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DocumentManaging taxonomic and functional diversity is the key to sustain aboveground biomass and soil microbial diversity: A synthesis from long-term forest restoration of southern China
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Exploring the biodiversity-ecosystem functioning relationship is one of the central goals of ecological research. Restoration is essential for supporting key ecosystem functions such as aboveground biomass production and managing soil microbial diversity. However, the relative importance of functional versus taxonomic diversity in explaining aboveground biomass and soil microbial diversity during restoration is poorly understood. Here, we used a trait-based approach to test for the importance of multiple plant diversity attributes in regulating aboveground biomass and soil microbial diversity in four 30- years-old restored subtropical forests in southern China. High-throughput Illumina sequencing was applied for detecting fungal and bacterial diversity. We show that both taxonomic and functional diversities are significant and positive regulators of aboveground biomass; however, functional diversity (FD) was more important than taxonomic diversity (TD) in controlling aboveground biomass. FD had the strongest direct effect on aboveground biomass compared with TD, soil properties, and community weighted mean (CWM) traits. Our results further indicate that leaf and root morphological traits and traits related to the nutrient content in plant tissues showed acquisitive resource use strategy which influenced aboveground biomass. In contrast to aboveground biomass, taxonomic diversity explained more of the soil microbial diversity than the FD and soil properties. Prediction of fungal richness was better than that of bacterial richness. In addition, root traits explained more variation of soil microbes than the leaf traits. Our results suggest that both TD and FD play a role in shaping aboveground biomass and soil microbial diversity; but FD is more important in supporting aboveground biomass while TD for belowground microbial diversity. These results imply that enhancing TD and FD is important to restoring and managing degraded forest landscapes. Key words: Biodiversity-Ecosystem functions; soil microbial diversity, taxonomic diversity, functional diversity, forest restoration ID: 3486373 -
ArticleMucuri Springs project: a long-term vision for the preservation of water resources
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The Mucuri Springs Project aims to promote the rational use of natural resources in rural properties located in the Mucuri River Basin, in the northeast of Minas Gerais and the extreme south of Bahia States, Brazil, encouraging the conservation and recovery of springs, watercourses, and permanent preservation areas. The prject also seeks to promote the transition to agroecology, guiding farmers, farmer’s families, and rural communities for more sustainable production, combining food security, income generation, and delivering environmental education throughout public reading policies and training in rural communities. It started in three municipalities of Minas Gerais State, and in 2019 a study was carried out to map the most critical areas in terms of a greater potential for soil loss and for water production. Within over three years of experience, the project has served 49 rural communities and has involved 1,506 families, 351 of which are partners. Altogether, 1,468 springs were mapped and characterized, of which 402 were protected, totaling more than 200 hectares in process of restoration and over 30 thousand native seedlings planted. These areas are being monitored and, when needed, restoration techniques have been proposed. Regarding agroecological practices, until now, agroforestry systems have been implemented in two properties and more than 2,600 seedlings have been supplied and planted to enrich the productive yard and preservation areas. Rotational management was also implemented in eleven pasture areas, 3,30 hectares of family crops were fertilized organically, and 21,50 hectares were given techniques for soil recovery. The Project has the perspective of operating strategically on priority areas of the Mucuri River basin, seeking regular flow and reducing impacts of climate change for the region . Keywords: Partnerships, Deforestation and forest degradation, Economic Development, Education, Sustainable forest management. ID: 3487504 -
ArticleCoupling machine learning and forest simulations to promote the applicability of long-term forest projections under climate change perspectives
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Projecting forest dynamics is the foundation for sound decision support in adaptive forest management. However, due to their complexity, many forest modeling techniques addressing global changes in terrestrial ecosystems are limited to scientific applications. Integrating conventional research and artificial intelligence technologies has the potential to bridge research and practical use. In this study, we propose a Machine Learning (ML) framework that facilitates the implementation of long-term forest projections under climate change scenarios. Our approach combines ML and forest simulations based on process-based models to project forest dynamics. The goal is to leverage the complementary strength of process-based and state-of-the-art ML models to improve predictions at a reduced computational cost. We use environmental data and periodic field measurements at a national scale to train ML models to predict forest growth. By integrating process-based simulations we investigate how the additional variables can improve the prediction accuracy. The proposed hybrid ML framework identifies forest dynamics processes and drivers across spatial and temporal scales, contributing at many levels to the climate change adaptation: from increasing awareness of the climate-induced hazards to enhancing education and assisting in sustainable natural resource management and planning. Keywords: adaptive forest management, climate change, forest growth modelling, machine learning ID: 3623078
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