Wildlife disease ecology from the individual to the population: Insights from a long-term study of a naturally infected European badger population.

Long-term individual-based datasets on host-pathogen systems are a rare and valuable resource for understanding the infectious disease dynamics in wildlife. A study of European badgers (Meles meles) naturally infected with bovine tuberculosis (bTB) at Woodchester Park in Gloucestershire (UK) has produced a unique dataset, facilitating investigation of a diverse range of epidemiological and ecological questions with implications for disease management. Since the 1970s, this badger population has been monitored with a systematic mark-recapture regime yielding a dataset of >15,000 captures of >3,000 individuals, providing detailed individual life-history, morphometric, genetic, reproductive and disease data. The annual prevalence of bTB in the Woodchester Park badger population exhibits no straightforward relationship with population density, and both the incidence and prevalence of Mycobacterium bovis show marked variation in space. The study has revealed phenotypic traits that are critical for understanding the social structure of badger populations along with mechanisms vital for understanding disease spread at different spatial resolutions. Woodchester-based studies have provided key insights into how host ecology can influence infection at different spatial and temporal scales. Specifically, it has revealed heterogeneity in epidemiological parameters; intrinsic and extrinsic factors affecting population dynamics; provided insights into senescence and individual life histories; and revealed consistent individual variation in foraging patterns, refuge use and social interactions. An improved understanding of ecological and epidemiological processes is imperative for effective disease management. Woodchester Park research has provided information of direct relevance to bTB management, and a better appreciation of the role of individual heterogeneity in disease transmission can contribute further in this regard. The Woodchester Park study system now offers a rare opportunity to seek a dynamic understanding of how individual-, group- and population-level processes interact. The wealth of existing data makes it possible to take a more integrative approach to examining how the consequences of individual heterogeneity scale to determine population-level pathogen dynamics and help advance our understanding of the ecological drivers of host-pathogen systems.

Climatic conditions produce contrasting influences on demographic traits in a long-distance Arctic migrant.

The manner in which patterns of variation and interactions among demographic rates contribute to population growth rate (λ) is key to understanding how animal populations will respond to changing climatic conditions. Migratory species are likely to be particularly sensitive to climatic conditions as they experience a range of different environments throughout their annual cycle. However, few studies have provided fully integrated demographic analyses of migratory populations in response to changing climatic conditions. Here, we employed integrated population models to demonstrate that the environmental conditions experienced during a short but critical period play a central role in the demography of a long-distance migrant, the light-bellied Brent goose (Branta bernicla hrota). Female survival was positively associated with June North Atlantic Oscillation (NAO) values, whereas male survival was not. In contrast, breeding productivity was negatively associated with June NAO, suggesting a trade-off between female survival and reproductive success. Both adult female and adult male survival showed low temporal variation, whereas there was high temporal variation in recruitment and breeding productivity. In addition, while annual population growth was positively correlated with annual breeding productivity, a sensitivity analysis revealed that population growth was most sensitive to changes in adult survival. Our results demonstrate that the environmental conditions experienced during a relatively short-time window at the start of the breeding season play a critical role in shaping the demography of a long-distant Arctic migrant. Crucially, different demographic rates responded in opposing directions to climatic variation, emphasising the need for integrated analysis of multiple demographic traits when understanding population dynamics.

Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population.

Demographic buffering allows populations to persist by compensating for fluctuations in vital rates, including disease-induced mortality. Using long-term data on a badger (Meles meles Linnaeus, 1758) population naturally infected with Mycobacterium bovis, we built an integrated population model to quantify impacts of disease, density and environmental drivers on survival and recruitment. Badgers exhibit a slow life-history strategy, having high rates of adult survival with low variance, and low but variable rates of recruitment. Recruitment exhibited strong negative density-dependence, but was not influenced by disease, while adult survival was density independent but declined with increasing prevalence of diseased individuals. Given that reproductive success is not depressed by disease prevalence, density-dependent recruitment of cubs is likely to compensate for disease-induced mortality. This combination of slow life history and compensatory recruitment promotes the persistence of a naturally infected badger population and helps to explain the badger's role as a persistent reservoir of M. bovis.

Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance.

Free-roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false-positive detections and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data.

Engaging with Socio-Economically Disadvantaged Communities and Their Cats: Human Behaviour Change for Animal and Human Benefit.

The overpopulation of free-roaming domestic cats (Felis catus) is fuelled by uncontrolled breeding of both owned and unowned populations and has been identified as a particular problem in socio-economically deprived areas. Consequently, for sustainable change, it is recommended that Trap-Neuter-Return activities are linked with community engagement to encourage positive behaviours towards cats. This paper assesses the acceptability and impact of a community-partnership program called "Bulwell Cat Watch" (BCW), set-up to control cat numbers in Bulwell, UK. The data are based on a (1) cross-sectional survey (n = 478); (2) pre-post analysis (n = 21); and (3) targeted survey of people known to engage with BCW (n = 34). We found significant associations between awareness of BCW and an increased likelihood of reporting unowned cats now compared to previous years. Respondents reported increased self-efficacy and confidence to help cats. Our pre-post study corroborated these findings with residents significantly more likely to report unowned cats compared to when surveyed pre-BCW. An indirect benefit to residents engaged with the program was the positive impact on confidence and self-esteem. Taken in combination these results show community partnerships can effectively engage often hard-to-reach populations and foster sustainable management by overcoming barriers to helping cats, alongside the potential for wider community benefits.

Prior Precision, Prior Accuracy, and the Estimation of Disease Prevalence Using Imperfect Diagnostic Tests.

Estimates of disease prevalence in any host population are complicated by uncertainty in the outcome of diagnostic tests on individuals. In the absence of gold standard diagnostics (tests that give neither false positives nor false negatives), Bayesian latent class inference can be applied to batteries of diagnostic tests, providing posterior estimates of the sensitivity and specificity of each test, alongside posterior estimates of disease prevalence. Here we explore the influence of precision and accuracy of prior information on the precision and accuracy of posterior estimates of these key parameters. Our simulations use three diagnostic tests, yielding eight possible diagnostic outcomes per individual. Seven degrees of freedom allow the estimation of seven parameters: sensitivity and specificity of each test, and disease prevalence. We show that prior precision begets posterior precision but only when priors are accurate. We also show that analyses without gold standard can use imprecise priors as long as they are initialised with accuracy. Imprecise priors risk the divergence of MCMC chains towards inaccurate posterior estimates, if inaccurate initial values are used. We note that inaccurate priors can yield inaccurate and imprecise inference. Bounded priors should certainly not be used unless their accuracy is well established. Inaccurate estimates of sensitivity or specificity can yield wildly inaccurate estimates of disease prevalence. Our analyses are motivated by studies of bovine tuberculosis in a wild badger population.

Divergent demographic strategies of plants in variable environments.

One of the best-supported patterns in life history evolution is that organisms cope with environmental fluctuations by buffering their most important vital rates against them. This demographic buffering hypothesis is evidenced by a tendency for temporal variation in rates of survival and reproduction to correlate negatively with their contribution to fitness. Here, we show that widespread evidence for demographic buffering can be artefactual, resulting from natural relationships between the mean and variance of vital rates. Following statistical scaling, we find no significant tendency for plant life histories to be buffered demographically. Instead, some species are buffered, whereas others have labile life histories with higher temporal variation in their more important vital rates. We find phylogenetic signal in the strength and direction of variance-importance correlations, suggesting that clades of plants are prone to being either buffered or labile. Species with simple life histories are more likely to be demographically labile. Our results suggest important evolutionary nuances in how species deal with environmental fluctuations.

Transients drive the demographic dynamics of plant populations in variable environments.

The dynamics of structured plant populations in variable environments can be decomposed into the 'asymptotic' growth contributed by vital rates, and 'transient' growth caused by deviation from stable stage structure.We apply this framework to a large, global data base of longitudinal studies of projection matrix models for plant populations. We ask, what is the relative contribution of transient boom and bust to the dynamic trajectories of plant populations in stochastic environments? Is this contribution patterned by phylogeny, growth form or the number of life stages per population and per species?We show that transients contribute nearly 50% or more to the resulting trajectories, depending on whether transient and stable contributions are partitioned according to their absolute or net contribution to population dynamics.Both transient contributions and asymptotic contributions are influenced heavily by the number of life stages modelled. We discuss whether the drivers of transients should be considered real ecological phenomena, or artefacts of study design and modelling strategy. We find no evidence for phylogenetic signal in the contribution of transients to stochastic growth, nor clear patterns related to growth form. We find a surprising tendency for plant populations to boom rather than bust in response to temporal changes in vital rates and that stochastic growth rates increase with increasing tendency to boom. Synthesis. Transient dynamics contribute significantly to stochastic population dynamics but are often overlooked in ecological and evolutionary studies that employ stochastic analyses. Better understanding of transient responses to fluctuating population structure will yield better management strategies for plant populations, and better grasp of evolutionary dynamics in the real world.

What do you mean, 'resilient'?

In a world beset by environmental disasters and anthropogenic disturbances, resilience might be the key to the persistence of natural systems. Yet, the 'measurement' of resilience is hampered by the multiple (and often conflicting) processes that yield the response of systems to insult. We recommend the simultaneous consideration of 'resistance' and 'recovery' as measurable components that together represent resilience.