Biodiversity and Conservation

, Volume 16, Issue 9, pp 2649–2675

Identification of Biodiversity Conservation Priorities using Predictive Modeling: An Application for the Equatorial Pacific Region of South America

Authors

    • Department of Geography and the EnvironmentUniversity of Texas at Austin
  • Rodrigo Sierra
    • Department of Geography and the EnvironmentUniversity of Texas at Austin
  • Kenneth R. Young
    • Department of Geography and the EnvironmentUniversity of Texas at Austin
  • Carmen Ulloa- Ulloa
    • Missouri Botanical Garden
Original paper

DOI: 10.1007/s10531-006-9077-y

Cite this article as:
Peralvo, M., Sierra, R., Young, K.R. et al. Biodivers Conserv (2007) 16: 2649. doi:10.1007/s10531-006-9077-y

Abstract

We used predictive modeling of species distributions to identify conservation priority areas in the equatorial Pacific region of western Ecuador and northwestern Peru. Museum and herbarium data and predictive models of species distributions are increasingly being used to assess the conservation status of individual species. In this study, we assembled occurrence data for 28 species of vascular plants, birds, and mammals to assess the conservation priorities of the set of natural communities that they represent. Environmental variables were used to predict the species’ distributions using correlative modeling as an alternative to point data, which has been the traditional approach to identify critical areas. Specific priority sites for conservation were identified using an area-selection algorithm based on simulated annealing. Four scenarios of prioritization were created using different criteria for the spatial compactness of the selected sites and fragmentation of remnant habitat. The results provide a preliminary assessment of conservation priorities for the dry ecosystems of the Equatorial Pacific region, and will serve as guidelines to focus future fieldwork.

Keywords

Area-selection algorithmsEcuadorPeruSpecies distribution modelingSystematic conservation planningTropical dry forest

Abbreviations

BLM

Boundary length modifier

CDC

Conservation Data Center

CRU CL 2.0

Climatic Research Unit’s Climatologic database

DEM

Digital Elevation Model

DINAREN

National Directorate of Natural Resources of Ecuador

ENSO

El Niño/Southern Oscillation

GARP

Genetic Algorithm for Rule Set Prediction

GIScience

Geographic Information Science

INRENA

National Institute of Natural Resources of Peru

IUCN

International Union for the Conservation of Nature and Natural Resources

LULC

Land Use/Land Cover

MBG

Missouri Botanical Garden

PET

Potential evapotranspiration

PSAD

Provisional South-American Datum

SPOT

Spatial Portfolio Optimization Tool

SRTM

Shuttle Radar Topographic Mission

TNC

The Nature Conservancy

UTM

Universal Transversal Mercator

VAST

Missouri Botanical Garden’s VAScular Tropicos database

Copyright information

© Springer Science+Business Media B.V. 2006