Demographic patterns in Lyme borreliosis seasonality over 25 years.

Lyme borreliosis, the most common vector-borne disease in Europe and North America, is attracting growing concern due to its expanding geographic range. The growth in incidence and geographic spread is largely attributed to climate and land-use changes that support the tick vector and thereby increase disease risk. Despite a wide range of symptoms displayed by Lyme borreliosis patients, the demographic patterns in clinical manifestations and seasonal case timing have not been thoroughly investigated and may result from differences in exposure, immunity and pathogenesis. We analysed 25 years of surveillance data from Norway, supplemented by population demography data, using a Bayesian modelling framework. The analyses aimed to detect differences in case seasonality and clinical manifestations of Lyme borreliosis across age and sex differentiated patient groups. The results showed a bimodal pattern of incidence over age, where children (0-9 years) had the highest incidence, young adults (20-29 years) had low incidence and older adults had a second incidence peak in the ages 70-79 years. Youth (0-19 years) presented with a higher proportion of neuroborreliosis cases and a lower proportion of arthritic manifestations compared to adults (20+ years). Adult males had a higher overall incidence than adult females and a higher proportion of arthritis cases. The seasonal timing of Lyme borreliosis consistently occurred around 4.4 weeks earlier in youth compared to adults, regardless of clinical manifestation. All demographic groups exhibited a shift towards an earlier seasonal timing over the 25-year study period, which appeared unrelated to changes in population demographics. However, the disproportionate incidence of Lyme borreliosis in seniors requires increased public awareness and knowledge about this high-risk group as the population continues to age concurrently with disease emergence. Our findings highlight the importance of considering patient demographics when analysing the emergence and seasonal patterns of vector-borne diseases using long-term surveillance data.

The emergence and shift in seasonality of Lyme borreliosis in Northern Europe.

Climate change has had a major impact on seasonal weather patterns, resulting in marked phenological changes in a wide range of taxa. However, empirical studies of how changes in seasonality impact the emergence and seasonal dynamics of vector-borne diseases have been limited. Lyme borreliosis, a bacterial infection spread by hard-bodied ticks, is the most common vector-borne disease in the northern hemisphere and has been rapidly increasing in both incidence and geographical distribution in many regions of Europe and North America. By analysis of long-term surveillance data (1995-2019) from across Norway (latitude 57°58'-71°08' N), we demonstrate a marked change in the within-year timing of Lyme borreliosis cases accompanying an increase in the annual number of cases. The seasonal peak in cases is now six weeks earlier than 25 years ago, exceeding seasonal shifts in plant phenology and previous model predictions. The seasonal shift occurred predominantly in the first 10 years of the study period. The concurrent upsurgence in case number and shift in case timing indicate a major change in the Lyme borreliosis disease system over recent decades. This study highlights the potential for climate change to shape the seasonal dynamics of vector-borne disease systems.

Optimal energy allocation trade-off driven by size-dependent physiological and demographic responses to warming.

Body size-dependent physiological effects of temperature influence individual growth, reproduction, and survival, which govern animal population responses to global warming. Considerable knowledge has been established on how such effects can affect population growth and size structure, but less is known of their potential role in temperature-driven adaptation in life-history traits. In this study, we ask how warming affects the optimal allocation of energy between growth and reproduction and disentangle the underlying fitness trade-offs. To this end, we develop a novel dynamic energy budget integral projection model (DEB-IPM), linking individuals' size- and temperature-dependent consumption and maintenance via somatic growth, reproduction, and size-dependent energy allocation to emergent population responses. At the population level, we calculate the long-term population growth rate (fitness) and stable size structure emerging from demographic processes. Applying the model to an example of pike (Esox lucius), we find that optimal energy allocation to growth decreases with warming. Furthermore, we demonstrate how growth, fecundity, and survival contribute to this change in optimal allocation. Higher energy allocation to somatic growth at low temperatures increases fitness through survival of small individuals and through the reproduction of larger individuals. In contrast, at high temperatures, increased allocation to reproduction is favored because warming induces faster somatic growth of small individuals and increased fecundity but reduced growth and higher mortality of larger individuals. Reduced optimum allocation to growth leads to further reductions in body size and an increasingly truncated population size structure with warming. Our study demonstrates how, by incorporating general physiological mechanisms driving the temperature dependence of life-history traits, the DEB-IPM framework is useful for investigating the adaptation of size-structured organisms to warming.

Life history adaptations to fluctuating environments: Combined effects of demographic buffering and lability.

Demographic buffering and lability have been identified as adaptive strategies to optimise fitness in a fluctuating environment. These are not mutually exclusive, however, we lack efficient methods to measure their relative importance for a given life history. Here, we decompose the stochastic growth rate (fitness) into components arising from nonlinear responses and variance-covariance of demographic parameters to an environmental driver, which allows studying joint effects of buffering and lability. We apply this decomposition for 154 animal matrix population models under different scenarios to explore how these main fitness components vary across life histories. Faster-living species appear more responsive to environmental fluctuations, either positively or negatively. They have the highest potential for strong adaptive demographic lability, while demographic buffering is a main strategy in slow-living species. Our decomposition provides a comprehensive framework to study how organisms adapt to variability through buffering and lability, and to predict species responses to climate change.

Size- and stage-dependence in cause-specific mortality of migratory brown trout.

Evidence-based management of natural populations under strong human influence frequently requires not only estimates of survival but also knowledge about how much mortality is due to anthropogenic vs. natural causes. This is the case particularly when individuals vary in their vulnerability to different causes of mortality due to traits, life history stages, or locations. Here, we estimated harvest and background (other cause) mortality of landlocked migratory salmonids over half a century. In doing so, we quantified among-individual variation in vulnerability to cause-specific mortality resulting from differences in body size and spawning location relative to a hydropower dam. We constructed a multistate mark-recapture model to estimate harvest and background mortality hazard rates as functions of a discrete state (spawning location) and an individual time-varying covariate (body size). We further accounted for among-year variation in mortality and migratory behaviour and fit the model to a unique 50-year time series of mark-recapture-recovery data on brown trout (Salmo trutta) in Norway. Harvest mortality was highest for intermediate-sized trout, and outweighed background mortality for most of the observed size range. Background mortality decreased with body size for trout spawning above the dam and increased for those spawning below. All vital rates varied substantially over time, but a trend was evident only in estimates of fishers' reporting rate, which decreased from over 50% to less than 10% throughout the study period. We highlight the importance of body size for cause-specific mortality and demonstrate how this can be estimated using a novel hazard rate parameterization for mark-recapture models. Our approach allows estimating effects of individual traits and environment on cause-specific mortality without confounding, and provides an intuitive way to estimate temporal patterns within and correlation among different mortality sources.

Effects of size- and sex-selective harvesting: An integral projection model approach.

Harvesting is often size-selective, and in species with sexual size dimorphism, it may also be sex-selective. A powerful approach to investigate potential consequences of size- and/or sex-selective harvesting is to simulate it in a demographic population model. We developed a population-based integral projection model for a size- and sex-structured species, the commonly exploited pike (Esox lucius). The model allows reproductive success to be proportional to body size and potentially limited by both sexes. We ran all harvest simulations with both lower size limits and slot limits, and to quantify the effects of selective harvesting, we calculated sex ratios and the long-term population growth rate (λ). In addition, we quantified to what degree purely size-selective harvesting was sex-selective, and determined when λ shifted from being female to male limited under size- and sex-selective harvesting. We found that purely size-selective harvest can be sex-selective, and that it depends on the harvest limits and the size distributions of the sexes. For the size- and sex-selective harvest simulations, λ increased with harvest intensity up to a threshold as females limited reproduction. Beyond this threshold, males became the limiting sex, and λ decreased as more males were harvested. The peak in λ, and the corresponding sex ratio in harvest, varied with both the selectivity and the intensity of the harvest simulation. Our model represents a useful extension of size-structured population models as it includes both sexes, relaxes the assumption of female dominance, and accounts for size-dependent fecundity. The consequences of selective harvesting presented here are especially relevant for size- and sex-structured exploited species, such as commercial fisheries. Thus, our model provides a useful contribution toward the development of more sustainable harvesting regimes.

Individual heterogeneity and early life conditions shape growth in a freshwater top predator.

Body size can have profound impacts on survival, movement, and reproductive schedules shaping individual fitness, making growth a central process in ecological and evolutionary dynamics. Realized growth is the result of a complex interplay between life history schedules, individual variation, and environmental influences. Integrating all of these aspects into growth models is methodologically difficult, depends on the availability of repeated measurements of identifiable individuals, and consequently represents a major challenge in particular for natural populations. Using a unique 30-yr time series of individual length measurements inferred from scale year rings of wild brown trout, we develop a Bayesian hierarchical model to estimate individual growth trajectories in temporally and spatially varying environments. We reveal a gradual decrease in average juvenile growth, which has carried over to adult life and contributed to decreasing sizes observed at the population level. Commonly studied environmental drivers like temperature and water flow did not explain much of this trend and overall persistent and among-year individual variation dwarfed temporal variation in growth patterns. Our model and results are relevant to a wide range of questions in ecology and evolution requiring a detailed understanding of growth patterns, including conservation and management of many size-structured populations.

Fitness consequences of early life conditions and maternal size effects in a freshwater top predator.

Conditions experienced in early life stages can be an important determinant of individual life histories. In fish, environmental conditions are known to affect early survival and growth, but recent studies have also emphasized maternal effects mediated by size or age. However, the relative sensitivity of the mean fitness (population growth rate λ) to different early life impacts remains largely unexplored. Using a female-based integral projection model (IPM) parameterized from unique long-term demographic data for pike (Esox lucius), we evaluated the relative fitness consequences of different early life impacts, including (i) maternal effects of length on egg weight, potentially affecting offspring (first year) survival, and (ii) effects of temperature on offspring growth and survival. Of the seven vital rates defining the model, offspring survival could not be directly estimated and four scenarios were defined for this rate. Elasticity analyses of the IPM were performed to calculate (i) the total contribution from different lengths to the elasticity of λ to the projection kernel, and (ii) the elasticity of λ to underlying variables of female current length, female offspring length at age 1, and temperature. These elasticities were decomposed into contributions from different vital rates across length. Egg weight increased with female length, as expected, but the effect leveled off for the largest females. However, λ was largely insensitive to this effect, even when egg weight was assumed to have a strong effect on offspring survival. In contrast, λ was sensitive to early temperature conditions through growth and survival. Among mature females, the total elasticity of λ to the projection kernel generally increased with length. The results were robust to a wide range of assumptions. These results suggest that environmental conditions experienced in early life represent a more important driver of mean population growth and fitness of pike than maternal effects of size on offspring survival. We discuss two general mechanisms underlying the weak influence of this maternal effect, suggesting that these may be general for long-lived and highly fecund fishes. This model and results are relevant for the management of long-lived top predators, including many commercially important fish species.

Environmental variation and population responses to global change.

Species' responses to environmental changes such as global warming are affected not only by trends in mean conditions, but also by natural and human-induced environmental fluctuations. Methods are needed to predict how such environmental variation affects ecological and evolutionary processes, in order to design effective strategies to conserve biodiversity under global change. Here, we review recent theoretical and empirical studies to assess: (1) how populations respond to changes in environmental variance, and (2) how environmental variance affects population responses to changes in mean conditions. Contrary to frequent claims, empirical studies show that increases in environmental variance can increase as well as decrease long-term population growth rates. Moreover, environmental variance can alter and even reverse the effects of changes in the mean environment, such that even if environmental variance remains constant, omitting it from population models compromises their ability to predict species' responses to changes in mean conditions. Drawing on theory relating these effects of environmental variance to the curvatures of population growth responses to the environment, we outline how species' traits such as phylogenetic history and body mass could be used to predict their responses to global change under future environmental variability.

Individual heterogeneity in life histories and eco-evolutionary dynamics.

Individual heterogeneity in life history shapes eco-evolutionary processes, and unobserved heterogeneity can affect demographic outputs characterising life history and population dynamical properties. Demographic frameworks like matrix models or integral projection models represent powerful approaches to disentangle mechanisms linking individual life histories and population-level processes. Recent developments have provided important steps towards their application to study eco-evolutionary dynamics, but so far individual heterogeneity has largely been ignored. Here, we present a general demographic framework that incorporates individual heterogeneity in a flexible way, by separating static and dynamic traits (discrete or continuous). First, we apply the framework to derive the consequences of ignoring heterogeneity for a range of widely used demographic outputs. A general conclusion is that besides the long-term growth rate lambda, all parameters can be affected. Second, we discuss how the framework can help advance current demographic models of eco-evolutionary dynamics, by incorporating individual heterogeneity. For both applications numerical examples are provided, including an empirical example for pike. For instance, we demonstrate that predicted demographic responses to climate warming can be reversed by increased heritability. We discuss how applications of this demographic framework incorporating individual heterogeneity can help answer key biological questions that require a detailed understanding of eco-evolutionary dynamics.

Effects of climate change on trait-based dynamics of a top predator in freshwater ecosystems.

Predicted universal responses of ectotherms to climate warming include increased maximum population growth rate and changes in body size through the temperature-size rule. However, the mechanisms that would underlie these predicted responses are not clear. Many studies have focused on proximate mechanisms of physiological processes affecting individual growth. One can also consider ultimate mechanisms involving adaptive explanations by evaluating temperature effects on different vital rates across the life history and using the information in a population dynamical model. Here, we combine long-term data for a top predator in freshwater ecosystems (pike; Esox lucius) with a stochastic integral projection model to analyze concurrent effects of temperature on vital rates, body size, and population dynamics. As predicted, the net effect of warming on population growth rate (fitness) is positive, but the thermal sensitivity of this rate is highly size- and vital rate-dependent. These results are not sensitive to increasing variability in temperature. Somatic growth follows the temperature-size rule, and our results support an adaptive explanation for this response. The stable length structure of the population shifts with warming toward an increased proportion of medium-sized but a reduced proportion of small and large individuals. This study highlights how demographic approaches can help reveal complex underlying mechanisms for population responses to warming.

Effects of demographic structure on key properties of stochastic density-independent population dynamics.

The development of stochastic demography has largely been based on age structured populations, although other types of demographic structure, especially permanent and dynamic heterogeneity, are likely common in natural populations. The combination of stochasticity and demographic structure is a challenge for analyses of population dynamics and extinction risk, because the population structure will fluctuate around the stable structure and the population size shows transient fluctuations. However, by using a diffusion approximation for the total reproductive value, density-independent dynamics of structured populations can be described with only three population parameters: the expected population growth rate, the environmental variance and the demographic variance. These parameters depend on population structure via the state-specific vital rates and transition rates. Once they are found, the diffusion approximation represents a substantial reduction in model complexity. Here, we review and compare the key population parameters across a wide range of demographic structure, from the case of no structure to the most general case of dynamic heterogeneity, and for both discrete and continuous types. We focus on the demographic variance, but also show how environmental stochasticity can be included. This study brings together results from recent models, each considering a specific type of population structure, and places them in a general framework for structured populations. Comparison across different types of demographic structure reveals that the reproductive value is an essential concept for understanding how population structure affects stochastic dynamics and extinction risk.

Integral projection models for finite populations in a stochastic environment.

Continuous types of population structure occur when continuous variables such as body size or habitat quality affect the vital parameters of individuals. These structures can give rise to complex population dynamics and interact with environmental conditions. Here we present a model for continuously structured populations with finite size, including both demographic and environmental stochasticity in the dynamics. Using recent methods developed for discrete age-structured models we derive the demographic and environmental variance of the population growth as functions of a continuous state variable. These two parameters, together with the expected population growth rate, are used to define a one-dimensional diffusion approximation of the population dynamics. Thus, a substantial reduction in complexity is achieved as the dynamics of the complex structured model can be described by only three population parameters. We provide methods for numerical calculation of the model parameters and demonstrate the accuracy of the diffusion approximation by computer simulation of specific examples. The general modeling framework makes it possible to analyze and predict future dynamics and extinction risk of populations with various types of structure, and to explore consequences of changes in demography caused by, e.g., climate change or different management decisions. Our results are especially relevant for small populations that are often of conservation concern.

Poor environmental tracking can make extinction risk insensitive to the colour of environmental noise.

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms-which probably act in many species-can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought.

Effects of climate change and variability on population dynamics in a long-lived shorebird.

Climate change affects both the mean and variability of climatic variables, but their relative impact on the dynamics of populations is still largely unexplored. Based on a long-term study of the demography of a declining Eurasian Oystercatcher (Haematopus ostralegus) population, we quantify the effect of changes in mean and variance of winter temperature on different vital rates across the life cycle. Subsequently, we quantify, using stochastic stage-structured models, how changes in the mean and variance of this environmental variable affect important characteristics of the future population dynamics, such as the time to extinction. Local mean winter temperature is predicted to strongly increase, and we show that this is likely to increase the population's persistence time via its positive effects on adult survival that outweigh the negative effects that higher temperatures have on fecundity. Interannual variation in winter temperature is predicted to decrease, which is also likely to increase persistence time via its positive effects on adult survival that outweigh the negative effects that lower temperature variability has on fecundity. Overall, a 0.1 degrees C change in mean temperature is predicted to alter median time to extinction by 1.5 times as many years as would a 0.1 degrees C change in the standard deviation in temperature, suggesting that the dynamics of oystercatchers are more sensitive to changes in the mean than in the interannual variability of this climatic variable. Moreover, as climate models predict larger changes in the mean than in the standard deviation of local winter temperature, the effects of future climatic variability on this population's time to extinction are expected to be overwhelmed by the effects of changes in climatic means. We discuss the mechanisms by which climatic variability can either increase or decrease population viability and how this might depend both on species' life histories and on the vital rates affected. This study illustrates that, for making reliable inferences about population consequences in species in which life history changes with age or stage, it is crucial to investigate the impact of climate change on vital rates across the entire life cycle. Disturbingly, such data are unavailable for most species of conservation concern.

Fixation of slightly beneficial mutations: effects of life history.

Recent studies of rates of evolution have revealed large systematic differences among organisms with different life histories, both within and among taxa. Here, we consider how life history may affect the rate of evolution via its influence on the fixation probability of slightly beneficial mutations. Our approach is based on diffusion modeling for a finite, stage-structured population with stochastic population dynamics. The results, which are verified by computer simulations, demonstrate that even with complex population structure just two demographic parameters are sufficient to give an accurate approximation of the fixation probability of a slightly beneficial mutation. These are the reproductive value of the stage in which the mutation first occurs and the demographic variance of the population. The demographic variance also determines what influence population size has on the fixation probability. This model represents a substantial generalization of earlier models, covering a large range of life histories.

Individual heterogeneity in vital parameters and demographic stochasticity.

Most population models assume that individuals have equal opportunities for survival and reproduction, although many natural populations consist of individuals with different vital parameters that remain different over time. Individual heterogeneity in vital parameters, which may depend on age or stage, can alter many population characteristics compared with a homogeneous population, affecting both deterministic and stochastic properties of the population process. Demographic variance is an important parameter influenced by heterogeneity. However, whether heterogeneity leads to increased or decreased demographic variance has been an unresolved question, except for special cases. Here, we present a general stochastic matrix model for a heterogeneous population that allows us to examine effects of heterogeneity on population dynamics, even when the degree of heterogeneity depends on age. Using this model, we found that the demographic variance may increase, decrease, or remain unaltered compared with a homogeneous comparison model, depending on the vital parameter values and on how these are distributed among individuals at each time step. Furthermore, if the reproductive value is the same for all individuals, heterogeneity has no effect on the demographic variance. Thus, we provide a general theoretical framework for analyzing how individual heterogeneity caused by different biological mechanisms affects fluctuations of especially small populations.