Through the Random Forest: Ontogeny as a study system to connect prediction to explanation

As modeling tools and approaches become more advanced, ecological models are becoming more complex and must be investigated with novel methods of analysis. Machine learning approaches are a powerful toolset for exploring such complexity. While these approaches are powerful, results may suffer from well-known trade-offs between predictive and explanatory power. We employ an empirically rooted ontogenetically stage-structured consumer-resource model to investigate how machine learning can be used as a tool to root model analysis in mechanistic ecological principles. Applying random forest models to model output using simulation parameters as feature inputs, we extended established feature analysis into a simple graphical analysis. We used this graphical analysis to reduce model behavior to a linear function of three ecologically based mechanisms. From this model, we find that stability depends on the interaction between internal plant demographics that control the distribution of plant density across ontogenetic stages and the distribution of consumer pressure across ontogenetic stages. Predicted outcomes from these linear models rival accuracy achieved by our random forests, while explaining results as a function of ecological interactions.