Studies conducted in Europe and North America have documented significant bat mortality at wind farms. According to Voigt et al. (2015), mortality rates range from 1 to 10 bats per turbine per year in Europe, with spikes of up to 40–50 individuals during migration seasons or in particularly sensitive areas.

The main causes of bat mortality include:

• Direct collision: Bats may fly into moving blades, especially during migration or in low-visibility conditions.
• Barotrauma: Sudden pressure changes near turbines can cause fatal internal injuries without physical contact.
• Attraction to turbines:

As noted by Cryan & Barclay (2009), bats may be attracted to turbines due to:

✓ Insect concentrations around the structures

✓ Mistaking turbines for potential roosting sites

✓ Curiosity about unfamiliar objects during exploratory behavior

Chiroptera—commonly known as bats—are mammals of critical ecological importance. Unfortunately, their populations have significantly declined in recent years worldwide, with many species now at risk of extinction.

While wind power is a vital component of the clean energy transition, wind turbines can pose a serious threat to bats due to several factors, including:

• Direct collisions with turbine blades
• Barotrauma (injuries from sudden air pressure changes)
• Habitat disruption or loss
• Barriers across migratory flight paths

  BAT SPECIES MOST AT RISK

  The species most affected in Italy and across Europe tend to be migratory and those that forage in open spaces, including:

  • Pipistrellus pipistrellus (Common Pipistrelle)
  • Pipistrellus kuhlii (Kuhl’s Pipistrelle)
  • Hypsugo savii (Savi’s Pipistrelle)
  • Nyctalus noctula (Common Noctule)
  • Nyctalus leisleri (Leisler’s Bat)
  • Miniopterus schreibersii (Schreibers’ Bent-winged Bat)
  • Tadarida teniotis (European Free-tailed Bat)

> Limitations of Traditional methods and Radar Systems for Bird and Bat Monitoring at Wind Farms

Monitoring wildlife activity at wind farms using conventional methods presents significant challenges that hamper effective conservation efforts. Human observers, while valuable for their expertise, face inherent restrictions including daylight-only observations, susceptibility to fatigue, and the impossibility of maintaining continuous vigilance. These constraints create substantial gaps in data collection, particularly during nocturnal periods when bat activity peaks and collision risks are highest.

Avian Radar systems offer technological advantages over human observation but come with their own set of limitations. Their prohibitively high cost represents a major barrier to widespread implementation, making them financially unfeasible for many wind farm operations, especially smaller installations. While radar can effectively penetrate through fog and some adverse weather conditions, they still frequently struggle to distinguish between species, making it nearly impossible to differentiate between birds, bats, and non-wildlife targets like insects or environmental debris. Their accuracy also diminishes considerably at the lower altitudes where many bat species typically fly, creating blind spots in coverage around turbine blades where collisions most commonly occur.

The data collection and analysis process suffers from inconsistent methodology across sites, making comparative studies problematic. Traditional monitoring approaches typically generate limited quantitative data suitable for rigorous scientific analysis, with sampling protocols frequently missing peak activity periods. This patchy data collection creates an incomplete understanding of wildlife behavior patterns and their correlation with environmental variables. Post-mortem surveys significantly underestimate mortality rates due to scavenger removal, further complicating accurate impact assessment.

From an operational perspective, conventional methods rarely provide the real-time data necessary for immediate protective actions. The gap between wildlife detection and implementing protective measures like turbine curtailment creates a critical delay during which collisions may occur. Large wind farm installations present particular challenges due to their extensive spatial requirements, which conventional monitoring struggles to cover adequately. Furthermore, integration difficulties between monitoring systems and turbine controls hamper efficient automated responses to wildlife presence.

These comprehensive limitations highlight the pressing need for advanced monitoring solutions that can overcome these constraints through continuous, accurate, and species-specific detection with automated response capabilities—precisely the gaps that modern AI-powered and thermal imaging systems like BCMS® Ventur aim to address.