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Effects of Timber Harvests and Silvicultural Edges on Terrestrial Salamanders

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  • Jami E MacNeil
  • Rod N Williams

Abstract

Balancing timber production and conservation in forest management requires an understanding of how timber harvests affect wildlife species. Terrestrial salamanders are useful indicators of mature forest ecosystem health due to their importance to ecosystem processes and sensitivity to environmental change. However, the effects of timber harvests on salamanders, though often researched, are still not well understood. To further this understanding, we used artificial cover objects to monitor the relative abundance of terrestrial salamanders for two seasons (fall and spring) pre-harvest and five seasons post-harvest in six forest management treatments, and for three seasons post-harvest across the edge gradients of six recent clearcuts. In total, we recorded 19,048 encounters representing nine species of salamanders. We observed declines in mean encounters of eastern red-backed salamanders (Plethodon cinereus) and northern slimy salamanders (P. glutinosus) from pre- to post-harvest in group selection cuts and in clearcuts. However, we found no evidence of salamander declines at shelterwoods and forested sites adjacent to harvests. Edge effects induced by recent clearcuts influenced salamanders for approximately 20 m into the forest, but edge influence varied by slope orientation. Temperature, soil moisture, and canopy cover were all correlated with salamander counts. Our results suggest silvicultural techniques that remove the forest canopy negatively affect salamander relative abundance on the local scale during the years immediately following harvest, and that the depth of edge influence of clearcuts on terrestrial salamanders is relatively shallow (

Suggested Citation

  • Jami E MacNeil & Rod N Williams, 2014. "Effects of Timber Harvests and Silvicultural Edges on Terrestrial Salamanders," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-27, December.
  • Handle: RePEc:plo:pone00:0114683
    DOI: 10.1371/journal.pone.0114683
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    References listed on IDEAS

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    1. J. Andrew Royle, 2004. "N-Mixture Models for Estimating Population Size from Spatially Replicated Counts," Biometrics, The International Biometric Society, vol. 60(1), pages 108-115, March.
    2. repec:ucp:bkecon:9780226320625 is not listed on IDEAS
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