4.7 Article

Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics

期刊

ECOLOGY
卷 91, 期 1, 页码 252-261

出版社

WILEY
DOI: 10.1890/08-0879.1

关键词

algorithmic models; boreal toad; Bufo boreas; ecoinformatics; ecological process; landscape ecology; landscape genetics; multiple scales; Random Forests; species connectivity; Yellowstone National Park, USA

类别

资金

  1. Yellowstone National Park [YELL-05452]
  2. IACUC [ASAF 3378]
  3. EPA-STAR [FP-916695]
  4. NSF [DEB-0608458, DEB-0548415]
  5. James King fellowship
  6. Theodore Roosevelt Memorial Fund
  7. Society for Wetland Science
  8. WSU Zoology alumni scholarship
  9. Canon National Parks
  10. Sigma-Xi
  11. Graduate Women in Science
  12. U.S. Environmental Protection Agency (EPA)

向作者/读者索取更多资源

A major objective of ecology is to understand how ecological processes limit population connectivity and species' distributions. By spatially quantifying ecological components driving functional connectivity, we can understand why some locally suitable habitats are unoccupied, resulting, in observed discontinuities in distribution. However, estimating connectivity may be difficult due to population stochasticity and violations of assumptions of parametric statistics. To address these issues, we present a novel application of Random Forests to landscape genetic data. We address the effects of three key ecological components oil Bufo boreas connectivity in Yellowstone National Park: ecological process, scale. and hierarchical organization. Habitat permeability, topographic morphology, and temperature-moisture regime are all significant ecological processes associated with B. boreas Connectivity. Connectivity was influenced by growing-season precipitation, 1988 Yellowstone fires, cover, temperature, impervious surfaces (roads and development), and topographic complexity (56% variation explained). We found that habitat permeability generally operates oil fine scales, while topographic morphology and temperature-moisture regime operate across Multiple scales, thus demonstrating the importance of cross-scale analysis for ecological interpretation. In a hierarchical analysis, we were able to explain more variation within genetic clusters as identified using Structure (a Bayesian algorithm) (74%; dispersal cover, growing-season precipitation, impervious surfaces) as opposed to between genetic clusters (45%; ridgelines, hot, dry slopes, length of hot season. and annual precipitation). Finally. the analytical methods we developed ire powerful and an be applied to any species or system with appropriate landscape genetic data.

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