Be intentional with model complexity when scaling functional traits
Yiluan Song
,
María Natalia Umaña,
Kai Zhu
ESA 2024 presentation by Dr. Kai Zhu
Code
Highlights
There are increasing interests in measuring and modeling finescale variations in functional traits.
With hierarchical Bayesian models, we showed in two cases that aggregation of traits might lead to similar inference with lower model complexity.
We argue that hierarchical models should be used to explore the structure of data and the scale of process, in order to inform model complexity.
Motivation
Size traits vary with latitude (
Joswig et al., 2021
).
Climatic and soil variables explain variance in traits (
Joswig et al., 2021
).
Dispersion of species, populations and individuals in the trait space (
Albert et al., 2010
).
Model complexity can be decomposed into multiple components (
Malmborg et al., 2024
).
Case study 1: Trait-environment relation
Variations of specific leaf area between and within communities.
Individual-level model.
Community-level model.
Hierarchical model.
Model complexity assessed following
Albert et al., 2010
.
Case study 2: Climate niche change over time
Changes in abundance of species with different temperature and temperature niches at grassland plots in Elkhorn Slough, California.
Community-level model.
Species-level model.
Hierarchical model.
Model complexity assessed following
Albert et al., 2010
.
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