首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Taxonomic And Regional Uncertainty In Species-area Relationships And The Identification Of Richness Hotspots
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Taxonomic And Regional Uncertainty In Species-area Relationships And The Identification Of Richness Hotspots

机译:物种-区域关系的分类学和区域不确定性以及富集热点的确定

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Species-area relationships (SARs) are fundamental to the study of key and high-profile issues in conservation biology and are particularly widely used in establishing the broad patterns of biodiversity that underpin approaches to determining priority areas for biological conservation. Classically, the SAR has been argued in general to conform to a power-law relationship, and this form has been widely assumed in most applications in the field of conservation biology. Here, using nonlinear regressions within an information theoretical model selection framework, we included uncertainty regarding both model selection and parameter estimation in SAR modeling and conducted a global-scale analysis of the form of SARs for vascular plants and major vertebrate groups across 792 terrestrial ecoregions representing almost 97% of Earth's inhabited land. The results revealed a high level of uncertainty in model selection across biomes and taxa, and that the power-law model is clearly the most appropriate in only a minority of cases. Incorporating this uncertainty into a hotspots analysis using multimodel SARs led to the identification of a dramatically different set of global richness hotspots than when the power-law SAR was assumed. Our findings suggest that the results of analyses that assume a power-law model may be at severe odds with real ecological patterns, raising significant concerns for conservation priority-setting schemes and biogeographical studies.
机译:物种-区域关系(SAR)是研究保护生物学中关键和备受关注的问题的基础,并且特别广泛地用于建立广泛的生物多样性模式,这些模式为确定生物保护重点领域的方法奠定了基础。传统上,SAR通常被认为符合幂律关系,并且这种形式已在保护生物学领域的大多数应用中被广泛采用。在这里,使用信息理论模型选择框架内的非线性回归,我们在SAR建模中包括了模型选择和参数估计方面的不确定性,并对代表792个陆地生态区的维管植物和主要脊椎动物群体的SAR形式进行了全球规模的分析。地球上几乎有97%的人居住。结果表明,跨生物群落和分类群的模型选择存在很大的不确定性,而幂律模型显然仅在少数情况下最合适。将这种不确定性纳入使用多模型SAR进行的热点分析中,可以确定与假设幂律SAR时截然不同的一组全球富裕热点。我们的发现表明,假设采用幂律模型的分析结果可能与真实的生态模式大相径庭,这引起了对保护优先级确定计划和生物地理研究的极大关注。

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