What kind of birds are more susceptible to avian malaria? A global analysis based on interpretable machine learning approach
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Aim
Avian malaria (genus Plasmodium ) is a mosquito-transmitted parasitic disease of birds. It has a wide distribution across the world, infecting more than 2,400 bird species, posing a great threat to bird health. However, over half of bird species have a cumulative sample size of fewer than 20 individuals, leading to a limited understanding of the global patterns and mechanisms of their susceptibility to avian malaria. Our aim is to use interpretable machine learning to identify the global ecological and evolutionary drivers shaping species-level avian malaria susceptibility in birds.
Location Global.
Time period 1994–2023.
Major taxa studied
Global bird species and their malaria parasites (genus Plasmodium )
Methods
Based on infection data and traits from 72,406 birds of 2,544 species worldwide, we developed an interpretable machine learning (IML) approach to identify the global drivers of species-level susceptibility and their trends. We further applied our model to predict the susceptibility of bird species with a sample size of less than 30 and tested multiple hypotheses related to differences in parasite susceptibility in birds.
Results
Our model distinguished susceptible birds with moderate accuracy (F1 score = 0.72) and predicted 752 bird species to be highly susceptible to avian malaria, including 16 threatened species. Susceptibility showed a moderate but significant phylogenetic signal, with most susceptible species belonging to Passeriformes. Highly susceptible species were generally characterized by larger body size, omnivory, ground-foraging behavior, wider geographic ranges, and medium diversification rates.
Main Conclusions
We show that ecological and evolutionary factors together shape species-level susceptibility of birds to avian malaria. Interpretable machine learning integrating host traits offers a global insights into infection mechanisms and supports the development of more precise surveillance and control strategies.
