Kinship And Care: Racial Disparities In Potential Dementia Caregiving In The U.S. From 2000 To 2060.

BACKGROUND: Although the family plays a pivotal role in older adults' care, there is limited research on how evolving demographic trends affect older adults' support networks and how the trends vary by race. To fill this gap, we examine the influence of shifting family demographics on future care needs for older adults with dementia, emphasizing the unequal health and potential caregiving burdens by race in the U.S. METHODS: Using demographic models of kinship, we estimate the availability of potential caregivers, and dementia prevalence among one's kin by race, kin type, and the age of a focal person from 2000 to 2060. We introduce an index called the Dementia Dependency Ratio to assess dementia caregiving demands at the population level, taking into account the age and kinship structure of the population. RESULTS: Our findings suggest that Black individuals tend to have more children, grandchildren, and nieces/nephews as they age. However, Black individuals also tend to have more kin with dementia compared to their White counterparts. This elevated prevalence of dementia among Black kinship networks counterbalances the advantage of having more kin as potential caregivers. A further projection analysis suggests that the racial gap in caregiving demand within the kinship network will widen in the next four decades if the racial gap in dementia prevalence remains unchanged. CONCLUSIONS: These findings emphasize the urgency of reducing racial inequality in dementia prevalence rates and increasing public support for families with extended members affected by dementia. With the shrinkage of nuclear families and population aging in the next few decades, extended family members may undertake more caregiving responsibilities for dementia. We call for a kinship perspective in understanding dementia care in future research.

Projections of human kinship for all countries.

Demographers have long attempted to project future changes in the size and composition of populations, but have ignored what these processes will mean for the size, composition, and age distribution of family networks. Kinship structures matter because family solidarity-a crucial source of informal care for millions of people around the world-is conditional on kin being alive. Here, we present innovative projections of biological kin for the 1950 to 2100 period and discuss what they imply for the availability of informal care. Overall, we project that the number of living kin for individuals will decline dramatically worldwide. While a 65-yo woman in 1950 could expect to have 41 living kin, a 65-yo woman in 2095 is projected to have just 25 [18.8 to 34.7] relatives (lower and upper 80% projection intervals). This represents a 38% [15 to 54] global decline. The composition of family networks is also expected to change, with the numbers of living grandparents and great-grandparents markedly increasing, and the numbers of cousins, nieces and nephews, and grandchildren declining. Family networks will age considerably, as we project a widening age gap between individuals and their kin due to lower and later fertility and longer lifespans. In Italy, for example, the average age of a grandmother of a 35-yo woman is expected to increase from 77.9 y in 1950 to 87.7 y [87.1 to 88.5] in 2095. The projected changes in kin supply will put pressure on the already stretched institutional systems of social support, as more individuals age with smaller and older family networks.

Causes and consequences of pair-bond disruption in a sex-skewed population of a long-lived monogamous seabird.

Many animals form long-term monogamous pair bonds, and the disruption of a pair bond (through either divorce or widowhood) can have significant consequences for individual vital rates (survival, breeding, and breeding success probabilities) and life-history outcomes (lifetime reproductive success [LRS], life expectancy). Here, we investigated the causes and consequences of pair-bond disruption in wandering albatross (Diomedea exulans). State-of-the-art statistical and mathematical approaches were developed to estimate divorce and widowhood rates and their impacts on vital rates and life-history outcomes. In this population, females incur a higher mortality rate due to incidental fishery bycatch, so the population is male-skewed. Therefore, we first posited that males would show higher widowhood rates negatively correlated with fishing effort and females would have higher divorce rates because they have more mating opportunities. Furthermore, we expected that divorce could be an adaptive strategy, whereby individuals improved breeding success by breeding with a new partner of better quality. Finally, we posited that pair-bond disruptions could reduce survival and breeding probabilities owing to the cost of remating processes, with important consequences for life-history outcomes. As expected, we showed that males had higher widowhood rates than females and females had higher divorce rates in this male-skewed population. However, no correlation was found between fishing effort and male widowhood. Secondly, contrary to our expectation, we found that divorce was likely nonadaptive in this population. We propose that divorce in this population is caused by an intruder who outcompetes the original partner in line with the so-called forced divorce hypothesis. Furthermore, we found a 16.7% and 18.0% reduction in LRS only for divorced and widowed males, respectively, owing to missing breeding seasons after a pair-bond disruption. Finally, we found that divorced individuals were more likely to divorce again, but whether this is related to specific individual characteristics remains an important area of investigation.

Individual stochasticity in the life history strategies of animals and plants.

The life histories of organisms are expressed as rates of development, reproduction, and survival. However, individuals may experience differential outcomes for the same set of rates. Such individual stochasticity generates variance around familiar mean measures of life history traits, such as life expectancy and the reproductive number R0. By writing life cycles as Markov chains, we calculate variance and other indices of variability for longevity, lifetime reproductive output (LRO), age at offspring production, and age at maturity for 83 animal and 332 plant populations from the Comadre and Compadre matrix databases. We find that the magnitude within and variability between populations in variance indices in LRO, especially, are surprisingly high. We furthermore use principal components analysis to assess how the inclusion of variance indices of different demographic outcomes affects life history constraints. We find that these indices, to a similar or greater degree than the mean, explain the variation in life history strategies among plants and animals.

Years of life lost during the Covid-19 pandemic in Sweden considering variation in life expectancy by level of geriatric care.

The Covid-19 pandemic has not affected the population evenly. This must be acknowledged when it comes to understanding the Covid-19 death toll and answering the question of how many life years have been lost. We use level of geriatric care to account for variation in remaining life expectancy among individuals that died during 2020. Based on a linkage of administrative registers, we estimate remaining life expectancy stratified by age, sex, and care status using an incidence-based multistate model and analyze the number of years of life lost (YLL) during 2020 in Sweden. Our results show that remaining life expectancy between individuals with and without care differs substantially. More than half of all Covid-19 deaths had a remaining life expectancy lower than 4 years. Yet, in a 1-year perspective, Covid-19 did not seem to replace other causes of death. Not considering the differences in remaining life expectancy in the affected populations overestimated YLL by 40% for women and 30% for men, or around 2 years per death. While the unadjusted YLL from Covid-19 amounted to an average of 7.5 years for women and 8.6 years for men, the corresponding YLL adjusted for care status were 5.4 and 6.6, respectively. The total number of YLL to Covid-19 in 2020 is comparable to YLL from ischemic heart disease in 2019 and 2020. Our results urge the use of subgroup specific mortality when counting the burden of Covid-19. YLL are considerably reduced when the varying susceptibility for death is considered, but even if most lifespans were cut in the last years of life, the YLL are still substantial.

When the going gets tough, the tough get going: Effect of extreme climate on an Antarctic seabird's life history.

Individuals differ in many ways. Most produce few offspring; a handful produce many. Some die early; others live to old age. It is tempting to attribute these differences in outcomes to differences in individual traits, and thus in the demographic rates experienced. However, there is more to individual variation than meets the eye of the biologist. Even among individuals sharing identical traits, life history outcomes (life expectancy and lifetime reproduction) will vary due to individual stochasticity, that is to chance. Quantifying the contributions of heterogeneity and chance is essential to understand natural variability. Interindividual differences vary across environmental conditions, hence heterogeneity and stochasticity depend on environmental conditions. We show that favourable conditions increase the contributions of individual stochasticity, and reduce the contributions of heterogeneity, to variance in demographic outcomes in a seabird population. The opposite is true under poor conditions. This result has important consequence for understanding the ecology and evolution of life history strategies.

The Role of Kinship in Racial Differences in Exposure to Unemployment.

Most studies on unemployment have assessed its individual-level costs. However, beyond its effects on individuals, unemployment incurs costs for their immediate families and extended kin. Close kin provide the majority of social support for unemployed adults. Applying demographic and statistical techniques to official statistics and using COVID-19 survey data on kinship and labor force experience, we assess the unemployment level and exposure to unemployment in the United States from a kinship perspective. The results indicate dramatic racial disparities in the number of unemployed kin and the number of kin affected by an unemployed person. Specifically, during the pandemic-induced recession, Black Americans had 1.7 unemployed people in their extended family compared with 1.2 among Whites. Further, every job loss in a Black extended family affected approximately 23 related members of the family through kinship ties, compared with approximately 20 among Whites. The racial gap in the number of unemployed kin is evident in all age-groups and escalates with age. This study's findings highlight the need to understand unemployment and its demographic implications, which are stratified by race.

The Contributions of Maternal Age Heterogeneity to Variance in Lifetime Reproductive Output.

AbstractVariance among individuals in fitness components reflects both genuine heterogeneity between individuals and stochasticity in events experienced along the life cycle. Maternal age represents a form of heterogeneity that affects both the mean and the variance of lifetime reproductive output (LRO). Here, we quantify the relative contribution of maternal age heterogeneity to the variance in LRO using individual-level laboratory data on the rotifer Brachionus manjavacas to parameterize a multistate age × maternal age matrix model. In B. manjavacas, advanced maternal age has large negative effects on offspring survival and fertility. We used multistate Markov chains with rewards to quantify the contributions to variance in LRO of heterogeneity and of the stochasticity inherent in the outcomes of probabilistic transitions and reproductive events. Under laboratory conditions, maternal age heterogeneity contributes 26% of the variance in LRO. The contribution changes when mortality and fertility are reduced to mimic more ecologically relevant environments. Over the parameter space where populations are near stationarity, maternal age heterogeneity contributes an average of 3% of the variance. Thus, the contributions of maternal age heterogeneity and individual stochasticity can be expected to depend strongly on environmental conditions; over most of the parameter space, the variance in LRO is dominated by stochasticity.

A demographic and evolutionary analysis of maternal effect senescence.

Maternal effect senescence-a decline in offspring survival or fertility with maternal age-has been demonstrated in many taxa, including humans. Despite decades of phenotypic studies, questions remain about how maternal effect senescence impacts evolutionary fitness. To understand the influence of maternal effect senescence on population dynamics, fitness, and selection, we developed matrix population models in which individuals are jointly classified by age and maternal age. We fit these models to data from individual-based culture experiments on the aquatic invertebrate, Brachionus manjavacas (Rotifera). By comparing models with and without maternal effects, we found that maternal effect senescence significantly reduces fitness for B. manjavacas and that this decrease arises primarily through reduced fertility, particularly at maternal ages corresponding to peak reproductive output. We also used the models to estimate selection gradients, which measure the strength of selection, in both high growth rate (laboratory) and two simulated low growth rate environments. In all environments, selection gradients on survival and fertility decrease with increasing age. They also decrease with increasing maternal age for late maternal ages, implying that maternal effect senescence can evolve through the same process as in Hamilton's theory of the evolution of age-related senescence. The models we developed are widely applicable to evaluate the fitness consequences of maternal effect senescence across species with diverse aging and fertility schedule phenotypes.

Variance as a life history outcome: Sensitivity analysis of the contributions of stochasticity and heterogeneity.

Variance in life history outcomes among individuals is a requirement for natural selection, and a determinant of the ecological dynamics of populations. Heterogeneity among individuals will cause such variance, but so will the inherently stochastic nature of their demography. The relative contributions of these variance components - stochasticity and heterogeneity - to life history outcomes are presented here in a general, demographic calculation. A general formulation of sensitivity analysis is provided for the relationship between the variance components and the demographic rates within the life cycle. We illustrate these novel methods with two examples; the variance in longevity within and between frailty groups in a laboratory population of fruit flies, and the variance in lifetime reproductive output within and between initial environment states in a perennial herb in a stochastic fire environment. In fruit flies, an increase in mortality would increase the variance due to stochasticity and reduce that due to heterogeneity. In the plant example, increasing mortality reduces, and increasing fertility increases both variance components. Sensitivity analyses such as these can provide a powerful tool in identifying patterns among life history stages and heterogeneity groups and their contributions to variance in life history outcomes.

The Paris Agreement objectives will likely halt future declines of emperor penguins.

The Paris Agreement is a multinational initiative to combat climate change by keeping a global temperature increase in this century to 2°C above preindustrial levels while pursuing efforts to limit the increase to 1.5°C. Until recently, ensembles of coupled climate simulations producing temporal dynamics of climate en route to stable global mean temperature at 1.5 and 2°C above preindustrial levels were not available. Hence, the few studies that have assessed the ecological impact of the Paris Agreement used ad-hoc approaches. The development of new specific mitigation climate simulations now provides an unprecedented opportunity to inform ecological impact assessments. Here we project the dynamics of all known emperor penguin (Aptenodytes forsteri) colonies under new climate change scenarios meeting the Paris Agreement objectives using a climate-dependent-metapopulation model. Our model includes various dispersal behaviors so that penguins could modulate climate effects through movement and habitat selection. Under business-as-usual greenhouse gas emissions, we show that 80% of the colonies are projected to be quasiextinct by 2100, thus the total abundance of emperor penguins is projected to decline by at least 81% relative to its initial size, regardless of dispersal abilities. In contrast, if the Paris Agreement objectives are met, viable emperor penguin refuges will exist in Antarctica, and only 19% and 31% colonies are projected to be quasiextinct by 2100 under the Paris 1.5 and 2 climate scenarios respectively. As a result, the global population is projected to decline by at least by 31% under Paris 1.5 and 44% under Paris 2. However, population growth rates stabilize in 2060 such that the global population will be only declining at 0.07% under Paris 1.5 and 0.34% under Paris 2, thereby halting the global population decline. Hence, global climate policy has a larger capacity to safeguard the future of emperor penguins than their intrinsic dispersal abilities.

Selection in two-sex stage-structured populations: Genetics, demography, and polymorphism.

The outcome of natural selection depends on the demographic processes of birth, death, and development. Here, we derive conditions for protected polymorphism in a population characterized by age- or stage-dependent demography with two sexes. We do so using a novel two-sex matrix population model including basic Mendelian genetics (one locus, two alleles, random mating). Selection may operate on survival, growth, or fertility, any or all of which may differ between the sexes. The model can therefore incorporate genes with arbitrary pleiotropic and sex-specific effects. Conditions for protected polymorphism are expressed in terms of the eigenvalues of the linearization of the model at the homozygote boundary equilibria. We show that in the absence of sexual dimorphism, polymorphism requires heterozygote superiority in the genotypic population growth rate. In the presence of sexual dimorphism, however, heterozygote superiority is not required; an inferior heterozygote may invade, reducing the population growth rate and even leading to extinction (so-called evolutionary suicide). Our model makes no assumptions about separation of time scales between ecological and evolutionary processes, and can thus be used to project sex×stage×genotype dynamics of eco-evolutionary processes. Empirical evidence that sexual dimorphism affects extinction risk is growing, yet sex differences are often ignored in evolutionary demography and in eco-evolutionary models. Our analysis highlights the importance of sexual dimorphism and suggests mechanisms by which an allele can be favored by selection, yet drive a population to extinction, as a result of the structure and interdependence of sex- and stage-specific processes.

Changing contribution of area-level deprivation to total variance in age at death: a population-based decomposition analysis.

OBJECTIVES: Two processes generate total variance in age at death: heterogeneity (between-group variance) and individual stochasticity (within-group variance). Limited research has evaluated how these two components have changed over time. We quantify the degree to which area-level deprivation contributed to total variance in age at death in Scotland between 1981 and 2011. DESIGN: Full population and mortality data for Scotland were obtained and matched with the Carstairs score, a standardised z-score calculated for each part-postcode sector that measures relative area-level deprivation. A z-score above zero indicates that the part-postcode sector experienced higher deprivation than the national average. A z-score below zero indicates lower deprivation. From the aggregated data we constructed 40 lifetables, one for each deprivation quintile in 1981, 1991, 2001 and 2011 stratified by sex. PRIMARY OUTCOME MEASURES: Total variance in age at death and the proportion explained by area-level deprivation heterogeneity (between-group variance). RESULTS: The most deprived areas experienced stagnating or slightly increasing variance in age at death. The least deprived areas experienced decreasing variance. For males, the most deprived quintile life expectancy was between 7% and 11% lower and the SD is between 6% and 25% higher than the least deprived. This suggests that the effect of deprivation on the SD of longevity is comparable to its effect on life expectancy. Decomposition analysis revealed that contributions from between-group variance doubled between 1981 and 2011 but at most only explained 4% of total variance. CONCLUSIONS: This study adds to the emerging body of literature demonstrating that socio-economic groups have experienced diverging trends in variance in age at death. The contribution from area-level deprivation to total variance in age at death, which we were able to capture, has doubled since 1981. Area-level deprivation may play an increasingly important role in mortality inequalities.

Stage-Structured Evolutionary Demography: Linking Life Histories, Population Genetics, and Ecological Dynamics.

Demographic processes and ecological interactions are central to understanding evolution and vice versa. We present a novel framework that combines basic Mendelian genetics with the powerful demographic approach of matrix population models. The ecological components of the model may be stage classified or age classified, linear or nonlinear, time invariant or time varying, and deterministic or stochastic. Genotypes may affect, in fully pleiotropic fashion, any mixture of demographic traits (viability, fertility, development) at any points in the life cycle. The dynamics of the stage × genotype structure of the population are given by a nonlinear population projection matrix. We show how to construct this matrix and use it to derive sufficient conditions for a protected genetic polymorphism for the case of linear, time-independent demography. These conditions demonstrate that genotype-specific population growth rates (λ) do not determine the outcome of selection. Except in restrictive special cases, heterozygote superiority in λ is neither necessary nor sufficient for a genetic polymorphism. As a consequence, the population growth rate does not always increase, and populations can be driven to extinction due to evolutionary suicide. We demonstrate the construction and analysis of the model using data on a color polymorphism in the common buzzard (Buteo buteo). The model exhibits a stable genetic polymorphism and declining growth rate, consistent with field data and previous models.

Ringed seal demography in a changing climate.

Climate change is affecting species' distributions and abundances worldwide. Baseline population estimates, against which future observations may be compared, are necessary if we are to detect ecological change. Arctic sea ice ecosystems are changing rapidly and we lack baseline population estimates for many ice-associated species. Provided we can detect them, changes in Arctic marine ecosystems may be signaled by changes in indicator species such as ringed seals (Pusa hispida). Ringed seal monitoring has provided estimates of survival and fertility rates, but these have not been used for population-level inference. Using matrix population models, we synthesized existing demographic parameters to obtain estimates of historical ringed seal population growth and structure in Amundsen Gulf and Prince Albert Sound, Canada. We then formalized existing hypotheses about the effects of emerging environmental stressors (i.e., earlier spring ice breakup and reduced snow depth) on ringed seal pup survival. Coupling the demographic model to ice and snow forecasts available from the Coupled Model Intercomparison Project resulted in projections of ringed seal population size and structure up to the year 2100. These projections showed median declines in population size ranging from 50% to 99%. Corresponding to these projected declines were substantial changes in population structure, with increasing proportions of ringed seal pups and adults and declining proportions of juveniles. We explored if currently collected, harvest-based data could be used to detect the projected changes in population stage structure. Our model suggests that at a present sample size of 100 seals per year, the projected changes in stage structure would only be reliably detected by mid-century, even for the most extreme climate models. This modeling process revealed inconsistencies in existing estimates of ringed seal demographic rates. Mathematical population models such as these can contribute both to understanding past population trends as well as predicting future ones, both of which are necessary if we are to detect and interpret future observations.

Age × stage-classified demographic analysis: a comprehensive approach.

This paper presents a comprehensive theory for the demographic analysis of populations in which individuals are classified by both age and stage. The earliest demographic models were age classified. Ecologists adopted methods developed by human demographers and used life tables to quantify survivorship and fertility of cohorts and the growth rates and structures of populations. Later, motivated by studies of plants and insects, matrix population models structured by size or stage were developed. The theory of these models has been extended to cover all the aspects of age-classified demography and more. It is a natural development to consider populations classified by both age and stage. A steady trickle of results has appeared since the 1960s, analyzing one or another aspect of age × stage-classified populations, in both ecology and human demography. Here, we use the vec-permutation formulation of multistate matrix population models to incorporate age- and stage-specific vital rates into demographic analysis. We present cohort results for the life table functions (survivorship, mortality, and fertility), the dynamics of intra-cohort selection, the statistics of longevity, the joint distribution of age and stage at death, and the statistics of life disparity. Combining transitions and fertility yields a complete set of population dynamic results, including population growth rates and structures, net reproductive rate, the statistics of lifetime reproduction, and measures of generation time. We present a complete analysis of a hypothetical model species, inspired by poecilogonous marine invertebrates that produce two kinds of larval offspring. Given the joint effects of age and stage, many familiar demographic results become multidimensional, so calculations of marginal and mixture distributions are an important tool. From an age-classified point of view, stage structure is a form of unobserved heterogeneity. From a stage-classified point of view, age structure is unobserved heterogeneity. In an age × stage-classified model, variance in demographic outcomes can be partitioned into contributions from both sources. Because these models are formulated as matrices, they are amenable to a complete sensitivity analysis. As more detailed and longer longitudinal studies are developed, age × stage-classified demography will become more common and more important.

Climate change and functional traits affect population dynamics of a long-lived seabird.

Recent studies unravelled the effect of climate changes on populations through their impact on functional traits and demographic rates in terrestrial and freshwater ecosystems, but such understanding in marine ecosystems remains incomplete. Here, we evaluate the impact of the combined effects of climate and functional traits on population dynamics of a long-lived migratory seabird breeding in the southern ocean: the black-browed albatross (Thalassarche melanophris, BBA). We address the following prospective question: "Of all the changes in the climate and functional traits, which would produce the biggest impact on the BBA population growth rate?" We develop a structured matrix population model that includes the effect of climate and functional traits on the complete BBA life cycle. A detailed sensitivity analysis is conducted to understand the main pathway by which climate and functional trait changes affect the population growth rate. The population growth rate of BBA is driven by the combined effects of climate over various seasons and multiple functional traits with carry-over effects across seasons on demographic processes. Changes in sea surface temperature (SST) during late winter cause the biggest changes in the population growth rate, through their effect on juvenile survival. Adults appeared to respond to changes in winter climate conditions by adapting their migratory schedule rather than by modifying their at-sea foraging activity. However, the sensitivity of the population growth rate to SST affecting BBA migratory schedule is small. BBA foraging activity during the pre-breeding period has the biggest impact on population growth rate among functional traits. Finally, changes in SST during the breeding season have little effect on the population growth rate. These results highlight the importance of early life histories and carry-over effects of climate and functional traits on demographic rates across multiple seasons in population response to climate change. Robust conclusions about the roles of various phases of the life cycle and functional traits in population response to climate change rely on an understanding of the relationships of traits to demographic rates across the complete life cycle.

Matrix methods in health demography: a new approach to the stochastic analysis of healthy longevity and DALYs.

BACKGROUND: Increases in human longevity have made it critical to distinguish healthy longevity from longevity without regard to health. Current methods focus on expectations of healthy longevity, and are often limited to binary health outcomes (e.g., disabled vs. not disabled). We present a new matrix formulation for the statistics of healthy longevity, based on health prevalence data and Markov chain theory, applicable to any kind of health outcome and which provides variances and higher moments as well as expectations of healthy life. METHOD: The model is based on a Markov chain description of the life course coupled with the moments of health outcomes ("rewards") at each age or stage. As an example, we apply the method to nine European countries using the SHARE survey data on the binary outcome of disability as measured by activities of daily living, and the continuous health outcome of hand grip strength. RESULTS: We provide analytical formulas for the mean, variance, coefficient of variation, skewness and other statistical properties of healthy longevity. The analysis is applicable to binary, categorical, ordinal, or interval scale health outcomes. The results are easily evaluated in any matrix-oriented software. The SHARE results reveal familiar patterns for the expectation of life and of healthy life: women live longer than men but spend less time in a healthy condition. New results on the variance shows that the standard deviation of remaining healthy life declines with age, but the coefficient of variation is nearly constant. Remaining grip strength years decrease with age more dramatically than healthy years but their variability pattern is similar to the pattern of healthy years. Patterns are similar across nine European countries. CONCLUSIONS: The method extends, in several directions, current calculations of health expectancy (HE) and disability-adjusted life years (DALYs). It applies to both categorical and continuous health outcomes, to combinations of multiple outcomes (e.g., death and disability in the formulation of DALYs) and to age- or stage-classified models. It reveals previously unreported patterns of variation among individuals in the outcomes of healthy longevity.

Inferring forest fate from demographic data: from vital rates to population dynamic models.

As population-level patterns of interest in forests emerge from individual vital rates, modelling forest dynamics requires making the link between the scales at which data are collected (individual stems) and the scales at which questions are asked (e.g. populations and communities). Structured population models (e.g. integral projection models (IPMs)) are useful tools for linking vital rates to population dynamics. However, the application of such models to forest trees remains challenging owing to features of tree life cycles, such as slow growth, long lifespan and lack of data on crucial ontogenic stages. We developed a survival model that accounts for size-dependent mortality and a growth model that characterizes individual heterogeneity. We integrated vital rate models into two types of population model; an analytically tractable form of IPM and an individual-based model (IBM) that is applied with stochastic simulations. We calculated longevities, passage times to, and occupancy time in, different life cycle stages, important metrics for understanding how demographic rates translate into patterns of forest turnover and carbon residence times. Here, we illustrate the methods for three tropical forest species with varying life-forms. Population dynamics from IPMs and IBMs matched a 34 year time series of data (albeit a snapshot of the life cycle for canopy trees) and highlight differences in life-history strategies between species. Specifically, the greater variation in growth rates within the two canopy species suggests an ability to respond to available resources, which in turn manifests as faster passage times and greater occupancy times in larger size classes. The framework presented here offers a novel and accessible approach to modelling the population dynamics of forest trees.

Correction to: Perturbation Analysis of Indices of Lifespan Variability.

We discovered an error in Eq. (12) (p. 1621).