A Multi-objective Optimization Approach Associated to Climate Change Analysis to Improve Systematic Conservation Planning

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• 作者：Shana Schlottfeldt (16) (17)
  Jon Timmis (17)
  Maria Emilia Walter (16)
  Andr茅 Carvalho (18)
  Lorena Simon (19)
  Rafael Loyola (19)
  Jos茅 Alexandre Diniz-Filho (19)
  
  16. Department of Computer Science ; University of Brasilia ; Brasilia ; Brazil
  17. Department of Electronics ; University of York ; York ; UK
  18. Department of Computer Science ; SCC-ICMC-USP ; S茫o Paulo ; Brazil
  19. Institute of Biological Sciences ; Federal University of Goi谩s ; Goi芒nia ; Brazil
  
• 关键词：Multi ; objective optimization ; Systematic conservation planning ; Spatial conservation prioritization ; Biodiversity conservation ; Climate change ; Uncertainty in simulations ; Parameter tuning
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9019
• 期：1
• 页码：458-472
• 全文大小：1,723 KB
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• 作者单位：Evolutionary Multi-Criterion Optimization
• 丛书名：978-3-319-15891-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Biodiversity conservation has been since long an academic community concern, leading scientists to propose strategies to effectively meet conservation goals. In particular, Systematic Conservation Planning (SCP) aims to determine the most cost effective way of investing in conservation actions. SCP can be formalized by the Set-Covering Problem, which is NP-hard. SCP is inherently multi-objective, although it has been usually treated with a monobjective and static approach. Here, we propose a multi-objective solution for SCP, increasing its flexibility and complexity, and, at the same time, augmenting the quality of provided information, which reinforces decision-making. We used ensemble forecasting, considering future climate simulations to estimate species occurrence projected to 2080. Our method identifies sites: 1) of high priority for conservation; 2) with significant risk of investment; and, 3) that may become attractive in the future. To the best of our knowledge, this application to a real-world problem in ecology is the first attempt to apply multi-objective optimization to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.