Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

Low signs of territorial behavior in the Eurasian otter during low-water conditions in a Mediterranean river.

The Eurasian otter Lutra lutra is a territorial semi-aquatic carnivore usually found at low densities in rivers, coastal areas, and wetlands. Its diet is based on prey associated with aquatic environments. Mediterranean rivers are highly seasonal, and suffer reduced flow during the summer, resulting in isolated river sections (pools) that sometimes can be left with a minimal amount of water, leading to concentrations of food for otters. To our knowledge, this process, which was known to field naturalists, has not been accurately described, nor have otter densities been estimated under these conditions. In this study, we describe the population size and movements of an aggregation of otters in an isolated pool in the Guadiana River in the Tablas de Daimiel National Park (central Spain), which progressively dried out during the spring-summer of 2022, in a context of low connectivity due to the absence of circulating water in the Guadiana and Gigüela rivers. Using non-invasive genetic sampling of 120 spraints collected along 79.4 km of sampling transects and spatial capture-recapture methods, we estimated the otter density at 1.71 individuals/km of river channel length (4.21 individuals/km2) in a progressively drying river pool, up to five times higher than previously described in the Iberian Peninsula. The movement patterns obtained with the spatial capture-recapture model are not quite different from those described in low density, which seems to indicate a wide home range overlap, with low signs of territoriality.

A capture-recapture modeling framework emphasizing expert opinion in disease surveillance.

In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.

Estimating the Undetected Burden of Respiratory Syncytial Virus Hospitalizations in Adults Through Capture-Recapture Methods.

INTRODUCTION: Traditional surveillance systems may underestimate the burden caused by respiratory syncytial virus (RSV). Capture-recapture methods provide alternatives for estimating the number of RSV-related hospitalizations in a population. METHODS: Capture-recapture methods were used to estimate the number of RSV-related hospitalizations in adults in Middle Tennessee from two independent hospitalization surveillance systems during consecutive respiratory seasons from 2016-2017 to 2019-2020. Data from the Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN) and the Emerging Infections Program (EIP) were used. Annual RSV hospitalization rates were calculated using the capture-recapture estimates weighted by hospitals' market share divided by the corresponding census population. RESULTS: Using capture-recapture methods, the estimated overall adult hospitalization rates varied from 8.3 (95% CI: 5.9-15.4) RSV-related hospitalizations per 10,000 persons during the 2016-2017 season to 28.4 (95% CI: 18.2-59.0) hospitalizations per 10,000 persons in the 2019-2020 season. The proportion of hospitalizations that HAIVEN determined ranged from 8.7% to 36.7% of the total capture-recapture estimated hospitalization, whereas EIP detected 23.5% to 52.7% of the total capture-recapture estimated hospitalizations. CONCLUSION: Capture-recapture estimates showed that individual traditional surveillance systems underestimated the hospitalization burden in adults. Using capture-recapture allows for a more comprehensive estimate of RSV hospitalizations.

The central role of the identifying assumption in population size estimation.

The problem of estimating the size of a population based on a subset of individuals observed across multiple data sources is often referred to as capture-recapture or multiple-systems estimation. This is fundamentally a missing data problem, where the number of unobserved individuals represents the missing data. As with any missing data problem, multiple-systems estimation requires users to make an untestable identifying assumption in order to estimate the population size from the observed data. If an appropriate identifying assumption cannot be found for a data set, no estimate of the population size should be produced based on that data set, as models with different identifying assumptions can produce arbitrarily different population size estimates-even with identical observed data fits. Approaches to multiple-systems estimation often do not explicitly specify identifying assumptions. This makes it difficult to decouple the specification of the model for the observed data from the identifying assumption and to provide justification for the identifying assumption. We present a re-framing of the multiple-systems estimation problem that leads to an approach that decouples the specification of the observed-data model from the identifying assumption, and discuss how common models fit into this framing. This approach takes advantage of existing software and facilitates various sensitivity analyses. We demonstrate our approach in a case study estimating the number of civilian casualties in the Kosovo war.

Population mortality before and during the COVID-19 epidemic in two Sudanese settings: a key informant study.

BACKGROUND: Population mortality is an important metric that sums information from different public health risk factors into a single indicator of health. However, the impact of COVID-19 on population mortality in low-income and crisis-affected countries like Sudan remains difficult to measure. Using a community-led approach, we estimated excess mortality during the COVID-19 epidemic in two Sudanese communities. METHODS: Three sets of key informants in two study locations, identified by community-based research teams, were administered a standardised questionnaire to list all known decedents from January 2017 to February 2021. Based on key variables, we linked the records before analysing the data using a capture-recapture statistical technique that models the overlap among lists to estimate the true number of deaths. RESULTS: We estimated that deaths per day were 5.5 times higher between March 2020 and February 2021 compared to the pre-pandemic period in East Gezira, while in El Obeid City, the rate was 1.6 times higher. CONCLUSION: This study suggests that using a community-led capture-recapture methodology to measure excess mortality is a feasible approach in Sudan and similar settings. Deploying similar community-led estimation methodologies should be considered wherever crises and weak health infrastructure prevent an accurate and timely real-time understanding of epidemics' mortality impact in real-time.

Intersectional inequities and longitudinal prevalence estimates of opioid use disorder in Massachusetts 2014-2020: a multi-sample capture-recapture analysis.

Background: As overdoses continue to increase worldwide, accurate estimates are needed to understand the size of the population at risk and address health disparities. Capture-recapture methods may be used in place of direct estimation at nearly any geographic level (e.g., city, state, country) to estimate the size of the population with opioid use disorder (OUD). We performed a multi-sample capture-recapture analysis with persons aged 18-64 years to estimate the prevalence of OUD in Massachusetts from 2014 to 2020, stratified by sex and race/ethnicity. Methods: We used seven statewide administrative data sources linked at the individual level. We developed log-linear models to estimate the unknown OUD-affected population. Uncertainty was characterized using 95% confidence intervals (95% CI) on the total counts and prevalence estimates. Findings: The estimated OUD prevalence increased from 5.47% (95% CI = 4.89%, 5.98%) in 2014 to 5.79% (95% CI = 5.34%, 6.19%) in 2020. Prevalence among Hispanic females doubled (2.46% in 2014 to 4.23% in 2020) and prevalence rose to nearly 10% among Black non-Hispanic males and Hispanic males from 2014 through 2019. Estimates for Black non-Hispanic females more than doubled from 2014 through 2019 (3.39% to 7.09%), and then decreased to 5.69% in 2020. Interpretation: This study is the first to provide OUD prevalence trend estimates by binary sex and race/ethnicity at a state level using capture-recapture methods. Using these methods as the international overdose crisis worsens can allow jurisdictions to appropriately allocate resources and targeted interventions to marginalised populations. Funding: NIDA.

Estimating the size of a closed population by modeling latent and observed heterogeneity.

The paper extends the empirical likelihood (EL) approach of Liu et al. to a new and very flexible family of latent class models for capture-recapture data also allowing for serial dependence on previous capture history, conditionally on latent type and covariates. The EL approach allows to estimate the overall population size directly rather than by adding estimates conditional to covariate configurations. A Fisher-scoring algorithm for maximum likelihood estimation is proposed and a more efficient alternative to the traditional EL approach for estimating the non-parametric component is introduced; this allows us to show that the mapping between the non-parametric distribution of the covariates and the probabilities of being never captured is one-to-one and strictly increasing. Asymptotic results are outlined, and a procedure for constructing profile likelihood confidence intervals for the population size is presented. Two examples based on real data are used to illustrate the proposed approach and a simulation study indicates that, when estimating the overall undercount, the method proposed here is substantially more efficient than the one based on conditional maximum likelihood estimation, especially when the sample size is not sufficiently large.

Where the small things are: Modelling edge effects on mouse lemur population density and distribution in northwestern Madagascar.

Edge effects result from the penetration to varying depths and intensities, of abiotic and biotic conditions from the surrounding non-forest matrix into the forest interior. Although 70% of the world's forests are within 1 km of a forest edge, making edge effects a dominant feature of most forest habitats, there are few empirical data on inter-site differences in edge responses in primates. We used spatially explicit capture-recapture (SECR) models to determine spatial patterns of density for two species of mouse lemurs (Microcebus murinus and Microcebus ravelobensis) in two forest landscapes in northwestern Madagascar. The goal of our study was to determine if mouse lemurs displayed spatially variable responses to edge effects. We trapped animals using Sherman live traps in the Mariarano Classified Forest (MCF) and in the Ambanjabe Forest Fragment Site (AFFS) site within Ankarafantsika National Park. We trapped 126 M. murinus and 79 M. ravelobensis at MCF and 78 M. murinus and 308 M. ravelobensis at AFFS. For M. murinus, our top model predicted a positive edge response, where density increased towards edge habitats. In M. ravelobensis, our top model predicted a negative edge response, where density was lower near the forest edges and increased towards the forest interior. At regional and landscape-specific scales, SECR models estimated different density patterns between M. murinus and M. ravelobensis as a result of variation in edge distance. The spatial variability of our results using SECR models indicate the importance of studying the population ecology of primates at varying scales that are appropriate to the processes of interest. Our results lend further support to the theory that some lemurs exhibit a form of ecological flexibility in their responses to forest loss, forest fragmentation, and associated edge effects.

Estimation of the Population Size of Street- and Venue-Based Female Sex Workers and Sexually Exploited Minors in Rwanda in 2022: 3-Source Capture-Recapture.

BACKGROUND: HIV surveillance among key populations is a priority in all epidemic settings. Female sex workers (FSWs) globally as well as in Rwanda are disproportionately affected by the HIV epidemic; hence, the Rwanda HIV and AIDS National Strategic Plan (2018-2024) has adopted regular surveillance of population size estimation (PSE) of FSWs every 2-3 years. OBJECTIVE: We aimed at estimating, for the fourth time, the population size of street- and venue-based FSWs and sexually exploited minors aged ≥15 years in Rwanda. METHODS: In August 2022, the 3-source capture-recapture method was used to estimate the population size of FSWs and sexually exploited minors in Rwanda. The field work took 3 weeks to complete, with each capture occasion lasting for a week. The sample size for each capture was calculated using shinyrecap with inputs drawn from previously conducted estimation exercises. In each capture round, a stratified multistage sampling process was used, with administrative provinces as strata and FSW hotspots as the primary sampling unit. Different unique objects were distributed to FSWs in each capture round; acceptance of the unique object was marked as successful capture. Sampled FSWs for the subsequent capture occasions were asked if they had received the previously distributed unique object in order to determine recaptures. Statistical analysis was performed in R (version 4.0.5), and Bayesian Model Averaging was performed to produce the final PSE with a 95% credibility set (CS). RESULTS: We sampled 1766, 1848, and 1865 FSWs and sexually exploited minors in each capture round. There were 169 recaptures strictly between captures 1 and 2, 210 recaptures exclusively between captures 2 and 3, and 65 recaptures between captures 1 and 3 only. In all 3 captures, 61 FSWs were captured. The median PSE of street- and venue-based FSWs and sexually exploited minors in Rwanda was 37,647 (95% CS 31,873-43,354), corresponding to 1.1% (95% CI 0.9%-1.3%) of the total adult females in the general population. Relative to the adult females in the general population, the western and northern provinces ranked first and second with a higher concentration of FSWs, respectively. The cities of Kigali and eastern province ranked third and fourth, respectively. The southern province was identified as having a low concentration of FSWs. CONCLUSIONS: We provide, for the first time, both the national and provincial level population size estimate of street- and venue-based FSWs in Rwanda. Compared with the previous 2 rounds of FSW PSEs at the national level, we observed differences in the street- and venue-based FSW population size in Rwanda. Our study might not have considered FSWs who do not want anyone to know they are FSWs due to several reasons, leading to a possible underestimation of the true PSE.

That's not the Mona Lisa! How to interpret spatial capture-recapture density surface estimates.

Spatial capture-recapture methods are often used to produce density surfaces, and these surfaces are often misinterpreted. In particular, spatial change in density is confused with spatial change in uncertainty about density. We illustrate correct and incorrect inference visually by treating a grayscale image of the Mona Lisa as an activity center intensity or density surface and simulating spatial capture-recapture survey data from it. Inferences can be drawn about the intensity of the point process generating activity centers, and about the likely locations of activity centers associated with the capture histories obtained from a single survey of a single realization of this process. We show that treating probabilistic predictions of activity center locations as estimates of the intensity of the process results in invalid and misleading ecological inferences, and that predictions are highly dependent on where the detectors are placed and how much survey effort is used. Estimates of the activity center density surface should be obtained by estimating the intensity of a point process model for activity centers. Practitioners should state explicitly whether they are estimating the intensity or making predictions of activity center location, and predictions of activity center locations should not be confused with estimates of the intensity.

Relationship between wintering site and survival in a migratory waterbird using different migration routes.

When wintering at different sites, individuals from the same breeding population can experience different conditions, with costs and benefits that may have implications throughout their lifetime. Using a dataset from a longitudinal study on Eurasian Spoonbills from southern France, we explored whether survival rate varied among individuals using different wintering sites. In the last 13 years, more than 3000 spoonbills have been ringed as chicks in Camargue. These birds winter in five main regions that vary in both migratory flyway (East Atlantic vs. Central European) and migration distance (long-distance vs. short-distance vs. resident). We applied Cormack-Jolly-Seber models and found evidence for apparent survival to correlate with migration distance, but not with flyway. During the interval between the first winter sighting and the next breeding period, long-distance migrants had the lowest survival, independently of the flyway taken. Additionally, as they age, spoonbills seem to better cope with migratory challenges and wintering conditions as no differences in apparent survival among wintering strategies were detected during subsequent years. As dispersal to other breeding colonies was rarely observed, the lower apparent survival during this period is likely to be partly driven by lower true survival. This supports the potential role of crossing of natural barriers and degradation of wintering sites in causing higher mortality rates as recorded for a variety of long-distance migrants. Our work confirms variation in demographic parameters across winter distribution ranges and reinforces the importance of longitudinal studies to better understand the complex demographics of migratory species.

Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture-recapture models.

BACKGROUND: Improved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning. METHODS: We used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces. RESULTS: Model results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52-20.6) for females and 9.27 bears/100 km2 (95% CI 7.70-11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity. CONCLUSIONS: Density-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.

Finite mixtures in capture-recapture surveys for modeling residency patterns in marine wildlife populations.

This work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture-recapture survey data. We combine the Open Jolly-Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference, discussing the appropriate choice of priors to mitigate label-switching and nonidentifiability issues, commonly associated with finite mixture models. We conduct a series of simulation experiments to illustrate the competitive advantage of our proposal over less specific alternatives. The proposed approach is applied to data collected on the common bottlenose dolphin population inhabiting the Tiber River estuary (Mediterranean Sea). Our results provide novel insights into this population's size and structure, shedding light on some of the ecological processes governing its dynamics.

Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River.

BACKGROUND: Hidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization. METHODS: In this paper, we describe a general framework for modelling multi-state capture-recapture data via HMMs using both maximum likelihood and Bayesian methods. We then apply an HMM to invasive silver carp telemetry data from the Illinois River and compare the results estimated by both methods. RESULTS: Our analysis demonstrates disadvantages of relying on a single approach and highlights insights obtained from implementing both methods together. While both methods often struggled to converge, our results show biologically informative priors for Bayesian methods and initial values for maximum likelihood methods can guide convergence toward realistic solutions. Incorporating prior knowledge of the system can successfully constrain estimation to biologically realistic movement and detection probabilities when dealing with sparse data. CONCLUSIONS: Biologically unrealistic estimates may be a sign of poor model convergence. In contrast, consistent convergence behavior across approaches can increase the credibility of a model. Estimates of movement probabilities can strongly influence the predicted population dynamics of a system. Therefore, thoroughly assessing results from HMMs is important when evaluating potential management strategies, particularly for invasive species.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Use of Capture-Recapture Analysis to Estimate the Number of Agricultural Fatalities and Severe Agricultural Injuries in Indiana.

Conducting surveillance of agricultural injuries and fatalities in the United States has been an ongoing challenge, with many cases falling outside the criteria of national and local surveillance systems. In this research, capture-recapture analysis was used to estimate the number of fatal agricultural injuries in Indiana between 2016 and 2020. A limited analysis of non-fatal injuries is also provided. This analysis was possible because of two publicly available datasets containing incident descriptions with sufficient detail for case matching. The first dataset consisted of summary lists of fatal and nonfatal agricultural injuries in Indiana published in annual agricultural fatality reports produced by the Purdue Extension. The second data source was AgInjuryNews, which gathers reports of agricultural injuries and fatalities published in news media and other publicly available sources. Results of the capture-recapture analysis estimate that, every year in Indiana, the Purdue Extension misses 18% of fatal incidents and AgInjuryNews misses approximately 60%. AgInjuryNews identifies approximately 3 fatal incidents per year that are missed by Purdue Extension. Analysis of nonfatal incidents was limited by the fact that both data sources only included nonfatal injuries that were extremely severe and/or connected to a fatality. The Purdue Extension is estimated to miss 22% and AgInjuryNews is estimated to miss 25% of nonfatal agricultural injuries meeting that narrow definition. While capture-recapture analysis only provides estimates of true injury rates, the results provide evidence that Purdue Extension's surveillance captures most agricultural fatalities in the state. AgInjuryNews has been able to identify cases missed by Purdue, and this research takes an important step forward in quantifying how media reports found in this data source differ from extension surveillance. This research also highlights the continuing limitations in the surveillance non-fatal injuries and the ways in which publicly available data can aid researchers in filling gaps in surveillance.

Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California.

The capture-recapture method is a common tool used in epidemiology to estimate the size of "hidden" populations and correct the underascertainment of cases, based on incomplete and overlapping lists of the target population. Log-linear models are often used to estimate the population size yet may produce implausible and unreliable estimates due to model misspecification and small cell sizes. A novel targeted minimum loss-based estimation (TMLE) model developed for capture-recapture makes several notable improvements to conventional modeling: "targeting" the parameter of interest, flexibly fitting the data to alternative functional forms, and limiting bias from small cell sizes. Using simulations and empirical data from the San Francisco, California, Department of Public Health's human immunodeficiency virus (HIV) surveillance registry, we evaluated the performance of the TMLE model and compared results with those of other common models. Based on 2,584 people observed on 3 lists reportable to the surveillance registry, the TMLE model estimated the number of San Francisco residents living with HIV as of December 31, 2019, to be 13,523 (95% confidence interval: 12,222, 14,824). This estimate, compared with a "ground truth" of 12,507, was the most accurate and precise of all models examined. The TMLE model is a significant advancement in capture-recapture studies, leveraging modern statistical methods to improve estimation of the sizes of hidden populations.

Combining cross-sectional survey and register data improved the estimate of hepatitis C prevalence among patients attending a psychiatric emergency department in Denmark.

BACKGROUND: The prevalence of hepatitis C (HCV) among psychiatric patients is elevated compared to the background population in many studies, but the prevalence among Danish psychiatric patients is unknown. The aim of the study was to determine the HCV prevalence and the proportion of the psychiatric patient population that remains to be diagnosed and treated in a Danish setting. METHODS: During a 5-month period, patients attending the psychiatric emergency room in Vejle, Denmark, were offered point-of-care anti-HCV testing. Previous hepatitis C tests for all patients attending the Psychiatric Department in the study period were extracted from the national laboratory database (DANVIR). We combined the survey and register data in a capture-recapture estimate of undiagnosed patients with HCV. RESULTS: During the study 24.9% (589 of 2364) patients seen at the psychiatric department attended the emergency room. The prevalence of anti-HCV among those tested in the emergency room was 1.6%. The laboratory register identified 595/2364 patients previously tested for anti-HCV with a positive prevalence of 6.1%. The undiagnosed anti-HCV positives among the 1483 never tested was estimated to 1.1%. Thus the total estimated prevalence of anti-HCV was 2.3% (54/2364, 95% CI 1.7%-3.0%) in the population, of whom 70.4% had been diagnosed, and 72.2% of diagnosed patients had received treatment or cleared HCV. CONCLUSION: Combining survey and register data showed that the WHO target of 90% diagnosed and 80% treated was not met. To eliminate HCV in the psychiatric population, both undiagnosed and untreated patients must be targeted.

Male fertility advantage within and between seasons in the perennial androdioecious plant Phillyrea angustifolia.

BACKGROUND AND AIMS: Androdioecy, the co-occurrence of males and hermaphrodites, is a rare reproductive system. Males can be maintained if they benefit from a higher male fitness than hermaphrodites, referred to as male advantage. Male advantage can emerge from increased fertility owing to resource reallocation. However, empirical studies usually compare sexual phenotypes over a single flowering season, thus ignoring potential cumulative effects over successive seasons in perennials. In this study, we quantify various components of male fertility advantage, both within and between seasons, in the long-lived perennial shrub Phillyrea angustifolia (Oleaceae). Although, owing to a peculiar diallelic self-incompatibility system and female sterility mutation strictly associated with a breakdown of incompatibility, males do not need fertility advantage to persist in this species, this advantage remains an important determinant of their equilibrium frequency. METHODS: A survey of >1000 full-sib plants allowed us to compare males and hermaphrodites for several components of male fertility. Individuals were characterized for proxies of pollen production and vegetative growth. By analysing maternal progeny, we compared the siring success of males and hermaphrodites. Finally, using a multistate capture-recapture model we assessed, for each sexual morph, how the intensity of flowering in one year impacts next-year growth and reproduction. KEY RESULTS: Males benefitted from a greater vegetative growth and flowering intensity. Within one season, males sired twice as many seeds as equidistant, compatible hermaphroditic competitors. In addition, males more often maintained intense flowering over successive years. Finally, investment in male reproductive function appeared to differ between the two incompatibility groups of hermaphrodites. CONCLUSION: Males, by sparing the cost of female reproduction, have a higher flowering frequency and vegetative growth, both of which contribute to male advantage over an individual lifetime. This suggests that studies analysing sexual phenotypes during only single reproductive periods are likely to provide inadequate estimates of male advantage in perennials.