Abstract
Many problems of modern forest ecology require analysis of the conjugated dynamics of processes occurring at different spatio-temporal scales of the functioning of plant communities and soils resulted from their interaction under the influence of all edaphic and anthropogenic factors. Mathematical models can be an effective tool for such analysis. The aim of this study is to present the implementation of new model system that makes it possible to reproduce in simulation experiments the spatial structure of forest phytocenoses formed by tree and grass-shrub layers, as well as associated heterogeneity of soil conditions and the diversity of ecological niches at different hierarchical levels. To determine the required level of detail of the spatial heterogeneity of forest biogeocenoses related to the processes of their multi-scale functioning, experimental studies were carried out on permanent sampling plots in the Prioksko-Terrasny State Natural Biosphere Reserve and in the «Kaluzhskie Zaseki» State Nature Reserve. The spatial structure of communities and related heterogeneity of ecological conditions were studied using traditional soil and geobotanical, as well as modern instrumental methods. The obtained data were used to construct the algorithms and to estimate the parameters of different blocks of the new system of models. The implementation of a spatially-explicit process-based system of models has shown its ability to reproduce the dynamics of forest ecosystems, taking into account the species composition and spatial structure of different layers of vegetation and the associated patchiness of soil conditions. Due to a wide range of interrelated ecosystem characteristics implemented in the system of models it is possible to simulate productivity, biological turnover of C and N, and the dynamics of forest ecosystems, taking into account their typical spatial structure at different scales. This improves understanding of ecosystem processes and their contribution to maintaining the sustainable functioning of forests, which can be used for predictive assessments of the efficiency of forest management techniques and in solving other forestry and environmental problems.
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