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242 results for “occupancy data”
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Sierra Nevada Barred Owl Occupancy Data 2017-2018
<p>Do not use without author's permission.</p>
Data from larval habitat occupancy and habitat attribute surveys.
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Data from the article titled: Transforming into community. Development process of the Algarabía Occupational Center as an expanded community of practice Transforming into community. Development process of the Algarabía Occupational Center as an expanded community of practice
<p>Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/<br>AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by<br>the [grant number PRE2021-098075]</p>
Data from: Modelling the area of occupancy of habitat types with remote sensing
1. A current challenge of biodiversity and conservation is the estimation of the spatial extent of habitat types across broad territories. In the absence of fine-resolution maps, predictive modelling helps in assessing the spatial distribution of vegetation cover. However, such approaches are still uncommon in regional planning and management. Here, we present a framework for mapping the area of occupancy (AOO) of habitat types that allows highly suitable estimates at different scales. 2. We model the potential AOO with abiotic variables related to topography and climate, resulting in broad AOO estimates that are subsequently downscaled to the local AOO with remote sensing. The combination of individual local AOO estimates allows the defining of the realized AOO, comprising locations with a high likelihood of occurrence and low uncertainty for each habitat. We applied this framework to mapping 24 protected habitat types of Natura 2000 sites in northern Spain. 3. Local and realized AOO were highly accurate, with a 70% overall accuracy for the realized AOO. Remote sensing data, and especially LiDAR, were the most important predictors in habitat types related to forests and shrubs, followed by rock outcrops and pastures. Environmental variables were also relevant for specific habitats subject to abiotic constraints. 4. The combination of ecological modelling with remote sensing offers multiple advantages over traditional field surveys and image interpretation, allowing the harmonization of habitat maps across large regions and through time. This is particularly useful for implementing conservation actions under Natura 2000 principles or assessing IUCN criteria for ecosystems.
Data from: An empirical and mechanistic explanation of abundance-occupancy relationships for a critically endangered nomadic migrant
The positive abundance-occupancy relationship (AOR) is a pervasive pattern in macroecology. Similarly, the association between occupancy (or probability of occurrence) and abundance is also usually assumed to be positive and in most cases constant. Examples of AORs for nomadic species with variable distributions are extremely rare. Here we examined temporal and spatial trends in the AOR over seven years for a critically endangered nomadic migrant which relies on dynamic pulses in food availability to breed. We predicted a negative temporal relationship, where local mean abundances increase when the number of occupied sites decreases, and a positive relationship between local abundances and the probability of occurrence. We also predicted that these patterns are largely attributable to spatiotemporal variation in food abundance. The temporal AOR was significantly negative and annual food availability was significantly positively correlated with the number of occupied sites, but negatively correlated with abundance. Thus, as food availability decreased, local densities of birds increased, and vice-versa. The abundance - probability of occurrence relationship was positive and non-linear, but varied between years due to differing degrees of spatial aggregation caused by changing food availability. Importantly, high abundance (or occupancy) did not necessarily equate to high quality habitat and may be indicative of resource bottlenecks or exposure to other processes affecting vital rates. Our results provide a rare empirical example that highlights the complexity of AORs for species that target aggregated food resources in dynamic environments.
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
MeCareNWL - baseline data concerning the psychosocial and occupational impact of COVID-19 among NHS and social care staff in NW London
<p>Online survey data collected at baseline for participants enrolled in the MeCareNWL study - see <a href="https://arc-nwl.nihr.ac.uk/research/covid-19/london-wide-covid-19-research/mecarenwl">MeCareNWL (nihr.ac.uk)</a>.</p> <p>Data extracted from the Qualtrics database on 13 July 2021. Some additional post-processing to satisfy anonymisation requirements.</p> <p>A pdf copy of the survey and the completed STROBE reporting checklist are also included.</p>
Physicochemical habitat data and multi-scale occupancy data for spring-associated fishes in Oklahoma streams
<p class="vC7TJ allowTextSelection">Spring-associated fishes occupy thermally unique habitats in groundwater-dominated streams that are often of high quality. However, outside of water temperature, little else is known about the physicochemical habitat requirements for many of these species. With human effects on streams increasing, it is important to conservation and management to characterize spring habitats and the species that occupy them. Our study objective was to determine the physicochemical factors related to occupancy of four spring-associated species in the Arbuckle Uplift and Ozark Highlands ecoregions, Oklahoma USA. We used a hierarchal approach to identify habitat relationships at multiple spatial scales. We collected detection and non-detection data using both snorkeling and seining methods. We examined the physicochemical relationships related to detection and occupancy for four spring-associated fishes. Data were analyzed using occupancy modeling in a Bayesian framework. Our results indicated water depth and water clarity were important factors affecting detection of spring-associated fishes. Occupancy of our target species differed by ecoregion, with least darter being less common in the Ozark Highlands ecoregion and subadult smallmouth bass being more common in the Ozark Highlands. Interestingly, we found water temperature occupancy relationship for only least darter and southern redbelly dace, whereas redspot chub and smallmouth bass were more likely to occur at sites with deeper pool habitats of larger streams. We documented both spatial and temporal differences in occurrence probabilities at ecoregion, reach, and riffle-run-pool complex scale. Furthermore, our results indicate snorkeling was a superior sampling method compared to seining for detecting most fishes in clear warmwater streams even at relatively low visibilities. Lastly, we demonstrate the importance of using multi-scale studies when developing conservation plans for warmwater fishes.</p>
Data from: Postwar wildlife recovery in an African savanna: Evaluating patterns and drivers of species occupancy and richness
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Data from: An empirical and mechanistic explanation of abundance-occupancy relationships for a critically endangered nomadic migrant
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Data from: Sex and occupation time influence niche space of a recovering keystone predator
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Data from: Insectivorous bat occupancy is mediated by drought and agricultural land use in a highly modified ecoregion
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Data from: Human activities influence the occupancy probability of mammalian carnivores in the Brazilian Caatinga
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Data from: Factors influencing ocelot occupancy in Brazilian Atlantic Forest reserves
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Data from: Tigers, terrain, and human settlement influence the occupancy of leopards (Panthera pardus) in southwestern Tarai, Nepal
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Data from: Stream community richness predicts apex predator occupancy dynamics in riparian systems
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Data from: A multistate dynamic site occupancy model for spatially aggregated sessile communities
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Data from: Ecological niche features override biological traits and taxonomic relatedness as predictors of occupancy and abundance in lake littoral macroinvertebrates
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Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
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