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98 results for “Capture recapture”
Multi-event capture-recapture analysis in Alpine chamois reveals contrasting responses to interspecific competition, within and between populations
<p>1. Understanding components of interspecific competition has long been a major goal in ecological studies. Classical models of competition typically consider equal responses of all individuals to the density of competitors, however responses may differ both among individuals from the same population, and between populations.</p> <p>2. Based on individual long-term monitoring of two chamois populations in sympatry with red deer, we built a multi-event capture-recapture model to assess how vital rates of the smaller chamois are affected by competition from the larger red deer.</p> <p>3. In both populations, mortality and breeding probabilities of female chamois depend on age and in most cases, of breeding status the preceding year. Successful breeders always performed better the next year, indicating that some females are of high quality. In one population where there was high spatial overlap between the two species, the survival of old female chamois that were successful breeders the preceding year (high-quality) was negatively related to an index of red deer population size suggesting that they tend to skip reproduction instead of jeopardizing their own survival when the number of competitors increases. The breeding probability of young breeders (ages 2 and 3) was similarly affected by red deer population size. In contrast, in the second site with low spatial overlap between the two species, the vital rates of female chamois were not related to red deer population size.</p> <p>4. We provide evidence for population-specific responses to interspecific competition and more generally, for context-, age- and state-dependent effects of interspecific competition.</p> <p>5. Our results also suggest that the classical assumption of equal responses of all individuals to interspecific competition should be relaxed, and emphasize the need to move towards more mechanistic approaches to better understand how natural populations respond to changes in their environment.</p>
Data from: Estimating population size in the presence of temporary migration using a joint analysis of telemetry and capture recapture data
1.Temporary migration – where individuals can leave and re-enter a sampled population – is a feature of many capture–mark–recapture (CMR) studies of mobile populations which, if unaccounted for, can lead to biased estimates of population capture probabilities and consequently biased estimates of population abundance. 2. We present a method for incorporating radiotelemetry data within a CMR study to eliminate bias due to temporary migration using a Bayesian state-space model. 3. Our results indicate that using a relatively small number of telemetry tags, it is possible to greatly reduce bias in estimates of capture probabilities using telemetry data to model transition probabilities in and out of the sampling area. In a capture–recapture data set for trout Cod in the Murray river, Australia, accounting for temporary migration led to overall higher estimates of capture probabilities than models assuming permanent or zero migration. Also, individual heterogeneity in detectability can be managed through explicit modelling. We show how accounting for temporary migration when estimating capture probabilities can be used to estimate the abundance and size distribution of a population as though it were closed. 4. Our model provides a basis for more complex models that might integrate telemetry data into other CMR scenarios, thus allowing for greater precision in estimates of vital rates that might otherwise be biased by temporary migration. Our results highlight the importance of accounting for migration in survey design and parameter estimation, and the potential scope for supplementing large-scale CMR data sets with a subset of auxiliary data that provide information on processes that are hidden to primary sampling processes.
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).
Data from: Reliable effective number of breeders/adult census size ratios in seasonal-breeding species: opportunity for integrative demographic inferences based on capture-mark-recapture data and multilocus genotypes
The ratio of the effective number of breeders (Nb) to the adult census size (Na), Nb/ Na, approximates the departure from the standard capacity of a population to maintain genetic diversity in one reproductive season. This information is relevant for assessing population status, understanding evolutionary processes operating at local scales and unraveling how life-history traits affect these processes. However, our knowledge on Nb/Na ratios in nature is limited because estimation of both parameters is challenging. The sibship frequency (SF) method is adequate for reliable Nb estimation because it is based on sibship and parentage reconstruction from genetic marker data, thereby providing demographic inferences that can be compared with field-based information. In addition, capture-mark-recapture (CMR) robust design methods are well suited for Na estimation in seasonal-breeding species. We used tadpole genotypes of three pond-breeding amphibian species (Epidalea calamita, Hyla molleri and Pelophylax perezi, n = 73-96 single-cohort tadpoles / species genotyped at 15-17 microsatellite loci) and candidate parental genotypes (n = 94-300 adults / species) to estimate Nb by the SF method. To assess the reliability of Nb estimates, we compared sibship and parentage inferences with field-based information and checked for the convergence of results in replicated subsampled analyses. Finally, we used CMR data from a 6-year monitoring program to estimate annual Na in the three species and calculate the Nb/Na ratio. Reliable ratios were obtained for E. calamita (Nb/Na = 0.18-0.28) and P. perezi (0.5), but in H. molleri Na could not be estimated and genetic information proved insufficient for reliable Nb estimation. Integrative demographic studies taking full advantage of SF and CMR methods can provide accurate estimates of the Nb/Na ratio in seasonal-breeding species. Importantly, the SF method provides results that can be readily evaluated for reliability. This represents a good opportunity for obtaining robust demographic inferences with wide applications for evolutionary and conservation research.
Data from: Examining disease prevalence for species of conservation concern using non-invasive spatial capture-recapture techniques
1. Non-invasive techniques have long been used to estimate wildlife population abundance and density. However, recent technological breakthroughs have facilitated non-invasive estimation of the proportion of animal populations with certain diseases. Giraffes Giraffa camelopardalisare increasingly becoming recognized as a species of conservation concern with decreasing population trajectories across their range in Africa. 2. Diseases may be an important component impacting giraffe population declines, and the emerging 'Giraffe Skin Disease' (GSD), characterized by the appearance of wrinkled skin and alopecic lesions on the limbs, neck, and chest of infected giraffe, may hinder movement causing increased susceptibility to predation. 3. We examined the prevalence of GSD in Tanzania's Ruaha National Park over a 4-month period in 2015, using photographic capture–recapture surveys via road-based transects. We divided the study area into five circuitous survey units, each approximately 100 km in length ($\bar x$ = 99.22 km, SD = 3.72), and surveyed for giraffes for four months. From these surveys, we developed a database of spatially-explicit giraffe photographs. 4. We processed these photos for individual identification and fitted spatial capture–recapture models to predict the spatial configuration of giraffe abundance and GSD prevalence within the study area. 5. Our results indicated that >86% of the giraffe population showed signs of GSD and that the disease was more prevalent in the northern and north-eastern portion of Ruaha National Park. 6. Synthesis and applications. Our research shows that data from non-invasive surveys can be used in spatial capture–recapture (SCR) models to estimate the proportion of a population affected by a visible disease. Researchers and conservationists can use SCR models to better examine the variation in parameters associated with these populations such as sex and age class, movement, and encounter rate, which may be linked to the prevalence of the disease, while incorporating broad spatial and temporal dimensions of the population in such areas. We discuss the implications of this research for conservation of threatened species with an emphasis on disease ecology and vulnerability to predations, and more broadly, for wildlife conservation.
Data from: Analyzing movement behavior and dynamic space-use strategies among habitats using multi-event capture-recapture modeling
1.The environment of most species is heterogeneous at different spatial and temporal scales; this heterogeneity can have a direct effect on various components of fitness. As a consequence, individual space-use and movement strategies are central issues in ecology and conservation and receive considerable attention from researchers. 2.In the last 30 years, this issue has led to the development of capture–recapture models that allow movement between sites to be quantified, while handling imperfect detection. For studies involving numerous recapture sites in which the emphasis is on dispersal or migration rather than movement between particular sites, Lagrange et al. recently proposed a parsimonious CR multi-event model that contrasts individuals that move and individuals that stay in place, irrespective of the sites involved. 3.In this study, we developed a generalized version of this model to allow survival probability and movement probability to differ for different types of habitat to which the individual sites may be assigned. We investigated the potential of this new parameterization by studying the movements of an amphibian, the yellow-bellied toad (Bombina variegata), in a set of breeding and resting/foraging ponds. 4.Our capture-recapture multi-event model provides a highly flexible tool allowing users to model movements within and between several habitats. This approach can be potentially used to study movement behavior and space-use strategies of a wide range of taxa.
Data from: An R package for analyzing survival using continuous-time open capture-recapture models
Capture–recapture software packages have proven to be very powerful tools for analysing factors affecting survival in wild populations. However, all such packages are limited to discrete-time protocols. Appropriate survival analysis tools are still lacking for data acquired from continuous-time protocols. We have developed a statistical method and propose an r package for analysing such data based on an extension of classical survival analysis models incorporating an inhomogeneous Poisson process for modelling capture histories. First, data were simulated from a continuous-time protocol. These data were used to (i) compare survival estimation biases of discrete- and continuous-time approaches and (ii) investigate the performance and accuracy of our r package for four types of covariates: factors varying between individuals (like sex), in time (like climatic factors), both in time and between individuals (like physical condition) and age (as a categorical factor). Secondly, the r package has been applied to a real data set for survival analysis of cats in the Kerguelen archipelago (regrouping 682 cats over 20 years) as an illustrative example. Results of the simulated data analysis show that the method performs better than its discrete-time counterpart for analysing data acquired from continuous-time protocols. It provides unbiased parameter estimates for all parameters except those that vary both in time and between individuals – which is not surprising, since in our case, these factors were not updated in continuous time (i.e. only upon capture). When applied to the Kerguelen cat data set, the results suggest that survival is lower in juveniles than in adults and subadults, varies between study sites and increases with physical condition, and this latter effect being more important in females than in males. Sex, season, temporal linear trend in survival and the NDVI vegetation index were also tested but were not found to be significant. However, confidence intervals were too large (due to a low recapture rate) for excluding such effects. Further analyses are still needed for rigorous covariate testing in this context. In conclusion, continuous-time approaches – such as that presented in this paper – should be preferred when data acquired from continuous-time protocols is analysed.
Data from: Markov-modulated Poisson processes as a new framework for analyzing capture-recapture data
1.Opportunistic capture-recapture data consists of observations over non-constant time-intervals and so fails to satisfy the basic assumptions of traditional capture-recapture models. Analyzing opportunistic capture-recapture data is often done by discretizing time-intervals or summarizing data, but without taking into account the continuous-time process of the state and/or the capture. 2.To deal with non-constant time-intervals, continuous-time closed capture-recapture models have been proposed by Yip et al. (1996), Hwang & Chao (2002), Schofield et al. (2017) for estimating population size. More recently, a continuous-time Cormack-Jolly-Seber model has been proposed by Fouchet et al. (2016) to reduce bias in survival rates, and a two-state process has been proposed by Choquet et al. (2017) to estimate reproduction rates and survival rates of young within a season. 3.The aim of the current study is to demonstrate how an approach based on a Markov-modulated Poisson process (MMPP) (Freed & Shepp, 1982) allows, in a similar way to a multistate model, to model opportunistic data using several states. To this end, several multistate models were rewritten as MMPP models, showing, the potential for this approach to address the ecological questions as multistate models, but using an extended data framework. In particular, it is a useful framework for dealing with data that has unordered levels of uncertainty. 4.The methods were illustrated using simulations and analysis of data on the Alpine ibex (Capraibex).
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture
1. Spatial capture-recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional non-spatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modelling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. 2. To mitigate the computational burden of large-scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state-space (LESS). Based on prior knowledge about a species' home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. 3. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half-normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57 fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore – the wolverine (Gulo gulo) – with an unprecedented resolution and across the species' entire range in Norway (more than 200 000 km2). 4. Our approach helps overcome a major computational obstacle to population and landscape-level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners that are working at scales that are relevant for conservation and management.
Data from: Applying the multistate capture-recapture robust design to characterize metapopulation structure
1. Population structure must be considered when developing mark-recapture (MR) study designs as the sampling of individuals from multiple populations (or subpopulations) may increase heterogeneity in individual capture probability. Conversely, the use of an appropriate MR study design which accommodates heterogeneity associated with capture-occasion varying covariates due to animals moving between 'states' (i.e. geographic sites) can provide insight into how animals are distributed in a particular environment and the status and connectivity of subpopulations. 2. The Multistate Closed Robust Design was chosen to investigate: 1) the demographic parameters of Indo-Pacific bottlenose dolphins (Tursiops aduncus) subpopulations in coastal and estuarine waters of Perth, Western Australia; and 2) how they are related to each other in a metapopulation. Using four years of year-round photo-identification surveys across three geographic sites, we accounted for heterogeneity of capture probability based on how individuals distributed themselves across geographic sites and characterized the status of subpopulations based on their abundance, survival and interconnection. 3. MSCRD models highlighted high heterogeneity in capture probabilities and demographic parameters between sites. High capture probabilities, high survival and constant abundances described a subpopulation with high fidelity in an estuary. In contrast, low captures, permanent and temporary emigration and fluctuating abundances suggested transient use and low fidelity in an open coastline site. 4. Estimates of transition probabilities also varied between sites, with estuarine dolphins visiting sheltered coastal embayments more regularly than coastal dolphins visited the estuary, highlighting some dynamics within the metapopulation. 6. Synthesis and applications. To date, bottlenose dolphin studies using mark-recapture approach have focussed on investigating single subpopulations. Here, in a heterogeneous coastal-estuarine environment, we demonstrated that spatially structured bottlenose dolphin subpopulations contained distinct suites of individuals and differed in size, demographics and connectivity. Such insights into the dynamics of a metapopulation can assist in local-scale species conservation. The MSCRD approach is applicable to species/populations consisting of recognizable individuals and is particularly useful for characterizing wildlife subpopulations that vary in their vulnerability to human activities, climate change or invasive species.
Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus
Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.
Genotyping validates the efficacy of photographic identification in a capture-mark-recapture study based on the head scale patterns of the prairie lizard (Sceloporus consobrinus)
Population studies often incorporate capture-mark-recapture (CMR) techniques to gather information on long-term biological and demographic characteristics. A fundamental requirement for CMR studies is that an individual must be uniquely and permanently marked to ensure reliable reidentification throughout its lifespan. Photographic identification involving automated photographic identification software has become a popular and efficient non-invasive method for identifying individuals based on natural markings. However, few studies have a) robustly assessed the performance of automated programs by using a double-marking system or b) determined their efficacy for long-term studies by incorporating multi-year data. Here, we evaluated the performance of the program Interactive Individual Identification System (I<sup>3</sup>S) by cross-validating photographic identifications based on the head scale pattern of the prairie lizard (<i>Sceloporus consobrinus</i>) with individual microsatellite genotyping (N=863). Further, we assessed the efficacy of the program to identify individuals over time by comparing error rates between within-year and between-year recaptures. Recaptured lizards were correctly identified by I<sup>3</sup>S in 94.1% of cases. We estimated a false rejection rate (FRR) of 5.9% and a false acceptance rate (FAR) of 0%. By using I<sup>3</sup>S we correctly identified 97.8% of within-year recaptures (FRR=2.2%; FAR=0%) and 91.1% of between-year recaptures (FRR=8.9%; FAR=0%). Misidentifications were primarily due to poor photo quality (N=4). However, two misidentifications were caused by indistinct scale configuration due to scale damage (N=1) and ontogenetic changes in head scalation between capture events (N=1). We conclude that automated photographic identification based on head scale patterns is a reliable and accurate method for identifying individuals over time. Because many lizard or reptilian species possess variable head squamation, this method has potential for successful application in many species.
Data from: Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks
<p>River otters (Lontra canadensis) were extirpated from New Mexico by the 1950s. A limited reintroduction occurred during 2008–2010 in which 33 otters sourced from Washington (WA) were translocated to the Upper Rio Grande Basin (URG) of New Mexico. We conducted a noninvasive genetic capture-recapture survey during the winter of 2018 by collecting fecal DNA samples from river otter scats found at latrines in the URG dendritic network of perennial waterways. Our objectives were to: 1) estimate genetic diversity and effective population size; 2) genetic divergence from the WA source population and potential connectivity with regionally proximal populations; 3) spatially explicit population density and size; and 4) population growth rate since the founder event. Between February and April 2018, we collected 1,184 fecal DNA samples from 622 individual scats at 20 latrines; genotyping was attempted at 10 otter-specific microsatellite loci for a subsample of 543 samples. A bottlenecking founder effect was strongly supported, which, combined with genetic drift, reduced genetic diversity and effective population size by 20–26% and 106–170%, respectively, compared with the WA source population. Estimated population density from spatial capture-recapture models was 0.23–0.28 otter/km of waterway, or 1 otter/3.57–4.35 km of waterway, corresponding to a total population size of 83–100 otters across 359 km of the perennial dendritic network from La Mesilla, New Mexico to Alamosa National Wildlife Refuge, Colorado. Estimated average annual population growth rate since the founder event was 1.12–1.15/year. Despite successful population establishment, the URG river otter population remains small, is genetically degraded, and does not yet meet the criteria for long-term reintroduction success. Projections suggested that the population could reach the recommended minimum viable population size of ≥400 otters by the years 2030–2033, though sufficient habitat may not exist in the URG Basin to support that many otters. </p>
Spatial capture-recapture data of the Darwin's frog
<p>These data and code are intentended to serve as complementary information to allow the full reproducibility of the results presented in the study "Natal dispersal is facilitated by routine movements integrated along a straight path in the Darwin’s frog" submitted to Ecography.</p> <p>The first file titled "data and code used to run the sCJS.rar" contains capture-recapture data from three populations of the Darwin's frog (<em>Rhinoderma darwinii</em>) collected between March 2014 and December 2016. The age of the individuals, population of origin, and x- and y-coordinates of each capture are provided. The x- and y-coordinates are in meters. The sampling design follows the Pollock's robust design. The capture-history matrices (one for the x-coordinates and another for the y-coordinates) contain 48 columns representing twelve evenly distributed primary capture periods, each one composed of four consecutive days of capture (secondary capture ocassions). The time-period between primary capture periods is 3 months. However, note that we did not visited the populations during all these periods, therefore, some of collumns contain only NA's. The rows of the capture-history matrices represent the different individuals that were captured across the study. There are 370 rows, but for seven of these individuals we don't have spatial data (these individuals are removed in the code). Then we provide the R code used to run the sCJS of Schaub and Royle (2014). This code is almost identical to the provided by these authors, but a few modifications were made in order to fit our study design. Results of the model are also provided.</p> <p>The second file "Data required to run the simulation model.rar" contains the data required to run the movement path simulation model presented in the Supplementary material, Appendix 2 of the paper. Further details about these files and model can be found in the article.</p> <p>Any question can be adressed to andresvalenzuela.zoo@gmail.com</p> <p>Enjoy it!</p>
Data from: Using spatial capture–recapture to elucidate population processes and space-use in herpetological studies
The cryptic behavior and ecology of herpetofauna make estimating the impacts of environmental change on demography difficult; yet, the ability to measure demographic relationships is essential for elucidating mechanisms leading to the population declines reported for herpetofauna worldwide. Recently developed spatial capture–recapture (SCR) methods are well suited to standard herpetofauna monitoring approaches. Individually identifying animals and their locations allows accurate estimates of population densities and survival. Spatial capture–recapture methods also allow estimation of parameters describing space-use and movement, which generally are expensive or difficult to obtain using other methods. In this paper, we discuss the basic components of SCR models, the available software for conducting analyses, and the experimental designs based on common herpetological survey methods. We then apply SCR models to Red-backed Salamander (Plethodon cinereus), to determine differences in density, survival, dispersal, and space-use between adult male and female salamanders. By highlighting the capabilities of SCR, and its advantages compared to traditional methods, we hope to give herpetologists the resource they need to apply SCR in their own systems.
Data from: Survival estimates strongly depend on capture-recapture designs in a disturbed environment inducing dispersal
Capture‐Recapture (CR) approaches are extensively used to estimate demographic parameters. Their robustness relies on the selection of suitable statistical models, but also on the sampling design and effort deployed in the field. In colonial or territorial species showing breeding site fidelity, recurrent local perturbations, such as predation‐induced breeding failure, may lead individuals to disperse locally or regionally. This might induce heterogeneity in individual CR histories and biases in demographic parameter estimates. Here, we assessed the effect of buffer areas and multi‐site designs on annual survival estimate accuracy and precision. First, using simulated data with and without a multi‐site design, we compared survival estimates for different scenarios involving contrasted local and regional dispersal. Then, using data from a local multi‐site long‐term monitoring survey carried out in a colony of black‐legged kittiwakes, we tested the homogeneity of individual CR histories and compared survival estimates from three datasets including one or several breeding cliffs with or without buffer areas. Results from simulations highlighted that a single‐site design consistently led to underestimated or less precise survival values compared to a multi‐site design. Similarly, a multi‐site design allowed detecting significant temporal trends while a single‐site one did not. In the field‐based study, adding buffer areas reduced individual heterogeneity, but all three datasets suffered from strong trap‐dependence possibly due to individual breeding success affecting nest‐site fidelity. Implementing a multi‐site design significantly improved the accuracy and precision of annual survival estimates, regardless of CR models applied. Adapting the spatial scale of sampling design to the local environment and species behaviour is essential to provide robust parameters of key relevance for population monitoring and management. We show that implementing buffer areas and/or multi‐site designs may be especially beneficial for long‐lived species facing regular local perturbation events leading to local dispersal.
Data from: Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data
<p>Population dynamics models combine density-dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density-dependence. This is typically addressed using state-space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture-recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state-space models.</p> <p>We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state-space models for density-dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model, using a conditional multinomial likelihood, followed by a Horvitz-Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R-package VGAM, for different parameter specifications. The methods were then applied to a real dataset of gray-sided voles <em>Myodes rufocanus</em>from Northern Norway.</p> <p>We found that density-dependence estimation was improved when explicitly modelling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.</p>
Capture-recapture dataset of Svalbard voles (1990-2007) with trap locations and rain-on-snow measurements
<p>Ecologists are still puzzled by the diverse population dynamics of herbivorous small mammals that range from high-amplitude, multi-annual cycles to stable dynamics. Theory predicts that this diversity results from combinations of climatic seasonality, weather stochasticity and density-dependent food web interactions. The almost ubiquitous 3-5-yr cycles in boreal and arctic climates may theoretically result from bottom-up (plant-herbivore) and top-down (predator-prey) interactions. Assessing empirically the roles of such interactions, and how they are influenced by environmental stochasticity, has been hampered by food web complexity. Here, we take advantage of a uniquely simple High-Arctic food web, which allowed us to analyze dynamics of a graminivorous vole population not subjected to top-down regulation. This population exhibited high-amplitude, non-cyclic fluctuations - partly driven by weather stochasticity. However, the predominant driver of the dynamics was overcompensatory density dependence in winter that caused the population to frequently crash. Model simulations showed that the seasonal pattern of density dependence would yield regular 2-year cycles in absence of stochasticity. While such short cycles have not yet been observed in mammals, they are theoretically plausible if graminivorous vole populations are deterministically bottom-up regulated. When incorporating weather stochasticity in the model simulations, cyclicity became disrupted and the amplitude was increased - akin to the observed dynamics. Our findings contrast with the 3-5-yr population cycles that are typical of graminivorous small mammals in more complex food webs, suggesting that top-down regulation is normally an important component of such dynamics.</p>
Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling
<p>Data and scripts for our paper:</p> <p>Lindsø, L.K., Dupont, P., Rød-Eriksen, L. <em>et al.</em> Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling. <em>Oecologia</em> <strong>198</strong>, 139–151 (2022). https://doi.org/10.1007/s00442-021-05087-3</p>
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Allen Brain Atlas
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ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.