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Earthworms can stimulate microbial activity and hence greenhouse gas (GHG) emissions from soils. However, the extent of this effect in the presence of plants and soil moisture fluctuations, which are influenced by earthworm burrowing activity, remains uncertain. Here, we report the effects of earthworms (without, anecic, endogeic, both) and plants (with, without) on GHG (CO2, N2O) emissions in a 3-month greenhouse mesocosm experiment simulating a simplified agricultural context. The mesocosms allowed for water drainage at the bottom to account for the earthworm engineering effect on water flow during two drying-wetting cycles. N2O cumulative emissions were 34.6% and 44.8% lower when both earthworm species and only endogeic species were present, respectively, and 19.8% lower in the presence of plants. The presence of the endogeic species alone or in combination with the anecic species slightly reduced CO2 emissions by 5.9% and 11.4%, respectively, and the presence of plants increased emissions by 6%. Earthworms, plants and soil water content interactively affected weekly N2O emissions, an effect controlled by increased soil dryness due to drainage via earthworm burrows and mesocosm evapotranspiration. Soil macroporosity (measured by X-ray tomography) was affected by earthworm species-specific burrowing activity. Both GHG emissions decreased with topsoil macropore volume, presumably due to reduced moisture and microbial activity. N2O emissions decreased with macropore volume in the deepest layer, likely due to the presence of fewer anaerobic microsites. Our results indicate that, under experimental conditions allowing for plant and earthworm engineering effects on soil moisture, earthworms do not increase GHG emissions, and endogeic earthworms may even reduce N2O emissions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ganault 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.)

While potassium fertilization increases growth yield in Brazilian eucalyptus plantations, it could also increase water requirements, making trees more vulnerable to drought. Sodium fertilization, which has been shown to promote eucalyptus growth compared to K-deficient trees, could partially mitigate this adverse effect of potassium. However, little is known about the influence of K and Na fertilization on the tree metabolic response to water deficit. The aim of the present study was thus to analyze the transcriptome of leaves sampled from Eucalyptus grandis trees subjected to 37% rainfall reduction, and fertilized with potassium (K), sodium (Na), compared to control trees (C). The multifactorial experiment was set up in a field with a throughfall exclusion system. Transcriptomic analysis was performed on leaves from two-year-old trees, and data analyzed using multifactorial statistical analysis and weighted gene co-expression network analysis (WGCNA). Significant sets of genes were seen to respond to rainfall reduction, in interaction with K or Na fertilization, or to fertilization only (regardless of the water supply regime). The genes were involved in stress signaling, primary and secondary metabolism, secondary cell wall formation and photosynthetic activity. Our focus on key genes related to cation transporters and aquaporins highlighted specific regulation of ion homeostasis, and plant adjustment to water deficit. While water availability significantly affects the transcriptomic response of eucalyptus species, this study points out that the transcriptomic response is highly dependent on the fertilization regime. Our study is based on the first large-scale field trial in a tropical region, specifically designed to study the interaction between water availability and nutrition in eucalyptus. To our knowledge, this is the first global transcriptomic analysis to compare the influence of K and Na fertilization on tree adaptive traits in water deficit conditions., Competing Interests: The authors have declared that no competing interests exist.

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., Competing Interests: The authors have declared that no competing interests exist.

Intensive monoculture coffee farms quickly expanded in Yunnan Province in the 1990's and 2000's. In 2012, local authorities in Pu'er and Xishuangbanna Prefectures, the main coffee producing centre in the province, initiated a large-scale conversion program of these farms towards coffee-agroforestry systems to promote "ecologically-friendly coffee". Shade tree inventories and household interviews were conducted in these two prefectures to characterize coffee farms and the Local Ecological Knowledge (LEK) of farmers on the provision of ecosystem services by associated tree species. This study on newly emerging coffee farming systems revealed a high level of tree species diversity at both farm and landscape levels despite the previous dominance of intensive coffee monoculture and the large-scale distribution of a limited number of shade tree species by the government. 162 tree species were encountered during farm inventories, out of which the community of coffee farmers was able to rank 30 against 9 ecosystem services and disservices. This study reveals that this LEK is a type of hybrid knowledge that still relies mostly on traditional knowledge of tree species combined with experience acquired from newly-implemented coffee-agroforestry practices. This study also pointed out knowledge gaps regarding the impact of mature trees on coffee yield, coffee quality and pest control. The participatory approach resulted in the identification of non-promoted species with a high potential to provide locally relevant ecosystem services in coffee-agroforestry systems. These results lead to the upgrade of an online tool (www.shadetreeadvice.org) which allows extension services generating lists of recommended shade tree species tailored to the local ecological context and individual farmers' needs. This tool will benefit farmers' livelihood, support landscape health and contribute to the sustainability of the emerging Yunnan coffee agriculture sector., Competing Interests: The authors have declared that no competing interests exist.