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37 results for “trap density”
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>
Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities
<p>The use of camera traps in ecology helps affordably address questions about the distribution and density of cryptic and mobile species. The Random encounter model (REM) is a camera-trap method that has been developed to estimate population densities using unmarked individuals. However, few studies have evaluated its reliability in the field, especially considering that this method relies on parameters obtained from collared animals (<i>i.e.</i> average speed, in km/h), which can be difficult to acquire at low cost and effort. Our objectives were to (1) assess the reliability of this camera-trap method and (2) evaluate the influence of parameters coming from different populations on density estimates. We estimated a reference density of black bears (<i>Ursus americanus</i>) in Forillon National Park (Québec, Canada) using a spatial capture-recapture estimator based on hair-snag stations. We calculated average speed using telemetry data acquired from four different bear populations located outside our study area and estimated densities using the REM. The reference density, determined with a Bayesian spatial capture-recapture model, was 2.87 individuals/10km<sup>2</sup> [95% CI: 2.41–3.45], which was slightly lower (although not significatively different) than the different densities estimated using REM (ranging from 4.06–5.38 bears/10km<sup>2 </sup>depending on the average speed value used). Average speed values obtained from different populations had minor impacts on REM estimates when the difference in average speed between populations was low. Bias in speed values for slow-moving species had more influence on REM density estimates than for fast-moving species. We pointed out that a potential overestimation of density occurs when average speed is underestimated, i.e. using GPS telemetry locations with large fix-rate intervals. Our study suggests that REM could be an affordable alternative to conventional spatial capture-recapture, but highlights the need for further research to control for potential bias associated with speed values determined using GPS telemetry data.</p>
Data accompanying: Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures
<p>These are the data accompanying the publication titled: <em>Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures</em></p> <p>The repository contains:</p> <ul> <li>FigurePlotting.ipynb : JupyterNotebook used to load the data and plot the Figures.</li> <li>FigureData : folder that contains the raw/analysed data needed for plotting the Figures.</li> </ul> <p>The JupyterNotebook is organised in sections corresponding to the Figures and Supplementary Figures of the last version (published) of the paper. For each Figure/SuppFigure the needed data (stored in .txt or .pkl files in the FigureData folder) is loaded and plotted.</p> <p> </p>
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: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
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Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities
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Data from: Estimating animal density without individual recognition using information derivable exclusively from camera traps
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Probing surface charge densities on optical fibers with a trapped ion
<p>We describe a novel method to measure the surface charge densities on optical fibers placed in the vicinity of a trapped ion, where the ion itself acts as the probe. Surface charges distort the trapping potential, and when the fibers are displaced, the ion’s equilibrium position and secular motional frequencies are altered. We measure the latter quantities for different positions of the fibers and compare these measurements to simulations in which unknown charge densities on the fibers are adjustable parameters. Values ranging from −10 to +50 e/µm2 were determined. Our results will benefit the design and simulation of miniaturized experimental systems combining ion traps and integrated optics, for example, in the fields of quantum computation, communication and metrology. Furthermore, our method can be applied to any setup in which a dielectric element can be displaced relative to a trapped charge-sensitive particle.</p>
Data from: Density-dependent space use affects interpretation of camera trap detection rates
<p>Camera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends.</p>
Camera trap data: Density dependence of daily activity in three ungulate species
<p><span><span><span><span><span><span><span><span><span><span><span>Daily activity in herbivores reflects a balance between finding food and safety. The safety-in-numbers theory predicts that living in higher population densities increases safety, which should affect this balance. High-density populations are thus expected to show a more even distribution of activity – i.e. spread – and higher activity levels across the day. We tested these predictions for three ungulate species; red deer (<i>Cervus elaphus</i>), roe deer (<i>Capreolus capreolus</i>) and wild boar (<i>Sus scrofa</i>). We used camera traps to measure the level and spread of activity across ten forest sites at the Veluwe, the Netherlands, that widely range in ungulate density. Food availability and hunting levels were included as covariates. Daily activity was more evenly distributed when population density was higher for all three species. Both deer species showed relatively more feeding activity in broad daylight and wild boar during dusk. Activity level increased with population density only for wild boar. Food availability and hunting showed no correlation with activity patterns. These findings indicate that ungulate activity is to some degree density dependent. However, while these patterns might result from larger populations feeling safer as the safety-in-numbers theory states, we cannot rule out that they are the outcome of greater intraspecific competition for food, forcing animals to forage during suboptimal times of the day. Overall, this study demonstrates that wild ungulates adjust their activity spread and level based on their population size.</span></span></span></span></span></span></span></span></span></span></span></p>
Tiwi Island cat density camera-trap data 2017 and 2018
<p>This data was collected as part of the National Environmental Science Program's Threatened Species Recovery Hub (Project 1.1.12 - Mitigating cat impacts on the brush-tailed rabbit-rat). This dataset includes all detections of feral cats recorded on large grids of camera-traps deployed at four locations on the Tiwi Islands. Each of these grids consisted of 70 camera-traps, deployed in 14 rows of five cameras, with each camera spaced ~500 m apart. Camera-traps remained continuously recording for eight weeks. The location of each camera-trap is also provided.</p>
Data for: Estimation of density distribution in unmarked populations using camera traps
<p>Reliable estimates of species distribution and density are essential to ecology. Camera traps have revolutionized wildlife monitoring, and camera-trap data are increasingly used to study animal distribution and density. </p> <p>We propose a general framework and present a statistical model to estimate the distribution and density of species for which individuals lack identifying marks. Numbers recorded at traps allow spatial variation in density to be modelled, while distances of detected animals from the cameras allow correction for missed animals in the detection sector, using distance sampling.</p> <p>We test the model by simulating a camera-trap survey of a population of single animals, and we apply the model to data from a field study of Reeves's muntjac. The simulation indicated that the estimates of population density were unbiased, and the model performed well in depicting spatial variation in density. In the field study, the model estimated that the overall population density of Reeves's muntjac was 4.1 ind/km<sup>2</sup>, and mapped its density distribution across the study area.</p> <p>We provide a method to estimate unmarked species' density distribution using camera-trap data. Application of the model can help investigate the distribution and density of many ground-dwelling solitary animal populations lacking individually recognizable markings. We expect our method to provide an effective means for wildlife monitoring.</p>
Tiwi Island cat density camera-trap data 2017 and 2018
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Camera trap data: Density dependence of daily activity in three ungulate species
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Data from: Density-dependent space use affects interpretation of camera trap detection rates
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Data for: Estimation of density distribution in unmarked populations using camera traps
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Neutrophil extracellular trap induction by alcohol generates unique low-density neutrophils with defective functions and impaired clearance contributing to liver damage in alcoholic hepatitis
GEO Series GSE171809. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
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