4.7 Article Book Chapter

A framework for using niche models to estimate impacts of climate change on species distributions

出版社

BLACKWELL SCIENCE PUBL
DOI: 10.1111/nyas.12264

关键词

biotic interactions; climate change; dispersal; ecological niche model; land use; species distribution

资金

  1. Division Of Environmental Biology
  2. Direct For Biological Sciences [1119915] Funding Source: National Science Foundation

向作者/读者索取更多资源

Predicting species geographic distributions in the future is an important yet exceptionally challenging endeavor. Overall, it requires a two-step process: (1) a niche model characterizing suitability, applied to projections of future conditions and linked to (2) a dispersal/demographic simulation estimating the species' future occupied distribution. Despite limitations, for the vast majority of species, correlative approaches are the most feasible avenue for building niche models. In addition to myriad technical issues regarding model building, researchers should follow critical principles for selecting predictor variables and occurrence data, demonstrating effective performance in prediction across space, and extrapolating into nonanalog conditions. Many of these principles relate directly to the niche space, dispersal/demographic noise, biotic noise, and human noise assumptions defined here. Issues requiring progress include modeling interactions between abiotic variables, integrating biotic variables, considering genetic heterogeneity, and quantifying uncertainty. Once built, the niche model identifying currently suitable conditions must be processed to approximate the areas that the species occupies. That estimate serves as a seed for the simulation of persistence, dispersal, and establishment in future suitable areas. The dispersal/demographic simulation also requires data regarding the species' dispersal ability and demography, scenarios for future land use, and the capability of considering multiple interacting species simultaneously.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据