Plant diversity moderates drought stress in grasslands: Implications from a large real-world study on 13C natural abundances

Land-use change and intensification play a key role in the current biodiversity crisis. The resulting species loss can have severe effects on ecosystem functions and services, thereby increasing ecosystem vulnerability to climate change. We explored whether land-use intensification (i.e. fertilization intensity), plant diversity and other potentially confounding environmental factors may be significantly related to water use (i.e. drought stress) of grassland plants. Drought stress was assessed using δ13C abundances in aboveground plant biomass of 150 grassland plots across a gradient of land-use intensity. Under water shortage, plants are forced to increasingly take up the heavier 13C due to closing stomata leading to an enrichment of 13C in biomass. Plants were sampled at the community level and for single species, which belong to three different functional groups (one grass, one herb, two legumes). Results show that plant diversity was significantly related to the δ13C signal in community, grass and legume biomass indicating that drought stress was lower under higher diversity, although this relation was not significant for the herb species under study. Fertilization, in turn, mostly increased drought stress as indicated by more positive δ13C values. This effect was mostly indirect by decreasing plant diversity. In line with these results, we found similar patterns in the δ13C signal of the organic matter in the topsoil, indicating a long history of these processes. Our study provided strong indication for a positive biodiversity-ecosystem functioning relationship with reduced drought stress at higher plant diversity. However, it also underlined a negative reinforcing situation: as land-use intensification decreases plant diversity in grasslands, this might subsequently increases drought sensitivity. Vice-versa, enhancing plant diversity in species-poor agricultural grasslands may moderate negative effects of future climate change.

Fog-water collection for community use

Fog is a potential source of water that could be exploited using the innovative technology of fog collection. Naturally, the potential of fog has proven its significance in cloud forests that are thriving from fog interception. Historically, the remains of artificial structures in different countries prove that fog has been collected as an alternative and/or supplementary water source. In the beginning of the 19th century, fog collection was investigated as a potential natural resource. After the mid-1980s, following success in Chile, fog-water collection commenced in a number of developing countries. Most of these countries are located in arid and semi-arid regions with topographic and climatic conditions that favour fog-water collection. This paper reviews the technology of fog collection with initial background information on natural fog collection and its historical development. It reviews the climatic and topographic features that dictate fog formation (mainly advection and orographic) and the innovative technology to collect it, focusing on the amount collected, the quality of fog water, and the impact of the technology on the livelihoods of beneficiary communities. By and large, the technology described is simple, cost-effective, and energy-free. However, fog-water collection has disadvantages in that it is seasonal, localised, and the technology needs continual maintenance. Based on the experience in several countries, the sustainability of the technology could be guaranteed if technical, economic, social, and management factors are addressed during its planning and implementation.

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0–10 %, an increase in soil loss by 25–35 %, and an increase in N-leaching by 30–45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to croplands.

SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization

SOMS is a general surrogate-based multistart algorithm, which is used in combination with any local optimizer to find global optima for computationally expensive functions with multiple local minima. SOMS differs from previous multistart methods in that a surrogate approximation is used by the multistart algorithm to help reduce the number of function evaluations necessary to identify the most promising points from which to start each nonlinear programming local search. SOMS’s numerical results are compared with four well-known methods, namely, Multi-Level Single Linkage (MLSL), MATLAB’s MultiStart, MATLAB’s GlobalSearch, and GLOBAL. In addition, we propose a class of wavy test functions that mimic the wavy nature of objective functions arising in many black-box simulations. Extensive comparisons of algorithms on the wavy testfunctions and on earlier standard global-optimization test functions are done for a total of 19 different test problems. The numerical results indicate that SOMS performs favorably in comparison to alternative methods and does especially well on wavy functions when the number of function evaluations allowed is limited.