Rare bird forecast: A combined approach using a long‐term dataset of an Arctic seabird and a numerical weather prediction model

Abstract

AbstractWildlife observation is a popular activity, and sightings of rare or difficult‐to‐find animals are often highly desired. However, predicting the sighting probabilities of these animals is a challenge for many observers, and it may only be possible by limited experts with intimate knowledge and skills. To tackle this difficulty, we developed user‐friendly forecast systems of the daily observation probabilities of a rare Arctic seabird (Ross's Gull Rhodostethia rosea) in a coastal area in northern Japan. Using a dataset gathered during 16 successive winters, we applied a machine learning technique of self‐organizing maps and explored how days with gull sightings were related to the meteorological pressure patterns over the Sea of Okhotsk (Method A). We also built a regression model that explains the relationship between gull sightings and local‐scale environmental factors (Method B). We then applied these methods with the operational global numerical weather prediction model (a computer simulation application about the fluid dynamics of Earth's atmosphere) to forecast the daily observation probabilities of our target. Method A demonstrated a strong dependence of gull sightings on the 16 representative weather patterns and forecasted stepwise observation probabilities ranging from 0% to 85.7%. Method B also showed that the strength of the northerly wind and the advancement of the season explained gull sightings and forecasted continuous observation probabilities ranging from 0% to 95.5%. Applying these two methods with the operational global numerical weather prediction model successfully forecasted the varied observation probabilities of Ross's Gull from 1 to 5 days ahead from November to February. A 2‐year follow‐up observation also validated both forecast systems to be effective for successful observation, especially when both systems forecasted higher observation probabilities. The developed forecast systems would therefore allow cost‐effective animal observation and may facilitate a better experience for a variety of wildlife observers.