Calibrated Ecosystem Models Cannot Predict the Consequences of Conservation Management Decisions

Area of Science:

• Ecology
• Ecological modeling
• Conservation science

Background:

• Ecosystem models are crucial for predicting management intervention outcomes in applied ecology and conservation.
• High-dimensional, nonlinear models often face limitations due to scarce data for calibration and validation, impacting their reliability as decision-support tools.

Purpose of the Study:

• To calibrate ecosystem models using time series data from experimental microcosm ecosystems.
• To assess the predictive accuracy of these calibrated models in forecasting ecosystem dynamics and responses to management interventions.

Main Methods:

• Calibration of ecosystem models against time series data from 110 experimental microcosm ecosystems.
• Assessment of model predictive performance for future dynamics and management scenarios.

Main Results:

• Multiple, divergent parameter sets achieved equivalent, good fits to the calibration data for each ecosystem.
• The calibrated models exhibited poor predictive accuracy for future ecosystem dynamics and responses to management interventions.
• Model failure was attributed to the inability of calibration to resolve the nature of interspecific interactions.

Conclusions:

• Calibration of ecosystem models with limited real-world data does not guarantee accurate predictions of management intervention consequences.
• The findings raise concerns about the utility of current ecosystem modeling approaches in supporting applied ecological decision-making.
• Further research is needed to improve the predictive power of ecosystem models, particularly in resolving species interactions.