Find research datasets worth reusing

opencc-zeroDec 2013View details →

Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey

<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>

F I G U R E 3 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments

F I G U R E 3 (a) Posterior distribution of the difference between the true value and posterior distribution estimates (in percentage of the true value) of annual total smolt abundance obtained from DM (Dirichlet-multinomial) model M5 (simulation exercise). Each line is a year, and the 10 (one for each replicate) distributions are overlaid. The overall average difference is indicated in the left corner of the panel and represented by a red dashed vertical line. (b) C.V. of the posterior distributions of total abundance obtained from DM model M5. Each blue dot corresponds to the C.V. of a unique year/replicate, and each dashed blue horizontal line corresponds to the average C.V. of a replicate across years. The average C.V. across years and replicates (red dashed line) is also indicated in the top-right corner of the panel.

F I G U R E 4 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments

F I G U R E 4 (a–c) Posterior distribution of the relative contribution of the smolt abundance associated with each upstream rotary screw trap (RST) and (d) posterior distribution of the relative contribution of the "rest" of the smolt abundance in relation to the total abundance for the DM (Dirichletmultinomial) model M5 (simulation exercise). The red dots correspond to the true values in the simulated dataset.

F I G U R E 1 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments

F I G U R E 1 Restigouche River catchment map with main streams and lakes. Red dots indicate the location of the rotary screw traps (RST). Black polygons indicate subcatchments draining into upstream RSTs. Top-right panels: zoom on the location of the two downstream RSTs and example of a smolt with a streamer tag. Photo credit: Marie-Camille St-Amour.

F I G U R E 5 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments

F I G U R E 5 (left panels, a–e) Posterior distributions of the estimated annual catchability θt,k at each rotary screw trap (RST), (middle panels, f– h) posterior distributions of the annual smolt abundance estimates associated with each upstream RST (Nmt,i), (i) the total smolt abundance for the Restigouche catchment (Nmtot), and (right panels, j–m) posterior distributions of the annual relative proportions of the total smolt abundance t of the Restigouche catchment estimated for each upstream RST and for the unsampled areas (rest, bottom-right panel) using the Restigouche dataset. In the catchability panels (a–e), the dashed gray line is the median, and the light and dark gray areas indicate the 2.5th–97.5th and 25th– 75th interquantile ranges, respectively, of the hyperparameter μθ, that is, the average catchability of each RST of the time series. In the relative k proportions panels (f–i) the thick dashed colored line in the top three panels indicates the average proportion of the total catchment abundance estimated at the upstream RST. The thick dashed gray lines indicate the proportions of the Restigouche catchment wetted areas of the subbasins sampled at the upstream RST(j–l) and the wetted area for the remainder of the Restigouche catchment not sampled by any upstream RST (m); the proportions for the rest of the catchment area change over years based on the specific upstream RSTs that operated in a given year. For all panels, the colored dot is the median, and the thin and thick vertical segments indicate the 2.5th–97.5th and 25th–75th interquantile ranges, respectively.

opencc-zeroJul 2022View details →

Multi-surveyor capture-mark-recapture as a powerful tool for butterfly population monitoring in the pre-imaginal stage

For many elusive insect species, which are difficult to cover by standard monitoring schemes, innovative survey methods are needed to gain robust data on abundance and population trends. The recording of pre-imaginal butterfly life stages provides great potential for ecological studies and conservation monitoring. However, using counts of pre-imaginal stages for quantitative research requires detection probability to be determined. We tested different removal and capture-mark-recapture (CMR) approaches to determine the detection probability for overwintering larvae of the endangered nymphalid butterfly Limenitis reducta. Classical removal and CMR studies require movement of the organisms under study but in our approach, we replaced movement of the study organisms by random movement of multiple different surveyors. The study was conducted in three plots within a spruce clear-cut in the 'Alb-Donau' region, Germany. Our dataset provides detection data of nine/ten different surveyors per study plot. The surveyors differed in their experience ('experts' vs. 'novices'). In the R-scripts we present the analysis of the data using i) the removal method, ii) CMR approaches with varying personnel expenditure. In addition, we test the validity of our method by comparing observed and simulated detection frequencies. The results of our study indicate that multi-surveyor removal/CMR techniques are highly suitable for estimating abundance of overwintering L. reducta larvae and that the proposed methodology has several strengths: long survey period, estimates of the absolute population size accompanied by uncertainty measures, estimates of overwinter mortality. The methods from our study can be adapted and used for several different butterfly species, other insect taxa with specific immobile life-stages, and some sessile organisms, e.g. elusive plants, fungi, or corals.

opencc-by-sa-4.0Aug 2018View details →

Spatial capture-recapture data of Darwin's frogs captured between 2014-2017

Search-encounter spatial capture-recapture data from R. darwinii individuals captured between 2014-2017 at two plots located near Neltume, Southern Chile. x and y coordinates in meters are provided for each capture as text files. These are matrices with 64 columns (secondary capture occasions) and 311 rows (frogs). The 16 primary capture occasions are separated by a 3-month period, and each of these occasion is composed of four secondary capture occasions. With these data you can reconstruct the capture-history matrix used in non-spatial capture-recapture models. Snout-to-vent length (SVL) during each capture occasion are provided in mm for each frog. With these data you can reconstruct the age of the individuals (juveniles or adults). Id data is provided for each individual. The first column represents the frog’s code, and the second one represents the plot where the frog was captured (1= HUI1, 2= HUI2). Any question can be addressed to andresvalenzuela.zoo@gmail.com

opencc-by-4.0Dec 2018View details →

Spatial partial identity model for spatial capture-recapture analysis of large carnivores in Kasungu National Park, Malawi

Overview: Decline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture-recapture is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two-flank spatial partial identity model (SPIM), offers a cost-effective approach which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We used SPIM to estimate density for leopard (Panthera pardus) and spotted hyaena (Crocuta crocuta), and report on the status of other large carnivores. Usage notes: These data are to estimate density for leopard and spotted hyaena in KNP, Malawi. They are provided as an example for using the spatial partial identity model for spatial capture-recapture analysis in populations where individuals are partially identified. Methods: Individual leopards and spotted hyaena were identified from photographs using their unique pelage patterns (Henschel & Ray, 2003). A database was maintained of identified individuals, with partial (single flank) or complete (two flank) identities, to build capture histories for SCR analysis. We identified individuals from left flank captures for both species, due to higher numbers of identified left flank individuals recorded during preliminary surveys. Complete identities were added where flanks were certain to come from the same individual (from baited stations outside of survey time, live captures, dual camera trap stations and multiple passes of a single camera trap). Leopards were sexed by visual determination of external genitalia, presence of the dewlap, frontal bossing and overall body size (Henschel & Ray, 2003; Devens et al. 2018). Sexing was not possible for spotted hyaena due to difficulties in determining sex from external genitalia and body size. Capture histories were developed for spatial captures and trap effort, with each day (24 hours) treated as a separate sampling occasion (Goldberg et al. 2015). Trap effort was measured through a binary matrix of active-inactive days, to improve estimates of detection probability, and included the spatial location of each camera location. Density was modelled using the package SPIM (Augustine, 2018) in R v.3.5.2 (R Development Core Team, 2018) to resolve the complete identity of individuals from single-flank samples probabilistically (see Augustine et al. 2018 for complete description of spatial partial identity model), and a Bernoulli observation model fitted, whereby an individual may be captured in each trap only once during each sampling occasion (Royle et al. 2013; Augustine et al. 2018). For Markov Chain Monte Carlo simulations, a single chain of 50,000 iterations per single session analysis was undertaken, with a burn-in of 500 iterations and data augmentation of 100-130 individuals for leopard and 125-250 for spotted hyaena. Analysis was conducted with an increasing buffer width from 10,000 to 25,000 metres (leopard) and 10,000 to 40,000 metres (spotted hyaena), using 5,000 metre increments, until density estimates stabilised (Chase-Grey et al. 2013; Devens et al. 2018).

opencc-by-4.0Dec 2020View details →

Capture-recapture histories used in our paper "High long-term survival and asymmetric movements in a reintroduced metapopulation of cinereous vultures" published by Ecosphere

These three datasets correspond to the capture-recapture histories used in the E-surge software (in the HEADED format) for both population models (Capture_recapture_histories_ALPS.txt for the Alps and Capture_recapture_histories_CAUSSES.txt for the Causses) and the metapopulation model (Capture_recapture_histories_METAPOPULATION.txt). Columns descriptor: <ul> <li>H:O1 to H:O25 correspond to each occasion (i.e. year)</li> <li>S: correspond to the sample size (i.e. associated number of animals)</li> <li>RC: correspond to the Right Censoring ( -1 if the animal is removed at the last capture or 0 if no right censoring)</li> <li>$COV:Group correspond to the group to which the individual belongs</li> </ul> Individuals were grouped by age (from the 1st year until ≥6th years), by release status (wild-born / hacking: released juveniles before fledging / aviary: released immatures, sub-adults or adults), by origin (Causses / Alps) and if a missing ring was replaced. All details for the groups are given in the Appendix B for both population models and in the Appendix C for the metapopulation model.

opencc-zeroSep 2023View details →

Forecasting wildlife movement with spatial capture-recapture

<ol> <li>Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture-recapture modeling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time.</li> <li>We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased, and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the performance of the models' abundance estimators and movement forecasts when fit to data simulated under different movement models. We also fit the models to spatial capture–recapture data collected on North Atlantic right whales off the Atlantic Coast of the southeastern United States.</li> <li>Random walk movement models improved abundance estimation and movement forecasts in our simulation study and received greater support from the data in the right whale case study than did activity center movement models.</li> <li>Forecasts of wildlife movement made under integrated spatial capture–recapture movement models will be most valuable when individuals have been observed recently, when sampling for individuals is extensive and efficient, and when the scale of individuals' movements is small relative to the scale of the study area and sampling process. </li> </ol>