Spatial conservation prioritization: Quantitative methods and computational tools

By Atte Moilanen, Kerrie A. Wilson, H. Possingham. 2009

383 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fdfe10da819146787194b12c8181eca2418dae5b

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Citations in the corpus

Computational Sustainability (2011, background): “From a mathematical point of view, the site selection or reserve design problem (Ando et al. 1998, Moilanen et al. 2009, and Polasky et al. 2008) concerns the selection of sites optimizing certain criteria (e.g., habitat suitability for certain species), while satisfying one or more constraints…”
From climate change predictions to actions – conserving vulnerable animal groups in hotspots at a regional scale (2010, methodology): “This could be achieved using reserve selection algorithms (e.g. Moilanen et al., 2009), which allow the identification the minimum set of areas necessary to represent all species at a given target.”
Establishing the link between habitat selection and animal population dynamics (2015, background): “Nevertheless, spatial prioritization (e.g., for purposes of reserve design) is still mostly driven by the location of usage/abundance hotspots (Loiselle et al. 2003, Moilanen et al. 2009, Zurell et al. 2009).”
Making protected area networks effective for marine top predators (2011, background): “Science-based design of protected areas relies on quantitative spatial prioritisation, allowing explicit and repeatable formulation (Moilanen et al. 2009).”
Conservation implications of species–genetic diversity correlations (2014, background): “In spatial conservation prioritization the conservation of diversity at the landscape scale is achieved by selecting localities that best complement each other with respect to the desired biodiversity features (Margules and Pressey, 2000; Arponen et al., 2008; Moilanen et al., 2009).”
Species in a dynamic world: Consequences of habitat network dynamics on conservation planning (2012, background): “Gradually, the following developments were implemented: first, computational efficiency was improved significantly, and second, population dynamics were incorporated in a more realistic manner (Cabeza and Van Teeffelen, 2009; Moilanen et al., 2009).”