Evaluation of modeling strategies for assessing self-thinning behavior and carrying capacity

Abstract

Self-thinning and site maximum carrying capacity are key concepts for understanding and predicting ecosystem dynamics as they represent the outcome of several fundamental ecological processes (e.g., mortality and growth). Relationships are often derived using alternative modeling strategies, depending on the statistical approach, model formulation, and underlying data with unclear implications of these various assumptions. In this analysis, the influence of contrasting modeling strategies for estimating the self-thinning relationship and maximum carrying capacity in long-term, permanent plot data (n = 130) from the mixed Nothofagus forests in southern Chile was assessed and compared. Seven contrasting modeling strategies were used including ordinary least squares, quantile, and nonlinear regression that were formulated based on static (no remeasurements) or dynamic data (with remeasurements). Statistically distinct differences among these seven approaches were identified with mean maximum carrying capacity ranging from 1,050 to 1,912 stems/ha depending on the approach. The population-level static approach based on quantile regression produced an estimate closest to the overall mean with site-level carrying capacity depending on tree species diversity and climate. Synthesis and applications. Overall, the findings highlight strong variability within and between contrasting methods of determining self-thinning and site maximum carry capacity, which may influence ecological inferences.

Self-thinning and site maximum carrying capacity are key concepts for understanding and predicting ecosystem dynamics as they represent the outcome of several fundamental ecological processes (e.g., mortality and growth). Relationships are often derived using alternative modeling strategies, depending on the statistical approach, model formulation, and underlying data with unclear implications of these various assumptions. In this analysis, the influence of contrasting modeling strategies for estimating the self-thinning relationship and maximum carrying capacity in long-term, permanent plot data (n = 130) from the mixed Nothofagus forests in southern Chile was assessed and compared. Seven contrasting modeling strategies were used including ordinary least squares, quantile, and nonlinear regression that were formulated based on static (no remeasurements) or dynamic data (with remeasurements). Statistically distinct differences among these seven approaches were identified with mean maximum carrying capacity ranging from 1,050 to 1,912 stems/ha depending on the approach. The population-level static approach based on quantile regression produced an estimate closest to the overall mean with site-level carrying capacity depending on tree species diversity and climate. Synthesis and applications. Overall, the findings highlight strong variability within and between contrasting methods of determining self-thinning and site maximum carry capacity, which may influence ecological inferences.