Literature DB >> 19582397

A quantitative climate-match score for risk-assessment screening of reptile and amphibian introductions.

Nicola J van Wilgen1, Núria Roura-Pascual, David M Richardson.   

Abstract

Assessing climatic suitability provides a good preliminary estimate of the invasive potential of a species to inform risk assessment. We examined two approaches for bioclimatic modeling for 67 reptile and amphibian species introduced to California and Florida. First, we modeled the worldwide distribution of the biomes found in the introduced range to highlight similar areas worldwide from which invaders might arise. Second, we modeled potentially suitable environments for species based on climatic factors in their native ranges, using three sources of distribution data. Performance of the three datasets and both approaches were compared for each species. Climate match was positively correlated with species establishment success (maximum predicted suitability in the introduced range was more strongly correlated with establishment success than mean suitability). Data assembled from the Global Amphibian Assessment through NatureServe provided the most accurate models for amphibians, while ecoregion data compiled by the World Wide Fund for Nature yielded models which described reptile climatic suitability better than available point-locality data. We present three methods of assigning a climate-match score for use in risk assessment using both the mean and maximum climatic suitabilities. Managers may choose to use different methods depending on the stringency of the assessment and the available data, facilitating higher resolution and accuracy for herpetofaunal risk assessment. Climate-matching has inherent limitations and other factors pertaining to ecological interactions and life-history traits must also be considered for thorough risk assessment.

Mesh:

Year:  2009        PMID: 19582397     DOI: 10.1007/s00267-009-9311-y

Source DB:  PubMed          Journal:  Environ Manage        ISSN: 0364-152X            Impact factor:   3.266


  10 in total

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  10 in total
  5 in total

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  5 in total

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