Multi-objective optimization applied to Systematic conservation Planning and spatial conservation priorities under climate change

TY - GEN

T1 - Multi-objective optimization applied to Systematic conservation Planning and spatial conservation priorities under climate change

AU - Schlottfeldt, Shana

AU - Timmis, Jon

AU - Walter, Maria Emilia M T

AU - Carvalho, Andre C P L F

AU - Diniz-Filho, Jose Alexandre F

AU - Simon, Lorena M.

PY - 2014

Y1 - 2014

N2 - Biodiversity problems require strategies to accomplish specific conservation goals. An underlying principle of these strategies is known as Systematic Conservation Planning (SCP). SCP is an inherently multi-objective (MO) problem but, in the literature, it has been usually dealt with a monobjective approach. In addition, SCP analysis tend to assume that conserved biodiversity does not change throughout time. In this paper we propose a MO approach to the SCP problem which increases flexibility through the inclusion of more objectives, which whilst increasing the complexity, significantly augments the amount of information used to provide users with an improved decision support system. We employed ensemble forecasting approach, enriching our analysis by taking into account future climate simulations to estimate species occurrence projected to 2080. Our approach is able to identify sites of high priority for conservation, regions with high risk of investment and sites that may become attractive options in the future. As far as we know, this is the first attempt to apply MO algorithms to a SCP problem associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.

AB - Biodiversity problems require strategies to accomplish specific conservation goals. An underlying principle of these strategies is known as Systematic Conservation Planning (SCP). SCP is an inherently multi-objective (MO) problem but, in the literature, it has been usually dealt with a monobjective approach. In addition, SCP analysis tend to assume that conserved biodiversity does not change throughout time. In this paper we propose a MO approach to the SCP problem which increases flexibility through the inclusion of more objectives, which whilst increasing the complexity, significantly augments the amount of information used to provide users with an improved decision support system. We employed ensemble forecasting approach, enriching our analysis by taking into account future climate simulations to estimate species occurrence projected to 2080. Our approach is able to identify sites of high priority for conservation, regions with high risk of investment and sites that may become attractive options in the future. As far as we know, this is the first attempt to apply MO algorithms to a SCP problem associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.

KW - Biodiversity conservation

KW - Climate change

KW - Multi-objective optimization

KW - Systematic Conservation Planning

UR - http://www.scopus.com/inward/record.url?scp=84905662446&partnerID=8YFLogxK

U2 - 10.1145/2598394.2598404

DO - 10.1145/2598394.2598404

M3 - Conference contribution

AN - SCOPUS:84905662446

SN - 9781450328814

SP - 177

EP - 178

BT - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

PB - ACM

T2 - 16th Genetic and Evolutionary Computation Conference, GECCO 2014

Y2 - 12 July 2014 through 16 July 2014

ER -