@article{fdi:010091380,
  title = {{I}ntegrating multi-objective optimization and ecological connectivity to strengthen {P}eru's protected area system towards the 30*2030 target},
  author = {{D}eleglise, {H}ugo and {J}usteau-{A}llaire, {D}imitri and {M}ulligan, {M}. and {E}spinoza, {J}. {C}. and {I}sasi-{C}atala, {E}. and {A}lvarez, {C}. and {C}ondom, {T}homas and {P}alomo, {I}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {K}unming-{M}ontreal {G}lobal {B}iodiversity {F}ramework ({GBF}) of the {C}onvention on {B}iological {D}iversity has set the target of protecting 30 % of the world's land and sea by 2030. {P}revious conservation planning approaches have been based primarily on biodiversity elements, particularly for {P}eru, a mega-biodiverse country whose protected areas network need to be expanded. {H}owever, achieving this ambitious 30 % target requires careful consideration of numerous ecological and social aspects. {T}o cover these aspects, we present a terrestrial conservation planning approach that integrates biodiversity, ecosystem services, human impact, ecological connectivity and ecoregional representativeness. {O}ur approach has been co-produced with national organisations and {NGO}s and includes advanced {A}rtificial {I}ntelligence ({AI}) methods. {O}ur results identify areas of high ecological value to supplement the 17.88 % of areas already protected, to reach 30 %. {T}he integration of these areas could close gaps in the current system, particularly those vital for water related ecosystem services, ecoregional representativity and ecological connectivity. {I}ntegrated {AI}-based optimization methods (i.e., integer linear programming, constraint programming, reference point method) enabled us to obtain optimal, constraint-satisfying and balanced protected areas selected on the basis of integrated variables, and constitute a robust alternative compared with heuristic methods (e.g., {M}arxan, {Z}onation) commonly used. {T}his work can be used as a fundamental component of {P}eru's territorial planning, and paves the way on future research on conservation planning, which should integrate advanced spatial conservation planning methods, ecological and social factors in an even more comprehensive way.},
  keywords = {{C}onservation planning ; {B}iodiversity ; {E}cosystem services ; {W}ater availability ; {C}arbon sequestration ; {A}rtificial {I}ntelligence ; {PEROU}},
  booktitle = {},
  journal = {{B}iological {C}onservation},
  volume = {299},
  numero = {},
  pages = {110799 [12 p.]},
  ISSN = {0006-3207},
  year = {2024},
  DOI = {10.1016/j.biocon.2024.110799},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091380},
}