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AbstractLarge tropical trees and a few dominant species were recently identified as the main structuring elements of tropical forests. However, such result did not translate yet into quantitative approaches which are essential to understand, predict and monitor forest functions and composition over large, often poorly accessible territories. Here we show that the above-ground biomass (AGB) of the whole forest can be predicted from a few large trees and that the relationship is proved strikingly stable in 175 1-ha plots investigated across 8 sites spanning Central Africa. We designed a generic model predicting AGB with an error of 14% when based on only 5% of the stems, which points to universality in forest structural properties. For the first time in Africa, we identified some dominant species that disproportionally contribute to forest AGB with 1.5% of recorded species accounting for over 50% of the stock of AGB. Consequently, focusing on large trees and dominant species provides precise information on the whole forest stand. This offers new perspectives for understanding the functioning of tropical forests and opens new doors for the development of innovative monitoring strategies.

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate’s temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE. PLoS ONE, 14 (2) ISSN:1932-6203

Smallholder farms represent the largest population of dairy cattle farms in Indonesia. Dairy activities can play an important role to secure the livelihood of smallholder farms. However, small farms face several constraints and challenges to be sustainable in the future. To assess the sustainability of smallholder dairy farms and to understand in what conditions farms are more sustainable, we conducted a study in two districts of the West Java Province. Our method was based on participatory meetings that allowed us to identify “critical features” of the local farming systems, and associated indicators. From discussions with local stakeholders, we proposed 6 “strategic indicators” of sustainability. Five of those indicators were related to the social and economic dimensions of sustainability, which appeared to be crucial in the local context. To assess the sustainability of farms based on those 6 indicators, we collected secondary data from the local cooperative, and carried out a formal field survey to 355 farmers from May to August 2015. Results showed that the most sustainable farms were those who had highest capital and diversified activity. Farms which had low capital but had additional activity were more sustainable than specialized ones. Whereas the level of farm income appears to be linked directly to farm capital, pluriactivity contributes to reduce risks related to dairy business and to gain benefit from synergies between activities. In the future, policies and projects to enhance farm capital and farmers’ pluriactivity will be needed to support the sustainability of smallholder farms.

This study investigates four decades of socio-economic and environmental change in a shifting cultivation landscape in the northern uplands of Laos. Historical changes in land cover and land use were analyzed using a chronological series of remote sensing data. Impacts of landscape change on local livelihoods were investigated in seven villages through interviews with various stakeholders. The study reveals that the complex mosaics of agriculture and forest patches observed in the study area have long constituted key assets for the resilience of local livelihood systems in the face of environmental and socio-economic risks. However, over the past 20 years, a process of segregating agricultural and forest spaces has increased the vulnerability of local land users. This process is a direct outcome of policies aimed at increasing national forest cover, eradicating shifting cultivation and fostering the emergence of more intensive and commercial agricultural practices. We argue that agriculture-forest segregation should be buffered in such a way that a diversity of livelihood opportunities and economic development pathways can be maintained.