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The need for understanding the factors that trigger human responses to climate change has opened inquiries on the role of indigenous and local ecological knowledge (ILK) in facilitating or constraining social adaptation processes. Answers to the question of how ILK is helping or limiting smallholders to cope with increasing disturbances to the local hydro-climatic regime remain very limited in adaptation and mitigation studies and interventions. Herein, we discuss a case study on ILK as a resource used by expert farmer-fishers (locally known as Caboclos) to cope with the increasing threats on their livelihoods and environments generated by changing flood patterns in the Amazon delta region. While expert farmer-fishers are increasingly exposed to shocks and stresses, their ILK plays a key role in mitigating impacts and in strengthening their adaptive responses that are leading to a process of incremental adaptation (PIA). We argue that ILK is the most valuable resource used by expert farmer-fishers to adapt the spatial configuration and composition of their land-/resource-use systems (agrodiversity) and their produced and managed resources (agrobiodiversity) at landscape, community and household levels. We based our findings on ILK on data recorded for over the last 30 years using detailed ethnographic methodologies and multitemporal landscape mapping. We found that the ILK of expert farmer-fishers and their “tradition of change” have facilitated the PIA to intensify a particular production system to optimize production across a broad range of flood conditions and at the same time to manage or conserve forests to produce resources and services.

More and more effective research is needed to help address complex sustainability problems. Many research approaches have adopted more transdisciplinary characteristics as a way to improve effectiveness. However, empirical evidence of the extent to which and how transdisciplinary research design and implementation contribute to (more) effective scientific and social outcomes remains limited. This paper reports a comparative analysis of five research-for-development projects implemented in Peru and Indonesia to: characterize the extent to which projects employed transdisciplinary principles; assess the extent to which and how intended project outcomes were achieved; analyze the relationship between transdisciplinary research approaches and outcomes; and provide lessons from the experience of using a theory-based approach to evaluate a set of case studies. Our analysis demonstrates that the projects employing more transdisciplinary principles in their design and implementation make more diverse contributions and have a greater breadth of influence.

Abstract. Extreme climatic events, such as droughts and heat stress induce anomalies in ecosystem-atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30 year time period. We then used FLUXNET eddy-covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they down-regulated GPP, resulting in a moderate reduction of the ecosystem’s carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco, and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a down-regulation after about two weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies, but as a novelty generalizes these findings at the global scale. Specifically, we find that the different response functions of the two antipodal land-atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.

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