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One size does not fit all: Priority areas for real world problems

Author

Listed:
  • Pimenta, Mayra
  • Andrade, André Felipe Alves de
  • Fernandes, Fernando Hiago Souza
  • Amboni, Mayra Pereira de Melo
  • Almeida, Renata Silva
  • Soares, Ana Hermínia Simões de Bello
  • Falcon, Guth Berger
  • Raíces, Daniel Santana Lorenzo
  • De Marco Júnior, Paulo

Abstract

Recently, much effort has been expended to improve Species Distribution Models (SDMs), particularly for use in systematic conservation planning, as they affect the arrangement and effectiveness of spatial prioritization. Protocol definition to create SMDs is a real and complex problem faced to protect threatened environments in large megadiverse areas, such as the Amazon basin. We compare spatial prioritizations based on different protocols to generate general and specific models of multiple taxa. While in general protocols we use only bioclimatic variables, in specific protocols we also added different environmental variables that are more appropriate to the sets of related species. Our results show greater precision and less commission error in the final models adjusted with specific protocols, especially for aquatic species with the inclusion of hydrological variables. We also demonstrate that modeling choices can play an important role in determining the priority of a region, with prioritization of different areas depending on the models adjusted based on general and specific protocols. We argue that niche models for multi-taxon prioritization studies should be more efficient when based on choices that capture the ecological requirements of different taxonomic groups.

Suggested Citation

  • Pimenta, Mayra & Andrade, André Felipe Alves de & Fernandes, Fernando Hiago Souza & Amboni, Mayra Pereira de Melo & Almeida, Renata Silva & Soares, Ana Hermínia Simões de Bello & Falcon, Guth Berger &, 2022. "One size does not fit all: Priority areas for real world problems," Ecological Modelling, Elsevier, vol. 470(C).
    • Handle: RePEc:eee:ecomod:v:470:y:2022:i:c:s0304380022001247
    DOI: 10.1016/j.ecolmodel.2022.110013
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