Gradient forests: calculating importance gradients on physical predictors.

Area of Science:

• Ecology
• Biodiversity research
• Environmental science

Background:

• Ecological analyses seek to understand species and community responses to environmental gradients.
• Existing methods like random forests assess predictor importance for individual species but not whole assemblages.
• There's a need to identify where along gradients significant compositional changes and thresholds occur for entire ecological communities.

Purpose of the Study:

• To develop and validate a novel method, "gradient forest," extending random forests to analyze whole ecological assemblages.
• To pinpoint critical environmental variables and their associated change points or thresholds influencing community composition.
• To improve predictions of biodiversity patterns for applications like bioregionalization and protected area design.

Main Methods:

• Gradient forest synthesizes cross-validated R2 and accuracy importance from univariate random forest analyses across multiple species and surveys.
• It generates a monotonic function for each predictor, representing compositional turnover along environmental gradients.
• The method was tested on both synthetic and real ecological data.

Main Results:

• Gradient forest successfully identified important predictors and change points in compositional shifts on a synthetic dataset.
• Application to Great Barrier Reef data revealed sediment mud fraction as the primary predictor.
• The highest community compositional turnover occurred at approximately 25% mud fraction, with similar insights for other predictors.

Conclusions:

• Gradient forest provides a powerful tool for analyzing community-level responses to environmental gradients.
• It accurately identifies key environmental drivers and thresholds influencing biodiversity patterns.
• This method offers refined information crucial for effective conservation planning and biodiversity assessment.