Who Wants to Save the Forest? Characterizing Community-Led Monitoring in Prey Lang, Cambodia.

Community monitoring is believed to be successful only where there is sustained funding, legislation for communities to enforce rules, clear tenure rights, and an enabling environment created by the state. Against this backdrop, we present the case of an autonomous grassroots-monitoring network that took the initiative to protect their forest, in a context, where no external incentives and rule enforcement power were provided. The aim was to analyze the socio-demographic and economic backgrounds, motivations and achievements of forest monitors, compared to non-monitors in the same communities. A total of 137 interviews were conducted in four villages bordering Prey Lang forest in Cambodia. We used binary logit models to identify the factors that influenced the likelihood of being a monitor. Results show that there were few (22%, n = 30) active monitors. Active monitors were intrinsically motivated forest-users, and not specifically associated with a particular gender, ethnicity, or residence-time in that area. The most common interventions were with illegal loggers, and the monitors had a general feeling of success in stopping the illegal activities. Most (73%, n = 22) of them had been threatened by higher authorities and loggers. Our results show that despite the lack of power to enforce rules, absence of external funding and land-ownership rights, and enduring threats of violence and conflicts, autonomous community monitoring may take place when community members are sufficiently motivated by the risk of losing their resources.

Can Community Members Identify Tropical Tree Species for REDD+ Carbon and Biodiversity Measurements?

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.

Vietnam's forest transition in retrospect: demonstrating weaknesses in business-as-usual scenarios for REDD.

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.