Explore the vast range of titles available.

Performance tradeoffs are ubiquitous in both ecological and evolutionary modeling, yet they are usually postulated and built into fitness and ecological landscapes. However, tradeoffs depend on genetic background and evolutionary history and can themselves evolve.We present a simple model capable of capturing the key feedback loop: evolutionary history shapes tradeoff strength, which, in turn, shapes evolutionary future. One consequence of this feedback is that genomes with identical fitness can have different evolutionary properties shaped by prior environmental exposure. Another is that, generically, the best adaptations to one environment may evolve in another. Our simple framework bridges the gap between the phenotypic Fisher’s Geometric Model and the genotypic properties, such as modularity and evolvability, and can serve as a rich playground for investigating evolution in multiple or changing environments.

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.

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.

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.

Among the factors that may reduce the predictability of evolution, chaos, characterized by a strong dependence on initial conditions, has received much less attention than randomness due to genetic drift or environmental stochasticity. It was recently shown that chaos in phenotypic evolution arises commonly under frequency-dependent selection caused by competitive interactions mediated by many traits. This result has been used to argue that chaos should often make evolutionary dynamics unpredictable. However, populations also evolve largely in response to external changing environments, and such environmental forcing is likely to influence the outcome of evolution in systems prone to chaos. We investigate how a changing environment causing oscillations of an optimal phenotype interacts with the internal dynamics of an eco-evolutionary system that would be chaotic in a constant environment. We show that strong environmental forcing can improve the predictability of evolution by reducing the probability of chaos arising, and by dampening the magnitude of chaotic oscillations. In contrast, weak forcing can increase the probability of chaos, but it also causes evolutionary trajectories to track the environment more closely. Overall, our results indicate that, although chaos may occur in evolution, it does not necessarily undermine its predictability.

Abstract Natural environments are seldom static and therefore it is important to ask how a population adapts in a changing environment. We consider a finite, diploid population evolving in a periodically changing environment and study how the fixation probability of a rare mutant depends on its dominance coefficient and the rate of environmental change. We find that, in slowly changing environments, the effect of dominance is the same as in the static environment, that is, if a mutant is beneficial (deleterious) when it appears, it is more (less) likely to fix if it is dominant. But, in fast changing environments, the effect of dominance can be different from that in the static environment and is determined by the mutant’s fitness at the time of appearance as well as that in the time-averaged environment. We find that, in a rapidly varying environment that is neutral on average, an initially beneficial (deleterious) mutant that arises while selection is decreasing (increasing) has a fixation probability lower (higher) than that for a neutral mutant as a result of which the recessive (dominant) mutant is favored. If the environment is beneficial (deleterious) on average but the mutant is deleterious (beneficial) when it appears in the population, the dominant (recessive) mutant is favored in a fast changing environment. We also find that, when recurrent mutations occur, dominance does not have a strong influence on evolutionary dynamics.

Organisms often respond to environmental change via phenotypic plasticity, in which an individual modulates its phenotype according to the environment. Highly variable or changing environments can exceed physiological limits and generate maladapted plastic phenotypes, which is termed nonadaptive plasticity. In some cases, selection may reduce the negative or disruptive impacts of environmental stress and produce locally adapted populations. Salt is an increasingly prevalent contaminant of freshwater systems and can induce nonadaptive plastic phenotypes for freshwater organisms like amphibians. Hyla cinerea is a frog species with populations inhabiting brackish, coastal habitats, so we use this species to test whether coastal populations are locally adapted to tolerate saltwater by determining how salt exposure during the embryonic and larval stages alters mortality and plastic developmental and metamorphic phenotypes of coastal and inland populations. Coastal frogs have higher survival, faster growth rates, and metamorphose sooner than inland frogs across salinities. Coastal frogs also metamorphose smaller (likely a consequence of earlier metamorphosis) yet maintain constant size, while higher salinities reduce metamorphic size for inland frogs. Coastal frogs evolved to minimize nonadaptive and disruptive impacts of saltwater during larval development and accelerate the larval period to reduce time spent in a stressful environment.

Quantifying flood risk depends on accurate probability estimation, which is challenging due to non‐stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time‐varying copulas was introduced for the treatment of compound flood risk and bivariate design in non‐stationary environments. Archimedean copulas were developed to diagnose the non‐stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non‐stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non‐stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non‐stationarity may lead to risk over or underestimation. It showed the limitations of the 1‐day scale and quantified the uncertainty of non‐stationary models. This study provided a flood risk assessment framework in a changing environment and a risk‐based design technique, which is essential for climate change adaptation and water management.

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.

This study examines runoff variations and their drivers in the Buha and Shaliu Rivers of the Qinghai Lake Basin (1960–2016), a key ecological area in China. Abrupt changes were detected using the Mann–Kendall and cumulative anomaly methods, while the Budyko framework attributed runoff variations to dominant factors. Correlation and grey relational analyses assessed multicollinearity, and a lake water balance model with climate elasticity theory quantified the effects of climate and land surface changes on runoff components and lake levels. Results indicate that the Buha River experienced an abrupt runoff change in 2004, while the Shaliu River exhibited a change beginning in 2003. Based on the trends and abrupt change points of each factor, the study period was divided into four segments: 1960–1993, 1994–2016, 1960–2003, and 2004–2016. The correlation coefficients are significantly different in different periods. The climate elasticity coefficients were as follows: P (precipitation), 1.98; ET0 (potential evapotranspiration), −0.98; Rn (net radiation), 0.66; T (average temperature), 0.02; U2 (wind speed at 2 m height), 0.16; RHU (relative umidity), −0.56. The elasticity coefficient of runoff with respect to precipitation is significantly higher than that for other climate variables. Net radiation and relative humidity contribute equally to runoff, while wind speed and temperature have relatively smaller effects. In the Qinghai Lake Basin, runoff is sensitive to precipitation (0.38), potential evapotranspiration (−0.07), and the underlying surface parameter ω (−98.32). Specifically, a 1 mm increase in precipitation raises runoff by 0.38 mm, while a 1 mm rise in potential evapotranspiration reduces it by 0.07 mm. A one-unit increase in ω leads to a significant runoff decrease of 98.32 mm. According to the lake water balance model, climate contributes 88.43% to groundwater runoff, while land surface changes contribute −11.57%. Climate change and land surface changes contribute 93.02% and 6.98%, respectively, to lake water levels. This study quantitatively evaluates the impacts of climate and land surface changes on runoff, providing insights for sustainable hydrological and ecological management in the Qinghai Lake Basin.