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1,630 results for “Occupations”
Leopard (Panthera pardus) occupancy in the Chure range of Nepal
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Leopard occupancy correlates with tiger and prey occurrences
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Returning neighbors: Eastern wild turkey (Meleagris gallopavo silvestris) occupancy in an urban landscape
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Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
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Occupancy patterns of the introduced, predatory sugar glider in Tasmanian forests
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A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
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Warming threatens habitat suitability and breeding occupancy of rear-edge alpine bird specialists
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Data from: Multi-trophic occupancy modeling connects temporal dynamics of woodpeckers and beetle sign following fire
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Targeted occupant surveys: A novel method to effectively relate occupant feedback with environmental conditions
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Ancient DNA-based sex determination of bison hide moccasins provides evidence for selective hunting strategies by Promontory Cave occupants
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Investigating the Distribution and Occupancy of Otter Species across Human-modified Landscapes in Sabah, Malaysia.
<b>Description: </b><p>A study on the occupancy of otters in streams across land-use gradient, within four habitat treatments: logged forest (LF), heavily degraded forest (HDF), riparian reserves within oil palm plantation (RR), and oil palm plantation (ROP).</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/171"><b>Investigating the Occupancy & Habitat Use of Tropical Otters Across Human Modified Landscapes in SAFE Project, Kalabakan, Sabah.</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Studentship)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence 2016/56)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3897377">here</a></p><p><b>Files: </b>This consists of 1 file: Otter_Data_Annabel_Pianzin.xlsx</p><p><b>Otter_Data_Annabel_Pianzin.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>Otter occupancy</b> (described in worksheet Otter_Occupancy)</p><p>Description: Otter occupancy</p><p>Number of fields: 12</p><p>Number of data rows: 36</p><p>Fields: </p><ul><li><b>site</b>: Location within the SAFE landscape (Field type: location)</li><li><b>subtransect_ID</b>: Point ID (Field type: id)</li><li><b>treatment</b>: Habitat type (Field type: categorical)</li><li><b>oc1</b>: Presence absence of otter (Field type: abundance)</li><li><b>oc2</b>: Presence absence of otter (Field type: abundance)</li><li><b>oc3</b>: Presence absence of otter (Field type: abundance)</li><li><b>oc4</b>: Presence absence of otter (Field type: abundance)</li><li><b>spraint</b>: Visual observation of spraint (Field type: abundance)</li><li><b>footprint</b>: Visual observation of otter footprint (Field type: abundance)</li><li><b>taxa</b>: Species detected at the site (Field type: taxa)</li><li><b>A.cinereus</b>: Presence absence of otter (Field type: abundance)</li><li><b>L.perspicillata</b>: Presence absence of otter (Field type: abundance)</li></ul></li><li><p><b>Occupancy Survey Date</b> (described in worksheet Occupancy_Survey_Date)</p><p>Description: Occupancy_Survey_Date</p><p>Number of fields: 7</p><p>Number of data rows: 36</p><p>Fields: </p><ul><li><b>site</b>: Location within the SAFE landscape (Field type: location)</li><li><b>subtransect_ID</b>: Point ID (Field type: id)</li><li><b>treatment</b>: Habitat type (Field type: categorical)</li><li><b>oc1_date</b>: Date of survey (Field type: date)</li><li><b>oc2_date</b>: Date of survey (Field type: date)</li><li><b>oc3_date</b>: Date of survey (Field type: date)</li><li><b>oc4_date</b>: Date of survey (Field type: date)</li></ul></li><li><p><b>Habitat covariates</b> (described in worksheet Habitat_Covariates)</p><p>Description: Habitat covariates</p><p>Number of fields: 20</p><p>Number of data rows: 36</p><p>Fields: </p><ul><li><b>site</b>: Location within the SAFE landscape (Field type: location)</li><li><b>point_ID</b>: Point ID (Field type: id)</li><li><b>treatment</b>: Habitat type (Field type: categorical)</li><li><b>width</b>: Stream width (Field type: numeric)</li><li><b>depth</b>: Stream depth (Field type: numeric)</li><li><b>altitude</b>: Altitude of site (Field type: numeric)</li><li><b>stmedge</b>: Width of stream edge (Field type: numeric)</li><li><b>canco</b>: Stream canopy cover (Field type: numeric)</li><li><b>ugro</b>: Undergrowth cover (Field type: numeric)</li><li><b>bankco</b>: Bank canopy cover (Field type: numeric)</li><li><b>bk.rock</b>: Bank rock cover (Field type: numeric)</li><li><b>bk.soil</b>: Bank soil cover (Field type: numeric)</li><li><b>bk.both</b>: Bank soil and rock cover (Field type: numeric)</li><li><b>no.tree</b>: Number of trees (Field type: numeric)</li><li><b>no.log</b>: Number of logs (Field type: numeric)</li><li><b>fquality</b>: Forest quality (Field type: numeric)</li><li><b>bankheight</b>: Bank height (Field type: numeric)</li><li><b>bankdbh</b>: Bank diatmeter at breast height (Field type: numeric)</li><li><b>human</b>: Distance from human settlements (Field type: numeric)</li><li><b>estate</b>: Distance from oil palm estate (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2017-02-22 to 2019-06-03</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Mustelidae <br> -  -  -  -  -  -  <i>Lutrogale</i> <br> -  -  -  -  -  -  -  <i>Lutrogale perspicillata</i> <br> -  -  -  -  -  -  <i>Aonyx</i> <br> -  -  -  -  -  -  -  <i>Aonyx cinereus</i> <br></div><p></p>
Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective
<p>Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective<br> R. Maria del Rio-Chanona, Penny Mealy, Anton Picheler, Francois Lafond, J. Doyne Farmer<br> contact:<br> Results <br> The supply, demand, and total shocks at the industry and occupation level are in files:</p> <p>industry_variables_and_shock.csv<br> occupation_variables_and_shock.csv</p> <p>To reproduce our results we also include<br> The employment data between industries and occupations<br> industry_occupation_employment.csv<br> The classification of work activities<br> iwa_remotelabor_labels.csv<br> The essential score of industries at the NAICS 4d level<br> essential_score_industries_naics_4d_rev.csv<br> </p> <p>Update with respect to the previous version</p> <p>We have now included our code to reproduce our study from scratch.</p>
Predictive multi-scale occupancy models at range-wide extents: effects of habitat and human disturbance on distributions of wetland birds
<p><span><i>Aim:</i> Predicting distributions is fundamental to ecology, yet hindered by spatially-restricted sampling, scale-dependent relationships, and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi-scale data, and used it to develop range-wide breeding-season SDMs for 14 marsh bird species of concern.</span></p> <p><span><i>Location: </i>USA.</span></p> <p><span><i>Methods: </i>We built SDMs using data from range-wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out-of-sample prediction, range-wide sampling over broad conditions, multi-scale analyses and scale-optimization, and species-specific detectability for a suite of wide-ranging species. </span></p> <p><span><i>Results: </i>Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban, and hydrologic disturbances operating from local scales (100 – 500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at > 1 scale. Five species showed evidence for continental-scale range contraction during the study.</span></p> <p><span><i>Main conclusions: </i>We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data-driven, empirical SDMs built at the range-wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine-scale wetland vegetation missed important broad-scale drivers of occupancy for marsh birds. </span></p>
Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods
Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time-to-first-detection (TTD) occupancy models have been proposed as a cost-effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy-detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala (Phascolarctos cinereus) populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness-of-fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy-detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness-of-fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy-detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.
Data from: TAF4, a subunit of transcription factor II D, directs promoter occupancy of nuclear receptor HNF4A during post-natal hepatocyte differentiation
The functions of the TAF subunits of mammalian TFIID in physiological processes remain poorly characterised. Here we describe a novel function of TAFs in directing genomic occupancy of a transcriptional activator. Using liver-specific inactivation in mice, we show that the TAF4 subunit of TFIID is required for post-natal hepatocyte maturation. TAF4 promotes pre-initiation complex (PIC) formation at post-natal expressed liver function genes and down-regulates a subset of embryonic expressed genes by increased RNA polymerase II pausing. The TAF4-TAF12 heterodimer interacts directly with HNF4A and in vivo TAF4 is necessary to maintain HNF4A-directed embryonic gene expression at post-natal stages and promotes HNF4A occupancy of functional cis-regulatory elements adjacent to the transcription start sites of post-natal expressed genes. Stable HNF4A occupancy of these regulatory elements requires TAF4-dependent PIC formation highlighting that these are mutually dependent events. Local promoter-proximal HNF4A-TFIID interactions therefore act as instructive signals for post-natal hepatocyte differentiation.
Data from: Habitat filtering determines the functional niche occupancy of plant communities worldwide
How the patterns of niche occupancy vary from species-poor to species-rich communities is a fundamental question in ecology that has a central bearing on the processes that drive patterns of biodiversity. As species richness increases, habitat filtering should constrain the expansion of total niche volume, while limiting similarity should restrict the degree of niche overlap between species. Here, by explicitly incorporating intraspecific trait variability, we investigate the relationship between functional niche occupancy and species richness at the global scale. We assembled 21 datasets worldwide, spanning tropical to temperate biomes and consisting of 313 plant communities representing different growth forms. We quantified three key niche occupancy components (the total functional volume, the functional overlap between species and the average functional volume per species) for each community, related each component to species richness, and compared each component to the null expectations. As species richness increased, communities were more functionally diverse (an increase in total functional volume), and species overlapped more within the community (an increase in functional overlap) but did not more finely divide the functional space (no decline in average functional volume). Null model analyses provided evidence for habitat filtering (smaller total functional volume than expectation), but not for limiting similarity (larger functional overlap and larger average functional volume than expectation) as a process driving the pattern of functional niche occupancy. Synthesis. Habitat filtering is a widespread process driving the pattern of functional niche occupancy across plant communities and coexisting species tend to be more functionally similar rather than more functionally specialized. Our results indicate that including intraspecific trait variability will contribute to a better understanding of the processes driving patterns of functional niche occupancy.
Data from: Dynamic occupancy modeling reveals a hierarchy of competition among fishers, grey foxes, and ringtails
1. Determining how species coexist is critical for understanding functional diversity, niche partitioning and interspecific interactions. Identifying the direct and indirect interactions among sympatric carnivores that enable their coexistence are particularly important to elucidate because they are integral for maintaining ecosystem function. 2. We studied the effects of removing 9 fishers (Pekania pennanti) on their population dynamics and used this perturbation to elucidate the interspecific interactions among fishers, grey foxes (Urocyon cinereoargenteus), and ringtails (Bassariscus astutus). Grey foxes (family: Canidae) are likely to compete with fishers due to their similar body sizes and dietary overlap, and ringtails (family: Procyonidae), like fishers, are semi-arboreal species of conservation concern. We used spatial capture-recapture to investigate fisher population numbers and dynamic occupancy models that incorporated interspecific interactions to investigate the effects members of these species had on the colonization and persistence of each other's site occupancy. 3. The fisher population showed no change in density for up to three years following the removals of fishers for translocations. In contrast, fisher site occupancy decreased in the years immediately following the translocations. During this same time period, site occupancy by grey foxes increased and remained elevated through the end of the study. 4. We found a complicated hierarchy among fishers, foxes, and ringtails. Fishers affected grey fox site persistence negatively but had a positive effect on their colonization. Foxes had a positive effect on ringtail site colonization. Thus, fishers were the dominant small carnivore where present and negatively affected foxes directly and ringtails indirectly. 5. Coexistence among the small carnivores we studied appears to reflect dynamic spatial partitioning. Conservation and management efforts should investigate how intraguild interactions may influence the recolonization of carnivores to previously occupied landscapes.
Data from: Biodiversity dynamics and environmental occupancy of fossil azooxanthellate and zooxanthellate scleractinian corals
Scleractinian corals have two fundamentally different life strategies, which can be inferred from morphological criteria in fossil material. In the non-photosymbiotic group nutrition comes exclusively from heterotrophic feeding, whereas the photosymbiotic group achieves a good part of its nutrition from algae hosted in the coral's tissue. These ecologic differences arose early in the evolutionary history of corals but with repeated evolutionary losses and presumably also gains of symbiosis since then. We assessed the biodiversity dynamics and environmental occupancy of both ecologic groups to identify times when the evolutionary losses of symbiosis as inferred from molecular analyses might have occurred and if these can be linked to environmental change. Two episodes are likely: The first was in the mid-Cretaceous when non-symbiotic corals experienced an origination pulse and started to become more common in deeper, non-reef habitats and on siliciclastic substrates initiating a long-term offshore trend in occupancy. The second was around the Cretaceous/Paleogene boundary with another origination pulse and increased occupancy of deep-water settings in the non-symbiotic group. Environmental factors such as rapid global warming associated with mid-Cretaceous anoxic events and increased nutrient concentrations in Late Cretaceous–Cenozoic deeper waters are plausible mechanisms for the shift. Turnover rates and durations are not significantly different between the two ecologic groups when compared over the entire history of scleractinians. However, the deep-water shift of non-symbiotic corals was accompanied by reduced extinction rates, supporting the view that environmental occupancy is a prominent driver of evolutionary rates.
Data from: On the measurement of occupancy in ecology and paleontology
Occupancy statistics in ecology and paleontology are biased upward by the fact that we generally do not have solid data on species that exist but are not found. The magnitude of this bias increases as the average occupancy probability decreases and as the number of sites sampled decreases. A maximum-likelihood method is developed to estimate the underlying distribution of occupancy probabilities of all species based only on the sample of observed species with nonzero occupancy. The method is based on determining the probability that the number of occupied sites will take on any specific value for a given occupancy probability, integrated over the entire distribution of occupancy probabilities. If the shape of the underlying distribution is well modeled, the resulting occupancy estimates circumvent the bias inherent in failing to observe some species and the fact that this bias depends on the number of sites. For occupancy data on marine animal genera drawn from the Paleobiology Database, the underlying distribution is reasonably approximated as a right-truncated log-normal, but the methods developed can be extended to any distribution. Examples are presented to illustrate some observations that are robust and others that need to be revised in light of this bias correction. The method is compared to a recently developed, distribution-free approach to the same problem.
Data from: Joint effect of education and main lifetime occupation on late life health: a cross-sectional study of older adults in Xiamen, China
Background: The effects of education and occupation on health have been well documented individually, but little is known about their joint effect, especially their cumulative joint effect on late life health. Methods: We enrolled 14,292 participants aged 60+ years by multistage sampling across 173 communities in Xiamen, China, in 2013. Heath status was assessed by the ability to perform six basic activities of daily life. Education was classified in four categories: 'Illiterate', 'Primary', 'Junior high school' and 'Senior high school and beyond'. Main lifetime occupation was also four categorized: 'Employed', 'Farmer', 'Jobless' and 'Others'. Odds ratios (ORs) were estimated by random-intercept multilevel models regressing health status on education and main lifetime occupation with or without their interactions, adjusting by some covariates. Results: Totally, 13,880 participants had complete data, of whom 12.5% suffered from disability, and 'Illiterate' and 'Farmer' took up the greatest proportion (33.01% and 42.72%, respectively). Participants who were higher educated had better health status (ORs = 0.62, 0.46, and 0.44 for the 'Primary', 'Junior high school', and 'Senior high school and beyond', respectively, in comparison with 'Illiterate'). Those who were long term jobless in early life had poorest heath (ORs = 1.88, 95% CI 1.47 to 2.40). Unexpectedly, for the farmers, the risk of poor health gradually increased in relation to higher education level (ORs = 1.26, 1.28, 1.40 and 2.24, respectively). For the 'Employed', similar ORs were obtained for the 'Junior high school' and 'Senior high school and beyond' educated (both ORs = 1.01). For the 'Farmer' and 'Jobless', participants who were 'Illiterate' and 'Primary' educated also showed similar ORs. Conclusions: Both education and main lifetime occupation were associated with late life health. Higher education was observed to be associated with better health, but such educational advantage was mediated by main lifetime occupation.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.