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Generalized Additive Modeling of Ecological Data With mgcv: New Adequacy Assessment Tools.

Julien Mainguy1, Rachel McInerney2, Russell B Millar3

  • 1Direction principale de l'expertise sur la faune aquatique Ministère de l'Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs Québec Québec Canada.

Ecology and Evolution
|January 12, 2026
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Summary

Generalized additive models (GAMs) offer better ecological analysis than GLMs by handling nonlinearities. New R package functions aid in validating GAM adequacy and detecting overfitting, improving ecological relationship interpretation.

Keywords:
adjusted deviance explaineddeviance residualshalf‐normal plot with a simulated envelopemgcViz scoreunder‐ and overfitting

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Area of Science:

  • Ecology
  • Statistics
  • Environmental Science

Background:

  • Generalized additive models (GAMs) extend generalized linear models (GLMs) to capture nonlinear ecological relationships.
  • Validating GAMs is complex due to smooth functions and potential overfitting from excessive flexibility.
  • Existing methods may not adequately assess GAM fit and flexibility in ecological contexts.

Purpose of the Study:

  • To present novel methods for assessing the adequacy of GAMs fitted using the mgcv package in R.
  • To introduce a metric for detecting under- and overfitting in GAMs.
  • To demonstrate the utility of these methods in fisheries-related ecological analyses.

Main Methods:

  • Utilizing half-normal plots with simulated envelopes from the hnp package for GAM adequacy assessment.
  • Employing a new metric from the mgcViz package to evaluate overfitting and underfitting.
  • Applying these techniques to analyze continuous, count, and discrete proportion data in fisheries.

Main Results:

  • The hnp package functions provide a robust visual tool for checking GAM assumptions.
  • The mgcViz metric effectively identifies issues of under- and overfitting.
  • These methods enhance the statistical rigor for interpreting nonlinear ecological patterns.

Conclusions:

  • The presented R-based tools improve the validation and interpretation of GAMs in ecological research.
  • These approaches offer valuable statistical context for understanding complex ecological relationships.
  • The methods are broadly applicable to various ecological datasets, including fisheries data.