Modelling the unpredictability of future biodiversity in ecological networks.

Area of Science:

• Ecology
• Biodiversity Science
• Conservation Biology

Background:

• Predicting future biodiversity loss is crucial for conservation efforts.
• Simple extinction models often assume independent species loss, ignoring ecological interactions.
• Interacting species face complex extinction risks, necessitating more sophisticated models.

Purpose of the Study:

• To evaluate how incorporating ecological dependencies affects the accuracy of biodiversity loss predictions.
• To compare extinction models with and without species interactions.
• To understand the impact of food web structure on extinction prediction uncertainty.

Main Methods:

• Comparison of two extinction models: one with prey-dependent extinction rates and one with independent extinctions.
• Utilizing simulations to analyze biodiversity loss over time.
• Assessing the variance in future biodiversity predictions under different ecological scenarios.

Main Results:

• Models incorporating ecological dependencies reveal greater uncertainty in biodiversity loss predictions.
• The expected loss of biodiversity is similar between models, but variance is higher with interactions.
• Increased uncertainty arises from the non-independence of species responses to extinctions.

Conclusions:

• Explicitly including ecological dependencies in extinction models increases predictive uncertainty.
• More information about species interactions does not necessarily improve biodiversity loss prediction accuracy.
• Food webs highly dependent on basal species exhibit the largest increase in extinction variance.