A nonlinear mixed-effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an...

Area of Science:

• Ecology
• Environmental Science
• Statistical Modeling

Background:

• Ecologists increasingly face complex data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structures.
• Nonlinear mixed-effects models (NLME) provide a flexible analytical framework but lack practical examples in ecological literature.
• Understanding vegetation moisture dynamics is crucial for ecological theory and practical fire management.

Purpose of the Study:

• To illustrate a step-by-step approach for applying nonlinear mixed-effects models to ecological data.
• To investigate variations in vegetation moisture dynamics among functional groups and aridity conditions in Patagonia.
• To compare the performance of NLME models against simpler statistical approaches.

Main Methods:

• Utilized field data on vegetation moisture dynamics from northwestern Patagonia, a Mediterranean-type climate region.
• Developed and progressively complexified a statistical model to incorporate various sources of variability and correlation.
• Provided R scripts and guidelines for reproducible data analysis and parameter estimation.

Main Results:

• Moisture dynamics were found to differ significantly between grasses and shrubs, and among grasses under varying aridity levels.
• The NLME model demonstrated superior goodness-of-fit and met statistical assumptions better than classical models.
• The NLME approach successfully accounted for spatial nesting, temporal dependence, variance heterogeneity, and seasonal patterns.

Conclusions:

• Nonlinear mixed-effects models offer a robust framework for analyzing complex ecological data, yielding ecologically relevant parameter estimates.
• The developed model can aid in forecasting critical fuel moisture levels for fire occurrence prediction.
• This work provides a valuable, reproducible example of NLME application for ecologists dealing with intricate datasets.