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In the context of ongoing global changes, long-term demographic monitoring data is crucial to assess the viability of natural populations. Such data is particularly important when large fluctuations in population size obfuscate background demographic trajectories. Here, we report results from the first 5 years of a long-term monitoring of a moonwort (Botrychium, Ophioglossaceae) community in Bic National Park, eastern Canada. Our objectives were to provide a first estimate of the composition, size, and demography of the moonwort colonies and to evaluate the putative influence of meteorological and microenvironmental variables on the density of aboveground sporophytes and sporophyte trait variability. Individuals were identified to the species level and tallied in each colony (n = 16 colonies) for the first 3 years (2019–2021) and then marked, monitored, and measured in permanent sample plots (n = 27 plots) for the last 3 years (2021–2023). Although colonies did not vary in composition (n = 8 Botrychium species), the number of emerged individuals differed yearly. Moonwort abundance, re-emergence, and trophophore height were associated with rainfall during the period of sporophyte emergence. Post-drought recovery occurred readily, which might reflect rapid recruitment or dormancy in Botrychium. Moonwort abundance declined as shrub cover and herbaceous vegetation height increased whereas trophophore size was smaller under closed tree canopy. Our results suggest spring rainfall as the limiting factor in opened habitats, whereas competition for sunlight may be limiting under closed canopy. The sizable and species-rich moonwort community of the Bic National Park provides key insights into Botrychium ecology and community dynamics. [ABSTRACT FROM AUTHOR]

Marine ecosystems exist in a noisy and uncertain environment, not governed by deterministic laws. The development of ecological communities is significantly influenced by variability, and the interaction between nonlinearity and stochastic processes can lead to phenomena that deterministic models cannot explain. Plankton, forming the base of the marine food web, are highly affected by stochastic fluctuations due to their short reproductive timescales. Investigating the effects of noise on plankton growth is essential for accurately describing and predicting marine health. We present a realistic biogeochemical model where multiplicative white noise represents environmental stochasticity affecting plankton. The model suggests ergodic properties in the presence of stochastic fluctuations, with temporal and ensemble distributions being coherent. Analytical and numerical analyses reveal that, given sufficiently low noise intensity, dynamics near equilibrium resemble an Ornstein-Uhlenbeck additive process. With higher noise intensities, resonance occurs, particularly when endogenous dynamics are periodic. The results indicate that low noise intensity can positively influence plankton persistence with an higher number of species coexisting, while higher noise intensity can establish a new equilibrium in the system.

Understanding the process of extinction in natural populations is crucial for the preservation of ecosystem stability and biodiversity, both theoretically and practically. The risk of extinction in these populations is often influenced by environmental stochasticity, which has a significant impact on birth and mortality rates. In this study, we propose a tri-trophic food chain model that incorporates random disturbances in the environment, represented by a chemostat, which is an ideal mathematical model for simulating diverse ecosystems. In the absence of noise, the model exhibits two types of bistability, indicating that the stochastic system has two distinct paths to extinction: from a stationary state or from an oscillatory state. For each type, we determine the tipping value of environmental stochasticity that leads to the extinction of top predators by constructing confidence regions for the corresponding coexisting attractor. Furthermore, we observe a high skewness and heavy-tailed distribution of extinction times for intermediate and high levels of environmental stochasticity, consistent with empirical data. To analyze extinction times, we employ the Lévy distribution, a statistical model that describes power-law tail distributions. Our findings demonstrate that, for a fixed dilution rate, increasing environmental stochasticity reduces the average extinction time, thereby accelerating species extinction. Additionally, for a certain level of stochasticity, the average extinction time decreases with the magnitude of the dilution rate due to the heavy-tailed nature of the extinction time distribution. [ABSTRACT FROM AUTHOR]

Climate refugia are areas where species can persist through climate change with little to no movement. Among the factors associated with climate refugia are high spatial heterogeneity, such that there is only a short distance between current and future optimal climates, as well as biotic or abiotic environmental factors that buffer against variability in time. However, these types of climate refugia may be declining due to anthropogenic homogenization of environments and degradation of environmental buffers. To quantify the potential for restoration of refugia‐like environmental conditions to increase population persistence under climate change, we simulated a population's capacity to track their temperature over space and time given different levels of spatial and temporal variability in temperature. To determine how species traits affected the efficacy of restoring heterogeneity, we explored an array of values for species' dispersal ability, thermal tolerance, and fecundity. We found that species were more likely to persist in environments with higher spatial heterogeneity and lower environmental stochasticity. When simulating a management action that increased the spatial heterogeneity of a previously homogenized environment, species were more likely to persist through climate change, and population sizes were generally higher, but there was little effect with mild temperature change. The benefits of heterogeneity restoration were greatest for species with limited dispersal ability. In contrast, species with longer dispersal but lower fecundity were more likely to benefit from a reduction in environmental stochasticity than an increase in spatial heterogeneity. Our results suggest that restoring environments to refugia‐like conditions could promote species' persistence under the influence of climate change in addition to conservation strategies such as assisted migration, corridors, and increased protection. [ABSTRACT FROM AUTHOR]

Temporal correlations among demographic parameters can strongly influence population dynamics. Our empirical knowledge, however, is very limited regarding the direction and the magnitude of these correlations and how they vary among demographic parameters and species' life histories. Here, we use long-term demographic data from 15 bird and mammal species with contrasting pace of life to quantify correlation patterns among five key demographic parameters: juvenile and adult survival, reproductive probability, reproductive success and productivity. Correlations among demographic parameters were ubiquitous, more frequently positive than negative, but strongly differed across species. Correlations did not markedly change along the slow-fast continuum of life histories, suggesting that they were more strongly driven by ecological than evolutionary factors. As positive temporal demographic correlations decrease the mean of the long-run population growth rate, the common practice of ignoring temporal correlations in population models could lead to the underestimation of extinction risks in most species.

For species primarily regulated by a common predator, the P* rule of Holt & Lawton (Holt & Lawton, 1993. Am. Nat. 142, 623-645. (doi:10.1086/285561)) predicts that the prey species that supports the highest mean predator density (P*) excludes the other prey species. This prediction is re-examined in the presence of temporal fluctuations in the vital rates of the interacting species including predator attack rates. When the fluctuations in predator attack rates are temporally uncorrelated, the P* rule still holds even when the other vital rates are temporally auto-correlated. However, when temporal auto-correlations in attack rates are positive but not too strong, the prey species can coexist due to the emergence of a positive covariance between predator density and prey vulnerability. This coexistence mechanism is similar to the storage effect for species regulated by a common resource. Negative or strongly positive auto-correlations in attack rates generate a negative covariance between predator density and prey vulnerability and a stochastic priority effect can emerge: with non-zero probability either prey species is excluded. These results highlight how temporally auto-correlated species' interaction rates impact the structure and dynamics of ecological communities.

Current environmental changes may increase temporal variability of life history traits of species thus affecting their long‐term population growth rate and extinction risk. If there is a general relationship between environmental variances (EVs) and mean annual survival rates of species, that relationship could be used as a guideline for analyses of population growth and extinction risk for populations, where data on EVs are missing. For this purpose, we present a comprehensive compilation of 252 EV estimates from 89 species belonging to five vertebrate taxa (birds, mammals, reptiles, amphibians and fish) covering mean annual survival rates from 0.01 to 0.98. Since variances of survival rates are constrained by their means, particularly for low and high mean survival rates, we assessed whether any observed relationship persisted after applying two types of commonly used variance stabilizing transformations: relativized EVs (observed/mathematical maximum) and logit‐scaled EVs. With raw EVs at the arithmetic scale, mean–variance relationships of annual survival rates were hump‐shaped with small EVs at low and high mean survival rates and higher (and widely variable) EVs at intermediate mean survival rates. When mean annual survival rates were related to relativized EVs the hump‐shaped pattern was less distinct than for raw EVs. When transforming EVs to logit scale the relationship between mean annual survival rates and EVs largely disappeared. The within‐species juvenile‐adult slopes were mainly positive at low (<0.5) and negative at high (>0.5) mean survival rates for raw and relativized variances while these patterns disappeared when EVs were logit transformed. Uncertainties in how to interpret the results of relativized and logit‐scaled EVs, and the observed high variation in EV's for similar mean annual survival rates illustrates that extrapolations of observed EVs and tests of life history drivers of survival–EV relationships need to also acknowledge the large variation in these parameters. [ABSTRACT FROM AUTHOR]

While polyploids are common in nature, existing models suggest that polyploid establishment should be difficult and rare. We explore this apparent paradox by focussing on the role of unreduced gametes, as their union is the main route for the formation of neopolyploids. Production of such gametes is affected by genetic and environmental factors, resulting in variation in the formation rate of unreduced gametes (u). Once formed, neopolyploids face minority cytotype exclusion (MCE) due to a lack of viable mating opportunities. More than a dozen theoretical models have explored factors that could permit neopolyploids to overcome MCE and become established. Until now, however, none have explored variability in u and its consequences for the rate of polyploid establishment. Here, we determine the distribution that best fits the available empirical data on u. We perform a global sensitivity analysis exploring the consequences of using empirical distributions of u to investigate effects on polyploid establishment. We determined that in many cases, u is best fit by a log-normal distribution. We found environmental stochasticity in u dramatically impacts model predictions when compared to a static u. Our results help reconcile previous modelling results suggesting high barriers to the polyploid establishment with the observation that polyploids are common in nature. [ABSTRACT FROM AUTHOR]

Life history traits are used to predict asymptotic odds of extinction from dynamic conditions. Less is known about how life history traits interact with stochasticity and population structure of finite populations to predict near‐term odds of extinction. Through empirically parameterized matrix population models, we study the impact of life history (reproduction, pace), stochasticity (environmental, demographic), and population history (existing, novel) on the transient population dynamics of finite populations of plant species. Among fast and slow pace and either a uniform or increasing reproductive intensity or short or long reproductive lifespan, slow, semelparous species are at the greatest risk of extinction. Long reproductive lifespans buffer existing populations from extinction while the odds of extinction of novel populations decrease when the reproductive effort is uniformly spread across the reproductive lifespan. Our study highlights the importance of population structure, pace, and two distinct aspects of parity for predicting near‐term odds of extinction. [ABSTRACT FROM AUTHOR]

We propose that the ecological resilience of communities to permanent changes of the environment can be based on how variation in the overall abundance of individuals affects the number of species. Community sensitivity is defined as the ratio between the rate of change in the log expected number of species and the rate of change in the log expected number of individuals in the community. High community sensitivity means that small changes in the total abundance strongly impact the number of species. Community resistance is the proportional reduction in expected number of individuals that the community can sustain before expecting to lose one species. A small value of community resistance means that the community can only endure a small reduction in abundance before it is expected to lose one species.Based on long‐term studies of four bird communities in European deciduous forests at different latitudes large differences were found in the resilience to environmental perturbations. Estimating the variance components of the species abundance distribution revealed how different processes contributed to the community sensitivity and resistance. Species heterogeneity in the population dynamics was the largest component, but its proportion varied among communities. Species‐specific response to environmental fluctuations was the second major component of the variation in abundance.Estimates of community sensitivity and resistance based on data only from a single year were in general larger than those based on estimates from longer time series. Thus, our approach can provide rapid and conservative assessment of the resilience of communities to environmental changes also including only short‐term data.This study shows that a general ecological mechanism, caused by increased strength of density dependence due to reduction in resource availability, can provide an intuitive measure of community resilience to environmental variation. Our analyses also illustrate the importance of including specific assumptions about how different processes affect community dynamics. For example, if stochastic fluctuations in the environment affect all species in a similar way, the sensitivity and resistance of the community to environmental changes will be different from communities in which all species show independent responses. [ABSTRACT FROM AUTHOR]

The mechanism of seed dispersal mutualism is fundamental to understanding vegetation diversity and its conservation. In this study, we propose a stochastic model that extends the classical framework of seed dispersal mutualism to explore the effects of environmental stochasticity on mutualistic interactions between seed dispersers and plants. We first provide a comprehensive picture of the long-term dynamics of seed dispersal mutualism in deterministic and stochastic environments. We then analyze the relationship between stochasticity and the probability and time that seed dispersal mutualism tips between stable states. Additionally, we evaluate the extinction risk of seed dispersal mutualism for different population values and accordingly assign extinction warning levels to these values. The analysis reveals that the impact of environmental stochasticity on tipping phenomena is scenario-dependent but follows some interpretable trends. The probability (resp., time) of tipping towards the extinction state typically increases (resp., decreases) monotonically with noise intensity, while the probability of tipping towards the coexistence state typically peaks at intermediate noise intensity. Noise in animal populations contributes to tipping toward the coexistence state, whereas noise in plant populations slows down the tipping toward the coexistence state. Noise-induced changes in warning levels of initial population values are most pronounced near the boundaries of the basin of attraction, but sufficiently loud noise (especially for plant populations) may alter the risk far from these boundaries. These findings provide a theoretical explanation for the effect of environmental stochasticity on multistability transitions in seed dispersal mutualism and can be utilized to study the interplay between other population systems and environmental stochasticity. [ABSTRACT FROM AUTHOR]

Abstract Current environmental changes may increase temporal variability of life history traits of species thus affecting their long‐term population growth rate and extinction risk. If there is a general relationship between environmental variances (EVs) and mean annual survival rates of species, that relationship could be used as a guideline for analyses of population growth and extinction risk for populations, where data on EVs are missing. For this purpose, we present a comprehensive compilation of 252 EV estimates from 89 species belonging to five vertebrate taxa (birds, mammals, reptiles, amphibians and fish) covering mean annual survival rates from 0.01 to 0.98. Since variances of survival rates are constrained by their means, particularly for low and high mean survival rates, we assessed whether any observed relationship persisted after applying two types of commonly used variance stabilizing transformations: relativized EVs (observed/mathematical maximum) and logit‐scaled EVs. With raw EVs at the arithmetic scale, mean–variance relationships of annual survival rates were hump‐shaped with small EVs at low and high mean survival rates and higher (and widely variable) EVs at intermediate mean survival rates. When mean annual survival rates were related to relativized EVs the hump‐shaped pattern was less distinct than for raw EVs. When transforming EVs to logit scale the relationship between mean annual survival rates and EVs largely disappeared. The within‐species juvenile‐adult slopes were mainly positive at low (0.5) mean survival rates for raw and relativized variances while these patterns disappeared when EVs were logit transformed. Uncertainties in how to interpret the results of relativized and logit‐scaled EVs, and the observed high variation in EV's for similar mean annual survival rates illustrates that extrapolations of observed EVs and tests of life history drivers of survival–EV relationships need to also acknowledge the large variation in these parameters.

Abstract Life history traits are used to predict asymptotic odds of extinction from dynamic conditions. Less is known about how life history traits interact with stochasticity and population structure of finite populations to predict near‐term odds of extinction. Through empirically parameterized matrix population models, we study the impact of life history (reproduction, pace), stochasticity (environmental, demographic), and population history (existing, novel) on the transient population dynamics of finite populations of plant species. Among fast and slow pace and either a uniform or increasing reproductive intensity or short or long reproductive lifespan, slow, semelparous species are at the greatest risk of extinction. Long reproductive lifespans buffer existing populations from extinction while the odds of extinction of novel populations decrease when the reproductive effort is uniformly spread across the reproductive lifespan. Our study highlights the importance of population structure, pace, and two distinct aspects of parity for predicting near‐term odds of extinction.

For species that inhabit environments where resource availability may be unpredictable, balance of resource allocation to life‐history traits can have heightened consequences for survival, reproduction, and ultimately, fitness. Acquisition and allocation of energy to maintenance, capital gain and reproduction should be in tune with the landscape an animal inhabits—environmental severity, food availability and population size all influence the resources animals have and dictate the ways they should be allocated.In seasonal environments, animals that experience periods of extreme resource limitation (e.g. harsh winters) may favour allocation of resources to body reserves to secure their survival at the cost of reproduction (i.e. risk averse). In contrast, the same accumulation of body reserves may not be necessary to survive in relatively benign landscapes where instead, allocation to reproduction is favoured (i.e. risk prone).According to the theory of risk‐sensitive allocation of resources, when animals are exposed to unprecedented or life‐threatening conditions, they may shift resource allocation to favour building capital over allocation in reproduction to preempt against encountering another life‐threatening event in the future.Using data from a long‐term project on a highly site‐faithful and long‐lived species, mule deer (Odocoileus hemionus), we evaluated how a life‐threatening winter and the associated changes in resource availability resulting from a population reduction influenced how animals acquired and allocated energy to survival (i.e. fat accumulation).Per capita precipitation, and the associated reduction in population abundance after the severe winter, had a positive influence of accrual of fat over summer. After the extreme physiological stress of a hard winter, deer starting spring with low body reserves accumulated 2.8 percentage points more fat over summer compared with before the experience of a bad winter and had an increased probability of recruiting fewer offspring. Fat stores can interact with environment, life history and behaviour to influence survival during periods of resource scarcity.For a long‐lived herbivore, we documented shifts in risk tolerance associated with fat accrual in preparation for winter, supporting the notion that risk‐sensitive allocation of resources may be plastic—an essential adaptation for animals to cope with rapidly changing landscapes. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Populations suffer two types of stochasticity: demographic stochasticity, from sampling error in offspring number, and environmental stochasticity, from temporal variation in the growth rate. By modelling evolution through phenotypic selection following an abrupt environmental change, we investigate how genetic and demographic dynamics, as well as effects on population survival of the genetic variance and of the strength of stabilizing selection, differ under the two types of stochasticity. We show that population survival probability declines sharply with stronger stabilizing selection under demographic stochasticity, but declines more continuously when environmental stochasticity is strengthened. However, the genetic variance that confers the highest population survival probability differs little under demographic and environmental stochasticity. Since the influence of demographic stochasticity is stronger when population size is smaller, a slow initial decline of genetic variance, which allows quicker evolution, is important for population persistence. In contrast, the influence of environmental stochasticity is population‐size‐independent, so higher initial fitness becomes important for survival under strong environmental stochasticity. The two types of stochasticity interact in a more than multiplicative way in reducing the population survival probability. Our work suggests the importance of explicitly distinguishing and measuring the forms of stochasticity during evolutionary rescue. [ABSTRACT FROM AUTHOR]

How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates using 'transient' LTREs have gained in popularity.Transient LTREs have been used particularly to understand how variation in vital rates translate into variation in growth for populations under long‐term study. For these, complete population models may be constructed to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking.We extend transient LTREs to further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows one to compare the resultant effect on the growth rate of different environmental factors, as well as density dependence, which may each act via multiple vital rates. We also show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.We illustrate these extensions with an integrated population model (IPM) for data from a 26 years study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density and unexplained random variation via multiple vital rates, and from demographic stochasticity.The case study suggests that variation in first year survival via the unexplained random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size. [ABSTRACT FROM AUTHOR]

Ecosystem dynamics is often considered driven by a coupling of species' resource consumption and its population size dynamics. Such resource-population dynamics is captured by MacArthur-type models. One biologically relevant feature that would also need to be captured by such models is the introduction of new and different species. Speciation introduces a stochastic component in the otherwise deterministic MacArthur theory. We describe here how speciation can be implemented to yield a model that is consistent with current theory on equilibrium resource-consumer models, but also displays readily observable rank diversity metric changes. The model also reproduces a priority effect. Adding speciation to a MacArthur-style model provides an attractively simple extension to explore the rich dynamics in evolving ecosystems. [ABSTRACT FROM AUTHOR]

Species interact in landscapes where environmental conditions vary in time and space. This variability impacts how species select habitat patches. Under equilibrium conditions, evolution of this patch selection can result in ideal free distributions where per capita growth rates are zero in occupied patches and negative in unoccupied patches. These ideal free distributions, however, do not explain why species occupy sink patches, why competitors have overlapping spatial ranges, or why predators avoid highly productive patches. To understand these patterns, we solve for coevolutionarily stable strategies (coESSs) of patch selection for multispecies stochastic Lotka-Volterra models accounting for spatial and temporal heterogeneity. In occupied patches at the coESS, we show that the differences between the local contributions to the mean and the variance of the long-term population growth rate are equalized. Applying this characterization to models of antagonistic interactions reveals that environmental stochasticity can partially exorcize the ghost of competition past, select for new forms of enemy-free and victimless space, and generate hydra effects over evolutionary timescales. Viewing our results through the economic lens of modern portfolio theory highlights why the coESS for patch selection is often a bet-hedging strategy coupling stochastic sink populations. Our results highlight how environmental stochasticity can reverse or amplify evolutionary outcomes as a result of species interactions or spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics.

Marine Protected Areas (MPAs) are being implemented worldwide, yet there are few cases where managers make specific predictions of the response of previously harvested populations to MPA implementation. Such predictions are needed to evaluate whether MPAs are working as expected, and if not, why. This evaluation is necessary to perform adaptive management, identifying whether and when adjustments to management might be necessary to achieve MPA goals. Using monitoring data and population models, we quantified expected responses of targeted species to MPA implementation and compared them to monitoring data. The model required two factors to explain observed responses in MPAs: (a) pre‐MPA harvest rates, which can vary at local spatial scales, and (b) recruitment variability before and after MPA establishment. Low recruitment years before MPA establishment in our study system drove deviations from expected equilibrium population size distributions and introduced an additional time lag to response detectability. Synthesis and applications. We combined monitoring data and population models to show how (a) harvest rates prior to Marine Protected Area (MPA) implementation, (b) variability in recruitment, and (c) initial population size structure determine whether a response to MPA establishment is detectable. Pre‐MPA harvest rates across MPAs plays a large role in MPA response detectability, demonstrating the importance of measuring this poorly known parameter. While an intuitive expectation is for response detectability to depend on recruitment variability and stochasticity in population trajectories after MPA establishment, we address the overlooked role of recruitment variability before MPA establishment, which alters the size structure at the time of MPA establishment. These factors provide MPA practitioners with reasons whether or not MPAs may lead to responses of targeted species. Our overall approach provides a framework for a critical step of adaptive management.

Abstract With species increasingly imperiled due to anthropogenic activities, conservation practitioners are tasked with determining conservation priorities to make the best use of limited resources. One way of setting priorities is to categorize species based on risk of extinction. The United States' Endangered Species Act (ESA) has two listing statuses into which imperiled species are placed to receive protections: threatened or endangered. Our objective was to identify differences between threatened and endangered (T&E) species beyond what is outlined in their ESA definitions. For 6 broad‐resolution threats (habitat modification, overutilization, pollution, species‐species interactions, environmental stochasticity, and demographic stochasticity), we investigated whether there is a difference in the number and types of threats which impacted T&E species at the time of their listing. We found that threatened (x̄ = 2.9, SD = 1.4) and endangered (x̄ = 3.0, SD = 1.1) species faced a similar number of threats at time of their listing. The only broad‐resolution threat that impacted endangered species more than threatened species was demographic stochasticity, with endangered species being 2.1 times (95% CI = 1.5–2.8) more likely to be impacted than threatened species. We examined demographic stochasticity by breaking it down into finer‐resolution threats to identify additional differences between T&E species. We found 4 finer‐resolution demographic stochasticity threats (few individuals in one population, few individuals in multiple populations, lack of reproduction, and genetic loss) to be strong predictors of endangered status. The similarities in the number and types of broad‐resolution threats faced by T&E species indicated that they are equally conservation reliant.

A common measure of generation time is the average distance between two recruitment events along a genetic lineage. In populations with stage structure that live in a constant environment, this generation time can be computed from the elasticities of stable population growth to fecundities, and it is equivalent to another common measure of generation time: the average parental age of reproductive-value-weighted offspring. Here, we show three things. First, when the environment fluctuates, the average distance between two recruitment events along a genetic lineage is computed from the elasticities of the stochastic growth rate to fecundities. Second, under environmental stochasticity, this measure of generation time remains equivalent to the average parental age of reproductive-value-weighted offspring. Third, the generation time of a population in a fluctuating environment may deviate from the generation time the population would have in the average environment. [ABSTRACT FROM AUTHOR]

Summary: The stochastic growth rate describes long‐run growth of a population that lives in a fluctuating environment. Perturbation analysis of the stochastic growth rate provides crucial information for population managers, ecologists and evolutionary biologists. This analysis quantifies the response of the stochastic growth rate to changes in demographic parameters. A form of this analysis deals with changes that only occur in some environmental states. Caswell put forth two conjectures about environment‐specific perturbations of the stochastic growth rate. The conjectures link the stationary distribution of the stochastic environmental process with the magnitude of some environment‐specific perturbations. This note disproves one conjecture and proves the other. [ABSTRACT FROM AUTHOR]

Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision‐making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well suited to the IPM approach. Herein, we present a stochastic size‐ and stage‐structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (CTS) (Ambystoma californiense). This Bayesian model reveals that CTS population dynamics show greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicated that long‐term viability is possible with surprisingly high (20%–50%) terrestrial mortality but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate‐dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically similar species. [ABSTRACT FROM AUTHOR]

Background. Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments. Methods. We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling. Results. The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence. Discussion. Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists. [ABSTRACT FROM AUTHOR]

Woody vegetation spread over former croplands in Europe has created new unexploited habitats for forest organisms. Their ability to colonise them and thrive depends on life‐history traits including fecundity, dormancy and dispersal ability.The effects of these traits on species distribution, abundance and community assembly have been extensively studied in fragmented landscapes. However, their consequences for genetic diversity and connectivity in local populations remain largely unknown.We investigated the genetic population structure and diversity of Curculio elephas and Curculio glandium (Coleoptera: Curculionidae), two sympatric acorn weevils with contrasting life‐history strategies, in a landscape with mature oak stands and plots of new expanding forests.Using a fragment of a mitochondrial gene cytochrome oxidase subunit 1 and nuclear DNA (80 single nucleotide polymorphisms [SNPs]), we found that gene flow between populations was significantly weaker in the poor disperser C. elephas, especially in isolated new forests. However, genetic neutrality tests did show population expansion in C. elephas, which suffers frequent population bottlenecks (probably linked to extended dormancy) and is a poor coloniser of isolated new forests. However, its greater fecundity allows it to recovery quickly if the number of reproductive individuals falls. Its populations are thus larger but genetically less diverse than those of C. glandiumWithin foraging guilds, the most fecund species will outcompete the others under a context of constrained dispersal. Hence, new landscapes of expanding forests represent a good opportunity for more mobile but less fecund species to colonise new habitats and so be temporarily released from competition. [ABSTRACT FROM AUTHOR]

Aim: Over the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher‐level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space‐use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data. Innovation: We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real‐world dataset containing 1346 year‐round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among‐colony variability in nonbreeding space use for one species, with significant effects of population size and longitude. Main conclusions: This work provides a useful, much‐needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives. [ABSTRACT FROM AUTHOR]

Marine ecosystems exist in a noisy and uncertain environment, not governed by deterministic laws. The development of ecological communities is significantly influenced by variability, and the interaction between nonlinearity and stochastic processes can lead to phenomena that deterministic models cannot explain. Plankton, forming the base of the marine food web, are highly affected by stochastic fluctuations due to their short reproductive timescales. Investigating the effects of noise on plankton growth is essential for accurately describing and predicting marine health. We present a realistic biogeochemical model where multiplicative white noise represents environmental stochasticity affecting plankton. The model suggests ergodic properties in the presence of stochastic fluctuations, with temporal and ensemble distributions being coherent. Analytical and numerical analyses reveal that, given sufficiently low noise intensity, dynamics near equilibrium resemble an Ornstein-Uhlenbeck additive process. With higher noise intensities, resonance occurs, particularly when endogenous dynamics are periodic. The results indicate that low noise intensity can positively influence plankton persistence with an higher number of species coexisting, while higher noise intensity can establish a new equilibrium in the system. • Marine ecosystem stochastic models show non linear response to noise. • Environmental stochasticity increases plankton persistence. • Noise diffuses through the trophic web and leads to resonance phenomena. • Dynamics near-equilibrium resembles Ornstein-Uhlenbec additive process for low noise. • The stochastic model solutions suggest ergodic properties. [ABSTRACT FROM AUTHOR]

Background Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments. Methods We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling. Results The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence. Discussion Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists.

To forecast extinction risks of natural populations under climate change and direct human impacts, an integrative understanding of both phenotypic plasticity and adaptive evolution is essential. To date, the evidence for whether, when, and how much plasticity facilitates adaptive responses in changing environments is contradictory. We argue that explicitly considering three key environmental change components – rate of change, variance, and temporal autocorrelation – affords a unifying framework of the impact of plasticity on adaptive evolution. These environmental components each distinctively effect evolutionary and ecological processes underpinning population viability. Using this framework, we develop expectations regarding the interplay between plasticity and adaptive evolution in natural populations. This framework has the potential to improve predictions of population viability in a changing world. Global biodiversity is jeopardised by unprecedented environmental change, the hallmark of the Anthropocene. To estimate the extinction risks of species, understanding how individuals and populations respond to changing environments is crucial. Adaptive evolution and phenotypic plasticity are two key mechanisms by which natural populations avoid extinction in the face of environmental change. However, the relative roles and interplay between the two are still unresolved. Whether plasticity hinders (H1) or facilitates (H2) adaptive evolution has been ardently researched, but without cross-study standardization of how changing environments impact whether (H1) or (H2) is more likely over time. We propose an integrative framework based on how key environmental components influence the 'building blocks' of ecoevolutionary responses to examine when plasticity aids or hinders adaptive evolution. We synthesise key microevolutionary and ecological processes regarding how natural populations respond to environmental change. Studies may benefit from this framework to deepen our understanding of how plasticity influences adaptive evolution by reframing H1 and H2 in the context of environmental change, and will thus increase our ability to forecast extinction risks in the Anthropocene. [ABSTRACT FROM AUTHOR]

Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by "Laplace's demon," an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here, we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increase predictability. [ABSTRACT FROM AUTHOR]

Pollination sustains terrestrial food webs and agricultural systems and links the dynamics of interacting plant and pollinator species. Although environmental stochasticity is ubiquitous and can propagate through communities via species interactions in a way that increases extinction risk, it is unknown whether stochasticity affects species uniformly across pollination networks. In this paper, we introduce a stochastic dynamic model that makes novel use of the birth function, and apply it to pollination networks of increasing size. We start with two‐ and four‐species networks, in order to first illustrate the effects of stochasticity per se and then how those effects combine with specialization. We then describe the relationship between partner number and stochastic extinction risk in empirical networks with > 20 species. In the two‐species network, increasing the variance of the stochastic term of the model increased the size of the region in parameter space where extinctions occur. In networks with four or more species, specialists were more vulnerable to extinction than generalists over a broad range of variances. Extinction risk in networks with > 20 species declined nonlinearly with increasing mutualist partner number. Our results demonstrate the importance of including species interactions and stochasticity when using population‐dynamic models to compare species' extinction risk. While models that omit either of these factors are likely to underestimate extinction risk, they disproportionately underestimate the vulnerability of specialists. [ABSTRACT FROM AUTHOR]

Invasibility, the chance of a population to grow from rarity and become established, plays a fundamental role in population genetics, ecology, epidemiology and evolution. For many decades, the mean growth rate of a species when it is rare has been employed as an invasion criterion. Recent studies show that the mean growth rate fails as a quantitative metric for invasibility, with its magnitude sometimes even increasing while the invasibility decreases. Here we provide two novel formulae, based on the diffusion approximation and a large‐deviations (Wentzel–Kramers–Brillouin) approach, for the chance of invasion given the mean growth and its variance. The first formula has the virtue of simplicity, while the second one holds over a wider parameter range. The efficacy of the formulae, including their accompanying data analysis technique, is demonstrated using synthetic time series generated from canonical models and parameterised with empirical data. [ABSTRACT FROM AUTHOR]

Our knowledge of the factors affecting species abundances is mainly based on time‐series analyses of a few well‐studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a joint species distribution model to long‐term time‐series data on British bird communities to examine the relative contribution of intra‐ and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatiotemporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single‐species dynamics are also strongly affecting dynamics of communities in time and space. [ABSTRACT FROM AUTHOR]

Understanding how a species' life history affects its capacity to cope with environmental changes is important in the context of rapid climate changes. Reinterpreting previous results from a well-developed theoretical framework, we show that a trade-off exists between a species' ability to genetically adapt to long-term gradual environmental changes and its ability to demographically resist short-term environmental perturbations, causing variation in its vital rates. Surprisingly, this important insight has not been made formally explicit before. Choosing archetypal life histories along the fast-slow pace-of-life continuum and modeling their eco-evolutionary dynamics, we further show that long-lived species have larger demographic robustness to interannual fluctuations but limited trait evolutionary responses in gradually changing environments. In contrast, short-lived species had larger evolvability but reduced demographic robustness. This trade-off bears heavily on extinction probabilities of populations tracking fast trait changes in stochastic environments. Faster trait evolution in short-lived species came at the expense of their higher sensitivity to short-term fluctuations, causing higher extinction rates than for long-lived species. Long-lived species persisted better on short timescales but built maladaptation and an extinction debt over time. This work shows how modeling species' eco-evolutionary dynamics can help to assess species vulnerability to environmental changes. [ABSTRACT FROM AUTHOR]

Abstract Stochastic environments shape life‐history traits and can promote selection for risk‐spreading strategies, such as bet‐hedging. Although the strategy has often been hypothesized to exist for various species, empirical tests providing firm evidence have been rare, mainly due to the challenge in tracking fitness across generations. Here, we take a ‘proof of principle’ approach to explore whether the reproductive strategy of multiple‐batch spawning constitutes a bet‐hedging. We used Atlantic cod (Gadus morhua) as the study species and parameterized an eco‐evolutionary model, using empirical data on size‐related reproductive and survival traits. To evaluate the fitness benefits of multiple‐batch spawning (within a single breeding period), the mechanistic model separately simulated multiple‐batch and single‐batch spawning populations under temporally varying environments. We followed the arithmetic and geometric mean fitness associated with both strategies and quantified the mean changes in fitness under several environmental stochasticity levels. We found that, by spreading the environmental risk among batches, multiple‐batch spawning increases fitness under fluctuating environmental conditions. The multiple‐batch spawning trait is, thus, advantageous and acts as a bet‐hedging strategy when the environment is exceptionally unpredictable. Our research identifies an analytically flexible, stochastic, life‐history modelling approach to explore the fitness consequences of a risk‐spreading strategy and elucidates the importance of evolutionary applications to life‐history diversity.

Many natural populations exhibit temporal fluctuations in abundance that are consistent with external forcing by a randomly changing environment. As fitness emerges from an interaction between the phenotype and the environment, such demographic fluctuations probably include a substantial contribution from fluctuating phenotypic selection. We study the stochastic population dynamics of a population exposed to random (plus possibly directional) changes in the optimum phenotype for a quantitative trait that evolves in response to this moving optimum. We derive simple analytical predictions for the distribution of log population size over time both transiently and at stationarity under Gompertz density regulation. These predictions are well matched by population- and individual-based simulations. The log population size is approximately reverse gamma distributed, with a negative skew causing an excess of low relative to high population sizes, thus increasing extinction risk relative to a symmetric (e.g., normal) distribution with the same mean and variance. Our analysis reveals how the mean and variance of log population size change with the variance and autocorrelation of deviations of the evolving mean phenotype from the optimum. We apply our results to the analysis of evolutionary rescue in a stochastic environment and show that random fluctuations in the optimum can substantially increase extinction risk by both reducing the expected growth rate and increasing the variance of population size by several orders of magnitude.

According to a classic result in evolutionary biodemography, selection on age-specific survival invariably declines with reproductive age. The result assumes proportional changes in survival and a constant environment. Here, we look at selection on age-specific survival when changes are still proportional but the environment fluctuates. We find that selection may or may not decline with reproductive age depending on how exactly survival is proportionally altered by mutations. However, interpreted in neutral terms, the mathematics behind the classic result capture a general property that the genetics of populations with age structure possess both in a constant and in a fluctuating environment. • In a constant environment, selection on survival always declines with age. • In a fluctuating environment, this is not necessarily so. • The age pattern of selection depends on how the distribution of survival changes. • Selection decline with age connects with neutral theory. [ABSTRACT FROM AUTHOR]

The endangered mountain yellow‐legged frog (Rana muscosa) has been reduced to <10 isolated populations in the wild. Due to frequent catastrophic events (floods, droughts, wildfires), the recent dynamics of these populations have been erratic, making the future of the species highly uncertain. In 2018, a recovery plan was developed to improve the species status by reducing the impacts of various threats (predation, disease, habitat destruction), as well as reinforcing wild populations through the reintroduction of captive‐bred frogs. The short‐term goal stated in this plan was to reach a minimum of 20 populations of 50 adults each (hereafter, the 20/50 target), before the species can be considered for downlisting from the U.S. Endangered Species Act. However, there is no guarantee that this 20/50 target will be sufficient to ensure the species persistence in the long run. Using 19 years of mark‐recapture data, we estimated populations' demographic trends and assessed the viability of R. muscosa from a starting state of 20 populations of 50 adults each (i.e., the downlisting criteria). Our results reveal that, from this 20/50 state, the species has high chances of persistence only at a short time horizon (50 years). Moreover, >80% of populations would be extinct 50 years later. Therefore, the species will not be able to persist without implementation of the reintroduction program. We found that it is more important to increase the number of suitable sites occupied by R. muscosa than to simply reinforce or augment existing populations. Expanding the current distribution by establishing new populations at suitable sites, even after the "20 populations" mark has been reached, would increase the likelihood of the species' persistence in the longer term. [ABSTRACT FROM AUTHOR]

For stochastic population models with periodic coefficients, traditional approaches used to study the optimal harvesting problems of autonomous stochastic models are invalid due to the inhomogeneity of the models. The aim of this report is to search for a workable way. By using the stochastic periodic solution of the model as a bridge, we obtain the explicit forms of the optimal harvesting effort and the maximum sustained yield for a periodic stochastic Gompertz model. This approach, which can also be used to research multi-species models, offers a possible way to explore the optimal harvesting of periodic stochastic population models. [ABSTRACT FROM AUTHOR]

Abstract Training in Population Ecology asks for scalable applications capable of embarking students on a trip from basic concepts to the projection of populations under the various effects of density dependence and stochasticity. Demography_Lab is an educational tool for teaching Population Ecology aspiring to cover such a wide range of objectives. The application uses stochastic models to evaluate the future of populations. Demography_Lab may accommodate a wide range of life cycles and can construct models for populations with and without an age or stage structure. Difference equations are used for unstructured populations and matrix models for structured populations. Both types of models operate in discrete time. Models can be very simple, constructed with very limited demographic information or parameter‐rich, with a complex density‐dependence structure and detailed effects of the different sources of stochasticity. Demography_Lab allows for deterministic projections, asymptotic analysis, the extraction of confidence intervals for demographic parameters, and stochastic projections. Stochastic population growth is evaluated using up to three sources of stochasticity: environmental and demographic stochasticity and sampling error in obtaining the projection matrix. The user has full control on the effect of stochasticity on vital rates. The effect of the three sources of stochasticity may be evaluated independently for each vital rate. The user has also full control on density dependence. It may be included as a ceiling population size controlling the number of individuals in the population or it may be evaluated independently for each vital rate. Sensitivity analysis can be done for the asymptotic population growth rate or for the probability of extinction. Elasticity of the probability of extinction may be evaluated in response to changes in vital rates, and in response to changes in the intensity of density dependence and environmental stochasticity.

Abstract The endangered mountain yellow‐legged frog (Rana muscosa) has been reduced to 80% of populations would be extinct 50 years later. Therefore, the species will not be able to persist without implementation of the reintroduction program. We found that it is more important to increase the number of suitable sites occupied by R. muscosa than to simply reinforce or augment existing populations. Expanding the current distribution by establishing new populations at suitable sites, even after the “20 populations” mark has been reached, would increase the likelihood of the species' persistence in the longer term.

Phenotypic plasticity and its evolution may help evolutionary rescue in a novel and stressful environment, especially if environmental novelty reveals cryptic genetic variation that enables the evolution of increased plasticity. However, the environmental stochasticity ubiquitous in natural systems may alter these predictions, because high plasticity may amplify phenotype-environment mismatches. Although previous studies have highlighted this potential detrimental effect of plasticity in stochastic environments, they have not investigated how it affects extinction risk in the context of evolutionary rescue and with evolving plasticity. We investigate this question here by integrating stochastic demography with quantitative genetic theory in a model with simultaneous change in the mean and predictability (temporal autocorrelation) of the environment. We develop an approximate prediction of long-term persistence under the new pattern of environmental fluctuations, and compare it with numerical simulations for short- and long-term extinction risk. We find that reduced predictability increases extinction risk and reduces persistence because it increases stochastic load during rescue. This understanding of how stochastic demography, phenotypic plasticity, and evolution interact when evolution acts on cryptic genetic variation revealed in a novel environment can inform expectations for invasions, extinctions, or the emergence of chemical resistance in pests.

Nonhuman primates are an essential part of tropical biodiversity and play key roles in many ecosystem functions, processes, and services. However, the impact of climate variability on nonhuman primates, whether anthropogenic or otherwise, remains poorly understood. In this study, we utilized age‐structured matrix population models to assess the population viability and demographic variability of a population of geladas (Theropithecus gelada) in the Simien Mountains, Ethiopia with the aim of revealing any underlying climatic influences. Using data from 2008 to 2019 we calculated annual, time‐averaged, and stochastic population growth rates (λ) and investigated relationships between vital rate variability and monthly cumulative rainfall and mean temperature. Our results showed that under the prevailing environmental conditions, the population will increase (λs = 1.021). Significant effects from rainfall and/or temperature variability were widely detected across vital rates; only the first year of infant survival and the individual years of juvenile survival were definitively unaffected. Generally, the higher temperature in the hot‐dry season led to lower survival and higher fecundity, while higher rainfall in the hot‐dry season led to increased survival and fecundity. Overall, these results provide evidence of greater effects of climate variability across a wider range of vital rates than those found in previous primate demography studies. This highlights that although primates have often shown substantial resilience to the direct effects of climate change, their vulnerability may vary with habitat type and across populations. [ABSTRACT FROM AUTHOR]

Biased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer‐intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed‐loop simulation of resource‐management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom water, apex predator in the North Sea, as a real‐world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, Btrigger, and target exploitation rate, Ftarget) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters—for saithe, raising Btrigger and lowering Ftarget—would safeguard against a overharvest risk (<3.5% probability of stock depletion) and provide short‐term stability in catch limit (<20% year‐to‐year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine‐tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty. [ABSTRACT FROM AUTHOR]

The social environment in which individuals live affects their fitness and in turn population dynamics as a whole. Birds with facultative cooperative breeding can live in social groups with dominants, subordinate helpers that assist with the breeding of others, and subordinate non‐helpers. Helping behaviour benefits dominants through increased reproductive rates and reduced extrinsic mortality, such that cooperative breeding might have evolved in response to unpredictable, harsh conditions affecting reproduction and/or survival of the dominants. Additionally, there may be different costs and benefits to both helpers and non‐helpers, depending on the time‐scale. For example, early‐life costs might be compensated by later‐life benefits. These differential effects are rarely analysed in the same study.We examined whether helping behaviour affects population persistence in a stochastic environment and whether there are direct fitness consequences of different life‐history tactics adopted by helpers and non‐helpers.We parameterised a matrix population model describing the population dynamics of female Seychelles warblers Acrocephalus sechellensis, birds that display facultative cooperative breeding. The stochastic density‐dependent model is defined by a (st)age structure that includes life‐history differences between helpers and non‐helpers and thus can estimate the demographic mechanisms of direct benefits of helping behaviour.We found that population dynamics are strongly influenced by stochastic variation in the reproductive rates of the dominants, that helping behaviour promotes population persistence and that there are only early‐life differences in the direct fitness of helpers and non‐helpers.Through a matrix population model, we captured multiple demographic rates simultaneously and analysed their relative importance in determining population dynamics of these cooperative breeders. Disentangling early‐life versus lifetime effects of individual tactics sheds new light on the costs and benefits of helping behaviour. For example, the finding that helpers and non‐helpers have similar lifetime reproductive outputs and that differences in reproductive values between the two life‐history tactics arise only in early life suggests that overall, helpers and non‐helpers have a similar balance of costs and benefits when analysing direct benefits. We recommend analysing the consequence of different life‐history tactics, during both early life and over the lifetime, as analyses of these different time frames may produce conflicting results. [ABSTRACT FROM AUTHOR]