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Journal article

Permanent sample plots for natural tropical forests: A rationale with special emphasis on Central Africa

Picard N, Magnussen S, Banak L, Namkosserena S, Yalibanda Y ...see all

Environmental Monitoring and Assessment, vol. 164, issue 1-4 (2010) pp. 279-295

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Abstract

Permanent sample plots (PSP), where trees are individually and permanently marked, have received increased interest in Central Africa as a tool to monitor vegetation changes. Although techniques for mounting PSP in tropical forests are well known, their planning still deserves attention. This study aims at defining a rationale for determining the size and number of replicates for setting up PSP in mixed tropical forests. It considers PSP as a sampling plan to estimate a target quantity with its associated margin of error. The target quantity considered here is the stock recovery rate, which is a key parameter for forest management in Central Africa. It is computed separately for each commercial species. The number of trees to monitor for each species defines the margin of error on the stock recovery rate. The size and number of replicated plots is obtained as the solution of an optimization problem that consists in minimizing the margin of error for every species while ensuring that the mounting cost remains below a given threshold. This rationale was applied using the data from the M'Baïki experimental site in the Central African Republic. It showed that the stock recovery rate is a highly variable quantity, and that the typical cost that forest managers are prone to devote to PSP leads to high margins of error. It also showed that the size and number of replicated plots is related to the spatial pattern of trees: clustered or spatially heterogeneous patterns favor many small plots, whereas regular or spatially homogeneous patterns favor few large plots.

Author-supplied keywords

  • Central African Republic
  • Forest dynamics
  • Permanent sample plot
  • Resource management
  • Tropical forest

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Authors

  • Nicolas Picard

  • Steen Magnussen

  • Ludovic Ngok Banak

  • Salomon Namkosserena

  • Yves Yalibanda

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