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Choosing among correlative, mechanistic, and hybrid models of species' niche and distribution

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INTEGRATIVE ZOOLOGY
卷 18, 期 1, 页码 93-109

出版社

WILEY
DOI: 10.1111/1749-4877.12618

关键词

climate change; ecological niche model; integrative model; species distribution model; vertebrates

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Researchers compared different models for estimating species' niche and distribution, finding that mechanistic and correlative models have different strengths and limitations. Hybrid models combining both approaches were considered promising. However, the best approach depends on the specific context and research objectives.
Different models are available to estimate species' niche and distribution. Mechanistic and correlative models have different underlying conceptual bases, thus generating different estimates of a species' niche and geographic extent. Hybrid models, which combining correlative and mechanistic approaches, are considered a promising strategy; however, no synthesis in the literature assessed their applicability for terrestrial vertebrates to allow best-choice model considering their strengths and trade-offs. Here, we provide a systematic review of studies that compared or integrated correlative and mechanistic models to estimate species' niche for terrestrial vertebrates under climate change. Our goal was to understand their conceptual, methodological, and performance differences, and the applicability of each approach. The studies we reviewed directly compared mechanistic and correlative predictions in terms of accuracy or estimated suitable area, however, without any quantitative analysis to support comparisons. Contrastingly, many studies suggest that instead of comparing approaches, mechanistic and correlative methods should be integrated (hybrid models). However, we stress that the best approach is highly context-dependent. Indeed, the quality and effectiveness of the prediction depends on the study's objective, methodological design, and which type of species' niche and geographic distribution estimated are more appropriate to answer the study's issue.

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