Bulls are naturalizing to urban habitats, machine learning study finds

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Predicted RIO for short-built gull based on machine learning ensemble. Training data are plotted above (pink dots = presence; green dots = absence). debt: Ecological information (2023) DOI: 10.1016/j.ecoinf.2023.102364

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Predicted RIO for short-built gull based on machine learning ensemble. Training data are plotted above (pink dots = presence; green dots = absence). debt: Ecological information (2023) DOI: 10.1016/j.ecoinf.2023.102364

A recent study Published Inside Ecological information A team of University of Alaska Fairbanks researchers has used artificial intelligence to shed more light on habitat displacement among short-legged bulls.

Gulls generally live along coastlines and near water sources such as rivers. They feed on bugs and other small mammals, fish or birds.

From May to August, the team found that short ravens occupied areas that would normally have been roosting grounds for ravens. These include supermarket and fast food restaurant parking lots and other man-made structures such as industrial gravel bars and landfills.

This is the first study to compile a three-year dataset using a citizen science-based, opportunistic research method to include a large sample of gulls and other sub-Arctic birds in urban Alaska. This study provides a current snapshot of habitat conversion to urban landscapes.

UAF professor Falk Hootman, first author on the paper, and his team used artificial intelligence modeling that provided predictors—environmental variables for specific locations—to extrapolate information about gull occurrences. similar, Previous course Studied the distribution of the great gray owl.

In this study, researchers used US Census data and urban municipality data such as distances to roads, restaurants, waterways and waste transfer stations.

„Using socioeconomic datasets like the US Census is a real game-changer,” said Morris Steiner, a graduate student in Huettmann's lab. „This allows us to mimic the real-world environment and simulate the situation as close to nature as possible by including variables in the models.”

The findings indicate that the shift of gulls from natural habitats to more urban landscapes is driven by human dietary and industrial changes.

„They're taking advantage of the wasted opportunity that humans have left behind,” said Hootman, who is associated with UAF's Institute of Arctic Biology.

The short bulls, known as meow bulls until 2021, are omnivorous and highly adaptable. Although bulls can find plenty of food in landfills and gravel pits, the food is often poor for longevity and can even cause death. Avian „dumpster diving” on readily available food, especially at fast food restaurants, can be fatal to birds due to high levels of salt, fat, sugar, grease and impurities.

Bulls are good indicators of disease in an ecosystem.

The team found an increase in disease hosts where the gulls congregated in the summer, sometimes up to 200 birds at each location. Bulls transmit infectious diseases such as bird flu and salmonella, which can be transferred to humans. An unrelated step studyThe first recorded outbreak of gull-linked salmonella occurred in 1959 and was recorded in Ketchikan in North America.

„Bulls are known to be leading vectors of disease. They are heavily affected by bird flu. What we show in the maps are disease reservoirs that are consistent with human development,” said Hootman, who holds a UAF college appointment. Natural Science and Mathematics.

For Huettmann, these studies are a further sign that what is referred to as „wildlife” is changing.

„This kind of information provides a more complete picture of how human-made influence on the environment is changing what we know as natural. Using machine learning can help us advocate for improved wildlife conservation,” said Hootman.

More information:
Falk Hootman et al., Model-based prediction of a vacant summer niche in a subarctic urban area: a multi-year open-access data analysis of 'niche swap' by short-billed gulls, Ecological information (2023) DOI: 10.1016/j.ecoinf.2023.102364

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