ArticleScientific reports2023
Interpreting random forest analysis of ecological models to move from prediction to explanation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Considerations for multivariate approaches to classify blow fly species based on cuticular hydrocarbon profiles.Journal of forensic sciences · 2026Article
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- Long-term monitoring reveals climate-driven seasonal dynamics of Zeugodacus cucurbitae (Coquillett) in Tanzania and prospects for pest control.BMC ecology and evolution · 2026Article
- Diet-Induced Modulation of Gut Microbiota Affects Meat Quality in Grass Carp (Microorganisms · 2026Article
- Risk factors associated with overall survival in patients with cervical cancer: a prospective cohort study in Western China comparing random survival forest and Cox proportional hazards models.Journal of gynecologic oncology · 2026Article
- Association between weaning stress and rumen microbiota in goat kids: evidence from granger causality and randomized controlled trial validation.Animal bioscience · 2026Article
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- A Precise Apple Quality Prediction Model Integrating Driving Factor Screening and BP Neural Network.Plants (Basel, Switzerland) · 2025Article
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- Exploring the importance of environmental justice variables for predicting energy burden in the contiguous United States.iScience · 2025Article
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- Application of AI in predicting postoperative infections using routine blood parameters.Bioinformation · 2025Article
- Correlation between oral microbiota and dry socket at different time periods on tooth extraction.Journal of oral microbiology · 2025Article
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- Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia.Global challenges (Hoboken, NJ) · 2023Article
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Authors and funding
3 authors.
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Abstract
As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to identify the nonlinearities and interactions emergent from such complexity, especially across broad swaths of parameter space. This limits understanding of the ecological mechanisms underlying model behavior. Machine learning approaches are a potential answer to this issue, given their predictive ability when applied to complex large datasets. While perceptions that machine learning is a "black box" linger, we seek to illuminate its interpretive potential in ecological modeling. To do so, we detail our process of applying random forests to complex model dynamics to produce both high predictive accuracy and elucidate the ecological mechanisms driving our predictions. Specifically, we employ an empirically rooted ontogenetically stage-structured consumer-resource simulation model. Using simulation parameters as feature inputs and simulation output as dependent variables in our random forests, we extended feature analyses into a simple graphical analysis from which we reduced model behavior to three core ecological mechanisms. These ecological mechanisms reveal the complex interactions between internal plant demography and trophic allocation driving community dynamics while preserving the predictive accuracy achieved by our random forests.
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