Methoden vergleichen

	Analyse der Nahrungsnetzt-Topologie	Nischenmodellierung	Populations-Viabilitäts-Analyse
Fachgebiet	Ökologie	Ökologie	Ökologie
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	2000	1999	1981
Urheber	Richard Williams and Neo Martinez	Steven Phillips and David Stockwell	Mark Shaffer
Typ	ecological network characterization	species distribution prediction	extinction risk assessment
Wegweisende Quelle	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.	Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.	Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134.
Aliasnamen	food web structure, network topology, trophic network, food chain analysis	species distribution modeling, habitat suitability modeling, ecological niche model, MaxEnt	PVA, extinction risk, minimum viable population, MVP
Verwandt	4	4	4
Zusammenfassung	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.	Niche modeling, also called species distribution modeling (SDM), predicts the geographic range and habitat suitability of species using presence-only or presence-background occurrence data and environmental variables. MaxEnt (Maximum Entropy, Phillips et al. 2006) and GARP (Genetic Algorithm for Rule-set Prediction, Stockwell & Peters 1999) are two prominent algorithms. These methods identify the environmental conditions under which species are likely to occur, enabling prediction of distribution beyond sampled areas and assessment of habitat suitability across landscapes.	Population Viability Analysis (PVA), introduced by Shaffer (1981), estimates the probability that a population will persist over a given time period under specified conditions. PVA combines demographic models (Leslie matrices, IPMs) with stochastic simulation to project population trajectories, quantifying extinction risk. This allows conservation planners to assess whether a population will likely persist, evaluate management scenarios, and estimate the minimum viable population (MVP) size for long-term persistence. PVA is a decision-support tool, not a precise predictor.