Characterizing tree trait variance over spatiotemporal scales.

Area of Science:

• Ecology
• Trait-based ecology
• Forest ecology

Background:

• Functional ecology often focuses on trait means, neglecting variance patterns.
• Understanding trait variance across spatiotemporal scales is crucial for ecological predictions.
• Existing models for taxonomic patterns lack application to functional trait variance.

Purpose of the Study:

• To apply Taylor's Power Law to functional trait variance.
• To identify general patterns of trait variance scaling across spatial and temporal scales.
• To investigate the influence of spatial versus temporal variability on trait variance.

Main Methods:

• Compiled 10-year monitoring data of tree seedling communities across 213 plots.
• Collected functional trait data from a subtropical forest in Puerto Rico.
• Applied Taylor's Power Law to examine trait variance at nested spatial and temporal scales.

Main Results:

• Trait variance scaling with the mean was idiosyncratic across different traits.
• Slopes of variance scaling varied more across spatial scales than temporal scales.
• Spatial environmental variability appears to be a stronger driver of trait variance than temporal variability.

Conclusions:

• Taylor's Power Law can offer insights into functional trait variance scaling.
• Idiosyncratic scaling suggests diverse drivers of variation across traits.
• Spatial variability plays a more significant role in trait variance than temporal variability, impacting predictive ecology.