TY - JOUR
T1 - Application of activity sensors for estimating behavioral patterns
AU - Roberts, Caleb P.
AU - Cain, James W.
AU - Cox, Robert D.
N1 - Funding Information:
We thank Texas Tech University Department of Natural Resources Management, the U.S. Forest Service, Valles Caldera National Preserve, and the Pueblo of Jemez for funding and equipment. The New Mexico Department of Game and Fish provided logistical support and help in capturing study animals. We thank the Valles Caldera National Preserve for access, logistical support and equipment, including R. R. Parmenter and M.A. Peyton. J. Daly, J. Kiehne, S. Gaffney, E. Cate, and S. Johnson-Bice assistance with data collection. We also thank M. Wallace for equipment. We thank the Associate Editor, M. Wallace, P. Krausman, and 2 anonymous reviewers for helpful comments on a previous draft of this manuscript. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Publisher Copyright:
© The Wildlife Society, 2016
PY - 2016/12/1
Y1 - 2016/12/1
N2 - The increasing use of Global Positioning System (GPS) collars in habitat selection studies provides large numbers of precise location data points with reduced field effort. However, inclusion of activity sensors in many GPS collars also grants the potential to remotely estimate behavioral state. Thus, only using GPS collars to collect location data belies their full capabilities. Coupling behavioral state with location data would allow researchers and managers to refine habitat selection models by using diel behavioral state changes to partition fine-scale temporal shifts in habitat selection. We tested the capability of relatively unsophisticated GPS-collar activity sensors to estimate behavior throughout diel periods using free-ranging female elk (Cervus canadensis) in the Jemez Mountains of north-central New Mexico, USA, 2013–2014. Collars recorded cumulative number of movements (hits) per 15-min recording period immediately preceding GPS fixes at 0000, 0600, 1200, and 1800 hr. We measured diel behavioral patterns of focal elk, categorizing active (i.e., foraging, traveling, vigilant, grooming) and inactive (i.e., resting) states. Active behaviors (foraging, traveling) produced more average hits (0.87 ± 0.69 hits/min, 4.0 ± 2.2 hits/min, respectively; 95% CI) and inactive (resting) behavior fewer hits (−1.1 ± 0.61 95% CI). We differentiated active and inactive behavioral states with a bootstrapped threshold of 5.9 ± 3.9 hits/15-min recording period. Mean cumulative activity-sensor hits corresponded with observed diel behavioral patterns: hits increased during crepuscular (0600, 1800 hr) observations when elk were most active (0000–0600 hr: d = 0.19; 1200–1800 hr: d = 0.64) and decreased during midday and night (0000 hr, 1200 hr) when elk were least active (1800–0000 hr: d = −0.39; 0600–1200 hr: d = −0.43). Even using relatively unsophisticated GPS-collar activity sensors, managers can remotely estimate behavioral states, approximate diel behavioral patterns, and potentially complement location data in developing habitat selection models.
AB - The increasing use of Global Positioning System (GPS) collars in habitat selection studies provides large numbers of precise location data points with reduced field effort. However, inclusion of activity sensors in many GPS collars also grants the potential to remotely estimate behavioral state. Thus, only using GPS collars to collect location data belies their full capabilities. Coupling behavioral state with location data would allow researchers and managers to refine habitat selection models by using diel behavioral state changes to partition fine-scale temporal shifts in habitat selection. We tested the capability of relatively unsophisticated GPS-collar activity sensors to estimate behavior throughout diel periods using free-ranging female elk (Cervus canadensis) in the Jemez Mountains of north-central New Mexico, USA, 2013–2014. Collars recorded cumulative number of movements (hits) per 15-min recording period immediately preceding GPS fixes at 0000, 0600, 1200, and 1800 hr. We measured diel behavioral patterns of focal elk, categorizing active (i.e., foraging, traveling, vigilant, grooming) and inactive (i.e., resting) states. Active behaviors (foraging, traveling) produced more average hits (0.87 ± 0.69 hits/min, 4.0 ± 2.2 hits/min, respectively; 95% CI) and inactive (resting) behavior fewer hits (−1.1 ± 0.61 95% CI). We differentiated active and inactive behavioral states with a bootstrapped threshold of 5.9 ± 3.9 hits/15-min recording period. Mean cumulative activity-sensor hits corresponded with observed diel behavioral patterns: hits increased during crepuscular (0600, 1800 hr) observations when elk were most active (0000–0600 hr: d = 0.19; 1200–1800 hr: d = 0.64) and decreased during midday and night (0000 hr, 1200 hr) when elk were least active (1800–0000 hr: d = −0.39; 0600–1200 hr: d = −0.43). Even using relatively unsophisticated GPS-collar activity sensors, managers can remotely estimate behavioral states, approximate diel behavioral patterns, and potentially complement location data in developing habitat selection models.
KW - Cervus canadensis
KW - New Mexico
KW - activity budget
KW - behavior
KW - diel
KW - elk
KW - scale
KW - telemetry
KW - ungulate
UR - http://www.scopus.com/inward/record.url?scp=85006459238&partnerID=8YFLogxK
U2 - 10.1002/wsb.717
DO - 10.1002/wsb.717
M3 - Article
AN - SCOPUS:85006459238
VL - 40
SP - 764
EP - 771
JO - Wildlife Society Bulletin
JF - Wildlife Society Bulletin
SN - 0091-7648
IS - 4
ER -