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For highly social species, population dynamics depend on hierarchical demography that links local processes, group dynamics, and population growth. Here, we describe a stage-structured matrix model of hierarchical demography, which provides a framework for understanding social influences on population change. Our approach accounts for dispersal and affords insight into population dynamics at multiple scales. The method has close parallels to integral projection models but focuses on a discrete characteristic (group size). Using detailed long-term records for meerkats (Suricata suricatta), we apply our model to explore patterns of local density dependence and implications of group size for group and population growth. Taking into account dispersers, the model predicts a per capita growth rate for social groups that declines with group size. It predicts that larger social groups should produce a greater number of new breeding groups; thus, dominant breeding females (responsible for most reproduction) are likely to be more productive in larger groups. Considering the potential for future population growth, larger groups have the highest reproductive value, but per capita reproductive value is maximized for individuals in smaller groups. Across a plausible range of dispersal conditions, meerkats' long-run population growth rate is maximized when individuals form groups of intermediate size.

Dispersal is a key ecological process that influences the dynamics of spatially and socially structured populations and consists of three stages-emigration, transience, and settlement-and each stage is influenced by different social, individual, and environmental factors. Despite our appreciation of the complexity of the process, we lack a firm empirical understanding of the mechanisms underlying the different stages. Here, using data from 65 GPS-collared dispersing female coalitions of the cooperatively breeding meerkat (Suricata suricatta), we present a comprehensive analysis of the effects of population density, mate availability, dispersing coalition size, and individual factors on each of the three stages of dispersal in a wild population. We expected a positive effect of density on dispersal due to increased kin competition at high densities. We further anticipated positive effects of mate availability, coalition size, and body condition on dispersal success. We observed increasing daily emigration and settlement probabilities at high population densities. In addition, we found that emigration and settlement probabilities also increased at low densities and were lowest at medium densities. Daily emigration and settlement probabilities increased with increasing female coalition size and in the presence of unrelated males. Furthermore, the time individuals spent in the transient stage increased with population density, whereas coalition size and presence of unrelated males decreased dispersal distance. The observed nonlinear relationship between dispersal and population density is likely due to limited benefits of cooperation at low population densities and increased kin competition at high densities. Our study provides empirical validation for the theoretical predictions that population density is an important factor driving the evolution of delayed dispersal and philopatry in cooperative breeders.

We studied the temperature relations of wild and zoo Aldabra giant tortoises (Aldabrachelys gigantea) focusing on (1) the relationship between environmental temperature and tortoise activity patterns (n = 8 wild individuals) and (2) on tortoise body temperature fluctuations, including how their core and external body temperatures vary in relation to different environmental temperature ranges (seasons; n = 4 wild and n = 5 zoo individuals). In addition, we surveyed the literature to review the effect of body mass on core body temperature range in relation to environmental temperature in the Testudinidae. Diurnal activity of tortoises was bimodally distributed and influenced by environmental temperature and season. The mean air temperature at which activity is maximized was 27.9°C, with a range of 25.8–31.7°C. Furthermore, air temperature explained changes in the core body temperature better than did mass, and only during the coldest trial, did tortoises with higher mass show more stable temperatures. Our results, together with the overall Testudinidae overview, suggest that, once variation in environmental temperature has been taken into account, there is little effect of mass on the temperature stability of tortoises. Moreover, the presence of thermal inertia in an individual tortoise depends on the environmental temperatures, and we found no evidence for inertial homeothermy. Finally, patterns of core and external body temperatures in comparison with environmental temperatures suggest that Aldabra giant tortoises act as mixed conformer–regulators. Our study provides a baseline to manage the thermal environment of wild and rewilded populations of an important island ecosystem engineer species in an era of climate change.

Temporal autocorrelation in demographic processes is an important aspect of population dynamics, but a comprehensive examination of its effects on different life-history strategies is lacking. We use matrix population models from 454 plant and animal populations to simulate stochastic population growth rates (log lambda(s)) under different temporal autocorrelations in demographic rates, using simulated and observed covariation among rates. We then test for differences in sensitivities, or changes of log lambda(s) to changes in autocorrelation among two major axes of life-history strategies, obtained from phylogenetically informed principal component analysis: the fast-slow and reproductive-strategy continua. Fast life histories exhibit highest sensitivities to simulated autocorrelation in demographic rates across reproductive strategies. Slow life histories are less sensitive to temporal autocorrelation, but their sensitivities increase among highly iteroparous species. We provide cross-taxonomic evidence that changes in the autocorrelation of environmental variation may affect a wide range of species, depending on complex interactions of life-history strategies.

Understanding how the natural world will be impacted by environmental change over the coming decades is one of the most pressing challenges facing humanity. Addressing this challenge is difficult because environmental change can generate both population level plastic and evolutionary responses, with plastic responses being either adaptive or non-adaptive. We develop an approach that links quantitative genetic theory with data-driven structured models to allow prediction of population responses to environmental change via plasticity and adaptive evolution. After introducing general new theory, we construct a number of example models to demonstrate that evolutionary responses to environmental change over the short-term will be considerably slower than plastic responses, and that the rate of adaptive evolution to a new environment depends upon whether plastic responses are adaptive or non-adaptive. Parameterization of the models we develop requires information on genetic and phenotypic variation and demography that will not always be available, meaning that simpler models will often be required to predict responses to environmental change. We consequently develop a method to examine whether the full machinery of the evolutionarily explicit models we develop will be needed to predict responses to environmental change, or whether simpler non-evolutionary models that are now widely constructed may be sufficient.

Summary Habitat suitability models (HSMs) are commonly used in conservation practise to assess the potential of an area to be occupied and colonised. A major limitation of these models, however, is the omission of spatially explicit understanding of human acceptance towards the focal species. As wildlife is more and more subject to human-dominated landscapes, ignoring the sociological component will result in misrepresentation of the observed processes and inappropriate management. We distributed 10 000 questionnaires across Switzerland and identified key socio-demographical factors correlated with human acceptance of the wolf. We then created a spatially explicit acceptance model based on geo-referenced socio-demographical, social and geographical information. Finally, we combined our acceptance model with a HSM to obtain a unified socio-ecological suitability model, which included human and ecological components. We showed that the key factors associated with human acceptance were perception of how harmful the wolf is, interest in wolf-related issues, need for livestock protection, and fear of the wolf. Perceived harmfulness was in turn correlated with direct and indirect experience with the wolf, and level of education. Our acceptance map predicted decreasing acceptance with increasing altitude of residency and proximity to locations of confirmed wolf presence. This resulted in the overall opposition to the wolf for the Alpine region, albeit substantial regional differences. We found little spatial overlap (6% of Switzerland) between areas where the wolf was accepted and areas of suitable habitat. These areas of socio-ecological suitability were concentrated in the Jura Mountains and in the eastern and southern Alps, and were absent in the western and central Alps. Particularly in the Jura region, which is yet to be colonised, management of human acceptance will be a crucial conservation target. Synthesis and applications. We developed an integrative, socio-ecological approach that allowed us to accurately reproduce recent wolf recolonisation. We anticipate our framework to be a powerful tool to reliably evaluate overall suitable habitats and predict short to medium-term range expansion for species whose distribution is also dependent on human attitudes. Because our approach is sensitive to both the ecological and human component, it is ideally suited to identify key regions where proactive and targeted socio-ecological management plans are needed.

Understanding species distribution and predicting range shifts are major goals of ecology and biogeography. Obtaining reliable predictions of how species distribution might change in response to habitat change requires knowledge of habitat availability, occupancy, use for breeding, and spatial autocorrelation in these parameters. Amphibians in alpine areas provide an excellent model system for disentangling habitat drivers of occupancy from that of breeding while explicitly accounting for spatial autocorrelation. We focused on the widespread common frog (Rana temporaria) inhabiting alpine lakes in the Southern Carpathians, Romania. We used single season multistate occupancy models developed to account for imperfect detection and spatial autocorrelation to estimate the occupancy and breeding probabilities and to evaluate their response to habitat characteristics. We found that frogs do not occur in all water bodies [occupancy probability: 0.697; 95% credible interval (0.614, 0.729)] and do not breed in a substantial proportion of water bodies where they occur [breeding probability conditional on occupancy: 0.707; 95% credible interval (0.670, 0.729)]. Habitat characteristics explain water body occupancy but not breeding probability; and altitude, water body surface area, water body sinuosity and permanency, presence of invertebrates, and grazing along the banks all had positive effects on occupancy. We also detected strong spatial autocorrelation in occupancy and breeding probabilities. Thus, our results indicate that habitat choice by montane amphibians is influenced by both spatial autocorrelation and habitat characteristics. Because spatial autocorrelations matter and because the presence of adults is not the same as the presence of a reproducing population, it will be difficult to predict the effects of habitat change on high altitude amphibian populations.

Life-history adaptations to environmental change can be studied retrospectively in organisms that produce dormant propagules using methods of resurrection ecology. Here, we investigated such responses in a planktonic freshwater rotifer, Brachionus calyciflorus. We resurrected 14 clonal lineages from resting eggs extracted from three distinct sediment layers--representing periods of high, medium and low copper pollution--of a previously contaminated lake (Lake Orta, Italy). We exposed the resurrected clones to four copper concentrations over 14 days and recorded population densities at 48 h intervals. If the original populations in Lake Orta had adapted to the changing pollution levels, we expected to find demographic evidence of this adaptation in the resurrected lineages. However, we found high clonal variation in population-growth dynamics, which was more pronounced within than between pollution periods. Moreover, intrinsic population growth rates (r) increased chronologically. As such, the results did not reveal signs of adaptive evolution. Furthermore, we found that lineages from the period of medium copper pollution invested less into sexual reproduction than lineages from the other periods. By using this bio-demographic perspective, our analysis of resurrected rotifers provides insights into the life-history responses of an aquatic invertebrate in an ever-changing environment.

Organisms must cope with both short- and long-term environmental changes to persist. In this study we investigated whether life histories trade-off between their robustness to short-term environmental perturbations and their ability to evolve directional trait changes. We could confirm the tradeoff by modeling the eco-evolutionary dynamics of life-histories along the fast-slow pace-of-life continuum. Offspring dormancy and high adult survival rates allowed for large population sizes to be maintained in face of interannual environmental fluctuations but limited the speed of trait evolution with ongoing environmental change. In contrast, precocious offspring maturation and short-living adults promoted evolvability while lowering demographic robustness. This tradeoff had immediate consequences on extinction dynamics in variable environments. High evolvability allowed short-lived species to cope with long-lasting gradual environmental change, but came at the expense of more pronounced population declines and extinction rates from environmental variability. Higher robustness of slow life-histories helped them persist better on short timescales.

Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS’ performance decreases with decreasing length and resolution. Our simulations suggest a relative time-series length between ten and five generations and a resolution of half a generation are the minimum requirements for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths and resolutions half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

Timing matters How a species responds to rapid climate change is complicated. Paniw et al. used long-term data on the Kalahari meerkat, an arid specialist, to explore how predicted changes might affect population persistence over time. Warming and rainfall changes in one part of the year had a negative impact on survival and persistence, whereas similar changes during another part of the year had the opposite effect. Understanding such variability will be essential as we attempt to understand the broader influence of climate change. Science , this issue p. 631

Five appendices. ‘Appendix S1: Synchronisation of the animal-borne IMU with the hand-held camera’, ‘Appendix S2: Metrics for performance evaluation of classification models’, ‘Appendix S3: Feature Selection’, ‘Appendix S4: LOIO Results’, and ‘Appendix S5: Variation in the Norm of Magnetometer Data’. (DOCX 988 kb)

Figure S1: Net displacement curve of dispersing meerkats; Figure S2: Residuals diagnostics for statistical models; Table S1: Linear mixed effects model 1a (Comparison of net proportional daily mass change among emigrants, returners, and residents); Table S2: Linear mixed effects model 1b (Comparison of faecal glucocorticoid metabolite (fGCM) concentrations among emigrants, returners, and residents); Table S3: Linear mixed effects model 2a (Variation in net proportional daily mass change between post-eviction and transience stages in emigrants); Table S4: Linear mixed effects model 2b (Variation in faecal glucocorticoid metabolite (fGCM) concentrations between post-eviction and transience stages in emigrants)

Life-history theory states that, during the lifetime of an individual, resources are allocated to either somatic maintenance or reproduction. Resource allocation trade-offs determine the evolution and ecology of life-history strategies and determine an organism position along the fast-slow continuum. Theory predicts that environmental stochasticity is an important driver of resource allocation and therefore life-history evolution. Highly stochastic environments are expected to increase uncertainty in reproductive success and select for iteroparity and a slowing down of the life history. To date, most empirical studies have used comparisons among species to examine these theoretical predictions. By contrast, few have investigated how environmental stochasticity affects life-history strategies at the intraspecific level. In this study, we examined how variation in breeding site stochasticity (among-year variability in pond volume and hydroperiod) promotes the co-occurrence of different life-history strategies in a spatially structured population, and determines life-history position along the fast-slow continuum in the yellow-bellied toad (Bombina variegata). We collected mark-recapture data from a metapopulation and used multievent capture-recapture models to estimate survival, recruitment and breeding probabilities. We found higher survival and longer lifespans in populations inhabiting variable sites compared to those breeding in stable ones. In addition, probabilities of recruitment and skipping a breeding event were higher in variable sites. The temporal variance of survival and recruitment probabilities as well as the probability to skip breeding was higher in variable sites. Taken together, these findings indicate that populations breeding in variable sites experienced a slowing down of the life-history. Our study thus revealed similarities in the macroevolutionary and microevolutionary processes shaping life-history evolution.

Fission–fusion dynamics hypothetically enable animals to exploit dispersed and ephemeral food resources while minimizing predation risk. Disentangling factors affecting group size and composition of fission–fusion species facilitates their management and conservation. We used a 6-year data set of 2888 group formations of Masai giraffes in Tanzania to investigate determinants of social group size and structure. We tested whether ecological (lion density, vegetation structure, and prevalence of primary forage plants), anthropogenic (proximity to human settlements), temporal (rainy or dry season), and social (local giraffe density, adult sex ratio, and proportion of calves) factors explained variation in group size and sex- and age-class composition. Food availability rather than predation risk mediated grouping dynamics of adult giraffes, while predation risk was the most important factor influencing congregations with calves. Smallest group sizes occurred during the food-limiting dry season. Where predation risk was greatest, groups with calves were in bushlands more than in open grasslands, but the groups were smaller in size, suggesting mothers adopted a strategy of hiding calves rather than a predator-detection-and-dilution strategy. Groups with calves also were farther from towns but closer to traditional human compounds (bomas). This may be due to lower predator densities, and thus reduced calf predation risk, near bomas but higher human disturbance near towns. Sex- and age-based differences in habitat use reflected nursing mothers’ need for high-quality forage while also protecting their young from predation. Our results have implications for conservation and management of giraffes and other large-bodied, herd-forming ungulates in heterogeneous environments subject to anthropogenic threats.

Critical transitions are qualitative changes of state that occur when a stochastic dynamical system is forced through a critical point. Many critical transitions are preceded by characteristic fluctuations that may serve as model‐independent early warning signals, implying that these events may be predictable in applications ranging from physics to biology. In nonbiological systems, the strength of such early warning signals has been shown partly to be determined by the speed at which the transition occurs. It is currently unknown whether biological systems, which are inherently high dimensional and typically display low signal‐to‐noise ratios, also exhibit this property, which would have important implications for how ecosystems are managed, particularly where the forces exerted on a system are anthropogenic. We examine whether the rate of forcing can alter the strength of early warning signals in (1) a model exhibiting a fold bifurcation where a state shift is driven by the harvesting of individuals, and (2) a model exhibiting a transcritical bifurcation where a state shift is driven by increased grazing pressure. These models predict that the rate of forcing can alter the detectability of early warning signals regardless of the underlying bifurcation the system exhibits, but that this result may be more pronounced in fold bifurcations. These findings have important implications for the management of biological populations, particularly harvested systems such as fisheries, and suggest that knowing the class of bifurcations a system will manifest may help discriminate between true‐positive and false‐positive signals.

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy-modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red-list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence-only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.

1. Population dynamics are the result of an interplay between extrinsic and intrinsic environmental drivers. Predicting the effects of environmental change on wildlife populations therefore requires a thorough understanding of the mechanisms through which different environmental drivers interact to generate changes in population size and structure. 2. In this study, we disentangled the roles of temperature, food availability and population density in shaping short‐ and long‐term population dynamics of the African striped mouse, a small rodent inhabiting a semidesert with high intra‐ and interannual variation in environmental conditions. 3. We parameterized a female‐only stage‐structured matrix population model with vital rates depending on temperature, food availability and population density, using monthly mark–recapture data from 1609 mice trapped over 9 years (2005–2014). We then applied perturbation analyses to determine relative strengths and demographic pathways of these drivers in affecting population dynamics. Furthermore, we used stochastic population projections to gain insights into how three different climate change scenarios might affect size, structure and persistence of this population. 4. We identified food availability, acting through reproduction, as the main driver of changes in both short‐ and long‐term population dynamics. This mechanism was mediated by strong density feedbacks, which stabilized the population after high peaks and allowed it to recover from detrimental crashes. Density dependence thus buffered the population against environmental change, and even adverse climate change scenarios were predicted to have little effect on population persistence (extinction risk over 100 years

Predicting population responses to environmental change is an on-going challenge in ecology. Studies investigating the links between fitness-related phenotypic traits and demography have shown that trait dynamic responses to environmental change can sometimes precede population dynamic responses, and thus, can be used as an early warning signal. However, it is still unknown under which ecological and evolutionary circumstances, shifts in fitness-related traits can precede population responses to environmental perturbation. Here, we take a trait-based demographic approach and investigate both trait and population dynamics in a density-regulated population in response to a gradual change in the environment. We explore the ecological and evolutionary constraints under which shifts in a fitness-related trait precedes a decline in population size. We show both analytically and with experimental data that under medium-to-slow rate of environmental change, shifts in trait value can precede population decline. We further show the positive influence of environmental predictability, average reproductive rate, plasticity, and genetic variation on shifts in trait dynamics preceding potential population declines. These results still hold under non-constant genetic variation and environmental stochasticity. Our study highlights ecological and evolutionary circumstances under which a fitness-related trait can be used as an early warning signal of an impending population decline.

Anthropogenic degradation of natural habitats is a global driver of wildlife population declines. Local population responses to such environmental perturbations are generally well understood, but in socially structured populations, interactions between environmental and social factors may influence population responses. Thus, understanding how habitat degradation affects the dynamics of these populations requires simultaneous consideration of social and environmental mechanisms underlying demographic responses. Here we investigated the effect of habitat degradation through commercial forestry on spatiotemporal dynamics of a group-living bird, the Siberian jay, Perisoreus infaustus, in boreal forests of northern Sweden. We assessed the interacting effects of forestry, climate and population density on stage-specific, seasonal life-history rates and population dynamics, using long-term, individual-based demographic data from 70 territories in natural and managed forests. Stage-specific survival and reproductive rates, and consequently population growth, were lower in managed forests than in natural forests. Population growth was most sensitive to breeder survival and was more sensitive to early dispersing juveniles than those delaying dispersal. Forestry decreased population growth in managed forests by reducing reproductive success and breeder survival. Increased snow depth improved winter survival, and warmer spring temperatures enhanced reproductive success, particularly in natural forests. Population growth was stable in natural forests but it was declining in managed forests, and this difference accelerated under forecasted climate scenarios. Thus, climatic change could exacerbate the rate of forestry-induced population decline through reduced snow cover in our study species, and in other species with similar life-history characteristics and habitat requirements. This is a post-peer-review, pre-copyedit version of an article published in Oecologia. The final authenticated version is available online at: https://doi.org/10.1007/s00442-018-4100-z

Predicting population collapse in the face of unprecedented anthropogenic pressures is a key challenge in conservation. Abundance-based early warning signals have been suggested as a possible solution to this problem; however, they are known to be susceptible to the spatial and temporal subsampling ubiquitous to abundance estimates of wild population. Recent work has shown that composite early warning methods that take into account changes in fitness-related phenotypic traits - such as body size - alongside traditional abundance-based signals are better able to predict collapse, as trait dynamic estimates are less susceptible to sampling protocols. However, these previously developed composite early warning methods weighted the relative contribution of abundance and trait dynamics evenly. Here we present an extension to this work where the relative importance of different data types can be weighted in line with the quality of available data. Using data from a small-scale experimental system we demonstrate that weighted indicators can improve the accuracy of composite early warning signals by >60%. Our work shows that non-uniform weighting can increase the likelihood of correctly detecting a true positive early warning signal in wild populations, with direct relevance for conservation management.

Dispersal is a key process governing the dynamics of socially and spatially structured populations and involves three distinct stages: emigration, transience and settlement. At each stage, individuals have to make movement decisions, which are influenced by social, environmental and individual factors. Yet, a comprehensive understanding of the drivers that influence such decisions is still lacking, particularly for the transient stage during which free-living individuals are inherently difficult to follow. Social circumstances such as the likelihood of encountering conspecifics can be expected to strongly affects decision-making during dispersal, particularly in territorial species where encounters with resident conspecifics are antagonistic. Here, we analysed the movement trajectories of 47 dispersing coalitions of Kalahari meerkats Suricata suricatta through a landscape occupied by constantly monitored resident groups, while simultaneously taking into account environmental and individual characteristics. We used GPS locations collected on resident groups to create a georeferenced social landscape representing the likelihood of encountering resident groups. We used a step-selection function to infer the effect of social, environmental and individual covariates on habitat selection during dispersal. Finally, we created a temporal mismatch between the social landscape and the dispersal event of interest to identify the temporal scale at which dispersers perceive the social landscape. Including information about the social landscape considerably improved our representation of the dispersal trajectory compared to analyses that only accounted for environmental variables. The latter were only marginally selected or avoided by dispersers. Before leaving their natal territory, dispersers selected areas frequently used by their natal group. In contrast, after leaving their natal territory, they selectively used areas where they were less likely to encounter unrelated groups. This pattern was particularly marked in larger dispersing coalitions and when unrelated males were part of the dispersing coalition. Our results suggest that, in socially and spatially structured species, dispersers gather and process social information during dispersal, and that reducing risk of aggression from unrelated resident groups outweighs benefits derived from conspecific attraction. Finally, our work underlines the intimate link between the social structure of a population and dispersal, which affect each other reciprocally.