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Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

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Published on: March 13, 2014

Advances in modeling trait-based plant community assembly.

Daniel C Laughlin¹, David E Laughlin

¹Department of Biological Sciences, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand.

Trends in Plant Science

|June 4, 2013

Summary

Two new trait-based models predict species abundance using different math. Maxent and Traitspace offer distinct ecological predictions, useful for understanding community assembly and restoration efforts.

Keywords:

environmental filtering hierarchical Bayesian model intraspecific trait variation limiting similarity maximum entropy natural range of variability niche differentiation species distribution modeling trait convergence trait divergence

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Area of Science:

Ecology
Community Ecology
Ecological Modeling

Background:

Predicting species relative abundance is crucial for community ecology.
Trait-based models offer a mechanistic approach to understanding community assembly.
Existing models vary in their mathematical foundations and predictive power.

Purpose of the Study:

To review and compare two novel trait-based models of community assembly.
To analyze how these models predict species relative abundance from a regional pool.
To evaluate their differing mathematical approaches and potential outcomes.

Main Methods:

Examination of the Maxent model, which maximizes probability distribution under trait constraints.
Analysis of the Traitspace model, which uses environmental filtering.
Comparison of the mathematical underpinnings and predictive outputs of both models.

Main Results:

Maxent and Traitspace employ fundamentally different mathematical strategies.
Predictions from the two models can diverge significantly.
Neither model inherently maximizes functional diversity due to prioritizing environmental filtering.

Conclusions:

Traitspace can assess limiting similarity effects by including intraspecific trait variation.
The range of outcomes from both models can inform natural variability in restoration projects.
These models provide valuable tools for ecological forecasting and conservation.