4.5 Article

Identifying Conservation and Restoration Priorities for Saproxylic and Old-Growth Forest Species: A Case Study in Switzerland

Journal

ENVIRONMENTAL MANAGEMENT
Volume 44, Issue 1, Pages 105-118

Publisher

SPRINGER
DOI: 10.1007/s00267-009-9281-0

Keywords

Dead wood; Ecological niche factor analysis; Hotspots; Old-growth forest species; Saproxylic species; Swiss forests

Funding

  1. Federal Office for the Environment
  2. Swiss Centre of Cartography of the Fauna
  3. Swiss Ornithological Institute
  4. Swiss Federal Statistical Office

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Saproxylic (dead-wood-associated) and old-growth species are among the most threatened species in European forest ecosystems, as they are susceptible to intensive forest management. Identifying areas with particular relevant features of biodiversity is of prime concern when developing species conservation and habitat restoration strategies and in optimizing resource investments. We present an approach to identify regional conservation and restoration priorities even if knowledge on species distribution is weak, such as for saproxylic and old-growth species in Switzerland. Habitat suitability maps were modeled for an expert-based selection of 55 focal species, using an ecological niche factor analyses (ENFA). All the maps were then overlaid, in order to identify potential species' hotspots for different species groups of the 55 focal species (e.g., birds, fungi, red-listed species). We found that hotspots for various species groups did not correspond. Our results indicate that an approach based on richness hotspots may fail to conserve specific species groups. We hence recommend defining a biodiversity conservation strategy prior to implementing conservation/restoration efforts in specific regions. The conservation priority setting of the five biogeographical regions in Switzerland, however, did not differ when different hotspot definitions were applied. This observation emphasizes that the chosen method is robust. Since the ENFA needs only presence data, this species prediction method seems to be useful for any situation where the species distribution is poorly known and/or absence data are lacking. In order to identify priorities for either conservation or restoration efforts, we recommend a method based on presence data only, because absence data may reflect factors unrelated to species presence.

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