The occurrence of false positive detections in presence-absence data, even when they occur infrequently, can lead to severe bias when estimating species occupancy patterns. Building upon previous efforts to account for this source of observational error, we established a general framework to model false positives in occupancy studies and extend existing modeling approaches to encompass a broader range of sampling designs. Specifically, we identified three common sampling designs that are likely to cover most scenarios encountered by researchers. The different designs all included ambiguous detections, as well as some known-truth data, but their modeling differed in the level of the model hierarchy at which the known-truth information was incorporated (site level or observation level). For each model, we provide the likelihood, as well as R and BUGS code needed for implementation. We also establish a clear terminology and provide guidance to help choosing the most appropriate design and modeling approach.

Individual variation in reproductive success is a key feature of evolution, but also has important implications for predicting population responses to variable environments. Although such individual variation in reproductive outcomes has been reported in numerous studies, most analyses to date have not considered whether these realized differences were due to latent individual heterogeneity in reproduction or merely random chance causing different outcomes among like individuals. Furthermore, latent heterogeneity in fitness components might be expressed differently in contrasted environmental conditions, an issue that has only rarely been investigated. Here, we assessed (i) the potential existence of latent individual heterogeneity and (ii) the nature of its expression (fixed vs. variable) in a population of female Weddell seals (Leptonychotes weddellii), using a hierarchical modeling approach on a 30-year mark-recapture data set consisting of 954 individual encounter histories. We found strong support for the existence of latent individual heterogeneity in the population, with "robust" individuals expected to produce twice as many pups as "frail" individuals. Moreover, the expression of individual heterogeneity appeared consistent, with only mild evidence that it might be amplified when environmental conditions are severe. Finally, the explicit modeling of individual heterogeneity allowed us to detect a substantial cost of reproduction that was not evidenced when the heterogeneity was ignored.

Since amphibian declines were first proposed as a global phenomenon over a quarter century ago, the conservation community has made little progress in halting or reversing these trends. The early search for a “smoking gun” was replaced with the expectation that declines are caused by multiple drivers. While field observations and experiments have identified factors leading to increased local extinction risk, evidence for effects of these drivers is lacking at large spatial scales. Here, we use observations of 389 time-series of 83 species and complexes from 61 study areas across North America to test the effects of 4 of the major hypothesized drivers of declines. While we find that local amphibian populations are being lost from metapopulations at an average rate of 3.79% per year, these declines are not related to any particular threat at the continental scale; likewise the effect of each stressor is variable at regional scales. This result - that exposure to threats varies spatially, and populations vary in their response - provides little generality in the development of conservation strategies. Greater emphasis on local solutions to this globally shared phenomenon is needed.

Summary1. Life-history theory predicts that those vital rates that make larger contributions to population growth rate ought to be more strongly buffered against environmental variability than are those that are less important. Despite the importance of the theory for predicting demographic responses to changes in the environment, it is not yet known how pervasive demographic buffering is in animal populations because the validity of most existing studies has been called into question because of methodological deficiencies. 2. We tested for demographic buffering in the southern-most breeding mammal population in the world using data collected from 5558 known-age female Weddell seals over 30 years. We first estimated all vital rates simultaneously with mark-recapture analysis and then estimated process variance and covariance in those rates using a hierarchical Bayesian approach. We next calculated the population growth rate's sensitivity to changes in each of the vital rates and tested for evidence of demographic buffering by comparing properly scaled values of sensitivity and process variance in vital rates. 3. We found evidence of positive process covariance between vital rates, which indicates that all vital rates are affected in the same direction by changes in annual environment. Despite the positive correlations, we found strong evidence that demographic buffering occurred through reductions in variation in the vital rates to which population growth rate was most sensitive. Process variation in vital rates was inversely related to sensitivity measures such that variation was greatest in breeding probabilities, intermediate for survival rates of young animals and lowest for survival rates of older animals. 4. Our work contributes to a small but growing set of studies that have used rigorous methods on long-term, detailed data to investigate demographic responses to environmental variation. The information from these studies improves our understanding of life-history evolution in stochastic environments and provides useful information for predicting population responses to future environmental change. Our results for an Antarctic apex predator also provide useful baselines from a marine ecosystem when its top-and middle-trophic levels were not substantially impacted by human activity.

The development and use of automated species detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide cost‐ and time‐effective means to process information‐rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors. We developed a hierarchical modelling framework for estimating species occupancy from automated species detection data. We explore design and optimization of data post‐processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false‐positive errors and utilizes information about both occurrence and abundance of detections to improve estimation. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders. When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post‐validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post‐validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated. Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programmes.

Summary1. Prospecting allows individuals to gather information on the local quality of potential future breeding sites. In a variable and heterogeneous environment, it plays a major role in breeding habitat selection and potentially helps individuals make optimal dispersal decisions. Although prospecting movements, involving visits to other breeding sites, have been observed in many species at relatively fine spatial scales, little is known about their occurrence at larger scales. Furthermore, the adaptive value of dispersal strategies in response to environmental changes remains poorly investigated. 2. Here, our main objective is to highlight in what ways tracking devices could constitute powerful tools to study prospecting behaviour at various spatial scales. First, we stress the importance of considering prospecting movements involved in breeding habitat selection and we detail the type of data that can be collected. Then, we review the advantages and constraints associated with the use of tracking devices in this context, and we suggest new perspectives to investigate the behavioural strategies adopted by individuals during breeding habitat selection processes and dispersal decisions. 3. The rapid development of new powerful electronic tools for tracking individual behaviour thus opens a wide range of opportunities. More specifically, it may allow a more thorough understanding of the role of scaledependent dispersal behaviour in population responses to environmental changes.

Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross‐validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.

Extreme events have been suggested to play a disproportionate role in shaping ecological processes, but our understanding of the types of environmental conditions that elicit extreme consequences in natural ecosystems is limited. Here, we investigated the impact of a massive iceberg on the dynamics of a population of Weddell seals. Reproductive rates of females were reduced, but survival appeared unaffected. We also found suggestive evidence for a prolonged shift towards higher variability in reproductive rates. The annual number of females attending colonies showed unusual swings during the iceberg period, a pattern that was apparently the consequence of changes in sea-ice conditions. In contrast to the dramatic effects that were recorded in nearby populations of emperor penguins, our results suggest that this unusual environmental event did not have an extreme impact on the population of seals in the short-term, as they managed to avoid survival costs and were able to rapidly re-achieve high levels of reproduction by the end of the perturbation. Nevertheless, population projections suggest that even this modest impact on reproductive rates could negatively affect the population in the long run if such events were to occur more frequently, as is predicted by models of climate change.