Confronting species distribution model predictions with species functional traits

Area of Science:

• Ecology
• Biogeography
• Climate Change Biology

Background:

• Species distribution models (SDMs) typically use only climate and presence data.
• Functional traits and biological data are rarely incorporated into SDM development or validation.
• Understanding the link between climate suitability and species traits is crucial for ecological studies.

Purpose of the Study:

• To implement a species distribution model (Maxent) for predicting global climate habitat suitability for Grass Carp (Ctenopharyngodon idella).
• To test the relationship between predicted climate habitat suitability and Grass Carp individual growth rates in wild and stocked populations.
• To explore the potential of SDMs to infer biological mechanisms beyond species occurrence.

Main Methods:

• Implemented the Maxent algorithm to model Grass Carp habitat suitability globally.
• Collected Grass Carp growth rate data from wild (N=17) and stocked (N=51) populations across six continents.
• Utilized correlation analysis to assess the relationship between predicted suitability and observed growth rates.

Main Results:

• The Grass Carp Maxent model showed high accuracy in reflecting global occurrence data (AUC = 0.904).
• A significant positive correlation was found between predicted climate suitability and wild Grass Carp growth rates (r = 0.5).
• No significant correlation was observed between predicted suitability and stocked Grass Carp growth rates (r = -0.26).

Conclusions:

• Species distribution models can provide insights into the relationship between climate suitability and species' functional traits, like growth rates.
• SDMs offer a valuable tool for understanding the underlying biological mechanisms driving species distributions.
• The findings suggest that SDMs can be extended to predict ecological processes beyond simple species presence/absence.