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Plant Ecology

, Volume 217, Issue 5, pp 533–547 | Cite as

Do community-level models account for the effects of biotic interactions? A comparison of community-level and species distribution modeling of Rocky Mountain conifers

  • Paige E. Copenhaver-Parry
  • Shannon E. Albeke
  • Daniel B. Tinker
Article

Abstract

Community-level models (CLMs) aim to improve species distribution modeling (SDM) methods by attempting to explicitly incorporate the influences of interacting species. However, the ability of CLMs to appropriately account for biotic interactions is unclear. We applied CLM and SDM methods to predict the distributions of three dominant conifer tree species in the U.S. Rocky Mountains and compared CLM and SDM predictive accuracy as well as the ability of each approach to accurately reproduce species co-occurrence patterns. We specifically evaluated the performance of two statistical algorithms, MARS and CForest, within both CLM and SDM frameworks. Across all species, differences in SDM and CLM predictive accuracy were slight and can be attributed to differences in model structure rather than accounting for the effects of biotic interactions. In addition, CLMs generally over-predicted species co-occurrence, while SDMs under-predicted co-occurrence. Our results demonstrate no real improvement in the ability of CLMs to account for biotic interactions relative to SDMs. We conclude that alternative modeling approaches are needed in order to accurately account for the effects of biotic interactions on species distributions.

Keywords

Conditional random forests Co-occurrence Douglas-fir Lodgepole pine Multivariate adaptive regression splines Ponderosa pine 

Notes

Acknowledgments

The authors would like to thank Chris Woodall and Brian Walters for providing the data used in this study, as well as the students of the 2014 Advanced Spatial Analysis course at the University of Wyoming for their feedback and assistance. We are also grateful to two anonymous reviewers whose comments substantially improved the quality of this manuscript. P. Copenhaver-Parry was supported by a National Science Foundation Fellowship (G-K12 Project #0841298) and a fellowship from the Wyoming NASA Space Grant Consortium during the development of this manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Paige E. Copenhaver-Parry
    • 1
  • Shannon E. Albeke
    • 2
  • Daniel B. Tinker
    • 1
  1. 1.Program in Ecology and Department of BotanyUniversity of WyomingLaramieUSA
  2. 2.Wyoming Geographic Information Sciences Center and Department of GeographyUniversity of WyomingLaramieUSA

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