METHODS published: 17 November 2020 doi: 10.3389/fsufs.2020.600363 Frontiers in Sustainable Food Systems | www.frontiersin.org 1 November 2020 | Volume 4 | Article 600363 Edited by: Pablo Gregorini, Lincoln University, New Zealand Reviewed by: Emilio Andres Laca, University of California, Davis, United States Nicholas Tyler, Arctic University of Norway, Norway *Correspondence: Daniel Fortin daniel.fortin@bio.ulaval.ca Specialty section: This article was submitted to Agroecology and Ecosystem Services, a section of the journal Frontiers in Sustainable Food Systems Received: 29 August 2020 Accepted: 21 October 2020 Published: 17 November 2020 Citation: Fortin D, Brooke CF, Lamirande P, Fritz H, McLoughlin PD and Pays O (2020) Quantitative Spatial Ecology to Promote Human-Wildlife Coexistence: A Tool for Integrated Landscape Management. Front. Sustain. Food Syst. 4:600363. doi: 10.3389/fsufs.2020.600363 Quantitative Spatial Ecology to Promote Human-Wildlife Coexistence: A Tool for Integrated Landscape Management Daniel Fortin 1,2 *, Christopher F. Brooke 3 , Patricia Lamirande 4,5 , Hervé Fritz 3,6 , Philip D. McLoughlin 7 and Olivier Pays 8 1 Centre d’étude de la forêt, Centre Interdisciplinaire en Modélisation Mathématique de l’Université Laval, Québec, QC, Canada, 2 Department of Biology, Université Laval, Québec, QC, Canada, 3 School of Natural Resource Management, Nelson Mandela University, George Campus, George, South Africa, 4 Groupe Interdisciplinaire de Recherche en Éléments Finis, Centre Interdisciplinaire en Modélisation Mathématique de l’Université Laval, Centre d’étude de la forêt, Québec, QC, Canada, 5 Department of Mathematics and Statistics, Université Laval, Québec, QC, Canada, 6 REHABS International Research Laboratory, Centre national de la recherche scientifique-Université Lyon 1-Nelson Mandela University, George Campus, George, South Africa, 7 Department of Biology, University of Saskatchewan, Saskatoon, SK, Canada, 8 UMR CNRS 6554 LETG-Angers, UFR Sciences, University of Angers, Angers, France Understanding, predicting and controlling animal movement is a fundamental problem of conservation and management ecology. The need to mitigate human-wildlife conflicts, such as crop raiding by large herbivores, is becoming increasingly urgent. Because of the substantial costs or the possibility of unsuitable outcomes on wildlife, managers are often encouraged to deploy interventions that can achieve their objective while minimizing the impact on animal populations. We propose an adaptive management framework that can identify cost-effective solutions to reduce human-wildlife conflicts, while also minimizing constraints on animal movement and distribution. We focus on conflicts involving animals for which conflict zones occupy only a portion of their home-range. The adaptive management approach includes four basic steps: define and spatialize conflict areas, predict animal distribution from functional connectivity and patch residency time, predict the impact of management actions on animal distribution, and test predictions and revise predictive models. Key to the process is development of a mathematical model that can predict how habitat-animal interactions shape animal movement dynamics within patch networks. In our model, networks consist of a set of high-quality patches connected by links (i.e., potential inter-patch movements). Inter-patch movement rules and determinants of patch residency time need to be determined empirically. These data then provide information to parameterize a reaction-advection-diffusion model that can predict animal distribution dynamics given habitat features and movement taxis toward (or against) conflict areas depending on management actions. Illustrative simulations demonstrate how quantitative predictions can be used to make spatial adjustments in management interventions (e.g., length of diversionary fences) with respect of conflict areas. Simulations also show that the impact of multiple interventions cannot be considered as simply having additive effect, and their relative impact on animal equilibrium distribution depends on how they are added and deployed across the network. Following the principles of adaptive, integrated landscape management,