abdullahi H.,
Altmann Jeanne,
Antunes Pamela Castro,
Belant Jerrold L.,
Beyer Dean,
Blaum Niels,
BöhningGaese Katrin,
Cullen Laury,
Paula Rogerio Cunha,
Dekker Jasja,
DrescherLehman Jonathan,
Farwig Nina,
Fichtel Claudia,
Fischer Christina,
Ford Adam T.,
Goheen Jacob R.,
Janssen René,
Jeltsch Florian,
Kauffman Matthew,
Kappeler Peter M.,
Koch Flávia,
LaPoint Scott,
Markham A. Catherine,
Medici Emilia Patricia,
Morato Ronaldo G.,
Nathan Ran,
OliveiraSantos Luiz Gustavo R.,
Olson Kirk A.,
Patterson Bruce D.,
Paviolo Agustin,
Ramalho Emiliano Esterci,
Rösner Sascha,
Schabo Dana G.,
Selva Nuria,
Sergiel Agnieszka,
Xavier da Silva Marina,
Spiegel Orr,
Thompson Peter,
Ullmann Wiebke,
Zięba Filip,
ZwijaczKozica Tomasz,
Fagan William F.,
Mueller Thomas,
Calabrese Justin M.
Publication year - 2019
Publication title -
ecological monographs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.254
H-Index - 156
eISSN - 1557-7015
pISSN - 0012-9615
DOI - 10.1002/ecm.1344
Subject(s) - estimator , statistics , autocorrelation , range (aeronautics) , home range , computer science , mathematics , kernel density estimation , econometrics , ecology , engineering , habitat , biology , aerospace engineering
Abstract Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation ( KDE ) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [ AKDE ], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed ( IID ) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N ^ area ) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID ‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasingN ^ area . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for smallN ^ area . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had anN ^ area >1,000, where 30% had anN ^ area <30. In this frequently encountered scenario of smallN ^ area , AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.