Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt

  • Authors:

  • M. Convertino;R. Muñoz-Carpena;M. L. Chu-Agor;G. A. Kiker;I. Linkov

  • Affiliations:

  • HumNat Lab, Division of Environmental Health Sciences, School of Public Health, University of Minnesota Twin-Cities, Minneapolis, MN, USA and Graduate Faculty at Department of Industrial & Systems ...;Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA;Center of Environmental Sciences, Saint Louis University, MO, USA;Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA;Risk and Decision Science Team, Environmental Laboratory, Engineering Research and Development Center, US Army Corps of Engineers, Vicksburg, MS, USA and Department of Engineering and Public Polic ...

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2014

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Abstract

Untangling drivers of systems and uncertainty for species distribution models (SDMs) is important to provide reliable predictions that are useful for conservation campaigns. This is particularly true for species whose habitat is threatened by climate change that enhances the uncertainty in future species distributions. Global sensitivity and uncertainty analyses (GSUA) is a robust method to globally investigate the uncertainty of SDMs and the importance of species distributions' drivers in space and time. Here we apply GSUA to MaxEnt that is one of the popular presence-only SDMs. We consider the Snowy Plover (Charadrius alexandrinus nivosus) (SP) in Florida that is a shorebird whose habitat is affected by sea level rise due to climate change. The importance of intrinsic and exogenous input factors to the uncertainty of the species distribution is evaluated for MaxEnt. GSUA is applied for three projections of the habitat (2006, 2060, and 2100) according to the A1B sea level rise scenario. The large land cover variation determines a moderate decrease in habitat suitability in 2060 and 2100 prospecting a low risk of decline for the SP. The regularization parameter for the environmental features, the uncertainty into the classification of salt-marsh, transitional marsh, and ocean beach, and the maximum number of iterations for the model training are in this order the most important input factors for the average habitat suitability. These results are related to the SP but, in general MaxEnt appears as a very non-linear model where uncertainty mostly derives from the interactions among input factors. The uncertainty of the output is a species-specific variable. Thus, GSUA need be performed for each case considering local exogenous input factors of the model. GSUA allows quantitative informed species-management decisions by providing scenarios with controlled uncertainty and confidence over factors' importance that can be used by resource managers.