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A major goal in ecology is to understand mechanisms that increase invasion success of exotic species. A recent hypothesis implicates altered species interactions resulting from ungulate herbivore overabundance as a key cause of exotic plant domination. To test this hypothesis, we maintained an experimental demography deer exclusion study for 6 y in a forest where the native ungulate Odocoileus virginianus (white-tailed deer) is overabundant and Alliaria petiolata (garlic mustard) is aggressively invading. Because population growth is multiplicative across time, we introduce metrics that correctly integrate experimental effects across treatment years, the cumulative population growth rate, λc, and its geometric mean, λper-year, the time-averaged annual population growth rate. We determined λc and λper-year of the invader and of a common native, Trillium erectum. Our results conclusively demonstrate that deer are required for the success of Alliaria; its projected population trajectory shifted from explosive growth in the presence of deer (λper-year = 1.33) to decline toward extinction where deer are excluded (λper-year = 0.88). In contrast, Trillium's λper-year was suppressed in the presence of deer relative to deer exclusion (λper-year = 1.04 vs. 1.20, respectively). Retrospective sensitivity analyses revealed that the largest negative effect of deer exclusion on Alliaria came from rosette transitions, whereas the largest positive effect on Trillium came from reproductive transitions. Deer exclusion lowered Alliaria density while increasing Trillium density. Our results provide definitive experimental support that interactions with overabundant ungulates enhance demographic success of invaders and depress natives' success, with broad implications for biodiversity and ecosystem function worldwide.

Recent studies have examined the risk of poverty throughout the life course, but few have considered how transitioning in and out of poverty shape the dynamic heterogeneity and mortality disparities of a cohort at each age. Here we use state-by-age modeling to capture individual heterogeneity in crossing one of three different poverty thresholds (defined as 1×, 2× or 3× the \"official\" poverty threshold) at each age. We examine age-specific state structure, the remaining life expectancy, its variance, and cohort simulations for those above and below each threshold. Survival and transitioning probabilities are statistically estimated by regression analyses of data from the Health and Retirement Survey RAND data-set, and the National Longitudinal Survey of Youth. Using the results of these regression analyses, we parameterize discrete state, discrete age matrix models. We found that individuals above all three thresholds have higher annual survival than those in poverty, especially for mid-ages to about age 80. The advantage is greatest when we classify individuals based on 1× the \"official\" poverty threshold. The greatest discrepancy in average remaining life expectancy and its variance between those above and in poverty occurs at mid-ages for all three thresholds. And fewer individuals are in poverty between ages 40-60 for all three thresholds. Our findings are consistent with results based on other data sets, but also suggest that dynamic heterogeneity in poverty and the transience of the poverty state is associated with income-related mortality disparities (less transience, especially of those above poverty, more disparities). This paper applies the approach of age-by-stage matrix models to human demography and individual poverty dynamics. In so doing we extend the literature on individual poverty dynamics across the life course.

Both means and year-to-year variances of climate variables such as temperature and precipitation are predicted to change. However, the potential impact of changing climatic variability on the fate of populations has been largely unexamined. We analyzed multiyear demographic data for 36 plant and animal species with a broad range of life histories and types of environment to ask how sensitive their long-term stochastic population growth rates are likely to be to changes in the means and standard deviations of vital rates (survival, reproduction, growth) in response to changing climate. We quantified responsiveness using elasticities of the long-term population growth rate predicted by stochastic projection matrix models. Short-lived species (insects and annual plants and algae) are predicted to be more strongly (and negatively) affected by increasing vital rate variability relative to longer-lived species (perennial plants, birds, ungulates). Taxonomic affiliation has little power to explain sensitivity to increasing variability once longevity has been taken into account. Our results highlight the potential vulnerability of short-lived species to an increasingly variable climate, but also suggest that problems associated with short-lived undesirable species (agricultural pests, disease vectors, invasive weedy plants) may be exacerbated in regions where climate variability decreases.

1. The understorey of tropical forests is heterogeneous across time, and plants that inhabit this layer may exhibit adaptations (e.g. trait plasticity) that enable them to exploit this variability to their advantage. We tested the hypothesis that two widespread understorey herbs would perform equally well in a variable as in a constant environment, using a 2-year shade-house experiment. 2. We measured demographic traits (growth and survival), a physiological trait (maximum photosynthetic capacity), and life-history traits (leaf life span and biomass allocation) of Heliconia tortuosa and Calathea crotalifera. We investigated how these traits were affected by light availability at the seedling stage, precipitation, and whether individuals experienced a constant or variable light environment. 3. Whether or not a variable environment was favourable for plants depended upon precipitation and the environment in which individuals started life. At low precipitation, plants in a variable light environment grew more than those in a constant environment, but only when individuals had lived as seedlings in low light. At high precipitation, plants in a constant environment grew more than those in a variable environment, regardless of early conditions. Survival was lower in a variable environment at low precipitation, and more so at high precipitation. Photosynthetic capacity was lower for individuals in a variable environment than in a constant environment when they had lived in high light as seedlings. 4. Calathea grew faster and survived more poorly than Heliconia, independently of the treatments. Calathea grew more at high than low precipitation while Heliconia grew more at low than high precipitation. Leaf life span and biomass allocation did not differ among treatments, although Calathea had a significantly greater proportion of its biomass above-ground vs. that of Heliconia. 5. Synthesis. Environmental variability had a neutral or positive effect on biomass allocation, photosynthetic capacity, and leaf life span for these species. Survival was the only trait that was always lower in a variable environment. The effect of environmental variability was dependent on early life conditions as well as precipitation, suggesting that generalist species may experience high fitness as forest environments become more variable by maintaining high growth at the expense of survival.

Despite considerable interest in the dynamics of populations subject to temporally varying environments, alternate population growth rates and their sensitivities remain incompletely understood. For a Markovian environment, we compare and contrast the meanings of the stochastic growth rate ( \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{S}\\,}$ \\end{document} ), the growth rate of average population ( \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{M}\\,}$ \\end{document} ), the growth rate for average transition rates ( \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{A}\\,}$ \\end{document} ), and the growth rate of an aggregate represented by a megamatrix (shown here to equal \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{M}\\,}$ \\end{document} ). We distinguish these growth rates by the averages that define them. We illustrate our results using data on an understory shrub in a hurricane‐disturbed landscape, employing a range of hurricane frequencies. We demonstrate important differences among growth rates: \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{S}\\,}< \\lambda _{\\mathrm{M}\\,}$ \\end{document} , but \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{A}\\,}$ \\end{document} can be < or > \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{M}\\,}$ \\end{document} . We show that stochastic elasticity, \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $E^{\\mathrm{S}\\,}_{ij}$ \\end{document} , and megamatrix elasticity, \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $E^{\\mathrm{M}\\,}_{ij}$ \\end{document} , describe a complex perturbation of both means and variances of rates by the same proportion. Megamatrix elasticities respond slightly and stochastic elasticities respond strongly to changing the frequency of disturbance in the habitat (in our example, the frequency of hurricanes). The elasticity \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $E^{\\mathrm{A}\\,}_{ij}$ \\end{document} of \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\lambda _{\\mathrm{A}\\,}$ \\end{document} does not predict changes in the other elasticities. Because \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $E^{\\mathrm{S}\\,}$ \\end{document} , although commonly utilized, is difficult to interpret, we introduce elasticities with a more direct interpretation: \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $E^{\\mathrm{S}\\,\\mu }$ \\end{document} for perturbations of means and \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $E^{\\mathrm{S}\\,\\sigma }$ \\end{document} for variances. We argue that a fundamental tool for studying selection pressures in varying environments is the response of growth rate to vital rates in all habitat states.

Mortality plateaus at advanced ages have been found in many species, but their biological causes remain unclear. Here, we exploit age‐from‐stage methods for organisms with stage‐structured demography to study cohort dynamics, obtaining age patterns of mortality by weighting one‐period stage‐specific survivals by expected age‐specific stage structure. Cohort dynamics behave as a killed Markov process. Using as examples two African grasses, one pine tree, a temperate forest perennial herb, and a subtropical shrub in a hurricane‐driven forest, we illustrate diverse patterns that may emerge. Age‐specific mortality always reaches a plateau at advanced ages, but the plateau may be reached rapidly or slowly, and the trajectory may follow positive or negative senescence along the way. In variable environments, birth state influences mortality at early but not late ages, although its effect on the level of survivorship persists. A new parameter \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $\\mu _{\\omega }$ \\end{document} summarizes the risk of mortality averaged over the entire lifetime in a variable environment. Recent aging models for humans that employ nonobservable abstract states of “vitality” are also known to produce diverse trajectories and similar asymptotic behavior. We discuss connections, contrasts, and implications of our results to these models for the study of aging.

In tropical rain forests, rates of forest turnover and tree species' life-history differences are shaped by the life expectancy of trees and the time taken by seedlings to reach the canopy. These measures are therefore of both theoretical and applied interest. However, the relationship between size, age, and life expectancy is poorly understood. In this paper, we show how to obtain, in a dynamic environment, age-related population parameters from data on size and light transitions and survival of individuals over single time steps. We accomplish this goal by combining two types of analysis (integral projection modeling and age-from-stage analysis for variable environments) in a new way. The method uses an index of crown illumination (CI) to capture the key tree life-history axis of movement through the light environment. We use this method to analyze data on nine tropical tree species, chosen to sample two main gradients, juvenile recruitment niche (gap/nongap) and adult crown position niche (subcanopy, canopy-emergent). We validate the method using independent estimates of age and size from growth rings and ¹⁴C from some of the same species at the same site and use our results to examine correlations among age-related population parameters. Finally, we discuss the implications of these new results for life histories of tropical trees.

Stage-based demographic data are now available on many species of plants and some animals, and they often display temporal and spatial variability. We provide exact formulas to compute age-specific life expectancy and survivorship from stage-based data for three models of temporal variability: cycles, serially independent random variation, and a Markov chain. These models provide a comprehensive description of patterns of temporal variation. Our formulas describe the effects of cohort (birth) environmental condition on mortality at all ages, and of the effects on survivorship of environmental variability experienced over the course of life. This paper complements existing methods for time-invariant stage-based data, and adds to the information on population growth and dynamics available from stochastic demography.

Understanding actual and potential selection on traits of invasive species requires an assessment of the sources of variation in demographic rates. While some of this variation is assignable to environmental, biotic or historical factors, unexplained demographic variation also may play an important role. Even when sites and populations are chosen as replicates, the residual variation in demographic rates can lead to unexplained divergence of asymptotic and transient population dynamics. This kind of divergence could be important for understanding long- and short- term differences among populations of invasive species, but little is known about it. We investigated the demography of a small invasive tree Psidium cattleianum Sabine in the rainforest of Hawaiʻi at four sites chosen for their ecological similarity. Specifically, we parameterized and analyzed integral projection models (IPM) to investigate projected variability among replicate populations in: (1) total population size and annual per capita population growth rate during the transient and asymptotic periods; (2) population structure initially and asymptotically; (3) three key parameters that characterize transient dynamics (the weighted distance of the structure at each time step from the asymptotic structure, the strength of the sub-dominant relative to the dominant dynamics, and inherent cyclicity in the subdominant); and (4) proportional sensitivity (elasticity) of population growth rates (both asymptotic and transient) to perturbations of various components of the life cycle. We found substantial variability among replicate populations in all these aspects of the dynamics. We discuss potential consequences of variability across ecologically similar sites for management and evolutionary ecology in the exotic range of invasive species.

Extreme events significantly impact ecosystems and are predicted to increase in frequency and/or magnitude with climate change. Generalized extreme value (GEV) distributions describe most ecologically relevant extreme events, including hurricanes, wildfires, and disease spread. In climate science, the GEV is widely used as an accurate and flexible tool over large spatial scales (>10⁵ km²) to study how changes in climate shift extreme events. However, ecologists rarely use the GEV to study how climate change affects populations. Here we show how to estimate a GEV for hurricanes at an ecologically relevant (<10³ km²) spatial scale, and use the results in a stochastic, empirically based, matrix population model. As a case study, we use an understory shrub in southeast Florida, USA with hurricane-driven dynamics and measure the effects of change using the stochastic population growth rate. We use sensitivities to analyze how population growth rate is affected by changes in hurricane frequency and intensity, canopy damage levels, and canopy recovery rates. Our results emphasize the importance of accurately estimating location-specific storm frequency. In a rapidly changing world, our methods show how to combine realistic extreme event and population models to assess ecological impacts and to prioritize conservation actions for at-risk populations.