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Mathematical optimization ideas for biodiversity conservation

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  • Billionnet, Alain

Abstract

Several major environmental issues like biodiversity loss and climate change currently concern the international community. These topics that are related to the development of human societies have become increasingly important since the United Nations Conference on Environment and Development (UNCED) or Earth Summit in Rio de Janeiro in 1992. In this article, we are interested in the first issue. We present here many examples of the help that using mathematical programming can provide to decision-makers in the protection of biodiversity. The examples we have chosen concern the selection of nature reserves, the control of adverse effects caused by landscape fragmentation, including the creation or restoration of biological corridors, the ecological exploitation of forests, the control of invasive species, and the maintenance of genetic diversity. Most of the presented models are – or can be approximated with – linear-, quadratic- or fractional-integer formulations and emphasize spatial aspects of conservation planning. Many of them represent decisions taken in a static context but temporal dimension is also considered. The problems presented are generally difficult combinatorial optimization problems, some are well solved and others less well. Research is still needed to progress in solving them in order to deal with real instances satisfactorily. Moreover, relations between researchers and practitioners have to be strengthened. Furthermore, many recent achievements in the field of robust optimization could probably be successfully used for biodiversity protection, a domain in which many data are uncertain.

Suggested Citation

  • Billionnet, Alain, 2013. "Mathematical optimization ideas for biodiversity conservation," European Journal of Operational Research, Elsevier, vol. 231(3), pages 514-534.
  • Handle: RePEc:eee:ejores:v:231:y:2013:i:3:p:514-534
    DOI: 10.1016/j.ejor.2013.03.025
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    5. Álvarez-Miranda, Eduardo & Salgado-Rojas, José & Hermoso, Virgilio & Garcia-Gonzalo, Jordi & Weintraub, Andrés, 2020. "An integer programming method for the design of multi-criteria multi-action conservation plans," Omega, Elsevier, vol. 92(C).
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    7. Kıbış, Eyyüb Y. & Büyüktahtakın, İ. Esra, 2017. "Optimizing invasive species management: A mixed-integer linear programming approach," European Journal of Operational Research, Elsevier, vol. 259(1), pages 308-321.
    8. Álvarez-Miranda, Eduardo & Goycoolea, Marcos & Ljubić, Ivana & Sinnl, Markus, 2021. "The Generalized Reserve Set Covering Problem with Connectivity and Buffer Requirements," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1013-1029.
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    12. Kayla Stan & Arturo Sanchez-Azofeifa & Mário Espírito-Santo & Carlos Portillo-Quintero, 2015. "Simulating Deforestation in Minas Gerais, Brazil, under Changing Government Policies and Socioeconomic Conditions," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-19, September.
    13. Stephanie A. Snyder & Robert G. Haight, 2016. "Application of the Maximal Covering Location Problem to Habitat Reserve Site Selection," International Regional Science Review, , vol. 39(1), pages 28-47, January.
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    15. Haider, Zulqarnain & Charkhgard, Hadi & Kwon, Changhyun, 2018. "A robust optimization approach for solving problems in conservation planning," Ecological Modelling, Elsevier, vol. 368(C), pages 288-297.
    16. Alfandari, Laurent & Hassanzadeh, Alborz & Ljubic, Ivana, 2020. "An Exact Method for Assortment Optimization under the Nested Logit Model," ESSEC Working Papers WP2001, ESSEC Research Center, ESSEC Business School, revised 2020.
    17. Pascual, Adrián & Guerra-Hernández, Juan, 2022. "Spatial connectivity in tree-level decision-support models using mathematical optimization and individual tree mapping," Forest Policy and Economics, Elsevier, vol. 139(C).
    18. Gatmiry, Zohreh S. & Hafezalkotob, Ashkan & Khakzar bafruei, Morteza & Soltani, Roya, 2021. "Food web conservation vs. strategic threats: A security game approach," Ecological Modelling, Elsevier, vol. 442(C).
    19. Eyyüb Y. Kıbış & İ. Esra Büyüktahtakın & Robert G. Haight & Najmaddin Akhundov & Kathleen Knight & Charles E. Flower, 2021. "A Multistage Stochastic Programming Approach to the Optimal Surveillance and Control of the Emerald Ash Borer in Cities," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 808-834, May.
    20. Laurent Alfandari & Alborz Hassanzadeh & Ivana Ljubić, 2021. "An Exact Method for Assortment Optimization under the Nested Logit Model," Working Papers hal-02463159, HAL.
    21. Álvarez-Miranda, Eduardo & Garcia-Gonzalo, Jordi & Pais, Cristobal & Weintraub, Andrés, 2019. "A multicriteria stochastic optimization framework for sustainable forest decision making under uncertainty," Forest Policy and Economics, Elsevier, vol. 103(C), pages 112-122.
    22. Hao Ye & Yongyong Song & Dongqian Xue, 2022. "Multi-Scenario Simulation of Land Use and Habitat Quality in the Guanzhong Plain Urban Agglomeration, China," IJERPH, MDPI, vol. 19(14), pages 1-22, July.

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