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Uneven soil depth and low water availability are the key limiting factors to vegetation restoration and reconstruction in limestone soils such as in vulnerable karst regions. Belowground competition will possibly increase under limited soil resources. Here, we investigate whether low resource availability (including shallow soil, low water availability, and shallow soil and low water availability combined) stimulates the competition between grasses with different root systems in karst soil, by assessing their growth response, biomass allocation, and morphological plasticity. In a full three-way factorial blocked design of soil depth by water availability by neighbor identity, we grew Festuca arundinacea (deep-rooted) and Lolium perenne (shallow-rooted) under normal versus shallow soil depth, high versus low water availability, and in monoculture (conspecific neighbor) versus mixture (neighbor of the other species). The key results were as follows: (1) total biomass and aboveground biomass in either of the species decreased with reduction of resources but were not affected by planting patterns (monoculture or mixture) even at low resource levels. (2) For F. arundinacea, root biomass, root mass fraction, total root length, and root volume were higher in mixture than in monoculture at high resource level (consistent with resource use complementarity), but lower in mixture than in monoculture at low resource levels (consistent with interspecific competition). In contrast for L. perenne, either at high or low resource level, these root traits had mostly similar values at both planting patterns. These results suggest that deep-rooted and shallow-rooted plant species can coexist in karst regions under current climatic regimes. Declining resources, due to shallow soil, a decrease in precipitation, or combined shallow soil and karst drought, increased the root competition between plants of deep-rooted and shallow-rooted species. The root systems of deep-rooted plants may be too small to get sufficient water and nutrients from dry, shallow soil, while shallow-rooted plants will maintain a dominant position with their already adaptive strategy in respect of root biomass allocation and root growth.

Promoting the use of solar photovoltaic (PV) systems in global cities can be an effective way to cope with severe environmental problems caused by the consuming of fossil fuels. However, a complex urban environment challenges the effective use of PV systems for practical applications. Essentially, this is a spatial optimization problem, where the goal is maximizing the harvesting of solar energy while minimizing occupied urban surfaces. To address this problem, this paper proposes three hierarchical optimizations. First, computational optimization provides a parallel architecture for an established 3D solar estimation model to achieve spatially scalable computation with high spatio-temporal resolution. Second, priority optimization determines the use of different urban partitions considering various constraints. Third, capacity optimization analyzes the spatial and quantitative distribution of solar potential, constrained by the smallest solar irradiation and the minimum surface area to be used. The overall optimization framework is then set to obtain the minimum PV installation capacity required to meet the real demand with the identification of urban surfaces to be equipped with PV modules. By using smart meter data with high temporal resolution in the city of Bologna, Italy, our analysis not only provides executable plans to meet the real demand but also reveals that rebalance and storage capacity are needed to achieve a real-time self-supportive architecture. The proposed analytic and optimization framework can promote distributed PV systems in urban areas and facilitate energy transition adapted to a variety of applications.