Date (Creation)

2025-04-16

Date (Revision)

2025-04-16

Date (Publication)

2025-04-16

Date (released)

2025-04-16

Edition

1.0

Unique resource identifier

https://doi.org/10.5285/c8d8ffe6-0aff-493c-b40f-e63f0c35f081

Codespace

doi

Unique resource identifier

GB/NERC/BAS/PDC/02047

Codespace

https://data.bas.ac.uk/

Unique resource identifier

NE/Y00115X/1

Codespace

award

Other citation details

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

Credit

No credit.

Status

Completed

Point of contact

Organisation name	Individual name	Electronic mail address	Role
British Antarctic Survey	Fretwell, Peter T		Author
British Antarctic Survey	Bamford, Connor C G		Author
British Antarctic Survey	Skachkova, Aliaksandra		Author
British Antarctic Survey	Trathan, Philip N		Author
British Antarctic Survey	Forcada, Jaume		Author
NERC EDS UK Polar Data Centre		PDCServiceDesk@bas.ac.uk	Point of contact

Maintenance and update frequency

As needed

Maintenance note

Completed

Global Change Master Directory (GCMD) Science Keywords

• 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

• Antarctica

• Bayesian analysis

• Climate change

• Emperor penguins

• Population trajectory

• Sea ice

Place

• Weddell Sea Antarctica

• Bellingshausen Sea Antarctica

• Dronning Maud Land Antarctica

• Antarctic peninsula Antarctica

GEMET - INSPIRE themes, version 1.0

• Habitats and biotopes

Access constraints

Other restrictions

Other constraints

no limitations to public access

Access constraints

Other restrictions

Other constraints

no limitations

Use constraints

License

Other constraints

Open Government Licence v3.0

Use constraints

Other restrictions

Other constraints

Data supplied under Open Government Licence v3.0

Use constraints

Other restrictions

Other constraints

No restrictions apply.

Unique resource identifier

url

Codespace

url

Association Type

Cross reference

Unique resource identifier

url

Codespace

url

Association Type

Cross reference

Spatial representation type

Text, table

Language

English

Character set

UTF8

Topic category

• Geoscientific information

Begin date

2008-08-01

End date

2023-12-31

Supplemental Information

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.

Title

European Petroleum Survey Group (EPSG) Geodetic Parameter Registry

Date (Publication)

2008-11-12

Cited responsible party

Organisation name	Individual name	Electronic mail address	Role
European Petroleum Survey Group		EPSGadministrator@iogp.org	Publisher

Unique resource identifier

urn:ogc:def:crs:EPSG::3031

Version

6.18.3

Hierarchy level

Dataset

Statement

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.

Metadata

File identifier

c8d8ffe6-0aff-493c-b40f-e63f0c35f081 XML

Metadata language

English

Character set

UTF8

Hierarchy level

Dataset

Hierarchy level name

dataset

Date stamp

2025-04-16

Metadata standard name

ISO 19115 Geographic Information - Metadata

Metadata standard version

ISO 19115:2003(E)

Metadata author

Organisation name	Individual name	Electronic mail address	Role
NERC EDS UK Polar Data Centre		polardatacentre@bas.ac.uk	Point of contact