8/8 Huge thanks to my coauthors and collaborators for making this possible🙌

MaskSDM is just the beginning: there’s a lot more to explore in combining AI and ecology to tackle the biodiversity crisis💚🌍

7/8 Many more results are available in the paper (and in its Appendix):
📄 doi.org/10.1111/2041...

We hope MaskSDM becomes a practical tool for ecologists and conservation scientists:
💻 lnkd.in/ePeWmbZs

6/8 A major contribution is our new Shapley value computation method, which avoids common linear assumptions by leveraging MaskSDM’s flexible input design. This provides more precise insights into how different environmental factors shape species distributions, locally and globally📊

5/8 A single MaskSDM model performs nearly as well on each tested subset of inputs as an oracle model trained specifically on that subset. This makes it possible to obtain predictions, performance metrics, and maps for any variable subset using only simple inference passes 📈🗺️

4/8 Leveraging MaskSDM, we modeled the distributions of 12,738 plant species worldwide using the sPlotOpen dataset. MaskSDM can be applied anywhere and adapts to the data available, making it ideal for global biodiversity assessments 🌍🌏🌎

3/8 MaskSDM uses transformer-based masked modeling (e.g., BERT, MAE, 4M) adapted for ecology. This lets the model learn from incomplete inputs and still predicts reliably. It’s also multimodal: tabular data, satellite images, time series, and more. More coming soon… stay tuned 😉

2/8 MaskSDM brings three key advantages for species distribution modeling (SDM):
🧩 Flexibility — choose which variables and modalities to use
⚙️ Robustness — works even with missing data
🔍 Explainability — a new Shapley values method shows which factors matter most for each species