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43 results for “distance sampling”
Data from: Estimating feral cat densities using distance sampling in an urban environment
1. Estimating feral cat population densities in urban environments can be difficult due to lack of public space and human interference. The purpose of this study was to use distance sampling in a citywide landscape to determine population size and areas of high abundance to inform trap-neuter-release management programs. 2. Line transect distance sampling was used to estimate density of the feral cat population in Windsor, Ontario from June to July 2014. Windsor has a human population of 217,188 and is about 146 km2 in size. Most transects were placed along local roads. 3. Density was estimated at about 13.3 (95% CI 9.7 – 18.1) cats per km2, and an estimated population size of 1858 cats (95% CI 1361 – 2537) with the highest relative density occurring in West and Central Windsor. 4. Urban wildlife managers could utilize these methods to monitor feral cat populations and evaluate the effectiveness of trap-neuter-release programs.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
Western Antarctic marine mammal and seabird distance sampling data
<p>These datasets are:</p> <p>1) Raw (MS Access) IFAW Logger2010 tables (<a href="http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/">http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/</a>) and</p> <p>2) .RData objects preprocessed by the R package LoggeR (<a href="https://github.com/embiuw/LoggeR">https://github.com/embiuw/LoggeR</a>), for distance sampling of marine mammals and seabirds from two ships of opportunity along the Western Antarctic Peninsula, Drake Passage and Scotia Sea during the 2019 - 2020 austral summer. The ships were the MS Fram and MS Midnatsol of the Hurtigruten fleet, with data collected from the start of December 2019 until late January 2020. </p>
Distance sampling visual observation sightings data for cetaceans from Antarctic Tourist vesssels
<p>The following is two summer sampling seasons of distance sampling data collected by trained observer teams of two from Antarctic tourist vessels. The data set contains 5 key dataframes. This data has be cleaned and quality controlled. </p> <p>Effort - details the type of visual observation effort, the observer on effort and other information </p> <p>Environment - details the environmental conditions under which the data were collected. </p> <p>Sightings - details the observations made, and distance estiamtes (relative to the ship) for cetceans. </p> <p>Resightings - used in select cases, see protocols.</p> <p>gpsData - automated collection of location data at 5 sec intervals, some gaps exists, which were interpolated later for analysis</p> <p>Work is ongoing with the dataset, please contact authors about its use. </p>
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
Aerial survey is an important, widely employed approach for estimating free‐ranging wildlife over large or inaccessible study areas. We studied how a distance covariate influenced probability of double‐observer detections for birds counted during a helicopter survey in Canada's central Arctic. Two observers, one behind the other but visually obscured from each other, counted birds in an incompletely shared field of view to a distance of 200 m. Each observer assigned detections to one of five 40‐m distance bins, guided by semi‐transparent marks on aircraft windows. Detections were recorded with distance bin, taxonomic group, wing‐flapping behavior, and group size. We compared two general model‐based estimation approaches pertinent to sampling wildlife under such situations. One was based on double‐observer methods without distance information, that provide sampling analogous to that required for mark–recapture (MR) estimation of detection probability, urn:x-wiley:20457758:media:ece34824:ece34824-math-0001, and group abundance, urn:x-wiley:20457758:media:ece34824:ece34824-math-0002, along a fixed‐width strip transect. The other method incorporated double‐observer MR with a categorical distance covariate (MRD). A priori, we were concerned that estimators from MR models were compromised by heterogeneity in urn:x-wiley:20457758:media:ece34824:ece34824-math-0003 due to un‐modeled distance information; that is, more distant birds are less likely to be detected by both observers, with the predicted effect that urn:x-wiley:20457758:media:ece34824:ece34824-math-0004 would be biased high, and urn:x-wiley:20457758:media:ece34824:ece34824-math-0005 biased low. We found that, despite increased complexity, MRD models (ΔAICc range: 0–16) fit data far better than MR models (ΔAICc range: 204–258). However, contrary to expectation, the more naïve MR estimators of urn:x-wiley:20457758:media:ece34824:ece34824-math-0006 were biased low in all cases, but only by 2%–5% in most cases. We suspect that this apparently anomalous finding was the result of specific limitations to, and trade‐offs in, visibility by observers on the survey platform used. While MR models provided acceptable point estimates of group abundance, their far higher stranded errors (0%–40%) compared to MRD estimates would compromise ability to detect temporal or spatial differences in abundance. Given improved precision of MRD models relative to MR models, and the possibility of bias when using MR methods from other survey platforms, we recommend avian ecologists use MRD protocols and estimation procedures when surveying Arctic bird populations.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
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Data from: Accommodating temporary emigration in spatial distance sampling models
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Data from: Distance sampling with camera traps
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Data from: Relationship type affects the reliability of dispersal distance estimated using pedigree inferences in partially sampled populations: a case study involving invasive American mink in Scotland
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Data from: Fine-scale sampling reveals distinct isolation by distance patterns in chum salmon (Oncorhynchus keta) populations occupying a glacially dynamic environment
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Data from: Estimating feral cat densities using distance sampling in an urban environment
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Scripts from: Performance of generalized distance sampling models with temporary emigration: a simulation study
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Data from: Hierarchical distance sampling to estimate population sizes of common lizards across a desert ecoregion
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Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
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The potential of fallow management to promote steppe bird conservation within the next EU Common Agricultural Policy reform: Distance sampling dataset on farmland bird community
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Data from: Estimating abundance of the federally endangered Mitchell’s satyr butterfly using hierarchical distance sampling
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Coalescent-based species delimitation is sensitive to geographic sampling and isolation by distance
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Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities
Distance sampling is a common survey method in wildlife studies, because it allows accounting for imperfect detection. The framework has been extended to hierarchical distance sampling (HDS), which accommodates the modelling of abundance as a function of covariates, but rare and elusive species may not yield enough observations to fit such a model. We integrate HDS into a community modelling framework that accommodates multi-species spatially replicated distance sampling data. The model allows species-specific parameters, but these come from a common underlying distribution. This form of information sharing enables estimation of parameters for species with sparse data sets that would otherwise be discarded from analysis. We evaluate the performance of the model under varying community sizes with different species-specific abundances through a simulation study. We further fit the model to a seabird data set obtained from shipboard distance sampling surveys off the East Coast of the USA. Comparing communities comprised of 5, 15 or 30 species, bias of all community-level parameters and some species-level parameters decreased with increasing community size, while precision increased. Most species-level parameters were less biased for more abundant species. For larger communities, the community model increased precision in abundance estimates of rarely observed species when compared to single-species models. For the seabird application, we found a strong negative association of community and species abundance with distance to shore. Water temperature and prey density had weak effects on seabird abundance. Patterns in overall abundance were consistent with known seabird ecology. The community distance sampling model can be expanded to account for imperfect availability, imperfect species identification or other missing individual covariates. The model allowed us to make inference about ecology of species communities, including rarely observed species, which is particularly important in conservation and management. The approach holds great potential to improve inference on species communities that can be surveyed with distance sampling.
Data from: Model selection with overdispersed distance sampling data
1. Distance sampling (DS) is a widely-used framework for estimating animal abundance. DS models assume that observations of distances to animals are independent. Non-independent observations introduce overdispersion, causing model selection criteria such as AIC or AICc to favour overly complex models, with adverse effects on accuracy and precision. 2. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor (c ̂), and associated adjusted model selection criteria (QAIC) for use with overdispersed DS data. In other contexts, a single value of c ̂ is calculated from the "global" model, i.e., the most highly-parameterized model in the candidate set, and used to calculate QAIC for all models in the set; the resulting QAIC values, and associated ΔQAIC values and QAIC weights, are comparable across the entire set. Candidate models of the DS detection function include models with different general forms (e.g., half-normal, hazard rate, uniform), so it may not be possible to identify a single global model. We therefore propose a two-step model selection procedure by which QAIC is used to select among models with the same general form, and then a goodness-of-fit statistic is used to select among models with different forms. A drawback of this approach is that QAIC values are not comparable across all models in the candidate set. 3. Relative to AIC, QAIC and the two-step model selection procedure avoided overfitting and improved the accuracy and precision of densities estimated from simulated data. When applied to six real data sets, adjusted criteria and procedures selected either the same model as AIC or a model that yielded a more accurate density estimate in 5 cases, and a model that yielded a less accurate estimate in 1 case. 4. Many DS surveys yield overdispersed data, including cue counting surveys of songbirds and cetaceans, surveys of social species including primates, and camera-trapping surveys. Methods that adjust for overdispersion during the model selection stage of DS analyses therefore address a conspicuous gap in the DS analytical framework as applied to species of conservation concern.
Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities
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Annotated Behaviour and Observability Dataset (ABODe)
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