Should Conservation Cut-In Wind Speed Be Tailored to Site-Specific Conditions? Insights from Bat Activity Patterns at Windfarms in Northern Portugal

1. Introduction

Wind energy production is one of the strategies contributing to reduce human-induced greenhouse gas emissions; by decreasing the dependency from fossil fuels [1]. However, its spatial positioning should be planned within a rigorous ecological framework [2]. Indeed, wind turbines may cause barrier effects, disturbance and displacement of species, habitat loss and fragmentation, apart from casualties of birds and bats [3]. To minimize impacts on wildlife, it is important to use optimal sitting strategies for wind turbines, for example by avoiding sensitive habitats [4] or by keeping minimum distances from raptor nests and bat roosts [5].

Bats are long-lived, low fecundity mammals and, therefore, may be particularly vulnerable to large-scale sustained mortality events, such as those resulting from wind turbines [6]. As global wind energy development expands to meet energy demands [7] and mitigate climate change [8], it is essential to manage these impacts, especially since several species of bats are endangered and most provide crucial ecosystem services (i.e, pest control, pollination, plant dispersion) [9].

Effective positioning of wind farms is a promising approach to reduce impacts on bats, but because many species’ habitat requirements exhibit temporal variation over the annual cycle, it remains a challenging task [10]. A promising approach to mitigate on-site impacts on bats is to increase the cut-in wind speed— the threshold at which a wind turbine starts rotating and generating power (usually circa 3m/s) - to higher wind speeds. Even if there is no single, universal "standard" cut-in speed for conservation, it often involves increasing this value to 5.0 m/s (sometimes to higher values) [11]. Since bats exhibit peak flight activity at low wind speeds, this adjustment is considered to significantly reduce their risk of collision, while the resulting energy production losses remain modest [12].

Drawing on data from three wind farms in northern Portugal, we assess the wind speeds at 55 meters above ground associated with peak bat activity, to evaluate whether regular curtailment thresholds align with local activity patterns. Objectives include debating whether general rules associated with curtailing turbine operation results in effective lowering of bat mortality risk or if they should be tailored to specific locations and periods.

2. Materials and Methods

2.1. Study Area

This study was conducted in three wind farms located in mainland north and central Portugal, Gevancas windfarm (Serra do Alvão), Dom João e Feirão windfarm (Serra de Montemuro) and Azinheira windfarm (Serra do Viso), from 2017 to 2025, combining historical monitoring datasets with additional field surveys.

2.2. Estimating Wind Speeds

According to the Portuguese national authority for nature conservation (ICNF- Institute for Nature Conservation and Forests) and UNEP/EUROBATS guidelines, approximately 90% of bat activity near ground level occurs at wind speeds up to 3m/s [26]. However, this threshold refers to near- ground conditions and cannot be directly applied to turbine rotor height (bat impact zone). Therefore, bat activity was monitored at 55 m above ground level, encompassing both near-ground flight paths and the lower turbine rotor-swept zone. Due the impossibility of collecting wind speed at this height, the wind speed measurements were obtained from anemometers NRG S1 [13] placed in nearby meteorological towers or wind turbines (at 45m or 85m, respectively). To relate this regulatory reference value to bat activity and wind conditions, wind speed at 55m was extrapolated using the wind profile power law [14]. This procedure aims to translate a ground- level management threshold to the height at which wind turbines operate (bat impact zone). To calculate the wind speed at 55m, the wind power law or Hellmann power law was applied [14]:

$$\mu (z)=\mu (Z_{ref})({\displaystyle \frac{z}{Z_{ref}}}{)}^{\propto }$$

where:

• μ(z) is the estimated wind speed at the desired height at 55 m
• μ(Zref) is the known wind speed at the reference height at 45m (meteorological towers) or 85m (wind turbines);
• Z is the target height for wind speed estimation (55m);
• Zref is the reference height at which wind speed is known (45m or 85m);
• α is the roughness factor corresponding to moderately rough terrain typical of mountainous rural landscapes with shrub and forest cover, which characterize the study areas (0.18).

2.3. Bat Activity Monitoring

Bat activity recording was carried out using fixed monitoring points at 55 meters. For this purpose, meteorological towers at each wind farm were used to mount the equipment (automatic detectors Song Meter SM4BAT FS and microphones SMM-U2 Ultrasonic Microphone) [15,16], configured specifically for the targeted bat community. Bat activity was systematically monitored for seven consecutive nights each month, from March to October, totalling 56 nights of sampling (each night, recordings began at sunset and continued until sunrise). Wind speed values at each sampling point were obtained from anemometers installed on the meteorological towers (at 45m) or at the wind turbine (85m) (see please 2.2). The collected parameters allowed an analysis of the influence of wind speed on bat activity (measured by the number of bat passes/hour).

2.4. Data Processing

The identification of acoustic recordings collected were carried out using Kaleidoscope Pro® sound (Wildlife Acoustics Inc.) using the AutoID function to identify species or groups of species, followed by manual checking in order to detect possible mistakes [17,18]. The pulse identification, if necessary, was based on [19,20]. The number of bat passes detected per unit of time (bat passes/hour) was used for evaluating the patterns of activity. The cumulative bat activity, as a function of wind speed at 55 meters, was used to determine the wind speeds corresponding to 80% and 90% of total bat activity, considered thresholds for cut-in wind speeds of the wind turbines at each wind farm. To assess the influence of wind speed on bat activity patterns recorded at 55 meters, a Generalized Linear Mixed Model (GLMMs) were implemented, considering the random effects associated with the windfarm and month of the year. As the data consisted of counts, the Poisson distribution with a canonical (logarithmic) link function was found to be the most suitable. All statistical analyses were performed using the R statistical software and the lme4 package [21].

3. Results

The relationship between bat activity and wind speed differed across wind farms. At Gevancas (Figure 1), 80% to 90% of cumulative bat activity at 55m occurred at wind speeds up to 8.87 m/s. At the Azinheira (Figure 2), 80% of cumulative bat activity at 55m occurred at wind speeds up to 4.70 m/s, and 90% occurred at wind speed of up to 6.02 m/s. Finally, at Dom João e Feirão (Figure 3), 80% of the cumulative bat activity at 55 m occurred at wind speeds up to 5.24 m/s and 90% occurred at wind speeds up to 6.19 m/s.

The mixed-effects model depicts the random influence of time (month; σ² = 1.66) and location (wind farm; σ² = 0.23) in the patterns of bat activity observed. Even when considering the previous randomness, bat activity decreased significantly with increasing wind speed (β = −0.48 ± 0.01 SE, z = −30.93, p < 0.001) (Table 1).

4. Discussion

The results of this study demonstrate that bat activity is strongly influenced by wind speed, with higher activity occurring under low wind conditions. This pattern aligns with previous studies reporting reduced bat activity at higher wind speed [12,22]. However, the wind speed thresholds encompassing most bat activity differed substantially among the three wind farms, highlighting the importance of local ecological and landscape factors in mediating this relationship [23]. In fact, the substantial variation observed among wind farms suggests that the relationship between bat activity and wind speed is strongly site- dependent, probably reflecting differences in bat community composition, habitat structure, prey availability, and proximity to landscape features such as forest edges and watercourses [23,24]. Studies have proven that wind farms located in more complex and heterogeneous landscapes tend to concentrate higher bat activity, which can increase collision risk [23].

The risk of bat collisions with wind turbines is directly related to both activity levels and turbine operation [12,24]. Although most activity occurs up to wind speeds of 9m/s, bats tend to be more active under moderate wind speeds conditions (below 6–7m/s). Windmills typically begin operating at wind speeds above 3m/s, coinciding with the wind speed at which bat activity is highest. Therefore, the periods of greatest risk occur when turbines are active and bat activity is high [25].

Based on UNEP/EUROBATS guidelines and related scientific reports, the recommended wind speed threshold to trigger a cut-in-speed (curtailment) for wind turbines to protect bats is typically around 5 m/s (from 3 to 6.5 m/s), but with no clear indication on the heights associated [26]. Anyway, the risk of collision is highest for taller turbines and, therefore, mitigation must be tailored to the rotor-swept zone, especially when bat activity is high at the nacelle height. [26]. Additionally, current thresholds for wind speed emerge from approximately 90% of bat activity near ground level (5m) occurring under such low-wind conditions [11]. However, bat activity at ground level is associated with a low risk zone, whereas the heights at which turbine rotors operate correspond to areas of real risk of collision [27]. This threshold therefore does not account for the vertical gradient in wind speed and bat activity and its implication for turbine related mortality. By translating this reference value to turbine relevant heights, our results suggest that a uniform cut- in speed may not adequately capture site specific risk conditions [28]. In some cases, applying generalized thresholds could even lead to an unnecessary energy production loss [29].

Overall, these findings emphasize the need for adaptive, site-specific mitigation strategies at wind farms. Rather than relying on uniform curtailment thresholds, management measures should be informed by local bat activity patterns and wind conditions. Such tailored approaches have the potential to enhance bat conservation outcomes while maintaining wind energy efficiency and sustainability. Importantly, this approach does not aim to predict bat activity at rotor height, but rather to contextualize regulatory thresholds within turbine operational conditions, ensuring that mitigation measures are effective where the risk of mortality is real. These results also highlight the need for robust preconstruction studies at proposed wind farm sites. Gathering detailed, specific data on bat activity and wind speed profiles is essential to effectively determine the most appropriate cut-in speeds and implemented mitigation measures that maximize bat protection while minimizing energy production loss.

5. Conclusions

Given the variation in results across the studied windfarms, the authors advocate that turbine cut-in speeds should not be pre-defined or standardized, but rather adjusted based on prior studies and site-specific data to effectively reduce bat mortality risk. This study provides empirical support by moving beyond uniform curtailment thresholds, underscoring the importance of integrating site-specific ecological data into wind farm planning and management.