Deep Learning Model Predicts Invasive Plant Spread Across Michigan Under Climate Change
A new study used a deep learning framework called Deepbiosphere, combined with citizen science and remote sensing data, to predict the distribution of 1,553 vascular plant species in Michigan, with a focus on two invasive species. The model outperformed baseline approaches by nearly 11% on average, and by over 56% and 74% respectively for the two invasive species studied. The findings highlight how AI-driven species distribution models can better inform early intervention strategies as climate change expands invasive species ranges.
Researchers applied the Deepbiosphere deep learning framework to map current and future distributions of native and invasive plant species across Michigan, achieving a mean AUC-ROC of 0.79 across 1,553 vascular plant species — roughly 11% better than traditional machine learning baselines. For two focal invasive species, common buckthorn (Rhamnus cathartica) and tree of heaven (Ailanthus altissima), the model improved predictive accuracy by 56% and 75% respectively. Current projections show R. cathartica is already broadly suitable across much of Michigan, while A. altissima remains more restricted to southern areas but is projected to expand strongly northward under future climate scenarios. The study also mapped prediction uncertainty, finding that differences between general circulation models (GCMs) were the dominant source of spatial uncertainty across most of the state. By explicitly quantifying and visualizing uncertainty alongside risk, the researchers argue that managers can make more informed decisions about monitoring and intervention priorities under climate change.
What's missing
The study's own limitations include its regional focus on Michigan, which may constrain generalizability of the Deepbiosphere framework to other geographies or species assemblages. Citizen science occurrence data used to train the model may carry spatial sampling bias toward populated or accessible areas, potentially affecting predictions in remote regions. The study does not address how model performance might change as climate conditions shift further beyond the training data distribution, nor does it validate future projections against observed range changes.
What different sources said
- bioRxivCenter
Mapping distribution of invasive plant species and uncertainty using citizen science, remote sensing, and deep learning
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