Population regulation throughout a complex life cycle.

Research Highlight: McIntosh, A. R., Greig, H. S., & Howard, S. (2022). Regulation of open populations of a stream insect through larval density dependence. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13696. Despite decades of research on population regulation through density dependence, it remains challenging to identify and understand the relative importance of mechanisms governing open populations of organisms with complex life cycles. McIntosh et al. (2022) manipulated density of aquatic invertebrates in the field, and then followed populations for 2 years to track the effects on abundance through multiple life-history stages. The authors found that their density manipulation, performed on larvae that were about to pupate, had minimal effects on the number of emergent adults collected several months later. The manipulation had a similarly negligible influence on the number of egg masses laid at study locations. The authors attribute this to stochasticity around dispersal of flying adults through the terrestrial environment. However, later in the study, the authors found evidence of density-dependent population regulation among larval stages, seemingly controlled by resource availability. These results suggest that population dynamics depend on multiple mechanisms operating at different points in organisms' life history, which could either help or hinder population persistence with disturbance or environmental change.

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.

Open population maximum likelihood spatial capture-recapture.

Open population capture-recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture-recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack-Jolly-Seber and Jolly-Seber models, with and without activity center movement. The method is applied to a 12-year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root-mean-square error in predicting population density compared to closed population models.

Levels of depression and life exhaustion in the open population of the middle urbanized Siberian city: gender differences.

AIM: The aim of the study was to determine the levels of depression and life exhaustion in men and women of the open urban population in the age range. MATERIALS AND METHODS: A single-stage epidemiological study was conducted among people of both sexes aged 25-64 in Tyumen. A representative sample was formed from the electoral lists of citizens by the method of "random numbers" - 2000 men and women with a response among men 85.0%, among women - 70.3%. The study of depression was conducted according to the algorithms of the program of the world health organization "MONICA-psychosocial". RESULTS: The prevalence of depression in the Tyumen population and in the age and sex groups showed a predominance of the average level over the high, in the age categories 25-34 and 35-44 years - significantly higher prevalence of high levels of depression in women. The higher prevalence of the average level of men and women IN the open population was determined to be relatively high. The average level of LIFE significantly prevails in women in the older age categories and in the population as a whole, the high level of LIFE - at the age of 25-34 years in women and at the age of 55-64 years in men. CONCLUSION: Therefore, in the open population of the middle-urbanized Siberian city there is a need to form an integrated approach to the prevention of non-infectious diseases, especially cardiovascular diseases, as it is established that prevention programs lead to a reduction in the burden of depression and, and effective approaches to the prevention of psycho-emotional States at the level of individual communities include school-oriented programs to teach positive thinking among the population, starting from a young age.

Stress in the family - association with prevalence of cardiovascular diseases in men of the open city population.

AIM: The aim of the study was to establish associations of the prevalence of coronary artery disease (CAD), metabolic syndrome (MS) and arterial hypertension (AH) with family stress in open male population of Tyumen aged 25-64 years. MATERIALS AND METHODS: Cross-sectional epidemiological survey was conducted on representative sample of 1000 individuals (250 in every 4 decades: 25-34, 35-44, 45-54, 55-64 years) formed from the election list of males in one of the administrative districts of Tyumen. The response to cardiac screening was 85.0% - 850 patients. Different types of CAD were defined based on standard methods in epidemiological surveys. "Certain" CAD and "possible" CAD were determined according to extended epidemiological criteria. IDF criteria (2005) were used for the MS assessment, AH was considered as a component of the MS. A questionnaire of WHO MONICA «Knowledge and attitude to health¼ was used to analyze stress in families. RESULTS: The results of the study showed that more than 80% of males in open urban population, mostly older individuals, had a stable marital status last twelve months, while one-third of the male population had conflicts in their families and the fifth part did not have the opportunity to relax at home. Male patients with CAD had family stress more frequently during last 12 months on extended epidemiological criteria. In the open male population aged 25-64 years with MS and AH there was found more stable marital status versus comparison groups. CONCLUSION: Standardized methods and the accumulated database should be used to continue objective monitoring of the epidemiological situation of cardiovascular diseases and psychosocial risk factors in open urban population during regional preventive program.

State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales.

North Atlantic right whales (Eubalaena glacialis Müller 1776) present an interesting problem for abundance and trend estimation in marine wildlife conservation. They are long lived, individually identifiable, highly mobile, and one of the rarest of cetaceans. Individuals are annually resighted at different rates, primarily due to varying stay durations among several principal habitats within a large geographic range. To date, characterizations of abundance have been produced that use simple accounting procedures with differing assumptions about mortality. To better characterize changing abundance of North Atlantic right whales between 1990 and 2015, we adapted a state-space formulation with Jolly-Seber assumptions about population entry (birth and immigration) to individual resighting histories and fit it using empirical Bayes methodology. This hierarchical model included accommodation for the effect of the substantial individual capture heterogeneity. Estimates from this approach were only slightly higher than published accounting procedures, except for the most recent years (when recapture rates had declined substantially). North Atlantic right whales' abundance increased at about 2.8% per annum from median point estimates of 270 individuals in 1990 to 483 in 2010, and then declined to 2015, when the final estimate was 458 individuals (95% credible intervals 444-471). The probability that the population's trajectory post-2010 was a decline was estimated at 99.99%. Of special concern was the finding that reduced survival rates of adult females relative to adult males have produced diverging abundance trends between sexes. Despite constraints in recent years, both biological (whales' distribution changing) and logistical (fewer resources available to collect individual photo-identifications), it is still possible to detect this relatively recent, small change in the population's trajectory. This is thanks to the massive dataset of individual North Atlantic right whale identifications accrued over the past three decades. Photo-identification data provide biological information that allows more informed inference on the status of this species.

A comparison of abundance estimates from extended batch-marking and Jolly-Seber-type experiments.

Little attention has been paid to the use of multi-sample batch-marking studies, as it is generally assumed that an individual's capture history is necessary for fully efficient estimates. However, recently, Huggins et al. (2010) present a pseudo-likelihood for a multi-sample batch-marking study where they used estimating equations to solve for survival and capture probabilities and then derived abundance estimates using a Horvitz-Thompson-type estimator. We have developed and maximized the likelihood for batch-marking studies. We use data simulated from a Jolly-Seber-type study and convert this to what would have been obtained from an extended batch-marking study. We compare our abundance estimates obtained from the Crosbie-Manly-Arnason-Schwarz (CMAS) model with those of the extended batch-marking model to determine the efficiency of collecting and analyzing batch-marking data. We found that estimates of abundance were similar for all three estimators: CMAS, Huggins, and our likelihood. Gains are made when using unique identifiers and employing the CMAS model in terms of precision; however, the likelihood typically had lower mean square error than the pseudo-likelihood method of Huggins et al. (2010). When faced with designing a batch-marking study, researchers can be confident in obtaining unbiased abundance estimators. Furthermore, they can design studies in order to reduce mean square error by manipulating capture probabilities and sample size.

Semivarying coefficient models for capture-recapture data: colony size estimation for the little penguin Eudyptula minor.

To accommodate seasonal effects that change from year to year into models for the size of an open population we consider a time-varying coefficient model. We fit this model to a capture-recapture data set collected on the little penguin Eudyptula minor in south-eastern Australia over a 25 year period using Jolly-Seber type estimators and nonparametric P-spline techniques. The time-varying coefficient model identified strong changes in the seasonal pattern across the years which we further examined using functional data analysis techniques. To evaluate the methodology we also conducted several simulation studies that incorporate seasonal variation.