Protecting Fruit Bat Populations While Powering the Grid
Modeling the long-term impact of wind farms on bat populations to guide biodiversity-safe energy development in South Africa.
Camissa recently developed population models for two fruit bat species to assess the long-term impact of wind farm fatalities. The goal was to support evidence-based decision-making around monitoring and mitigation — ensuring that wind energy development can proceed without undermining national conservation objectives.
Balancing Energy and Ecology
Can we expand wind energy without pushing fruit bats to decline?
Wind farms are essential to South Africa’s renewable energy strategy, but they can also impact wildlife — including vulnerable bat species. At two Eastern Cape wind farms, estimated bat fatalities raised questions: Are these deaths sustainable? Or are we quietly putting national populations at risk? The clients needed robust scientific insight to understand impacts and ensure their projects do not impact the long term viability of fruit bat populations. However, one of the major challenges in the sector is knowing when fatalities are ecologically significant. Natural populations can be resilient, but without understanding when mortality begins to affect long-term viability, it’s difficult to know whether mitigation actions are truly needed — or not.
Modeling the Risk
A counterfactual approach to understanding population-level impacts
Population models provide a structured way to assess whether fatalities are likely to lead to population declines over time. Camissa built a stochastic population model to simulate how two fruit bat species — the Egyptian fruit bat and Wahlberg’s epauletted fruit bat — might fare under different wind turbine fatality rates over 100 years. The model incorporated key demographic processes, including age-specific survival, reproductive output, maturation rates, and annual fatalities attributable to wind energy.
Using a matched runs approach, two parallel models were run for each species — one with wind turbine fatalities and one without — to isolate the specific impact of wind energy. This counterfactual comparison makes it possible to explore how population trajectories diverge over time due to anthropogenic mortality. Because both scenarios use identical parameters, the approach is relatively robust to uncertainty in inputs — making it especially useful in data-limited contexts. It allows decision-makers to evaluate whether observed impacts are likely to drive meaningful ecological change, without relying on specific thresholds.
From Insight to Action
Using science to support nature-positive energy
The modeling showed that wind energy impacts can be assessed in a structured, repeatable way — even where data are limited. Both studied wind farms were found to pose a low risk of population-level impacts based on current fatality patterns. However, the analysis also highlighted opportunities to strengthen monitoring and apply adaptive management where needed. The approach gives operators a tangible tool to manage ecological risks — and can be replicated across the country to guide responsible wind energy expansion.
ANY QUESTIONS?
To find out more about this project, please get in touch.
Jonathan Aronson