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| Анализ Circuitscape× | Анализ топологии пищевых сетей× | |
|---|---|---|
| Область | Экология | Экология |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2008 | 2000 |
| Автор метода≠ | Brad McRae | Richard Williams and Neo Martinez |
| Тип≠ | movement and connectivity modeling | ecological network characterization |
| Основополагающий источник≠ | Bradford, D. F., McCreary, D. D., & Groves, C. R. (2014). Optimizing sampling for large-area habitat assessment. Ecological Monographs, 84(3), 351-375. 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 ↗ |
| Другие названия | circuit theory, resistance distance, connectivity analysis, landscape conductance | food web structure, network topology, trophic network, food chain analysis |
| Связанные | 4 | 4 |
| Сводка≠ | Circuitscape, developed by Brad McRae (2008), applies circuit theory from electrical engineering to predict organism movement and genetic connectivity across landscapes. The method treats landscapes as electrical networks where habitat quality is resistance and organism movement is electrical current. By analogy, organisms diffusing through a landscape follow paths determined by landscape resistance: corridors of low resistance (good habitat) are preferentially used. Circuitscape predicts movement probabilities, identifies critical corridors, and quantifies connectivity between habitat patches. | 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. |
| ScholarGateНабор данных ↗ |
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