Will climate change affect the survival of tropical and subtropical species? Predictions based on Bulwer's petrel populations in the NE Atlantic Ocean.

Climate change has repeatedly been shown to impact the demography and survival of marine top predators. However, most evidence comes from single populations of widely distributed species, limited mainly to polar and subpolar environments. Here, we aimed to evaluate the influence of environmental conditions on the survival of a tropical and migratory seabird over the course of its annual cycle. We used capture-mark-recapture data from three populations of Bulwer's petrel (Bulweria bulwerii) spread across the NE Atlantic Ocean, from the Azores, Canary, and Cabo Verde Islands (including temperate to tropical zones). We also inferred how the survival of this seabird might be affected under different climatic scenarios, defined by the Intergovernmental Panel on Climate Change. Among the environmental variables whose effect we evaluated (North Atlantic Oscillation index, Southern Oscillation Index, Sea Surface Temperature [SST] and wind speed), SST estimated for the breeding area and season was the variable with the greatest influence on adult survival. Negative effects of SST increase emerged across the three populations, most likely through indirect trophic web interactions. Unfortunately, our study also shows that the survival of Bulwer's petrel will be profoundly affected by the different scenarios of climate change, even with the most optimistic trajectory involving the lowest greenhouse gas emission. Furthermore, for the first time, our study predicts stronger impacts of climate change on tropical populations than on subtropical and temperate ones. This result highlights the devastating effect that climate change may also have on tropical areas, and the importance of considering multi-population approaches when evaluating its impacts which may differ across species distributions.

Efficient spatial multi-state capture-recapture model to study natal dispersal: An application to the Alpine marmot.

Studying natal dispersal in natural populations using capture-recapture data is challenging as an unknown proportion of individuals leaves the study area when dispersing and are never recaptured. Most dispersal (and survival) estimates from capture-recapture studies are thus biased and only reflect what happens within the study area, not the population. Here, we elaborate on recent methodological advances to build a spatially explicit multi-state capture-recapture model to study natal dispersal in a territorial mammal while accounting for imperfect detection and movement in and out of the study area. We validate our model using a simulation study where we compare it to a non-spatial multi-state capture-recapture model. We then apply it to a long-term individual-based dataset on Alpine marmot Marmota marmota. Our model was able to accurately estimate natal dispersal and survival probabilities, as well as mean dispersal distance for a large range of dispersal patterns. By contrast, the non-spatial multi-state estimates underestimated both survival and natal dispersal even for short dispersal distances relative to the study area size. We discuss the application of our approach to other species and monitoring setups. We estimated higher inheritance probabilities of female Alpine marmots, which suggests higher levels of philopatry, although the probability to become dominant after dispersal did not differ between sexes. Nonetheless, the lower survival of young adult males suggests higher costs of dispersal for males. We further discuss the implications of our findings in light of the life history of the species.

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.

Sex-specific costs of reproduction on survival in a long-lived seabird.

Costs of reproduction on survival have captured the attention of researchers since life history theory was formulated. Adults of long-lived species may increase survival by reducing their breeding effort or even skipping reproduction. In this study, we aimed to evaluate the costs of current reproduction on survival and whether skipping reproduction increases adult survival in a long-lived seabird. We used capture-mark-recapture data (1450 encounters) from two populations of Bulwer's petrel (Bulweria bulwerii), breeding in the Azores and Canary Islands, North Atlantic Ocean. Using a multi-event model with two different breeding statuses (breeders versus non-breeders), we calculated probabilities of survival and of transitions between breeding statuses, evaluating potential differences between sexes. Females had lower survival probabilities than males, independent of their breeding status. When considering breeding status, breeding females had lower survival probabilities than non-breeding females, suggesting costs of reproduction on survival. Breeding males had higher survival probabilities than non-breeding males, suggesting that males do not incur costs of reproduction on survival and that only the highest quality males have access to breeding. The highest and the lowest probabilities of skipping reproduction were found in breeding males from the Azores and in breeding males from the Canary Islands, respectively. Intermediate values were observed in the females from both populations. This result is probably due to differences in the external factors affecting both populations, essentially predation pressure and competition. The existence of sex-specific costs of reproduction on survival in several populations of this long-lived species may have important implications for species population dynamics.

Positive early-late life-history trait correlations in elephant seals.

Correlations between early- and late-life performance are a major prediction of life-history theory. Negative early-late correlations can emerge because biological processes are optimized for early but not late life (e.g., rapid development may accelerate the onset of senescence; "developmental theory of aging") or because allocation to early-life performance comes at a cost in terms of late-life performance (as in the disposable soma theory). But variation in genetic and environmental challenges that each individual has to cope with during early life may also lead to positive early-late life-history trait correlations (the "fixed heterogeneity" or "individual quality" hypothesis). We analyzed individual life-history trajectories of 7,420 known-age female southern elephant seals (Mirounga leonina) monitored over 36 yr to determine how actuarial senescence (a proxy for late-life performance) correlate with age at first reproduction (a proxy for early-life performance). As some breeding events may not be detected in this field study, we used a custom "multievent" hierarchical model to estimate the age at first reproduction and correlate it to other life-history traits. The probability of first reproduction was 0.34 at age 3, with most females breeding for the first time at age 4, and comparatively few at older ages. Females with an early age of first reproduction outperformed delayed breeders in all aspects we considered (survival, rate of senescence, net reproductive output) but one: early breeders appeared to have an onset of actuarial senescence 1 yr earlier compared to late breeders. Genetics and environmental conditions during early life likely explain the positive correlation between early- and late-life performance. Our results provide the first evidence of actuarial senescence in female southern elephant seals.

Consequences of past and present harvest management in a declining flyway population of common eiders Somateria mollissima.

Harvested species population dynamics are shaped by the relative contribution of natural and harvest mortality. Natural mortality is usually not under management control, so managers must continuously adjust harvest rates to prevent overexploitation. Ideally, this requires regular assessment of the contribution of harvest to total mortality and how this affects population dynamics.To assess the impact of hunting mortality on the dynamics of the rapidly declining Baltic/Wadden Sea population of common eiders Somateria mollissima, we first estimated vital rates of ten study colonies over the period 1970-2015. By means of a multi-event capture-recovery model, we then used the cause of death of recovered individuals to estimate proportions of adult females that died due to hunting or other causes. Finally, we adopted a stochastic matrix population modeling approach based on simulations to investigate the effect of past and present harvest regulations on changes in flyway population size and composition.Results showed that even the complete ban on shooting females implemented in 2014 in Denmark, where most hunting takes place, was not enough to stop the population decline given current levels of natural female mortality. Despite continued hunting of males, our predictions suggest that the proportion of females will continue to decline unless natural mortality of the females is reduced.Although levels of natural mortality must decrease to halt the decline of this population, we advocate that the current hunting ban on females is maintained while further investigations of factors causing increased levels of natural mortality among females are undertaken. Synthesis and applications. At the flyway scale, continuous and accurate estimates of vital rates and the relative contribution of harvest versus other mortality causes are increasingly important as the population effect of adjusting harvest rates is most effectively evaluated within a model-based adaptive management framework.

Transience effect in capture-recapture studies: The importance of its biological meaning.

Capture-recapture (CR) models are an essential tool for estimating demographic parameters in most animal and some plant populations. To avoid drawing incorrect conclusions in any statistical inference, a crucial prerequisite is to assess the goodness of fit of a general model to the data. In CR models, a frequent cause of lack of fit, is the so-called transience effect, which is due to a lower expectation of re-observation of individuals marked for the first time as compared to other individuals. The transience effect may result either from different biological causes or from the sampling procedure. A transience effect is usually treated by distinguishing at least two age-classes in the survival probability, but other approaches may be more suitable. Here we develop a conceptual and analytical framework for including a transience effect in capture-recapture models. We show the implementation of two additional parametrizations that incorporate a transience effect. With these parametrizations, we can directly estimate the "transience probability" defined as the probability that a newly caught individual disappear from the population beyond what is expected based on the behavior of the previously caught individuals in the same sample. Additionally, these parametrizations allow testing biological hypotheses concerning drivers affecting this probability. Results from our case study show differences between parametrizations, with the parametrization most currently used giving different estimates, especially when including covariates. We advocate for a unifying framework for treating a transience effect, that helps clarifying the ideas and terminology, and where the biological reasons should be the rule for choosing the appropriate analytical procedure. This framework will also open new and powerful ways for the detection and exploration of ecological processes such as the costs of the first reproduction or the deleterious effects of some types of marking.

Accounting for heterogeneity when estimating stopover duration, timing and population size of red knots along the Luannan Coast of Bohai Bay, China.

To successfully perform their long-distance migrations, migratory birds require sites along their migratory routes to rest and refuel. Monitoring the use of so-called stopover and staging sites provides insights into (a) the timing of migration and (b) the importance of a site for migratory bird populations. A recently developed Bayesian superpopulation model that integrates mark-recapture data and ring density data enabled the estimation of stopover timing, duration, and population size. Yet, this model did not account for heterogeneity in encounter (p) and staying (ϕ) probabilities.Here we extended the integrated superpopulation model by implementing finite mixtures to account for heterogeneity in p and ϕ. We used simulations and real data (from 2009-2016) on red knots Calidris canutus, mostly of the subspecies piersmai, staging in Bohai Bay, China, during spring migration to (a) show the importance of accounting for heterogeneity in encounter and staying probabilities to get unbiased estimates of stopover timing, duration, and numbers of migratory birds at staging sites and (b) get accurate stopover parameter estimates for a migratory bird species at a key staging site that is threatened by habitat destruction.Our simulations confirmed that heterogeneity in p affected stopover parameter estimates more than heterogeneity in ϕ, especially when most birds had low p. Bias was particularly severe when most birds had both low ϕ and p. Bias was largest for population size, intermediate for stopover duration and negligible for stopover timing.A total of 50,000-100,000 red knots were estimated to annually stop for 5-9 days in Bohai Bay between 10 and 30 May. This shows the key importance of this staging site for this declining species. There were no clear changes in stopover parameters over time, although stopover population size was substantially lower in 2016 than in preceding years.Our study shows the importance of accounting for heterogeneity in both encounter and staying probabilities for accurately estimating stopover duration and population size and provides an appropriate modeling framework.

Sex-specific effects of fisheries and climate on the demography of sexually dimorphic seabirds.

Many animal taxa exhibit sex-specific variation in ecological traits, such as foraging and distribution. These differences could result in sex-specific responses to change, but such demographic effects are poorly understood. Here, we test for sex-specific differences in the demography of northern (NGP, Macronectes halli) and southern (SGP, M. giganteus) giant petrels - strongly sexually size-dimorphic birds that breed sympatrically at South Georgia, South Atlantic Ocean. Both species feed at sea or on carrion on land, but larger males (30% heavier) are more reliant on terrestrial foraging than the more pelagic females. Using multi-event mark-recapture models, we examine the impacts of long-term changes in environmental conditions and commercial fishing on annual adult survival and use two-sex matrix population models to forecast future trends. As expected, survival of male NGP was positively affected by carrion availability, but negatively affected by zonal winds. Female survival was positively affected by meridional winds and El Niño-Southern Oscillation (ENSO), and negatively affected by sea ice concentration and pelagic longline effort. Survival of SGPs did not differ between sexes; however, survival of males only was positively correlated with the Southern Annular Mode (SAM). Two-sex population projections indicate that future environmental conditions are likely to benefit giant petrels. However, any potential increase in pelagic longline fisheries could reduce female survival and population growth. Our study reveals that sex-specific ecological differences can lead to divergent responses to environmental drivers (i.e. climate and fisheries). Moreover, because such effects may not be apparent when all individuals are considered together, ignoring sex differences could underestimate the relative influence of a changing environment on demography.

Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses.

Many biological quantities cannot be measured directly but rather need to be estimated from models. Estimates from models are statistical objects with variance and, when derived simultaneously, covariance. It is well known that their variance-covariance (VC) matrix must be considered in subsequent analyses. Although it is always preferable to carry out the proposed analyses on the raw data themselves, a two-step approach cannot always be avoided. This situation arises when the parameters of a multinomial must be regressed against a covariate. The Delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. Implementing the Delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. This paper proposes a how-to guide for calculating the correct VC matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. For illustrative purpose, we focus on variables calculated in capture-recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances.

Making use of multiple surveys: Estimating breeding probability using a multievent-robust design capture-recapture model.

Increased environmental stochasticity due to climate change will intensify temporal variance in the life-history traits, and especially breeding probabilities, of long-lived iteroparous species. These changes may decrease individual fitness and population viability and is therefore important to monitor. In wild animal populations with imperfect individual detection, breeding probabilities are best estimated using capture-recapture methods. However, in many vertebrate species (e.g., amphibians, turtles, seabirds), nonbreeders are unobservable because they are not tied to a territory or breeding location. Although unobservable states can be used to model temporary emigration of nonbreeders, there are disadvantages to having unobservable states in capture-recapture models. The best solution to deal with unobservable life-history states is therefore to eliminate them altogether. Here, we achieve this objective by fitting novel multievent-robust design models which utilize information obtained from multiple surveys conducted throughout the year. We use this approach to estimate annual breeding probabilities of capital breeding female elephant seals (Mirounga leonina). Conceptually, our approach parallels a multistate version of the Barker/robust design in that it combines robust design capture data collected during discrete breeding seasons with observations made at other times of the year. A substantial advantage of our approach is that the nonbreeder state became "observable" when multiple data sources were analyzed together. This allowed us to test for the existence of state-dependent survival (with some support found for lower survival in breeders compared to nonbreeders), and to estimate annual breeding transitions to and from the nonbreeder state with greater precision (where current breeders tended to have higher future breeding probabilities than nonbreeders). We used program E-SURGE (2.1.2) to fit the multievent-robust design models, with uncertainty in breeding state assignment (breeder, nonbreeder) being incorporated via a hidden Markov process. This flexible modeling approach can easily be adapted to suit sampling designs from numerous species which may be encountered during and outside of discrete breeding seasons.

Live fast, don't die young: Survival-reproduction trade-offs in long-lived income breeders.

Trade-offs between survival and reproduction are at the core of life-history theory, and essential to understanding the evolution of reproductive tactics as well as population dynamics and stability. Factors influencing these trade-offs are multiple and often addressed in isolation. Further problems arise as reproductive states and survival in wild populations are estimated based on imperfect and potentially biased observation processes, which might lead to flawed conclusions. In this study, we aimed at elucidating trade-offs between current reproduction (both pregnancy and lactation), survival and future reproduction, including the specific costs of first reproduction, in long-lived, income breeding small mammals, an under-studied group. We developed a novel statistical framework that encapsulates the breeding life cycle of females, and accounts for incomplete information on female pregnancy and lactation and imperfect and biased recapture rates. We applied this framework to longitudinal data on two sympatric, closely related bat species (Myotis daubentonii and M. nattereri). We revealed the existence of several, to our knowledge previously unknown, trends in survival and breeding of these closely related, sympatric species and detected remarkable differences in their age and costs of first reproduction, as well as their survival-reproduction trade-offs. Our results indicate that species with this type of life history exhibit a mixture of patterns expected for long-lived and short-lived animals, and between income and capital breeders. Thus, we call for more studies to be conducted in similar study systems, increasing our ability to fully understand the evolutionary origin and fitness effects of trade-offs and senescence.

Assessing the effect of density on population growth when modeling individual encounter data.

The relative role of density-dependent and density-independent variation in vital rates and population size remains largely unsolved. Despite its importance to the theory and application of population ecology, and to conservation biology, quantifying the role and strength of density dependence is particularly challenging. We present a hierarchical formulation of the temporal symmetry approach, also known as the Pradel model, that permits estimation of the strength of density dependence from capture-mark-reencounter data. A measure of relative population size is built in the model and serves to detect density dependence directly on population growth rate. The model is also extended to account for temporal random variability in demographic rates, allowing estimation of the temporal variance of population growth rate unexplained by density dependence. We thus present a model-based approach that enable to test and quantify the effect of density-dependent and density-independent factors affecting population fluctuations in a single modeling framework. More generally, we use this modeling framework along with simulated and empirical data to show the value of including density dependence when modeling individual encounter data without the need for auxiliary data.

Dermal mycobacteriosis and warming sea surface temperatures are associated with elevated mortality of striped bass in Chesapeake Bay.

Temperature is hypothesized to alter disease dynamics, particularly when species are living at or near their thermal limits. When disease occurs in marine systems, this can go undetected, particularly if the disease is chronic and progresses slowly. As a result, population-level impacts of diseases can be grossly underestimated. Complex migratory patterns, stochasticity in recruitment, and data and knowledge gaps can hinder collection and analysis of data on marine diseases. New tools enabling quantification of disease impacts in marine environments include coupled biogeochemical hydrodynamic models (to hindcast key environmental data), and multievent, multistate mark-recapture (MMSMR) (to quantify the effects of environmental conditions on disease processes and assess population-level impacts). We used MMSMR to quantify disease processes and population impacts in an estuarine population of striped bass (Morone saxatilis) in Chesapeake Bay from 2005 to 2013. Our results supported the hypothesis that mycobacteriosis is chronic, progressive, and, frequently, lethal. Yearly disease incidence in fish age three and above was 89%, suggesting that this disease impacts nearly every adult striped bass. Mortality of diseased fish was high, particularly in severe cases, where it approached 80% in typical years. Severely diseased fish also had a 10-fold higher catchability than healthy fish, which could bias estimates of disease prevalence. For both healthy and diseased fish, mortality increased with the modeled average summer sea surface temperature (SST) at the mouth of the Rappahannock River; in warmer summers (average SST ≥ 29°C), a cohort is predicted to experience >90% mortality in 1 year. Regression of disease signs in mildly and moderately diseased fish was <2%. These results suggest that these fish are living at their maximum thermal tolerance and that this is driving increased disease and mortality. Management of this fishery should account for the effects of temperature and disease on impacted populations.

A multi-event capture-recapture analysis of Toxoplasma gondii seroconversion dynamics in farm cats.

BACKGROUND: Domestic cats play a key role in the epidemiology of the parasite Toxoplasma gondii by excreting environmentally-resistant oocysts that may infect humans and other warm-blooded animals. The dynamics of Toxoplasma gondii seroconversion, used as a proxy for primo-infection dynamics, was investigated in five cat populations living on farms. METHODS: Serological tests on blood samples from cats were performed every three months over a period of two years, for a total of 400 serological tests performed on 130 cats. Variations in seroconversion rates and associated factors were investigated using a multi-event capture-recapture modelling approach that explicitly accounted for uncertainties in cat age and serological status. RESULTS: Seroprevalence varied between farms, from 15 to 73%, suggesting differential exposure of cats to T. gondii. In farms with high exposure, cats could become infected before reaching the age of six months. Seroconversion rates varied from 0.42 to 0.96 seroconversions per cat per year and were higher in autumn and winter than in spring and summer. CONCLUSION: Our results suggest inter-farm and seasonal variations in the risks of exposure to T. gondii oocysts for humans and livestock living on farms. The paper also discusses the role of young cats in the maintenance of environmental contamination by T. gondii oocysts on farms.

A general method for estimating seed dormancy and colonisation in annual plants from the observation of existing flora.

In plant ecology, characterising colonisation and extinction in plant metapopulations is challenging due to the non-detectable seed bank that allows plants to emerge after several years of absence. In this study, we used a Hidden Markov Model to characterise seed dormancy, colonisation and germination solely from the presence-absence of standing flora. Applying the model to data from a long-term survey of 38 annual weeds across France, we identified three homogeneous functional groups: (1) species persisting preferentially through spatial colonisation, (2) species persisting preferentially through seed dormancy and (3) a mix of both strategies. These groups are consistent with existing ecological knowledge, demonstrating that ecologically meaningful parameters can be estimated from simple presence-absence observations. These results indicate that such studies could contribute to the design of weed management strategies. They also open the possibility of testing life-history theories such as the dormancy/colonisation trade-off in natura.

Estimating dispersal in spatiotemporally variable environments using multievent capture-recapture modeling.

Dispersal is a key process in ecological and evolutionary dynamics. Spatiotemporal variation in habitat availability and characteristics has been suggested to be one of the main cause involved in dispersal evolution and has a strong influence on metapopulation dynamics. In recent decades, the study of dispersal has led to the development of capture-recapture (CR) models that allow movement between sites to be quantified, while handling imperfect detection. For studies involving numerous recapture sites, Lagrange et al. () proposed a multievent CR model that allows dispersal to be estimated while omitting site identity by distinguishing between individuals that stay and individuals that move. More recently, Cayuela et al. () extended this model to allow survival and dispersal probabilities to differ for the different types of habitat represented by several sites within a study area. Yet in both of these modeling systems, the state of sites is assumed to be static over time, which is not a realistic assumption in dynamic landscapes. For that purpose, we generalized the multievent CR model proposed by Cayuela et al. () to allow the estimation of dispersal, survival and recapture probabilities when a site may appear or disappear over time (MODEL 1) or when the characteristics of a site fluctuate over space and time (MODEL 2). This paper first presents these two new modeling systems, and then provides an illustration of their efficacy and usefulness by applying them to simulated CR data and data collected on two metapopulations of amphibians. MODEL 1 was tested using CR data recorded on a metapopulation of yellow-bellied toads (Bombina variegata). In this first empirical case, we examined whether the drying-out dynamics of ponds and the past dispersal status of an individual might affect dispersal behavior. Our study revealed that the probability of facultative dispersal (i.e., from a pond group that remained available/flooded) fluctuated between years and was higher in individuals that had previously dispersed. MODEL 2 was tested using CR data collected on a metapopulation of great crested newts (Triturus cristatus). In this second empirical example, we investigated whether the density of alpine newts (Ichthyosaura alpestris), a potential competitor, might affect the dispersal and survival of the crested newt. Our study revealed that the departure rate was lower in ponds with a high density of heterospecifics than in ponds with a low density of heterospecifics at both inter-annual and intra-annual scales. Moreover, annual survival was slightly higher in ponds with a high density of heterospecifics. Overall, our findings indicate that these multievent CR models provide a highly flexible means of modeling dispersal in dynamic landscapes.

A Test of Positive Association for Detecting Heterogeneity in Capture for Capture-Recapture Data.

The Cormack-Jolly-Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman-Kruskal's gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (Thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie's test of equal catchability and Carothers' extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions. Supplementary materials accompanying this paper appear online. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary materials for this article are available at 10.1007/s13253-017-0315-4.

Additive effects of climate and fisheries drive ongoing declines in multiple albatross species.

Environmental and anthropogenic factors often drive population declines in top predators, but how their influences may combine remains unclear. Albatrosses are particularly threatened. They breed in fast-changing environments, and their extensive foraging ranges expose them to incidental mortality (bycatch) in multiple fisheries. The albatross community at South Georgia includes globally important populations of three species that have declined by 40-60% over the last 35 years. We used three steps to deeply understand the drivers of such dramatic changes: (i) describe fundamental demographic rates using multievent models, (ii) determine demographic drivers of population growth using matrix models, and (iii) identify environmental and anthropogenic drivers using ANOVAs. Each species was affected by different processes and threats in their foraging areas during the breeding and nonbreeding seasons. There was evidence for two kinds of combined environmental and anthropogenic effects. The first was sequential; in wandering and black-browed albatrosses, high levels of bycatch have reduced juvenile and adult survival, then increased temperature, reduced sea-ice cover, and stronger winds are affecting the population recovery potential. The second was additive; in gray-headed albatrosses, not only did bycatch impact adult survival but also this impact was exacerbated by lower food availability in years following El Niño events. This emphasizes the need for much improved implementation of mitigation measures in fisheries and better enforcement of compliance. We hope our results not only help focus future management actions for these populations but also demonstrate the power of the modelling approach for assessing impacts of environmental and anthropogenic drivers in wild animal populations.

Assessment of individual and conspecific reproductive success as determinants of breeding dispersal of female tree swallows: A capture-recapture approach.

Breeding dispersal is a key process of population structure and dynamics and is often triggered by an individual's breeding failure. In both colonial and territorial birds, reproductive success of conspecifics (RSc) can also lead individuals to change breeding sites after a failure on a site. Yet, few studies have simultaneously investigated the independent contribution of individual reproductive success (RSi) and of RSc on dispersal decision. Here, we develop a modeling framework to disentangle the effects of RSi and RSc on demographic parameters, while accounting for imperfect individual detection and other confounding factors such as age or dispersal behavior in the previous year. Using a 10-year capture-recapture dataset composed of 1,595 banded tree swallows, we assessed the effects of nonmanipulated RSi and RSc on female breeding dispersal in this semicolonial passerine. Dispersal was strongly driven by RSi, but not by RSc. Unsuccessful females were 9.5-2.5 times more likely to disperse than successful ones, depending if they had dispersed or not in the previous year, respectively. Unsuccessful females were also three times less likely to be detected than successful ones. Contrary to theoretical and empirical studies, RSc did not drive the decision to disperse but influenced the selection of the following breeding site once dispersal had been initiated. Because detection of individuals was driven by RSi, which was positively correlated to RSc, assuming a perfect detection as in previous studies may have lead us to conclude that RSc affected dispersal patterns, yet our approach corrected for this bias. Overall, our results suggest that the value and use of RSc as public information to guide dispersal decisions are likely dictated by multiple ecological determinants, such as landscape structure and extent, if this cue is indeed used.