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Predicting species distributions for conservation decisions.

Antoine Guisan¹, Reid Tingley, John B Baumgartner

¹Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland; Institute of Earth Surface Dynamics, University of Lausanne, 1015, Lausanne, Switzerland; ARC Centre of Excellence for Environmental Decisions (CEED), School of Biological Sciences, The University of Queensland, St Lucia, Brisbane, Qld, 4072, Australia; CSIRO Ecosystem Sciences, Ecosciences Precinct, Dutton Park, Brisbane, Qld, 4102, Australia.

Ecology Letters

|October 19, 2013

Summary

This summary is machine-generated.

Species distribution models (SDMs) can effectively guide conservation actions, but their successful application is often hidden in grey literature. Integrating SDMs into structured decision-making processes is key to improving conservation outcomes.

Keywords:

Biological invasions conservation planning critical habitats environmental suitability reserve selection species distribution model structured decision making translocation

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Area of Science:

Ecology
Conservation Biology
Environmental Science

Background:

Species distribution models (SDMs) are increasingly recognized for their potential to inform conservation strategies.
However, documented evidence of SDMs directly supporting on-ground conservation solutions remains limited in scientific literature.
The decision-making frameworks guiding SDM application are often not explicitly stated.

Purpose of the Study:

To investigate the extent to which species distribution models (SDMs) are successfully applied in real-world conservation.
To identify barriers and facilitators for the effective use of SDMs in conservation decision-making.
To propose a framework for tailoring SDMs to diverse conservation contexts.

Main Methods:

Analysis of case studies across various conservation applications, including biological invasions, habitat identification, reserve selection, and species translocation.
Examination of the integration of SDMs within explicit decision-making processes.
Identification of successful examples often found in grey literature.

Main Results:

Successful applications of SDMs in conservation exist but are predominantly found in grey literature, limiting broader scientific learning.
SDMs can be effectively tailored for different decision-making contexts when embedded in structured and transparent processes.
Explicit decision frameworks are crucial for maximizing the utility of SDMs.

Conclusions:

Bridging the gap between SDM modeling and practical conservation requires better understanding of decision processes by modelers and feedback from decision-makers.
The role of 'translators' is vital for facilitating communication between modelers and decision-makers.
Encouraging modeler involvement in real decision-making processes can enhance scientific knowledge and improve conservation outcomes.