Mixed Evidence for Species Diversity Affecting Ecological Forecasts in Constant Versus Declining Light

Area of Science:

• Ecology
• Environmental Science
• Conservation Biology

Background:

• Accurate ecological forecasts are crucial for management and conservation, but factors influencing forecastability remain unclear.
• Environmental change and biodiversity are often presumed to hinder ecological forecasting, yet this has lacked experimental validation due to data demands.

Purpose of the Study:

• To experimentally test how species richness and environmental change (light levels) affect the accuracy of ecological forecasts.
• To investigate the drivers of forecastability in ecological systems using a long-term microcosm experiment.

Main Methods:

• Conducted a long-term microcosm experiment with 30 protist communities, manipulating species richness and light conditions (constant vs. decreasing).
• Collected high-resolution time series data on species abundances, community biomass, and oxygen concentrations over 41 weeks.
• Applied data-driven forecasting methods to predict ecological variables and analyzed forecast accuracy in relation to richness and light.

Main Results:

• Species richness had a minor interactive effect on species abundance forecasts, decreasing accuracy in constant light but increasing it in declining light.
• Forecast accuracy for aggregate properties (biomass, oxygen) was not influenced by richness or light and was comparable to species-level forecasts.
• Time series characteristics like variability and autocorrelation partially explained the observed effects of richness and light on forecast accuracy.

Conclusions:

• Found no strong evidence that higher diversity reduces ecological forecastability; instead, diversity may enhance predictability under changing environmental conditions.
• Results suggest that conserving biodiversity could improve decision-making in conservation and management by increasing the predictability of species populations.
• Promoting species diversity may be a viable strategy to enhance the resilience and predictability of ecosystems facing environmental change.