Biology subjects

Caswell, H.

Publications and source records attributed to Caswell, H..

12 recordsLinked to original sources

bioRxiv2026

Sensitivity of stochastic growth rate and lifetime reproductive output to kin effects: A nonlinear feedback model

Humans exhibit a coevolutionary relationship between the trajectory of social evolution and senescence. A focal individual's kinship network is a function of his/her life history, while that network in turn shapes the focal individual's life history through kin effects on reproduction, generating a dynamic, nonlinear feedback loop across the population. Assessing directional selection on kin effects, therefore, requires explicitly modelling the feedback between the kinship network and life history. Here, we develop such a framework and apply it to examine selection pressures on the cooperative behaviour of closely related kin, including mothers, grandmothers, and older sisters. We hypothesise that the negative association of grandmothers, mothers, and older sisters (>10 years) with child mortality are under positive selection because increasing the absolute values of these kin effects would increase the stochastic growth rate. Furthermore, we hypothesise that the positive association of older sisters with child mortality is subject to negative selection, as increasing this effect would reduce the stochastic growth rate. We estimate kin-specific slope coefficients for child mortality using a Bayesian hierarchical model and solve a system of nonlinear equations linking kinship structure and survival to account for the nonlinear feedback between kinship and child survival. Thereafter, we compute stochastic sensitivities of both the stochastic growth rate and the mean lifetime reproductive output to these slope coefficients. Consistent with our hypotheses, all slope coefficients for kin presence on child mortality are negatively sensitive to both fitness metrics. We discuss the applications of our framework to examine the coevolution of social behaviours and senescence in age-structured populations.

evolutionary biology↗

bioRxiv2025

The formal demography of kinship. VII. Lifetime kin overlap within and across generations

BackgroundInteractions among kin have important consequences, including resource transfers, alloparenting, health care, and economic support. Some interactions require that the lives of the interacting relatives overlap. The overlap over a lifetime (lifetime kin overlap, LKO) depends on mortality (longer lives give more opportunity for overlap) and fertility (higher fertility produces more kin with which to overlap). Here we provide a general solution to the problem of calculating lifetime kin overlap. ObjectivesTo develop a demographic model for the mean and variance of the lifetime overlap of any types of kin over the life of a focal individual. MethodsThe matrix kinship model is used to provide the age distribution of kin as an age-specific property of Focal. The mean and variance of lifetime overlap with kin of any type are then calculated using Markov chains with rewards. ResultsWe obtain the statistics of LKO with numbers of kin, with selected age classes of kin, and with at least one kin, in both prospective and retrospective directions, for each sex separately and with sexes combined. Simultaneous overlap with two or more types of kin (e.g., parents and children) describes sandwich generations. We provide an example comparing Japan under 1947 rates (low survival, high fertility) and 2019 rates (high survival, low fertility). ContributionIt is now possible to compute the mean and variance of the projected LKO with any type of kin, in one-sex or two-sex models based on age or combinations of age and stage.

ecology↗

bioRxiv2024

Inequality, heterogeneity, and chance: Multiple factors and their interactions

A heterogeneous population is a mixture of groups differing in vital rates. In such a population, some of the variance in demographic outcomes (e.g., longevity, lifetime reproduction) is due to heterogeneity and some is the result of stochastic demographic processes. Many studies have partitioned variance into its between-group and within-group components, but have focused on single factors. Especially for longevity, variance due to stochasticity is far greater than that due to heterogeneity. Here we extend analysis to multiple-factor studies, making it possible to calculate the contributions to variance of each factor and each of the interactions among factors. We treat the population as a mixture and use the marginal mixing distributions to compute variance components. Examples are presented: longevity as a function of sex, race, and U.S. state of residence, lifetime reproduction among set of developed countries and as a result of resource availability and pesticide exposure.

ecology↗

bioRxiv2024

The formal demography of kinship VI: Demographic stochasticity, variance, and covariance in the kinship network

BackgroundThe matrix model for kinship networks includes many demographic processes but is deterministic, projecting expected values of age-stage distributions of kin. It provides no information on (co)variances. Because kin populations are small, demographic stochasticity is expected to create appreciable inter-individual variation. ObjectivesTo develop a stochastic kinship model to project (co)variances of kin age-stage distributions, and functions thereof, including demographic stochasticity. MethodsKin populations are described by multitype branching processes. Means and covariances are projected using matrices that are generalizations of the deterministic model. The analysis requires only an age-specific mortality and fertility schedule. Both linear and non-linear transformations of the kin age distribution are treated as outputs accompanying the state equations. ResultsThe stochastic model follows the same mathematical framework as the deterministic model, modified to treat initial conditions as mixture distributions. Variances in numbers of most kin are compatible with Poisson distributions. Variances for parents and ancestors are compatible with binomial distributions. Prediction intervals are provided, as are probabilities of having at least one or two kin of each type. Prevalences of conditions are treated either as fixed or random proportions. Dependency ratios and their variances are calculated for any desired group of kin types. An example compares Japan under 1947 rates (high mortality, high fertility) and 2019 rates (low mortality, low fertility). ContributionPrevious versions of the kinship model have acknowledged their limitation to expected values. That limitation is now removed; means and variances are easily and quickly calculated with minimal modification of code.

ecology↗

bioRxiv2024

Family matters: Linking population growth, kin interactions, and African elephant social groups

In many species, individuals are embedded in a network of kin with whom they interact. Interactions between kin can affect the survival and fertility rates, and thus the life history of individuals. These interactions indirectly affect both the network of kin and the dynamics of the population. In this way, a non-linear feedback between the kin network and individual vital rates emerges. We describe a framework for integrating these kin interactions into a matrix model by linking the individual kin network to a matrix model. We demonstrate the use of this framework for African elephant populations under varying poaching pressure. For this example, we incorporate effects of the maternal presence and matriarchal age on juvenile survival, and effects of the presence of a sister on young female fecundity. We find that the feedback resulting from the interactions between family members shifts and reduces the expected kin network. The reduction in family size and structure severely reduces the positive effects of family interactions, leading to an additional decrease in population growth rate on top of the direct decrease due to the additional mortality. Our analysis provides a framework that can be applied to a wide range of social species.

ecology↗

bioRxiv2023

A standard protocol to report discrete stage-structured demographic information

O_LIStage-based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology, and conservation science. Accordingly, MPMs now exist for over 3,000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open-access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. C_LIO_LIHere, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. C_LIO_LIOver 80% of surveyed researchers (n=60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility, and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardized protocol to present MPMs in publications. This standard is linked to www.compadre-db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open-access repositories such as DRYAD, Figshare, and Zenodo. C_LIO_LICombining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology, and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility. C_LI

ecology↗

bioRxiv2022

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 CO_SCPLOWOMADREC_SCPLOW and CO_SCPLOWOMPADREC_SCPLOW 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 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.

ecology↗

bioRxiv2022

The formal demography of kinship IV: Two-sex models

BackgroundPrevious kinship models analyze female kin through female lines of descent, neglecting male kin and male lines of descent. Because males and females differ in mortality and fertility, including both sexes in kinship models is an important unsolved problem. ObjectivesThe objectives are to develop a kinship model including female and male kin through all lines of descent, to explore approximations when full sex-specific rates are unavailable, and to apply the model to several populations as an example. MethodsThe kin of a focal individual form an agexsex-classified population and are projected as Focal ages using matrix methods, providing expected age-sex structures for every type of kin at every age of Focal. Initial conditions are based on the distribution of ages at maternity and paternity. ResultsThe equations for two-sex kinship dynamics are presented. As an example, the model is applied to populations with large (Senegal), medium (Haiti), and small (France) differences between female and male fertility. Results include numbers and sex ratios of kin as Focal ages. An approximation treating female and male rates as identical provides some insight into kin numbers, even when male and female rates are very different. ContributionMany demographic and sociological parameters (e.g., aspects of health, bereavement, labor force participation) differ markedly between the sexes. This model permits analysis of such parameters in the context of kinship networks. The matrix formulation makes it possible to extend the two-sex analysis to include kin loss, multistate kin demography, and time varying rates.

ecology↗

bioRxiv2021

The formal demography of kinship III: Kinship dynamics with time-varying demographic rates

BackgroundKinship models generally assume time-invariant demographic rates, and compute the kinship structures implied by those rates. It is important to compute the consequences of time variation in demographic rates for kinship stuctures. ObjectivesOur goal is to develop a matrix model for the dynamics of kinship networks subject to arbitrary temporal variation in survival, fertility, and population structure. MethodsWe develop a system of equations for the dynamics of the age structure of each type of kin of a Focal individual. The matrices describing survival and fertility vary with time. The initial conditions in the time-invariant model are replaced with a set of boundary conditions for initial time and initial age. ResultsThe time-varying model maintains the network structure of the time-invariant model. In addition to the results of the time-invariant model, it provides kinship structures by period, cohort, and age. It applies equally to historical sequences of past rates and to projections of future rates. As an illustration, we analyze the kinship structure of Sweden from 1891 to 2120. ContributionThe time-varying kinship model makes it possible to analyze the consequences of changing demographic rates, in the past or the future. It is easily computable, requires no simulations, and is readily extended to include additional, more distant relatives in the kinship network. The method can also be used to show the growth of families, lineages, and dynasties in populations across time and place and between social groups.

ecology↗

bioRxiv2020

The formal demography of kinship. II. Multistate models, parity, and sibship

BackgroundRecent kinship models focus on the age structures of kin as a function of the age of the focal individual. However, variables in addition to age have important impacts. Generalizing age-specific models to multistate models including other variables is an important and hitherto unsolved problem. ObjectivesOur aim is to develop a multistate kinship model, classifying individuals jointly by age and other criteria (generically, "stages"). MethodsWe use the vec-permutation method to create multistate projection matrices including age- and stage-dependent survival, fertility, and transitions. These matrices operate on block-structured population vectors that describe the agexstage structure of each kind of kin, at each age of a focal individual. ResultsThe new matrix formulation is directly comparable to, and greatly extends, the recent age-classified kinship model of Caswell (2019a). As an application, we derive a model that includes age and parity. We obtain, for all types of kin, the joint agexparity structure, the marginal age and parity structures, and the (normalized) parity distributions, at every age of the focal individual. We show how to use the agexparity distributions to calculate the distributions of sibship sizes of kin. As an example, we apply the model to Slovakia (1960-2014). The results include a dramatic shift in the parity distribution as the frequency of low-parity kin increased and that of high-parity kin decreased. ContributionThe new model extends the formal demographic analysis of kinship to agexstage-classified models. In addition to parity, other stage classifications, including marital status, maternal age effects, and sex are now open to analysis.

ecology↗

bioRxiv2019

Maternal Effect Senescence and Fitness: A Demographic Analysis of a Novel Model Organism

Maternal effect senescence--a decline in offspring fitness with maternal age--has been demonstrated in a range of taxa, including humans. Despite decades of phenotypic studies, it remains unclear how maternal effect senescence impacts population structure or evolutionary fitness. To understand the impact of maternal effect senescence on population dynamics, fitness, and selection, we used data from individual-based culture experiments on the microscopic aquatic invertebrate, Brachionus manjavacas (Rotifera), to develop a series of matrix population models in which individuals are classified jointly by age and maternal age. By comparing the results derived from models with and without maternal effects, we found that the fitness difference due to maternal effect senescence arises primarily through decreased fertility, particularly at maternal ages corresponding to the peak reproductive output. In all models, selection gradients decrease with increasing age. They also decrease with maternal age for large maternal ages, implying that maternal effect senescence can evolve through the same process as in Hamiltons theory of the evolution of demographic senescence. We find that maternal effect senescence significantly alters population structure and fitness for B. manjavacas, a species with high maternal investment and maximum reproduction in early-to mid-life. The models developed here were built with data from an emerging model organism, and are widely applicable to evaluate the fitness consequences of maternal effect senescence across species with diverse aging and fertility schedule phenotypes.

evolutionary biology↗

bioRxiv2019

When the going gets tough, the tough get going: effect of extreme climate on an Antarctic seabird’s life history

1Individuals 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 out-comes 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 will vary due to individual stochasticity, i.e., to chance. Quantifying the contributions of heterogeneity and chance is essential to understanding natural variability. Inter-individual differences vary across environmental conditions. Heterogeneity and stochasticity depend on environmental conditions. We show that favorable 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.

ecology↗