Guillaume Péron

Age at the onset of senescence in birds and mammals is predicted by early-life performance.

Life-history theory predicts that traits involved in maturity, reproduction and survival correlate along a fast–slow continuum of life histories. Evolutionary theories and empirical results indicate that senescence-related traits vary along this continuum, with slow species senescing later and at a slower pace than fast species. Because senescence patterns are typically difficult to estimate from studies in the wild, here we propose to predict the associated trait values in the frame of life-history theory. From a comparative analysis based on 81 free-ranging populations of 72 species of birds and mammals, we find that a nonlinear combination of fecundity, age at first reproduction and survival over the immature stage can account for ca two-thirds of the variance in the age at the onset of actuarial senescence. Our life-history model performs better than a model predicting the onset based on generation time, and it only includes life-history traits during early life as explanatory variables, i.e. parameters that are both theoretically expected to shape senescence and are measurable within relatively short studies. We discuss the good-fit of our life-history model to the available data in the light of current evolutionary theories of senescence. We further use it to evaluate whether studies that provided no evidence for senescence lasted long enough to include the onset of senescence.

Compensation and additivity of anthropogenic mortality: life-history effects and review of methods

Demographic compensation, the increase in average individual performance following a perturbation that reduces population size, and, its opposite, demographic overadditivity (or superadditivity) are central processes in both population ecology and wildlife management. A continuum of population responses to changes in cause-specific mortality exists, of which additivity and complete compensation constitute particular points. The position of a population on that continuum influences its ability to sustain exploitation and predation. Here I describe a method for quantifying where a population is on the continuum. Based on variance-covariance formulae, I describe a simple metric for the rate of compensation-additivity. I synthesize the results from 10 wildlife capture-recapture monitoring programmes from the literature and online databases, reviewing current statistical methods and the treatment of common sources of bias. These results are used to test hypotheses regarding the effects of life-history strategy, population density, average cause-specific mortality and age class on the rate of compensation-additivity. This comparative analysis highlights that long-lived species compensate less than short-lived species and that populations below their carrying capacity compensate less than those above.

Estimation of bird and bat mortality at wind-power farms with superpopulation models

Summary 1. Collision of birds and bats with turbines in utility-scale wind farms is an increasing cause of concern. 2. Carcass counts conducted to quantify the ‘take’ of protected species need to be corrected for carcass persistence probability (removal by scavengers and decay) and detection probability (searcher efficiency). These probabilities may vary with time since death, because of intrinsic changes in carcass properties with age and of heterogeneity (preferential removal of easyto-detect carcasses). 3. In this article, we describe the use of superpopulation capture–recapture models to perform the required corrections to the raw count data. We review how to make such models age specific and how to combine trial experiments with carcass searches in order to accommodate the fact that carcasses are stationary (which affects the detection process). 4. We derive information about optimal sampling design (proportion of the turbines to sample, number of sampling occasions, interval between sampling occasions) and use simulations to illustrate the expected precision of mortality estimates. We analyse data from a small wind farm in New Jersey, in which we find the estimated number of fatalities to be twice the number of carcasses found. 5. Synthesis and applications. Our approach to estimation of wind farm mortality based on data from carcass surveys is flexible and can accommodate a range of different sampling designs and biological hypotheses. Resulting mortality estimates can be used (1) to quantify the required amount of compensation actions, (2) to inform mortality projections for proposed wind development sites and (3) to inform decisions about management of existing wind farms.

Evidence of reduced individual heterogeneity in adult survival of long-lived species

The canalization hypothesis postulates that the rate at which trait variation generates variation in the average individual fitness in a population determines how buffered traits are against environmental and genetic factors. The ranking of a species on the slow‐fast continuum – the covariation among life‐history traits describing species‐specific life cycles along a gradient going from a long life, slow maturity, and low annual reproductive output, to a short life, fast maturity, and high annual reproductive output – strongly correlates with the relative fitness impact of a given amount of variation in adult survival. Under the canalization hypothesis, long‐lived species are thus expected to display less individual heterogeneity in survival at the onset of adulthood, when reproductive values peak, than short‐lived species. We tested this life‐history prediction by analysing long‐term time series of individual‐based data in nine species of birds and mammals using capture‐recapture models. We found that individual heterogeneity in survival was higher in species with short‐generation time (< 3 years) than in species with long generation time (> 4 years). Our findings provide the first piece of empirical evidence for the canalization hypothesis at the individual level from the wild.

Uncovering periodic patterns of space use in animal tracking data with periodograms, including a new algorithm for the Lomb-Scargle periodogram and improved randomization tests

BackgroundPeriodicity in activity level (rest/activity cycles) is ubiquitous in nature, but whether and how these periodicities translate into periodic patterns of space use by animals is much less documented. Here we introduce an analytical protocol based on the Lomb-Scargle periodogram (LSP) to facilitate exploration of animal tracking datasets for periodic patterns. The LSP accommodates missing observations and variation in the sampling intervals of the location time series.ResultsWe describe a new, fast algorithm to compute the LSP. The gain in speed compared to other R implementations of the LSP makes it tractable to analyze long datasets (>106 records). We also give a detailed primer on periodicity analysis, focusing on the specificities of movement data. In particular, we warn against the risk of flawed inference when the sampling schedule creates artefactual periodicities and we introduce a new statistical test of periodicity that accommodates temporally autocorrelated background noise. Applying our LSP-based analytical protocol to tracking data from three species revealed that an ungulate exhibited periodicity in its movement speed but not in its locations, that a central place-foraging seabird tracked moon phase, and that the movements of a range-resident canid included a daily patrolling component that was initially masked by the stochasticity of the movements.ConclusionThe new, fast algorithm tailored for movement data analysis and now available in the R-package ctmm makes the LSP a convenient exploratory tool to detect periodic patterns in animal movement data.

Estimating nest abundance while accounting for time-to-event processes and imperfect detection

Birds and their population dynamics are often used to understand and document anthropogenic effects on biodiversity. Nest success is a critical component of the breeding output of birds in different environments; but to obtain the complete picture of how bird populations respond to perturbations, we also need an estimate of nest abundance or density. The problem is that raw counts generally underestimate actual nest numbers because detection is imperfect and because some nests may fail or fledge before being subjected to detection efforts. Here we develop a state-space superpopulation capture-recapture approach in which inference about detection probability is based on the age at first detection, as opposed to the sequence of re-detections in standard capture-recapture models. We apply the method to ducks in which (1) the age of the nests and their initiation dates can be determined upon detection and (2) the duration of the different stages of the breeding cycle is a priori known. We fit three model variants with or without assumptions about the phenology of nest initiation dates, and use simulations to evaluate the performance of the approach in challenging situations. In an application to Blue-winged Teal Anas discors breeding at study sites in North and South Dakota, USA, nesting stage (egg-laying or incubation) markedly influenced nest survival and detection probabilities. Two individual covariates, one binary covariate (presence of grazing cattle at the nest site), and one continuous covariate (Robel index of vegetation), had only weak effects. We estimated that 5-10% of the total number of nests were available for detection but were missed by field crews. An additional 6-15% were never available for detection. These percentages are expected to be larger in less intense, more typical sampling designs. User-friendly software nestAbund is provided to assist users in implementing the method.

Partitioning prediction uncertainty in climate-dependent population models

The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate–demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose (Chen caeruslescens atlantica), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.