Science Advances

This PDF file includes:

• Supplementary Materials and Methods
• fig. S1. Fine-resolution (5 m) spatial predictions of temperature metrics at the HJA based on BRT models.
  
• fig. S2. Partial dependence plots showing the relationship between selected microtopographic variables and microclimate.
  
• fig. S3. Partial dependence plots showing the relationship between selected vegetation structure variables and microclimate.
  
• fig. S4. Key interactions identified from BRT models testing the effects of elevation, microtopography, and vegetation structure on microclimate.
  
• fig. S5. RI of variables measured at 25- and 250-m scales for each temperature metric in both years.
  
• fig. S6. Comparison of observed microclimate data by year.
  
• fig. S7. Comparison of predicted microclimate metrics by year.
  
• fig. S8. Photo of the HOBO temperature sensor in the field.
  
• table S1. BRT model settings (learning rate, number of trees), performance diagnostics (deviance, deviance SE, CV corr, CV SE), and tests for spatial autocorrelation in the BRT model residuals (Moran’s I and P).
  
• table S2. Pearson’s correlation coefficients (r) and associated P values for both observed and predicted values between years.
  
• table S3. Results from a PCA of all vegetation structure predictor variables.
  
• table S4. Summary statistics and t tests showing differences in LiDAR metrics between mature plantations and mature/old-growth forests.
  
• table S5. Results from Welch two-sample t tests comparing measures of biomass and canopy cover for plantation sites and mature/old-growth forest sites.
  
• table S6. Temperature metrics used in our study and associated summary statistics.
  
• table S7. Predictor variables used to predict patterns in microclimate metrics.
  
• References (59–65)

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