Metabolic energetics underlying attractors in neural models.

Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular-level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as ∼2 × 109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered to exhibit steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The energy cost of limit cycles, with oscillations much faster than the average neuronal firing rate of the population, is tracked more closely with the firing rate than the limit cycle frequency. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. As an example of a simple network in which each node is a WC model, a combination of three nodes can serve as a flexible circuit element that turns on with an oscillating output when input passes a threshold and then persists after the input ends (an "on-switch"), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.NEW & NOTEWORTHY This work bridges two approaches for understanding brain function: cellular-level studies of the metabolic energy costs of different aspects of neural activity and neural population modeling, including the role of neural attractors. The proposed modeling framework connects energetic costs, in ATP consumed per second per neuron, to the more abstract variables used in neural population modeling. In particular, this work anchors potential neural attractors to physiologically plausible bioenergetics requirements.

A mathematical framework for evo-devo dynamics.

Natural selection acts on phenotypes constructed over development, which raises the question of how development affects evolution. Classic evolutionary theory indicates that development affects evolution by modulating the genetic covariation upon which selection acts, thus affecting genetic constraints. However, whether genetic constraints are relative, thus diverting adaptation from the direction of steepest fitness ascent, or absolute, thus blocking adaptation in certain directions, remains uncertain. This limits understanding of long-term evolution of developmentally constructed phenotypes. Here we formulate a general, tractable mathematical framework that integrates age progression, explicit development (i.e., the construction of the phenotype across life subject to developmental constraints), and evolutionary dynamics, thus describing the evolutionary and developmental (evo-devo) dynamics. The framework yields simple equations that can be arranged in a layered structure that we call the evo-devo process, whereby five core elementary components generate all equations including those mechanistically describing genetic covariation and the evo-devo dynamics. The framework recovers evolutionary dynamic equations in gradient form and describes the evolution of genetic covariation from the evolution of genotype, phenotype, environment, and mutational covariation. This shows that genotypic and phenotypic evolution must be followed simultaneously to yield a dynamically sufficient description of long-term phenotypic evolution in gradient form, such that evolution described as the climbing of a fitness landscape occurs in "geno-phenotype" space. Genetic constraints in geno-phenotype space are necessarily absolute because the phenotype is related to the genotype by development. Thus, the long-term evolutionary dynamics of developed phenotypes is strongly non-standard: (1) evolutionary equilibria are either absent or infinite in number and depend on genetic covariation and hence on development; (2) developmental constraints determine the admissible evolutionary path and hence which evolutionary equilibria are admissible; and (3) evolutionary outcomes occur at admissible evolutionary equilibria, which do not generally occur at fitness landscape peaks in geno-phenotype space, but at peaks in the admissible evolutionary path where "total genotypic selection" vanishes if exogenous plastic response vanishes and mutational variation exists in all directions of genotype space. Hence, selection and development jointly define the evolutionary outcomes if absolute mutational constraints and exogenous plastic response are absent, rather than the outcomes being defined only by selection. Moreover, our framework provides formulas for the sensitivities of a recurrence and an alternative method to dynamic optimization (i.e., dynamic programming or optimal control) to identify evolutionary outcomes in models with developmentally dynamic traits. These results show that development has major evolutionary effects.

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.

Early-life food stress hits females harder than males in insects: A meta-analysis of sex differences in environmental sensitivity.

Fitness consequences of early-life environmental conditions are often sex-specific, but corresponding evidence for invertebrates remains inconclusive. Here, we use meta-analysis to evaluate sex-specific sensitivity to larval nutritional conditions in insects. Using literature-derived data for 85 species with broad phylogenetic and ecological coverage, we show that females are generally more sensitive to food stress than males. Stressful nutritional conditions during larval development typically lead to female-biased mortality and thus increasingly male-biased sex ratios of emerging adults. We further demonstrate that the general trend of higher sensitivity to food stress in females can primarily be attributed to their typically larger body size in insects and hence higher energy needs during development. By contrast, there is no consistent evidence of sex-biased sensitivity in sexually size-monomorphic species. Drawing conclusions regarding sex-biased sensitivity in species with male-biased size dimorphism remains to wait for the accumulation of relevant data. Our results suggest that environmental conditions leading to elevated juvenile mortality may potentially affect the performance of insect populations further by reducing the proportion of females among individuals reaching reproductive age. Accounting for sex-biased mortality is therefore essential to understanding the dynamics and demography of insect populations, not least importantly in the context of ongoing insect declines.

Recent exposure to environmental stochasticity does not determine the demographic resilience of natural populations.

Escalating climatic and anthropogenic pressures expose ecosystems worldwide to increasingly stochastic environments. Yet, our ability to forecast the responses of natural populations to this increased environmental stochasticity is impeded by a limited understanding of how exposure to stochastic environments shapes demographic resilience. Here, we test the association between local environmental stochasticity and the resilience attributes (e.g. resistance, recovery) of 2242 natural populations across 369 animal and plant species. Contrary to the assumption that past exposure to frequent environmental shifts confers a greater ability to cope with current and future global change, we illustrate how recent environmental stochasticity regimes from the past 50 years do not predict the inherent resistance or recovery potential of natural populations. Instead, demographic resilience is strongly predicted by the phylogenetic relatedness among species, with survival and developmental investments shaping their responses to environmental stochasticity. Accordingly, our findings suggest that demographic resilience is a consequence of evolutionary processes and/or deep-time environmental regimes, rather than recent-past experiences.

Marsupial position on life-history continua and the potential contribution of life-history traits to population growth.

Previous studies have suggested that mammal life history varies along the fast-slow continuum and that, in eutherians, this continuum is linked to variation in the potential contribution of survival and reproduction to population growth rate (λ). Fast eutherians mature early, have large litters and short lifespans, and exhibit high potential contribution of age at first reproduction and fertility to λ, while slow eutherians show high potential contribution of survival to λ. However, marsupials have typically been overlooked in comparative tests of mammalian life-history evolution. Here, we tested whether the eutherian life-history pattern extends to marsupials, and show that marsupial life-history trade-offs are organized along two major axes: (i) the reproductive output and dispersion axis, and (ii) the fast-slow continuum, with an additional association between adult survival and body mass. Life-history traits that potentially drive changes in λ are similar in eutherians and marsupials with slow life histories, but differ in fast marsupials; age at first reproduction is the most important trait contributing to λ and fertility contributes little. Marsupials have slower life histories than eutherians, and differences between these clades may derive from their contrasting reproductive modes; marsupials have slower development, growth and metabolism than eutherians of equivalent size.

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.

Value of combining transect counts and telemetry data to determine short-term population trends in a globally threatened species.

To evaluate conservation interventions, it is necessary to obtain reliable population trends for short (<10 years) time scales. Telemetry can be used to estimate short-term survival rates and is a common tool for assessing population trends, but it has limitations and can be biased toward specific behavioral traits of tagged individuals. Encounter rates calculated from transects can be useful for assessing changes across multiple species, but they can have large confidence intervals and be affected by variations in survey conditions. The decline of African vultures has been well-documented, but understanding of recent trends is lacking. To examine population trends, we used survival estimates from telemetry data collected over 6 years (primarily for white-backed vultures [Gyps africanus]) and transect counts conducted over 8 years (for 7 scavenging raptors) in 3 large protected areas in Tanzania. Population trends were estimated using survival analysis combined with the Leslie Lefkovitch matrix model from the telemetry data and using Bayesian mixed effects generalized linear regression models from the transect data. Both methods showed significant declines for white-backed vultures in Ruaha and Nyerere National Parks. Only telemetry estimates suggested significant declines in Katavi National Park. Encounter rates calculated from transects also showed declines in Nyerere National Park for lappet-faced vultures (38% annual declines) and Bateleurs (18%) and in Ruaha National Park for white-headed vultures (Trigonoceps occipitalis) (19%). Mortality rates recorded and inferred from telemetry suggested that poisoning is prevalent. However, only 6 mortalities of the 26 presumed mortalities were confirmed to be caused by poisoning, highlighting the challenges of determining the cause of death when working across large landscapes. Despite declines, our data provide evidence that southern Tanzania has higher current encounter rates of African vultures than elsewhere in East Africa. Preventing further declines will depend greatly on mitigating poisoning. Based on our results, we suggest that the use of multiple techniques improves understanding of population trends over the short term.

Importancia de combinar los conteos de transectos y los datos de telemetría para determinar las tendencias poblacionales a corto plazo de especies amenazadas a nivel mundial Resumen Para evaluar las intervenciones de conservación es necesario obtener tendencias poblacionales confiables para escalas temporales cortas (<10 años). La telemetría puede usarse para estimar las tasas de supervivencia a corto plazo, además de que es una herramienta común para analizar las tendencias poblacionales, pero tiene limitantes y puede sesgarse con el comportamiento específico de los individuos marcados. Las tasas de encuentro calculadas a partir de transectos pueden ser útiles para analizar cambios en varias especies, aunque pueden tener intervalos grandes de confianza y verse afectadas por las variantes en las condiciones del censo. La declinación de los buitres africanos está bien documentada, pero hace falta el conocimiento sobre las tendencias recientes. Usamos las estimaciones de supervivencia tomadas de datos telemétricos recolectados durante seis años (principalmente del buitre Gyps africanus) y los conteos de transecto de siete especies carroñeras realizados durante ocho años en tres áreas protegidas en Tanzania. Estimamos las tendencias poblacionales con la combinación de análisis de supervivencia y el modelo de matriz Leslie Lefkovitch hecho con los datos telemétricos y usando modelos bayesianos de regresión lineal generalizada de efectos mixtos hechos con los datos de los transectos. Ambos métodos indicaron declinaciones significativas de Gyps africanus en los Parques Nacionales Ruaha y Nyerere. Sólo las estimaciones telemétricas sugirieron una declinación significativa en el Parque Nacional Katavi. Las tasas de encuentro calculadas a partir de los transectos también indicaron declinaciones de Torgos tracheliotos (38% de declinaciones anuales) y de Terathopius ecaudutus (18%) en el Parque Nacional Nyerere y de Trigonoceps occipitalis (19%) en el Parque Nacional Ruaha. Las muertes registradas e inferidas a partir de la telemetría sugieren que el envenenamiento es prevalente. Sin embargo, sólo se confirmaron seis muertes por envenenamiento de las 26 supuestas, lo que resalta los obstáculos para determinar la causa de muerte cuando se trabaja en paisajes amplios. A pesar de las declinaciones, nuestros datos proporcionan evidencia de que el sur de Tanzania tiene tasas actuales de encuentro con buitres africanos más altas que en cualquier otra parte del occidente de África. La prevención de declinaciones en el futuro dependerá principalmente de evitar el envenenamiento. Con base en nuestros resultados, sugerimos que el uso de técnicas múltiples incrementa el conocimiento sobre las tendencias poblacionales a corto plazo.

Effects of Stage Structure on Coexistence: Mixed Benefits.

The properties of competition models where all individuals are identical are relatively well-understood; however, juveniles and adults can experience or generate competition differently. We study here less well-known structured competition models in discrete time that allow multiple life history parameters to depend on adult or juvenile population densities. A numerical study with Ricker density-dependence suggested that when competition coefficients acting on juvenile survival and fertility reflect opposite competitive hierarchies, stage structure could foster coexistence. We revisit and expand those results. First, through a Beverton-Holt two-species juvenile-adult model, we confirm that these findings do not depend on the specifics of density-dependence or life cycles, and obtain analytical expressions explaining how this coexistence emerging from stage structure can occur. Second, we show using a community-level sensitivity analysis that such emergent coexistence is robust to perturbations of parameter values. Finally, we ask whether these results extend from two to many species, using simulations. We show that they do not, as coexistence emerging from stage structure is only seen for very similar life-history parameters. Such emergent coexistence is therefore not likely to be a key mechanism of coexistence in very diverse ecosystems, although it may contribute to explaining coexistence of certain pairs of intensely competing species.

Optimal control of bioproduction in the presence of population heterogeneity.

Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics this is typically accounted for. However, on the population level this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous counterparts and may thus lead to severely suboptimal control of bioproduction. In this work, we introduce a comprehensive mathematical framework for solving bioproduction optimal control problems in the presence of heterogeneity. We study population-level models in which such heterogeneity is retained, and propose order-reduction approximation techniques. The reduced-order models take forms typical of homogeneous bioproduction models, making them a useful benchmark by which to study the importance of heterogeneity. Moreover, the derivation from the heterogeneous setting sheds light on parameter selection in ways a direct homogeneous outlook cannot, and reveals the source of approximation error. With view to optimally controlling bioproduction in microbial communities, we ask the question: when does optimising the reduced-order models produce strategies that work well in the presence of population heterogeneity? We show that, in some cases, homogeneous approximations provide remarkably accurate surrogate models. Nevertheless, we also demonstrate that this is not uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.

Modeling the effects of ration on individual growth of Oncorhynchus tshawytscha under controlled conditions.

Fed aquaculture is one of the fastest-growing and most valuable food production industries in the world. The efficiency with which farmed fish convert feed into biomass influences both environmental impact and economic revenue. Salmonid species, such as king salmon (Oncorhynchus tshawytscha), exhibit high levels of plasticity in vital rates such as feed intake and growth rates. Accurate estimations of individual variability in vital rates are important for production management. The use of mean trait values to evaluate feeding and growth performance can mask individual-level differences that potentially contribute to inefficiencies. Here, the authors apply a cohort integral projection model (IPM) framework to investigate individual variation in growth performance of 1625 individually tagged king salmon fed one of three distinct rations of 60%, 80%, and 100% satiation and tracked over a duration of 276 days. To capture the observed sigmoidal growth of individuals, they compared a nonlinear mixed-effects (logistic) model to a linear model used within the IPM framework. Ration significantly influenced several aspects of growth, both at the individual and at the cohort level. Mean final body mass and mean growth rate increased with ration; however, variance in body mass and feed intake also increased significantly over time. Trends in mean body mass and individual body mass variation were captured by both logistic and linear models, suggesting the linear model to be suitable for use in the IPM. The authors also observed that higher rations resulted in a decreasing proportion of individuals reaching the cohort's mean body mass or larger by the end of the experiment. This suggests that, in the present experiment, feeding to satiation did not produce the desired effects of efficient, fast, and uniform growth in juvenile king salmon. Although monitoring individuals through time is challenging in commercial aquaculture settings, recent technological advances combined with an IPM approach could provide new scope for tracking growth performance in experimental and farmed populations. Using the IPM framework might allow the exploration of other size-dependent processes affecting vital rate functions, such as competition and mortality.

The dynamics of relapses during treatment switch in relapsing-remitting multiple sclerosis.

Based on reported trends in relapse incidence among patients with relapsing-remitting multiple sclerosis, an original model for the response to disease modifying therapies is proposed. With a population approach and separate states for patients accounting for their risk of relapses, a system of nonlinear equations is formulated, similarly to established epidemiological models. Different parameters describe the effect of drugs and treatment switch in reducing the frequency of relapses. The model allows for a good fit to previously published data for experiments where different drugs are used. It also shows that different treatments maintain a high degree of similarity, with analogous dynamical features: a pre-treatment increment in relapse frequency leading to a distinct peak, a rapid drop after treatment switch and a plateau corresponding to a new base relapse activity, which seems dependant on the treatment chosen. A sensitivity analysis shows that the uncertainty in the initial proportions of different populations and the frequency of relapses can modify the overall dynamics of the response to treatment. Drugs are observed to induce effects that depend on patient sample's intrinsic characteristics, producing two clearly distinct and independent dynamics of relapse response. This confirms the clinical observation that certain drugs may be overall more successful in lowering the rate of relapses more significantly than others, notwithstanding the fact that patients behave differently across experiments.

Relative effects of sample size, detection probability, and study duration on estimation in integrated population models.

Understanding mechanistic causes of population change is critical for managing and conserving species. Integrated population models (IPMs) allow for quantifying population changes while directly relating environmental drivers to vital rates, but power of IPMs to detect trends and environmental effects on vital rates remains understudied. We simulated data for an IPM fewer than 41 scenarios to determine the power to detect trends and environmental effects on vital rates based on study duration, sample size, detection probability, and effect size. Our results indicated that temporal duration of a study and effect size, rather than sample size of each individual data set or detection probability, had the greatest influence on the power to identify trends in adult survival and fecundity. When using only 10 years of data, we were unable to identify a 50% increase in adult survival but were able to identify this increase with 22 years of data. When using only capture-recapture data in a traditional Cormack-Jolly-Seber analysis, we lacked sufficient power to identify trends in survival, and power of the Cormack-Jolly-Seber model was always less than the IPM. The IPM had greater power to identify trends and environmental effects on fecundity (e.g., we detected a 58% change in fecundity using 12 years of data). Models with effects of environmental variables on vital rates had less power than trends, likely to be due to increased annual variation in the vital rate when modeling responses to environmental effects that varied by year. Lack of power in the Cormack-Jolly-Seber analysis could be due to the relatively small variability in adult survival compared with fecundity, given the life history of our simulated species. As interannual variation in environmental conditions will probably increase with climate change, this type of analysis can help to inform the study duration needed, which may be a shifting target given future climate uncertainty and the complex nature of environmental correlations with demography.

The power of modelling pulsatile profiles.

The quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration-time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

Blindfold learning of an accurate neural metric.

The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli.

Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal.

Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development and reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor-analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual-based demography from yellow-bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneously, negative responses of winter survival and reproductive-status change to declining environmental quality result in a higher risk of population quasi-extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models.

We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the population response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.NEW & NOTEWORTHY Population models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.

Parental Age Effects and the Evolution of Senescence.

Most theory on the evolution of senescence implicitly assumes that all offspring are of equal quality. However, in addition to age-related declines in survival and fecundity (classically defined senescence), many organisms exhibit age-related declines in offspring quality, a phenomenon known as a parental age effect. Theoretical work suggests that parental age effects may alter age trajectories of selection and therefore shape the evolution of senescence; however, to date, these analyses have been limited to idealized life cycles and models of maternal care in human populations. To gain a broader understanding of how parental age effects may shape age trajectories of selection, we extend the classic age-structured population projection model to also account for parental age structure and apply this model to empirical data from an aquatic plant known to exhibit parental age effects (the duckweed Lemna minor), as well as a diverse set of simulated life cycles. Our results suggest that parental age effects alter predictions from classic theory on the evolution of senescence. Age-related declines in offspring quality reduce the relative value of late-life reproduction, leading to steeper age-related declines in the force of natural selection than would otherwise be expected and potentially favoring the evolution of more rapid rates of senescence.

Heterogeneous alpha-cell population modeling of glucose-induced inhibition of electrical activity.

Glucagon release from the pancreatic alpha-cells is regulated by glucose, but the underlying mechanisms are far from understood. It is known that the alpha-cell population is very heterogeneous, but - compared to the insulin-secreting beta-cells - the consequences of this cell-to-cell variation are much less studied. Since the alpha-cells are not electrically coupled, large differences in the single cell responses are to be expected, and this variation may contribute to the confusion regarding the mechanisms of glucose-induced suppression of glucagon release. Using mathematical modeling of alpha-cells with realistic cell-to-cell parameter variation based on recent experimental results, we show that the simulated alpha-cells exhibit great diversity in their electrophysiological behavior. To robustly reproduce experimental recordings from alpha-cell exposed to a rise in glucose levels, we must assume that both intrinsic mechanisms and paracrine signals contribute to glucose-induced changes in electrical activity. Our simulations suggest that the sum of different electrophysiological responses due to alpha-cell heterogeneity is involved in glucose-suppressed glucagon secretion, and that more than one mechanism contribute to control the alpha-cell populations' behavior. Finally, we apply regression analysis to our synthetic alpha-cell population to infer which membrane currents influence electrical activity in alpha-cells at different glucose levels. The results from such statistical modeling suggest possible disturbances underlying defect regulation of alpha-cell electrical behavior in diabetics. Thus, although alpha-cells appear to be inherently complex and heterogeneous as reflected in published data, realistic modeling of the alpha-cells at the population level provides insight into the mechanisms of glucagon release.

Changes in age-structure over four decades were a key determinant of population growth rate in a long-lived mammal.

A changing environment directly influences birth and mortality rates, and thus population growth rates. However, population growth rates in the short term are also influenced by population age-structure. Despite its importance, the contribution of age-structure to population growth rates has rarely been explored empirically in wildlife populations with long-term demographic data. Here we assessed how changes in age-structure influenced short-term population dynamics in a semi-captive population of Asian elephants Elephas maximus. We addressed this question using a demographic dataset of female Asian elephants from timber camps in Myanmar spanning 45 years (1970-2014). First, we explored temporal variation in age-structure. Then, using annual matrix population models, we used a retrospective approach to assess the contributions of age-structure and vital rates to short-term population growth rates with respect to the average environment. Age-structure was highly variable over the study period, with large proportions of juveniles in the years 1970 and 1985, and made a substantial contribution to annual population growth rate deviations. High adult birth rates between 1970 and 1980 would have resulted in large positive population growth rates, but these were prevented by a low proportion of reproductive-aged females. We highlight that an understanding of both age-specific vital rates and age-structure is needed to assess short-term population dynamics. Furthermore, this example from a human-managed system suggests that the importance of age-structure may be accentuated in populations experiencing human disturbance where age-structure is unstable, such as those in captivity or for endangered species. Ultimately, changes to the environment drive population dynamics by influencing birth and mortality rates, but understanding demographic structure is crucial for assessing population growth.