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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Niche variability and its consequences for species distribution modeling.

Matt J Michel1, Jason H Knouft

  • 1Department of Biology, Saint Louis University, St Louis, Missouri, United States of America. mmichel3@slu.edu

Plos One
|September 13, 2012
PubMed
Summary

Species niche variability impacts species distribution models (SDMs). Accounting for seasonal shifts in habitat use improves SDM accuracy for predicting species distributions under environmental change.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Environmental Science

Background:

  • Species distribution models (SDMs) assume niche conservatism, but empirical evidence shows niche variability.
  • Understanding niche plasticity is crucial for accurate ecological predictions.

Purpose of the Study:

  • To investigate the effect of seasonal niche variability on Maxent SDM projection accuracy.
  • To compare standard SDM projections with those using transformed environmental data.

Main Methods:

  • Analyzed habitat and locality data for five stream fish species across seasons.
  • Constructed SDMs using original and scale-transformed environmental data.
  • Evaluated projection accuracy using probability ratios at known presences versus other locations.

Main Results:

  • Species exhibited seasonal niche variation, shifting habitat use in response to environmental changes like canopy cover and flow rate.
  • SDMs using original data accurately predicted occurrences for some species across seasons.
  • SDMs using transformed data improved model performance in 10 out of 14 tested cases.

Conclusions:

  • Niche variability is prevalent and must be considered in SDM applications for predicting species distributions.
  • A framework incorporating niche variability, such as data transformation, can enhance SDM prediction accuracy.