TY - JOUR
T1 - Erratum
T2 - Does estimator choice influence our ability to detect changes in home-range size? [Anim Biotelemetry. 3, (2015)(16)] DOI: 10.1186/s40317-015-0051-x
AU - Signer, Johannes
AU - Balkenhol, Niko
AU - Ditmer, Mark
AU - Fieberg, John
N1 - Publisher Copyright:
© 2015 Signer et al.
PY - 2015/9/4
Y1 - 2015/9/4
N2 - The original version of this article unfortunately contains mistakes. The last two paragraphs in Background were moved to make the Conclusion and this is not correct. Please find below the correct text for each section. The correct Background is below: Animals interact with conspecifics and their environment, leading to non-random patterns of space-use [1]. Several different analytical methods have been proposed for quantifying these patterns, including home-range estimation (e.g., [2, 3]), habitat and step selection models (e.g., [4, 5]), and Bayesian state-space models that fit a mixture of random walks to movement data (e.g., [6, 7]). Whereas the latter two approaches often require customwritten code and fine-tuning to fit a specific data set, a variety of off-the-shelf home-range estimators can be easily implemented in multiple software platforms (R, ArcGIS, etc.). Because of their accessibility, home-range estimators are frequently used to compare space-use patterns for animals living in different landscapes (e.g., [8, 9]) or along spatial gradients (e.g., [10]). With the increase of finescale spatio-temporal data afforded by Global Positioning Technology (GPS), short-term (weekly, monthly) estimates of home-range size are now also commonly used to explore changes in space-use patterns over time (e.g., [3, 11, 12]).
AB - The original version of this article unfortunately contains mistakes. The last two paragraphs in Background were moved to make the Conclusion and this is not correct. Please find below the correct text for each section. The correct Background is below: Animals interact with conspecifics and their environment, leading to non-random patterns of space-use [1]. Several different analytical methods have been proposed for quantifying these patterns, including home-range estimation (e.g., [2, 3]), habitat and step selection models (e.g., [4, 5]), and Bayesian state-space models that fit a mixture of random walks to movement data (e.g., [6, 7]). Whereas the latter two approaches often require customwritten code and fine-tuning to fit a specific data set, a variety of off-the-shelf home-range estimators can be easily implemented in multiple software platforms (R, ArcGIS, etc.). Because of their accessibility, home-range estimators are frequently used to compare space-use patterns for animals living in different landscapes (e.g., [8, 9]) or along spatial gradients (e.g., [10]). With the increase of finescale spatio-temporal data afforded by Global Positioning Technology (GPS), short-term (weekly, monthly) estimates of home-range size are now also commonly used to explore changes in space-use patterns over time (e.g., [3, 11, 12]).
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U2 - 10.1186/s40317-015-0066-3
DO - 10.1186/s40317-015-0066-3
M3 - Comment/debate
AN - SCOPUS:85018524417
SN - 2050-3385
VL - 3
JO - Animal Biotelemetry
JF - Animal Biotelemetry
IS - 1
M1 - 28
ER -