Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator

Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the...

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Bibliographic Details
Published in:Ecology (Durham) Vol. 96; no. 5; pp. 1182 - 1188
Main Authors: Fleming, C. H, Fagan, W. F, Mueller, T, Olson, K. A, Leimgruber, P, Calabrese, J. M
Format: Journal Article
Language:English
Published: United States Ecological Society of America 01.05.2015
Subjects:
Animal Distribution - physiology
Animal populations
Animals
Antelopes - physiology
Autocorrelation
Brownian bridge
Computer Simulation
Conservation
Conservation biology
Correlation analysis
data collection
Data Interpretation, Statistical
Data ranges
Data sampling
Density estimation
Ecosystem
Estimating techniques
Estimation methods
Estimators
Gazelles
home range
Homing Behavior - physiology
kernel density
minimum convex polygon
Models, Biological
Models, Statistical
Mongolian gazelle
Mongolian gazelle, Procapra gutturosa
Movement
Procapra gutturosa
Relocation
Signal bandwidth
Statistics
tracking data
utilization distribution
wildlife
Wildlife ecology
Wildlife management
ISSN:0012-9658, 1939-9170
Online Access:Get full text
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Summary:Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.
Bibliography:http://dx.doi.org/10.1890/14-2010.1
Corresponding Editor: E. G. Cooch.
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ISSN:0012-9658
1939-9170
DOI:10.1890/14-2010.1