Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ топологии пищевых сетей× | Анализ жизнеспособности популяций× | |
|---|---|---|
| Область | Экология | Экология |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2000 | 1981 |
| Автор метода≠ | Richard Williams and Neo Martinez | Mark Shaffer |
| Тип≠ | ecological network characterization | extinction risk assessment |
| Основополагающий источник≠ | 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 ↗ | Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134. DOI ↗ |
| Другие названия | food web structure, network topology, trophic network, food chain analysis | PVA, extinction risk, minimum viable population, MVP |
| Связанные | 4 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
|
|