Density of a cryptic Australian small mammal: The threatened Julia Creek dunnart (Sminthopsis douglasi).

Globally, hundreds of mammal species face the threat of extinction in the coming decades, and in many cases, their ecology remains poorly understood. Fundamental ecological knowledge is crucial for effective conservation management of these species, but it is particularly lacking for small, cryptic mammals. The Julia Creek dunnart (Sminthopsis douglasi), a threatened, cryptic carnivorous marsupial that occurs in scattered populations in the central west of Queensland, Australia, was once so poorly studied that it was believed extinct. Sporadic research since its rediscovery in the early 1990s has revealed that S. douglasi is distributed across land at risk from many threats. Fundamental knowledge of S. douglasi population density is urgently required to inform conservation management at key sites, yet the species has historically proven hard to detect. Indeed, the status of the largest known population of S. douglasi, in Bladensburg National Park, is unknown. Here, we conducted a population study on S. douglasi at two sites within Bladensburg National Park via live mark-recapture surveys during 2022 and 2023. From likelihood-based spatially explicit capture-recapture (SECR) modelling we provide the first estimates of density and population size for S. douglasi. Live trapping resulted in captures of 49 individual S. douglasi (with 83 captures total, including recaptures). We estimated S. douglasi to occur at a density of 0.38 individuals ha-1 (0.25-0.58) at one site and 0.16 individuals ha-1 (0.09-0.27) at another site, with an estimated mean population size in suitable habitat at Bladensburg National Park of 1211 individuals (776-1646). Our S. douglasi density estimates were similar to that reported for other threatened small mammals in Australia. We also found evidence of extreme S. douglasi population fluctuations over time at Bladensburg National Park, which is of concern for its future conservation. Our study has provided the first estimate of density for S. douglasi, a threatened dasyurid species from the Mitchell Grass Downs of central western Queensland, Australia. Our research provides crucial population data to assist the management of this poorly studied species. We demonstrate a method that can be applied to species with low detection probability to ultimately help address the mammal extinction crisis faced by Australia and the rest of the world.

Wetland butterfly thriving in abandoned jungle: Neptis rivularis in the Czech Republic.

With ongoing insect declines, species expanding in distribution and abundance deserve attention, as understanding their success may help design conservation strategies for less successful species. Common causes of these successes include warmer climates, novel resources, and exploiting land use change, including land abandonment. These factors affect the nymphalid butterfly Neptis rivularis, developing on Spiraea spp. shrubs and reaching the north-western limits of its trans-Palearctic distribution in Central Europe. We combined mark-recapture, behaviour analysis, and distribution modelling to study N. rivularis in wetlands of the Trebonsko Protected Landscape (IUCN category V). The long-living adults (up to 4 weeks) spent a considerable amount of time searching for partners, ovipositing and nectaring at Spiraea shrubs, alternating this with stays in tree crowns, where they located cool shelters, spent nights, and presumably fed on honeydew. They formed high-density populations (310 adults/ha), exploiting high host plant abundance. They adhered to floodplains and to conditions of relatively mild winters. The ongoing Spiraea encroachment of abandoned alluvial grasslands is, thus, a transient situation, ultimately followed by forest encroachment. Rewilding the habitats by introducing native ungulates presents an opportunity to restore the disturbance regime of the sites. The increased resource supply combined with a warming climate has opened up temperate Europe to colonization by N. rivularis.

Development of underground detection system using a metal detector and aluminum tag for, Copris ochus (Coleoptera: Scarabaeidae).

Tracking of soil-dwelling insects poses greater challenges compared to aboveground-dwelling animals in terrestrial systems. A metal detector system consisting of a commercially available detector and aluminum tags was developed for detecting dung beetle, Copris ochus Motschulsky (Coleoptera: Scarabaeidae). First, detection efficacy of the system was evaluated by varying volumes of aluminum tags attached on a plastic model of the insect and also by varying angles. Then, detection efficacy was evaluated by varying depths of aluminum-tagged models under soil in 2 vegetation types. Finally, the effects of tag attachment on C. ochus adults were assessed for survivorship, burrowing depth, and horizontal movement. Generally, an increase in tag volume resulted in greater detection distance in semi-field conditions. Maximum detection distance of aluminum tag increased up to 17 cm below soil surface as the tag size (0.5 × 1.0 cm [width × length]) and thickness (16 layers) were maximized, resulting in a tag weight of 31.4 mg, comprising ca. 9% of average weight of C. ochus adult. Furthermore, the detection efficacy did not vary among angles except for 90°. In the field, metal detectors successfully detected 5 aluminum-tagged models in 20 × 10 m (W × L) arena within 10 min with detection rates ≥85% for up to depth of 10 cm and 45%-60% at depth of 20 cm. Finally, aluminum tagging did not significantly affect survivorship and behaviors of C. ochus. Our study indicates the potential of metal detector system for tracking C. ochus under soil.

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.

Long-distance dispersal in the short-distance dispersing house sparrow (Passer domesticus).

The house sparrow (Passer domesticus) is a small passerine known to be highly sedentary. Throughout a 30-year capture-mark-recapture study, we have obtained occasional reports of recoveries far outside our main metapopulation study system, documenting unusually long dispersal distances. Our records constitute the highest occurrence of long-distance dispersal events recorded for this species in Scandinavia. Such long-distance dispersals radically change the predicted distribution of dispersal distances and connectedness for our study metapopulation. Moreover, it reveals a much greater potential for colonization than formerly recorded for the house sparrow, which is an invasive species across four continents. These rare and occasional long-distance dispersal events are challenging to document but may have important implications for the genetic composition of small and isolated populations and for our understanding of dispersal ecology and evolution.

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.

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.

Juvenile survival of little owls decreases with snow cover.

Global environmental changes are associated with warmer average temperatures and more extreme weather events, potentially affecting wildlife population dynamics by altering demographic processes. Extreme weather events can reduce food resources and survival in all seasons of the year. Estimates of season-specific survival probabilities are therefore crucial to understand the moderating effect of extreme events on annual mortality. Here, we analysed survival probabilities of 307 radio-tracked juvenile little owls (Athene noctua) over two-week periods from fledging to their first breeding attempt in the following spring to assess the contribution of extreme weather events. Survival probabilities were typically lowest during the first weeks after fledging in summer but were moderated by seasonal extremes in winter. The duration of snow cover in winter had a strong negative effect on survival probability, while being food supplemented during the nestling stage increased survival during the first weeks after fledging in summer and ultimately led to a larger proportion of birds surviving the first year. Overall annual survival probability over the first year varied by 34.3% between 0.117 (95% credible interval 0.052-0.223) and 0.178 (0.097-0.293) depending on the severity of the winter, and was as high as 0.233 (0.127-0.373) for food-supplemented fledglings. In years with mild winters, the season with the lowest survival was the summer post-fledging period (0.508; 0.428-0.594), but in years with extensive snow cover the winter was the season with the lowest survival (0.481; 0.337-0.626). We therefore show that extreme weather events occurring in a particular season reduced the proportion of first-year survivors. Increasing extreme weather events can moderate seasonal survival probability through altering food supply of juvenile little owls either during the nestling period or in winter, with similarly large effects on annual survival and the viability of populations.

Estimating migration timing and abundance in partial migratory systems by integrating continuous antenna detections with physical captures.

Many populations migrate between two different habitats (e.g. wintering/foraging to breeding area, mainstem-tributary, river-lake, river-ocean, river-side channel) as part of their life history. Detection technologies, such as passive integrated transponder (PIT) antennas or sonic receivers, can be placed at boundaries between habitats (e.g. near the confluence of rivers) to detect migratory movements of marked animals. Often, these detection systems have high detection probabilities and detect many individuals but are limited in their ability to make inferences about abundance because only marked individuals can be detected. Here, we introduce a mark-recapture modelling approach that uses detections from a double-array PIT antenna system to imply movement directionality from arrays and estimate migration timing. Additionally, when combined with physical captures, the model can be used to estimate abundances for both migratory and non-migratory groups and help quantify partial migration. We first test our approach using simulation, and results indicate our approach displayed negligible bias for total abundance (less than ±1%) and slight biases for state-specific abundance estimates (±1%-6%). We fit our model to array detections and physical captures of three native fishes (humpback chub [Gila cypha], flannelmouth sucker [Catostomus latipinnis] and bluehead sucker [Catostomus discobolus]) in the Little Colorado River (LCR) in Grand Canyon, AZ, a system that exhibits partial migration (i.e. includes residents and migrants). Abundance estimates from our model confirm that, for all three species, migratory individuals are much more numerous than residents. There was little difference in movement timing between 2021 (a year without preceding winter/spring floods) and 2022 (a year with a small flood occurring in early April). In both years, flannelmouth sucker arrived in mid-March whereas humpback chub and bluehead sucker arrivals occurred early- to mid-April. With humpback chub and flannelmouth sucker, movement timing was influenced by body size so that large individuals were more likely to arrive early compared to smaller individuals. With more years of data, this model framework could be used to evaluate ecological questions pertaining to flow cues and movement timing or intensity, relative trends in migrants versus residents and ecological drivers of skipped spawning.

The effect of environmental variation on the relationship between survival and risk-taking behaviour in a migratory songbird.

Temporal changes in environmental conditions may play a major role in the year-to-year variation in fitness consequences of behaviours. Identifying environmental drivers of such variation is crucial to understand the evolutionary trajectories of behaviours in natural contexts. However, our understanding of how environmental variation influences behaviours in the wild remains limited. Using data collected over 14 breeding seasons from a collared flycatcher (Ficedula albicollis) population, we examined the effect of environmental variation on the relationship between survival and risk-taking behaviour, a highly variable behavioural trait with great evolutionary and ecological significance. Specifically, using annual recapture probability as a proxy of survival, we evaluated the specific effect of predation pressure, food availability, and mean temperature on the relationship between annual recapture probability and risk-taking behaviour (measured as flight initiation distance [FID]). We found a negative trend, as the relationship between annual recapture probability and FID decreased over the study years and changed from positive to negative. Specifically, in the early years of the study, risk-avoiding individuals exhibited a higher annual recapture probability, whereas in the later years, risk-avoiders had a lower annual recapture probability. However, we did not find evidence that any of the considered environmental factors mediated the variation in the relationship between survival and risk-taking behaviour.

Synchrony in adult survival is remarkably strong among common temperate songbirds across France.

Synchronous variation in demographic parameters across species increases the risk of simultaneous local extinction, which lowers the probability of subsequent recolonization. Synchrony therefore tends to destabilize meta-populations and meta-communities. Quantifying interspecific synchrony in demographic parameters, like abundance, survival, or reproduction, is thus a way to indirectly assess the stability of meta-populations and meta-communities. Moreover, it is particularly informative to identify environmental drivers of interspecific synchrony because those drivers are important across species. Using a Bayesian hierarchical multisite multispecies mark-recapture model, we investigated temporal interspecific synchrony in annual adult apparent survival for 16 common songbird species across France for the period 2001-2016. Annual adult survival was largely synchronous among species (73%, 95% credible interval [47%-94%] of the variation among years was common to all species), despite species differing in ecological niche and life history. This result was robust to different model formulations, uneven species sample sizes, and removing the long-term trend in survival. Synchrony was also shared across migratory strategies, which suggests that environmental forcing during the 4-month temperate breeding season has a large-scale, interspecific impact on songbird survival. However, the strong interspecific synchrony was not easily explained by a set of candidate weather variables we defined a priori. Spring weather variables explained only 1.4% [0.01%-5.5%] of synchrony, while the contribution of large-scale winter weather indices may have been stronger but uncertain, accounting for 12% [0.3%-37%] of synchrony. Future research could jointly model interspecific variation and covariation in breeding success, age-dependent survival, and age-dependent dispersal to understand when interspecific synchrony in abundance emerges and destabilizes meta-communities.

Social and genetic connectivity despite ecological variation in a killer whale network.

Philopatric kin-based societies encourage a narrow breadth of conservative behaviours owing to individuals primarily learning from close kin, promoting behavioural homogeneity. However, weaker social ties beyond kin, and across a behaviourally diverse social landscape, could be sufficient to induce variation and a greater ecological niche breadth. We investigated a network of 457 photo-identified killer whales from Norway (548 encounters in 2008-2021) with diet data available (46 mixed-diet individuals feeding on both fish and mammals, and 411 exclusive fish-eaters) to quantify patterns of association within and between diet groups, and to identify underlying correlates. We genotyped a subset of 106 whales to assess patterns of genetic differentiation. Our results suggested kinship as main driver of social bonds within and among cohesive social units, while diet was most likely a consequence reflective of cultural diffusion, rather than a driver. Flexible associations within and between ecologically diverse social units led to a highly connected network, reducing social and genetic differentiation between diet groups. Our study points to a role of social connectivity, in combination with individual behavioural variation, in influencing population ecology in killer whales.

Disentangling the influence of entanglement on recruitment in North Atlantic right whales.

North Atlantic right whales are Critically Endangered and declining, with entanglements in fishing gear a key contributor to their decline. Entanglement events can result in lethal and sub-lethal (i.e. increased energetic demands and reduced foraging ability) impacts, with the latter influencing critical life-history states, such as reproduction. Using a multi-event framework, we developed a Bayesian mark-recapture model to investigate the influence of entanglement severity on survival and recruitment for female right whales. We used information from 199 known-aged females sighted between 1977 and 2018, combined with known entanglements of varying severity that were classified as minor, moderate or severe. Severe entanglements resulted in an average decline in survival of 27% for experienced non-breeders, 9% for breeders and 26% for pre-breeding females compared with other entanglements and unentangled individuals. Surviving individuals with severe entanglements had low transitional probabilities to breeders, but surprisingly, individuals with minor entanglements had the lowest transitional probabilities, contrary to expectations underpinning current management actions. Management actions are needed to address the lethal and sub-lethal impacts of entanglements, regardless of severity classification.

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.

Changes in capture availability due to infection can lead to detectable biases in population-level infectious disease parameters.

Correctly identifying the strength of selection that parasites impose on hosts is key to predicting epidemiological and evolutionary outcomes of host-parasite interactions. However, behavioral changes due to infection can alter the capture probability of infected hosts and thereby make selection difficult to estimate by standard sampling techniques. Mark-recapture approaches, which allow researchers to determine if some groups in a population are less likely to be captured than others, can be used to identify infection-driven capture biases. If a metric of interest directly compares infected and uninfected populations, calculated detection probabilities for both groups may be useful in identifying bias. Here, we use an individual-based simulation to test whether changes in capture rate due to infection can alter estimates of three key metrics: 1) reduction in the reproductive success of infected parents relative to uninfected parents, 2) the relative risk of infection for susceptible genotypes compared to resistant genotypes, and 3) changes in allele frequencies between generations. We explore the direction and underlying causes of the biases that emerge from these simulations. Finally, we argue that short series of mark-recapture sampling bouts, potentially implemented in under a week, can yield key data on detection bias due to infection while not adding a significantly higher burden to disease ecology studies.

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.

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.

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.

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.