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Remote sensing of emperor penguin abundance and breeding success.

Alexander WinterlSebastian RichterAymeric HoustinTéo BarrachoMatthieu BoureauClément CornecDouglas CouetRobin CristofariClaire EiseltBen FabryAdélie KrellensteinChristoph MarkAstrid MainkaDelphine MénardJennifer MorinaySusie PottierElodie SchloesingCeline Le BohecDaniel P Zitterbart
Published in: Nature communications (2024)
Emperor penguins (Aptenodytes forsteri) are under increasing environmental pressure. Monitoring colony size and population trends of this Antarctic seabird relies primarily on satellite imagery recorded near the end of the breeding season, when light conditions levels are sufficient to capture images, but colony occupancy is highly variable. To correct population estimates for this variability, we develop a phenological model that can predict the number of breeding pairs and fledging chicks, as well as key phenological events such as arrival, hatching and foraging times, from as few as six data points from a single season. The ability to extrapolate occupancy from sparse data makes the model particularly useful for monitoring remotely sensed animal colonies where ground-based population estimates are rare or unavailable.
Keyphrases
  • electronic health record
  • big data
  • deep learning
  • convolutional neural network
  • machine learning
  • optical coherence tomography
  • risk assessment
  • human health