c8d8ffe6-0aff-493c-b40f-e63f0c35f081 eng utf8 dataset dataset NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom polardatacentre@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact 2025-04-16 ISO 19115 Geographic Information - Metadata ISO 19115:2003(E) European Petroleum Survey Group (EPSG) Geodetic Parameter Registry 2008-11-12 publication European Petroleum Survey Group EPSGadministrator@iogp.org https://www.epsg-registry.org/ EPSG Geodetic Parameter Dataset The EPSG Geodetic Parameter Dataset is a structured dataset of Coordinate Reference Systems and Coordinate Transformations, accessible through this online registry. information publisher urn:ogc:def:crs:EPSG::3031 6.18.3 Model input and output statistics on fifteen-year assessment of emperor penguin breeding populations from the Bellingshausen sea to the Weddell sea from VHR satellite imagery, 2009-2023 2025-04-16 creation 2025-04-16 revision 2025-04-16 publication 2025-04-16 released 1.0 https://doi.org/10.5285/c8d8ffe6-0aff-493c-b40f-e63f0c35f081 doi GB/NERC/BAS/PDC/02047 https://data.bas.ac.uk/ NE/Y00115X/1 award Please cite this item as: Fretwell, P., Bamford, C., Skachkova, A., Trathan, P., & Forcada, J. (2025). Model input and output statistics on fifteen-year assessment of emperor penguin breeding populations from the Bellingshausen sea to the Weddell sea from VHR satellite imagery, 2009-2023 (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/c8d8ffe6-0aff-493c-b40f-e63f0c35f081 This dataset contains model input and output data on emperor penguin population dynamics for a Bayesian analysis carried out on multivariate classification results. Model input data comprises multivariate classification analysis results derived from very-high resolution (VHR) satellite imagery pertaining to 16 emperor penguin colonies, spanning the Bellingshausen Sea to the Weddell Sea between 2009 to 2023. Model output data comprises population estimates for each year for each colony, global trends per year, global change for the dataset overall, global abundance pertaining to individual colonies, as well as statistical parameter estimates provided by the model. Data collection was carried out by personnel at BAS. Funding from WWF UK (GB095701), project NE/Y00115X/1 "Understanding emperor penguin populations in the Weddell Sea and Antarctic Peninsula" and previous WWF funding over the 15 year period. No credit. completed Fretwell, Peter T British Antarctic Survey Researcher author Bamford, Connor C G British Antarctic Survey Researcher author Skachkova, Aliaksandra British Antarctic Survey Researcher author Trathan, Philip N British Antarctic Survey Researcher author Forcada, Jaume British Antarctic Survey Researcher author NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact asNeeded completed EARTH SCIENCE > Biosphere > Ecological Dynamics > Endangered Species EARTH SCIENCE > Biosphere > Ecological Dynamics > Population Dynamics EARTH SCIENCE > Biosphere > Ecological Dynamics > Post-breeding EARTH SCIENCE > Biosphere > Ecological Dynamics > Survival EARTH SCIENCE > Spectral/Engineering > Visible Wavelengths > Visible Imagery EARTH SCIENCE > Biosphere > Zoology theme Global Change Master Directory (GCMD) Science Keywords 2026-01-27 publication 23 Global Change Data Center, Science and Exploration Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA) Greenbelt MD United States of America https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords Global Change Master Directory (GCMD) Keywords The information provided on this page seeks to define how the GCMD Keywords are structured, used and accessed. It also provides information on how users can participate in the further development of the keywords. information publisher Antarctica Bayesian analysis Climate change Emperor penguins Population trajectory Sea ice theme Weddell Sea Antarctica Bellingshausen Sea Antarctica Dronning Maud Land Antarctica Antarctic peninsula Antarctica place Habitats and biotopes theme GEMET - INSPIRE themes, version 1.0 2021-01-06 revision otherRestrictions no limitations to public access otherRestrictions no limitations license Open Government Licence v3.0 otherRestrictions Data supplied under Open Government Licence v3.0 otherRestrictions No restrictions apply. url url crossReference url url crossReference textTable eng utf8 geoscientificInformation -77.0 -64.0 0.0 90.0 2008-08-01 2023-12-31 It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk. NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information distributor text/csv application/pdf bytes 179200 http://ramadda.data.bas.ac.uk/repository/entry/show?entryid=c8d8ffe6-0aff-493c-b40f-e63f0c35f081 WWW:LINK-1.0-http--link Get Data Download data download bytes 179200 http://ramadda.data.bas.ac.uk/repository/entry/show?entryid=c8d8ffe6-0aff-493c-b40f-e63f0c35f081 WWW:LINK-1.0-http--link Get Data Download data download dataset Methodology: This dataset contains csv tables showing supplemental data from the results of the Bayesian analysis of the multivariate classification results. Table 5 summarises the Bayesian results, while tables 1-3 give specific outputs and statistical fits. Table 4 includes the underlying results from the multivariate classification analysis derived from satellite imagery of each of the 16 emperor penguin colonies in the segment of Antarctica studied over the 15 year period between 2009 and 2023. This was done using ArcGIS using VHR satellite imagery, details of each and the associated locations are recorded in the table. It also contains the information used on image quality, and satellite information used to parameterise the Bayesian model. Tables are described in more detail in the data storage section. Satellite imagery We used optical satellite data from the MAXAR VHR satellite constellation (https://resources.maxar.com/brochures/the-maxar-constellation) that was speculatively tasked over each colony location each year, accessed from the MAXAR search and Discovery platform (https://discover.maxar.com/ and similar previous versions). A single section of each image with a minimum window of 25 km2 was obtained for each colony each year. In several cases, images were unsuitable as there was low cloud, low sun angle or poor environmental conditions obscuring or making penguins difficult to identify. Where possible, additional images were acquired. Additional information of image ID and scene quality is contained in Table 4. The analysis was restricted to the austral spring, between the end of August, when the sun comes up at the more northerly colonies, and the end of November before the adults depart the colonies as chicks fledge. The general method of pre-processing followed that of LaRue et al. 2024, first loading each image into ArcGIS (ESRI ArcGIS version 10.6 2024 and earlier versions), and projecting the images into the local UTM projection for more accurate area assessment. Imagery was pan-sharpened using ArcGIS, and enhanced using a "Standard Deviation" histogram stretch. The location of the colony was then isolated, and the colony was cropped from the surrounding image manually to avoid excessive processing time. A supervised classification analysis was then conducted on each image, using a multivariate classification analysis in ArcGIS, which involves training a model on manually chosen pixels of penguin, guano, snow and shadow. These training data were chosen manually by experienced observers and usually equated to between 50-200 examples from each class, depending upon the homogeneity of the background image. Once the classes have been identified the multivariate classification algorithm divides the image into the available classes, one of which will be penguins. This process is iterative and usually training data needs to be refined multiple times before an acceptable confidence (>95% agreement between manual and automated observations) is reached. From these results the penguin pixels were isolated and converted into a shapefile to calculate. The shapefile gives the area occupied by penguins in each image. The area information from each shapefile are included in Table 5 as well as image IDs and image quality. This process was undertaken for a total of 241 images over the 15-year period. Model fitting and assessment: We used Markov Chain Montel Carlo methods in program JAGS, run from R (v.4.4.0; R Core Team, 2024) using package jagsUI to fit the state-space models. We used 550,000 iterations of three Markov chains using dispersed parameter values as starting values and discarded the first 50,000 samples of each chain as burn-in, thinning the remainder to every 50th sample, which produced 10,000 posterior samples from each Markov chain. We assessed chain convergence visually using trace plots, through the mixing of the ...(7) Data quality: To convert the area of penguins in each image to an index of abundance, we followed an approach similar to that used by LaRue et al. 2024, using a state-space analysis of emperor penguin population dynamics and observation process, and modified for satellite observations but without additional aerial or ground counts. The population-process accounted for daily changes in satellite counts over the survey period (August to November) in each of 16 analysed colonies. Colony-level trends and annual fluctuations were assessed considering the persistence of colonies at the same locations given physical changes (e.g., fast ice conditions), and occupancy (i.e., presence or absence for unknown reasons). The observation process accommodated bias and precision in satellite image counts due to data collection, and changes over survey period due to chick mortality and subsequent emigration by attendant and non-attendant adults. Expected abundance at colony and year was modelled according to LaRue et al. 2024. Due to a relatively low sample size and to the sparse data of some colonies, colony-level effects were modelled as random effects. Initial population states were assumed to have a log-normal distribution with hyperparameters estimated empirically from the data. The observation process assumed that the size of the areas occupied by penguins in satellite images were normally distributed and corrected for estimate bias due to interpretation of satellite images. Further, a log-linear effect was used to consider the number of adults that decline over the spring survey period, where the variable described the proportional change in expected count for each day elapsed in the survey. To accommodate observer bias, image quality scores were related to these variables in a discriminant analysis of principal components, using R package adegenet . Here, we selected the optimum number of principal components using cross-validation and used observer-assigned image quality as grouping variable in a discriminant analysis. The results provided a covariate with predicted image quality, and the subsequently fitted beta coefficients assumed that satellite observations had a constant proportional bias. asNeeded completed