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

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.

Alternate title: Polar Data Centre (PDC) record GB/NERC/BAS/PDC/02047

Date (Publication): 2025-04-30

Citation identifier: http://www.antarctica.ac.uk/dms/metadata.php?id= / GB/NERC/BAS/PDC/02047

Point of contact

Organisation name	Individual name	Electronic mail address	Role
British Antarctic Survey		pdc@bas.ac.uk	Custodian
NERC EDS UK Polar Data Centre	Fretwell, P., Bamford, C., Skachkova, A., Trathan, P., & Forcada, J.	pdc@bas.ac.uk	Originator

Maintenance and update frequency: Unknown

Keywords

NDGO0001

NERC OAI Harvesting

NERC_DDC

GCMD Parameter Valids

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 > Biosphere > Zoology
EARTH SCIENCE > Spectral/Engineering > Visible Wavelengths > Visible Imagery

BAS Free-text keywords

Antarctica
Bayesian analysis
Climate change
Emperor penguins
Population trajectory
Sea ice

Use limitation: Data supplied under Open Government Licence v3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.

Access constraints: Other restrictions

Other constraints: Data supplied under Open Government Licence v3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.

Language: English

Topic category

Geoscientific information

Begin date: 2008-08-01

End date: 2023-12-31

Reference system identifier: OGP / urn:ogc:def:crs:EPSG::4326

Distribution format

Distributor contact

Organisation name	Individual name	Electronic mail address	Role
Polar Data Centre - British Antarctic Survey		pdc@bas.ac.uk	Distributor

OnLine resource

Protocol	Linkage	Name
http		GET DATA

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Title: INSPIRE Data Specification on unknown theme Guidelines

Date

Explanation: See the referenced specification

Pass: No

Statement: 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.