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Анализ топологии пищевых сетей×Метод отбора проб по расстоянию×
ОбластьЭкологияЭкология
СемействоProcess / pipelineProcess / pipeline
Год появления20001993
Автор методаRichard Williams and Neo MartinezStephen Buckland
Типecological network characterizationpopulation abundance estimation
Основополагающий источникDunne, J. A., Williams, R. J., & Martinez, N. D. (2002). Network structure and robustness of marine food webs. The American Naturalist, 160(1), 117-129. link ↗Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (1993). Distance Sampling: Estimating Abundance of Biological Populations. Chapman and Hall, London. link ↗
Другие названияfood web structure, network topology, trophic network, food chain analysisline transect, point transect, distance estimation, detection probability
Связанные44
СводкаFood web topology analysis characterizes the structure of predator-prey interactions within ecological communities using network metrics. Pioneered by Williams and Martinez (2000) and extended by Dunne and colleagues (2002), this approach maps which species eat which and quantifies network properties (connectivity, clustering, robustness). Understanding food web structure reveals how ecosystems are organized, how stable they are to species loss, and what roles different species play in ecosystem function.Distance sampling is a statistical method for estimating population abundance from data on distances between observers and detected individuals. Developed by Buckland and colleagues (1993) and formalized in the software Distance, this approach accounts for imperfect detection: animals far from an observer are less likely to be detected. By modeling the detection function (probability of detecting an animal at various distances), distance sampling produces unbiased estimates of abundance and density even when detection is incomplete.
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  2. 3 Источники
  3. PUBLISHED
  1. v1
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ScholarGateСравнение методов: Food Web Topology · Distance Sampling. Получено 2026-06-19 из https://scholargate.app/ru/compare