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The article discusses the transformation processes that have taken place in the management of enterprises in extremely changing conditions of their functioning, which is war. For sustainability and development, these processes require, in particular, rapid adaptation, revision of the following: paradigms of management, security, personnel policy, information flows, business processes and business models; implementation of the principles of inclusiveness and accessibility, cost optimization; introduction of modern digital technologies; increase of productivity. The aim of the article is to substantiate and systematize the transformation processes of enterprise management during the wartime. To achieve this goal, the following tasks have been performed: the main problems in the management of enterprises that hinder their development in wartime have been systematized and characterized; the main blocks that unite the transformation processes in the management of enterprises during wartime are allocated on the basis of an analysis of the experience of various Ukrainian and international companies, this will allow other economic entities to lead their enterprises to sustainability, success, and efficiency. As result of the study, the authors determined that the main challenge in management in peacetime and in wartime is the rapid variability of factors of external influence, which leads to ultra-risky managerial decisions. During the war, it is difficult to foresee all sources of risks in management decisions that affect the business processes of the enterprise. At that, challenges appear quickly due to the variability of external factors, which leads to the rapid adaptation of managerial decisions and the need for their urgent solution. The loss of planning ability directly affects the decrease in the efficiency of the enterprise. Therefore, enterprises are trying to form certain patterns to stabilize their activities in extremely changing conditions of their functioning. Obviously, management has become the key instrument in this. The ability to make management decisions quickly, adapt to changing external factors, and implement high-quality communication with the team are crucial for success. Despite the lack of a one-size-fits-all approach, adhering to these core principles helps leaders navigate wartime and lead their businesses to sustainability, success, and efficiency.

Phenology is a critical mirror reflecting vegetation growth and has a major impact on terrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that has experienced considerable vegetation changes. However, understanding the dynamics of vegetation phenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces. This study combined the multi-climatic and anthropogenic datasets to characterize the interactions between phenology shifts and environmental variables. The principal findings were as follows: (1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness (R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the end of the growing season) delays in both the southeastern and northwestern regions. (2) SOS and EOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes vary depending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS, within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong response to the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), and temperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition, agriculture and urbanization play a significant role in shaping the spatial variations of SOS. These findings provide a basis for a systematic understanding of the processes that affect vegetation growth, which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems. [ABSTRACT FROM AUTHOR]

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Changing environment markedly shapes plant diversity and vegetation across all spatial scales. It leads to innumerable local, regional and sometimes global changes in plant communities. Here, we present eleven full-text research articles of the Special Issue "Plant communities in changing environment", which focuses on the evaluation of plant diversity and vegetation changes in different plant communities. We conclude that the results of all research studies show that plants, habitat and functional diversity are strongly conditioned in space and time by various factors, including climate, water regime, snow conditions, and human activities. These changes are also among most significant potential threats for plant diversity. [ABSTRACT FROM AUTHOR]

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194 days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments. [ABSTRACT FROM AUTHOR]

Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff (RR) simulation methods under a changing environment scenario, several ML-based RR simulation models implemented in novel continuous and non-real-time correction manners were constructed. The proposed models incorporated categorical boosting (CatBoost), a multi-hidden-layer BP neural network (MBP), and a long short-term memory neural network (LSTM) as the input–output simulators. This study focused on the Dongwan catchment of the Yiluo River Basin to carry out daily RR simulations for the purpose of verifying the model's applicability. Model performances were evaluated based on statistical indicators such as the deterministic coefficient, peak flow error, and runoff depth error. The research findings indicated that (1) ML-based RR simulation by using a consistency-disrupted dataset exhibited significant bias. During the validation phase for the three models, the R2 index decreased to around 0.6, and the peak flow error increased to over 20%. (2) Identifying data consistency transition points through data analysis and conducting staged RR simulations before and after the transition point can improve simulation accuracy. The R2 values for all three models during both the baseline and change periods were above 0.85, with peak flow and runoff depth errors of less than 20%. Among them, the CatBoost model demonstrated superior phased simulation accuracy and smoother simulation processes and closely matched the measured runoff processes across high, medium, and low water levels, with daily runoff simulation results surpassing those of the BP neural network and LSTM models. (3) When simulating the entire dataset without staged treatment, it is impossible to achieve good simulation results by adopting uniform extraction of the training samples. Under this scenario, the MBP exhibited the strongest generalization capability, highest prediction accuracy, better algorithm stability, and superior simulation accuracy compared to the CatBoost and LSTM simulators. This study offers new ideas and methods for enhancing the runoff simulation capabilities of machine learning models in changing environments. [ABSTRACT FROM AUTHOR]

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Phenology is a critical mirror reflecting vegetation growth and has a major impact on terrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that has experienced considerable vegetation changes. However, understanding the dynamics of vegetation phenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces. This study combined the multi-climatic and anthropogenic datasets to characterize the interactions between phenology shifts and environmental variables. The principal findings were as follows: (1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness (R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the end of the growing season) delays in both the southeastern and northwestern regions. (2) SOS and EOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes vary depending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS, within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong response to the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), and temperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition, agriculture and urbanization play a significant role in shaping the spatial variations of SOS. These findings provide a basis for a systematic understanding of the processes that affect vegetation growth, which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems.

The Jinsha River basin (JRB), known as China's largest hydropower base, has been facing a surge in hydrological drought occurrences in the past several years. This study used the drought index model and soil and water assessment tool (SWAT) hydrological model to uncover the linkages between meteorological and hydrological drought using long-term datasets in the JRB. The results revealed that: (1) Over the past six decades, the JRB has experienced recurrent meteorological droughts, with the upper reaches being the most affected, accounting for a frequency of 17.5%. However, the frequency of drought in the middle and lower reaches has shown a marked increase in the last 15 years. (2) The frequency of hydrological drought in the JRB has been on the rise over the past six decades, with a particularly notable increase observed in the last two decades. Furthermore, a noticeable upward trend has been observed in the duration of these hydrological droughts. (3) The propagation durations from meteorological drought to hydrological drought exhibited noticeable seasonal differences in the JRB. The transmission duration during the flood season was shorter, whereas in the dry season, it was more protracted. Additionally, the connection between meteorological drought and hydrological drought demonstrates a weakening trend. The findings of this study hold significant implications for crafting an efficient reservoir dispatching strategy to safeguard the water security of the JRB. [ABSTRACT FROM AUTHOR]

Precipitation (P) and temperature (T) are key components of the hydrometeorological system, and their dependence structures exhibit significant dynamic changes, including non-stationary behavior, in response to environmental variations. These changes affect local hydrological processes and impact the predictability of the hydrometeorological system. However, the dynamics of dependence structures among meteorological factors during corresponding and adjacent seasons, as well as their underlying causes, have not been fully revealed. Therefore, this study comprehensively explored the dynamics of the precipitation-temperature dependence structure (PTDS) and temperature-temperature dependence structure (TTDS), and their possible causes. Firstly, non-stationary of PTDS was identified using a copula model. Then the main drivers of PTDS were determined by the random forest (RF) model and variable projection importance (VIP) criteria. These drivers include both conventional factors such as local meteorological factors (e.g., P, T, wind speed (WS), vapor pressure, relative humidity and sunshine duration (SD)) and teleconnection factors (e.g., Sunspots, the Arctic Oscillation, Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO)). Additionally, the normalized difference vegetation index (NDVI) was used to investigate the response of dependence structure to vegetation dynamics. Finally, the ridge regression model was applied to construct driver models for the dynamics of dependence structures. The Loess Plateau was selected as the study area because of its high ecological sensitivity and typical human afforestation activities. The results show that: (1) non-stationarity in the PTDS occurred in different seasons and at various stations; (2) the primary drivers of PTDS and TTDS dynamics are predominantly local meteorological factors; (3) there is a strong correlation between SD and ENSO, and the impacts of PDO on local meteorological factors (WS and T) play a crucial role in driving the PTDS dynamics; and (4) NDVI is the main driver, primarily influencing T and ultimately affecting the dynamics of PTDS and TTDS. These findings suggest that there are significant ecological impacts through radiative or non-radiative feedback mechanisms under warming scenarios. Overall, this study provides new insights into the drivers and mechanisms behind the dynamics of dependence structures among meteorological elements. It contributes to a deeper understanding of the changing local hydrometeorological processes. [ABSTRACT FROM AUTHOR]

Water quality parameters are key indicators of quality of water and can indicate the algal biomass and eutrophication in lakes. Therefore, this study spectrally inverted and evaluated the water quality of the Poyang Lake in China by analysing the differences between the measured water quality parameters and the observed image spectra of Sentinel‐2 remote sensing. This analysis was done using statistical regression models (SRMs) and various machine learning models (e.g., support vector machine [SVM] and random forest [RF]). The following major conclusions were drawn: (1) nutrients accumulated more in summer (June–August) than in winter (December–February). For example, in local areas of the main river, total nitrogen (TN) concentration reached 3.4 mg/L in summer of 2016, whereas total phosphorus (TP) concentrations were below 0.052 mg/L in winters of 2016 and 2017. (2) The three models, SRM, RF and SVM, achieved good results in the inversions of Secchi depth and permanganate index. However, differences were noted in the inversions of other parameters. For example, the goodness‐of‐fit (r2) between the inversion and measured values of TN concentration from RF was 0.81, while that from SR was 0.73. (3) The spatial distribution patterns of the water quality parameters showed differences. The ammoniacal nitrogen concentration was higher in the central region than that in the western region of the lake, whereas TP concentration was higher at the shoreline of the lake at 0.07 mg/L on 2 April 2017 but was relatively low in the main channel. [ABSTRACT FROM AUTHOR]

Global navigation satellite system precise point positioning (PPP) technology can be significantly affected by multipath errors of code and phase observations. Previous studies using multipath hemispherical map (MHM) to mitigate multipath highly rely on stable surrounding environments, and limited work emphasizes multipath mitigation in changing environments (i.e., the surrounding environments are variable but the user maybe static). We propose a new multipath mitigation method using multipath hierarchy (MH) derived from the C/N0 discrepancy in azimuth and elevation grid in changing environments. The main processing procedures using MH in GNSS PPP are given. Two dedicated static datasets are collected to analyze the multipath discrepancy of normal MHM and further conduct the C/N0-based MH. The performance using MH is carefully analyzed in terms of positioning accuracy and residual reduction. Specifically, the normal MHM exhibits multipath discrepancy in changing environments; thus, the C/N0-based MH is of great necessity to further mitigate multipath. Compared to the normal MHM, the MH-corrected positioning errors are improved, especially at the initial epochs. By calculating the three-dimensional positioning accuracy of all epochs, the MH-corrected positioning accuracy can be improved by approximately 6 and 3 cm compared to the uncorrected and MHM-corrected results in centimeter-level PPP, respectively. Also, the standard deviations of MH-corrected code and phase residuals are smaller than MHM-corrected results for each satellite. In this sense, the proposed multipath mitigation method using MH is highly appreciated for the performance of positioning accuracy and residual reduction in changing environments. [ABSTRACT FROM AUTHOR]

This study aims to investigate the transformation of organizational culture during year of the of the active phase of the russian-Ukrainian war that contributes to the survival of Bogdan Khmelnitsky Melitopol State Pedagogical University as an organization and is a factor in the transition from the survival to the phase of successful and dynamic development. Due to the war and the occupation of certain territories of Ukraine, the university was forced to relocate to the free territory in the city of Zaporizhzhia. It lost all its buildings and other material resources; around 25% of students, 25% of faculty, and 25% of funding. Despite the extreme conditions of the university’s functioning and the stress of teachers and students under constant attacks on civilians by the occupiers, the university has resumed its educational process and research. A formal sign of the success of the scientific work is the growth of the university’s position in the Consolidated ranking of Ukrainian universities. In the educational sphere, interdisciplinary academic programs have become a significant trend during the war. Innovations in management ensured that the university quickly adapted to the extreme environment. These innovations were primarily related to transitioning from a hierarchy to an adhocratic organizational culture. The main areas of changes in the university management were: renewal of the personal composition of the university management, reorganization of structural units, updating of the system of university regulations, and complete transition to electronic document management.

Climate change and anthropogenic interventions have obviously altered the eco-hydrological regimes. A quantitative evaluation and attribution of the eco-hydrological alterations are urgently required. In this study, we evaluated the various attributions of eco-hydrological regimes in the Weihe River Basin (WRB). Firstly, the trends and change-point analysis of hydrological elements were examined, and the natural streamflow was reproduced based on the variable infiltration capacity model. Then, the most ecologically relevant hydrological indicators (ERHIs) were selected and combined with the eco-deficit and eco-surplus indicators to assess the degree of eco-hydrological regime alterations. Finally, the relative contributions to eco-hydrological alterations were quantified using the ‘simulated–observed comparison’ method. The results showed that (1) the streamflow of the WRB exhibited significant decreasing trends (p < 0.01), and a significant change point (p < 0.01) of the streamflow series was identified in 1990. (2) Seven representative indicators of hydrological alteration were selected as ERHIs. (3) During the human-induced period (1991–2017), human activities were the dominant factors in the eco-hydrological alterations as well as the variations of the ERHI indexes and the eco-deficit and eco-surplus metrics. Overall, the proposed framework may improve the understanding of the driving forces of eco-hydrological regime alterations under a changing environment. HIGHLIGHTS The principal component analysis was used to remove redundancy and correlation among the indicators of hydrological alterations and determine the ecologically relevant hydrological indicators.; A combination of ERHIs and eco-metrics to assess the eco-hydrological regime alterations at different time scales is used.; The effects of climate change and anthropogenic activities on the eco-hydrological regime alterations were quantitatively distinguished.;

Precipitation and wind, as the main external erosion forces in wind–water erosion crisscross regions, have profound impacts on water and soil loss. Meanwhile, with the intensification of climate change and human activities, the variation characteristics and risks caused by erosion forces need to be reassessed. In this study, we explored the time-varying characteristics, differences in action period and spatial distribution, and temporal evolution of risk for the compound events of two erosion forces, including precipitation and wind, in the Pisha sandstone area, one of the most seriously eroding and difficult-to-control areas in the Loess Plateau. The results indicated that: (1) the stationarity of regional precipitation was not destroyed, but the mean change existed in the five subseries divided by the detected change points in wind; (2) wind acted earlier than precipitation and increased from southeast to northwest, while precipitation did the opposite; and (3) precipitation-led erosion has become the main erosion type in this area. The above results reveal the evolution and dominant types of regional external erosion forces in a changing environment and thus have implications for regional erosion studies and policy adjustments. [ABSTRACT FROM AUTHOR]

In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions. [ABSTRACT FROM AUTHOR]

Changes in the hydrological cycle and water resources are inevitable consequences of environmental change, and runoff is an important element of the hydrological cycle. Therefore, the assessment of runoff changes is crucial for water resources management and socio-economic development. As an inland river basin in the arid zone of northwest China, the Shiyang River Basin is very vulnerable to environmental changes. Consequently, this study evaluated the past runoff evolution of the Shiyang River basin using a variety of statistical tools. In addition, the improved Soil and Water Assessment Tool (SWAT) was used to predict runoff trends from 2019 to 2050 under potential future climate change and land use projection scenarios in the future for the Shiyang River Basin. In the inland river basins, water resources mainly come from headwaters of the rivers in the upper mountainous regions, where they are more sensitive. Therefore, this study not only examined the mainstream of the Shiyang River, but also the six tributaries in the upper stream. The results indicate that the mainstream of the Shiyang River Basin and its six upstream tributaries all showed declining trends from the 1950s to 2019, and most of the rivers will continue to insignificantly decrease until 2050. Furthermore, there are two main timescales for runoff in the past as well as future: one is around 40 years and another is 20–30 years. In the meantime, the Shiyang River and its tributaries have relatively consistent change characteristics. The results of this study will provide assistance to basin management agencies in developing more appropriate water resource management plans. [ABSTRACT FROM AUTHOR]

Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff (RR) simulation methods under a changing environment scenario, several ML-based RR simulation models implemented in novel continuous and non-real-time correction manners were constructed. The proposed models incorporated categorical boosting (CatBoost), a multi-hidden-layer BP neural network (MBP), and a long short-term memory neural network (LSTM) as the input–output simulators. This study focused on the Dongwan catchment of the Yiluo River Basin to carry out daily RR simulations for the purpose of verifying the model’s applicability. Model performances were evaluated based on statistical indicators such as the deterministic coefficient, peak flow error, and runoff depth error. The research findings indicated that (1) ML-based RR simulation by using a consistency-disrupted dataset exhibited significant bias. During the validation phase for the three models, the R2 index decreased to around 0.6, and the peak flow error increased to over 20%. (2) Identifying data consistency transition points through data analysis and conducting staged RR simulations before and after the transition point can improve simulation accuracy. The R2 values for all three models during both the baseline and change periods were above 0.85, with peak flow and runoff depth errors of less than 20%. Among them, the CatBoost model demonstrated superior phased simulation accuracy and smoother simulation processes and closely matched the measured runoff processes across high, medium, and low water levels, with daily runoff simulation results surpassing those of the BP neural network and LSTM models. (3) When simulating the entire dataset without staged treatment, it is impossible to achieve good simulation results by adopting uniform extraction of the training samples. Under this scenario, the MBP exhibited the strongest generalization capability, highest prediction accuracy, better algorithm stability, and superior simulation accuracy compared to the CatBoost and LSTM simulators. This study offers new ideas and methods for enhancing the runoff simulation capabilities of machine learning models in changing environments.

Abstract In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

There are a lot of uncertainties in the water resources system, which makes the water allocation plan very risky. In order to analyze the risks of water resources allocation under uncertain conditions, a new methodology called the stochastic simulation-based risk assessment approach is developed in this paper. First, the main hydrological stochastic variable is fitted by a proper probability distribution. Second, suitable two-stage stochastic programming is constructed to obtain the expected benefit and optimized water allocation targets. Third, the Monte Carlo method is used to obtain a suitable stochastic sample of the hydrological variable. Fourth, a pre-allocated water optimization model is proposed to obtain optimized actual benefit. The methodology can give a way for risk analysis of water allocation plans obtained by uncertain optimization models, which provides reliable assistance to water managers in decision-making. The proposed methodology is applied to the Zhanghe Irrigation District and the risk of the water allocation plan obtained under the randomness of annual inflow is assessed. In addition, three different division methods of the annual inflow are applied in the first step, namely three levels, five levels and seven levels, respectively. From the results, the risk of the water allocation scheme obtained by the TSP model is 0.372–0.411 and decreases with the increase of the number of hydrological levels. Considering both the risk and model complexity, seven hydrological levels are recommended when using the TSP model to optimize water allocation under stochastic uncertainty. HIGHLIGHTS A stochastic simulation-based risk assessment method is proposed to analyze risks in water allocation plans obtained by uncertain models.; The suitable number of hydrological levels is revealed for using TSP model to optimize water allocation under stochastic uncertainty.;

Water resources are being impacted by anthropogenic climate change worldwide, the nature and severity of that impact is determined by geographic position, local topography, underlying geology, and human influences such as land use, water availability, and water regulation. As global temperatures continue to rise due to greenhouse gas emissions, it is imperative that we better understand how water availability fluctuates with changes in air temperature, rainfall, snowpack, and glacial ice. Traditionally, management of water resources has focused on surface water or ground water as if they were separate entities. As development of land and water resources increases, it is apparent that development of either of these resources affects the quantity and quality of the other. Nearly all surface-water features (streams, lakes, reservoirs, wetlands, and estuaries) interact with groundwater. These interactions take many forms. In many situations, surface-water bodies gain water and solutes from ground-water systems and in others the surface-water body is a source of ground-water recharge and causes changes in ground-water quality. As a result, withdrawal of water from streams can deplete ground water or conversely, pumpage of ground water can deplete water in streams, lakes, or wetlands. Pollution of surface water can cause degradation of ground-water quality and conversely pollution of ground water can degrade surface water. Thus, effective land and water management requires a clear understanding of the linkages between ground water and surface water as it applies to any given hydrologic setting. This study presents an overview of current understanding of the interaction of ground water and surface water, in terms of both quantity and quality, as applied to a variety of landscapes across the Nation. It serves as a general educational document rather than a report of new scientific findings. Its intent is to help the Federal, State, and local agencies build a firm scientific foundation for policies governing the management and protection of aquifers and watersheds. Effective policies and management practices must be built on a foundation that recognizes that surface water and ground water are simply two manifestations of a single integrated resource. It is our hope that this study will contribute to the use of such effective policies and management practices. [ABSTRACT FROM AUTHOR]

Drought is one of the most frequent and devastating natural disasters. Based on future climate scenarios and land use/land cover (LULC) patterns, the copula framework was employed to calculate the probabilities of meteorological and hydrological drought risks for the next 30 years (2021–2050) in the Wanquan River Basin, meanwhile, the relationship between hydrological and meteorological droughts was revealed by correlation analysis and cross-wavelet transform (XWT). The results are as follows: (1) In the next 30 years, the risk of intra-seasonal meteorological drought (short-term drought) in the WRB is high at a probability of 40–70%, while the risk of inter-seasonal meteorological drought is relatively small at a probability of close to 30%; (2) compared with meteorological drought, the risk of intra-seasonal hydrological drought is small, but the probability of inter-seasonal hydrological drought (medium- or long-term drought) is 30–50%, and the risk of hydrological drought in the upstream is greater than that in the downstream; (3) the future meteorological and hydrological droughts in the WRB are significantly and positively correlated, and that hydrological drought lags behind meteorological drought. [ABSTRACT FROM AUTHOR]

Modeling methodologies capable of coping with changing catchment conditions are becoming more important and are critical to ensure credible model predictions because the hydrologic process of the catchment is inevitably affected by climate change and human activities. A recently developed hydrological modeling framework that treats model parameters to be time-variant by building functions based on identified catchment properties is applied to the Upper Ganjiang River Basin in China experienced soil and water conservation constructions. The efficacy of the time-variant parameter method is investigated, and we further explore the impacts of model structure on hydrological modeling through this method. The approach is tested using two conceptual monthly water balance models, i.e., the ab model derived from the abcd model, and a two-parameter monthly water balance model (TWBM), which have the same model inputs and outputs, number of parameters and state variables. Results of the case study show that through the comparative analysis with the constant parameter models, both time-variant parameter models provide improved runoff simulations, especially for low and high flows. Moreover, the time-variant parameter method brings relatively larger runoff improvements for the TWBM model. For the actual evapotranspiration simulations, the ab model can yield improvements by treating parameter b (i.e., upper soil zone water holding capacity) to be time-variant, whereas the time-variant parameter method shows no efficacy in that for the TWBM model. The results demonstrate that the choice of hydrological model plays an important role in modeling performance of the time-variant parameter framework under a changing environment. Modification of the evapotranspiration calculation module for the TWBM model could be considered as further study to investigate the potential model improvement that compared with only time-variant parameters. This study can provide beneficial reference to comprehensively understand the impacts of changing environment on catchment hydrological modeling and thus improve the regional strategy for future water resource management. [ABSTRACT FROM AUTHOR]

Feedbacks between strategies and the environment are common in social-ecological, evolutionary ecological and even psychological-economic systems. Using common resources is always a dilemma for community members, like the tragedy of the commons. Here, we consider replicator dynamics with feedback-evolving games, where the pay-offs switch between two different matrices. Although each pay-off matrix on its own represents an environment where cooperators and defectors cannot coexist stably, we show that it is possible to design appropriate switching control laws and achieve persistent oscillations of strategy abundance. This result should help guide the widespread problem of population state control in microbial experiments and other social problems with eco-evolutionary feedback loops. [ABSTRACT FROM AUTHOR]

Abstract Frequency‐dependent (FD) selection, whereby fitness and selection depend on the genetic or phenotypic composition of the population, arises in numerous ecological contexts (competition, mate choice, crypsis, mimicry, etc.) and can strongly impact evolutionary dynamics. In particular, negative frequency‐dependent selection (NFDS) is well known for its ability to potentially maintain stable polymorphisms, but it has also been invoked as a source of persistent, predictable frequency fluctuations. However, the conditions under which such fluctuations persist are not entirely clear. In particular, previous work rarely considered that FD is unlikely to be the sole driver of evolutionary dynamics when it occurs, because most environments are not static but instead change dynamically over time. Here, we investigate how FD interacts with a temporally fluctuating environment to shape the dynamics of population genetic change. We show that a simple metric introduced by Lewontin, the slope of frequency change against frequency near equilibrium, works as a key criterion for distinguishing microevolutionary outcomes, even in a changing environment. When this slope D is between 0 and –2 (consistent with the empirical examples we review), substantial fluctuations would not persist on their own in a large population occupying a constant environment, but they can still be maintained indefinitely as quasi‐cycles fueled by environmental noise or genetic drift. However, such moderate NFDS buffers and temporally shifts evolutionary responses to periodic environments (e.g., seasonality). Stronger FD, with slope D < –2, can produce self‐sustained cycles that may overwhelm responses to a changing environment, or even chaos that fundamentally limits predictability. This diversity of expected outcomes, together with the empirical evidence for both FD and environment‐dependent selection, suggests that the interplay of internal dynamics with external forcing should be investigated more systematically to reach a better understanding and prediction of evolution.

Prediction of microbial growth is the main topic within the area of predictive microbiology. A commonly used framework is the so-called gamma concept which is to view the bacterial growth rate as a product of a bacteria- and medium specific maximum instantaneous factor, and several environmental factors. Traditionally environmental factors are assumed to be constant, leading to exponential growth. In the current study, we expand the gamma concept to include changing dynamic environmental factors, exemplified by pH, and show how this might affect estimates for Listeria monocytogenes growth rates and thereby predicted shelf-life. The study shows how substantial environmental changes might lead to large biases for the estimated growth and shelf-life, if not accounted for in the model. Finally, the study shed some light on how accuracy for the growth rate estimates might be improved by allocating more weight to observations prone to favorable than unfavorable environmental conditions. • Changing environment during storage leads to biased estimates for bac-terial growth. • In the study the gamma principle is extended to include changing environment. • The study exemplifies the theory with a model for pH evolution. • Shelf life is overestimated with decreasing growth conditions if unaccounted for. • Observations should be allocated weight in line with their environ-mental conditions. [ABSTRACT FROM AUTHOR]

The Jinsha River basin (JRB), known as China’s largest hydropower base, has been facing a surge in hydrological drought occurrences in the past several years. This study used the drought index model and soil and water assessment tool (SWAT) hydrological model to uncover the linkages between meteorological and hydrological drought using long-term datasets in the JRB. The results revealed that: (1) Over the past six decades, the JRB has experienced recurrent meteorological droughts, with the upper reaches being the most affected, accounting for a frequency of 17.5%. However, the frequency of drought in the middle and lower reaches has shown a marked increase in the last 15 years. (2) The frequency of hydrological drought in the JRB has been on the rise over the past six decades, with a particularly notable increase observed in the last two decades. Furthermore, a noticeable upward trend has been observed in the duration of these hydrological droughts. (3) The propagation durations from meteorological drought to hydrological drought exhibited noticeable seasonal differences in the JRB. The transmission duration during the flood season was shorter, whereas in the dry season, it was more protracted. Additionally, the connection between meteorological drought and hydrological drought demonstrates a weakening trend. The findings of this study hold significant implications for crafting an efficient reservoir dispatching strategy to safeguard the water security of the JRB.

The purpose of this study is to examine how the changing Nigerian marketing environment affects consumer behavior with emphasis on the role of government and the criticisms that follow. A total of 323 questionnaires were distributed to the respondents cutting across consumers of various categories of products in the Nigerian manufacturing sector including employees and government officials of business regulatory agencies in the 36 States of the six geo-political zones of the Nigerian Federation including Abuja, the Federal Capital Territory. The study adopted simple random sampling technique to select the sample of the study. The study was analyzed using descriptive statistics, Chi-square and multiple linear regression analysis and qualitative descriptive method to help in achieving the research objectives. The study revealed that changes brought about by the environment are caused by several macro-environmental factors such as economic, technological, socio-cultural, legal/political and international factors which have significant effect on consumers’ buying characteristics (social, cultural, personal and psychological) which subsequently affect consumers’ satisfaction and purchase decision. Economic and legal/political variables were found to exhibit the most significant impact while the technological variable exhibits the least impact.

Hydrological drought forecasting can mitigate the socio-economic and ecological impacts of drought. It is an important disaster reduction strategy to forecast the occurrence of hydrological drought according to the forecasting system. In this paper, the conditional distribution model with human activity factor as exogenous variable was constructed to forecast the hydrological drought based on meteorological drought, and then compared with the traditional normal distribution model and conditional distribution model. The results show that the runoff series of Luanhe River Basin from 1961 to 2010 was non-stationary. For the traditional conditional probability models, the transition probabilities of drought were affected by SPI time scales and forecasting periods. In order to analyze the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. Subsequently, the conditional distribution models involving human index were constructed and the influence of human activities on drought transition probability was analyzed. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. Finally, a scoring mechanism was applied to evaluate the performance of three drought forecasting models. According to the scores of the three drought forecasting models, the conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in Luanhe River Basin. [ABSTRACT FROM AUTHOR]

The reduction of greenhouse gases (GHGs) emission by replacing fossil energy stocks with carbon–neutral fuels is a major topic of the political and scientific debate on environmental sustainability. Such shift in energy sources is expected to curtail the accumulation rate of atmospheric CO2, which is a strong infrared absorber and thus contributes to the global warming effect. Although such change would produce desirable outputs, the consequences of a drastic decrease in atmospheric CO2 (the substrate of photosynthesis) should be carefully considered in the light of its potential impact on ecosystems stability and agricultural productivity. Indeed, plants regulate CO2 uptake and water loss through the same anatomical structure: the leaf stomata. A reduced CO2 availability is thus expected to enhance transpiration rate in plants decreasing their water use efficiency and imposing an increased water demand for both agricultural and wild ecosystems. We suggest that this largely underestimated issue should be duly considered when implementing policies that aim at the mitigation of global environmental changes and, at the same time, promote sustainable agricultural practices, include the preservation of biodiversity. Also, we underlie the important role(s) that modern biotechnology could play to tackle these global challenges by introducing new traits aimed at creating crop varieties with enhanced CO2 capture and water- and light-use efficiency. [ABSTRACT FROM AUTHOR]

The site frequency spectrum (SFS) is an important statistic that summarizes the molecular variation in a population, and is used to estimate population-genetic parameters and detect natural selection. Here, we study the SFS in a randomly mating, diploid population in which both the population size and selection coefficient vary periodically with time using a diffusion theory approach, and derive simple analytical expressions for the time-averaged SFS in slowly and rapidly changing environments. We show that for strong selection and in slowly changing environments where the population experiences both positive and negative cycles of the selection coefficient, the time-averaged SFS differs significantly from the equilibrium SFS in a constant environment. The deviation is found to depend on the time spent by the population in the deleterious part of the selection cycle and the phase difference between the selection coefficient and population size, and can be captured by an effective population size. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In addition to agriculture, the water–land nexus (WLN) also profoundly affects the sustainable development of industry and residents' lives. However, little research has been designed to assess the water–land nexus from the perspective of industry development and people's quality of life. In the current paper, Wi, a regional industrial water–land nexus matching index, and Wd, a matching index of the domestic water–land nexus, were proposed for evaluating the water–land nexus from the industry development and quality of life perspectives separately in the current paper. Furthermore, climate change and human activities have significant impacts on the water–land nexus. The WLNs were assessed spatially and temporally for the first time based on these two indexes in 128 municipalities in the Huang-Huai-Hai River Basin of China from 1951 to 2017 to analyze the impacts of the changing environment on them. The impact of changing environment was explored based on changes of some climate factors and land use. The value of Wi are higher in the eastern and southern cities than the western and northern cities, while Zhenjiang city in Jiangsu Province has the highest Wi. For Wd, there are two low Wd zones across the basin, while the minimum values occurred in Linxia Hui Autonomous Region (Wd = 35.34 mm). Wi and Wd in most cities in the basin showed a significant downward trend, and some cities in the southwest of the basin have the fastest-decreasing of Wd. Wt and Wa were also calculated to assess the total and agricultural water–land nexus separately based on existing research. The Wt for the Huang-Huai-Hai River Basin gradually increases from northwest to southeast, and its spatial distribution characteristics are similar to precipitation in the river basin. In addition, the government should simultaneously implement water transfer plans to reduce the agricultural water pressure in Ningxia and Gansu provinces. Dynamic driving factors of change of the four assessment indexes (Wt, Wa, Wi, Wd) are briefly analyzed in the end of the paper. [ABSTRACT FROM AUTHOR]

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 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.;

Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment in which the agent operates. A common assumption of GR is that the environment is static. However, in many real-world scenarios, for example, recognizing customers' preferences, it is necessary to recognize the goals of multiple agents or multiple goals of a single agent over an extended period. Therefore, it is reasonable to expect the environment to change throughout a series of goal recognition tasks. This paper presents three process mining-based solutions to the problem of adaptive GR in a changing environment implemented as different control strategies of a system for solving standard GR problems. As a standard GR system that gets controlled, we use the system grounded in process mining techniques, as it can adjust its internal GR mechanisms based on data collected while observing the operating agents. We evaluated our control strategies over synthetic and real-world datasets. The synthetic datasets were generated using the extended version of the Goal Recognition Amidst Changing Environments (GRACE) tool. The datasets account for different types of changes and drifts in the environment. The evaluation results demonstrate a trade-off between the GR performance over time and the effort invested in adaptations of the GR mechanisms of the system, showing that few well-planned adaptations can lead to a consistently high GR performance. [ABSTRACT FROM AUTHOR]

Extensive environmental changes increase the complex uncertainty surrounding water allocation systems, and irrational water allocation further exacerbates competition among users, posing formidable challenges to achieving dynamic and sustainable water resource management in inter-basin water diversion (IBWD) projects. This study established a dynamic multi-objective water allocation framework to address environmental change, uncertainty of water systems, and coordination between water allocation and deficit risk. The proposed framework focuses on water allocation as a continuous, multi-period decision-making process and considers reasonable water allocation from the individual to the overall multidimensionality. The feasibility and practicality of the framework were verified by long-term water allocation planning of the Hanjiang-to-Weihe IBWD in Shaanxi Province, China. The framework integrates the Soil and Water Assessment Tool model, three classical scenarios of the Coupled Model Intercomparison Project Phase 6, and optimization models to realize future sustainable water allocation optimal planning. The study found that the framework enables sustainable water allocation management and provides decisions for decision-makers under different water deficit conditions. Global optimal water allocation and the maximization of system benefits were achieved using multi-objective dynamic modeling. The average satisfaction level exceeded 0.9, and the Gini coefficient was below 0.05, both of which have a synergistic effect, with the former enhancing satisfaction within each subarea, and the latter ensuring equity among the subareas. The proposed framework aims to manage water resources of IBWD sustainably and provide optimal water allocation schemes to decision-makers under changing environments, which will alleviate water scarcity and interregional competition to promote regional sustainable development. [Display omitted] • Environmental changes exacerbate uncertainty in water allocation system. • Combining predictive and optimization models for optimal water allocation. • Developing a framework for water allocation through multiobjective dynamic modeling. • Global optimal water allocation and maximized system benefits have been achieved. • Contribute to the sustainable management of water allocation. [ABSTRACT FROM AUTHOR]

Understanding the genetic basis of how species respond to changing environments is essential to the conservation of species. However, the molecular mechanisms of adaptation remain largely unknown for long-lived tree species which always have large population sizes, long generation time, and extensive gene flow. Recent advances in landscape genomics can reveal the signals of adaptive selection linking genetic variations and landscape characteristics and therefore have created novel insights into tree conservation strategies. In this review article, we first summarized the methods of landscape genomics used in tree conservation and elucidated the advantages and disadvantages of these methods. We then highlighted the newly developed method "Risk of Non-adaptedness," which can predict the genetic offset or genomic vulnerability of species via allele frequency change under multiple scenarios of climate change. Finally, we provided prospects concerning how our introduced approaches of landscape genomics can assist policymaking and improve the existing conservation strategies for tree species under the ongoing global changes. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The adoption of adaptive management plans has been advocated in order to ensure the most effective management of natural habitats. Here, it is demonstrated how a hierarchical structural equation model that is fitted to temporal ecological monitoring data from a number of sites may be used to generate quantitative local ecological predictions and how these predictions may form the basis of adaptive management plans. Local ecological predictions will be made for the cover of the dwarf shrub cross-leaved heath (Erica tetralix) on Danish wet heathlands, which is an indicator of the conservation status of wet heathlands under different management scenarios. Based on a realistic example, the model predictions conclude that grazing by livestock on wet heathlands with a relatively low cover of cross-leaved heath cannot be recommended as the only management practice. Generally, ecological monitoring data may be used to generate quantitative and credible local adaptive management plans where uncertainty is taken into account. [ABSTRACT FROM AUTHOR]

The present study looks at the impact of the changing environment on workplace well-being among MNC employees. The sample consists of 113 employees working in Multinational Companies in Bangalore, India. Changing environment includes the disturbances caused due to human interference in natural processes like climate change, greenery, infrastructure and traffic. A self-developed survey questionnaire was used to collect the data and the results showed that the climate change impacted the work stress, motivation and productivity moderately. Greenery and traffic impacted the workplace well-being very much and extremely respectively. The common response for infrastructure was that it impacted well-being very much. [ABSTRACT FROM AUTHOR]

Frequency‐dependent (FD) selection, whereby fitness and selection depend on the genetic or phenotypic composition of the population, arises in numerous ecological contexts (competition, mate choice, crypsis, mimicry, etc.) and can strongly impact evolutionary dynamics. In particular, negative frequency‐dependent selection (NFDS) is well known for its ability to potentially maintain stable polymorphisms, but it has also been invoked as a source of persistent, predictable frequency fluctuations. However, the conditions under which such fluctuations persist are not entirely clear. In particular, previous work rarely considered that FD is unlikely to be the sole driver of evolutionary dynamics when it occurs, because most environments are not static but instead change dynamically over time. Here, we investigate how FD interacts with a temporally fluctuating environment to shape the dynamics of population genetic change. We show that a simple metric introduced by Lewontin, the slope of frequency change against frequency near equilibrium, works as a key criterion for distinguishing microevolutionary outcomes, even in a changing environment. When this slope D is between 0 and –2 (consistent with the empirical examples we review), substantial fluctuations would not persist on their own in a large population occupying a constant environment, but they can still be maintained indefinitely as quasi‐cycles fueled by environmental noise or genetic drift. However, such moderate NFDS buffers and temporally shifts evolutionary responses to periodic environments (e.g., seasonality). Stronger FD, with slope D < –2, can produce self‐sustained cycles that may overwhelm responses to a changing environment, or even chaos that fundamentally limits predictability. This diversity of expected outcomes, together with the empirical evidence for both FD and environment‐dependent selection, suggests that the interplay of internal dynamics with external forcing should be investigated more systematically to reach a better understanding and prediction of evolution. [ABSTRACT FROM AUTHOR]