Phytoplankton traits predict ecosystem function in a global set of lakes

Area of Science:

• Ecology
• Limnology
• Trait-based ecology

Background:

• Predicting ecosystem function from environmental conditions is crucial in ecosystem ecology.
• Traditional ecosystem models often lack broad applicability, requiring multiple regional versions.
• Trait-based approaches show promise for predicting ecosystem function, with limited prior application in aquatic systems.

Purpose of the Study:

• To evaluate the effectiveness of a trait-based model for predicting gross primary production (GPP) in lake ecosystems.
• To compare the performance of a trait-based GPP model against traditional ecosystem models.
• To demonstrate the utility of trait-based modeling for ecosystem function prediction across varied conditions.

Main Methods:

• Developed a trait-based model using data from over 1000 US lakes.
• Incorporated laboratory-generated phytoplankton trait data into the model.
• Validated the model using GPP observations from a global set of lakes.

Main Results:

• The trait-based model demonstrated strong predictive performance for GPP.
• Model efficacy was comparable to or better than two traditional ecosystem models.
• Successful spatial and temporal validation across diverse environmental settings was achieved.

Conclusions:

• Trait-based models are effective for predicting lake ecosystem GPP.
• This approach offers a unified and broadly applicable method for ecosystem function prediction.
• Phytoplankton traits provide a robust basis for understanding and forecasting ecosystem processes.