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Biological organisms exhibit diverse strategies for adapting to varying environments. For example, a population of organisms may express the same phenotype in all environments (“unvarying strategy”) or follow environmental cues and express alternative phenotypes to match the environment (“tracking strategy”), or diversify into coexisting phenotypes to cope with environmental uncertainty (“bet-hedging strategy”). We introduce a general framework for studying how organisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the long-term growth rate of a population. The previously studied strategies emerge as special cases of our model: The tracking strategy is favorable when environmental cues are accurate, whereas when cues are noisy, organisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation, which can even be random.

A formidable challenge for global change biologists is to predict how natural populations will respond to the emergence of conditions not observed at present, termed novel climates. Popular approaches to predict population vulnerability are based on the expected degree of novelty relative to the amplitude of historical climate fluctuations experienced by a population. Here, we argue that predictions focused on amplitude may be inaccurate because they ignore the predictability of environmental fluctuations in driving patterns of evolution and responses to climate change. To address this disconnect, we review major findings of evolutionary theory demonstrating the conditions under which phenotypic plasticity is likely to evolve in natural populations, and how plasticity decreases population vulnerability to novel environments. We outline key criteria that experimental studies should aim for to effectively test theoretical predictions, while controlling for the degree of climate novelty. We show that such targeted tests of evolutionary theory are rare, with marine systems being overall underrepresented in this venture despite exhibiting unique opportunities to test theory. We conclude that with more robust experimental designs that manipulate both the amplitude and predictability of fluctuations, while controlling for the degree of novelty, we may better predict population vulnerability to climate change.

The relationship between biological diversity and ecological stability has fascinated ecologists for decades. Determining the generality of this relationship, and discovering the mechanisms that underlie it, are vitally important for ecosystem management. Here, we investigate how species richness affects the temporal stability of biomass production by reanalyzing 27 recent biodiversity experiments conducted with primary producers. We find that, in grasslands, increasing species richness stabilizes whole-community biomass but destabilizes the dynamics of constituent populations. Community biomass is stabilized because species richness impacts mean biomass more strongly than its variance. In algal communities, species richness has a minimal effect on community stability because richness affects the mean and variance of biomass nearly equally. Using a new measure of synchrony among species, we find that for both grasslands and algae, temporal correlations in species biomass are lower when species are grown together in polyculture than when grown alone in monoculture. These results suggest that interspecific interactions tend to stabilize community biomass in diverse communities. Contrary to prevailing theory, we found no evidence that species’ responses to environmental variation in monoculture predicted the strength of diversity’s stabilizing effect. Together, these results deepen our understanding of when and why increasing species richness stabilizes community biomass.

Thermal performance curves have been widely used to model the ecological responses of ectotherms to variable thermal environments and climate change. Such models ignore the effects of time dependence—the temporal pattern and duration of temperature exposure—on performance. We developed and solved a simple mathematical model for growth rate of ectotherms, combining thermal performance curves for ingestion rate with the temporal dynamics of gene expression and protein production in response to high temperatures to predict temporal patterns of growth rate in constant and diurnally fluctuating temperatures. We used the model to explore the effects of heat shock proteins on larval growth rates of Manduca sexta. The model correctly captures two empirical patterns for larval growth rate: first, maximal growth rate and optimal temperature decline with increasing duration of temperature exposure; second, mean growth rates decline with time in diurnally fluctuating temperatures at higher mean temperatures. These qualitative results apply broadly to cases where proteins or other molecules produced in response to high temperatures reduce growth rates. We discuss some of the critical assumptions and predictions of the model and suggest potential extensions and alternatives. Incorporating time-dependent effects will be essential for making more realistic predictions about the physiological and ecological consequences of temperature fluctuations and climate change.

Environmental heterogeneity constitutes an evolutionary challenge for organisms. While evolutionary dynamics under variable conditions has been explored for decades, we still know relatively little about the cellular and molecular mechanisms involved. It is of paramount importance to examine these molecular bases because they may play an important role in shaping the course of evolution. In this review, we examine the diversity of adaptive mechanisms in the face of environmental changes. We exploit the recent literature on microbial systems because those have benefited the most from the recent emergence of genetic engineering and experimental evolution followed by genome sequencing. We identify four emerging trends: (i) an adaptive molecular change in a pathway often results in fitness trade-off in alternative environments but the effects are dependent on a mutation's genetic background; (ii) adaptive changes often modify transcriptional and signalling pathways; (iii) several adaptive changes may occur within the same molecular pathway but be associated with pleiotropy of different signs across environments; (iv) because of their large associated costs, macromolecular changes such as gene amplification and aneuploidy may be a rapid mechanism of adaptation in the short-term only. The course of adaptation in a variable environment, therefore, depends on the complexity of the environment but also on the molecular relationships among the genes involved and between the genes and the phenotypes under selection.

An organism’s phenotype can be influenced by maternal cues and directly perceived environmental cues, as well as by its genotype at polymorphic loci, which can be interpreted as a genetic cue. In fluctuating environments, natural selection favors organisms that efficiently integrate different sources of information about the likely success of phenotypic alternatives. In such situations, it can be beneficial to pass on maternal cues that offspring can respond to. A maternal cue could be based on environmental cues directly perceived by the mother but also partly on cues that were passed on by the grandmother. We have used a mathematical model to investigate how the passing of maternal cues and the integration of different sources of information evolve in response to qualitatively different kinds of temporal and spatial environmental fluctuations. The model shows that the passing of maternal cues and the transgenerational integration of sources of information readily evolve. Factors such as the degree of temporal autocorrelation, the predictive accuracy of different environmental cues, and the level of gene flow strongly influence the expression of adaptive maternal cues and the relative weights given to different sources of information. We outline the main features of the relation between the characteristics of environmental fluctuations and the adaptive systems of phenotype determination and compare these predictions with empirical studies on cue integration.

Archaeological accounts of cultural change reveal a fundamental conflict: Some suggest that change is gradual, accelerating over time, whereas others indicate that it is punctuated, with long periods of stasis interspersed by sudden gains or losses of multiple traits. Existing models of cultural evolution, inspired by models of genetic evolution, lend support to the former and do not generate trajectories that include large-scale punctuated change. We propose a simple model that can give rise to both exponential and punctuated patterns of gain and loss of cultural traits. In it, cultural innovation comprises several realistic interdependent processes that occur at different rates. The model also takes into account two properties intrinsic to cultural evolution: the differential distribution of traits among social groups and the impact of environmental change. In our model, a population may be subdivided into groups with different cultural repertoires leading to increased susceptibility to cultural loss, whereas environmental change may lead to rapid loss of traits that are not useful in a new environment. Taken together, our results suggest the usefulness of a concept of an effective cultural population size.

Temporal variation in natural selection is predicted to strongly impact the evolution and demography of natural populations, with consequences for the rate of adaptation, evolution of plasticity, and extinction risk. Most of the theory underlying these predictions assumes a moving optimum phenotype, with predictions expressed in terms of the temporal variance and autocorrelation of this optimum. However, empirical studies seldom estimate patterns of fluctuations of an optimum phenotype, precluding further progress in connecting theory with observations. To bridge this gap, we assess the evidence for temporal variation in selection on breeding date by modeling a fitness function with a fluctuating optimum, across 39 populations of 21 wild animals, one of the largest compilations of long-term datasets with individual measurements of trait and fitness components. We find compelling evidence for fluctuations in the fitness function, causing temporal variation in the magnitude, but not the direction of selection. However, fluctuations of the optimum phenotype need not directly translate into variation in selection gradients, because their impact can be buffered by partial tracking of the optimum by the mean phenotype. Analyzing individuals that reproduce in consecutive years, we find that plastic changes track movements of the optimum phenotype across years, especially in bird species, reducing temporal variation in directional selection. This suggests that phenological plasticity has evolved to cope with fluctuations in the optimum, despite their currently modest contribution to variation in selection.

Natural environments are never truly constant, but the evolutionary implications of temporally varying selection pressures remain poorly understood. Here we investigate how the fate of a new mutation in a fluctuating environment depends on the dynamics of environmental variation and on the selective pressures in each condition. We find that even when a mutation experiences many environmental epochs before fixing or going extinct, its fate is not necessarily determined by its time-averaged selective effect. Instead, environmental variability reduces the efficiency of selection across a broad parameter regime, rendering selection unable to distinguish between mutations that are substantially beneficial and substantially deleterious on average. Temporal fluctuations can also dramatically increase fixation probabilities, often making the details of these fluctuations more important than the average selection pressures acting on each new mutation. For example, mutations that result in a trade-off between conditions but are strongly deleterious on average can nevertheless be more likely to fix than mutations that are always neutral or beneficial. These effects can have important implications for patterns of molecular evolution in variable environments, and they suggest that it may often be difficult for populations to maintain specialist traits, even when their loss leads to a decline in time-averaged fitness.

The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques. As long as the rate of change of irradiance does not exceed a specific limit, the HC mode is applied to track the global maximum power point (GMPP). Once a high rate of change in irradiation is detected, the SSA mode is activated. Moreover, the proposed algorithm employs the concept of boundary conditions to handle fast and slow fluctuating irradiance patterns. A comprehensive comparative evaluation of the proposed hybrid SSA-HC with state-of-the-art MPPT algorithms has been undertaken. Four distinct cases have been examined, including irradiance conditions with varying rates of change and partial shading conditions. The proposed hybrid SSA-HC algorithm has been validated and tested using a developed hardware setup, simulated in MATLAB for solar photovoltaic (PV) systems, and compared with standard SSA and HC. The performance of the tracking capability of this proposed hybrid technique at both steady-state and dynamic conditions under rapid and gradual irradiance changes demonstrates its superiority over recent state-of-the-art algorithms.