Using animal movement paths to measure response to spatial scale

被引:114
|
作者
Nams, VO [1 ]
机构
[1] Nova Scotia Agr Coll, Dept Environm Sci, Truro, NS B2N 5E3, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
fractal; domain; heterogeneity; hierarchy; tortuousity;
D O I
10.1007/s00442-004-1804-z
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Animals live in an environment that is patchy and hierarchical. I present a method of detecting the scales at which animals perceive their world. The hierarchical nature of habitat causes movement path structure to vary with spatial scale, and the patchy nature of habitat causes movement path structure to vary throughout space. These responses can be measured by a combination of path tortuousity (measured with fractal dimension) versus spatial scale, the variation in tortuousity of small path segments along the movement path, and the correlation between tortuousities of adjacent path segments. These statistics were tested using simulated animal movements. When movement paths contained no spatial heterogeneity, then fractal D and variance continuously increased with scale, and correlation was zero at all scales. When movement paths contained spatial heterogeneity, then fractal D sometimes showed a discontinuity at transitions between domains of scale, variation showed peaks at transitions, and correlations showed a statistically significant positive value at scales smaller than patch size, decreasing to below zero at scales greater than patch size. I illustrated these techniques with movement paths from deer mice and red-backed voles. These new analyses should help understand how animals perceive and react to their landscape structure at various spatial scales, and to answer questions about how habitat structure affects animal movement patterns.
引用
收藏
页码:179 / 188
页数:10
相关论文
共 50 条
  • [41] Novel n-back spatial working memory task using eye movement response
    Cameron B. Jeter
    Saumil S. Patel
    Anne B. Sereno
    Behavior Research Methods, 2011, 43 : 879 - 887
  • [42] Novel n-back spatial working memory task using eye movement response
    Jeter, Cameron B.
    Patel, Saumil S.
    Sereno, Anne B.
    BEHAVIOR RESEARCH METHODS, 2011, 43 (03) : 879 - 887
  • [43] Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths
    Wang, Weixin
    Adamczyk, Peter Gabriel
    SENSORS, 2019, 19 (08):
  • [44] Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects
    Guillen, Rafael Arce
    Lindgren, Finn
    Muff, Stefanie
    Glass, Thomas W.
    Breed, Greg A.
    Schlagel, Ulrike E.
    METHODS IN ECOLOGY AND EVOLUTION, 2023, 14 (10): : 2639 - 2653
  • [45] Determining the spatial scale of species' response to habitat
    Holland, JD
    Bert, DG
    Fahrig, L
    BIOSCIENCE, 2004, 54 (03) : 227 - 233
  • [46] Predicting Indoor Spatial Movement Using Data Mining and Movement Patterns
    Lam, Luan D. M.
    Tang, Antony
    Grundy, John
    2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), 2017, : 223 - 230
  • [47] The evaluation of indices of animal abundance using spatial simulation of animal trapping
    Ramsey, D
    Efford, M
    Ball, S
    Nugent, G
    WILDLIFE RESEARCH, 2005, 32 (03) : 229 - 237
  • [48] Analyzing animal movement patterns using potential functions
    Preisler, Haiganoush K.
    Ager, Alan A.
    Wisdom, Michael J.
    ECOSPHERE, 2013, 4 (03):
  • [49] Confronting spatial capture-recapture models with realistic animal movement simulations
    Theng, Meryl
    Milleret, Cyril
    Bracis, Chloe
    Cassey, Phillip
    Delean, Steven
    ECOLOGY, 2022, 103 (10)
  • [50] Spatial and temporal scaling of population density and animal movement: A power law approach
    Schaefer, JA
    Mahoney, SP
    ECOSCIENCE, 2003, 10 (04): : 496 - 501