Caution is warranted when using animal space-use and movement to infer behavioral states

被引:14
作者
Buderman, Frances E. [1 ]
Gingery, Tess M. [2 ]
Diefenbach, Duane R. [3 ]
Gigliotti, Laura C. [4 ]
Begley-Miller, Danielle [5 ]
McDill, Marc M. [1 ]
Wallingford, Bret D. [6 ]
Rosenberry, Christopher S. [6 ]
Drohan, Patrick J. [1 ]
机构
[1] Penn State Univ, Dept Ecosyst Sci & Management, University Pk, PA 16802 USA
[2] Penn State Univ, Penn Cooperat Fish & Wildlife Res Unit, University Pk, PA 16802 USA
[3] Penn State Univ, US Geol Survey, Penn Cooperat Fish & Wildlife Res Unit, University Pk, PA 16802 USA
[4] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA
[5] Teatown Lake Reservat, Ossining, NY 10562 USA
[6] Penn Game Commiss, Harrisburg, PA 17110 USA
基金
美国食品与农业研究所;
关键词
Breeding; Mate search strategy; Odocoileus virginianus; White-tailed deer; Hidden Markov models; Home-range; Utilization distribution; Brownian bridge; Behavioral state; State identification; WHITE-TAILED DEER; HIDDEN MARKOV-MODELS; DYNAMIC INTERACTION; BREEDING SUCCESS; WILDLIFE; SEARCH; RANGE; TOOLS;
D O I
10.1186/s40462-021-00264-8
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Background Identifying the behavioral state for wild animals that can't be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. Methods Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. Results For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual's UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. Conclusions Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.
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页数:12
相关论文
共 70 条
[1]   Courtship strategies of white-tailed deer and mule deer males when living in sympatry [J].
Airst, Jason ;
Lingle, Susan .
BEHAVIOUR, 2019, 156 (3-4) :307-330
[2]   Male size and alternative mating tactics in white-tailed deer and mule deer [J].
Airst, Jason, I ;
Lingle, Susan .
JOURNAL OF MAMMALOGY, 2020, 101 (05) :1231-1243
[3]   Soil chemistry, and not short-term (1-2 year) deer exclusion, explains understory plant occupancy in forests affected by acid deposition [J].
Begley-Miller, Danielle R. ;
Diefenbach, Duane R. ;
McDill, Marc E. ;
Drohan, Patrick J. ;
Rosenberry, Christopher S. ;
Domoto, Emily H. Just .
AOB PLANTS, 2019, 11 (05) :1-15
[4]  
BEIER P, 1990, WILDLIFE MONOGR, P5
[5]   Search and foraging behaviors from movement data: A comparison of methods [J].
Bennison, Ashley ;
Bearhop, Stuart ;
Bodey, Thomas W. ;
Votier, Stephen C. ;
Grecian, W. James ;
Wakefield, Ewan D. ;
Hamer, Keith C. ;
Jessopp, Mark .
ECOLOGY AND EVOLUTION, 2018, 8 (01) :13-24
[6]  
Beringer J, 2004, WILDLIFE SOC B, V32, P648, DOI 10.2193/0091-7648(2004)032<0648:RVROFS>2.0.CO
[7]  
2
[8]   Mechanistic modelling of animal dispersal offers new insights into range expansion dynamics across fragmented landscapes [J].
Bocedi, Greta ;
Zurell, Damaris ;
Reineking, Bjoern ;
Travis, Justin M. J. .
ECOGRAPHY, 2014, 37 (12) :1240-1253
[9]   Determining Kill Rates of Ungulate Calves by Brown Bears Using Neck-Mounted Cameras [J].
Brockman, Christopher J. ;
Collins, William B. ;
Welker, Jeffery M. ;
Spalinger, Donald E. ;
Dale, Bruce W. .
WILDLIFE SOCIETY BULLETIN, 2017, 41 (01) :88-97
[10]   Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds [J].
Browning, Ella ;
Bolton, Mark ;
Owen, Ellie ;
Shoji, Akiko ;
Guilford, Tim ;
Freeman, Robin .
METHODS IN ECOLOGY AND EVOLUTION, 2018, 9 (03) :681-692