A concise guide to developing and using quantitative models in conservation management. Issue 2 (15th April 2019)

• Record Type: Journal Article
• Authors: García‐Díaz, Pablo; Prowse, Thomas A.A.; Anderson, Dean P.; Lurgi, Miguel; Binny, Rachelle N.; Cassey, Phillip
• Is Part Of: Conservation science and practice. Volume 1:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-15
• Subjects: applied conservation -- ecological models -- prediction -- projection -- simulation model -- statistical model -- uncertainty; Biodiversity conservation -- Periodicals; Biodiversity conservation; Periodicals; 333.951605
• DOI: 10.1111/csp2.11
• Languages: English
• ISSNs: 2578-4854

Abstract

Abstract : Quantitative models are powerful tools for informing conservation management and decision‐making. As applied modeling is increasingly used to address conservation problems, guidelines are required to clarify the scope of modeling applications and to facilitate the impact and acceptance of models by practitioners. We identify three key roles for quantitative models in conservation management: (a) to assess the extent of a conservation problem; (b) to provide insights into the dynamics of complex social and ecological systems; and, (c) to evaluate the efficacy of proposed conservation interventions. We describe 10 recommendations to facilitate the acceptance of quantitative models in conservation management, providing a basis for good practice to guide their development and evaluation in conservation applications. We structure these recommendations within four established phases of model construction, enabling their integration within existing workflows: (a) design (two recommendations); (b) specification (two); (c) evaluation (one); and (d) inference (five). Quantitative modeling can support effective conservation management provided that both managers and modelers understand and agree on the place for models in conservation. Our concise review and recommendations will assist conservation managers and modelers to collaborate in the development of quantitative models that are fit‐for‐purpose, and to trust and use these models appropriately while understanding key drivers of uncertainty.