• DOI:10.1111/cobi.12499
• Corpus ID: 40834299

Improving effectiveness of systematic conservation planning with density data.

@article{Veloz2015ImprovingEO,
  title={Improving effectiveness of systematic conservation planning with density data.},
  author={Sam Veloz and Leo A. Salas and Bob Altman and John D. Alexander and Dennis Jongsomjit and Nathan Elliott and Grant Ballard},
  journal={Conservation biology : the journal of the Society for Conservation Biology},
  year={2015},
  volume={29 4},
  pages={
          1217-27
        }
}

• S. Veloz, L. Salas, +4 authors G. Ballard
• Published 1 August 2015
• Environmental Science
• Conservation biology : the journal of the Society for Conservation Biology

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High-density model Zonation rankings protected more individuals per species when networks protected the highest priority 10-40% of the landscape. Compared with density-based models, the occurrence-based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density-based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts.