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Author (up) Guitet, S.; Sabatier, D.; Brunaux, O.; Herault, B.; Aubry-Kientz, M.; Molino, J.-F.; Baraloto, C. url  openurl
  Title Estimating tropical tree diversity indices from forestry surveys: A method to integrate taxonomic uncertainty Type Journal Article
  Year 2014 Publication Forest Ecology and Management Abbreviated Journal For. Ecol. Manage.  
  Volume 328 Issue Pages 270-281  
  Keywords Diversity; French guiana; Landscape scale; Monte-Carlo process; Rainforest; Tree community; Reliability; Surveys; Timber; Uncertainty analysis; Diversity; French Guiana; Landscape scale; Rainforest; Tree community; Forestry; estimation method; forest inventory; herbarium; landscape; numerical model; species diversity; taxonomy; uncertainty analysis; Forestry; Forests; Reliability; Surveys; French Guiana  
  Abstract Analyses of tree diversity and community composition in tropical rain forests are usually based either on general herbarium data or on a restricted number of botanical plots. Despite their high taxonomic accuracy, both types of data are difficult to extrapolate to landscape scales. Meanwhile, forestry surveys provide quantitative occurrence data on large areas, and are thus increasingly used for landscape-scale analyses of tree diversity. However, the reliability of these approaches has been challenged because of the ambiguity of the common (vernacular) names used by foresters and the complexity of tree taxonomy in those hyper-diverse communities.We developed and tested a novel approach to evaluate taxonomic reliability of forestry surveys and to propagate the resulting uncertainty in the estimates of several diversity indicators (alpha and beta entropy, Fisher-alpha and Sørensen similarity). Our approach is based on Monte-Carlo processes that simulate communities by taking into account the expected accuracy and reliability of common names. We tested this method in French Guiana, on 9 one-hectare plots (4279 trees – DBH. ≥. 10. cm) for which both common names and standardized taxonomic determinations were available. We then applied our method of community simulation on large forestry inventories (560. ha) at the landscape scale and compared the diversity indices obtained for 10 sites with those computed from precise botanical determination situated at the same localities.We found that taxonomic reliability of forestry inventories varied from 22% (species level) to 83% (family level) in this Amazonian region. Indices computed directly with raw forestry data resulted in incorrect values, except for Gini-Simpson beta-diversity. On the contrary, our correction method provides more accurate diversity estimates, highly correlated with botanical measurements, for almost all diversity indices at both regional and local scales. We obtained a robust ranking of sites consistent with those shown by botanical inventories.These results show that (i) forestry inventories represent a significant part of taxonomic information, (ii) the relative diversity of regional sites can be successfully ranked using forestry inventory data using our method and (iii) forestry inventories can valuably contribute to the detection of large-scale diversity patterns when biases are well-controlled and corrected.The tools we developed as R-functions are available in supplementary material and can be adapted with local parameters to be used for forest management and conservation issues in other regional contexts. © 2014 Elsevier B.V.  
  Address INRA, UMR EcoFoG, 97387 Kourou Cedex, French Guiana  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 03781127 (Issn) ISBN Medium  
  Area Expedition Conference  
  Notes Export Date: 30 July 2014; Coden: Fecmd; Correspondence Address: Guitet, S.; INRA, UMR Amap, TA A51/PS2, 34398 Montpellier Cedex 5, France; email: Approved no  
  Call Number EcoFoG @ webmaster @ Serial 552  
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