Raising offspring increases ageing: Differences in senescence among three populations of a long-lived seabird, the Atlantic puffin.

Actuarial senescence, the decline of survival with age, is well documented in the wild. Rates of senescence vary widely between taxa, to some extent also between sexes, with the fastest life histories showing the highest rates of senescence. Few studies have investigated differences in senescence among populations of the same species, although such variation is expected from population-level differences in environmental conditions, leading to differences in vital rates and thus life histories. We predict that, within species, populations differing in productivity (suggesting different paces of life) should experience different rates of senescence, but with little or no sexual difference in senescence within populations of monogamous, monomorphic species where the sexes share breeding duties. We compared rates of actuarial senescence among three contrasting populations of the Atlantic puffin Fratercula arctica. The dataset comprised 31 years (1990-2020) of parallel capture-mark-recapture data from three breeding colonies, Isle of May (North Sea), Røst (Norwegian Sea) and Hornøya (Barents Sea), showing contrasting productivities (i.e. annual breeding success) and population trends. We used time elapsed since first capture as a proxy for bird age, and productivity and the winter North Atlantic Oscillation Index (wNAO) as proxies for the environmental conditions experienced by the populations within and outside the breeding season, respectively. In accordance with our predictions, we found that senescence rates differed among the study populations, with no evidence for sexual differences. There was no evidence for an effect of wNAO, but the population with the lowest productivity, Røst, showed the lowest rate of senescence. As a consequence, the negative effect of senescence on the population growth rate (λ) was up to 3-5 times smaller on Røst (Δλ = -0.009) than on the two other colonies. Our findings suggest that environmentally induced differences in senescence rates among populations of a species should be accounted for when predicting effects of climate variation and change on species persistence. There is thus a need for more detailed information on how both actuarial and reproductive senescence influence vital rates of populations of the same species, calling for large-scale comparative studies.

Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.

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

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

Temporal correlations among demographic parameters are ubiquitous but highly variable across species.

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.

A hierarchical inventory of the world's mountains for global comparative mountain science.

A standardized delineation of the world's mountains has many applications in research, education, and the science-policy interface. Here we provide a new inventory of 8616 mountain ranges developed under the auspices of the Global Mountain Biodiversity Assessment (GMBA). Building on an earlier compilation, the presented geospatial database uses a further advanced and generalized mountain definition and a semi-automated method to enable globally standardized, transparent delineations of mountain ranges worldwide. The inventory is presented on EarthEnv at various hierarchical levels and allows users to select their preferred level of regional aggregation from continents to small subranges according to their needs and the scale of their analyses. The clearly defined, globally consistent and hierarchical nature of the presented mountain inventory offers a standardized resource for referencing and addressing mountains across basic and applied natural as well as social sciences and a range of other uses in science communication and education.

From design to analysis: A roadmap for predicting distributions of rare species.

Rare species are challenging to study, in part because rarity can take many forms. Jeliazkov et al. guide us through the multiple decisions to be made-from sampling designs to field methods and analytical, integrated models. Improved monitoring methods are needed to improve our understanding of rare species importance for ecosystem structure and functions. This is a commentary on Jeliazkov et al., 2022, https://doi.org/10.1111/gcb.16114.

Detecting climate signals in populations across life histories.

Climate impacts are not always easily discerned in wild populations as detecting climate change signals in populations is challenged by stochastic noise associated with natural climate variability, variability in biotic and abiotic processes, and observation error in demographic rates. Detection of the impact of climate change on populations requires making a formal distinction between signals in the population associated with long-term climate trends from those generated by stochastic noise. The time of emergence (ToE) identifies when the signal of anthropogenic climate change can be quantitatively distinguished from natural climate variability. This concept has been applied extensively in the climate sciences, but has not been explored in the context of population dynamics. Here, we outline an approach to detecting climate-driven signals in populations based on an assessment of when climate change drives population dynamics beyond the envelope characteristic of stochastic variations in an unperturbed state. Specifically, we present a theoretical assessment of the time of emergence of climate-driven signals in population dynamics ( ToE pop ). We identify the dependence of ToE pop on the magnitude of both trends and variability in climate and also explore the effect of intrinsic demographic controls on ToE pop . We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction), and the relationships between climate and demographic rates yield population dynamics that filter climate trends and variability differently. We illustrate empirically how to detect the point in time when anthropogenic signals in populations emerge from stochastic noise for a species threatened by climate change: the emperor penguin. Finally, we propose six testable hypotheses and a road map for future research.

Fine-scale spatial segregation in a pelagic seabird driven by differential use of tidewater glacier fronts.

In colonially breeding marine predators, individual movements and colonial segregation are influenced by seascape characteristics. Tidewater glacier fronts are important features of the Arctic seascape and are often described as foraging hotspots. Albeit their documented importance for wildlife, little is known about their structuring effect on Arctic predator movements and space use. In this study, we tested the hypothesis that tidewater glacier fronts can influence marine bird foraging patterns and drive spatial segregation among adjacent colonies. We analysed movements of black-legged kittiwakes (Rissa tridactyla) in a glacial fjord by tracking breeding individuals from five colonies. Although breeding kittiwakes were observed to travel up to ca. 280 km from the colony, individuals were more likely to use glacier fronts located closer to their colony and rarely used glacier fronts located farther away than 18 km. Such variation in the use of glacier fronts created fine-scale spatial segregation among the four closest (ca. 7 km distance on average) kittiwake colonies. Overall, our results support the hypothesis that spatially predictable foraging patches like glacier fronts can have strong structuring effects on predator movements and can modulate the magnitude of intercolonial spatial segregation in central-place foragers.

Influence of reproductive output on divorce rates in polar seabirds.

The high occurrence of social monogamy in birds has led to questions about partner fidelity, or the perennial nature of monogamy from one breeding season to another. Despite the evolutionary advantages of partner fidelity, divorce occurs among 95% of bird species. We aimed to describe patterns of divorce and partner fidelity in five seabird species breeding in Arctic and Antarctic regions and investigated the influence of breeding status on pair bond maintenance. For four out of the five species considered, we observed low divorce rates (respectively 1.9%, 3.3%, 2.5%, and 0.0% for Brünnich's guillemot, glaucous gull, Antarctic petrel, and south polar skua), while the divorce rate was much higher (19.1%) for the black-legged kittiwake. For kittiwakes, the divorce rate was lower for pairs that managed to raise their chick to 15 days of age, while the effect of breeding success on divorce in the four other species could not be tested due to the rareness of divorce events. Our results emphasize the potentially large temporal (interannual) variations that should be taken into account in understanding divorce and partner fidelity in seabirds.

Climate variability and density-dependent population dynamics: Lessons from a simple High Arctic ecosystem.

Ecologists are still puzzled by the diverse population dynamics of herbivorous small mammals that range from high-amplitude, multiannual cycles to stable dynamics. Theory predicts that this diversity results from combinations of climatic seasonality, weather stochasticity, and density-dependent food web interactions. The almost ubiquitous 3- to 5-y cycles in boreal and arctic climates may theoretically result from bottom-up (plant-herbivore) and top-down (predator-prey) interactions. Assessing, empirically, the roles of such interactions and how they are influenced by environmental stochasticity has been hampered by food web complexity. Here, we take advantage of a uniquely simple High Arctic food web, which allowed us to analyze the dynamics of a graminivorous vole population not subjected to top-down regulation. This population exhibited high-amplitude, noncyclic fluctuations-partly driven by weather stochasticity. However, the predominant driver of the dynamics was overcompensatory density dependence in winter that caused the population to frequently crash. Model simulations showed that the seasonal pattern of density dependence would yield regular 2-y cycles in the absence of stochasticity. While such short cycles have not yet been observed in mammals, they are theoretically plausible if graminivorous vole populations are deterministically bottom-up regulated. When incorporating weather stochasticity in the model simulations, cyclicity became disrupted and the amplitude was increased-akin to the observed dynamics. Our findings contrast with the 3- to 5-y population cycles that are typical of graminivorous small mammals in more complex food webs, suggesting that top-down regulation is normally an important component of such dynamics.

Multiple configurations and fluctuating trophic control in the Barents Sea food-web.

The Barents Sea is a subarctic shelf sea which has experienced major changes during the past decades. From ecological time-series, three different food-web configurations, reflecting successive shifts of dominance of pelagic fish, demersal fish, and zooplankton, as well as varying trophic control have been identified in the last decades. This covers a relatively short time-period as available ecological time-series are often relatively short. As we lack information for prior time-periods, we use a chance and necessity model to investigate if there are other possible configurations of the Barents Sea food-web than those observed in the ecological time-series, and if this food-web is characterized by a persistent trophic control. We perform food-web simulations using the Non-Deterministic Network Dynamic model (NDND) for the Barents Sea, identify food-web configurations and compare those to historical reconstructions of food-web dynamics. Biomass configurations fall into four major types and three trophic pathways. Reconstructed data match one of the major biomass configurations but is characterized by a different trophic pathway than most of the simulated configurations. The simulated biomass displays fluctuations between bottom-up and top-down trophic control over time rather than persistent trophic control. Our results show that the configurations we have reconstructed are strongly overlapping with our simulated configurations, though they represent only a subset of the possible configurations of the Barents Sea food-web.

Sedimentary ancient DNA shows terrestrial plant richness continuously increased over the Holocene in northern Fennoscandia.

The effects of climate change on species richness are debated but can be informed by the past. Here, we generated a sedimentary ancient DNA dataset covering 10 lakes and applied novel methods for data harmonization. We assessed the impact of Holocene climate changes and nutrients on terrestrial plant richness in northern Fennoscandia. We find that richness increased steeply during the rapidly warming Early Holocene. In contrast to findings from most pollen studies, we show that richness continued to increase thereafter, although the climate was stable, with richness and the regional species pool only stabilizing during the past three millennia. Furthermore, overall increases in richness were greater in catchments with higher soil nutrient availability. We suggest that richness will increase with ongoing warming, especially at localities with high nutrient availability and assuming that human activity remains low in the region, although lags of millennia may be expected.

Centennial relationships between ocean temperature and Atlantic puffin production reveal shifting decennial trends.

The current warming of the oceans has been shown to have detrimental effects for a number of species. An understanding of the underlying mechanisms may be hampered by the non-linearity and non-stationarity of the relationships between temperature and demography, and by the insufficient length of available time series. Most demographic time series are too short to study the effects of climate on wildlife in the classical sense of meteorological patterns over at least 30 years. Here we present a harvest time series of Atlantic puffins (Fratercula arctica) that goes back as far as 1880. It originates in the world's largest puffin colony, in southwest Iceland, which has recently experienced a strong decline. By estimating an annual chick production index for 128 years, we found prolonged periods of strong correlations between local sea surface temperature (SST) and chick production. The sign of decennial correlations switches three times during this period, where the phases of strong negative correlations between puffin productivity and SST correspond to the early 20th century Arctic warming period and to the most recent decades. Most of the variation (72%) in chick production is explained by a model in which productivity peaks at an SST of 7.1°C, clearly rejecting the assumption of a linear relationship. There is also evidence supporting non-stationarity: The SST at which puffins production peaked has increased by 0.24°C during the 20th century, although the increase in average SST during the same period has been more than three times faster. The best supported models indicate that the population's decline is at least partially caused by the increasing SST around Iceland.

Modeling the demography of species providing extended parental care: A capture-recapture multievent model with a case study on polar bears (Ursus maritimus).

In species providing extended parental care, one or both parents care for altricial young over a period including more than one breeding season. We expect large parental investment and long-term dependency within family units to cause high variability in life trajectories among individuals with complex consequences at the population level. So far, models for estimating demographic parameters in free-ranging animal populations mostly ignore extended parental care, thereby limiting our understanding of its consequences on parents and offspring life histories.We designed a capture-recapture multievent model for studying the demography of species providing extended parental care. It handles statistical multiple-year dependency among individual demographic parameters grouped within family units, variable litter size, and uncertainty on the timing at offspring independence. It allows for the evaluation of trade-offs among demographic parameters, the influence of past reproductive history on the caring parent's survival status, breeding probability, and litter size probability, while accounting for imperfect detection of family units. We assess the model performance using simulated data and illustrate its use with a long-term dataset collected on the Svalbard polar bears (Ursus maritimus).Our model performed well in terms of bias and mean square error and in estimating demographic parameters in all simulated scenarios, both when offspring departure probability from the family unit occurred at a constant rate or varied during the field season depending on the date of capture. For the polar bear case study, we provide estimates of adult and dependent offspring survival rates, breeding probability, and litter size probability. Results showed that the outcome of the previous reproduction influenced breeding probability.Overall, our results show the importance of accounting for i) the multiple-year statistical dependency within family units, ii) uncertainty on the timing at offspring independence, and iii) past reproductive history of the caring parent. If ignored, estimates obtained for breeding probability, litter size, and survival can be biased. This is of interest in terms of conservation because species providing extended parental care are often long-living mammals vulnerable or threatened with extinction.

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data.

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state-space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture-recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state-space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state-space models for density-dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz-Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R-package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray-sided voles Myodes rufocanus from Northern Norway. We found that density-dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.

Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species.

Temperatures in mountain areas are increasing at a higher rate than the Northern Hemisphere land average, but how fauna may respond, in particular in terms of phenology, remains poorly understood. The aim of this study was to assess how elevation could modify the relationships between climate variability (air temperature and snow melt-out date), the timing of plant phenology and egg-laying date of the coal tit (Periparus ater). We collected 9 years (2011-2019) of data on egg-laying date, spring air temperature, snow melt-out date, and larch budburst date at two elevations (~1,300 m and ~1,900 m asl) on a slope located in the Mont-Blanc Massif in the French Alps. We found that at low elevation, larch budburst date had a direct influence on egg-laying date, while at high-altitude snow melt-out date was the limiting factor. At both elevations, air temperature had a similar effect on egg-laying date, but was a poorer predictor than larch budburst or snowmelt date. Our results shed light on proximate drivers of breeding phenology responses to interannual climate variability in mountain areas and suggest that factors directly influencing species phenology vary at different elevations. Predicting the future responses of species in a climate change context will require testing the transferability of models and accounting for nonstationary relationships between environmental predictors and the timing of phenological events.

The Demographic Buffering Hypothesis: Evidence and Challenges.

In (st)age-structured populations, the long-run population growth rate is negatively affected by temporal variation in vital rates. In most cases, natural selection should minimize temporal variation in the vital rates to which the long-run population growth is most sensitive, resulting in demographic buffering. By reviewing empirical studies on demographic buffering in wild populations, we found overall support for this hypothesis. However, we also identified issues when testing for demographic buffering. In particular, solving scaling problems for decomposing, measuring, and comparing stochastic variation in vital rates and accounting for density dependence are required in future tests of demographic buffering. In the current context of climate change, demographic buffering may mitigate the negative impact of environmental variation and help populations to persist in an increasingly variable environment.

End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers.

Sustainable management of wildlife populations can be aided by building models that both identify current drivers of natural dynamics and provide near-term predictions of future states. We employed a Strategic Foresight Protocol (SFP) involving stakeholders to decide the purpose and structure of a dynamic state-space model for the population dynamics of the Willow Ptarmigan, a popular game species in Norway. Based on local knowledge of stakeholders, it was decided that the model should include food web interactions and climatic drivers to provide explanatory predictions. Modeling confirmed observations from stakeholders that climate change impacts Ptarmigan populations negatively through intensified outbreaks of insect defoliators and later onset of winter. Stakeholders also decided that the model should provide anticipatory predictions. The ability to forecast population density ahead of the harvest season was valued by the stakeholders as it provides the management extra time to consider appropriate harvest regulations and communicate with hunters prior to the hunting season. Overall, exploring potential drivers and predicting short-term future states, facilitate collaborative learning and refined data collection, monitoring designs, and management priorities. Our experience from adapting a SFP to a management target with inherently complex dynamics and drivers of environmental change, is that an open, flexible, and iterative process, rather than a rigid step-wise protocol, facilitates rapid learning, trust, and legitimacy.