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Almost all life on earth is facing environmental change, and understanding how populations will respond to these changes is of urgent importance. One factor that is known to affect the speed by which a population can evolve when faced with changes in the environment is strong sexual selection. This increases the adaptive capacity of a population by increasing reproductive skew toward well-adapted (usually) males who will, on average, be best able to compete for matings. This effect could potentially be disrupted when males pursue alternative reproductive tactics (ARTs), whereby males within a species exhibit qualitatively different behaviors in their pursuit of matings. ARTs are diverse, but one common class is those expressed through condition-dependent polyphenism such that high-quality, well-adapted males compete aggressively for mates and low-quality, poorly adapted males attempt to acquire matings via other, nonaggressive behaviors. Here, using an individual-based modeling approach, we consider the possible impacts of ARTs on adaptation and evolutionary rescue. When the ART is simultaneous, meaning that low-quality males not only engage in contests but also pursue other tactics, adaptive capacity is reduced and evolutionary rescue, where a population avoids extinction by adapting to a changing environment, becomes less likely. This is because the use of the ART allows low-quality males to contribute more maladaptive genes to the population than would happen otherwise. When the ART is fixed, however, such that low-quality males will only use the alternative tactic and do not engage in contests, we find the opposite: adaptation happens more quickly and evolutionary rescue when the environment changes is more likely. This surprising effect is caused by an increase in the mating success of the highest quality males who face many fewer competitors in this scenario—counterintuitively, the presence of males pursuing the ART increases reproductive skew toward those males in the best condition. [ABSTRACT FROM AUTHOR]

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The fairness-aware online learning framework has emerged as a potent tool within the context of continuous lifelong learning. In this scenario, the learner's objective is to progressively acquire new tasks as they arrive over time, while also guaranteeing statistical parity among various protected sub-populations, such as race and gender when it comes to the newly introduced tasks. A significant limitation of current approaches lies in their heavy reliance on the i.i.d (independent and identically distributed) assumption concerning data, leading to a static regret analysis of the framework. Nevertheless, it's crucial to note that achieving low static regret does not necessarily translate to strong performance in dynamic environments characterized by tasks sampled from diverse distributions. In this article, to tackle the fairness-aware online learning challenge in evolving settings, we introduce a unique regret measure, FairSAR, by incorporating long-term fairness constraints into a strongly adapted loss regret framework. Moreover, to determine an optimal model parameter at each time step, we introduce an innovative adaptive fairness-aware online meta-learning algorithm, referred to as FairSAOML. This algorithm possesses the ability to adjust to dynamic environments by effectively managing bias control and model accuracy. The problem is framed as a bi-level convex-concave optimization, considering both the model's primal and dual parameters, which pertain to its accuracy and fairness attributes, respectively. Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints. Our experimental evaluation of various real-world datasets in dynamic environments demonstrates that our proposed FairSAOML algorithm consistently outperforms alternative approaches rooted in the most advanced prior online learning methods. [ABSTRACT FROM AUTHOR]

The genetic covariance between traits can affect the evolution of a population through selection, drift, and migration. Conversely, research has demonstrated the reciprocal effect of evolutionary processes on changing genetic covariances, in part through mutational covariance, correlational selection, and plasticity. In this article, we propose that correlated changes in selective optima over generations can cause the evolution of genetic covariance and the G-matrix in such a way that the population can, in the future, evolve faster. We use individual-based simulations of populations exposed to three types of changing environments that differ in the correlation of the change between selective pressures. Our simulation experiments demonstrate that selection pressures for different traits changing in a correlated pattern over generations can lead to stronger trait correlations compared to the case with independently changing selective optima. Our findings show that correlated selective pressures result in significantly higher genetic trait covariance and that pleiotropy accounts for the majority of the difference in covariance between treatments. We also observe that the mutational variance evolves according to the environment that the populations were exposed to. Moreover, we show that clustered patterns of changes in selection can allow the evolution of genetic modularity. We show that the pattern of change in the selective environment affects the pace at which fitness evolves, with populations experiencing correlated change in optima having on average higher mean fitness than those experiencing uncorrelated environment change. Lay Summary: Trait covariance describes the relationship between the variance of two or more traits. The genetic covariance is the heritable portion of the covariance, and it can influence how a biological population changes through time. Researchers have also shown how the reciprocal effect is true, that is, how the evolution of a population may also impact the genetic covariance of traits. Here we are proposing one way in which evolution, specifically evolution through natural selection, can change the genetic covariance of traits. We call the mechanism we are proposing "correlated environmental change" because it describes a changing environment where the amount and direction of change over different traits are the same. Our results show that when the environment changes in such a way, compared to a not correlated way, genetic covariance between traits increases. Additionally, most of the covariance we find is a result of a heritable element (i.e., gene) impacting multiple traits. We also show how the future change given by mutations that impact these traits are more likely to happen in the direction of the changing environment. Finally, according to our results, populations become adapted to the way the environment changes, and are able to reach higher fitness faster when reexposed to the same type of change they were originally exposed to compared to a new type of change. [ABSTRACT FROM AUTHOR]

Many organisms have the ability to learn, but the costs and benefits of learning are difficult to quantify. We construct a minimal mathematical model of learning in which a forager attempts to maximize the amount of resources (food) it collects in a changing environment. Our model has two learning parameters: α, corresponding to the duration of the forager's memory, and ϵ , corresponding to how much the forager explores the environment to learn more about it. We analyse the effect of different regimes of environmental change on the optimal memory and exploration parameters α∗,ϵ∗. By comparing the fitness outcomes from learning foragers to the outcomes from foragers following fixed strategies, we explicitly quantify the fitness benefit (or cost) of learning as a function of environmental change. We find that in many environments, the marginal benefit of learning is surprisingly small. In every environment, it is possible to implement learning in such a way that performance is as bad or worse than following a fixed strategy. In some environments, even the best implementations of our minimal model of learning perform worse than the best fixed strategy. Finally, we find that variance in resource values negatively biases foragers' estimates for those values, potentially explaining experimental results showing that animals prefer less variable resources. [ABSTRACT FROM AUTHOR]

Navigating in a perpetually changing world can provide the basis for numerous challenging autonomous robotic applications. With a view to long-term autonomy, visual place recognition (vPR) systems should be able to robustly operate under extreme appearance changes in their environment. Typically, the utilized data representations are heavily influenced by those changes, negatively affecting the vPR performance. In this article, we propose a sequence-based technique that decouples such changes from the similarity estimation procedure. This is achieved by remapping the sequential representation data into the distance-space domain, i.e., a domain in which we solely consider the distances between image instances, and subsequently normalize them. In such a way, perturbations related to different environmental conditions and embedded into the original representation vectors are avoided, therefore the scene recognition efficacy is enhanced. We evaluate our framework under multiple different instances, with results indicating a significant performance improvement over other approaches. [ABSTRACT FROM AUTHOR]

To cope with changing environments, strongly adaptive algorithms that almost enjoy the optimal performance on every time interval have been proposed for online learning. However, the best regret bound of existing algorithms on each time interval with length τ is O ( τ log T ) , and their complexities are increasing with a factor of O(log T), where T is the time horizon. In real-world applications, T could go to infinity, which means that even these logarithmic factors are unacceptable. In this paper, we propose to remove the logarithmic factors of existing algorithms by utilizing prior information of environments. Specifically, we assume a lower bound τ1 and an upper bound τ2 on how long the environment changes are given, and only focus on the performance over time intervals with length in [τ1, τ2]. Then, we propose a new algorithm with a refined set of intervals that can reduce the complexity and a simple weighting method that can cooperate with our interval set. Theoretical analysis reveals that the regret bound of our algorithm on any focused interval is optimal up to a constant factor. Both the regret bound and the computational cost per iteration are independent of T. Experimental results show that our algorithm outperforms the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

This special issue delves into the local worlds of Svalbard, Estonia, Slovenia, Serbia and Albania and attests to different ways of knowing, experiencing, and living with the changing environments. Unlike many contemporary studies of environmental change, which focus on the realm of the environment undergoing unprecedented changes, this issue focuses on the processes of changes taking place in particular environments. It asks how these changes evolve, what they mean, and how people make sense of them in their daily lives. By exploring their spatial and temporal realms and the different scales at which they proliferate, this issue emphasizes the processes of changes in relation to environments and not just the other way around. Rather than rashly clinging to crisis or apocalyptic scenarios, it is important to pause and rethink the meanings, rhythms, affects, and effects of these changes in particular local worlds. [ABSTRACT FROM AUTHOR]

Organisms that are adapting to long term environmental change almost always deal with mul- tiple environments and trade-offs that affect their optimal phenotypic strategy. Here we combine the idea of repeated variation or heterogeneity, like seasonal shifts, with long-term directional dy- namics. Using the framework of fitness sets, we determine the dynamics of the optimal phenotype in two competing environments encountered with different frequencies, one of which changes with time. When such an optimal strategy is selected for in simulations of evolving populations, we observe rich behavior that is qualitatively different from and more complex than adaptation to long-term change in a single environment. The probability of survival and the critical rate of environmental change above which populations go extinct depend crucially on the relative fre- quency of the two environments and the strength and asymmetry of their selection pressures. We identify a critical frequency for the stationary environment, above which populations can escape the pressure to constantly evolve by adapting to the stationary optimum. In the neighborhood of this critical frequency, we also find the counter-intuitive possibility of a lower bound on the rate of environmental change, below which populations go extinct, and above which a process of evolutionary rescue is possible., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Society for the Study of Evolution (SSE).)

Bioenergetic organelles—mitochondria and plastids—retain their own genomes (mtDNA and ptDNA), and these organelle DNA (oDNA) molecules are vital for eukaryotic life. Like all genomes, oDNA must be able to evolve to suit new environmental challenges. However, mixed oDNA populations in cells can challenge cellular bioenergetics, providing a penalty to the appearance and adaptation of new mutations. Here we show that organelle “bottlenecks,” mechanisms increasing cell-to-cell oDNA variability during development, can overcome this mixture penalty and facilitate the adaptation of beneficial mutations. We show that oDNA heteroplasmy and bottlenecks naturally emerge in evolutionary simulations subjected to fluctuating environments, demonstrating that this evolvability is itself evolvable. Usually thought of as a mechanism to clear damaging mutations, organelle bottlenecks therefore also resolve the tension between intracellular selection for pure cellular oDNA populations and the “bet-hedging” need for evolvability and adaptation to new environments. This general theory suggests a reason for the maintenance of organelle heteroplasmy in cells, and may explain some of the observed diversity in organelle maintenance and inheritance across taxa.

Ecology and evolution under changing environments are important in many subfields of biology with implications for medicine. Here, we explore an example: the consequences of fluctuating environments on the emergence of antibiotic resistance, which is an immense and growing problem. Typically, high doses of antibiotics are employed to eliminate the infection quickly and minimize the time under which resistance may emerge. However, this strategy may not be optimal. Since competition can reduce fitness and resistance typically has a reproductive cost, resistant mutants' fitness can depend on their environment. Here we show conditions under which environmental varying fitness can be exploited to prevent the emergence of resistance. We develop a stochastic Lotka-Volterra model of a microbial system with competing phenotypes: a wild strain susceptible to the antibiotic, and a mutant strain that is resistant. We investigate the impact of various pulsed applications of antibiotics on population suppression. Leveraging competition, we show how a strategy of environmental switching can suppress the infection while avoiding resistant mutants. We discuss limitations of the procedure depending on the microbe and pharmacodynamics and methods to ameliorate them. [ABSTRACT FROM AUTHOR]

In order to migrate over large distances, cells within tissues and organisms rely on sensing local gradient cues which are irregular, conflicting, and changing over time and space. The mechanism how they generate persistent directional migration when signals are disrupted, while still remaining adaptive to signal’s localization changes remain unknown. Here, we find that single cells utilize a molecular mechanism akin to a working memory to satisfy these two opposing demands. We derive theoretically that this is characteristic for receptor networks maintained away from steady states. Time-resolved live-cell imaging of Epidermal growth factor receptor (EGFR) phosphorylation dynamics shows that cells transiently memorize position of encountered signals via slow-escaping remnant of the polarized signaling state, a dynamical ‘ghost’, driving memory-guided persistent directional migration. The metastability of this state further enables migrational adaptation when encountering new signals. We thus identify basic mechanism of real-time computations underlying cellular navigation in changing chemoattractant fields.

Navigating in a perpetually changing world can provide the basis for numerous challenging autonomous robotic applications. With a view to long-term autonomy, visual place recognition (vPR) systems should be able to robustly operate under extreme appearance changes in their environment. Typically, the utilized data representations are heavily influenced by those changes, negatively affecting the vPR performance. In this article, we propose a sequence-based technique that decouples such changes from the similarity estimation procedure. This is achieved by remapping the sequential representation data into the distance-space domain, i.e., a domain in which we solely consider the distances between image instances, and subsequently normalize them. In such a way, perturbations related to different environmental conditions and embedded into the original representation vectors are avoided, therefore the scene recognition efficacy is enhanced. We evaluate our framework under multiple different instances, with results indicating a significant performance improvement over other approaches.

Premise: Shifts in abiotic factors can affect many plant traits, including floral volatiles. This study examined the response of floral volatiles to water availability and whether phenotypic plasticity to water availability differs among populations. It also investigated genetic differentiation in floral volatiles, determined the effect of temperature on phenotypic plasticity to water availability, and assessed temporal variation in floral scent emission between day and evening, since pollinator visitation differs at those times. Methods: Rocky Mountain columbine plants (Aquilegia coerulea), started from seeds collected in three wild populations in Colorado, Utah, and Arizona, were grown under two water treatments in a greenhouse in Madison, Wisconsin, United States. One population was also grown under the two water treatments, at two temperatures. Air samples were collected from enclosed flowers using dynamic headspace methods and floral volatiles were identified and quantified by gas chromatography (GC) with mass spectrometry (MS). Results: Emission of three floral volatiles increased in the wetter environment, indicating phenotypic plasticity. The response of six floral volatiles to water availability differed among populations, suggesting genetic differentiation in phenotypic plasticity. Five floral volatiles varied among populations, and emission of most floral volatiles was greater during the day. Conclusions: Phenotypic plasticity to water availability permits a quick response of floral volatiles in changing environments. The genetic differentiation in phenotypic plasticity suggests that phenotypic plasticity can evolve but complicates predictions of the effects of environmental changes on a plant and its pollinators. [ABSTRACT FROM AUTHOR]

Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially "predictive" behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing. [ABSTRACT FROM AUTHOR]

The environment is one of the key factors in the emergence of intelligent creatures, but it has received little attention within the Evolutionary Robotics literature. This article investigates the effects of changing environments on morphological and behavioral traits of evolvable robots. In particular, we extend a previous study by evolving robot populations under diverse changing-environment setups, varying the magnitude, frequency, duration, and dynamics of the changes. The results show that long-lasting effects of early generations occur not only when transitioning from easy to hard conditions, but also when going from hard to easy conditions. Furthermore, we demonstrate how the impact of environmental scaffolding is dependent on the nature of the environmental changes involved. [ABSTRACT FROM AUTHOR]

This article proposes a robust image-sequence-based framework to deal with two challenges of visual place recognition in changing environments: 1) viewpoint variations and 2) environmental condition variations. Our framework includes two main parts. The first part is to calculate the distance between two images from a reference image sequence and a query image sequence. In this part, we remove the deep features of nonoverlap contents in these two images and utilize the remaining deep features to calculate the distance. As the deep features of nonoverlap contents are caused by viewpoint variations, removing these deep features can improve the robustness to viewpoint variations. Based on the first part, in the second part, we first calculate the distances of all pairs of images from a reference image sequence and a query image sequence, and obtain a distance matrix. Afterward, we design two convolutional operators to retrieve the distance submatrix with the minimum diagonal distribution. The minimum diagonal distribution contains more environmental information, which is insensitive to environmental condition variations. The experimental results suggest that our framework exhibits better performance than several state-of-the-art methods. Moreover, the analysis of runtime shows that our framework has the potential to satisfy real-time demands. [ABSTRACT FROM AUTHOR]

Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.

From the homeostasis of human health to the cycling of Earth's elements, microbial activities underlie environmental, medical and industrial processes. These activities occur in chemical and physical landscapes that are highly dynamic and experienced by bacteria as fluctuations. In this review, we first discuss how bacteria can experience both spatial and temporal heterogeneity in their environments as temporal fluctuations of various timescales (seconds to seasons) and types (nutrient, sunlight, fluid flow, etc.). We then focus primarily on nutrient fluctuations to discuss how bacterial communities, populations and single cells respond to environmental fluctuations. Overall, we find that environmental fluctuations are ubiquitous and diverse, and strongly shape microbial behavior, ecology and evolution when compared with environments in which conditions remain constant over time. We hope this review may serve as a guide toward understanding the significance of environmental fluctuations in microbial life, such that their contributions and implications can be better assessed and exploited. [ABSTRACT FROM AUTHOR]

A great temperature difference (GTD) in changing environments has a great impact on human comfort and health. Thus, the optimization of human comfort in the GTD environment becomes extremely important. In this paper, the human thermal regulation and clothing heat transfer in the GTD environment were simulated. The predicted skin temperature was verified by the experimental results in the GTD environment. The absolute error percentage of skin temperature was less than 5 %, indicating that the model has good accuracy for prediction of skin temperature in the GTD environment. Influencing factors of thermal comfort in the GTD environment were studied by the validated model. It was found that the influence of clothing thermal resistance had a critical value on the thermal comfort. When the effective thermal resistance was less than the critical value, increasing clothing thermal resistance improved the thermal comfort, and vice versa. The influence of metabolic equivalent change on the thermal comfort in a low-temperature environment was greater than that in a high-temperature environment. Finally, the thermal comfort time of human was predicted by the model, which could predict the duration of thermal comfort state under different GTD environments. The findings have great significance to adjust the comfortable dress of human under the GTD environment. • A numerical model on thermal comfort of dressed body was developed. • The numerical model was comprehensively validated with human trials in GTD environments. • Influencing factors of thermal comfort in GTD environments were analyzed. • Thermal comfort time under various GTD environments was predicted. [ABSTRACT FROM AUTHOR]

Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially “predictive” behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.

Many species are subject to seasonal cycles in resource availability, affecting the timing of their reproduction. Using a stage-structured consumer-resource model in which juvenile development and maturation are resource dependent, we study how a species' reproductive schedule evolves, dependent on the seasonality of its resource. We find three qualitatively different reproduction modes. First, continuous income breeding (with adults reproducing throughout the year) evolves in the absence of significant seasonality. Second, seasonal income breeding (with adults reproducing unless they are starving) evolves when resource availability is sufficiently seasonal and juveniles are more efficient resource foragers. Third, seasonal capital breeding (with adults reproducing partly through the use of energy reserves) evolves when resource availability is sufficiently seasonal and adults are more efficient resource foragers. Such capital breeders start reproduction already while their offspring are still experiencing starvation. Changes in seasonality lead to continuous transitions between continuous and seasonal income breeding, but the change between income and capital breeding involves a hysteresis pattern, such that a population's evolutionarily stable reproduction pattern depends on its initial one. Taken together, our findings show how adaptation to seasonal environments can result in a rich array of outcomes, exhibiting seasonal or continuous reproduction with or without energy reserves. [ABSTRACT FROM AUTHOR]

As organizations adapt to significant challenges, such as a pandemic and multiple generations entering the -workplace, it is important to have followers who are team players and have skills for the new environment. Understanding and learning followership skills will be necessary for organizational effectiveness. We identified the following three major categories of followership skills and placed them into a hierarchy: Professional, Interpersonal, and Political. Each of these classifications has specific skill sets. A model is also offered as followers progress over time, learning these skills as they become leaders. The progression includes (1) Novice/Apprentice, (2) Team player, (3) Decision Maker, and (4) Leader/Manager. [ABSTRACT FROM AUTHOR]

Taking a representative catchment of the Yangtze River Delta region as the study area, this research evaluated sub-daily rainstorm variability and its potential effects on flood processes based on an integrated approach of the HEC-HMS model and design storm hyetographs. The results show that the intensities of rainfall on sub-daily scale are getting more extreme. The annual maximum 1-, 2- and 3-hour rainstorms followed significant upward trends with increases of 0.32, 0.43 and 0.44 mm per year, respectively, while the annual maximum 6-, 12- and 24-h events had non-significant rising trends. The detected significant trends in short-duration rainstorms were then used to redesign storm hyetographs to drive the HEC-HMS model, the results show that these changes in short-duration rainstorm characteristics would increase the flood peak discharge and flood volume. These findings indicate that regional flood control capabilities must be improved to manage the adverse impacts of rainfall variation under changing environments. [ABSTRACT FROM AUTHOR]

Many species facing climate change have complex life cycles, with individuals in different stages differing in their sensitivity to a changing climate and their contribution to population growth. We use a quantitative genetics model to predict the dynamics of adaptation in a stage-structured population confronted with a steadily changing environment. Our model assumes that different optimal phenotypic values maximize different fitness components, consistent with many empirical observations. In a constant environment, the population evolves toward an equilibrium phenotype, which represents the best compromise given the trade-off between vital rates. In a changing environment, however, the mean phenotype in the population will lag behind this optimal compromise. We show that this lag may result in a shift along the trade-off between vital rates, with negative consequences for some fitness components but, less intuitively, improvements in some others. Complex eco-evolutionary dynamics can emerge in our model due to feedbacks between population demography and adaptation. Because of such feedback loops, selection may favor further shifts in life history in the same direction as those caused by maladaptive lags. These shifts in life history could be wrongly interpreted as adaptations to the new environment, while in reality they only reflect the inability of the population to adapt fast enough. [ABSTRACT FROM AUTHOR]

Fisheries productivity in the Laurentian Great Lakes has changed dramatically over the past century. Invasions of non-native species and anthropogenically induced environmental changes in habitat quality and quantity have significantly altered the species composition and abundance of Great Lakes fishes, thereby affecting the social and economic well-being of coastal communities that rely on the good and services that these fishes provide. Our increased ability to locate, access, catch, preserve, and transport fish while modifying their habitats has resulted in the loss of native fish populations, which has profoundly impacted the ecological functioning and thus the productivity, structure, and services of Great Lakes ecosystems. Further, our lack of predictable scientific knowledge and control over factors affecting the productivity of the various Great Lakes fisheries, coupled with the failure of fisheries governance systems to manage these resources sustainably, have often left Great Lakes commercial, recreational, and subsistence fisheries and their local fishing communities impoverished and in disarray. In this paper, we discuss the environmental, cultural, and socioeconomic changes that have characterized the Great Lakes basin in the last century. We also share our perspectives and personal stories about the impacts of these changes on ecosystems, fisheries, and the local and regional communities and economies that depend on them for their health and well-being. A key lesson learned was, that if we are to ensure the integrity and productivity of Great Lakes fisheries in the future, we must become better stewards, possessing a more predictable scientific and ecosystem-based understanding of fishes and their habitats while communicating the value of fisheries in food, recreational opportunities, and the economic and social wealth of local communities. The fate of Great Lakes fisheries and the quality of life of the people who use these resources are inextricably linked and can only be sustained in productive, well-governed, and well-balanced fisheries managed holistically at the ecosystem level. [ABSTRACT FROM AUTHOR]

What makes visual place recognition difficult to solve is the variation of the real-world places. In this work, an effective similarity measurement is proposed for visual place recognition in changing environments, based on Convolutional Neural Networks (CNNs) and content-based multi-scale landmarks. The image is firstly segmented into multi-scale landmarks with content information in order to adapt variations of viewpoint, then highly representative features of landmarks are derived from Convolutional Neural Networks (CNNs), which are robust against appearance variations. In the similarity measurement, the similarity between images is determined by analyzing both spatial and scale distributions of matched landmarks. Moreover, an efficient feature extraction and reduction strategy are proposed to generate all features of landmarks at one time. The efficiency of the proposed method makes it suitable for real-time applications. The proposed method is evaluated on two widespread datasets with varied viewpoint and appearance conditions and achieves superior performance against four other state-of-the-art methods, such as the bag-of-words model DBoW3 and the CNN-based Edge Boxes landmarks. Extensive experimentation demonstrates that integrating global and local information can provide more invariance in severe appearance changes, and considering the spatial distribution of landmarks can improve the robustness against viewpoint changes. [ABSTRACT FROM AUTHOR]

Indigenous knowledge systems (IKS) refer to local, peasant, traditional knowledge, or African farming systems. They are continually influenced by creativity, experimentation and contact with external systems. This study reviewed literature on the applications of various indigenous knowledge practices and systems in crop production. Significant contributions to global knowledge originated from indigenous people, such as in medicine and veterinary medicine with intimate understanding of local environments. Some forms of IK are expressed in stories, legends, folklore, rituals, songs and laws. Perspectives and applications of IKS constitute the unique unity in diversity and its innate and embodied essence of humanity, spirituality, society and environment. The two main IKS theories reviewed were indigenous knowledge innovation systems (IKIS) and postmodernism. Challenges in adoption of IKS practices included farmers who perceive IKS as outdated, lack of institutional focus on preserving and promoting indigenous knowledge and poor government support in incorporating IKS into agricultural policy frameworks. Future work may focus on determining the link between the IK system and the western technology, maintaining the value of indigenous knowledge base. [ABSTRACT FROM AUTHOR]

Abstract Learning is an important aspect of cognition that is crucial for the success of many species, and has been a factor involved in the evolution of distinct patterns of life history that depend on the environments in question. The extent to which different degrees of social and individual learning emerge follows from various species-dependent factors, such as the fidelity of information transmission between individuals, and that has previously been modelled in agent-based simulations with meme-based representations of learned knowledge and behaviours. A limitation of that previous work is that it was based on fixed environments, and it is known that different learning strategies will emerge depending on the variability of the environment. This paper will address that limitation by extending the existing modelling framework to allow the simulation of life history evolution and the emergence of appropriate learning strategies in changing environments. [ABSTRACT FROM AUTHOR]

In this paper we discuss recent results regarding the question of adaptation to changing environments in intermediate timescales and the quantification of the amount of information a cell needs about its environment, connecting the theoretical approaches with relevant experimental results. We first show how advances in the study of noise in genetic circuits can inform a detailed description of intracellular information flow and allow for simplified descriptions of the phenotypic state of a cell. We then present the different types of strategies that cells can use to respond to changing environments, and what a quantitative description of this process implies about the long term fitness of the population. We present an early approach connecting the transmission of information to the average fitness, and then move on to a full model of the process. This model is then simplified to obtain analytical results for a few cases. We present the necessary notation but avoid technical detail as much as possible, as our goal is to emphasize the biological interpretation and significance of the mathematical results. We focus on how carefully constructed models can answer the long-standing objection to the use of information theory in biology based on decision-theoretic considerations of the difference between the amount of information and its fitness value.

In a non-stationary environment, newly received data may have different knowledge patterns from the data used to train learning models. As time passes, a learning model’s performance may become increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years. However, very few existing methods pay attention to small regional drifts, and their accuracy may vary due to differing statistical significance tests. This paper presents a novel concept drift detection method, based on regional-density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first is a k -nearest neighbor-based space-partitioning schema (NNPS), which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation. The second is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall differences. The third component is a tailored statistical significance test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift and has been proven to follow a normal distribution. As a result, the NN-DVI’s accuracy and false-alarm rate are statistically guaranteed. Additionally, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing problems related to concept drift-detection. [ABSTRACT FROM AUTHOR]

This article explores the global impacts of a changing environment on the sustainability of resources. In a global context characterised by continued population growth and accelerated urbanisation in emerging economies and the least developed regions of the world, pressures on environmental resources are intensifying. Extreme effects on ecosystems in both urban and rural communities are of enduring concern, as evidenced in water and food insecurity, and poor air quality. The author compares varying approaches to the collection and use of evidence, and the ways in which researchers may influence policy decisions and their implementation. Drawing on large and small-scale studies conducted in different regions of the world from a range of disciplinary perspectives, the article seeks to unravel the triangular relationship between research evidence, policy and impact, while paying attention to the tools used to assess impact on, and of, policy. In conclusion, the author considers how co-ordinated efforts by academics, public, private and third-sector practitioners across disciplines and national borders might produce stronger evidence and knowledge with which to inform decision-makers, empower citizens and achieve sustainable development, thereby supporting the needs of present and future generations. [ABSTRACT FROM AUTHOR]

A major challenge in community ecology is to understand how species respond to environmental changes. Previous studies have shown that the reorganization of interactions among co-occurring species can modulate their chances to adapt to novel environmental conditions. Moreover, empirical evidence has shown that these ecological dynamics typically facilitate the persistence of groups of species rather than entire communities. However, so far, we have no systematic methodology to identify those groups of species with the highest or lowest chances to adapt to new environments through a reorganization of their interactions. Yet, this could prove extremely valuable for developing new conservation strategies. Here, we introduce a theoretical framework to estimate the effect of the reorganization of interactions on the adaptability of a group of species, within a community, to novel environmental conditions. We introduce the concept of the adaptation space of a group of species based on a feasibility analysis of a population dynamics model. We define the adaptation space of a group as the set of environmental conditions that can be made compatible with its persistence thorough the reorganization of interactions among species within the group. The larger the adaptation space of a group, the larger its likelihood to adapt to a novel environment. We show that the interactions in the community outside a group can act as structural constraints and be used to quantitatively compare the size of the adaptation space among different groups of species within a community. To test our theoretical framework, we perform a data analysis on several pairs of natural and artificially perturbed ecological communities. Overall, we find that the groups of species present in both control and perturbed communities are among the ones with the largest adaptation space. We believe that the results derived from our framework point out towards new directions to understand and estimate the adaptability of species to changing environments. [ABSTRACT FROM AUTHOR]

This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters.

Empirical studies have found that the mutualistic interactions forming the structure of plant-pollinator networks are typically more nested than expected by chance alone. Additionally, theoretical studies have shown a positive association between the nested structure of mutualistic networks and community persistence. Yet, it has been shown that some plant-pollinator networks may be more nested than others, raising the interesting question of which factors are responsible for such enhanced nested structure., It has been argued that ordered network structures may increase the persistence of ecological communities under less predictable environments. This suggests that nested structures of plant-pollinator networks could be more advantageous under highly seasonal environments. While several studies have investigated the link between nestedness and various environmental variables, unfortunately, there has been no unified answer to validate these predictions. Here, we move from the problem of describing network structures to the problem of comparing network structures. We develop comparative statistics, and apply them to investigate the association between the nested structure of 59 plant-pollinator networks and the temperature seasonality present in their locations., We demonstrate that higher levels of nestedness are associated with a higher temperature seasonality. We show that the previous lack of agreement came from an extended practice of using standardized measures of nestedness that cannot be compared across different networks., Importantly, our observations complement theory showing that more nested network structures can increase the range of environmental conditions compatible with species coexistence in mutualistic systems, also known as structural stability. This increase in nestedness should be more advantageous and occur more often in locations subject to random environmental perturbations, which could be driven by highly changing or seasonal environments. This synthesis of theory and observations could prove relevant for a better understanding of the ecological processes driving the assembly and persistence of ecological communities. [ABSTRACT FROM AUTHOR]