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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Response to technical comment 'A cautionary note for users of linear diversification dependencies'.

Hélène Morlon¹, Jonathan Rolland^2,3, Fabien L Condamine⁴

¹Institut de Biologie de l'ENS (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005, Paris, France.

Ecology Letters

|May 19, 2020

Summary

Researchers using the R-package RPANDA need to carefully interpret results from linear diversification analyses. This work clarifies how to correctly analyze functional diversification dependencies with time or environmental factors.

Keywords:

Extinction RPANDA macroevolution maximum-likelihood rate dependencies speciation

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Area of Science:

Ecology
Evolutionary Biology
Computational Biology

Background:

The R-package RPANDA is widely used for analyzing ecological and evolutionary diversification.
Interpreting results from diversification analyses, particularly linear dependencies, requires careful consideration.
A recent comment highlighted potential pitfalls in interpreting RPANDA outputs.

Purpose of the Study:

To provide clarifications for users of the R-package RPANDA.
To guide the interpretation of functional diversification dependencies.
To address the accurate analysis of diversification rates in relation to time and environment.

Main Methods:

Review and clarification of statistical approaches for diversification analyses.
Explanation of how to interpret functional diversification dependencies.
Guidance on incorporating time and environmental variables in diversification models.

Main Results:

Users must exercise caution when interpreting linear diversification dependencies.
Clearer guidelines are provided for analyzing functional diversification relationships.
The study emphasizes the importance of context (time, environment) in diversification analyses.

Conclusions:

Accurate interpretation of diversification analyses is crucial for ecological and evolutionary research.
This clarification enhances the utility of tools like RPANDA.
Users can now more reliably assess diversification patterns using functional dependencies.