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The use of habitat suitability models and species–area relationships to predict extinction debts in coastal forests, South Africa

Authors

  • Pieter I. Olivier,

    1. Conservation Ecology Research Unit, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
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  • Rudi J. van Aarde,

    Corresponding author
    1. Conservation Ecology Research Unit, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
    • Correspondence: Rudi J. van Aarde, Conservation Ecology Research Unit, Department of Zoology and Entomology, University of Pretoria, Hatfield, Pretoria 0083, South Africa

      E-mail: rjvaarde@zoology.up.ac.za

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  • Amanda T. Lombard

    1. Conservation Ecology Research Unit, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
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Abstract

Aim

Predicting extinctions before they are realized has proven difficult, yet is increasingly important for biodiversity conservation as habitat destruction continues unabated around the world. We evaluated whether habitat suitability models can be used in conjunction with species–area relationships (SAR) to detect apparent extinction debts as implicated by the conservation status assigned to bird species.

Location

KwaZulu-Natal province, South Africa.

Methods

We modelled historic distributions of coastal forests using MaxEnt, a presence-only technique for modelling species distributions. The model provided an estimate of forest loss. We then conducted 293 point counts to survey birds within remaining forest fragments and employed an information-theoretic framework to test for the best fit SAR model. Extinction debts were calculated using the estimate of forest loss and the empirical SAR data.

Results

Our model suggests extensive forest loss (82%) within a naturally fragmented landscape. The power function provided the best fit for bird SAR. Fourteen bird species are predicted to go extinct from coastal forests. Predicted extinctions closely matched the number of threatened species locally but not globally. Predicted extinctions also only matched globally threatened species that reach their northernmost distribution limit within coastal forests, but not species that reach their southernmost distribution limit here.

Main conclusions

We found that habitat suitability models could be used in conjunction with SAR to estimate extinction debt implied by conservation statuses of extant species. Our approach assumed that forest loss drives extinction debts but also provided the opportunity to link forest loss and the likelihood of extinction. Models of historical forest distribution may provide guidelines of where to implement restoration actions. Maintaining matrix habitats that link forest fragments and targeted landscape level restoration that increases fragment area and link isolated fragments will be important to prevent predicted extinctions.

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