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.

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.

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.

Increased eye size is favoured in Trinidadian killifish experimentally transplanted into low light, high competition environments.

Intraspecific variation in vertebrate eye size is well known. Ecological factors such as light availability are often correlated with shifts in relative eye size. However, experimental tests of selection on eye size are lacking. Trinidadian killifish (Anablepsoides hartii) are found in sites that differ in predation intensity. Sites that lack predators are characterized by lower light, high killifish densities, low resource availability, and intense competition for food. We previously found that killifish in sites that lack predators have evolved a larger "relative" eye size (eye size corrected for body size) than fish from sites with predators. Here, we used transplant experiments to test how selection operates on eye size when fish that are adapted to sites with predators are translocated into sites where predators are absent. We observed a significant "population × relative eye size" interaction; the relationship between relative eye size and a proxy for fitness (rates of individual growth) was positive in the transplanted fish. The trend was the opposite for resident fish. Such results provide experimental support that larger eyes enhance fitness and are favoured in environments characterized by low light and high competition.

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.

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.

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.

Towards automated animal density estimation with acoustic spatial capture-recapture.

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually, but identifying target species calls in recordings is non-trivial. Machine learning (ML) techniques can do detection quickly but may miss calls and produce false positives, i.e., misidentify calls from other sources as being from the target species. While abundance estimation methods can address the former issue effectively, methods to deal with false positives are under-investigated. We propose an acoustic spatial capture-recapture (ASCR) method that deals with false positives by treating species identity as a latent variable. Individual-level outputs from ML techniques are treated as random variables whose distributions depend on the latent identity. This gives rise to a mixture model likelihood that we maximize to estimate call density. We compare our method to existing methods by applying it to an ASCR survey of frogs and simulated acoustic surveys of gibbons based on real gibbon acoustic data. Estimates from our method are closer to ASCR applied to the dataset without false positives than those from a widely used false positive "correction factor" method. Simulations show our method to have bias close to zero and accurate coverage probabilities and to perform substantially better than ASCR without accounting for false positives.

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.

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.

Multifaceted density dependence: Social structure and seasonality effects on Serengeti lion demography.

Interactions between density and environmental conditions have important effects on vital rates and consequently on population dynamics and can take complex pathways in species whose demography is strongly influenced by social context, such as the African lion, Panthera leo. In populations of such species, the response of vital rates to density can vary depending on the social structure (e.g. effects of group size or composition). However, studies assessing density dependence in populations of lions and other social species have seldom considered the effects of multiple socially explicit measures of density, and-more particularly for lions-of nomadic males. Additionally, vital-rate responses to interactions between the environment and various measures of density remain largely uninvestigated. To fill these knowledge gaps, we aimed to understand how a socially and spatially explicit consideration of density (i.e. at the local scale) and its interaction with environmental seasonality affect vital rates of lions in the Serengeti National Park, Tanzania. We used a Bayesian multistate capture-recapture model and Bayesian generalized linear mixed models to estimate lion stage-specific survival and between-stage transition rates, as well as reproduction probability and recruitment, while testing for season-specific effects of density measures at the group and home-range levels. We found evidence for several such effects. For example, resident-male survival increased more strongly with coalition size in the dry season compared with the wet season, and adult-female abundance affected subadult survival negatively in the wet season, but positively in the dry season. Additionally, while our models showed no effect of nomadic males on adult-female survival, they revealed strong effects of nomads on key processes such as reproduction and takeover dynamics. Therefore, our results highlight the importance of accounting for seasonality and social context when assessing the effects of density on vital rates of Serengeti lions and of social species more generally.

Environmental heterogeneity modifies the link between personality and survival in fluctuating small mammal populations.

Despite numerous studies examining the fitness consequences of animal personalities, predictions concerning the relationship between personality and survival are not consistent with empirical observations. Theory predicts that individuals who are risky (i.e. bold, active and aggressive) should have higher rates of mortality; however, empirical evidence shows high levels of variation in behaviour-survival relationships in wild populations. We suggest that this mismatch between predictions under theory and empirical observations results from environmental contingencies that drive heterogeneity in selection. This uncertainty may constrain any universal directional relationships between personality traits and survival. Specifically, we hypothesize that spatiotemporal fluctuations in perceived risk that arise from variability in refuge abundance and competitor density alter the relationship between personality traits and survival. In a large-scale manipulative experiment, we trapped four small mammal species in five subsequent years across six forest stands treated with different management practices in Maine, United States. Stands all occur within the same experimental forest but contain varying amounts of refuge and small mammal densities fluctuate over time and space. We quantified the effects of habitat structure and competitor density on the relationship between personality traits and survival to assess whether directional relationships differed depending on environmental contingencies. In the two most abundant species, deer mice and southern red-backed voles, risky behaviours (i.e. higher aggression and boldness) predicted apparent monthly survival probability. Mice that were more aggressive (less docile) had higher survival. Voles that were bolder (less timid) had higher survival, but in the risky forest stands only. Additionally, traits associated with stress coping and de-arousal increased survival probability in both species at high small mammal density but decreased survival at low density. In the two less abundant study species, there was no evidence for an effect of personality traits on survival. Our field experiment provides partial support for our hypothesis: that spatiotemporal fluctuations in refuge abundance and competitor density alter the relationship between personality traits and survival. Our findings also suggest that behaviours associated with stress coping and de-arousal may be subject to density-dependent selection and should be further assessed and incorporated into theory.

Early-life variation in migration is subject to strong fluctuating survival selection in a partially migratory bird.

Population dynamic and eco-evolutionary responses to environmental variation and change fundamentally depend on combinations of within- and among-cohort variation in the phenotypic expression of key life-history traits, and on corresponding variation in selection on those traits. Specifically, in partially migratory populations, spatio-seasonal dynamics depend on the degree of adaptive phenotypic expression of seasonal migration versus residence, where more individuals migrate when selection favours migration. Opportunity for adaptive (or, conversely, maladaptive) expression could be particularly substantial in early life, through the initial development of migration versus residence. However, within- and among-cohort dynamics of early-life migration, and of associated survival selection, have not been quantified in any system, preventing any inference on adaptive early-life expression. Such analyses have been precluded because data on seasonal movements and survival of sufficient young individuals, across multiple cohorts, have not been collected. We undertook extensive year-round field resightings of 9359 colour-ringed juvenile European shags Gulosus aristotelis from 11 successive cohorts in a partially migratory population. We fitted Bayesian multi-state capture-mark-recapture models to quantify early-life variation in migration versus residence and associated survival across short temporal occasions through each cohort's first year from fledging, thereby quantifying the degree of adaptive phenotypic expression of migration within and across years. All cohorts were substantially partially migratory, but the degree and timing of migration varied considerably within and among cohorts. Episodes of strong survival selection on migration versus residence occurred both on short timeframes within years, and cumulatively across entire first years, generating instances of instantaneous and cumulative net selection that would be obscured at coarser temporal resolutions. Further, the magnitude and direction of selection varied among years, generating strong fluctuating survival selection on early-life migration across cohorts, as rarely evidenced in nature. Yet, the degree of migration did not strongly covary with the direction of selection, indicating limited early-life adaptive phenotypic expression. These results reveal how dynamic early-life expression of and selection on a key life-history trait, seasonal migration, can emerge across seasonal, annual, and multi-year timeframes, yet be substantially decoupled. This restricts the potential for adaptive phenotypic, microevolutionary, and population dynamic responses to changing seasonal environments.

Path analysis reveals combined winter climate and pollution effects on the survival of a marine top predator.

Marine ecosystems are experiencing growing pressure from multiple threats caused by human activities, with far-reaching consequences for marine food webs. Determining the effects of multiple stressors is complex, in part, as they can affect different aspects of biological organisation (behaviour, individual traits and demographic rates). Determining the combined effects of stressors, through different biological pathways, is key to predict the consequences for the viability of populations threatened by global change. Due to their position in the food chain, top predators such as seabirds are considered more sensitive to environmental changes. Climate change is affecting the prey resources available for seabirds, through bottom-up effects, while organic pollutants can bioaccumulate in food chains with the greatest impacts on top predators. However, knowledge of their combined effects on population dynamics is scarce. Using a path analysis, we quantify the effects of climate change and pollution on the survival of adult great black-backed gulls, both directly and through effects of individuals' body mass. Warmer ocean temperatures in gulls' winter foraging areas in the North Sea were correlated with higher survival, potentially explained by shifts in prey availability associated with global climate change. We also found support for indirect negative effects of organochlorines, highly toxic pollutants to seabirds, on survival, which acted, in part, through a negative effect on body mass. The results from this path analysis highlight how, even for such long-lived species where variance in survival tends to be limited, two stressors still have had a marked influence on adult survival and illustrate the potential of path models to improve predictions of population variability under multiple stressors.

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.

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.

Estimating the Prevalence of Dementia in New Zealand Using Capture-Recapture Analysis on Routinely Collected Health Data.

OBJECTIVES: Community based dementia prevalence studies are expensive and resource intensive. Aotearoa New Zealand (NZ) has never had a community based dementia prevalence study representing all major ethnic groups. In recent years, dementia prevalence estimates have been derived from routinely collected health data but issues of underdiagnosis and undercoding limit their utility. Capture-recapture techniques can estimate the number of dementia cases missing from health datasets by modelling the ascertained overlaps between linked data sources. METHODS: Three routinely collected national health data sets-interRAI, Public hospital discharges, and Pharmaceuticals-were linked and all prevalent cases of dementia in NZ for the year 1 January 2021-31 December 2021 were identified. Capture-recapture analysis fitted eight loglinear models to the data, with the best fitting model used to estimate the number of prevalent cases missing from all three datasets. RESULTS: We estimated that almost half (47.8%) of dementia cases are not present in any of the three datasets. Dementia prevalence increased from 3.7% to 7.1% (95% CI 6.9%-7.4%) in the NZ 60+ population and from 4.9% to 9.2% (95% CI 8.9%-9.6%) in the NZ 65+ population when missing cases were included. Estimates of missing cases were significantly higher (p < 0.001) in Maori (49.2%), Pacific peoples (50.6%) and Asian (59.6%) compared to Europeans (46.4%). CONCLUSIONS: This study provides updated estimates of dementia prevalence in NZ and the proportion of undiagnosed dementia in NZ, highlighting the need for better access to dementia assessment and diagnosis.

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.

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.