Natural populations consist of phenotypically diverse individuals that exhibit variation in their demographic parameters and intra-and interspecific interactions. Recent experimental work suggests that such variation can have significant ecological effects. However, ecological models typically disregard this variation and focus instead on trait means and total population density. Under what situations is this simplification appropriate? Why might intraspecific variation alter ecological dynamics? In this review, we synthesize recent theory, identifying six general mechanisms by which trait variation changes the outcome of ecological interactions. These include several direct effects of trait variation per se, and indirect effects arising from genetic variation's role in trait evolution.

Increases in the frequency, severity and duration of temperature extremes are anticipated in the near future. Although recent work suggests that changes in temperature variation will have disproportionately greater effects on species than changes to the mean, much of climate change research in ecology has focused on the impacts of mean temperature change. Here, we couple finegrained climate projections (2050-2059) to thermal performance data from 38 ectothermic invertebrate species and contrast projections with those of a simple model. We show that projections based on mean temperature change alone differ substantially from those incorporating changes to the variation, and to the mean and variation in concert. Although most species show increases in performance at greater mean temperatures, the effect of mean and variance change together yields a range of responses, with temperate species at greatest risk of performance declines. Our work highlights the importance of using fine-grained temporal data to incorporate the full extent of temperature variation when assessing and projecting performance.

Paramount to our ability to manage and protect biological communities from impending changes in the environment is an understanding of how communities will respond. General mathematical models of community dynamics are often too simplistic to accurately describe this response, partly to retain mathematical tractability and partly for the lack of biologically pleasing functions representing the model/environment interface. We address these problems of tractability and plausibility in community/environment models by incorporating the Boltzmann factor (temperature dependence) in a bioenergetic consumer-resource framework. Our analysis leads to three predictions for the response of consumer-resource systems to increasing mean temperature (warming). First, mathematical extinctions do not occur with warming; however, stable systems may transition into an unstable (cycling) state. Second, there is a decrease in the biomass density of resources with warming. The biomass density of consumers may increase or decrease depending on their proximity to the feasibility (extinction) boundary. Third, consumer biomass density is more sensitive to warming than resource biomass density (with some exceptions). These predictions are in line with many current observations and experiments. The model presented and analyzed here provides an advancement in the testing framework for global change scenarios and hypotheses of latitudinal and elevational species distributions.

Changing temperature can substantially shift ecological communities by altering the strength and stability of trophic interactions. Because many ecological rates are constrained by temperature, new approaches are required to understand how simultaneous changes in multiple rates alter the relative performance of species and their trophic interactions. We develop an energetic approach to identify the relationship between biomass fluxes and standing biomass across trophic levels. Our approach links ecological rates and trophic dynamics to measure temperature-dependent changes to the strength of trophic interactions and determine how these changes alter food web stability. It accomplishes this by using biomass as a common energetic currency and isolating three temperature-dependent processes that are common to all consumer-resource interactions: biomass accumulation of the resource, resource consumption and consumer mortality. Using this framework, we clarify when and how temperature alters consumer to resource biomass ratios, equilibrium resilience, consumer variability, extinction risk and transient vs. equilibrium dynamics. Finally, we characterise key asymmetries in species responses to temperature that produce these distinct dynamic behaviours and identify when they are likely to emerge. Overall, our framework provides a mechanistic and more unified understanding of the temperature dependence of trophic dynamics in terms of ecological rates, biomass ratios and stability.

Biological populations are strongly influenced by the random variation in their environment. The spectrum of frequencies in noise is particularly important to dynamics and persistence. Here we present an analysis of the variance spectra of a wide variety of long‐term time series of environmental variables. Spectra were well approximated by the inverse power law 1/fβ within the appropriate range of frequencies f; however, the majority of spectra were “flattened” at low frequencies. With some qualification we found the spectral exponents (β) to corroborate an earlier suggestion that terrestrial noise tends to be “white” (β < 0.5), while marine environments tend to be “red” (β ≈ 1) or “brown” (β ≈ 2). As well, we found a tendency for whiter noise in temperate latitudes than in either high or low latitudes. These results have wide‐ranging consequences for ecosystem fragility and species conservation.

Spatially synchronized fluctuations in system state are common in physical and biological systems ranging from individual atoms to species as diverse as viruses, insects and mammals. Although the causal factors are well known for many synchronized phenomena, several processes concurrently have an impact on spatial synchrony of species, making their separate effects and interactions difficult to quantify. Here we develop a general stochastic model of predator-prey spatial dynamics to predict the outcome of a laboratory microcosm experiment testing for interactions among all known synchronizing factors: (1) dispersal of individuals between populations; (2) spatially synchronous fluctuations in exogenous environmental factors (the Moran effect); and (3) interactions with other species (for example, predators) that are themselves spatially synchronized. The Moran effect synchronized populations of the ciliate protist Tetrahymena pyriformis; however, dispersal only synchronized prey populations in the presence of the predator Euplotes patella. Both model and data indicate that synchrony depends on cyclic dynamics generated by the predator. Dispersal, but not the Moran effect, 'phase-locks' cycles, which otherwise become 'decoherent' and drift out of phase. In the absence of cycles, phase-locking is not possible and the synchronizing effect of dispersal is negligible. Interspecific interactions determine population synchrony, not by providing an additional source of synchronized fluctuations, but by altering population dynamics and thereby enhancing the action of dispersal. Our results are robust to wide variation in model parameters representative of many natural predator-prey or host-pathogen systems. This explains why cyclic systems provide many of the most dramatic examples of spatial synchrony in nature.

Recent studies suggest that selection can allow coexistence in situations where ecological dynamics lead to competitive exclusion, provided that there is a trade-off between traits optimal for interacting with conspecifics and traits optimal for interacting with heterospecifics. Despite compelling empirical evidence, there is no general framework for elucidating how and when selection will allow coexistence in natural communities. Here we develop such a framework for a mechanism that we term "neighbor-dependent selection." We show that this mechanism can both augment coexistence when ecological conditions allow for niche partitioning and enable coexistence when ecological conditions lead to competitive exclusion. The novel insight is that when ecological conditions lead to exclusion, neighbor-dependent selection can allow coexistence via cycles driven by an intransitive loop; selection causes one species to be a superior interspecific competitor when it is rare and an inferior interspecific competitor when it is abundant. Our framework predicts the conditions under which selection can enable coexistence, as opposed to merely augmenting it, and elucidates the effects of heritability on the eco-evolutionary feedbacks that drive coexistence. Given increasing evidence that evolution operates on ecological timescales, our approach provides one means for evaluating the role of selection and trait evolution in species coexistence.

Community biomass is often less variable than the biomasses of populations within the community, yet attempts to implicate compensatory dynamics between populations as a cause of this relationship often fail. In part, this may be due to the lack of appropriate metrics for variability, but there is also great potential for large-scale processes such as seasonality or longer-term environmental change to obscure important dynamics at other temporal scales. In this study, we apply a scale-resolving method to long-term plankton data, to identify the specific temporal scales at which community-level variability is influenced by synchrony or compensatory dynamics at the population level. We show that variability at both the population and community level is influenced strongly by a few distinct temporal scales: in phytoplankton, ciliate, rotifer, and crustacean communities, synchronous dynamics are predominant at most temporal scales. However, in phytoplankton and crustacean communities, compensatory dynamics occur at a sub-annual scale (and at the annual scale in crustaceans) leading to substantial reductions in community-level variability. Aggregate measures of population and community variability do not detect compensatory dynamics in these communities; thus, resolving their scale dependence unmasks dynamics that are important for community stability in this system. The methods and results presented herein will ultimately lead to a better understanding of how stability is achieved in communities.