The utility of mortality hazard rates in population analyses

Higher Mortality Is Not a Universal Cost of Dispersal: A Case Study in African Wild Dogs

AbstractMortality is considered one of the main costs of dispersal. A reliable evaluation of mortality, however, is often hindered by a lack of information about the fate of individuals that disappear under unexplained circumstances (i.e., missing individuals). Here, we addressed this uncertainty by applying a Bayesian mortality analysis that inferred the fate of missing individuals according to information from individuals with known fate. Specifically, we tested the hypothesis that mortality during dispersal is higher than mortality among nondispersers using 32 years of mark-resighting data from a free-ranging population of the endangered African wild dog (Lycaon pictus) in northern Botswana. Contrary to expectations, we found that mortality during dispersal was lower than mortality among nondispersers, indicating that higher mortality is not a universal cost of dispersal. Our findings suggest that group living can incur costs for certain age classes, such as limited access to resources as group density increases, that exceed the mortality costs associated with dispersal. By challenging the accepted expectation of higher mortality during dispersal, we urge for further investigations of this key life history trait and propose a robust statistical approach to reduce bias in mortality estimates.

Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models

Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.

Recovered frog populations coexist with endemic Batrachochytrium dendrobatidis despite load-dependent mortality

Novel infectious diseases, particularly those caused by fungal pathogens, pose considerable risks to global biodiversity. The amphibian chytrid fungus (Batrachochytrium dendrobatidis, Bd) has demonstrated the scale of the threat, having caused the greatest recorded loss of vertebrate biodiversity attributable to a pathogen. Despite catastrophic declines on several continents, many affected species have experienced population recoveries after epidemics. However, the potential ongoing threat of endemic Bd in these recovered or recovering populations is still poorly understood. We investigated the threat of endemic Bd to frog populations that recovered after initial precipitous declines, focusing on the endangered rainforest frog Mixophyes fleayi. We conducted extensive field surveys over 4 years at three independent sites in eastern Australia. First, we compared Bd infection prevalence and infection intensities within frog communities to reveal species-specific infection patterns. Then, we analyzed mark-recapture data of M. fleayi to estimate the impact of Bd infection intensity on apparent mortality rates and Bd infection dynamics. We found that M. fleayi had lower infection intensities than sympatric frogs across the three sites, and cleared infections at higher rates than they gained infections throughout the study period. By incorporating time-varying individual infection intensities, we show that healthy M. fleayi populations persist despite increased apparent mortality associated with infrequent high Bd loads. Infection dynamics were influenced by environmental conditions, with Bd prevalence, infection intensity, and rates of gaining infection associated with lower temperatures and increased rainfall. However, mortality remained constant year-round despite these fluctuations in Bd infections, suggesting major mortality events did not occur over the study period. Together, our results demonstrate that while Bd is still a potential threat to recovered populations of M. fleayi, high rates of clearing infections and generally low average infection loads likely minimize mortality caused by Bd. Our results are consistent with pathogen resistance contributing to the coexistence of M. fleayi with endemic Bd. We emphasize the importance of incorporating infection intensity into disease models rather than infection status alone. Similar population and infection dynamics likely exist within other recovered amphibian-Bd systems around the globe, promising longer-term persistence in the face of endemic chytridiomycosis.

Density-dependence produces spurious relationships among demographic parameters in a harvested species

Harvest of wild organisms is an important component of human culture, economy, and recreation, but can also put species at risk of extinction. Decisions that guide successful management actions therefore rely on the ability of researchers to link changes in demographic processes to the anthropogenic actions or environmental changes that underlie variation in demographic parameters. Ecologists often use population models or maximum sustained yield curves to estimate the impacts of harvest on wildlife and fish populations. Applications of these models usually focus exclusively on the impact of harvest and often fail to consider adequately other potential, often collinear, mechanistic drivers of the observed relationships between harvest and demographic rates. In this study, we used an integrated population model and long-term data (1973-2016) to examine the relationships among hunting and natural mortality, the number of hunters, habitat conditions, and population size of blue-winged teal Spatula discors, an abundant North American dabbling duck with a relatively fast-paced life history strategy. Over the last two and a half decades of the study, teal abundance tripled, hunting mortality probability increased slightly ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>&lt;</mml:mo> <mml:mn>0.02</mml:mn></mml:mrow> </mml:semantics> </mml:math> ), and natural mortality probability increased substantially ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>&gt;</mml:mo> <mml:mn>0.1</mml:mn></mml:mrow> </mml:semantics> </mml:math> ) at greater population densities. We demonstrate strong density-dependent effects on natural mortality and fecundity as population density increased, indicative of compensatory harvest mortality and compensatory natality. Critically, an analysis that only assessed the relationship between survival and hunting mortality would spuriously indicate depensatory mortality due to multicollinearity between abundance, natural mortality and hunting mortality. Our findings demonstrate that models that only consider the direct effect of hunting on survival or natural mortality can fail to accurately assess the mechanistic impact of hunting on population dynamics due to multicollinearity among demographic drivers. This multicollinearity limits inference and may have strong impacts on applied management actions globally.

Demographic consequences of changing body size in a terrestrial salamander

Changes in climate can alter individual body size, and the resulting shifts in reproduction and survival are expected to impact population dynamics and viability. However, appropriate methods to account for size-dependent demographic changes are needed, especially in understudied yet threatened groups such as amphibians. We investigated individual- and population-level demographic effects of changes in body size for a terrestrial salamander using capture-mark-recapture data. For our analysis, we implemented an integral projection model parameterized with capture-recapture likelihood estimates from a Bayesian framework. Our study combines survival and growth data from a single dataset to quantify the influence of size on survival while including different sources of uncertainty around these parameters, demonstrating how selective forces can be studied in populations with limited data and incomplete recaptures. We found a strong dependency of the population growth rate on changes in individual size, mediated by potential changes in selection on mean body size and on maximum body size. Our approach of simultaneous parameter estimation can be extended across taxa to identify eco-evolutionary mechanisms acting on size-specific vital rates, and thus shaping population dynamics and viability.

When to stay and when to leave? Proximate causes of dispersal in an endangered social carnivore

Reliable estimates of birth, death, emigration and immigration rates are fundamental to understanding and predicting the dynamics of wild populations and, consequently, inform appropriate management actions. However, when individuals disappear from a focal population, inference on their fate is often challenging. Here we used 30 years of individual-based mark-recapture data from a population of free-ranging African wild dogs Lycaon pictus in Botswana and a suite of individual, social and environmental predictors to investigate factors affecting the decision to emigrate from the pack. We subsequently used this information to assign an emigration probability to those individuals that were no longer sighted within their pack (i.e. missing individuals). Natal dispersal (i.e. emigration from the natal pack) showed seasonal patterns with female dispersal peaking prior to the mating season and male dispersal peaking at the beginning of the wet season. For both sexes, natal dispersal rate increased in the absence of unrelated individuals of the opposite sex in the pack. Male natal dispersal decreased with increasing number of pups in the pack and increased in larger packs. Female natal dispersal decreased with increasing number of pups in larger packs, but increased with increasing number of pups in smaller packs. Individuals of both sexes were less likely to exhibit secondary dispersal (i.e. emigration from a pack other than the natal pack) if they were dominant and if many pups were present in the pack. Our models predicted that 18% and 25% of missing females and males, respectively, had likely dispersed from the natal pack, rather than having died. A misclassification of this order of magnitude between dispersal and mortality can have far-reaching consequences in the evaluation and prediction of population dynamics and persistence, and potentially mislead conservation actions. Our study showed that the decision to disperse is context-dependent and that the effect of individual, social and environmental predictors differs between males and females and between natal and secondary dispersal related to different direct and indirect fitness consequences. Furthermore, we demonstrated how a thorough understanding of the proximate causes of dispersal can be used to assign a dispersal probability to missing individuals. Knowledge of causes of dispersal can then be used within an integrated framework to more reliably estimate mortality rates.

Size- and stage-dependence in cause-specific mortality of migratory brown trout

Evidence-based management of natural populations under strong human influence frequently requires not only estimates of survival but also knowledge about how much mortality is due to anthropogenic vs. natural causes. This is the case particularly when individuals vary in their vulnerability to different causes of mortality due to traits, life history stages, or locations. Here, we estimated harvest and background (other cause) mortality of landlocked migratory salmonids over half a century. In doing so, we quantified among-individual variation in vulnerability to cause-specific mortality resulting from differences in body size and spawning location relative to a hydropower dam. We constructed a multistate mark-recapture model to estimate harvest and background mortality hazard rates as functions of a discrete state (spawning location) and an individual time-varying covariate (body size). We further accounted for among-year variation in mortality and migratory behaviour and fit the model to a unique 50-year time series of mark-recapture-recovery data on brown trout (Salmo trutta) in Norway. Harvest mortality was highest for intermediate-sized trout, and outweighed background mortality for most of the observed size range. Background mortality decreased with body size for trout spawning above the dam and increased for those spawning below. All vital rates varied substantially over time, but a trend was evident only in estimates of fishers' reporting rate, which decreased from over 50% to less than 10% throughout the study period. We highlight the importance of body size for cause-specific mortality and demonstrate how this can be estimated using a novel hazard rate parameterization for mark-recapture models. Our approach allows estimating effects of individual traits and environment on cause-specific mortality without confounding, and provides an intuitive way to estimate temporal patterns within and correlation among different mortality sources.

Using incidental mark-encounter data to improve survival estimation

Obtaining robust survival estimates is critical, but sample size limitations often result in imprecise estimates or the failure to obtain estimates for population subgroups. Concurrently, data are often recorded on incidental reencounters of marked individuals, but these incidental data are often unused in survival analyses.We evaluated the utility of supplementing a traditional survival dataset with incidental data on marked individuals that were collected ad hoc. We used a continuous time-to-event exponential survival model to leverage the matching information contained in both datasets and assessed differences in survival among adult and juvenile and resident and translocated Mojave desert tortoises (Gopherus agassizii).Incorporation of the incidental mark-encounter data improved precision of all annual survival point estimates, with a 3.4%-37.5% reduction in the spread of the 95% Bayesian credible intervals. We were able to estimate annual survival for three subgroup combinations that were previously inestimable. Point estimates between the radiotelemetry and combined datasets were within |0.029| percentage points of each other, suggesting minimal to no bias induced by the incidental data.Annual survival rates were high (>0.89) for resident adult and juvenile tortoises in both study sites and for translocated adults in the southern site. Annual survival rates for translocated juveniles at both sites and translocated adults in the northern site were between 0.73 and 0.76. At both sites, translocated adults and juveniles had significantly lower survival than resident adults. High mortality in the northern site was driven primarily by a single pulse in mortalities.Using exponential survival models to leverage matching information across traditional survival studies and incidental data on marked individuals may serve as a useful tool to improve the precision and estimability of survival rates. This can improve the efficacy of understanding basic population ecology and population monitoring for imperiled species.

Cyclical and stochastic thermal variability affects survival and growth in brook trout

Directional changes in temperature have well-documented effects on ectotherms, yet few studies have explored how increased thermal variability (a concomitant of climate change) might affect individual fitness. Using a common-garden experimental protocol, we investigated how bidirectional temperature change can affect survival and growth of brook trout (Salvelinus fontinalis) and whether the survival and growth responses differ between two populations, using four thermal-variability treatments (mean: 10 °C; range: 7-13 °C): (i) constancy; (ii) cyclical fluctuations every two days; (iii) low stochasticity (random changes every 2 days); (iv) high stochasticity (random changes daily). Recently hatched individuals were monitored under thermal variability (6 weeks) and a subsequent one-month period of thermal constancy. We found that variability can positively influence survival, relative to thermal constancy, but negatively affect growth. The observations reported here can be interpreted within the context of Jensen's Inequality (performance at average conditions is unequal to average performance across a range of conditions). Projections of future population viability in the context of climate change would be strengthened by increased experimental attention to the fitness consequences of stochastic and non-stochastic thermal variability.

Taxonomy-based hierarchical analysis of natural mortality: polar and subpolar phocid seals

Knowledge of life-history parameters is frequently lacking in many species and populations, often because they are cryptic or logistically challenging to study, but also because life-history parameters can be difficult to estimate with adequate precision. We suggest using hierarchical Bayesian analysis (HBA) to analyze variation in life-history parameters among related species, with prior variance components representing shared taxonomy, phenotypic plasticity, and observation error. We develop such a framework to analyze U-shaped natural mortality patterns typical of mammalian life history from a variety of sparse datasets. Using 39 datasets from seals in the family Phocidae, we analyzed 16 models with different formulations for natural morality, specifically the amount of taxonomic and data-level variance components (subfamily, species, study, and dataset levels) included in mortality hazard parameters. The highest-ranked model according to DIC included subfamily-, species-, and dataset-level parameter variance components and resulted in typical U-shaped hazard functions for the 11 seal species in the study. Species with little data had survival schedules shrunken to the mean. We suggest that evolutionary and population ecologists consider employing HBA to quantify variation in life-history parameters. This approach can be useful for increasing the precision of estimates resulting from a collection of (often sparse) datasets, and for producing prior distributions for populations missing life-history data.