We develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture-recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual-based formulation of a Jolly-Seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.
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http://dx.doi.org/10.1890/09-0804.1 | DOI Listing |
Sci Data
January 2025
RTI International, 3040 Cornwallis Rd., P.O. Box 12194, Research Triangle Park, NC, 27709, USA.
Geospatially explicit and statistically accurate person and household data allow researchers to study community-and neighborhood-level effects and design and test hypotheses that would otherwise not be possible without the generation of synthetic data. In this article, we demonstrate the workflow for generating spatially explicit household- and individual-level synthetic populations for the United States representing the year 2019. We use publicly available U.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Department of Medical Imaging, Pingyin people's Hospital, Jinan 250400, China.
Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality.
View Article and Find Full Text PDFSci Total Environ
January 2025
School of Biological Sciences, University of Adelaide, Adelaide, SA 5000, Australia; The Environment Institute, University of Adelaide, Adelaide, SA 5000, Australia; Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark; Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark. Electronic address:
Human overexploitation contributed strongly to the loss of hundreds of bird species across Oceania, including nine giant, flightless birds called moa. The inevitability of anthropogenic moa extinctions in New Zealand has been fiercely debated. However, we can now rigorously evaluate their extinction drivers using spatially explicit demographic models capturing species-specific interactions between moa, natural climates and landscapes, and human colonists.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Royal Danish Library, Special Collections, Søren Kierkegaards Plads. 1, 1221, Copenhagen K, Denmark.
Historical topographical maps contain valuable, spatially and thematically detailed information about past landscapes. Yet, for analyses of landscape dynamics through geographical information systems, it is necessary to "unlock" this information via map processing. For two study areas in northern and central Jutland, Denmark, we apply object-based image analysis, vector GIS, colour image segmentation, and machine learning processes to produce machine-readable layers for the land use and land cover categories forest, wetland, heath, dune sand, and water bodies from topographic maps from the late nineteenth century.
View Article and Find Full Text PDFSci Data
January 2025
Centre for Ecosystem Science, School of Biological, Earth, and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
Antarctica, Earth's least understood and most remote continent, is threatened by human disturbances and climate-related changes, underscoring the imperative for biodiversity inventories to inform conservation. Antarctic ecosystems support unique species and genetic diversity, deliver essential ecosystem services and contribute to planetary stability. We present Antarctica's first comprehensive ecosystem classification and map of ice-free lands, which host most of the continent's biodiversity.
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