Biodiversity & Conservation

, Volume 11, Issue 6, pp 1011–1024 | Cite as

A sequential approach to minimise threats within selected conservation areas

  • Miguel B. Araújo
  • Paul H. Williams
  • Andy Turner


Core–periphery models allow predictions of persistence to be madewith relatively little data. The rationale is that populations in the core oftheir geographical or ecological ranges occupy suitable habitats and exhibithigher and less variable densities. Populations along the peripheries tend to bemore fragmented and therefore less likely to receive immigrants from otherpopulations. A population's probability of persistence is expected to correlatepositively with habitat suitability and immigration rate and to correlatenegatively with demographic variability. These predictions may be invalidated bythe effect of threats, which may cause some peripheral populations to persistrather than populations in the core. We expect that predictions of persistencefrom core–periphery models will be improved by incorporating informationon threats, and illustrate one way in which threat could be integrated withinquantitative area-selection methods. We illustrate this for Europe by showingthat important areas for biodiversity, selected with presence data, haveconsistently more people than expected by chance, but that incorporating humandensity as a constraint to area selection can reduce substantially this level ofpressure. We also show that areas selected using simple core–peripherymodels have fewer people than areas selected with presence data only. Theseresults support the idea that there are opportunities to identify importantareas for the persistence of species that are located in areas with low humandensity.

Area selection Biodiversity conservation Core–periphery Human-induced threat Persistence 


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Miguel B. Araújo
    • 1
  • Paul H. Williams
    • 1
  • Andy Turner
    • 2
  1. 1.Biogeography and Conservation LaboratoryThe Natural History MuseumLondonUK
  2. 2.Centre for Computational Geography, School of GeographyUniversity of LeedsLeedsUK

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