A concise guide to developing and using quantitative models in conservation management

Pablo García-Díaz* (Corresponding Author), Thomas A.A. Prowse, Dean P. Anderson, Miguel Lurgi, Rachelle N. Binny, Phillip Cassey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
9 Downloads (Pure)


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.
Original languageEnglish
Article numbere11
JournalConservation Science and Practice
Issue number2
Early online date15 Apr 2019
Publication statusPublished - 2019

Bibliographical note

European Union’s Horizon 2020, Grant/Award Number: 726176; French ANR, Grant/Award Numbers: ANR‐10‐LABX‐41, ANR‐11‐IDEX‐002‐02; French ꁆMidi‐Pyrénées Region, Grant/Award Number: CNRS 121090; ꁆEuropean Research Council, Grant/Award Number: 726176


  • applied conservation
  • ecological models
  • prediction
  • projection
  • simulation model
  • statistical model
  • uncertainty


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