9 December 2016

New publication: Can community members identify tropical tree species for REDD+ carbon and biodiversity measurements?

This news is redirected by The Secretariat for Development Cooperation at SCIENCE from Department of Food and Resource Economics.

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.


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.


Original language English
Article number e0152061
Journal PloS one
Volume 11
Issue number 11
Number of pages 16
ISSN 1932-6203
State Published - 2016