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International audience; This paper has two aspects. Mathematically, in the context of global optimization, it provides the existence of an optimum of a perturbed optimization problem that generalizes the celebrated Ekeland variational principle and equivalent formulations (Caristi, Takahashi), whenever the perturbations need not satisfy the triangle inequality. Behaviorally, it is a continuation of the recent variational rationality approach of stay (stop) and change (go) human dynamics. It gives sufficient conditions for the existence of traps in a changing environment. In this way it emphasizes even more the striking correspondence between variational analysis in mathematics and variational rationality in psychology and behavioral sciences.

The water resources system is in a vulnerable situation because of the influence of climate change and the changing environment. The adaptation mechanism was discussed through the analysis of the process of passive response, adaptation and even active adjustment of water resources system under changing environment. The adaptability of the water resources system can be described as natural resilience (NR) of natural system and artificial adaptation (AA) social (artificial) system. The natural resilience indexes were identified and analyzed from the aspects of water quantity, water quality and water ecology. The artificial adaptation indexes were identified and analyzed from the aspects of resource, eco-environment, socio-economic and technical factors. On this basis, the index system was constructed in accord with process mechanism of water resources system adaptability. Besides, to address the two-dimension factors of water resources system adaptability, a method of system analysis based on connection numbers–fuzzy risk matrix was proposed based on the theory of risk matrix. The synthesis interval [Apess, Aopt] of water resources system adaptability is obtained, by defining the pessimistic criterion when two-dimension factors meet the evaluation standard at the same time, and the optimistic criterion when either of the two-dimension factors meets evaluation standard. Finally, the case study in the Huaihe River basin in China was carried out. The results show that the adaptability level of water resources system in the Huaihe River basin expressed fluctuating uprising tendency in 2006–2015. The adaptability level is the lowest [1.856, 2.625] in 2009, the highest [2.500, 3.536] in 2015. HIGHLIGHTS First, the adaptation mechanism of water resources system under changing environment was discussed.; Second, the method of system analysis based on connection numbers–fuzzy risk matrix is proposed based on the theory of risk matrix.; Third, the synthesis interval [Apess, Aopt] of water resources system adaptability was obtained by defining pessimistic criterion and optimistic criterion.

Evaluating the changes in runoff and analyzing its attribution under the changing environment is of great significance to water resources management. In this study, eight hydrological stations at the outlets of tributaries of the Upstream Yangtze River are selected. Based on the observed runoff data from 1951 to 2013, the spatial-temporal characteristics in runoff change are identified from time series analysis. Our results show that runoff in the Upstream Yangtze River decreases significantly with a rate of −7.6 km3 per ten years in general. The most significant declines in runoff are observed in the mainstream, Minjiang River, Tuojiang River, and Jialing River, while slight increase in runoff is found in the source area of the Yangtze River. Furthermore, the effects on runoff change from climate change and human activities are evaluated using the Soil and Water Assessment Tool (SWAT) and modified Fixing-Changing (MFC) method at multiple scales. Our results suggest that the main contributions to runoff change are from climate change variabilities (70%), land use/cover change (LUCC, 10%), and other human influence (20%). When examined at different spatial and temporal scales, climate change always appears to be the main cause of runoff change, although its contribution decreases over time. HIGHLIGHTS Runoff in the Upstream Yangtze River decreased significantly.; Runoff decline is attributed to climate change, LUCC and other human influence.; Climate change appears to be the main cause of runoff change at different spatial and temporal scales.; Contribution of climate change decreases over time.; Precipitation and temperature are the main meteorological causes of runoff change.

The environment changes constantly at various time scales and, in order to survive, species need to keep adapting. Whether these species succeed in avoiding extinction is a major evolutionary question. Using a multilocus evolutionary model of a mutation‐limited population adapting under strong selection, we investigate the effects of the frequency of environmental fluctuations on adaptation. Our results rely on an “adaptive‐walk” approximation and use mathematical methods from evolutionary computation theory to investigate the interplay between fluctuation frequency, the similarity of environments, and the number of loci contributing to adaptation. First, we assume a linear additive fitness function, but later generalize our results to include several types of epistasis. We show that frequent environmental changes prevent populations from reaching a fitness peak, but they may also prevent the large fitness loss that occurs after a single environmental change. Thus, the population can survive, although not thrive, in a wide range of conditions. Furthermore, we show that in a frequently changing environment, the similarity of threats that a population faces affects the level of adaptation that it is able to achieve. We check and supplement our analytical results with simulations.

Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

With a shifting climate pattern and enhancement of human activities, coastal areas are exposed to threats of groundwater environmental issues. This work takes the eastern coast of Laizhou Bay as a research area to study the response of a coastal groundwater system to natural and human impacts with a combination of statistical, hydrogeochemical, and fuzzy classification methods. First, the groundwater level dynamics from 1980 to 2017 were analyzed. The average annual groundwater level dropped 13.16 m with a descent rate of 0.379 m/a. The main external environmental factors that affected the groundwater level were extracted, including natural factors (rainfall and temperature), as well as human activities (irrigated area, water-saving irrigated area, sown area of high-water-consumption crops, etc.). Back-propagation artificial neural network was used to model the response of groundwater level to the above driving factors, and sensitivity analysis was conducted to measure the extent of impact of these factors on groundwater level. The results verified that human factors including irrigated area and water-saving irrigated area were the most important influencing factors on groundwater level dynamics, followed by annual precipitation. Further, groundwater samples were collected over the study area to analyze the groundwater hydrogeochemical signatures. With the hydrochemical diagrams and ion ratios, the formation of groundwater, the sources of groundwater components, and the main hydrogeochemical processes controlling the groundwater evolution were discussed to understand the natural background of groundwater environment. The fuzzy C-means clustering method was adopted to classify the groundwater samples into four clusters based on their hydrochemical characteristics to reveal the spatial variation of groundwater quality in the research area. Each cluster was spatially continuous, and there were great differences in groundwater hydrochemical and pollution characteristics between different clusters. The natural and human factors resulted in this difference were discussed based on the natural background of the groundwater environment, and the types and intensity of human activity.

A fast pacing climate change exacerbates the multitude of human impacts. Several reports highlighted (e.g., Magurran, 2016; Lewis et al., 2017; Diffenbaugh et al., 2018) that the degree of impact imposed by human activities on all the ecosystem components are unprecedented. We entered a new era, the Anthropocene (Malhi, 2017; Laurence, 2019). In this new era, soil and water resources are exposed to tremendous pressure, and our life depends on it. Soils and water environments (e.g., freshwater, coastal and marine) provide a wide range of direct and indirect regulating (e.g., carbon sequestration, climate regulation, water purification and storage, flood retention), provisioning (e.g., food, fiber, wood), and cultural (e.g., education, recreation, landscape aesthetics) ecosystem services (ES). Therefore, both soils and water are the key elements to humankind (Barbier, 2017; Pereira et al., 2018; Jorda-Capdevila et al., 2019). Soils and water environments interact in a continuum through very complex processes and feedbacks. For example, agriculture practices have implications on land degradation, water bodies eutrophication and pollution. Here, we present numerous studies focusing on how land abandonment (Tarolli et al., 2019), urbanization (Ferreira et al., 2018), agriculture intensification (Panagos et al., 2016), mining (Zibret et al., 2018), warfare activities in relation to land degradation (Certini et al., 2013) and climate change (Plaza et al., 2019) are accelerating soil and water resources degradation, and reducing their capacity to provide ES in quality and quantity. These drivers of change either interact individually or coupled at different spatio-temporal scales., The guest editors are extremely thankful to prof. José Luis Domingo Roig for the support in the organization of this special number. We want to acknowledge the authors and reviewers for their work, to Jia Yang, Yixuan Zhao and Nikita Deep. Paulo Pereira was supported by the project A09.3.3-LMT-K-712-01-0104 Lithuanian National Ecosystem Services Assessment and Mapping (LINESAM) is funded by the European Social Fund according to the activity “Improvement of researchers” qualification by implementing world-class R&D projects.

Conventional reservoir operation rule curves are based on the assumption of hydrological stationarity. The aggravating non-stationarity under the changing environment rocked this foundation. The hedging theory is one of the options for adaptive operation based on hydrological forecasts, which can provide a practical tool for optimal reservoir operation under a changing environment. However, the connections between hedging theory and rule curves are not clear. This paper establishes the linkage of rule curves and hedging theory by analyzing three fundamental problems surrounding the design of conventional rule curves, namely the law and design of water supply rule curves, the determination of flood control storage, and the division of refill and drawdown circle. The general interpretation of the conventional water supply rule curves with hedging theory is conducted. Both the theoretical analyses and the Danjiangkou Reservoir case study reveal that, based on the historical records, the rule curves can be interpreted as a specific expression of hedging theory. This intrinsic linkage allows us to propose a more general and scientific method of updating rule curves in the context of non-stationarity. On this basis, the rule-curve-based climate adaptation strategies are figured out using hedging theory. This research is helpful for practical adaptive operation of reservoirs in the changing environment.

International audience; We use individual-based stochastic simulations and analytical deterministic predictions to investigate the interaction between drift, natural selection and gene flow on the patterns of local adaptation across a fragmented species' range under clinally varying selection. Migration between populations follows a stepping-stone pattern and density decreases from the centre to the periphery of the range. Increased migration worsens gene swamping in small marginal populations but mitigates the effect of drift by replenishing genetic variance and helping purge deleterious mutations. Contrary to the deterministic prediction that increased connectivity within the range always inhibits local adaptation, simulations show that low intermediate migration rates improve fitness in marginal populations and attenuate fitness heterogeneity across the range. Such migration rates are optimal in that they maximize the total mean fitness at the scale of the range. Optimal migration rates increase with shallower environmental gradients, smaller marginal populations and higher mutation rates affecting fitness.

This paper establishes a water resources vulnerability framework based on sensitivity, natural resilience and artificial adaptation, through the analyses of the four states of the water system and its accompanying transformation processes. Furthermore, it proposes an analysis method for water resources vulnerability based on connection entropy, which extends the concept of contact entropy. An example is given of the water resources vulnerability in Anhui Province of China, which analysis illustrates that, overall, vulnerability levels fluctuated and showed apparent improvement trends from 2001 to 2015. Some suggestions are also provided for the improvement of the level of water resources vulnerability in Anhui Province, considering the viewpoint of the vulnerability index.

International audience; Population persistence in a new and stressful environment can be influenced by the plastic phenotypic responses of individuals to this environment, and by the genetic evolution of plasticity itself. This process has recently been investigated theoretically, but testing the quantitative predictions in the wild is challenging because (i) there are usually not enough population replicates to deal with the stochasticity of the evolutionary process, (ii) environmental conditions are not controlled, and (iii) measuring selection and the inheritance of traits affecting fitness is difficult in natural populations. As an alternative, predictions from theory can be tested in the laboratory with controlled experiments. To illustrate the feasibility of this approach, we briefly review the literature on the experimental evolution of plasticity, and on evolutionary rescue in the laboratory, paying particular attention to differences and similarities between microbes and multicellular eukaryotes. We then highlight a set of questions that could be addressed using this framework, which would enable testing the robustness of theoretical predictions, and provide new insights into areas that have received little theoretical attention to date.

Global change calls for an understanding of how temperature and flow regimes influence aquatic ecosystems. Fish assemblages are a major component of river ecosystems and are thought to exhibit more integrative informative responses than single species to environmental variations, whether rare and sudden or gradual and continuous. The use of long-term datasets is thus of primary importance, allied to statistical modeling. For each of three previously identified species clusters, we performed Bayesian variable selection and inference within a hierarchical log Poisson Generalized Linear Model using a spike and slab normal prior to pinpoint which subset of environmental variables is of importance for each fish assemblage. Fish counts from electrofishing experiments are known to provide overdispersed data and, not surprisingly, the contribution of recorded environmental effects is found to be weak compared with those of other intra-assemblage sources of variation. The posterior distribution of the regression parameters is in coherence with what was expected from biological knowledge of the three species clusters. In particular, thermophilic species tend to benefit from warmer waters, whereas the recruitment of cold water species decreases due to global warming effects. Our study provides an example of the advantages of hierarchical modeling for quantifying interspecies ecological effects and selecting common environmental variables of importance.

Background Carassius gibelio, a cyprinid fish from Eurasia, has the ability to reproduce both sexually and asexually. This fish is also known as an invasive species which colonized almost all continental Europe, most likely originating from Asia and Eastern Europe. Populations of both sexually and asexually reproducing individuals exist in sympatry. In this study we try to elucidate the advantages of such a mixed type of reproduction. We investigate the dynamics of two sympatric populations with sexual and asexual reproduction in a periodically fluctuating environment. We define an individual-based computational model in which genotypes are represented by L loci, and the environment is composed of L resources for which the two populations compete. Results Our model demonstrates advantageous population dynamics where the optimal percentage of asexual reproduction depends on selection strength, on the number of selected loci and on the timescale of environmental fluctuations. We show that the sexual reproduction is necessary for "generating" fit genotypes, while the asexual reproduction is suitable for "amplifying" them. The simulations show that the optimal percentage of asexual reproduction increases with the length of the environment stability period and decrease with the strength of the selection and the number of loci. Conclusions In this paper we addressed the advantages of a mixed type of sexual and asexual reproduction in a changing environment and explored the idea that a species that is able to adapt itself to environmental fluctuation can easily colonize a new habitat. Our results could provide a possible explanation for the rapid and efficient invasion of species with a variable ratio of sexual and asexual reproduction such as Carassius gibelio.