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Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. We extend regression LTREs to incorporate nonlinear (second‐order) terms and compare the accuracy of both approaches for three previously published demographic datasets. We show that the second‐order approach equals or outperforms the linear approach for all three case studies, even when all of the underlying vital rate functions are linear. Nonlinear vital rate responses to driver changes contributed most to population growth rate responses, but life history changes also made substantial contributions. Our results suggest that moving from linear to second‐order LTRE analyses could improve our understanding of population responses to changing environments.

In the first comprehensive assessment of the reproductive rates of critically endangered California Condors (Gymnogyps californianus) recovering from complete extirpation in the wild, we analyzed 20 years (1999–2018) of data from condor flocks in southern and central California. We found that several anthropogenic threats affected reproductive rates: (1) coastal space use by female condors was associated with lower hatch probability, presumably due to foraging on marine mammals and associated DDE exposure; (2) trash ingestion by chicks decreased fledging probability prior to implementation of trash management in 2007; and (3) all parent deaths during rearing resulted in chick or early fledgling deaths, and most parental deaths were due to lead poisoning. We also detected several effects on reproductive rates from the complex individual-based management of condors, which involves ongoing releases of captive-bred individuals and health interventions including treatment of lead poisoning. Recruitment rates were lower for new release sites, which we attribute to a lack of individual- and flock-level experience. In addition, the number of free-flying days in the wild in the year before first breeding and in the 8 weeks before subsequent breeding was positively associated with female and male recruitment and with female rebreeding probabilities, respectively, indicating that removing individuals from the wild may reduce their breeding success. Finally, probabilities of recruitment, rebreeding, and fledging all increased with age, and given the age distribution skew of the recovering flocks toward younger individuals, overall reproductive success was lower than would be expected at the stable age distribution. Thus, reproductive rates should increase over time as the mean age of California Condors increases if current and emerging threats to reproduction, including the loss of breeders due to lead poisoning, can be addressed.

Hybridization between taxa generates new pools of genetic variation that can lead to different environmental responses and demographic trajectories over time than seen in parental lineages. The potential for hybrids to have novel environmental tolerances may be increasingly important in mountainous regions, which are rapidly warming and drying due to climate change. Demographic analysis makes it possible to quantify within‐ and among‐species responses to variation in climate and to predict population growth rates as those conditions change. We estimated vital rates and population growth in 13 natural populations of two cinquefoil taxa (Potentilla hippianaandP. pulcherrima) and their hybrid across elevation gradients in the Southern Rockies. Using three consecutive years of environmental and demographic data, we compared the demographic responses of hybrid and parental taxa to environmental variation across space and time. All three taxa had lower predicted population growth rates under warm, dry conditions. However, the magnitude of these responses varied among taxa and populations. Hybrids had consistently lower predicted population growth rates thanP. hippiana. In contrast, hybrid performance relative toP. pulcherrimavaried with population and climate, with the hybrid maintaining relatively stable growth rates while populations ofP. pulcherrimashrank under warm, dry conditions. Our findings demonstrate that hybrids in this system are neither intrinsically unfit nor universally more vigorous than parents, suggesting that the demographic consequences of hybridization are context‐dependent. Our results also imply that shifts to warmer and drier conditions could have particularly negative repercussions forP. pulcherrima, which is currently the most abundant taxon in the study area, possibly as a legacy of more favorable historical climates. More broadly, the distributions of these long‐lived taxa are lagging behind their demographic trajectories, such that the currently less commonP. hippianacould become the most abundant of thePotentillataxa as this region continues to warm and dry.

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.