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| SIAR混合模型× | 食物网拓扑结构分析× | |
|---|---|---|
| 领域 | 生态学 | 生态学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2010 | 2000 |
| 提出者≠ | Andrew Parnell | Richard Williams and Neo Martinez |
| 类型≠ | diet and source apportionment analysis | ecological network characterization |
| 开创性文献≠ | Parnell, A. C., Inger, R., Bearhop, S., & Jackson, A. L. (2010). Source partitioning using stable isotopes: coping with too much variation. PLoS ONE, 5(3), e9672. DOI ↗ | 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 ↗ |
| 别名 | isotope mixing model, Bayesian mixing model, source apportionment, diet analysis | food web structure, network topology, trophic network, food chain analysis |
| 相关 | 4 | 4 |
| 摘要≠ | The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates isotopic data from potential food sources and consumers to infer diets. It accounts for uncertainty in isotope fractionation (the shift in isotope ratios between diet and tissue) and natural variation among source populations, producing probability distributions rather than point estimates of diet composition. | 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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