Open Population Spatial Capture-Recapture (OPSCR) models provide a unifying framework to simultaneously model demography and movement while accounting for imperfect detection of individuals. In OPSCR models, movements of individual home ranges between primary occasions usually follow a random walk process that neglects the role of the landscape. Here, we developed a non-Euclidean OPSCR model to explicitly estimate the extent to which home range shifts are shaped by spatial descriptors of the landscape, also referred to as landscape connectivity. We used simulations to validate the robustness of the model and then applied it to a 5-year, noninvasive genetic monitoring dataset of brown bears (Ursus arctos) in the Pyrenees mountain range (France, Spain, and Andorra). We found that male bears' home range movements were smaller close to main roads. The estimated resistance of the distance to roads was negative (-1.49 95% CrI [-2.86, -0.33]), meaning that the cost of moving their home ranges was higher close to roads. Our new OPSCR model provides a data-driven tool to assess the impact of landscape fragmentation on population connectivity at the scale of home range movement using noninvasive spatial capture-recapture data.

Abstract This study explores the proximal and biological mechanisms underlying male same-sex orientation, with a focus on the Fraternal Birth Order Effect (FBOE), a robust phenomenon whereby same-sex oriented men tend to have more older brothers than the population average, and its relationship with the Sororal Birth Order Effect (SBOE), whereby older sisters also appear to influence sexual orientation, albeit less consistently. The Maternal Immune Hypothesis (MIH), which posits that maternal immune responses to male-specific antigens accumulate across successive male pregnancies, provides a compelling proximal explanation for the FBOE, but it fails to fully account for the SBOE and other birth order patterns, such as the elevated prevalence of same-sex orientation among only-children compared to firstborns in larger sibships. Through explicit modelling of the MIH, our simulations reveal that the correlation between the number of older brothers and sisters generates a spurious SBOE, which disappears when controlling for older brothers. However, this control becomes insufficient when miscarriages are included in the simulations. Additionally, the increased prevalence of same-sex orientation among only-children, relative to firstborns with siblings, only emerges when miscarriages are incorporated into the model. Empirical analyses across eight diverse populations (Indonesia, France, French Polynesia, Greece, Canada, Czech Republic, Samoa, Iran) confirm the presence of an overall significant FBOE and, critically, an overall significant SBOE even after controlling for the number of older brothers. The higher frequency of same-sex orientation men among only-children, compared to firstborns in larger sibships, supports a possible role of miscarriage for this SBOE. However, the estimated miscarriage rates (37% - 58%) explaining the observed SBOE slightly exceeds reported rates (30% - 45%) which conservatively include early pregnancy losses. This possibly suggests that additional mechanisms contribute to a spurious SBOE or that a genuine SBOE coexists with the FBOE. Limitations of this study are discussed, as well as whether the MIH framework can be extended to accommodate these findings, or if alternative explanations are needed to resolve these discrepancies.

Death is an inherently spatial process. It happens to someone, somewhere, but often remains undetected in nature. Death is also the primary means by which humans regulate wildlife populations. Using a novel analytical method that accounts for the cryptic nature of the fate of individuals and one of the world's most comprehensive non-invasive genetic monitoring datasets, we were able to map cause-specific mortality of the entire Scandinavian grey wolf (Canis lupus) population despite the fact that most mortality events (mean = 65%; [95% CrI: 50.3-76.8%]) remained undetected. Our analysis revealed strong spatial variation in mortality with, for example, areas with a high risk of mortality linked with the current wolf management policies. Furthermore, we showed that the risk of legal mortality increased, while the risk of mortality due to causes other than legal mortality decreased with local wolf population density. This illustrates the complex interactions between spatial determinants and cause-specific mortality and therefore the importance of considering spatial variation when estimating mortality. Maps of mortality can inform wildlife management and conservation by capturing an elusive process in population dynamics as it unfolds in time and space.

Markov chain Monte Carlo (MCMC), the predominant algorithm for fitting hierarchal models to data in a Bayesian setting, relies on the ability to sample from the full conditional distributions of unobserved parameters. Covariance or precision matrices offer a unique sampling challenge due to the constraints on the elements of symmetric positive‐definite matrices. Historically, MCMC algorithms have relied upon conjugate prior‐likelihood forms for such matrices to sample directly from the conditional distribution. In the absence of conjugacy, Metropolis–Hastings sampling is problematic due to the challenge of generating suitable proposal values. Here, we develop a general Metropolis–Hastings sampling algorithm for covariance matrices, which makes proposals on an unconstrained domain through use of a bijective transformation. Our main contribution is a derivation of the Metropolis–Hastings acceptance probability for proposals made using this transformation. Pseudocode is provided for implementing our algorithm, and we also present a simulation study analyzing asymptotic performance under varying matrix dimensions and correlation structures. We present a real data example using this methodology to fit a hierarchical model for species abundance and detection using correlated random effects, for several bird species in the White Mountain National Forest, New Hampshire. Our method provides a simple and general‐purpose approach for MCMC sampling of nonconjugate covariance matrices in arbitrary hierarchical model structures.

The lion has experienced a major decline in its habitat and population size in recent years. This is explained by the transformation of its habitat and the reduction of its potential prey. Our study therefore focused on modelling its habitat and its prey and the analysis of its diet to contribute to its conservation in the Niokolo-Koba National Park (NKNP). Elevation, annual precipitation, NDVI, land cover type, distance from large water bodies, prey availability, poaching pressure and livestock pressure were used as variables in modelling the lion habitat with MaxEnt software. The model obtained with the modelling of the lion habitat performs well, with an AUC of 0.96, and indicates that the most favourable habitat for the lion is located in the centre of the park. This distribution is influenced by elevation, annual rainfall, prey availability, poaching pressure and livestock pressure.

This study quantifies the individual space use patterns of Pacific Coast Feeding Group gray whales (Eschrichtius robustus) from photographic capture-recapture data, collected in central Oregon, U.S.A., within a Bayesian framework. We evaluate the potential exposure of individuals to six anthropogenic stressors given their space use patterns. We used an 8-year dataset of spatially explicit encounter histories collected via photo-identification during continuous boat surveys to inform a Bayesian spatially explicit capture-recapture model and estimate space use of individual whales. Space use estimates were combined with exposure values of four static (distance from two ports, distance from an effluent discharge site, area of whale watching) and two dynamic (commercial Dungeness crab pots, recreational fishing) anthropogenic stressors or their proxies to estimate relative individual stressor exposure. The influence of age and sex on space use patterns and stressor exposure was assessed post hoc. Space use, and thereby stressor exposure, was highly variable among individuals, both within and between years. Some individuals displayed remarkable long-term and fine-spatial-scale site fidelity, not typically documented for large baleen whales. Juveniles concentrate their space use in a distinct area that is proximal to a port and center of whale watch activity. Exposure to stressors is highly variable across individuals and years given the heterogeneity of individual space use within the population and of stressor distribution, underscoring the complexity of managing wildlife populations. While population management plans need to be implemented at a population level, the recognition and incorporation of intraspecific variation can improve regulation efficacy since individual performance has relevant consequences on population health.

During migration, migratory bird species often aggregate at the same stopover sites due to geographical features that channel migratory routes (coasts, valleys) or locally abundant food resources (e.g. reedbeds, fruiting bushes). In migration ecology, however, stopover behavior is often studied on a single species, limiting the generality of inferences and their application to habitat management. If stopover decisions are similar across co‐occurring species, characterizing stopover ecology at a community level could provide more comprehensive insights. Using recent advances in modelling, we adapted a multi‐species capture‐recapture model to 1) quantify synchrony in stopover departure probabilities across species and 2) identify covariates driving this synchrony. We applied this model to three migratory songbirds commonly captured at French stopover sites: the sedge warbler Acrocephalus schoenobaenu s, reed warbler Acrocephalus scirpaeus and bluethroat Luscinia svecica . Departure decisions were largely synchronous across species, with time since arrival (TSA) as the primary synchronizing factor and weather conditions as a secondary influence. High synchrony in departure timing produced waves of migrant departures and consistent patterns in departure decisions across species, suggesting shared fuelling and timing strategies. Although this phenomenon has been documented via visual counts and radar, it has not been formally quantified at the species‐level using capture‐recapture methods. Our model is flexible and can test hypotheses regarding spatial synchrony in departure decisions. With decades of capture–recapture data available across Europe and North America, our approach offers new potential for studying stopover ecology at the community level, over large geographic and temporal scales.

center dot Ecologists can learn a lot about species by studying the precise locations in which they do (and do not) occur, but the location information associated with many species records is imprecise.center dot This is true of the North American Breeding Bird Survey (BBS), in which volunteer observers have surveyed birds at points along consistent routes across the United States for over 55 years.center dot As the BBS was designed for large-scale analyses, detailed location information for each bird count is not recorded.center dot We estimate location uncertainty in point count locations for BBS data and present a modeling method that accounts for this uncertainty in a way that opens new possibilities for fine-scale uses of this extensive dataset, unlocking its potential to advance the study of the relationships between birds and their immediate habitat. Las inferencias ecol & oacute;gicas a menudo se basan en las ubicaciones en las que est & aacute;n presentes las especies, pero muchos registros de especies tienen una incertidumbre sustancial en los metadatos espaciales, lo que limita su utilidad para an & aacute;lisis a escala fina. Esto es especialmente frecuente en registros hist & oacute;ricos, como los espec & iacute;menes de museos, y en algunos datos de ciencia ciudadana. Por ejemplo, el Censo de Aves Reproductoras de Am & eacute;rica del Norte (BBS por sus siglas en ingl & eacute;s) tiene m & aacute;s de 55 a & ntilde;os de datos de aves de transectos regulares ("rutas") a lo largo del continente, pero no fue dise & ntilde;ado para capturar el componente espacial de los eventos de conteo puntual, lo que limita los an & aacute;lisis de las relaciones especie-h & aacute;bitat para los cuales, de otro modo, ser & iacute;a adecuado. Presentamos una nueva metodolog & iacute;a para cuantificar la incertidumbre de la ubicaci & oacute;n en registros de BBS utilizando ubicaciones de paradas estimadas digitalizadas, derivando las correspondientes distribuciones de incertidumbre de covariables ambientales e incorporando esta informaci & oacute;n en modelos jer & aacute;rquicos de distribuci & oacute;n de especies utilizando valores bayesianos informativos previos. Este enfoque permite estimar las relaciones especie-ambiente de una manera que tiene en cuenta completamente la incertidumbre espacial subyacente. Cuantificamos la incertidumbre de las ubicaciones de parada en los datos de BBS en el centro de los Estados Unidos, modelamos las relaciones entre aves y cobertura del suelo en la parte superior del Medio Oeste, y validamos nuestro m & eacute;todo comparando las estimaciones posteriores de la cobertura del suelo con valores conocidos de covariables para un subconjunto de ubicaciones de paradas digitalizadas con GPS. Proporcionamos el c & oacute;digo para implementar este m & eacute;todo en R. Las estimaciones posteriores de la cobertura del suelo (bosques, pastizales/hierba, y cobertura desarrollada), basadas en nuestros valores informativos previos, estuvieron altamente correlacionadas con los valores conocidos de cobertura del suelo de las ubicaciones de paradas digitalizadas con GPS. Nuestro enfoque, por lo tanto, hace posible aprovechar de manera responsable grandes bases de datos hist & oacute;ricos y de ciencia ciudadana, como la BBS, para an & aacute;lisis ecol & oacute;gicos a escala fina.

Markov chain Monte Carlo (MCMC) algorithms are widely used for fitting hierarchical models to data.MCMC is the predominant tool used in Bayesian analyses to generate samples from the posterior distribution of model parameters conditional on observed data.MCMC is not a single algorithm, but rather a framework in which various sampling methods (samplers) are assigned to operate on subsets of unobserved parameters.There exists a vast set of valid samplers to draw upon, which differ in complexity, autocorrelation of samples produced, and applicability.Hamiltonian Monte Carlo [HMC; Radford M. Neal ( 2011)] sampling is one such technique, applicable to continuous-valued parameters, which uses gradients to generate large transitions in parameter space.The resulting samples have low autocorrelation, and therefore have high information content, relative for example to an equal-length sequence of highly autocorrelated samples.The No-U-Turn (NUTS) variety of HMC sampling [HMC-NUTS; Hoffman & Gelman (2014)] greatly increases the usability of HMC by introducing a recursive tree of numerical integration steps that makes it unnecessary to pre-specify a fixed number of steps.Hoffman & Gelman (2014) also introduce a self-tuning scheme for the step size, resulting in a fully automated HMC sampler with no need for manual tuning.

Research on the biological determinants of male homosexual preference has long realized that the older brother effect (FBOE, i.e., a higher fraternal birth rank of homosexuals) and the antagonist effect (AE, i.e., more fertile women have a higher chance of having a homosexual son) can both generate family data where homosexual men have more siblings and more older siblings than heterosexual men. Various statistical approaches were proposed in the recent literature to evaluate whether the action of FBOE or AE could be discriminated from empirical data, by controlling for the other effect. Here, we used simulated data to formally compare all the approaches that we could find in the relevant literature for their ability to reject the null hypothesis in the presence of a specified alternative hypothesis (tests based on regression, Bayesian modeling, or contingency tables). When testing for the FBOE, the relative performance of the different tests was different depending on the specific function generating the older brother effect. Even if no tests were found to always perform better than the others, some tests performed systematically poorly, and some tests displayed a systematic high rate of type-I error. For testing the AE, the relative performance of the tests was generally not changed across all parameter values assayed, providing a clear ranking of the various proposed approaches. Pros and cons for each candidate test are discussed, taking into consideration power and the rate of type-I error but also practicability, the possibility to control for confounding variables, and to consider alternative hypotheses.

Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.

Changes in lunar illumination alter the balance of risks and opportunities for animals, influencing activity patterns and species interactions. We examined if and how terrestrial mammals respond to the lunar cycle in some of the darkest places: the floors of tropical forests. We analysed long-term camera trapping data on 86 mammal species from 17 protected forests on three continents. Conservative categorization of activity during the night revealed pronounced avoidance of moonlight (lunar phobia) in 12 species, compared with pronounced attraction to moonlight (lunar philia) in only three species. However, half of all species in our study responded to lunar phases, either changing how nocturnal they were, altering their overall level of activity, or both. Avoidance of full moon was more common, exhibited by 30% of all species compared with 20% of species that exhibited attraction. Nocturnal species, especially rodents, were over-represented among species that avoided full moon. Artiodactyla were more prominent among species attracted to full moon. Our findings indicate that lunar phases influence animal behaviour even beneath the forest canopy. Such impacts may be exacerbated in degraded and fragmented forests. Our study offers a baseline representing relatively intact and well-protected contexts together with an intuitive approach for detecting activity shifts in response to environmental change.

Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.

Over the last decades, large-scale ecological projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large-scale population dynamics from a single monitoring program is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, integrated models combine multiple data types into a single analysis to quantify population dynamics of a targeted population. When working at large geographical scales, integrated spatial models have the potential to produce spatialised ecological estimates that would be difficult to obtain if data were analysed separately. In this paper, we illustrate how spatial integrated modelling offers a relevant framework for conducting ecological inference at large scales. Focusing on the Mediterranean bottlenose dolphins ( Tursiops truncatus ), we combined 21,464 km of photo-identification boat surveys collecting spatial capture-recapture data with 24,624 km of aerial line-transect following a distance-sampling protocol. We analysed spatial capture-recapture data together with distance-sampling data to estimate abundance and density of bottlenose dolphins. We compared the performances of the distance sampling model and the spatial capture-recapture model fitted independently, to our integrated spatial model. The outputs of our spatial integrated models inform bottlenose dolphin ecological status in the French Mediterranean Sea and provide ecological indicators that are required for regional scale ecological assessments like the EU Marine Strategy Framework Directive. We argue that integrated spatial models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales.

As air temperature increases, it has been suggested that smaller individual body size may be a general response to climate warming. However, for ectotherms inhabiting cold, highly seasonal environments, warming temperatures may increase the scope for growth and result in larger body size. In a long-term study of individual brook trout Salvelinus fontinalis and brown trout Salmo trutta inhabiting a small stream network, individual lengths increased over the course of 15 years. As size-selective gains and losses to the population acted to reduce body sizes and mean body size at first tagging in the autumn (<60 mm) were not observed to change substantially over time, the increase in body size was best explained by higher individual growth rates. For brook trout, increasing water temperatures during the spring (when both trout species accomplish most of their total annual growth) was the primary driver of growth rate for juvenile fish and the environmental factor which best explained increases in individual body size over time. For brown trout, by contrast, reduction in and subsequent elimination of juvenile Atlantic salmon Salmo salar midway through the study period explained most of the increases in juvenile growth and body size. In addition to these major trends, a considerable amount of interannual variation in trout growth and body size was explained by other abiotic (stream flow) and biotic (population density) factors with the direction and magnitude of these effects differing by season, age-class and species. For example, stream flow was the dominant growth rate driver for adult fish with strong positive effects in the summer and autumn, but flow variation could not explain increases in body size as we observed no trend in flow. Overall, our work supports the general contention that for high-latitude ectotherms, increasing spring temperatures associated with a warming climate can result in increased growth and individual body size (up to a point), but context-dependent change in other factors can substantially contribute to both interannual variation and longer-term effects.

Male homosexual orientation remains a Darwinian paradox, as there is no consensus on its evolutionary (ultimate) determinants. One intriguing feature of homosexual men is their higher male birth rank compared to heterosexual men. This can be explained by two non-exclusive mechanisms: an antagonistic effect (AE), implying that more fertile women have a higher chance of having a homosexual son and to produce children with a higher mean birth rank, or a fraternal birth effect (FBOE), where each additional older brother increases the chances for a male embryo to develop a homosexual orientation due to an immunoreactivity process. However, there is no consensus on whether both FBOE and AE are present in human populations, or if only one of these mechanisms is at play with its effect mimicking the signature of the other mechanism. An additional sororal birth order effect (SBOE) has also recently been proposed. To clarify this situation, we developed theoretical and statistical tools to study FBOE and AE independently or in combination, taking into account all known sampling biases. These tools were applied on new individual data, and on various available published data (two individual datasets, and all relevant aggregated data). Support for FBOE was apparent in aggregated data, with the FBOE increasing linearly with fertility. The FBOE was also supported in two individual datasets. An SBOE is generated when sampling in presence of FBOE, suggesting that controlling for FBOE is required to avoid artefactual SBOE. AE was not supported in individual datasets, including the analysis of the extended maternal family. The evolutionary implications of these findings are discussed.