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Title: Can community members identify tropical tree species for REDD+ carbon and biodiversity measurements?
Authors: Mingxu Zhao
Søren Brofeldt
Qiaohong Li
Jianchu Xu
Finn Danielsen
Simon Bjarke Lægaard Læssøe
Michael Køie Poulsen
Anna Gottlieb
James Franklin Maxwell
Ida Theilade
Keywords: Agricultural and Biological Sciences
Biochemistry, Genetics and Molecular Biology
Issue Date: 1-Nov-2016
Abstract: © 2016 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Biodiversity conservation is a required co-benefit of REDD+. Biodiversity monitoring is therefore needed, yet in most areas it will be constrained by limitations in the available human professional and financial resources. REDD+ programs that use forest plots for biomass monitoring may be able to take advantage of the same data for detecting changes in the tree diversity, using the richness and abundance of canopy trees as a proxy for biodiversity. If local community members are already assessing the above-ground biomass in a representative network of forest vegetation plots, it may require minimal further effort to collect data on the diversity of trees. We compare community members and trained scientists' data on tree diversity in permanent vegetation plots in montane forest in Yunnan, China. We show that local community members here can collect tree diversity data of comparable quality to trained botanists, at one third the cost. Without access to herbaria, identification guides or the Internet, community members could provide the ethno-taxonomical names for 95% of 1071 trees in 60 vegetation plots. Moreover, we show that the community-led survey spent 89% of the expenses at village level as opposed to 23% of funds in the monitoring by botanists. In participatory REDD+ programs in areas where community members demonstrate great knowledge of forest trees, community-based collection of tree diversity data can be a cost-effective approach for obtaining tree diversity information.
ISSN: 19326203
Appears in Collections:CMUL: Journal Articles

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