The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela

Authors

  • Robert P. Anderson,

    Corresponding author
    1. Department of Biology, City College of the City University of New York, New York, NY, USA
    2. Division of Vertebrate Zoology (Mammalogy), American Museum of Natural History, New York, NY, USA
      Correspondence: Robert P. Anderson, Department of Biology, City College of the City University of New York, 526 Marshak Science Building, 160 Convent Avenue, New York, NY 10031, USA.
      E-mail: anderson@sci.ccny.cuny.edu
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  • Ali Raza

    1. Department of Biology, City College of the City University of New York, New York, NY, USA
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Correspondence: Robert P. Anderson, Department of Biology, City College of the City University of New York, 526 Marshak Science Building, 160 Convent Avenue, New York, NY 10031, USA.
E-mail: anderson@sci.ccny.cuny.edu

Abstract

Aim  Various techniques model a species’ niche and potential distribution by comparing the environmental conditions of occurrence localities with those of the overall study region (via a background or pseudoabsence sample). Here, we examine how changes in the extent of the study region (ignored or under-appreciated in most studies) affect models of two rodents, Nephelomys caracolus and Nephelomys meridensis.

Location  North-central South America.

Methods  We used Maxent to model the species' potential distributions via two methods of defining the study region. In Method 1 (typical of most studies to date), we calibrated the model in a large study region that included the ranges of both species. In Method 2, we calibrated the model using a smaller study region surrounding the localities of the focal species, and then applied it to the larger region. Because the study region of Method 1 is likely to include areas of suitable conditions that are unoccupied because of dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the occupied localities. In contrast, Method 2 should avoid such problems but may require further assumptions (‘clamping’ in Maxent) to make predictions for areas with environmental conditions beyond those found in the smaller study region. For each method, we calculated several measures of geographic interpredictivity between predictions for the species (cross-species AUC, cross-species omission rate, and proportional geographic overlap).

Results  Compared with Method 1, Method 2 revealed a larger predicted area for each species, less concentrated around known localities (especially for N. caracolus). It also led to higher cross-species AUC values, lower cross-species omission rates and higher proportions of geographic overlap. Clamping was minimal and occurred primarily in regions unlikely to be suitable.

Main conclusions  Method 2 led to more realistic predictions and higher estimates of niche conservatism. Conclusions reached by many studies depend on the selection of an appropriate study region. Although detailed information regarding dispersal limitations and/or biotic interactions will typically be difficult to obtain, consideration of coarse distributional patterns, topography and vegetational zones often should permit delimitation of a much more reasonable study region than the extremely large ones currently in common use.

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