Species and environment representation: Selecting reserves for the retention of avian diversity in KwaZulu-Natal, South Africa

  • Fairbanks D
  • Reyers B
  • Van Jaarsveld A
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Multivariate analyses and complementarity-based reserve selection algorithms were used as tools for delimiting representative conservation areas in KwaZulu-Natal, South Africa, based on environmental variables and records for 566 breeding and non-breeding birds. The relationships between species assemblages and 20 environmental variables were investigated; including climate, topography, vegetation, landscape attributes, and land-use. Detrended correspondence analysis, canonical correspondence analysis, and hierarchical classification suggested that the major gradients of bird species variation are primarily related to climatic variables such as growing season temperature and seasonality of precipitation, reflecting the strong east-west gradient in oceanicity in KwaZulu-Natal, and water balance, reflecting a strong north-south gradient in aridity. Hierarchical classification was applied to the ordination data and revealed five representative types of bird communities. Four of the five bird communities and 37 species are under-represented in the existing reserve system, and the environmental processes required for long-term maintenance of birds have not been adequately sampled, showing that additional reserves are needed to ensure a more functional representative system. As an alternative, the inclusion of species spatial turnover structure together with associated environmental gradients were employed in both rarity and richness-based Complementary reserve selection algorithms to provide a better representation of birds and environmental processes. Improvements on the current generation of reserve selection techniques derived by including ordination analysis and spatial structure are discussed. © 2001 Elsevier Science Ltd.

Author-supplied keywords

  • Birds
  • Climate
  • Conservation area selection
  • Ordination
  • Pattern and process
  • South Africa
  • Spatial autocorrelation

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