Literature DB >> 12418159

Assessing state-wide biodiversity in the Florida Gap analysis project.

L G Pearlstine1, S E Smith, L A Brandt, C R Allen, W M Kitchens, J Stenberg.   

Abstract

The Florida Gap (Fl-Gap) project provides an assessment of the degree to which native animal species and natural communities are or are not represented in existing conservation lands. Those species and communities not adequately represented in areas being managed for native species constitute 'gaps' in the existing network of conservation lands. The United States Geological Survey Gap Analysis Program is a national effort and so, eventually, all 50 states will have completed it. The objective of Fl-Gap was to provide broad geographic information on the status of terrestrial vertebrates, butterflies, skippers and ants and their respective habitats to address the loss of biological diversity. To model the distributions and potential habitat of all terrestrial species of mammals, breeding birds, reptiles, amphibians, butterflies, skippers and ants in Florida, natural land cover was mapped to the level of dominant or co-dominant plant species. Land cover was classified from Landsat Thematic Mapper (TM) satellite imagery and auxiliary data such as the national wetlands inventory (NWI), soils maps, aerial imagery, existing land use/land cover maps, and on-the-ground surveys. Wildlife distribution models were produced by identifying suitable habitat for each species within that species' range. Mammalian models also assessed a minimum critical area required for sustainability of the species' population. Wildlife species richness was summarized against land stewardship ranked by an area's mandates for conservation protection.

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Mesh:

Year:  2002        PMID: 12418159     DOI: 10.1006/jema.2002.0551

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  4 in total

1.  Estimating the cumulative ecological effect of local scale landscape changes in south Florida.

Authors:  Dianna M Hogan; William Labiosa; Leonard Pearlstine; David Hallac; David Strong; Paul Hearn; Richard Bernknopf
Journal:  Environ Manage       Date:  2011-10-29       Impact factor: 3.266

2.  Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

Authors:  Zoltan Szantoi; Francisco J Escobedo; Amr Abd-Elrahman; Leonard Pearlstine; Bon Dewitt; Scot Smith
Journal:  Environ Monit Assess       Date:  2015-04-17       Impact factor: 2.513

3.  Efficiency of a protected-area network in a Mediterranean region: a multispecies assessment with raptors.

Authors:  María D Abellán; José E Martínez; José A Palazón; Miguel A Esteve; José F Calvo
Journal:  Environ Manage       Date:  2011-03-04       Impact factor: 3.266

Review 4.  A Review of Wetland Remote Sensing.

Authors:  Meng Guo; Jing Li; Chunlei Sheng; Jiawei Xu; Li Wu
Journal:  Sensors (Basel)       Date:  2017-04-05       Impact factor: 3.576

  4 in total

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