Quantifying the causes and consequences of variation in satellite‐derived population indices: a case study of emperor penguins
Bibliographic record
Abstract
Abstract Very high‐resolution satellite (VHR) imagery is a promising tool for estimating the abundance of wildlife populations, especially in remote regions where traditional surveys are limited by logistical challenges. Emperor penguins Aptenodytes forsteri were the first species to have a circumpolar population estimate derived via VHR imagery. Here we address an untested assumption from Fretwell et al. (2012) that a single image of an emperor penguin colony is a reasonable representation of the colony for the year the image was taken. We evaluated satellite‐related and environmental variables that might influence the calculated area of penguin pixels to reduce uncertainties in satellite‐based estimates of emperor penguin populations in the future. We focused our analysis on multiple VHR images from three representative colonies: Atka Bay, Stancomb‐Wills (Weddell Sea sector) and Coulman Island (Ross Sea sector) between September and December during 2011. We replicated methods in Fretwell et al. (2012), which included using supervised classification tools in ArcGIS 10.7 software to calculate area occupied by penguins (hereafter referred to as ‘population indices’) in each image. We found that population indices varied from 2 to nearly 6‐fold, suggesting that penguin pixel areas calculated from a single image may not provide a complete understanding of colony size for that year. Thus, we further highlight the important roles of: (i) sun azimuth and elevation through image resolution and (ii) penguin patchiness (aggregated vs. distributed) on the calculated areas. We found an effect of wind and temperature on penguin patchiness. Despite intra‐seasonal variability in population indices, simulations indicate that reliable, robust population trends are possible by including satellite‐related and environmental covariates and aggregating indices across time and space. Our work provides additional parameters that should be included in future models of population size for emperor penguins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".