TY - JOUR
T1 - A comprehensive analysis of autocorrelation and bias in home range estimation
AU - Noonan, Michael J.
AU - Tucker, Marlee A.
AU - Fleming, Christen H.
AU - Akre, Thomas S.
AU - Alberts, Susan C.
AU - Ali, Abdullahi H.
AU - Altmann, Jeanne
AU - Antunes, Pamela Castro
AU - Belant, Jerrold L.
AU - Beyer, Dean
AU - Blaum, Niels
AU - Böhning-Gaese, Katrin
AU - Cullen, Laury
AU - de Paula, Rogerio Cunha
AU - Dekker, Jasja
AU - Drescher-Lehman, Jonathan
AU - Farwig, Nina
AU - Fichtel, Claudia
AU - Fischer, Christina
AU - Ford, Adam T.
AU - Goheen, Jacob R.
AU - Janssen, René
AU - Jeltsch, Florian
AU - Kauffman, Matthew
AU - Kappeler, Peter M.
AU - Koch, Flávia
AU - LaPoint, Scott
AU - Markham, A. Catherine
AU - Medici, Emilia Patricia
AU - Morato, Ronaldo G.
AU - Nathan, Ran
AU - Oliveira-Santos, Luiz Gustavo R.
AU - Olson, Kirk A.
AU - Patterson, Bruce D.
AU - Paviolo, Agustin
AU - Ramalho, Emiliano Esterci
AU - Rösner, Sascha
AU - Schabo, Dana G.
AU - Selva, Nuria
AU - Sergiel, Agnieszka
AU - Xavier da Silva, Marina
AU - Spiegel, Orr
AU - Thompson, Peter
AU - Ullmann, Wiebke
AU - Zięba, Filip
AU - Zwijacz-Kozica, Tomasz
AU - Fagan, William F.
AU - Mueller, Thomas
AU - Calabrese, Justin M.
N1 - Publisher Copyright:
© 2019 by the Ecological Society of America
PY - 2019/5
Y1 - 2019/5
N2 - 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 ((Formula presented.)) 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 decreasing (Formula presented.). 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 small (Formula presented.). While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an (Formula presented.) >1,000, where 30% had an (Formula presented.) <30. In this frequently encountered scenario of small (Formula presented.), AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
AB - 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 ((Formula presented.)) 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 decreasing (Formula presented.). 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 small (Formula presented.). While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an (Formula presented.) >1,000, where 30% had an (Formula presented.) <30. In this frequently encountered scenario of small (Formula presented.), AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
KW - animal movement
KW - kernel density estimation
KW - local convex hull
KW - minimum convex polygon
KW - range distribution
KW - space use
KW - telemetry
KW - tracking data
UR - http://www.scopus.com/inward/record.url?scp=85060907618&partnerID=8YFLogxK
U2 - 10.1002/ecm.1344
DO - 10.1002/ecm.1344
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AN - SCOPUS:85060907618
SN - 0012-9615
VL - 89
JO - Ecological Monographs
JF - Ecological Monographs
IS - 2
M1 - e01344
ER -