LOCALITY UNCERTAINTY AND THE DIFFERENTIAL PERFORMANCE OF FOUR COMMON NICHE-BASED MODELING TECHNIQUES

Authors

  • Miguel Fernandez
  • Stanley Blum Research Informatics, California Academy of Sciences
  • Steffen Reichle The Nature Conservancy
  • Qinghua Guo Sierra Nevada Research Institute, University of California Merced
  • Barbara Holzman Department of Geography, San Francisco State University
  • Healy Hamilton Center for Biodiversity & Research, California Academy of Sciences

DOI:

https://doi.org/10.17161/bi.v6i1.3314

Keywords:

georeferencing, spatial uncertainty, ecological niche modeling, comparative performance, fuzzy kappa

Abstract

We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections.

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Author Biographies

  • Stanley Blum, Research Informatics, California Academy of Sciences
    Head of Research Informatics, California Academy of Sciences
  • Steffen Reichle, The Nature Conservancy
    Science Training Manager,Conservation Strategies Division, The Nature Conservancy
  • Qinghua Guo, Sierra Nevada Research Institute, University of California Merced
    School of Engineering, Assistant Professor
  • Barbara Holzman, Department of Geography, San Francisco State University
    Department of Geography & Human Environmental Studies, Professor
  • Healy Hamilton, Center for Biodiversity & Research, California Academy of Sciences
    Head of the Center for Biodiversity Research, California Academy of Sciences

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Published

2009-09-05

Issue

Section

Articles (peer-reviewed)

How to Cite

Fernandez, Miguel, Stanley Blum, Steffen Reichle, Qinghua Guo, Barbara Holzman, and Healy Hamilton. 2009. “LOCALITY UNCERTAINTY AND THE DIFFERENTIAL PERFORMANCE OF FOUR COMMON NICHE-BASED MODELING TECHNIQUES”. Biodiversity Informatics 6 (1): 36-52. https://doi.org/10.17161/bi.v6i1.3314.