Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges

被引:522
作者
Cagnacci, Francesca [1 ]
Boitani, Luigi [2 ]
Powell, Roger A. [3 ]
Boyce, Mark S. [4 ]
机构
[1] Edmund Mach Fdn, Environm & Nat Resources Area, Res & Innovat Ctr, I-38010 San Michele All Adige, Trento, Italy
[2] Univ Roma La Sapienza, Dept Anim & Human Biol, I-00185 Rome, Italy
[3] N Carolina State Univ, Dept Biol, Raleigh, NC 27695 USA
[4] Univ Alberta, Dept Biol Sci, Edmonton, AB T6G 2E9, Canada
关键词
global positioning system technology; biotelemetry; animal movement; autocorrelation; mechanistic models; fitness; GLOBAL POSITIONING SYSTEM; STATE-SPACE MODELS; RESOURCE SELECTION; HABITAT SELECTION; MOVEMENT; HOME; AUTOCORRELATION; TRACKING; WILDLIFE; BEHAVIOR;
D O I
10.1098/rstb.2010.0107
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Global positioning system (GPS) telemetry technology allows us to monitor and to map the details of animal movement, securing vast quantities of such data even for highly cryptic organisms. We envision an exciting synergy between animal ecology and GPS-based radiotelemetry, as for other examples of new technologies stimulating rapid conceptual advances, where research opportunities have been paralleled by technical and analytical challenges. Animal positions provide the elemental unit of movement paths and show where individuals interact with the ecosystems around them. We discuss how knowing where animals go can help scientists in their search for a mechanistic understanding of key concepts of animal ecology, including resource use, home range and dispersal, and population dynamics. It is probable that in the not-so-distant future, intense sampling of movements coupled with detailed information on habitat features at a variety of scales will allow us to represent an animal's cognitive map of its environment, and the intimate relationship between behaviour and fitness. An extended use of these data over long periods of time and over large spatial scales can provide robust inferences for complex, multi-factorial phenomena, such as meta-analyses of the effects of climate change on animal behaviour and distribution.
引用
收藏
页码:2157 / 2162
页数:6
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