Temporal variability can promote migration between habitats.

Understanding the conditions that promote the evolution of migration is important in ecology and evolution. When environments are fixed and there is one most favorable site, migration to other sites lowers overall growth rate and is not favored. Here we ask, can environmental variability favor migration when there is one best site on average? Previous work suggests that the answer is yes, but a general and precise answer remained elusive. Here we establish new, rigorous inequalities to show (and use simulations to illustrate) how stochastic growth rate can increase with migration when fitness (dis)advantages fluctuate over time across sites. The effect of migration between sites on the overall stochastic growth rate depends on the difference in expected growth rates and the variance of the fluctuating difference in growth rates. When fluctuations (variance) are large, a population can benefit from bursts of higher growth in sites that are worse on average. Such bursts become more probable as the between-site variance increases. Our results apply to many (≥ 2) sites, and reveal an interplay between the length of paths between sites, the average differences in site-specific growth rates, and the size of fluctuations. Our findings have implications for evolutionary biology as they provide conditions for departure from the reduction principle, and for ecological dynamics: even when there are superior sites in a sea of poor habitats, variability and habitat quality across space determine the importance of migration.

Advancing methods for the biodemography of aging within social contexts.

Several social dimensions including social integration, status, early-life adversity, and their interactions across the life course can predict health, reproduction, and mortality in humans. Accordingly, the social environment plays a fundamental role in the emergence of phenotypes driving the evolution of aging. Recent work placing human social gradients on a biological continuum with other species provides a useful evolutionary context for aging questions, but there is still a need for a unified evolutionary framework linking health and aging within social contexts. Here, we summarize current challenges to understand the role of the social environment in human life courses. Next, we review recent advances in comparative biodemography and propose a biodemographic perspective to address socially driven health phenotype distributions and their evolutionary consequences using a nonhuman primate population. This new comparative approach uses evolutionary demography to address the joint dynamics of populations, social dimensions, phenotypes, and life history parameters. The long-term goal is to advance our understanding of the link between individual social environments, population-level outcomes, and the evolution of aging.

Hurricanes affect diversification among individual life courses of a primate population.

Extreme climatic events may influence individual-level variability in phenotypes, survival and reproduction, and thereby drive the pace of evolution. Climate models predict increases in the frequency of intense hurricanes, but no study has measured their impact on individual life courses within animal populations. We used 45 years of demographic data of rhesus macaques to quantify the influence of major hurricanes on reproductive life courses using multiple metrics of dynamic heterogeneity accounting for life course variability and life-history trait variances. To reduce intraspecific competition, individuals may explore new reproductive stages during years of major hurricanes, resulting in higher temporal variation in reproductive trajectories. Alternatively, individuals may opt for a single optimal life-history strategy due to trade-offs between survival and reproduction. Our results show that heterogeneity in reproductive life courses increased by 4% during years of major hurricanes, despite a 2% reduction in the asymptotic growth rate due to an average decrease in mean fertility and survival by that is, shortened life courses and reduced reproductive output. In agreement with this, the population is expected to achieve stable population dynamics faster after being perturbed by a hurricane ( ρ = 1.512 ; 95% CI: 1.488, 1.538), relative to ordinary years ρ = 1.482 ; 1.475 , 1.490 . Our work suggests that natural disasters force individuals into new demographic roles to potentially reduce competition during unfavourable environments where mean reproduction and survival are compromised. Variance in lifetime reproductive success and longevity are differently affected by hurricanes, and such variability is mostly driven by survival.

Adaption, neutrality and life-course diversity.

Heterogeneity among individuals in fitness components is what selection acts upon. Evolutionary theories predict that selection in constant environments acts against such heterogeneity. But observations reveal substantial non-genetic and also non-environmental variability in phenotypes. Here, we examine whether there is a relationship between selection pressure and phenotypic variability by analysing structured population models based on data from a large and diverse set of species. Our findings suggest that non-genetic, non-environmental variation is in general neither truly neutral, selected for, nor selected against. We find much variations among species and populations within species, with mean patterns suggesting nearly neutral evolution of life-course variability. Populations that show greater diversity of life courses do not show, in general, increased or decreased population growth rates. Our analysis suggests we are only at the beginning of understanding the evolution and maintenance of non-genetic non-environmental variation.

Reproductive dispersion and damping time scale with life-history speed.

Iteroparous species may reproduce at many different ages, resulting in a reproductive dispersion that affects the damping of population perturbations, and varies among life histories. Since generation time ( T c ) is known to capture aspects of life-history variation, such as life-history speed, does T c also determine reproductive dispersion ( S ) or damping time ( τ )? Using phylogenetically corrected analyses on 633 species of animals and plants, we find, firstly, that reproductive dispersion S scales isometrically with T c . Secondly, and unexpectedly, we find that the damping time ( τ ) does not scale isometrically with generation time, but instead changes only as T c b with b < 1 (also, there is a similar scaling with S ). This non-isometric scaling implies a novel demographic contrast: increasing generation times correspond to a proportional increase in reproductive dispersion, but only to a slower increase in the damping time. Thus, damping times are partly decoupled from the slow-fast continuum, and are determined by factors other than allometric constraints.

Quantifying the effect of genetic, environmental and individual demographic stochastic variability for population dynamics in Plantago lanceolata.

Simple demographic events, the survival and reproduction of individuals, drive population dynamics. These demographic events are influenced by genetic and environmental parameters, and are the focus of many evolutionary and ecological investigations that aim to predict and understand population change. However, such a focus often neglects the stochastic events that individuals experience throughout their lives. These stochastic events also influence survival and reproduction and thereby evolutionary and ecological dynamics. Here, we illustrate the influence of such non-selective demographic variability on population dynamics using population projection models of an experimental population of Plantago lanceolata. Our analysis shows that the variability in survival and reproduction among individuals is largely due to demographic stochastic variation with only modest effects of differences in environment, genes, and their interaction. Common expectations of population growth, based on expected lifetime reproduction and generation time, can be misleading when demographic stochastic variation is large. Large demographic stochastic variation exhibited within genotypes can lower population growth and slow evolutionary adaptive dynamics. Our results accompany recent investigations that call for more focus on stochastic variation in fitness components, such as survival, reproduction, and functional traits, rather than dismissal of this variation as uninformative noise.

Distributions of LRS in varying environments.

The lifetime reproductive success (LRS) of individuals is affected by random events such as death, realized growth or realized reproduction, and the outcomes of these events can differ even when individuals have identical probabilities. Another source of randomness arises when these probabilities also change over time in variable environments. For structured populations in stochastic environments, we extend our recent method to determine how birth environment and birth stage determine the random distribution of the LRS. Our results provide a null model that quantifies effects on LRS of just the birth size or stage. Using Roe deer Capreolus capreolus as a case study, we show that the effect of an individual's birth environment on LRS varies with the frequency of environments and their temporal autocorrelation, and that lifetime performance is affected by changes in the pattern of environmental states expected as a result of climate change.

Life-history strategy varies with the strength of competition in a food-limited ungulate population.

Fluctuating population density in stochastic environments can contribute to maintain life-history variation within populations via density-dependent selection. We used individual-based data from a population of Soay sheep to examine variation in life-history strategies at high and low population density. We incorporated life-history trade-offs among survival, reproduction and body mass growth into structured population models and found support for the prediction that different life-history strategies are optimal at low and high population densities. Shorter generation times and lower asymptotic body mass were selected for in high-density environments even though heavier individuals had higher probabilities to survive and reproduce. In contrast, greater asymptotic body mass and longer generation times were optimal at low population density. If populations fluctuate between high density when resources are scarce, and low densities when they are abundant, the variation in density will generate fluctuating selection for different life-history strategies, that could act to maintain life-history variation.

Skewed distributions of lifetime reproductive success: beyond mean and variance.

Lifetime reproductive performance is quantified here by the LRS (lifetime reproductive success), the random number of offspring an individual produces over its lifetime. Many field studies find that distributions of LRS among individuals are non-normal, zero-inflated and highly skewed. These results beg the question, what is the distribution of LRS predicted by demographic models when the only source of randomness is demographic stochasticity? Here we present the first exact analysis of the probability distribution of LRS for species described by age + stage models; our analysis starts with estimated vital rates. We illustrate with three examples: the Hadza, human hunter-foragers (age-only), the evergreen tree Tsuga canadensis (stage-only) and Roe deer, Capreolus capreolus (age + stage). For each we obtain the exact distribution of LRS, but also calculate and discuss the first three moments. Our results point to important questions about how such LRS distributions affect natural selection, and life history evolution.

Drivers of diversity in individual life courses: Sensitivity of the population entropy of a Markov chain.

Individuals differ in their life courses, but how this diversity is generated, how it has evolved and how it is maintained is less understood. However, this understanding is crucial to comprehend evolutionary and ecological population dynamics. In structured populations, individual life courses represent sequences of stages that end in death. These life course trajectories or sequences can be described by a Markov chain and individuals diversify over the course of their lives by transitioning through diverse discrete stages. The rate at which stage sequences diversify with age can be quantified by the population entropy of a Markov chain. Here, we derive sensitivities of the population entropy of a Markov chain to identify which stage transitions generate - or contribute - most to diversification in stage sequences, i.e. life courses. We then use these sensitivities to reveal potential selective forces on the dynamics of life courses. To do so we correlated the sensitivity of each matrix element (stage transition) with respect to the population entropy, to its sensitivity with respect to fitness λ, the population growth rate. Positive correlation between the two sensitivities would suggest that the stage transitions that selection has acted most strongly on (high sensitivities with respect to λ) are also those that contributed most to the diversification of life courses. Using an illustrative example on a seabird population, the Thick-billed Murres on Coats Island, that is structured by reproductive stages, we show that the most influential stage transitions for diversification of life courses are not correlated with the most influential transitions for population growth. Our finding suggests that observed diversification in life courses is neutral rather than adaptive, note this does not imply that the life histories themselves are not adaptive. We are at an early stage of understanding how individual level dynamics shape ecological and evolutionary dynamics, and many discoveries await.

Age distribution, trends, and forecasts of under-5 mortality in 31 sub-Saharan African countries: A modeling study.

BACKGROUND: Despite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates. METHODS AND FINDINGS: Data came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee-Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries. CONCLUSIONS: To our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

How climate affects extreme events and hence ecological population models.

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.

Advancing front of old-age human survival.

Old-age mortality decline has driven recent increases in lifespans, but there is no agreement about trends in the age pattern of old-age deaths. Some argue that old-age deaths should become compressed at advanced ages, others argue that old-age deaths should become more dispersed with age, and yet others argue that old-age deaths are consistent with little change in dispersion. However, direct analysis of old-age deaths presents unusual challenges: Death rates at the oldest ages are always noisy, published life tables must assume an asymptotic age pattern of deaths, and the definition of "old-age" changes as lives lengthen. Here we use robust percentile-based methods to overcome some of these challenges and show, for five decades in 20 developed countries, that old-age survival follows an advancing front, like a traveling wave. The front lies between the 25th and 90th percentiles of old-age deaths, advancing with nearly constant long-term shape but annual fluctuations in speed. The existence of this front leads to several predictions that we verify, e.g., that advances in life expectancy at age 65 y are highly correlated with the advance of the 25th percentile, but not with distances between higher percentiles. Our unexpected result has implications for biological hypotheses about human aging and for future mortality change.

Susceptibility of wild and colonized Anopheles stephensi to Plasmodium vivax infection.

BACKGROUND: As much as 80% of global Plasmodium vivax infections occur in South Asia and there is a shortage of direct studies on infectivity of P. vivax in Anopheles stephensi, the most common urban mosquito carrying human malaria. In this quest, the possible effects of laboratory colonization of mosquitoes on infectivity and development of P. vivax is of interest given that colonized mosquitoes can be genetically less divergent than the field population from which they originated. METHODS: Patient-derived P. vivax infected blood was fed to age-matched wild and colonized An. stephensi. Such a comparison requires coordinated availability of same-age wild and colonized mosquito populations. Here, P. vivax infection are studied in colonized An. stephensi in their 66th-86th generation and fresh field-caught An. stephensi. Wild mosquitoes were caught as larvae and pupae and allowed to develop into adult mosquitoes in the insectary. Parasite development to oocyst and sporozoite stages were assessed on days 7/8 and 12/13, respectively. RESULTS: While there were batch to batch variations in infectivity of individual patient-derived P. vivax samples, both wild and colonized An. stephensi were roughly equally susceptible to oocyst stage Plasmodium infection. At the level of sporozoite development, significantly more mosquitoes with sporozoite load of 4+ were seen in wild than in colonized populations.

Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model.

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.

Machine learning approaches to the social determinants of health in the health and retirement study.

BACKGROUND: Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. METHODS: A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. RESULTS: All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data (R2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. DISCUSSION: Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Demographic and clinical profiles of Plasmodium falciparum and Plasmodium vivax patients at a tertiary care centre in southwestern India.

BACKGROUND: Malaria remains an important cause of morbidity and mortality in India. Though many comprehensive studies have been carried out in Africa and Southeast Asia to characterize and examine determinants of Plasmodium falciparum and Plasmodium vivax malaria pathogenesis, fewer have been conducted in India. METHODS: A prospective study of malaria-positive individuals was conducted at Goa Medical College and Hospital (GMC) from 2012 to 2015 to identify demographic, diagnostic and clinical indicators associated with P. falciparum and P. vivax infection on univariate analysis. RESULTS: Between 2012 and 2015, 74,571 febrile individuals, 6287 (8.4%) of whom were malaria positive, presented to GMC. The total number of malaria cases at GMC increased more than two-fold over four years, with both P. vivax and P. falciparum cases present year-round. Some 1116 malaria-positive individuals (mean age = 27, 91% male), 88.2% of whom were born outside of Goa and 51% of whom were construction workers, were enroled in the study. Of 1088 confirmed malaria-positive patients, 77.0% had P. vivax, 21.0% had P. falciparum and 2.0% had mixed malaria. Patients over 40 years of age and with P. falciparum infection were significantly (p < 0.001) more likely to be hospitalised than younger and P. vivax patients, respectively. While approximately equal percentages of hospitalised P. falciparum (76.6%) and P. vivax (78.9%) cases presented with at least one WHO severity indicator, a greater percentage of P. falciparum inpatients presented with at least two (43.9%, p < 0.05) and at least three (29.9%, p < 0.01) severity features. There were six deaths among the 182 hospitalised malaria positive patients, all of whom had P. falciparum. CONCLUSION: During the four year study period at GMC, the number of malaria cases increased substantially and the greatest burden of severe disease was contributed by P. falciparum.

Racial and Socioeconomic Variation in Genetic Markers of Telomere Length: A Cross-Sectional Study of U.S. Older Adults.

BACKGROUND: Shorter telomere length (TL) has been associated with stress and adverse socioeconomic conditions, yet U.S. blacks have longer TL than whites. The role of genetic versus environmental factors in explaining TL by race and socioeconomic position (SEP) remains unclear. METHODS: We used data from the U.S. Health and Retirement Study (N=11,934) to test the hypothesis that there are differences in TL-associated SNPs by race and SEP. We constructed a TL polygenic risk score (PRS) and examined its association with race/ethnicity, educational attainment, assets, gender, and age. RESULTS: U.S. blacks were more likely to have a lower PRS for TL, as were older individuals and men. Racial differences in TL were statistically accounted for when controlling for population structure using genetic principal components. The GWAS-derived SNPs for TL, however, may not have consistent associations with TL across different racial/ethnic groups. CONCLUSIONS: This study showed that associations of race/ethnicity with TL differed when accounting for population stratification. The role of race/ethnicity for TL remains uncertain, however, as the genetic determinants of TL may differ by race/ethnicity. Future GWAS samples should include racially diverse participants to allow for better characterization of the determinants of TL in human populations.

Distinct genomic architecture of Plasmodium falciparum populations from South Asia.

Previous whole genome comparisons of Plasmodium falciparum populations have not included collections from the Indian subcontinent, even though two million Indians contract malaria and about 50,000 die from the disease every year. Stratification of global parasites has revealed spatial relatedness of parasite genotypes on different continents. Here, genomic analysis was further improved to obtain country-level resolution by removing var genes and intergenic regions from distance calculations. P. falciparum genomes from India were found to be most closely related to each other. Their nearest neighbors were from Bangladesh and Myanmar, followed by Thailand. Samples from the rest of Southeast Asia, Africa and South America were increasingly more distant, demonstrating a high-resolution genomic-geographic continuum. Such genome stratification approaches will help monitor variations of malaria parasites within South Asia and future changes in parasite populations that may arise from in-country and cross-border migrations.