<ul><li>Dryland vegetation ecosystems are susceptible to critical transitions between alternative stable states.</li><li>Spatial patterning of vegetation in drylands leads to complex and diverse dynamics, going beyond local bifurcations.</li><li>Deep neural networks show strong predictive capabilities in identifying dynamical signatures of critical transitions.</li><li>Model performance for neural Early Warning Signal detection varies when training and test data sources are interchanged, affecting generalization.</li></ul>

Neural models for prediction of spatially patterned phase transitions: methods and challenges

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