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Quantitatively Testing Predictions From Mechanistic Models: A Case Study for Island Biodiversity.

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Ecological models were tested for their ability to predict island biodiversity patterns beyond initial data. A neutral model demonstrated accurate predictions for species composition and diversity across 17 archipelagos.

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beta diversityimmigrationneutral modelnichespecies richness

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Area of Science:

  • Ecology
  • Biodiversity Science
  • Theoretical Ecology

Background:

  • Ecological models are crucial for understanding biodiversity patterns.
  • Previous research fitted models to island alpha diversity but lacked predictive validation for other biodiversity metrics.
  • Testing predictive power on unseen data is essential for model robustness.

Purpose of the Study:

  • To evaluate the predictive accuracy of ecological models using island alpha diversity data.
  • To assess if models fitted to alpha diversity can predict other biodiversity patterns, such as species composition similarity.
  • To determine the efficacy of neutral models in explaining island biodiversity.

Main Methods:

  • Fitted ecological models using existing island alpha diversity data.
  • Tested model predictions against independent biodiversity datasets, including inter-island species similarity.
  • Analyzed 17 distinct archipelagos to validate model performance.

Main Results:

  • A neutral model provided reasonable predictions for island biodiversity patterns across 17 archipelagos.
  • The model's confidence intervals accurately reflected the observed data, with a mean deviation of 0.19.
  • Observed island biodiversity patterns were largely explained by neutral competition and dispersal limitation.

Conclusions:

  • Neutral models offer a parsimonious explanation for island biodiversity patterns.
  • The study validates the predictive capability of neutral models for higher-order diversity statistics.
  • Ecological models fitted to alpha diversity can successfully predict other biodiversity measures.