# Wind turbine threats to bustards: spatially explicit risk modelling from empirical data

V Bretagnolle, A Merot, A Martineau, E Dedeban, R Chargé, E Debenest

► To cite this version:

V Bretagnolle, A Merot, A Martineau, E Dedeban, R Chargé, et al. Wind turbine threats to bustards: spatially explicit risk modelling from empirical data. Biological Conservation, 2025, 310, pp.111391. 10.1016/j.biocon.2025.111391. hal-05206263

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# Wind turbine threats to bustards: spatially explicit risk modelling from empirical data

V. Bretagnolle<sup>a,b,*</sup>, A. Merot<sup>c</sup>, A. Martineau<sup>c,d</sup>, E. Dedeban<sup>a</sup>, R. Chargé<sup>c</sup>, E. Debenest<sup>c</sup>

## ARTICLE INFO

### Keywords:

Tetrax tetrax

Rotor blade

Hazardous flights

Collision risk

Mortality

Flight altitude

## ABSTRACT

Wind energy is considered a renewable alternative to fossil fuels, but with the increasing development of wind farms, the threat to birds is a growing concern. Flight height is a critical behaviour trait when assessing risk with wind turbines, as it can affect both collision risk and avoidance behaviour. This study used 12 years of GPS data collected on the little bustard, to assess the potential risk of wind farms during breeding or migration. Long-term demographic data are unavailable for this species, and collision-mortality data largely absent. We thus estimated mortality by evaluating collision risk with wind turbines through flight behaviour characteristics. Using a dataset of 8,891,945 GPS fixes, we found that during breeding season, little bustards spent 0.8 % of their time flying, with males flying about twice as much as females. Maximal flight activity was at twilight, especially for females. 18–60 % of all flights were at a height within the range of blades: more at night and more in females than in males. Using the explicit spatial distribution of both bustards and wind turbines in the study region, we estimated mortality risk to be above 1 % per year, i.e. an increase in the adult mortality rate of about 10 %. This increased mortality seems unsustainable for this endangered population. Our method of explicit risk calculation could be transferred to any large-sized species for which GPS data of flight activity is available. We recommend avoiding the development of further turbines within 2000 m of any current male location.

## 1. Introduction

Wind energy is seen as a renewable alternative to fossil fuels in order to reach CO₂ emission targets (Veers et al., 2019). However, the exponential rate of development of wind farms (>10 % annually) and their increasing size (Conkling et al., 2022) are a growing concern in terms of their potential threat to flying organisms, in particular birds (Martín et al., 2018). Wind turbines can have four main effects: the indirect effects of displacement due to disturbance, barrier effects and habitat loss, and the direct effect of collision (Thaker et al., 2018). To determine risk, several factors are taken into account: exposure which is related to the species' potential co-occurrence with a wind farm, sensitivity which is related to the probability of mortality due to collision, and vulnerability, a combination of exposure and sensitivity (Michael et al., 2022). Vulnerability depends on species morphology, behaviour, demography, habitat or conservation status (Furness et al., 2013). The estimation of risk requires data on bird movements or on mortality rates, the latter

usually determined by searching for carcasses, since collision risk, habitat loss and disturbance effects have been shown to be highly taxon and site dependent (Fernández-Bellon et al., 2019). Data must therefore be collected for the species in question, which can be difficult if the species is rare, elusive or nocturnal. A crucial factor when assessing collision risks and avoidance behaviour are flight characteristics, including flight height (Furness et al., 2013; Schaub et al., 2019). In the past, studies analysing flight height relied on visual estimation of flight altitude, by eye or with radar (Péron et al., 2017). Today, high-resolution GPS telemetry has revolutionized data acquisition, as it is able to collect data at 1 s resolution (Schaub et al., 2024).

The little bustard *Tetrax tetrax* is a Eurasian steppe bird considered near threatened across its distribution and endangered in its western range, where it has declined by around 50 % over the last 10 years (Morales and Bretagnolle, 2022). Since 1970, the species has disappeared in at least 10 European countries, with a 94 % population decline in western and northern France between 1982 and 1996

<footnote><sup>a</sup> CEBC, UMR7273 CNRS & La Rochelle Université, 79360, Beauvoir sur Niort, France</footnote>

<footnote><sup>b</sup> LTSER Zone Atelier "Plaine & Val de Sèvre", 79360, Beauvoir sur Niort, France</footnote>

<footnote><sup>c</sup> Groupe Ornithologique des Deux-Sèvres, 79000, Niort, France</footnote>

<footnote><sup>d</sup> Ligue pour la Protection des Oiseaux France, 17305 Rochefort Cédex, France</footnote>

<footnote>* Corresponding author at: V. Bretagnolle, CEBC, UMR7273 CNRS & La Rochelle Université, 79360, Beauvoir sur Niort, France.
E-mail address: breta@cebc.cnrs.fr (V. Bretagnolle).</footnote>
(Morales and Bretagnolle, 2022). Factors of decline concern fecundity, e. g. decrease in invertebrate food resources, nest destruction during crop harvesting, and habitat loss and degradation (Bretagnolle et al., 2022). Adult mortality has also become an issue, including illegal killing and collision with power lines (Silva et al., 2014; Marcelino et al., 2017), and possibly wind turbines (Silva et al., 2022). The species has certain behaviours known to make species prone to collision (Bernardino et al., 2018). These include flocking behaviour, site fidelity, limited flight manoeuvrability (Janss, 2000), high flight speed (Martin and Shaw, 2010), and narrow sight fields (Martin and Shaw, 2010; Barrientos et al., 2012). In addition, unlike other bustards and large soaring birds, little bustards migrate by night (Villers et al., 2010), making them less able to detect the presence of infrastructure (Alonso et al., 2020). Little bustards inhabit lowland fields and grasslands, where wind farm construction projects are on the rise. In Spain, the risk of wind farms to little bustards has been evaluated through the number of collisions (Marques et al., 2021). In France, no studies have investigated the effects of wind farms on the little bustard's movements or mortality rates. Data on the number of collisions are unavailable, yet the species lives in an area subject to wind farm projects planning hundreds, or possibly thousands, of wind turbines.

To help filling this gap, this study assessed the potential risk of existing and planned wind farms within the migrating western French population's breeding area during breeding and migration. Due to the threatened status of the species, few turbines have so far been set up in close proximity to its display areas, so evaluation of avoidance behaviour or wind turbine presence has never been carried out. The study investigated the species' flight characteristics using advanced GPS technology and used these to quantify hazardous flights, i.e. flights within the rotor swept zone, considering the total duration of such

hazardous flights per breeding season as an indirect surrogate of colli-
sion risk to estimate potential mortality rates. We used spatially explicit
data of both bustards and wind farms positions to establish sex- and
period-specific risk models, as in this species male and female behav-
iour, ecology and life-history traits are strongly sexually dimorphic and
season-dependent.

## 2. Material and methods

2.1. Study area and species

The little bustard is the smallest of western Palearctic bustards (body
weight of males 908.0 ± 72.2, n = 18), females 883.4 ± 86.7, n = 12).
Our study area is located in Poitou-Charentes in Western France, where
bustards breed in arable farmlands. This population has declined from
7500 males in 1979, to just 330 in 2016 – a 96 % reduction in 35 years
(Morales and Bretagnolle, 2022). The study area was determined using
the locations of GPS fixes for the species (see Fig. 1), and hosted on
average 258 males between 2008 and 2020, i.e. c.90 % of the Poitou-
Charentes population. Since 2008, males have been surveyed here
annually each spring with standardized protocols (Devoucoux et al.,
2019; Bretagnolle et al., 2024), and each male each year is precisely
located and mapped. During this period, post-breeding gathering sites
were also surveyed annually, with 2 to 4 counts each year over the entire
region. Since 2004, 10 NATURA 2000 sites totalling about 170,000 ha
have been designated for the conservation of the little bustard, eight of
which currently host the species. Proactive conservation efforts include
three National Action Plans, agri-environment schemes (Berthet et al.,
2012), a captive-breeding programme, and a reinforcement programme
(Attié et al., 2022). The species' breeding ecology is reasonably well

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** A map of the study area showing the NATURA 2000 sites (in light grey) and the main NATURA 2000 site where the captures were performed (in dark grey). Panel 1A shows all GPS fixes obtained 2012–2023, as well as all flights of little bustards recorded 2012–2023. Panel 1B shows all current or planned wind farms, and the location of male little bustards.</figcaption></figure>
known (Bretagnolle et al., 2022), as is its movement ecology (Alonso et al., 2020), but its demographic parameters, such as adult survival rate, are virtually unknown (although see (Attié et al., 2022) which precludes any detailed explicit survival analysis.

## 2.2. Data collection

The data were obtained from GPS satellite trackers attached to wild birds (N = 31 individuals, 2012–2023) and captive-bred birds (N = 66, one in 2009, all others 2017–2022). Over 12 years, individuals were fitted with either UvA-BiTS (N = 10; Bouten et al., 2013; www.uva-bits.nl), Milsar RadioTag (N = 12) & Milsar GSM (N = 20, https://milsar.com), or Ornitela GPS/GSM (N = 57, https://www.ornitela.com/). Capture, handling and fitting the birds with GPS tags was carried out with the authorisation of the Museum National d'Histoire Naturelle (PP_479). Captures were done at night, using a thermal camera to locate and approach the birds, and then catching them with a net bell mounted on a 7-m perch. The weight of the GPS tags was about 1 % of a bustard's body weight. Two birds were retagged with new GPS trackers because the previous one stopped working, and 12 GPS trackers were successively fitted on different individuals including three on three birds due to mortality, so the number of individuals fitted differs from the number of GPS trackers (Table S1 in Supplementary Material 1). Since many GPS tags worked for more than one year, the actual number of individual per year was 133 (see Table S2A in SM1). While most of the little bustards in this study were caught in one of the NATURA 2000 sites (Fig. 1a), the GPS fixes cover almost all of the study area due to the movements of the tagged birds (Fig. 1a).

The trackers collected three-dimensional GPS positions and instantaneous speed, and were set to collect data at different temporal resolutions, which differed between GPS manufacturer and tracker year/ type (Fig. S1 in SM1). The altitude-above-ground level (in metres; hereafter, agl) for each tracking point was calculated by subtracting the ground elevation from digital elevation models (SRTM 25 m), from the altitude above sea level recorded by the tracker. Ground elevation data were obtained through the European Environment Agency (https://lan.d.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=mapview). Data were remotely downloaded from the trackers using the UvABiTS antenna system (Bouten et al., 2013), Milsar antenna RadioTag, or through the GSM network for the other two transmitters. In 2012–2022, to save battery life, high-resolution tracking data with fix interval of 3 s was collected mostly during a 1 h-period per day, with the period regularly shifted to prevent bias. This obtained fixes from a total of paired 106 individual by year, from 77 different individuals, for a total of 3,835,271 fixes. In 2023, the setting of the Ornitela devices was changed, and we used a switch that triggers 1-s interval fixes as soon as an individual was detected flying using a threshold value on the accelerometer. This resulted in 1,794,602 fixes that year, of which 42 % were in flight, from 22 different individuals (Table S2B). As the data were collected over 12 years using various types of trackers with varying time resolutions, we split the analyses into two separate data sets and checked whether the results were altered by data origin.

## 2.3. Data processing

The detailed procedure is given in SM 2 which includes calibration of altitude, a summary of which is presented below. Data were filtered, first selecting arbitrarily only fixes obtained with at least four satellites, second excluding those with positive altitudes over 300 m in breeding and post-breeding periods or 800 m in migration periods; third, excluding those with negative altitudes below –30 m. The latter threshold was deduced from GPS calibration as the altitude precision of the least accurate platforms was 30 m (see SM 2). However, negative altitudes between –30 m and 0 were kept to avoid bias resulting from removing these negative values while analysing the entire distribution of flight heights (Péron et al., 2020; Schaub et al., 2024). Fourth, we also

excluded flight speeds over 130 km/h (Villers et al., 2010). In total, filtering removed only 2.19 % of the entire dataset. Next, we divided survey periods into three stages: migration, combining both migrations, breeding and post-breeding. For males, post-breeding was set from 31 July until migration to Iberia, and for females from 15 August until migration. 60.8 % of birds did not migrate in specific years, especially released captive-bred bustards (N = 74). These birds were either sedentary and remained on breeding sites, or showed erratic movements during winter. In both cases, we differentiated the breeding from the non-breeding period by using, for a given year, the mean date of migration of the other birds. Therefore, for non-migrating birds, we had only two periods of interest, breeding and post-breeding (same rules for the start of the post-breeding period).

We found that the type of tracker affected the calculation of time spent in flight (Fig. S3 in SM2; see also Fig. 3D), with the Milsar and Ornitela tags used until 2022 underestimating this by about one-third compared to the Ornitela tags used in 2023. We therefore split the analyses into two periods, or two data sets: 2012–2022 and 2023. Using 2012–2022 data, we restricted this analysis to individuals that had >1 fix per hour, which eliminated 2012–2014 data for the calculation of time spent in flight. We considered as 'in flight' any fix with a ground speed over 15 km/h (see (Weiser et al., 2024)), whatever the altitude, and for speeds between 5 and 15 km/h, only fixes with an altitude above a value given by the equation in SM2. All other fixes were considered landed for this dataset, with an altitude of 0 m agl. Then, the percentage of time spent flying was derived as the proportion of fixes in flight out of the total number of fixes, a ratio calculated at different time frames (e.g. 24 h, week, season) or by sex, multiplied by 100. To calculate the exact duration of a flight, we considered either the first and last flight fix, which by definition, was preceded or followed by a landed fix. However, when these two flight fixes were separated by more than 90 s because of the GPS temporal resolution, we could not assume that the bird had been always flying. In such cases, we used R-package EnvStats to sample a random value of flight duration in seconds from a 0-truncated normal distribution centred at 45 s, with an SD of 13 s, with minimal and maximal values were 0 and 90 s. We thus obtained for each flight a duration, a distance covered, and a distribution of altitudes (one for each fix), from which we extracted the median and maximal values.

For the high-resolution 2023 dataset, percentage of time spent flying was more straightforward to obtain, since the GPS recorded a fix every second (see Fig. S4 in SM2 for dealing with batteries too low to obtain 1 fix per second, as well as false detections or very short flights). Despite the difference between the two datasets, we obtained a proportion of time spent flying per individual at any time step, in the form of percentage of fixes flying or length of time spent in flight out of total time surveyed, multiplied by 100, as well as flight duration.

## 2.4. Statistical analyses

We grouped the collected flight data into 24-h periods for each individual. For the 2012–2022 dataset, since there was no high resolution data, we simply calculated the percentage of time spent flying midnight to midnight as the proportion of fixes spent flying out of the total number of fixes that day multiplied by 100. For the 2023 dataset, we split the 24-h period into night and day using sunset and sunrise, and then added 30 min after and before, i.e. 1 h in total, to define twilight hours. The number of seconds spent flying day, twilight or night was obtained, allowing the calculation of percentage of time spent flying daily. In both datasets, to ensure meaningful statistical testing, we arbitrarily kept only days with at least 100 fixes for 2012–2022 (1322 ind by day were suppressed, removing 14 % of the data), while in the 2023 dataset, only day/night periods with at least 5000 s of monitoring were kept, removing 9 % of data. The data were only filtered for the statistical tests to allow statistical assumptions to be met; we chose not to filter the data for the figures in order to provide a picture of the complete data set. Using the 2023 dataset, differences between sexes or years in
number of seconds per day spent flying were tested using the R package GlmmTMB. Since the distribution of this variable was highly skewed and zero-inflated, the best model (by AIC) was a zero-inflated negative binomial distribution. We checked the residuals using the R package DHARMa (fit of residuals are presented in Fig. S9). In each model, individual was used as a random effect. The 2012–2022 dataset was slightly different in nature since variable was the percentage of fixes in flight, sowe used linear mixed models to test the sex or period effect on log-transformed % of fixes flying, with individual identity as a random effect.

We assessed heights at which little bustards would potentially interact with wind turbines as ‘at-risk heights’. Turbine height was derived from a government dataset (https://www.georisques.gouv.fr/donnees/bases-de-donnees/eolien-terrestre downloaded: 26/02/2024) (Fig. S6). Given the wide variation between turbines and the sensitivity of this parameter, especially lower heights, we used two sets of values representing the range of the rotor-swept zone and minimal height: 25–145 m and 40–190 m for minimal–maximal range height agl. All statistical analyses were performed with R4.4.1, using generalized linear mixed models (GlmmTMB or lme4 with lmer function for tests), with visual data exploration as a first step to decide the error distribution (Poisson or Gaussian), and random factors in the form of individual identity or year as a factor, depending on the model. When using lme4, effects were tested using car::Anova and type III wald Chi² tests.

## 2.5. Spatially explicit estimation of hazardous flights

The following terminology will be used throughout: i) **hazardous flights** refer to any flight behaviour that, given its altitude and length/ duration, might result in the flying bird being in the immediate vicinity of a wind turbine (Fig. S7). “Vicinity of a wind turbine” is approached by the ii) **rotor swept zone**, defined as the sphere delimited by the diameter of the blade, given that the rotor is able to rotate in order to stay perpendicular to wind direction (see Fig. S7). A bird flying within the rotor swept zone, as defined above, allows calculating the iii) **probability of encounter** between a flying bird and a turbine (Fig. S7), as an explicit risk of potential collision. The total time spent flying within the rotor swept zone for a certain time duration is defined as the iv) **potential hazard exposure** and is expressed in s/day or min/season. Finally, the v) **risk of collision** with the blades is evaluated through potential hazard exposure used as a proxy, and is assumed to increase linearly with the total time spent in hazard exposure within the breeding season (Fig. S7). Collision risk, in the complete absence of casualties, was thus approached by modelling hazardous flights through a probabilistic approach using flight parameters as obtained by the GPS data analyses (Fig. S7).

The total time of any given individual spent flying in hazardous flights as defined above further depended on the explicit spatial distance between the bird and the closest turbine. To simplify calculations, we used four theoretical discrete distances between a bird and the closest turbine: 200, 500, 1000 and 2000 m, defining four buffer zones. The time spent in the rotor-swept zone was determined by the total flight duration of each individual, based on sex and season, while also considering the time of day, which could either increase or decrease the risk factor. We thus evaluated the time at risk for individuals at an increasing distance from turbines (from 200 to 2000 m), and then transferred the simulated results to a real situation based on the explicit distribution of little bustards and wind turbines in the study area (Eqs. (1)–(3) below).

The probability of encounter, i.e. the likelihood of the bird's flight path crossing a turbine, depended on the length of the flight for a given distance to a turbine and flight altitude, with a higher proportion of flights within the turbine's blade range increasing the risk. Using the GPS data, we were able to estimate each of these parameters at different distances between a given bird and the nearest turbine (Fig. S7) at the

four distance classes (i). The first equation modelled the probability of encounter as the probability of a given flight to be hazardous. This probability, $P_{j,i}^{hazard}$, for any given individual j at distance i to the nearest turbine is given by;:

$$P_{j,i}^{hazard} = Alt_{j/(day,sex)}^{flight} \times C_{j}^{Day} \times P_{j,i}^{Dist} \times P_{j,i}^{Fligth} \quad (1)$$

where

- $Alt_{j/(day,sex)}^{flight}$, the flight altitude of the individual j as a % of overlap with blade rotor diameter, considering the entire distribution of flight altitudes rather than average flight height.

- $C_{j}^{Day}$, the aggravating (e.g. flying at night) or abating factors (e.g. flying at twilight). The values of C are 1 for night flights, arbitrarily set to 0.5 for twilight flights and 0 for day flights as we assumed that all birds are able to avoid a turbine they can see. At twilight, we assumed that flying birds could, at least in some cases, detect the turbine visually, and avoid it, hence an abating factor. These strong assumptions are discussed below.

- $P_{j,i}^{Dist}$, the probability that a bird in flight encounters a turbine, i.e. that a given flight of bird j is longer than the distance i between it and the nearest turbine since many flights are shorter in length and therefore bird j cannot reach the turbine.

- $P_{j,i}^{Fligth}$, the probability that an individual j flies through the turbine, given the diameter of the turbine set at 80-meter radius for simplicity. It can be expressed as the ratio of the horizontal surface area of a 80-m disk radius (the blade) and the flight disk area of radius i ($\pi 80^2/\pi i^2$), because the turbine rotates according to wind direction, assuming that bird j flies randomly in any direction.

The second equation calculated the potential hazard exposure, i.e. the time spent flying at risk in seconds, i.e. within the rotor swept zone, denoted as $T_{j,i}^{hazard}$ as a function of $P_{j,i}^{hazard}$ from (Eq. (1))

$$T_{j,i}^{hazard} = T_{j/(season,sex)}^{flying} \times P_{j,i}^{hazard} \quad (2)$$

where

- $T_{j/(season,sex)}^{flying}$ is the total time spent flying for individual j according to its sex and season.

Finally, to evaluate the potential number of casualties for the entire bustard population, $M_{(i)}$ (at a given distance i), we combined the number of exposed birds, $J_i$, which accounts for the distance i between any bird and the closest turbine (Fig. S7), and the probability or risk of collision $R_{(i)}$, which depends on the time spent at risk (Eq. (2)), i.e. the time spent flying within the rotor-swept zone ($T_{j,i}^{risk}$, Eq. (2)). The number $J_i$ was based on both actual bird and turbine distributions (see Fig. 1 and Fig. S6), and was directly obtained by the male surveys performed annually, and by extracting this number at various radii (buffers) around the spatially explicit map of the wind turbines, from 200 m to 2000 m. We arbitrarily assumed that the probability of collision was 1 above 120 s total time spent flying within the rotor-swept zone, otherwise divided by 120. $R_{(i)}$ is thus obtained as:

$$\left\{ \begin{array}{l} 0 < T_{i}^{hazard} \le 120s, M_{(i)} = \frac{T_{i}^{hazard}}{120} \times J_{i} \\ T_{i}^{hazard} > 120s, M_{(i)} = J_{i} \end{array} \right. \quad (3)$$

where $J_i$ is the number of birds found within an area of radius i around the turbine such as $J_i = \sum_{j=1}^{N} j_i$ with N the grand total of birds of the entire study area. The calculation was made per sex, but the number of
females was assumed to be the same as the number of males.

# 3. Results

## 3.1. Flight characteristics

### 3.1.1. Proportion of time spent flying

Using 2023 data, we found high inter-individual variation in flying activity (Fig. 2A; random effect GLMM with individual as a random effect, z = -17.6, n = 22 individuals and 3509 day/nights, p < 0.0001), with some birds flying on average over 15 min per day (Fig. 2A). Flight

time significantly varied with sex (z = 7.18, p < 0.0001), with males flying 7.82 ± 7.36 min/day (1.06 % per 24 h, from raw data), while females only flew 2.54 ± 6.36 min/day (Fig. 2A, C). There was also a clear seasonal pattern of flight activity, with higher values from April to June, which roughly corresponds to male displaying activity (Fig. 2B). Flying activity was reduced after July, when birds presumably started moulting. In the post-breeding period (z = 15.82, p < 0.0001), flying activity was 2.78 ± 4.16 min/day, similar between sexes (Fig. 2C). There was also a strong within-day pattern of flight activity (Fig. 2D–G), with night flights less frequent than day flights, although night flying was not rare. Twilight flights before sunrise were as frequent as day

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Number of minutes (±SD) spent flying daily by little bustards in 2023 by (A) individuals, classified by sex; (B) averaged weekly over the breeding season (from 1/4 to 30/7), irrespective of sex; (C) per sex and season (breeding versus post-breeding). (D) Males before and after sunrise; (E) females before and after sunrise; (F) males before and after sunset; and (G) females before and after sunset. The upper number in the figure indicates the number of individuals available. Data was first obtained per individual and per day (sample size in lower brackets), then averaged per individual.</figcaption></figure>
flights in males (Fig. 2D, F). Female's most active flight time was 1 h before sunrise, possibly in relation to copulation activity and visits to males at least early in spring (Fig. 2E). Over the entire breeding season standardized at 100 days, males flew a total of 13.03 ± 12.3 h (range 7.16–25.6 h) while females flew 5.11 ± 2.67 h (0.71–6.96 h).

Using 2012–2022 data (2012–2014 data excluded), we found similar patterns in flight time according to season and hour (Fig. 3), although differences between sexes and hours were less clearcut. The monthly variation in flight activity was more apparent (Fig. 3A). Flights increased at the very end of post-breeding in November, related to departure for migration (Fig. 3A). There was also a significant variation between years (F = 103.2, *p* < 0.00001), but no interaction with sex (F

= 2.06, *p* = 0.09). Time spent flying during the breeding season was 0.47 ± 0.59 % (0.68 ± 1.15 % during post-breeding), with high inter-individual variation (0.08–1.65 %, *n* = 25). Males spent twice as much time flying as females (0.54 ± 0.61 % versus 0.26 ± 0.51 %: Chi²<sub>sex</sub> = 3.67, *p* = 0.055, *n* = 3302 ind multiplied by day, from 25 individuals). Individuals flew on average 6.77 ± 8.5 min/day, a value that was very close to that obtained from the 2023 dataset when both sexes were combined (Figs. 2, 3). For 12 individuals providing data in both datasets, an underestimation by 40 % of time spent flying was found in the 2015–2022 dataset compared to 2023 (Fig. S8), thus we used the 2023 data set for this parameter in the following analysis.

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Percentage of points spent flying (±SD) per day (24 h) in little bustards in 2015–2022 during the breeding season according to: (A) season (breeding versus post-breeding); (B) day and night; (C) sex (male versus female); (D) type of GPS. Daily variation in the % of points spent flying according to: solar hour at sunrise in males (E) and females (F) and at sunset in males (G) and females (H); (I) per hours.</figcaption></figure>
### 3.1.2. Flight duration, speed and distance

Using 2023 data, flight bouts were 66.9 ± 87.6 s in males and 98.9 ± 289.4 s in females (range 6–9473, *n* = 14,824 flights), while distance covered was 940.5 ± 1316 m in males and 1410.2 ± 4490 m in females (18.2 m–142 km; Fig. 4B, D; Table 1). The 2012–2022 dataset suggested a similar pattern (Fig. 4C versus D), though the short flights less than 40 s in duration were almost completely missing, an expected outcome of the data sampling rate (one fix every 30 or 60 s). The 2023 data were therefore more representative. Distributions in 2023 suggested a mixture of several distributions (see Fig. S8 for examples with flight duration and distance). The bimodality in frequency distribution of flight durations allowed two types of flights to be differentiated: short (34.9 %) and longer flights (28.2 %; Fig. S10.C). A third type was ‘uncertain’ when in between the two modes (36.9 %). Short flights were on average 20.2 s (224.2 m) versus 165.2 s (2449.0 m) for long flights. Using duration, distance, average speed and maximum and median altitudes, a principal component analysis (PCA) confirmed the existence of these two types of flights, with a slight difference according to sex, but a marked increase in long flights in the post-breeding period (Fig. S10.D). Flight parameters per category, sex, period and season are given in Table 1. Overall, there was no sex effect on flight parameters except interaction with daylight on flight altitude, but there were strong seasonal effects, with post-breeding flights tending to be longer, and a strong effect of daylight on distance and duration (see Table S3 for statistical models). Night flights were longer in duration and covered longer distances than day flights in both sexes, but especially females (Table S3). A third category included migration flights (49 in total). These occurred at night, with birds departing mainly between 18:00 and 19:00 (twilight at this time of year, i.e. November) and then usually flying until morning. Migration flights were at a higher speed (55.67 ± 24.4 km/h), although the speed was similar to long flights (Table 1). The median altitude was 168.7 ± 146.9 m agl, which was much higher than long-duration flights.

### 3.1.3. Flight height in relation to blade diameter

The 2023 dataset indicated a mode of flight altitude around 5–10 m agl (Fig. 5B). Flight altitude varied significantly with sex interacting with daylight (Fig. 5D, F; Table S3), and with season. Birds flew higher in the post-breeding than breeding season, at night than in day, and females flew higher than males (Fig. 5; Table 1, Table S3). Short flights

were 15 m agl, while long fights were 23 m agl (Table 1). There was also a correlation between flight speed and altitude (GLMM, $Chi^2 = 1715.1$, $p < 0.0001$), irrespective of sex, period and season (data not shown). We then compared flight height parameters with turbine height as a proxy of collision risk. As there are many turbine models in use, we used two sets of parameters for these (Fig. S6): a small/lower (25–145 m) and a high/larger structure (40–190 m). The percentage of time spent flying within the range of the turbines did not differ between the two datasets (Fig. 5A, B): using the 2023 dataset, this ranged between 27.1 % and 60.0 % for lower blades to between 9.2 % and 48.2 % for higher blades, and differed between males and females and between day and night (Fig. 5). Overall, despite bustards flying less at night than during the day, the collision risk was higher, especially at twilight (Fig. 2).

### 3.1.4. A spatially explicit risk assessment model during breeding and migration periods

From the GPS results, a bird settled during the entire breeding season next to a turbine, flying 1.2 min/day at twilight or night, would thus spend 120mn in flight time over the 100 days of the breeding season, with about 40 % of this time within the rotor-swept zone. Although it is reasonable to expect that such bird would die from collision, not all birds display at a 0-m distance from a turbine (none in our study area). For a male with a home-range centroid 200 m from a wind turbine, 84.2 % of all its flights are longer than 200 m (from Fig. 4B), hence potentially through the turbine. However, given a disc with a 80-m radius, the proportion of flights likely to cross the blade disc is only 16 %, i.e. the ratio between the two discs. The percentage of flights likely to cross a low blade (25 m) is 34 % by night and 20 % by day (from Fig. 5C, D), arbitrarily halved at twilight, and set to 0 when flying by day (Table 2). Applying (Eq. (1)) resulted in $P_{j,i}^{hazard}$ being 0.046 by night and 0.023 at twilight. Since a male flies 104 min at night and 142 min at twilight over an entire breeding season, the bird potentially crosses the path of the blade a total of 8.02 min per season (Table 2). Using (Eq. (3)) and actual numbers of individuals within a 2000 m radius of an existing turbine, we estimated the number of casualties would potentially be up to 2.2 males and 2.8 females (Table 2).

We also evaluated the risk at post-breeding gathering sites. There are 85 such sites known in our study area (data not shown): 10 of these were <2000 m from existing turbines, and one <500 m. The latter hosts only a

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Flight parameters (distance covered and flight duration) according to the 2015–2022 dataset (A, C) versus the 2023 dataset (with very high time resolution; B, D). The four figures represent frequency distributions.</figcaption></figure>
<table><caption>**Table 1**
Flight parameters of the little bustard obtained from GPS tracking in 2023 (see 'Methods' for datasets). Six flight parameters are shown according to season, sex, flight type and diurnal cycle. N represents the total number of GPS fixes.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Post_Breeding</th><th colspan="2">Breeding</th><th colspan="2">Day</th><th colspan="2">Night</th><th colspan="2">Short duration flight</th><th colspan="2">Uncertain duration flight</th><th colspan="2">Long duration flight</th></tr><tr><th>Female<br>(N = 994)</th><th>Male<br>(N = 2239)</th><th>Female<br>(N = 1468)</th><th>Male<br>(N = 10,123)</th><th>Female<br>(N = 1604)</th><th>Male<br>(N = 9786)</th><th>Female<br>(N = 858)</th><th>Male<br>(N = 2576)</th><th>Female<br>(N = 823)</th><th>Male<br>(N = 4355)</th><th>Female<br>(N = 860)</th><th>Male<br>(N = 4612)</th><th>Female<br>(N = 779)</th><th>Male<br>(N = 3395)</th></tr></thead><tbody><tr><td colspan="15"><strong>Average speed (km/h)</strong></td></tr><tr><td>Mean ± SD<br>(N)</td><td>48.6 ± 11.2<br>(994)</td><td>50.4 ± 10.5<br>(2239)</td><td>46.1 ± 10.4<br>(1468)</td><td>46.9 ± 9.08<br>(10123)</td><td>46.4 ± 9.90<br>(1604)</td><td>47.0 ± 9.16<br>(9786)</td><td>48.6 ± 12.1<br>(858)</td><td>49.8 ± 10.1<br>(2576)</td><td>39.0 ±<br>9.28 (823)</td><td>40.6 ± 8.15<br>(4355)</td><td>49.2 ±<br>8.84 (860)</td><td>49.3 ± 7.62<br>(4612)</td><td>53.5 ± 8.59<br>(779)</td><td>54.1 ± 7.26<br>(3395)</td></tr><tr><td>Median<br>[Min,<br>Max]</td><td>48.7 [11.7,<br>107]</td><td>50.3 [18.0,<br>109]</td><td>46.0 [8.82,<br>87.8]</td><td>47.0 [7.36,<br>83.0]</td><td>46.7 [8.82,<br>103]</td><td>47.0 [7.36,<br>109]</td><td>48.1 [14.9,<br>107]</td><td>49.2 [7.67,<br>97.0]</td><td>39.4 [8.82,<br>83.2]</td><td>40.9 [7.36,<br>72.7]</td><td>48.0 [27.3,<br>89.0]</td><td>48.5 [20.7,<br>97.0]</td><td>52.6 [31.7,<br>107]</td><td>53.3 [26.0,<br>109]</td></tr><tr><td colspan="15"><strong>Maximum speed (km/h)</strong></td></tr><tr><td>Mean ± SD<br>(N)</td><td>60.6 ± 14.8<br>(994)</td><td>61.9 ± 13.9<br>(2239)</td><td>60.1 ± 14.9<br>(1468)</td><td>60.8 ± 13.0<br>(10123)</td><td>60.6 ± 14.4<br>(1604)</td><td>61.0 ± 13.2<br>(9786)</td><td>59.8 ± 15.7<br>(858)</td><td>61.1 ± 12.9<br>(2576)</td><td>49.6 ±<br>12.8 (823)</td><td>51.5 ± 10.4<br>(4355)</td><td>60.4 ±<br>12.2 (860)</td><td>62.0 ± 10.2<br>(4612)</td><td>71.5 ± 10.7<br>(779)</td><td>71.9 ± 10.8<br>(3395)</td></tr><tr><td>Median<br>[Min,<br>Max]</td><td>62.0 [15.0,<br>107]</td><td>63.0 [18.0,<br>110]</td><td>60.0 [12.0,<br>133]</td><td>60.0 [10.0,<br>117]</td><td>61.0 [12.0,<br>107]</td><td>61.0 [10.0,<br>115]</td><td>61.0 [15.0,<br>133]</td><td>61.0 [10.0,<br>117]</td><td>50.0 [12.0,<br>133]</td><td>51.0 [10.0,<br>97.0]</td><td>61.0 [29.0,<br>106]</td><td>62.0 [21.0,<br>105]</td><td>71.0 [38.0,<br>107]</td><td>71.0 [26.0,<br>117]</td></tr><tr><td colspan="15"><strong>Flight duration (s)</strong></td></tr><tr><td>Mean ± SD<br>(N)</td><td>104 ± 213<br>(994)</td><td>87.4 ± 133<br>(2239)</td><td>95.5 ± 332<br>(1468)</td><td>62.3 ± 73.1<br>(10123)</td><td>73.1 ± 102<br>(1604)</td><td>63.4 ± 76.2<br>(9786)</td><td>147 ± 466<br>(858)</td><td>80.2 ± 121<br>(2576)</td><td>20.6 ±<br>13.9 (823)</td><td>20.1 ± 15.1<br>(4355)</td><td>49.7 ±<br>40.1 (860)</td><td>50.6 ± 24.0<br>(4612)</td><td>236 ± 485<br>(779)</td><td>149 ± 130<br>(3395)</td></tr><tr><td>Median<br>[Min,<br>Max]</td><td>53.0 [10.0,<br>5190]</td><td>53.9 [10.0,<br>3310]</td><td>41.9 [10.0,<br>9470]</td><td>42.0 [10.0,<br>2880]</td><td>43.9 [10.0,<br>1820]</td><td>43.0 [10.0,<br>3310]</td><td>51.5 [10.0,<br>9470]</td><td>50.0 [10.0,<br>2880]</td><td>18.0 [10.0,<br>324]</td><td>18.0 [10.0,<br>559]</td><td>46.7 [20.0,<br>1150]</td><td>48.0 [20.0,<br>930]</td><td>135 [46.9,<br>9470]</td><td>114 [45.2,<br>3310]</td></tr><tr><td colspan="15"><strong>Distance covered (m)</strong></td></tr><tr><td>Mean ± SD<br>(N)</td><td>1450 ±<br>3010 (994)</td><td>1260 ±<br>1800<br>(2239)</td><td>1380 ±<br>5260 (1468)</td><td>870 ± 1170<br>(10123)</td><td>991 ± 1250<br>(1604)</td><td>879 ± 1010<br>(9786)</td><td>2180 ±<br>7350 (858)</td><td>1170 ±<br>2100<br>(2576)</td><td>222 ± 93.0<br>(823)</td><td>225 ± 89.9<br>(4355)</td><td>659 ± 182<br>(860)</td><td>681 ± 186<br>(4612)</td><td>3490 ±<br>7570 (779)</td><td>2210 ±<br>1970<br>(3395)</td></tr><tr><td>Median<br>[Min,<br>Max]</td><td>748 [30.1,<br>76500]</td><td>791 [78.4,<br>34200]</td><td>539 [20.3,<br>142000]</td><td>556 [18.2,<br>65800]</td><td>578 [20.3,<br>13300]</td><td>566 [18.2,<br>19000]</td><td>713 [30.1,<br>142000]</td><td>710 [22.7,<br>65800]</td><td>210 [20.3,<br>396]</td><td>215 [18.2,<br>397]</td><td>632 [397,<br>1060]</td><td>660 [397,<br>1060]</td><td>1950 [1060,<br>142000]</td><td>1680<br>[1060,<br>65800]</td></tr><tr><td colspan="15"><strong>Maximum altitude (m)</strong></td></tr><tr><td>Mean ± SD<br>(N)</td><td>38.3 ± 33.8<br>(994)</td><td>38.9 ± 36.5<br>(2239)</td><td>26.6 ± 28.3<br>(1468)</td><td>20.9 ± 15.1<br>(10123)</td><td>28.6 ± 27.4<br>(1604)</td><td>22.8 ± 19.5<br>(9786)</td><td>36.3 ± 36.6<br>(858)</td><td>29.5 ± 28.3<br>(2576)</td><td>15.9 ±<br>14.9 (823)</td><td>15.0 ± 11.6<br>(4355)</td><td>22.6 ±<br>18.1 (860)</td><td>21.7 ± 15.4<br>(4612)</td><td>57.3 ± 38.3<br>(779)</td><td>39.3 ± 29.8<br>(3395)</td></tr><tr><td>Median<br>[Min,<br>Max]</td><td>28.5 [1.08,<br>263]</td><td>29.8 [1.04,<br>293]</td><td>18.0 [1.04,<br>299]</td><td>17.6 [1.04,<br>235]</td><td>19.8 [1.24,<br>263]</td><td>18.0 [1.04,<br>276]</td><td>25.0 [1.04,<br>299]</td><td>22.4 [1.20,<br>293]</td><td>11.7 [1.04,<br>169]</td><td>12.2 [1.04,<br>127]</td><td>18.7 [1.12,<br>153]</td><td>18.5 [1.04,<br>235]</td><td>49.1 [2.56,<br>299]</td><td>31.2 [2.20,<br>293]</td></tr><tr><td colspan="15"><strong>Median altitude (m)</strong></td></tr><tr><td>Mean ± SD<br>(N)</td><td>26.4 ± 25.2<br>(994)</td><td>28.2 ± 32.0<br>(2239)</td><td>16.0 ± 19.2<br>(1468)</td><td>11.0 ± 9.83<br>(10123)</td><td>18.2 ± 20.3<br>(1604)</td><td>12.7 ± 14.1<br>(9786)</td><td>24.0 ± 25.3<br>(858)</td><td>19.4 ± 26.2<br>(2576)</td><td>9.81 ±<br>12.2 (823)</td><td>8.22 ± 8.30<br>(4355)</td><td>16.0 ±<br>17.1 (860)</td><td>12.9 ± 13.0<br>(4612)</td><td>35.9 ± 26.8<br>(779)</td><td>23.3 ± 25.9<br>(3395)</td></tr><tr><td>Median<br>[Min,<br>Max]</td><td>19.1<br>[0.520,<br>167]</td><td>19.6 [0.200,<br>293]</td><td>9.40 [0.280,<br>187]</td><td>8.44<br>[0.0400,<br>177]</td><td>11.2<br>[0.280,<br>154]</td><td>8.92<br>[0.0400,<br>244]</td><td>15.5 [0.400,<br>187]</td><td>12.3 [0.440,<br>293]</td><td>6.12<br>[0.400,<br>150]</td><td>6.18<br>[0.0400,<br>127]</td><td>11.6<br>[0.280,<br>153]</td><td>9.88<br>[0.520,<br>210]</td><td>29.8 [0.920,<br>187]</td><td>16.4 [1.04,<br>293]</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Flight altitude frequency of occurrence, according to dataset (2015–2022 in grey versus 2023 in black), sex and daily cycle. Two types of rotor-swept zone are shown, 25–145 m (green) and 40–190 m (blue). Coloured rectangles provide the percentage of overlap between flight altitude and blade diameter, according to rotor-swept zone, as well as sample size (fixes). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

small group (max 18 individuals, average 8.3). Applying the values of Table 2 with 60 days instead of 100 would result in a further 0.5–1 bird casualties. Finally, we analysed migration paths. For the 44 available trips, a bird flew within 2000 m of a turbine in 156 cases as a single bird during a trip might approach several turbines, 53 of these flights being <140 m agl (34 %). Restricting risk calculation to distances less than 200 m from a turbine, 12 trips (of 44: 27 %) were concerned. Hence, approximately 9 % of migration flights were within a 200-m distance from a turbine at risk altitude (see Fig. S11 for examples). As about 16 % of flights within 200 m of a blade may actually cross the rotor path (Table 2, column b), 1.5 % of migration flights may pass through rotors at night. Projecting the number of deaths is a matter of speculation, but

since about 500 birds migrate each season from our population, 7.5 birds would be expected to fly through rotors at night each migration, so 1 out of 15 individuals dying by collision is probably a reasonable number. Overall, a tentative estimate of total yearly mortality would be 7 birds: 5 during breeding, 1 during post-breeding, and 1 during migration.

## 4. Discussion

The transition to renewable energy could potentially conflict with biodiversity conservation as these facilities such as wind farms are often in areas with low human population density, which can be habitats for threatened species. However, estimating risk and how this may affect
<caption>**Table 2**</caption>

Explicit modelling of collision risk in the little bustard (see Eq. (1) in 'Methods'), according to distance to turbine (four distances shown) and sex. The columns break down collision risk calculation. For footnotes (c) and (g), two turbine types were used, 25–145 m and 40–190 m (see methods).

<table><thead><tr><th rowspan="2">Distance to turbine in m (buffer size)</th><th rowspan="2">Sex</th><th rowspan="2">No. of flights (%) more than the distance between the bird and the turbine<sup>a</sup></th><th rowspan="2">% of buffer area covered by wind turbine<sup>b</sup></th><th colspan="3">% flights overlap with blade height according to hour (hence luminosity)<sup>c</sup>. Two blade diameters separated by "/"</th><th colspan="3">Abatement factor according to hour (luminosity)<sup>d</sup></th><th colspan="2">Probability of flying through rotor swept zone<sup>e</sup>, see Eq. (1)</th><th colspan="2">Total time spent flying at night and twilight/24 h (in minute) for an average bird<sup>f</sup></th><th rowspan="2">Total time spent flying within rotor swept zone/ breeding season (minute)<sup>g</sup>, see Eq. (2). Left and right columns for large and small blades.</th><th rowspan="2">Average number of individuals within buffer distance from a turbine<sup>h</sup></th><th rowspan="2">Number possibly killed (per year)<sup>i</sup>, see Eq. (3)</th></tr><tr><th>Night</th><th>Twilight</th><th>Day</th><th>Night</th><th>Twilight</th><th>Day</th><th>Night</th><th>Twilight</th><th>Night</th><th>Twilight</th></tr></thead><tbody><tr><td>200</td><td>Male</td><td>84.2 %</td><td>16 %</td><td>34/18</td><td>34/18</td><td>20/9</td><td>1</td><td>0.5</td><td>0</td><td>0.04580</td><td>0.02829</td><td>1.04</td><td>1.42</td><td>8.02</td><td>4.24</td><td>0.76</td><td>0.76</td></tr><tr><td>500</td><td>Male</td><td>57.0 %</td><td>2.56 %</td><td>34/18</td><td>34/18</td><td>20/9</td><td>1</td><td>0.5</td><td>0</td><td>0.00496</td><td>0.00306</td><td>1.04</td><td>1.42</td><td>0.87</td><td>0.46</td><td>1.75</td><td>0.8</td></tr><tr><td>1000</td><td>Male</td><td>30.1 %</td><td>0.64 %</td><td>34/18</td><td>34/18</td><td>20/9</td><td>1</td><td>0.5</td><td>0</td><td>0.00065</td><td>0.00040</td><td>1.04</td><td>1.42</td><td>0.11</td><td>0.06</td><td>5.34</td><td>0.45</td></tr><tr><td>2000</td><td>Male</td><td>11.0 %</td><td>0.16 %</td><td>34/18</td><td>34/18</td><td>20/9</td><td>1</td><td>0.5</td><td>0</td><td>0.00005</td><td>0.00004</td><td>1.04</td><td>1.42</td><td>0.01</td><td>0.01</td><td>17.3</td><td>0.2</td></tr><tr><td>200</td><td>Female</td><td>84.2 %</td><td>16 %</td><td>60/48</td><td>60/48</td><td>41/27</td><td>1</td><td>0.5</td><td>0</td><td>0.08083</td><td>0.04446</td><td>1.16</td><td>0.41</td><td>11.03</td><td>8.83</td><td>0.76</td><td>0.76</td></tr><tr><td>500</td><td>Female</td><td>57.0 %</td><td>2.56 %</td><td>60/48</td><td>60/48</td><td>41/27</td><td>1</td><td>0.5</td><td>0</td><td>0.00875</td><td>0.00482</td><td>1.16</td><td>0.41</td><td>1.20</td><td>0.96</td><td>1.75</td><td>1.4</td></tr><tr><td>1000</td><td>Female</td><td>30.1 %</td><td>0.64 %</td><td>60/48</td><td>60/48</td><td>41/27</td><td>1</td><td>0.5</td><td>0</td><td>0.00115</td><td>0.00064</td><td>1.16</td><td>0.41</td><td>0.16</td><td>0.13</td><td>5.34</td><td>0.6</td></tr><tr><td>2000</td><td>Female</td><td>11.0 %</td><td>0.16 %</td><td>60/48</td><td>60/48</td><td>41/27</td><td>1</td><td>0.5</td><td>0</td><td>0.00010</td><td>0.00006</td><td>1.16</td><td>0.41</td><td>0.01</td><td>0.01</td><td>17.3</td><td>0.1</td></tr></tbody></table>

<footnote><sup>a</sup> This corresponds to the percentage of flights in the breeding season superior to the distance to turbine (Fig. 4b).</footnote>

<footnote><sup>b</sup> This was obtained by the ratio of two discs, the numerator being the area of the blade (80-m radius), the second corresponding to the radius of the distance to the turbine.</footnote>

<footnote><sup>c</sup> These were all obtained directly from Fig. 5.</footnote>

<footnote><sup>d</sup> We considered that flying by day entailed no risk (i.e. the bustard will systematically avoid the turbine), while flying at night entailed maximal risk. The twilight flights were intermediate, hence a 0.5 abatement factor.</footnote>

<footnote><sup>e</sup> Value obtained by multiplying all % and translated into a probability.</footnote>

<footnote><sup>f</sup> This was obtained from Fig. 2.</footnote>

<footnote><sup>g</sup> Multiplied by 100 days, corresponding to the breeding season.</footnote>

<footnote><sup>h</sup> The number of individuals positioned within a given radius of a wind turbine was obtained by the exact distribution of all males each year between 2008 and 2020 (except 2019) sampled by ornithologists every year, and using the distribution of wind turbines, whatever the year of operation. Then this number was divided by the total of males (3092) and multiplied by the average yearly number of males (258), giving a predicted number of males at each radius. The number of females equals the number of males.</footnote>

<footnote><sup>i</sup> The likely number killed was derived from the total number of minutes flying within the blade area (previous column), considering arbitrarily that the risk is 100 % for an individual flying more than 2 min, 50 % for flying more than 1 min, 10 % for flying more than 12 s, and 1 % for flying more than 1 s.</footnote>
reproductive and survival performance requires extensive observations (Miller et al., 2014; Certain et al., 2015), which can be costly or even impossible for rare species. To overcome this, GPS tracking allows precise information on movement to be collected (Marques et al., 2020) that can be used to assess overlaps between the area used by species and wind farms (Duffy and Capece, 2014). Rather than exploring collision risk according to various scenarios of blade types or turbine distribution, this study sought to estimate the probability of encounter between a flying bird and a turbine, in the absence of data on casualties (Schaub et al., 2019). Using time spent within the rotor swept zone as a potential hazardous exposure, and arbitrarily converting this time to collision risk, we estimated that potentially about 7 birds could be killed each year, i.e. 1.16 % of the population present (500–600 birds). This figure might be higher if considering the local density of males, i.e. lek size, as larger leks lead to more male interactions, and flights.

Literature on threats to bustards caused by wind turbines is scarce in comparison to power lines (Martin and Shaw, 2010; Silva et al., 2023). Only a single little bustard death has been documented in Spain (Silva et al., 2022), yet it has been ranked 150th of 9568 bird species vulnerable to wind turbine mortality (Thaxter et al., 2017). While avoidance behaviour of wind turbines is well developed in large birds (Fielding et al., 2024), this avoidance rate is completely unknown for little bustards, and using an arbitrary avoidance rate of 98 % seemed highly optimistic for this species when flying at night. We thus opted for a spatially explicit risk assessment based on the actual distribution of birds and turbines (both known), and accurate knowledge of flight parameters allowed by high-resolution GPS data. We took into account that error in estimation can be high (Péron et al., 2020), and noise in data can affect predictions of collision risk (Schaub et al., 2023). Imprecision may be mitigated by increasing GPS-fix frequency (Thaxter et al., 2018; Schaub et al., 2019), thus most of our analyses were restricted to the 2023 dataset collected at very high temporal resolution (1 s).

In this study neither mortality rate nor collision risk could be assessed directly. Our estimation thus relied on establishing a link between hazardous flight exposure (time) and risk of collision, with collision being equal to a casualty. This was largely inspired by the collision risk model, with mortality risk modelled explicitly by the total time spent flying within the rotor swept area. We arbitrarily considered that a bird flying through a turbine path for a total of 120 s at night would die, since it would inevitably be caught by a blade (a three-bladed rotor makes a rotation in 15 s, i.e. one blade every 5 s; hence the individual would cross the blades 24 times in 2 min), an assumption that currently cannot be verified. However, a major discovery of our study was the presence of night flights in this species. Night flight leads to maximum collision risk as birds cannot see the turbine (even though turbines in the area are equipped with a red light on the mast), and given the little bustard's very low flight manoeuvrability, mortality risk is expected to be maximal. Time spent flying in the rotor-swept zone is impossible to translate into a mortality rate with certainty. Assuming that a little bustard flying within the rotor swept zone for more than 120 s would have a probability of death of 1, we then assumed a probability of death as 1/120 for every second of flight within the rotor-swept zone (see Fig. S7). In daylight, we assumed that little bustards would see the turbine so considered avoidance behaviour as 100 %, since we have evidence that birds flying by day modify their trajectory when approaching a turbine (Fig. S11). Setting the abating factor at 0.02 (98 % avoidance rate) rather than 0 did not change the estimated numbers of birds killed (data not shown).

The total estimated mortality was expected to be around 7 individuals per year (both sexes combined), slightly over 1 % of the total population in the study area. It may seem surprising that after 10 years of wind farms in the study area, no carcass search has ever found a single little bustard (an expected 50 birds would be concerned for this time period). This could be explained by the fact that the carcasses of this species disappear extremely rapidly because its white wings attract predators – usually within 1 or 2 h (pers. obs.) – so search intensity

would have to be exceedingly high (Ravache et al., 2024). This expected mortality rate is within the range of a study using the known fate (based on GPS) of 1704 bird mortalities: around 2 % (Serratosa et al., 2024). Additionally, mortality rates may be further increased by power lines that carry electricity from the turbines to power station.

The ‘1% mortality criterion’ set by the EU ORNIS Committee on bird conservation (Schippers et al., 2020) is considered acceptable by the European Union for wind farm projects in compliance with protection measures and the precautionary principle. However, highly vulnerable species are affected by additional fatalities, and this threshold has been found to be overoptimistic (Schippers et al., 2020; Conkling et al., 2022) since even at a very low rate additive mortality can impair viability (Murgatroyd et al., 2021; Balotari-Chiebao et al., 2023). An excess 1 % adult mortality would be expected to have a significant effect on little bustard population dynamics through demographic parameters (Duriez et al., 2022); adult survival is almost 90 % (Attié et al., 2022), so this would represent a 10 % increase in mortality rate. The little bustard is a large-bodied species that is currently not limited by density dependence, but is constrained by the agricultural context (Bretagnolle et al., 2018), thus the population growth rate at low density would be low (Schippers et al., 2020).

Our analyses show that a buffer distance of 2000 m between any new turbine and displaying males seems safe, but shorter distances would inevitably increase casualties. A 2000 m buffer distance was highlighted by an expert panel for the French Ministry of Environment in 2022, and is currently the recommended distance for new turbines in impact studies. Interestingly, a strong effect of power lines up to 1600 m on the flight behaviour of great bustards has been similarly detected (Raab et al., 2011). Selecting sites for wind farms that minimize the expected impact on birds of conservation concern remains the most effective tool for reducing threats (Balotari-Chiebao et al., 2023). Observing and tracking behavioural reactions of birds, developing demographic and distributional (or spatial) models, and creating predictive maps will be important to refine population-level impacts to support decisions on wind farm development.

## Statement of inclusion

4 of the 6 authors are working for different NGOs dedicated to bird conservation, and as such, are directly involved with public institutions dealing with wind turbine development.

## CRediT authorship contribution statement

**V. Bretagnolle:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **A. Merot:** Methodology, Investigation, Data curation. **A. Martineau:** Project administration, Methodology, Investigation, Funding acquisition. **E. Dedeban:** Methodology, Investigation. **R. Chargé:** Resources, Methodology. **E. Debenest:** Project administration, Investigation, Funding acquisition.

## Author statement

No part of the research has been published elsewhere, and the MS is not being considered for publication elsewhere.

AI has not been used in any part of the MS.

## Declaration of competing interest

The authors declare strictly no conflict of interest.

## Acknowledgements

We thank Alexandre Villers, Gildas Lemonnier, Romain Bonnet, Pierre de Bouet du Portal, Mathieu Liaigre, Damien Chiron, Steve
Augiron, Samuel Peroteau, Landry Boussac, Gaspard Bernard, Marie Fretin, Jean-Baptiste Perrotin, Clotilde Simonelli, Guillaume Calonego, Benoit Ganglof, Pierre-Alexandre Dupeyras, Aymeric Legrand and Yohann Meuraillon for their help with field work. We also thank local NGOs and LPO that coordinate annual counts, and Agence Régionale de la Biodiversité for help in data caring. Funding was provided by DREAL and Région Nouvelle Aquitaine, CNRS, RTE, QENERGY and Fondation Lisea. Finally, we thank Alexandre Villers for his initial analyses and feedback on this study, and S. Gaba, A. Villers and C. Eraud for improving an initial draft.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.biocon.2025.111391.

## Data availability

GPS data are deposited on Movebank.

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