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Távolságmintavételezés×A táplálékhálózat-topológia elemzése×
TudományterületÖkológiaÖkológia
MódszercsaládProcess / pipelineProcess / pipeline
Keletkezés éve19932000
MegalkotóStephen BucklandRichard Williams and Neo Martinez
Típuspopulation abundance estimationecological network characterization
AlapműBuckland, 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 ↗Dunne, 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 ↗
Alternatív nevekline transect, point transect, distance estimation, detection probabilityfood web structure, network topology, trophic network, food chain analysis
Kapcsolódó44
ÖsszefoglalóDistance 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.Food 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.
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  3. PUBLISHED

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ScholarGateMódszerek összehasonlítása: Distance Sampling · Food Web Topology. Letöltve 2026-06-19, forrás: https://scholargate.app/hu/compare