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Conspecific populations living in adjacent but contrasting microenvironments represent excellent systems for studying natural selection. These systems are valuable because gene flow is expected to force genetic homogeneity except at loci experiencing divergent selection. A history of reciprocal transplant and common garden studies in such systems, and a growing number of genomic studies, have contributed to understanding how selection operates in natural populations. While selection can vary across different fitness components and life stages, few studies have investigated how this ultimately affects allele frequencies and the maintenance of divergence between populations. Here, we study two sunflower ecotypes in distinct, adjacent habitats by combining demographic models with genome‐wide sequence data to estimate fitness and allele frequency change at multiple life stages. This framework allows us to estimate that only local ecotypes are likely to experience positive population growth (λ > 1) and that the maintenance of divergent adaptation appears to be mediated via habitat‐ and life stage‐specific selection. We identify genetic variation, significantly driven by loci in chromosomal inversions, associated with different life history strategies in neighbouring ecotypes that optimize different fitness components and may contribute to the maintenance of distinct ecotypes.

Explaining large‐scale ordered patterns and their effects on ecosystem functioning is a fundamental and controversial challenge in ecology. Here, we coupled empirical and theoretical approaches to explore how competition and spatial heterogeneity govern the regularity of colony dispersion in fungus‐farming termites. Individuals from different colonies fought fiercely, and inter‐nest distances were greater when nests were large and resources scarce—as expected if competition is strong, large colonies require more resources and foraging area scales with resource availability. Building these principles into a model of inter‐colony competition showed that highly ordered patterns emerged under high resource availability and low resource heterogeneity. Analysis of this dynamical model provided novel insights into the mechanisms that modulate pattern regularity and the emergent effects of these patterns on system‐wide productivity. Our results show how environmental context shapes pattern formation by social‐insect ecosystem engineers, which offers one explanation for the marked variability observed across ecosystems.

The expectations of polar or upslope distributional shifts of species ranges in response to warming climate conditions have been recently questioned. Diverse responses of different life stages to changing temperature and moisture regimes may alter these predicted range dynamics. Furthermore, the climate driver(s) influencing demographic rates, and the contribution of each demographic rate to population growth rate (λ), may shift across a species range. We investigated these demographic effects by experimentally manipulating climate and measuring responses of λ in nine populations spanning the elevation range of an alpine plant (Ivesia lycopodioides). Populations exhibited stable growth rates (λ ~ 1) under naturally wet conditions and declining rates (λ < 1) under naturally dry conditions. However, opposing vital rate responses to experimental heating and watering lead to negligible or negative effects on population stability. These findings indicate that life stage–specific responses to changing climate can disrupt the current relationships between population stability and climate across species ranges.

With ongoing climate change, populations are expected to exhibit shifts in demographic performance that will alter where a species can persist. This presents unique challenges for managing plant populations and may require ongoing interventions, including in situ management or introduction into new locations. However, few studies have examined how climate change may affect plant demographic performance for a suite of species, or how effective management actions could be in mitigating climate change effects. Over the course of two experiments spanning 6 yr and four sites across a latitudinal gradient in the Pacific Northwest, United States, we manipulated temperature, precipitation, and disturbance intensity, and quantified effects on the demography of eight native annual prairie species. Each year we planted seeds and monitored germination, survival, and reproduction. We found that disturbance strongly influenced demographic performance and that seven of the eight species had increasingly poor performance with warmer conditions. Across species and sites, we observed 11% recruitment (the proportion of seeds planted that survived to reproduction) following high disturbance, but just 3.9% and 2.3% under intermediate and low disturbance, respectively. Moreover, mean seed production following high disturbance was often more than tenfold greater than under intermediate and low disturbance. Importantly, most species exhibited precipitous declines in their population growth rates (λ) under warmer‐than‐ambient experimental conditions and may require more frequent disturbance intervention to sustain populations.Aristida oligantha, a C4 grass, was the only species to have λ increase with warmer conditions. These results suggest that rising temperatures may cause many native annual plant species to decline, highlighting the urgency for adaptive management practices that facilitate their restoration or introduction to newly suitable locations. Frequent and intense disturbances are critical to reduce competitors and promote native annuals’ persistence, but even such efforts may prove futile under future climate regimes.

Predicting species' range shifts under future climate is a central goal of conservation ecology. Studying populations within and beyond multiple species' current ranges can help identify whether demographic responses to climate change exhibit directionality, indicative of range shifts, and whether responses are uniform across a suite of species.

Structured demographic models are among the most common and useful tools in population biology. However, the introduction of integral projection models (IPMs) has caused a profound shift in the way many demographic models are conceptualized. Some researchers have argued that IPMs, by explicitly representing demographic processes as continuous functions of state variables such as size, are more statistically efficient, biologically realistic, and accurate than classic matrix projection models, calling into question the usefulness of the many studies based on matrix models. Here, we evaluate how IPMs and matrix models differ, as well as the extent to which these differences matter for estimation of key model outputs, including population growth rates, sensitivity patterns, and life spans. First, we detail the steps in constructing and using each type of model. Second, we present a review of published demographic models, concentrating on size‐based studies, which shows significant overlap in the way IPMs and matrix models are constructed and analyzed. Third, to assess the impact of various modeling decisions on demographic predictions, we ran a series of simulations based on size‐based demographic data sets for five biologically diverse species. We found little evidence that discrete vital rate estimation is less accurate than continuous functions across a wide range of sample sizes or size classes (equivalently bin numbers or mesh points). Most model outputs quickly converged with modest class numbers (≥10), regardless of most other modeling decisions. Another surprising result was that the most commonly used method to discretize growth rates for IPM analyses can introduce substantial error into model outputs. Finally, we show that empirical sample sizes generally matter more than modeling approach for the accuracy of demographic outputs. Based on these results, we provide specific recommendations to those constructing and evaluating structured population models. Both our literature review and simulations question the treatment of IPMs as a clearly distinct modeling approach or one that is inherently more accurate than classic matrix models. Importantly, this suggests that matrix models, representing the vast majority of past demographic analyses available for comparative and conservation work, continue to be useful and important sources of demographic information.

Populations of many species are genetically adapted to local historical climate conditions. Yet most forecasts of species’ distributions under climate change have ignored local adaptation (LA), which may paint a false picture of how species will respond across their geographic ranges. We review recent studies that have incorporated intraspecific variation, a potential proxy for LA, into distribution forecasts, assess their strengths and weaknesses, and make recommendations for how to improve forecasts in the face of LA. The three methods used so far (species distribution models, response functions, and mechanistic models) reflect a trade‐off between data availability and the ability to rigorously demonstrate LA to climate. We identify key considerations for incorporating LA into distribution forecasts that are currently missing from many published studies, including testing the spatial scale and pattern of LA, the confounding effects of LA to nonclimatic or biotic drivers, and the need to incorporate empirically based dispersal or gene flow processes. We suggest approaches to better evaluate these aspects of LA and their effects on species‐level forecasts. In particular, we highlight demographic and dynamic evolutionary models as promising approaches to better integrate LA into forecasts, and emphasize the importance of independent model validation. Finally, we urge closer examination of how LA will alter the responses of central vs. marginal populations to allow stronger generalizations about changes in distribution and abundance in the face of LA.

Structured population models are among the most widely used tools in ecology and evolution. Integral projection models (IPMs) use continuous representations of how survival, reproduction and growth change as functions of state variables such as size, requiring fewer parameters to be estimated than projection matrix models (PPMs). Yet, almost all published IPMs make an important assumption that size‐dependent growth transitions are or can be transformed to be normally distributed. In fact, many organisms exhibit highly skewed size transitions. Small individuals can grow more than they can shrink, and large individuals may often shrink more dramatically than they can grow. Yet, the implications of such skew for inference from IPMs has not been explored, nor have general methods been developed to incorporate skewed size transitions into IPMs, or deal with other aspects of real growth rates, including bounds on possible growth or shrinkage.

Shifts in species geographic distributions in response to climate change have spurred numerous studies to determine which abiotic (e.g. climatic) and, less commonly, biotic (e.g. competitive) processes determine range limits. However, the impact of disturbances on range limits and their interactions with climatic and biotic effects is not well understood, despite their potential to alter competitive relationships between species or override climatic effects. Disturbance might have differential effects at contrasting range limits, based on Darwin's theory that biotic interactions set abiotically benign range limits and abiotic factors set abiotically stressful range limits.

Many predictions of how climate change will impact biodiversity have focused on range shifts using species‐wide climate tolerances, an approach that ignores the demographic mechanisms that enable species to attain broad geographic distributions. But these mechanisms matter, as responses to climate change could fundamentally differ depending on the contributions of life‐history plasticity vs. local adaptation to species‐wide climate tolerances. In particular, if local adaptation to climate is strong, populations across a species’ range—not only those at the trailing range edge—could decline sharply with global climate change. Indeed, faster rates of climate change in many high latitude regions could combine with local adaptation to generate sharper declines well away from trailing edges. Combining 15 years of demographic data from field populations across North America with growth chamber warming experiments, we show that growth and survival in a widespread tundra plant show compensatory responses to warming throughout the species’ latitudinal range, buffering overall performance across a range of temperatures. However, populations also differ in their temperature responses, consistent with adaptation to local climate, especially growing season temperature. In particular, warming begins to negatively impact plant growth at cooler temperatures for plants from colder, northern populations than for those from warmer, southern populations, both in the field and in growth chambers. Furthermore, the individuals and maternal families with the fastest growth also have the lowest water use efficiency at all temperatures, suggesting that a trade‐off between growth and water use efficiency could further constrain responses to forecasted warming and drying. Taken together, these results suggest that populations throughout species’ ranges could be at risk of decline with continued climate change, and that the focus on trailing edge populations risks overlooking the largest potential impacts of climate change on species’ abundance and distribution.