Assessing model adequacy leads to more robust phylogeographic inference

Area of Science:

• Evolutionary Biology
• Population Genetics
• Computational Biology

Background:

• Phylogeographic studies rely on complex demographic models and large datasets.
• Current applications of these models may lead to researcher misinterpretations and compromised inferences.
• The field faces challenges in model selection, results evaluation, and model assessment.

Purpose of the Study:

• To highlight critical challenges in the application of demographic models in phylogeography.
• To argue for the importance of model selection and model evaluation in phylogeographic analyses.
• To guide researchers toward more robust and reliable phylogeographic inferences.

Main Methods:

• Review of common practices in phylogeographic data analysis.
• Identification of key challenges in model application: model selection, results evaluation, and model evaluation.
• Conceptual argument for prioritizing model selection and evaluation.

Main Results:

• Phylogeographic research disproportionately emphasizes the evaluation of analytical results (parameter estimates, delimitations, topologies).
• Insufficient attention is paid to the crucial initial steps of selecting an appropriate model and statistically evaluating its fit to the data.
• This imbalance can lead to flawed interpretations and unreliable conclusions.

Conclusions:

• Model selection and model evaluation are essential for valid phylogeographic inference.
• A shift in focus towards these underemphasized aspects is necessary for advancing the field.
• Prioritizing rigorous model assessment will enhance the reliability of phylogeographic studies.