MD Léa Picard

PhD Alexandra Mailles

MD Pierre Fillâtre

MD, PhD Pierre Tattevin
email: pierre.tattevin@chu-rennes.fr

MD Jean-Paul Stahl

Prof Pierre Tattevin

Encephalitis in travellers: A prospective multicentre study Running title: Encephalitis in travellers

Keywords: Encephalitis, travellers, herpes simplex virus, arbovirus, prevention

Background: As the epidemiology of encephalitis varies from one country to another, international travel may be an important clue for the diagnostic workout of this puzzling disease.

Methods:

We performed an ancillary study using the ENCEIF prospective cohort conducted in 62 clinical sites in France from 2016 to 2019. All cases of encephalitis in adults that fulfilled a case definition derived from the International Encephalitis Consortium were included.

Travellers were defined as patients who spent at least one night in a foreign country within the last six months.

Results: Of the 494 encephalitis patients enrolled, 69 (14%) were travellers. As compared to non-travellers, they were younger (median age, 48 years [interquartile range, 36-69] vs. 66 , P<0.001), less likely to be immunocompromised: 2/69 (3%) vs 56/425 (13%), P=0.02, and reported more arthralgia: 7/69 (10%) vs. 11/425 (3%), P=0.007. The risk of poor outcome at hospital discharge (Glasgow outcome scale <3), was similar for travellers and for non-travellers after adjustment (aOR 0.80 [0.36 -1.80], P=0.594). Arboviruses were the main causes of encephalitis in travellers: 15/69 (22%) vs. 20/425 (5%) in non-travellers, P<0.001, and Herpes simplex virus (HSV) was the second (9/69, 13%). Of note, in 19% of cases (13/69), the risk of encephalitis in travellers may have been decreased with a vaccine.

Conclusions:

The two primary causes of encephalitis in travellers are arboviruses, and HSV.

Empirical treatment of encephalitis in travellers must include aciclovir. Vaccination may decrease the risk of encephalitis in travellers by up to 19%.

Background

Encephalitis is defined as an inflammatory process of brain tissue associated with neurologic dysfunction [START_REF] Venkatesan | Case Definitions, Diagnostic Algorithms, and Priorities in Encephalitis: Consensus Statement of the International Encephalitis Consortium[END_REF]. Encephalitis is a rare but debilitating disease with an estimated incidence ranging from 0.06 to 12.6/100,000 persons-year [START_REF] Venkatesan | Case Definitions, Diagnostic Algorithms, and Priorities in Encephalitis: Consensus Statement of the International Encephalitis Consortium[END_REF][START_REF] Granerod | New Estimates of Incidence of Encephalitis in England[END_REF]. Viruses are the most frequent pathogens associated with encephalitis but differential diagnosis include a wide range of infectious and non-infectious causes, including autoimmune, neoplastic and paraneoplastic causes [START_REF] Venkatesan | Acute encephalitis in immunocompetent adults[END_REF][START_REF] Boucher | Epidemiology of infectious encephalitis causes in 2016[END_REF][START_REF] Thomas | Autoimmune N-methyl-D-aspartate receptor encephalitis is a differential diagnosis of infectious encephalitis[END_REF]. As outlined in international guidelines, early diagnosis and appropriate empirical treatment are key factors for outcome [START_REF] Stahl | Guidelines on the management of infectious encephalitis in adults[END_REF][START_REF] Tunkel | The Management of Encephalitis: Clinical Practice Guidelines by the Infectious Diseases Society of America[END_REF]. However, 30-50% of encephalitis cases remain undocumented, which jeopardizes the optimal selection of empirical treatment [START_REF] Granerod | Challenge of the unknown. A systematic review of acute encephalitis in non-outbreak situations[END_REF][START_REF] Mailles | Changing profile of encephalitis: Results of a 4-year study in France[END_REF][START_REF] Mailles | Infectious encephalitis in France in 2007: a national prospective study[END_REF].

While some micro-organisms commonly responsible for encephalitis, like Herpes Simplex virus (HSV) are ubiquitous, many are unevenly distributed across the world, making international travel an important clue to guide diagnostic workout [START_REF] Kenfak | Diagnostic approach to encephalitis and meningoencephalitis in adult returning travellers[END_REF]. The epidemiology of encephalitis in travellers may be impacted by the changing profile of travel patterns over years, and by emerging pathogens [START_REF] Semenza | Climate change and infectious disease in Europe: Impact, projection and adaptation[END_REF]. This changing profile of encephalitis was illustrated by the emergence of West-Nile virus (WNV) and tick-borne encephalitis virus (TBEV) across Europe, in areas where they have not been reported before [START_REF] Bakonyi | West Nile virus keeps on moving up in Europe[END_REF][START_REF] Velay | A new hot spot for tick-borne encephalitis (TBE): A marked increase of TBE cases in France in 2016[END_REF]. Case reports and series suggest that travellers are more likely to be exposed to unusual causes of encephalitis [START_REF] Kenfak | Diagnostic approach to encephalitis and meningoencephalitis in adult returning travellers[END_REF], but these data are limited by small sample size, retrospective design, and publication biases. We report a prospective, multicentre, cohort study, ancillary to the ENCEIF study [START_REF] Mailles | Changing profile of encephalitis: Results of a 4-year study in France[END_REF], in which we aimed to describe the characteristics, aetiologies and management of encephalitis in travellers.

Methods

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The ENCEIF multi-centre prospective cohort study enrolled all consecutive patients with encephalitis suspected of infectious origin in 62 participating sites from January 2016 to December 2019 [START_REF] Mailles | Changing profile of encephalitis: Results of a 4-year study in France[END_REF]. The objective of the ENCEIF study was to describe the epidemiology, the characteristics (clinical, biological, imaging), the management, and the outcome of acute infectious encephalitis in France. All adult inpatients who fulfilled a case definition derived from the International Encephalitis Consortium [START_REF] Venkatesan | Case Definitions, Diagnostic Algorithms, and Priorities in Encephalitis: Consensus Statement of the International Encephalitis Consortium[END_REF] with no alternative diagnosis were invited to participate in the study (see below).

Briefly, encephalitis was defined as neurologic symptoms of acute onset, for more than 24 h, with no alternative diagnosis, and at least two of the following signs: fever ≥38°C, seizure, recent focal neurological symptoms, cerebrospinal fluid (CSF) white cells count ≥5/mm 3 , inflammatory brain lesion on magnetic resonance imaging (MRI) or computed tomography (CT)-scan, and suggestive abnormalities on electroencephalogram (EEG). Data were collected on a standardized questionnaire and computerized using a secure, web-based application Voozanoo (Epiconcept©, Paris), in accordance with the French regulation on medical data protection and the European General Data Protection Regulation. Travellers were defined as patients who stayed at least one night in a foreign country within the last six months. Among travellers, a distinction was made between those who usually live in France, but travelled abroad within the last 6 months before encephalitis diagnosis, and those who usually live in a foreign country, but were diagnosed with encephalitis while they were in France (thereafter referred to as foreigners). In-hospital outcome was categorized using the Glasgow outcome scale (GOS), ranging from 1 (death) to 5 (no or minor neuro-psychological deficits with no impact on daily life).

Statistical analyses were performed using the R 4.0.5 software. Categorical variables were described as frequencies and percentages, and quantitative variables as medians and interquartile range. Categorical variables were compared by chi-square or Fisher's exact test as ACCEPTED MANUSCRIPT / CLEAN COPY appropriate, and continuous variables were compared by Mann Whitney test. A P value <0.05 was considered significant. The odds ratio for poor outcome was computed using a logistic regression analysis and was adjusted for age and immunodeficiency as they were found to be the two major risk factors for poor outcome in previous studies on the same cohort [START_REF] Mailles | Changing profile of encephalitis: Results of a 4-year study in France[END_REF].

In accordance with French regulation, we obtained informed non-opposition for the use of medical data from all patients or from relatives responsible for legal matter, or from the attending physician in case of consciousness disorders. In the latter case, this non-opposition to the use of medical data had to be confirmed by patients once they were able to. This work received an authorization of the French Commission for data Protection. This work received an authorization of the French Commission for data Protection (CNIL), and was approved by the National Ethic Committee (No. 915148, authorization obtained on October 20, 2015).

Results

Of the 494 patients included in the ENCEIF cohort, 69 (14%) were travellers. As compared to non-travellers, travellers were younger (median age, 48 years [interquartile range, 36-69] vs. 66 [49-76], P<0.001), more likely to be employed (48.5% vs. 30.5%, P=0.004), and less likely to be immunocompromised (3% vs 13%, P=0.024), Table 1. Among travellers, 12 (17%) were foreigners.

Travel destinations were diverse, including all five continents. The most frequent were Europe and Africa which is consistent with the known repartition of air traffic in France during the same period, Supplementary figures 1 and 2. Although the ranking of destinations was similar, the repartition was different. Some destinations (Africa and Asia & Oceania) were overrepresented while Europe and America were under-represented compared to the "Bulletin statistique du trafic aérien" [START_REF]Statistiques du trafic aérien[END_REF].

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On admission, travellers were more likely to present with coma (15% vs 7%, P=0.041), headache (59% vs 45%, P=0.039), infra-tentorial signs (30% vs 17%, P=0.011), including cerebellar syndrome (17% vs 8%, P=0.015), and arthralgia (10% vs 3%, P=0.007), Table 2.

Arboviruses were the primary pathogens in travellers, responsible for 15/69 encephalitis (22%), vs. 20/425 (5%) in non-travellers, P<0.001 (Table 3). The most frequent arboviruses responsible for encephalitis in travellers were TBEV (6/69, 9%), and WNV (4/69, 6%), while TBEV was the only arbovirus encephalitis documented in non-travellers (20/425, 5%). HSV was the second cause of encephalitis in travellers, less frequent than in non-travellers (13% vs 29%, P=0.009), as was varicella-zoster virus encephalitis, responsible for 3/69 (4%) of encephalitis in travellers, vs. 62/425 (15%) in non-travellers, P=0.032. Tuberculosis was more common in travellers, responsible for 6/69 encephalitis (9%), as compared to 5/425 (1%) in non-travellers, P=0.002. Interestingly, of the 6 cases of tuberculosis encephalitis in travellers, 4 involved foreigners and 5 reported a history of travel to Africa. The proportion of encephalitis of unknown cause was similar in travellers (27/69, 39%), and in non-travellers (143/425, 34%).

The proportion of encephalitis in travellers that can be attributed with certainty to their travel destination was low (15/69, 22%), Table 4. Of note, in 13/69 (19%) cases of encephalitis in travellers, a vaccine recommended for travellers or the general population (ie, TBE, measles, influenza) may have limited the risk of infection. All travellers with encephalitis received aciclovir as empirical treatment (Table 5), with similar time from admission to aciclovir start as compared to non-travellers. In-hospital mortality was 9% (37/425) in non-travellers, as compared to 0/69 in travellers, P=0.021. However, the risk of poor outcome at hospital discharge (Glasgow outcome scale <3), was similar for travellers and for non-travellers after adjustment for age and immunodeficiency (aOR 0.80 [0.36-1.80], P=0.594).

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Discussion

The main finding of this prospective, multicentre study, are the followings: i) recent international travel is a common finding in patients with encephalitis, found in 69/494 encephalitis (13%) in France during years 2016-2019, before the COVID-19 pandemic and its travel restrictions; ii) arboviruses are the primary pathogens responsible for encephalitis in travellers (15/69, 22%), primarily TBE and WNV; iii) HSV is the second most frequent pathogen responsible for encephalitis in travellers (9/69, 13%), although less common than in non-travellers (13% vs 29%, P=0.009); iv) tuberculosis is more frequently responsible for encephalitis in travellers, than in non-travellers (9% vs. 1%), P=0.002; v) according to the etiologies, 19% of encephalitis in travellers may have been prevented, with a vaccine ; vi) encephalitis in returning travellers is not associated with poor outcome at hospital discharge (aOR 0.80 [0.36-1.80], P=0.5936).

Despite recent progress in the etiological diagnosis, the management of encephalitis remains a challenge [START_REF] Venkatesan | Acute encephalitis in immunocompetent adults[END_REF][START_REF] Granerod | Challenge of the unknown. A systematic review of acute encephalitis in non-outbreak situations[END_REF][START_REF] Bodilsen | Existing Data Sources for Clinical Epidemiology: The Danish Study Group of Infections of the Brain Database (DASGIB)[END_REF][START_REF] Nath | Major advances in neuroinfectious diseases in the past two decades[END_REF], and specific data for sub-populations of interest are warranted.

Prospective studies focused on children, and the elderlies [START_REF] Britton | Causes and Clinical Features of Childhood Encephalitis: A Multicenter, Prospective Cohort Study[END_REF][START_REF] Hansen | Clinical Epidemiology, Risk Factors, and Outcomes of Encephalitis in Older Adults[END_REF], but to our knowledge, no prospective study on travellers has been reported. Given the importance of appropriate empirical treatment for encephalitis, we aimed to provide specific data for travellers, to better inform therapeutic guidelines in this population [START_REF] Goulenok | Management of adult infectious encephalitis in metropolitan France[END_REF]. Although there is no specific curative treatment for the primary causes of encephalitis in travellers identified in our study, ie arboviruses, our findings have consequences, as these encephalitis might be prevented by vaccine or vector control. TBE is endemic in selected areas within North and Central Europe [START_REF] Slunge | Burden of Tick-Borne Encephalitis, Sweden[END_REF][START_REF] Ungureanu | Meningitis, meningoencephalitis and encephalitis in Bern: an observational study of 258 patients[END_REF], and the ENCEIF cohort study documented its emergence in France during years 2016-2019, becoming the third cause of encephalitis in adults in this country [START_REF] Mailles | Changing profile of encephalitis: Results of a 4-year study in France[END_REF]. Of note, only 6/26 cases of TBE occurred in travellers during the study period, which confirms that TBE is now endemic in several areas of metropolitan France. Although all cases of WNV encephalitis were ACCEPTED MANUSCRIPT / CLEAN COPY diagnosed in recent travellers, the risk of an outbreak due to WNV infections in France is real, as sporadic transmission of WNV has been documented in horses, and humans, in France. In addition, WNV lineage 2 is now well established in Europe, responsible for domestic outbreaks every year in neighbouring countries, over recent years [START_REF] Falcinella | Three case reports of West Nile virus neuroinvasive disease: lessons from real-life clinical practice[END_REF][START_REF] Schneider | Autochthonous West Nile virus infection in Germany: Increasing numbers and a rare encephalitis case in a kidney transplant recipient[END_REF][START_REF]West Nile virus infection -Annual Epidemiological Report for 2019[END_REF]. No case of rabies was reported in this prospective cohort. In Europe, the 18 consecutive cases of rabies reported during years 2006-2019 were all travel-related, mostly from Asia and Africa [START_REF] Gossner | Prevention of human rabies: a challenge for the European Union and the European Economic Area[END_REF].

The low proportion of cases attributed to travel (22%) highlights the need to keep the same first-line work-up and probabilistic treatment in travellers presenting with encephalitis. This is especially relevant given that HSV is the first treatable etiology in travellers as well as in non-travellers, with a dramatic impact of early treatment with aciclovir. Finally, it is important to keep in mind that encephalitis in travellers is a rare event, we report 69 cases over 4 years in 62 centres in France while over 145 million air travels happened each year during the study period.

The proportion of possibly preventable encephalitis (19%) in our study highlights the importance of pre-travel consultation that tackles vector protection and evaluates the need for vaccination, especially TBE vaccine [START_REF] Demicheli | Vaccines for preventing tick-borne encephalitis[END_REF][START_REF] Taba | EAN consensus review on prevention, diagnosis and management of tick-borne encephalitis[END_REF]. Unfortunately, we have no information on the vaccination status and use of repellents in the travellers' patients included in our study. The small number of arboviruses encephalitis did not allow for in-depth analysis of clinical features of arboviruses, and a previous study on the whole ENCEIF cohort using a factor analysis of mixed data could not identify any distinctive pattern or cluster of cases [START_REF] Maréchal | A prospective cohort study to identify clinical, biological and imaging features that predict etiology of acute encephalitis[END_REF]. The better prognosis of encephalitis in travellers may be mostly related to the lower proportion of comorbidities, and the younger age, of this population. Due to limited sample size, and the absence of any fatal outcome among travellers with encephalitis, our study was not powered to identify independent prognostic factors.

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This study has limitations: first, despite the large initial cohort, the number of returning travellers makes for a small sample size. As it only included adult patients with encephalitis managed in France during years 2016-2019, our findings may not be generalized to children, to other countries, and to other period of times. However, to the best of our knowledge, this is the first prospective cohort study on encephalitis in travellers, and by far the largest to date.

With a large number of participating sites, the enrolment of all consecutive cases of encephalitis, and the use of international inclusion criteria, our study provides robust data that may be of interest for other settings. Inclusions stopped in December 2019, immediately before the COVID-19 pandemic, and travel patterns were similar to the general population in France during the study period, with Europe (76.7%), and Africa (12.0%) as the two main destinations for intercontinental travel [START_REF]Statistiques du trafic aérien[END_REF].

In conclusion, a recent history of travel abroad in patients with encephalitis should not change the preliminary work-up, and the empirical treatment, which must include aciclovir pending HSV encephalitis is ruled out. However, arbovirus encephalitis, and tuberculosis, both more common in travellers than in non-travellers, must be suspected early in the process. Pretravel consultation should address the risk of arbovirus encephalitis for selected destinations, and consider specific vaccinations, and/or vector control advices.

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(

  Montpellier), Guillaune Marc (Saint-Nazaire), Alexandre Maria (Montpellier), Nathalie Marin (Paris), Aurélie Martin (Strasbourg), Guillaume Martin-Blondel (Toulouse), Martin Martinot (Colmar), Alexandre Mas (Montpellier), Philippe Mateu (Charleville-Mézière), Morgan Matt (Paris), Laurence Maulin (Aix-en-Provence), Frédéric Mechai (Paris), Eugénie Mutez (Lille), Jérémie Orain (Nantes), Anne Schieber-Pachart (Colmar), Nathalie Pansu (Montpellier), Solene Patrat-Delon (Rennes), Patricia Pavese (Grenoble), Hélène Pelerin (La Roch sur Yon), Véronique Pelonde-Erimée (Fort de France), Isabelle Pierre (Grenoble), Diane Ponscarme (Paris), Dimitri Psimaras (Paris), Mathilde Puges (Bordeaux), Mathilde Reveillon-Istin (Le Havre), Sylvain Rheims (Lyon), Aurélie Richard-Mornas (Lyon), Agnès Riché (Angoulème), Vincent Roubeau (Paris), Yvon Ruch (Strasbourg), Isabelle Runge (Orléans), Hélène Savini (Marseille), Romain Sonneville (Paris), Jean-Paul Stahl (Grenoble), Pierre Tattevin (Rennes), Kelly Tiercelet (Paris), Saber Touati (Grenoble), Jean-Marie Turmel (Fort-de-France), Isabelle Tyvaert (Nancy), Marc-Olivier Vareil (Bayonne), Magalie Vidal-Roux (Clermont-Ferrand), Virginie Vitrat (Annecy), Adrien Wang (Paris), Heidi Wille (Bayonne), Mathieu Zuber (Paris). *The scientific committee are: Laurent Almoyna-Martinez (Aix-en-Provence), Olivier Bouchaud (Bobigny), Thomas de Broucker (Saint Denis), Fabrice Bruneel (Versailles), Bernard Castan (Périgueux), Eric Denes (Limoges), Olivier Epaulard (Grenoble), Nadine Girard (Marseille), Jean-Louis Herrmann (Garches), Jérome Honnorat (Lyon), Alexandra Mailles (Saint Maurice), Patrice Morand, (Grenoble), François Raffi (Nantes), France Roblot (Poitiers), Jean-Paul Stahl (Grenoble), Romain Sonneville (Paris), Pierre Tattevin (Rennes).

Table 1 .

 1 Demographic characteristics of patients with encephalitis, travellers (n=69) vs. nontravellers (n=425), Enceif cohort, France 2016-19

		Travellers	Non-travellers
				P
		n=69	n=425
	Demographics
	Age, years	48 [36-69]	66 [49-76]	<0.001
	Gender, male	46 (66.7%)	257 (60.6%)	0.409
	Professional status			0.002
	Active, unemployed	9 (13.2%)	35 (8.4%)
	Active, employed	33 (48.5%)	128 (30.5%)
	Student	4 (5.9%)	14 (3.3%)
	Retired	22 (32.4%)	242 (57.8%)
	Co-morbidities
	Diabetes	4 (5.8%)	63 (15.0%)	0.062
	Cancer	3 (4.3%)	36 (8.5%)	0.342
	Hemopathy	4 (5.9%)	23 (5.5%)	1
	Immunodepression	2 (2.9%)	56 (13.2%)	0.024
	Chronic neurological
		1 (1.4%)	28 (6.7%)	0.151
	disorders
	Chronic psychiatric
	disorders	1 (1.4%)	27 (6.5%)	0.167
	Qualitative data presented as number (%), quantitative data as median [interquartile range]
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Table 2 .

 2 Clinical characteristics of encephalitis on admission, travellers (n=69), vs. nontravellers (n=425), Enceif

cohort, France 2016-19

		Travellers	Non-travellers
				P
		n=69	n=425
	Clinical findings
	Coma	10 (14.5%)	28 (6.6%)	0.041
	Altered mental status	30 (43.5%)	167 (39.3%)	0.599
	Confusion	36 (52.2%)	270 (63.5%)	0.095
	Epilepsy	7 (10.1%)	67 (15.8%)	0.302
	Headache	41 (59.4%)	192 (45.2%)	0.039
	Supra-tentorial signs	26 (37.7%)	180 (42.4%)	0.55
	Motor deficit	11 (15.9%)	48 (11.3%)	0.366
	Speech disorders	18 (26.1%)	152 (35.8%)	0.152
	Infra-tentorial signs	21 (30.4%)	71 (16.7%)	0.011
	Cranial nerve palsy	9 (13.0%)	46 (10.8%)	0.736
	Cerebellar syndrom	12 (17.4%)	32 (7.5%)	0.015
	Fever	58 (84.1%)	325 (76.5%)	0.213
	Rash	6 (8.7%)	45 (10.6%)	0.79
	Respiratory signs	7 (10.1%)	48 (11.3%)	0.94
	Digestive symptoms	15 (21.7%)	79 (18.6%)	0.65
	Arthralgia	7 (10.1%)	11 (2.6%)	0.007
	Cerebrospinal fluid
	Erythrocytes, per mm 3	11 [2-80]	15 [4-60.5]	0.978
	White cells, per mm 3	75 [19.5-243.5]	114 [32-273.5]	0.152
	Protein, g/L	0.87 [0.60-1.41]	0.90 [0.62-1.44]	0.749

Qualitative data presented as number (%), quantitative data as median [interquartile range]

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Table 3 .

 3 Causes of encephalitis, travellers (n=69), vs. non-travellers (n=425), Enceif cohort, France 2016-19 Coxiella burnettii, Parvovirus B19, Anaplasma sp., Rickettsia

	Travellers, n=69	Non-travellers, n=425	P

sp., Legionella pneumophila (one patient each) ACCEPTED MANUSCRIPT / CLEAN COPY

Table 4 .

 4 Encephalitis in travellers: travel destinations and travel-associated etiology, Enceif cohort, France 2016-19

	Total	Travel-associated etiology

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Table 5 .

 5 Encephalitis in travellers (n=69) vs. non-travellers (n=425): Treatment and outcome, Enceif cohort, France 2016-19 Qualitative data presented as number (%), quantitative data as median [interquartile range]

	Travellers	Non-travellers
	n=69	n=425	P

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Funding

French Infectious Diseases Society (Société de pathologie infectieuse de langue française, SPILF)

Data availability

The data underlying this article cannot be shared due to privacy concerns for the individuals that participated in the study. The data will be shared on reasonable request to the corresponding author.

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Alexandra Mailles, Jean-Paul Stahl, Pierre Tattevin ; data collection: The investigator's group ; analysis and interpretation of results: Pierre Fillâtre, Léa Picard ; draft manuscript preparation: Léa Picard, Pierre Tattevin, Alexandra Mailles, Pierre Fillâtre, Jean-Paul Stahl.

All authors reviewed the results and approved the final version of the manuscript.

Conflict of interest

The authors have declared no conflicts of interest.

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Kévin Barré
email: kevin.barre@mnhn.fr

Jérémy S P Froidevaux

Alejandro Sotillo

Charlotte Roemer

Christian Kerbiriou

Drivers of bat activity at wind turbines advocate for mitigating bat exposure using multicriteria algorithm-based curtailment

Keywords: acoustic monitoring, chiroptera, cut-in speed, collision risk, mitigation hierarchy

Wind turbine development is growing exponentially and faster than other sources of renewable energy worldwide. While multi-turbine facilities have small physical footprint, they are not free from negative impacts on wildlife. This is particularly true for bats, whose population viability can be threatened by wind turbines through mortality events due to collisions. Wind turbine curtailment (hereafter referred to as "blanket curtailment") in non-winter periods at low wind speeds and mild temperatures (i.e. when bats are active and wind energy production is low) can reduce fatalities, but show variable and incomplete effectiveness because other factors affect fatality risks including landscape features, rain, turbine functioning, and seasonality. The combined effects of these drivers, and their potential as criteria in algorithm-based curtailment, have so far received little attention. We compiled bat acoustic data recorded over four years at 34 wind turbine nacelles in France from post-construction regulatory studies, including 8,619 entire nights (251±58 nights per wind turbine on average). We modelled nightly bat activity in relation to its multiple drivers for three bat guilds, and assessed whether curtailment based on algorithm would be more efficient to limit bat exposure than blanket curtailment based on various combinations of unique wind speed and temperature thresholds. We found that landscape features, weather conditions, seasonality, and turbine functioning determine bat activity at nacelles. Algorithm-based curtailment is more efficient than blanket curtailment, and has the potential to drastically reduce bat exposure while sustaining the same energy production.

Compared to blanket curtailment, the algorithm curtailment reduces average exposure by 20 to 29% and 7 to 12% for the high-risk guilds of long-and mid-range echolocators, and by 24 to 31% for the low-risk guild of short-range echolocators. These findings call for the use of algorithm curtailment as both power production and biodiversity benefits will be higher in most situations.

wind energy

Introduction

Wind power generation produces near-zero greenhouse gas emissions during the operational phase, has short greenhouse gas payback time, and constitutes an efficient and sustainable way for the transition towards reduced global greenhouse gas emissions [START_REF] Dammeier | Space, Time, and Size Dependencies of Greenhouse Gas Payback Times of Wind Turbines in Northwestern Europe[END_REF][START_REF] Veers | Grand challenges in the science of wind energy[END_REF]. As a consequence and in line with international treaties such as the 2016 Paris agreement to reduce global greenhouse gas emissions, wind turbine installation has grown exponentially over the last 20 years and currently represents the most rapidly expanding form of renewable energy worldwide (GWEC, 2021). While wind farm installation can have a relatively small footprint in terms of land conversion compared to other development projects, it still entails negative impacts on wildlife, particularly for insectivorous bats through mortality events by collision. Such increases in mortality are likely to impinge on the viability of populations [START_REF] Friedenberg | Assessing fatality minimization for hoary bats amid continued wind energy development[END_REF][START_REF] Frick | Fatalities at wind turbines may threaten population viability of a migratory bat[END_REF]. This is especially true for migratory and long-range echolocating bat species, which are the most sensitive to collisions as they fly more often at the height at which turbines operate [START_REF] Roemer | Bat flight height monitored from wind masts predicts mortality risk at wind farms[END_REF]. In addition to mortality, some bat species avoid areas adjacent to wind turbines leading to a reduction of habitat availability [START_REF] Barré | Estimating habitat loss due to wind turbine avoidance by bats: Implications for European siting guidance[END_REF].

In the European Union and in many countries worldwide, wind energy developers must carry out an Environmental Impact Assessment (EIA) prior to any wind farm installation to evaluate potential environmental consequences and the measures required to avoid impacts. Developers must also monitor impacts during the operational phase (e.g. decree no. 0198 of August 27, 2011, in France). However, guidelines to avoid "areas where high bat activity has been determined by impact assessment" (EUROBATS; [START_REF] Rodrigues | Guidelines for consideration of bats in wind farm projects -Revision 2014[END_REF] appear to be poorly implemented [START_REF] Barré | Over a decade of failure to implement UNEP/EUROBATS guidelines in wind energy planning: A call for action[END_REF]. When impacts cannot be avoided, measures fot their reduction or, as a last resort, offsetting, must be implemented to achieve a state of no net loss of biodiversity (i.e. the mitigation hierarchy framework; Business and Biodiversity Offsets Programme (BBOP), 2012). Wind turbine curtailment at low wind speeds and mild temperatureswhen bats are highly active and energy production is low, hereafter referred to as "blanket curtailment"is a reduction measure that offers promising opportunities to reconcile bat conservation and wind energy [START_REF] Adams | A review of the effectiveness of operational curtailment for reducing bat fatalities at terrestrial wind farms in North America[END_REF][START_REF] Whitby | The State of the Science on Operational Minimization to Reduce Bat Fatality at Wind Energy Facilities[END_REF][START_REF] Voigt | Wildlife and renewable energy: German politics cross migratory bats[END_REF][START_REF] Arnett | Altering turbine speed reduces bat mortality at wind-energy facilities[END_REF][START_REF] Baerwald | A Large-Scale Mitigation Experiment to Reduce Bat Fatalities at Wind Energy Facilities[END_REF]. One of the most common blanket curtailment strategies is based on a simple combination of a maximum wind speed threshold (most often between 3.5 and 8 m/s) and a minimum temperature threshold (most often around 10°C). Respectively below and above those thresholds, the blades are turned to a different angle (i.e. feathered) to limit their rotation rate to less than one per minute, due to expected favourable conditions for bats. Blanket curtailment is mostly limited to non-winter periods. This approach can significantly reduce the fatality risk, but shows variable and incomplete effectiveness [START_REF] Voigt | Wind turbines without curtailment produce large numbers of bat fatalities throughout their lifetime : A call against ignorance and neglect[END_REF][START_REF] Adams | A review of the effectiveness of operational curtailment for reducing bat fatalities at terrestrial wind farms in North America[END_REF][START_REF] Whitby | The State of the Science on Operational Minimization to Reduce Bat Fatality at Wind Energy Facilities[END_REF][START_REF] Măntoiu | Wildlife and infrastructure: impact of wind turbines on bats in the Black Sea coast region[END_REF]. Besides wind speed and temperature, landscape features and other weather factors such as rain also drive bat fatality risk [START_REF] Thompson | Factors associated with bat mortality at wind energy facilities in the United States[END_REF][START_REF] Santos | Using species distribution modelling to predict bat fatality risk at wind farms[END_REF]. Indeed, bat activity at wind turbine nacelles, which links to fatality risk [START_REF] Peterson | Acoustic Exposure to Turbine Operation Quantifies Risk to Bats at Commercial Wind Energy Facilities[END_REF][START_REF] Korner-Nievergelt | Estimating bat and bird mortality occurring at wind energy turbines from covariates and carcass searches using mixture models[END_REF], also depends on the weather, season, landscape features, and wind turbine dimensions and rotation speed [START_REF] Roemer | Influence of landscape and time of year on bat-wind turbines collision risks[END_REF][START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF][START_REF] Cryan | Behavior of bats at wind turbines[END_REF][START_REF] Brinkmann | Entwicklung von Methoden zur Untersuchung und Reduktion des Kollisionsrisikos von Fledermäusen an Onshore-Windenergieanlagen[END_REF][START_REF] Horn | Behavioral Responses of Bats to Operating Wind Turbines[END_REF]. Consequently, curtailment strategies based on multifactor algorithms have the potential to be more efficient in reducing the fatality risk. Indeed, the use of an algorithm to curtail wind turbines in real-time based on weather factors, date, and nightly time, should allow avoiding most collisions while minimizing the loss of production [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF]. [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF][START_REF] Brinkmann | Entwicklung von Methoden zur Untersuchung und Reduktion des Kollisionsrisikos von Fledermäusen an Onshore-Windenergieanlagen[END_REF] are two of the few studies that propose this type of multicriteria framework to curtail wind turbines. These studies were based on data sampled in 2008 in Germany covering six months at 70 wind turbine nacelles and 35 different sites. To our knowledge, no peer-reviewed study has examined simultaneously and on a spatiotemporally extensive dataset all drivers of bat exposure (i.e. landscape features, weather conditions, date, and wind turbine characteristics), nor assessed the possibility to use them in guild specific algorithms to inform wind turbine curtailment.

To assess the potential of multicriteria curtailment algorithms, we compiled bat acoustic data recorded at wind turbine nacelles in France by wind energy developers in a context of postconstruction regulatory studies, while homogeneously re-analysing acoustic data (i.e. using the same automated bat call identification software). Reprocessed bat acoustic data allowed us to build a standardised bat activity metric at nacelle height known to be a good predictor of fatality risk [START_REF] Peterson | Acoustic Exposure to Turbine Operation Quantifies Risk to Bats at Commercial Wind Energy Facilities[END_REF][START_REF] Korner-Nievergelt | Estimating bat and bird mortality occurring at wind energy turbines from covariates and carcass searches using mixture models[END_REF]. Given the absence of national guidelines in France concerning the characteristics and settings of bat recorders for bat monitoring at nacelles and the large number of engineering consultants involved in data collection, we expected a large variation in the methods [START_REF] Coly | Études chiroptérologiques dans les dossiers réglementaires éoliens : disponibilité de l'information et conformité avec les recommandations nationales et européennes[END_REF]. Thus, our first objective was to assess whether monitoring methods (devices and settings) or confounding effects with landscape features, date, weather and wind turbine characteristics would bias the comparison of bat activity between wind turbines. This assessment was intended to filter out data from some wind turbines if necessary, and highlight the need for better national or international cooperation in the choice of materials and parameters in the case where the current situation would not allow meta-analyses. Once any method bias was controlled for, our second objective was to determine the main factors influencing bat activity at nacelles. We expected bat activity to increase with increasing landscape quality (e.g. by an increasing amount of forests, proximity to wetlands, or land use heterogeneity; [START_REF] Put | Bats respond negatively to increases in the amount and homogenization of agricultural land cover[END_REF][START_REF] Sirami | Artificial wetlands and surrounding habitats provide important foraging habitat for bats in agricultural landscapes in the Western Cape, South Africa[END_REF]Boughey et al., 2011a) and decreasing blade rotation speed [START_REF] Cryan | Behavior of bats at wind turbines[END_REF][START_REF] Horn | Behavioral Responses of Bats to Operating Wind Turbines[END_REF], and to be higher during nights with good weather conditions (i.e. high temperature, low wind speed and no rain; [START_REF] Voigt | Wildlife and renewable energy: German politics cross migratory bats[END_REF][START_REF] Erickson | The influence of regional climate and nightly weather conditions on activity patterns of insectivorous bats[END_REF] and at the end of summer [START_REF] Heim | Seasonal activity patterns of European bats above intensively used farmland[END_REF]. Finally, our third objective was to compare on a per-night scale the performance of a curtailment algorithm based on multiple factors to that of a blanket curtailment method based on various combinations of unique wind speed and temperature thresholds, in terms of both bat activity exposure and energy production. We expected the algorithm-based curtailment to be more efficient in reducing bat exposure compared to blanket curtailment, by avoiding a larger percentage of bat activity occurring when blades are moving, while involving smaller losses of energy production.

Methods

Acoustic data collection and processing

We compiled existing raw acoustic data (i.e. sound files in raw or wav format) of 14,937 complete recording nights at 59 wind turbine nacelles (including 20 models) located on 55 wind farms in France (Fig. 1; Table S1). These data were provided by nine wind farm developers and produced by 12 consulting firms and non-governmental organizations as part of regulatory postimplementation impact monitoring studies. Each of the 59 wind turbines was monitored on average for 251 nights (min: 103; max: 514). The monitored nights covered all months of the year and spanned four years between 2017 and 2020; 10% of the sites were monitored for more than one year. The year 2017 represents 2% of nights, 2018 18% of nights, 2019 79% of nights and 2020 0.4% of nights (Fig. S1). Depending on the analyses conducted, the complete set or subset of these data were used (see Statistical analysis section).

Three types of recorders were used: Batcorder at 18 wind turbines (versions 1, 2 and 3; ecoObs), Batmode S+ at 34 wind turbines (bat bioacoustics technology GmbH), and Song Meters at eight wind turbines (SM3BAT and SM4BAT; Wildlife Acoustics). All recorders were positioned at the bottom of the nacelle. Each was associated with one to three triggering thresholds, i.e. a built-in recording control algorithm which started the recording when a sound event exceeded a given sound level (see Supporting information S1 for more details).

Acoustic monitoring was always performed throughout the night, from sunset to sunrise. We used the number of bat passes (hereafter referred to as "activity") or the presence/absence (hereafter referred to as "occurrence") recorded during a night as a measure of bat visits with exposure (see section 2.3 for more details). We defined a bat pass as one or more echolocation calls within a five-second interval [START_REF] Kerbiriou | Bat Pass Duration Measurement: An Indirect Measure of Distance of Detection[END_REF]. All 731,717 bat passes were automatically classified to the closest taxonomic level using the Tadarida software [START_REF] Bas | Tadarida : A Toolbox for Animal Detection on Acoustic Recordings[END_REF]. Since most bat species had very low occurrence (Table S1), we pooled together species into three guilds based on their similar echolocation call structures and therefore similar foraging strategies: long-range echolocators (LRE), mid-range echolocators (MRE) and shortrange echolocators (SRE) [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF], see Table S2 for species composition.

Long-range echolocators are especially sensitive to fatality risks with wind turbines due to the great part of the time they spend at height (i.e. 20 to 45 m above ground level), followed by the mid-range echolocators (Table S2; [START_REF] Roemer | Bat flight height monitored from wind masts predicts mortality risk at wind farms[END_REF]. Although grouping species into these three guilds prevented misidentification problems between cryptic species, noise in the nacelle due to wind turbine functioning generated many false positives, especially at very high blade speeds. We followed the approach of [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] and applied a maximum false positive tolerance of 50% to discard these interferences (see [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] for more details), which reduced the dataset to 98,627 bat passes. This reduction led to discard 6.55 to 9.93% fewer false positives for Batmode data compared to other recorders.

Environmental and wind turbine variables

To determine which factors influence bat activity and occurrence at wind turbine nacelles, we collected or computed variables related to landscape composition and heterogeneity, weather conditions, and wind turbine functioning and dimensions.

Landscape variables -We considered variables representing the surface cover of five land-use types known to positively or negatively affect bats: impervious surfaces [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF][START_REF] Dixon | Relationship between land cover and insectivorous bat activity in an urban landscape[END_REF], arable lands [START_REF] Put | Bats respond negatively to increases in the amount and homogenization of agricultural land cover[END_REF], grasslands [START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: implications for conservation[END_REF][START_REF] Roeleke | Habitat use of bats in relation to wind turbines revealed by GPS tracking[END_REF][START_REF] Lentini | Bats in a Farming Landscape Benefit from Linear Remnants and Unimproved Pastures[END_REF], forests [START_REF] Heim | The relevance of vegetation structures and small water bodies for bats foraging above farmland[END_REF]Boughey et al., 2011a) and water bodies [START_REF] De Conno | Testing the performance of bats as indicators of riverine ecosystem quality[END_REF][START_REF] Sirami | Artificial wetlands and surrounding habitats provide important foraging habitat for bats in agricultural landscapes in the Western Cape, South Africa[END_REF]. These variables were computed around the 59 wind turbines as proportions of the total area for variables presenting enough variations (impervious surfaces, arable lands, grasslands and forests), in ten area buffers around wind turbines (50,100,250,500,1000,2000,3000,4000,5000 and 10000 m radius) to use the most relevant scale for each variable [START_REF] Kalda | Multi-scale ecology of insectivorous bats in agricultural landscapes[END_REF], see Statistical Analysis section for more details). We also calculated the Euclidean distance to the nearest impervious surfaces, forests and water bodies. Moreover, we computed landscape metrics depicting landscape configurational and compositional heterogeneity [START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF], including edge density (i.e. the density of ecotones in m/ha), conditional entropy (i.e. an increasing index with increasing landscape complexity), patch richness density (i.e. the number of patch types standardised by the surface), and Shannon diversity index of habitat patches. These landscape metrics were computed using the R package landscapemetrics [START_REF] Hesselbarth | landscapemetrics: an open-source R tool to calculate landscape metrics[END_REF], for the ten radius sizes presented above. All landscape variables were extracted from the highresolution CES OSO land cover map 2018 available at https://www.theialand.fr/en/ceslist/land-cover-sec/ [START_REF] Derksen | Geometry aware evaluation of handcrafted superpixel-based features and convolutional neural networks for land cover mapping using satellite imagery[END_REF].

Weather variables -We collected the average wind speed (m/s) and temperature (°C) recorded by wind turbine nacelle weather stations in 10-minute intervals and averaged them on a nightly scale at each wind turbine (i.e. on the same scale as acoustic data). Since the amount of rainfall was not recorded by the nacelle weather stations, we collected the daily cumulated rain (mm) (i.e. over the 24-hour period from midnight of the day when the recording night started) using the weather database from E-OBS (https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#datafiles).

Wind turbines variables -We collected dimensions of wind turbines which measured 45 to 139 m (92 m on average) in nacelle height and 44 to 126 m (94 m on average) in rotor diameter (Table S1). We also collected the average rotation speed (km/h) at the tip of the blade in 10minute intervals, and averaged it on a nightly scale at each wind turbine.

Statistical analysis

We assessed drivers of measures of bat activity and occurrence around wind turbine nacelles, including factors related to landscape composition and heterogeneity, weather conditions, the Julian day, wind turbine functioning and dimensions and recording methods (i.e. the recorder type and the trigger sensitivity). In the first step, since we compiled data produced by different contributors, we expected the existence of multiple combinations between the recorder type and the trigger sensitivity. However, these recording methods deeply affect the number of bat passes recorded [START_REF] Adams | Do you hear what I hear? Implications of detector selection for acoustic monitoring of bats[END_REF]. Confounding effects between recording methods and factors of interest (e.g. landscape composition) could prevent modeling them simultaneously. Using all compiled data, we therefore tested for trends in the landscape composition and heterogeneity, weather conditions, and wind turbine functioning and dimensions between recording methods (i.e. a discrete variable including seven combinations between the recorder type and the trigger sensitivity), using Kruskal-Wallis tests and box plots. We then computed the proportion of variance explained by each variable (pseudo-R²) to assess whether the importance of the factors of interest (actual drivers of bat activity) was biased by the different recording methods. To

properly study the drivers of bat activity at wind turbine nacelles, a prerequisite was that the recording methods do not capture an overwhelming part of the variance compared to the factors known to affect bat activity. To achieve this, we built one full Generalised Linear Mixed Model (GLMM, R package glmmTMB; [START_REF] Brooks | {glmmTMB} Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling[END_REF] per bat guild, using LRE and MRE activity and SRE occurrence as response variables, and the landscape (see Supporting information S2

for landscape variable selection and composition), weather conditions (i.e. average wind speed, average temperature and cumulated rain), the Julian day, and wind turbine functioning (i.e.

average blades rotation speed) and dimensions (i.e. nacelle height and rotor diameter) as fixed effects (hereafter referred to as "explanatory variables"). Since we had a relatively small number of sites, we restricted the number of landscape variables to three; i.e. the same number as the other types of variables (i.e. three weather variables and three wind turbine functioning and dimension variables available). With such approach, models were always constituted of ten variables, allowing to avoid overparameterization. For landscape variables we pre-selected the best computing area buffer and in a second step selected the three oneswithin the five landscape variableswith the best conjoint contributions (see Supporting Information S2 for more details). We used the wind turbine identifier and year as random effects to account for pseudo-replication (i.e. many recording nights per wind turbine) and inter-year variations in activity, associated with a negative binomial distribution for LRE and MRE guilds and a binomial distribution for SRE guild (see Supporting information S2 and Table S4 for the composition of full models). Then, we computed the pseudo-R² of each variable by subtracting the marginal R² of the full model and that of the model without the target variable, using the R package sjstats.

The preliminary analysis showed that recording methods resulted in confounding effects with most other variables of interest and captured the largest variance part (Table S5;

Figs. 2 & S2).
Thus, to model bat activity or occurrence as a function of explanatory variables, we selected in a second step only one combination between the recorder type and the trigger sensitivity that removed any variation in recording methods. We chose the combination of the Batmode set to a trigger sensitivity of 37 dBSPL which had the largest dataset resulting in 34 wind turbines, 8,619 nights and 65,775 bat passes. Based on this subset, we performed the same GLMMs workflow as presented above (see Supporting information S2 and Table S6 for more details) to assess the respective effects of explanatory variables on the LRE and MRE activity and SRE occurrence. For each explanatory variable, we checked the potential need for adding a nonlinear effect by visual inspection of Generalised Additive Mixed Models (GAMM, R package mgcv; [START_REF] Wood | Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models[END_REF] see Table 1 for variables that required quadratic or cubic effects). We also checked the absence of multicollinearity by calculating the Variance Inflation Factor (VIF) for each explanatory variable (R package performance; [START_REF] Lüdecke | Performance: assessment of regression models performance[END_REF]. All variables showed a VIF<2, implying no evidence of multicollinearity [START_REF] Chatterjee | Regression analysis by example[END_REF]. It should be noted that wind speed and blade speed were not excessively correlated thanks to maintenance periods that stopped the turbines in all wind conditions (Fig. S3). Overall model validation was performed using diagnostic plots (R packages DHARMa and performance; [START_REF] Lüdecke | Performance: assessment of regression models performance[END_REF]. Full models were compared to null ones using the Akaike information criterion (AIC) [START_REF] Burnham | Model Selection and Multimodel Inference. A practical information-Theoretic approach[END_REF], and goodness of fit was assessed using the marginal R² (variance explained by the fixed effects) and conditional R² (variance explained by both fixed and random factors) values [START_REF] Nakagawa | A general and simple method for obtaining R2 from generalized linear mixed-effects models[END_REF]. All analyses were performed using a significance threshold of 5% in R statistical software v.4.0.3 (R Core Team, 2020).

Assessing the effectiveness of using model equations to limit bat exposure compared to conventional curtailments

Using the same Batmode dataset, we assessed whether curtailment of wind turbines based on multiple-factor models could be more efficient in limiting bat activity exposure at the scale of all wind turbines than commonly used blanket curtailment methods. For that, we trained full models for each guild on a 50% fully random subset of the dataset (hereafter referred to as "training dataset") and predicted bat activity on the other 50% (hereafter referred to as "prediction dataset"), and this 100 times. Then, we computed for each prediction dataset the remaining percentage of bat activity (for LRE and MRE guilds) or occurrence (for SRE guild) (i.e. the real bat activity or occurrence recorded while the blades were moving) and the percentage of lost blade rotations (i.e. as a proxy of lost energy production) resulting of curtailing wind turbines following either of two methods: (i) curtailing above thresholds of bat activity predicted from full models (hereafter referred to as "multicriteria curtailment algorithm"), and (ii) curtailing below wind speed thresholds and this either without temperature threshold or with different minimum temperatures required from 2 to 18°C (hereafter referred to as "blanket curtailment"). Finally, we plotted the relationship between the remaining percentage of bat activity or occurrence and the percentage of lost blade rotations for both curtailment methods to evaluate their effectiveness in limiting exposure (Fig. 3A). The comparison of both curtailment methods was conducted for the non-winter periods only.

To assess whether the effectiveness was relevant for all wind turbines, we also plotted the relationship between the remaining percentage of bat activity and the percentage of lost blade rotations for each wind turbine independently. We computed Area Under Curve (AUC) values for both curtailment methods to evaluate which one was the most effective (i.e. with the highest AUC value) (R package MESS). We also estimated to what extent the effectiveness of curtailment methods was preserved when wind turbines included in the training dataset differed from those in the prediction dataset. For that, we repeated the procedure explained above, but using a training dataset constituted of data from 33 out of 34 wind turbines and a prediction dataset constituted of data from the 34 th wind turbine, and we repeated it for each wind turbine to present its results while computing AUC values for both curtailment methods. These turbineby-turbine assessments were only conducted for LRE and MRE guilds for which we had enough data for each wind turbines.

Finally, because the percentage of lost blade rotations did not constitute a perfect proxy of lost energy production, we assessed whether the relative comparison of lost blade rotations between curtailment methods as a proxy of energy production losses was biased (e.g. one method for a given level of lost blade rotations involving slower blade speeds, and in turn lower energy losses, than the other method). For that we compared the distribution of blade speeds inside lost blade rotations between the two curtailment methods.

Results

Bat monitoring

A total 98,627 bat passes were recorded at 59 wind turbines and 14,937 nights. However, as described above, in order to avoid confounding effects between recording methods and other explanatory variables, we only selected wind turbines monitored using Batmode recorders which exhibited no trigger sensitivity variation, while including most of the data (i.e. 34 wind turbines out of 59 and 8,619 nights out of 14,937). Data from Batmode resulted in a total of 65,775 bat passes recorded, with 43,519 passes of LRE, 22,135 passes of MRE and 121 passes of SRE (see Table S3 for species composition). At least one pass of LRE, MRE and SRE was recorded in 35%, 18% and 1% of nights, respectively (Table S3).

Drivers of bat activity around nacelles

Full models of bat activity and occurrence showed smaller AIC than null models (delta AIC of full models from -50 to -1468), with 33%, 55% and 51% variance explained by fixed effects and 78%, 60% and 54% by both fixed and random effects, for LRE, MRE and SRE guilds, respectively (Table S6).

Regarding landscape variables, LRE activity was positively affected by the landscape Shannon diversity index of habitat patches at the 10,000 m radius scale while MRE activity increased with increasing patch richness density at the 1,000 m radius scale and forest proportion at the 10,000 m radius scale. We also found significant positive relationships between SRE occurrence and edge density at the 10,000 m radius scale and the proportion of impervious surfaces at the 100 m radius scale (Fig. 4; Table 1). Concerning wind turbine functioning and dimensions, increasing average blade speed significantly reduced the activity/occurrence of all guilds, while no effect of nacelle height and rotor size were found (Figs. 4; Table 1). Concerning weather conditions, average temperature positively and non-linearly affected the activity of LRE and MRE guilds, while the average wind speed and the cumulated rain negatively affected (non-linearly and linearly, respectively) the activity/occurrence of all guilds (Fig. 4; Table 1).

Finally, we found seasonality in the activity of the LRE and MRE guilds, manifested as a cubic and quadratic relationship, respectively, with the Julian date: increasing between January and August, and decreasing from September to December (Fig. 4; Table 1).

Effectiveness of model equations to limit bat exposure compared to conventional curtailments

For the blanket curtailment, we found that increasing wind speed thresholds below which wind turbines should be curtailed almost always linearly decreased the real remaining bat activity for all guilds (Fig. S4A-C). For the multicriteria curtailment algorithm, we found that decreasing the predicted bat activity above which wind turbines should be curtailed decreased the actual bat activity or occurrence exposed: exponentially for LRE activity, logistically for MRE activity and linearly for SRE occurrence. (Fig. S4A-C). Moreover, expanding curtailment increased the percentage of lost blade rotations differently between methods: with logistic increases when using wind speed and temperature criteria, and exponential increases when using a multicriteria curtailment algorithm. (Fig. 4D-F).

When we linked the real bat activity or occurrence exposed with the percentage of lost blade rotations, we found that the multicriteria curtailment algorithm was more efficient than the blanket curtailment for all guilds (Figs. 3B &5). We found that the multicriteria curtailment algorithm at the scale of all wind turbines exhibited on average 20% and 9% less bat activity exposed than blanket curtailment without temperature threshold for LRE and MRE guilds, respectively, and 24% less occurrence exposed for SRE guild (Fig. 3B1). When blanket curtailment included temperature thresholds, the multicriteria curtailment algorithm exhibited on average 20 to 29%, 7 to 12% and 24 to 31% less exposure for LRE, MRE and SRE guilds, respectively, depending on the temperature threshold considered in blanket curtailment (Figs.

3B2 & 5). The higher efficiency of the multicriteria curtailment algorithm was confirmed by its AUC values which were higher than those of blanket curtailment for a 10°C threshold at 81 and Finally, blade speed distributions did not differ between lost blade rotations of both curtailment methods, thus suggesting that the relative comparison of lost blade rotations between curtailment methods as a proxy of energy production losses was not biased (Fig. S9).

Discussion

Identifying drivers of bat exposure to wind turbines from acoustic monitoring at nacelles, and the possibility of their combined use as criteria in algorithm-based curtailment, have so far received little attention in the scientific literature in the context of wind turbine impact mitigation. Our study shows that recording methods should be accounted for when using acoustic data continuously produced in post-construction regulatory studies, before analysing the drivers of bat exposure. Once detection method biases were avoided, results showed that it is possible to disentangle the main drivers. Our findings revealed that landscape features, weather conditions, seasonality, and wind turbine functioning determine the activity of all bat guilds at nacelles. Algorithms including all these drivers to curtail wind turbines above a given level of predicted bat activity are more efficient than common blanket curtailment methods based on unique wind speed and temperature thresholds on the activity period of bats, as they reduce more exposure while sustaining the same energy production.

Assessing bias in recording methods

One of the aims of this study was to take advantage of the numerous pre-existing data from post-construction monitoring studies instead of designing a field study that would require paramount monetary and time investments. A prerequisite for using all aggregated data was the absence of biases related to the recording methods. Unfortunately, when considering data collected using different recording methods, the combination of recorder type and triggering sensitivity explained much more variance than all well-known drivers of bat activity [START_REF] Roemer | Influence of landscape and time of year on bat-wind turbines collision risks[END_REF][START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF][START_REF] Cryan | Behavior of bats at wind turbines[END_REF][START_REF] Horn | Behavioral Responses of Bats to Operating Wind Turbines[END_REF], minimizing their relative importance in the models. All gradients of drivers strongly varied among recorder type/trigger sensitivity combinations, thus preventing any modelling of the effects of drivers on bat activity based on the full dataset due to confounding effects. Indeed, different recorder type/trigger sensitivity combinations can lead to very different levels of bat activity between sites due to the different detection distances generated by the material specificities and settings [START_REF] Darras | High microphone signal-to-noise ratio enhances acoustic sampling of wildlife[END_REF][START_REF] Adams | Do you hear what I hear? Implications of detector selection for acoustic monitoring of bats[END_REF]. We opted to compensate for this problem by modelling the effect of the drivers on bat activity after separating the recorder type/trigger sensitivity combinations.

However, harmonising monitoring methods across all sites would avoid such partitioning and loss of data. Alternatively, future studies could assess the possibility of using corrective coefficients of activity between different recorder type/trigger sensitivity combinations, or establish longer bat pass units, to make the sites monitored in different ways comparable.

Drivers of bat activity around nacelles

Our results highlight the high importance of accounting for all drivers (i.e. landscape, wind turbine functioning, weather, and date) to better account for the variation of bat activity at nacelle height. As expected from previous studies looking at fatality risk or bat activity at nacelle height, we found a joint effect of all types of drivers on bat activity [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF][START_REF] Thompson | Factors associated with bat mortality at wind energy facilities in the United States[END_REF][START_REF] Cryan | Behavior of bats at wind turbines[END_REF][START_REF] Santos | Using species distribution modelling to predict bat fatality risk at wind farms[END_REF][START_REF] Horn | Behavioral Responses of Bats to Operating Wind Turbines[END_REF].

Specifically, LRE and MRE activity increased with the Shannon diversity index of habitat patches and patch richness density, respectively, as previously reported by [START_REF] Froidevaux | Tree size, microhabitat diversity and landscape structure determine the value of isolated trees for bats in farmland[END_REF][START_REF] Mendes | Bat richness and activity in heterogeneous landscapes: guild-specific and scale-dependent?[END_REF][START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF]. Edge density also positively affected the SRE guild occurrence, as previously documented for hedgerow density [START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF][START_REF] Verboom | The importance of linear landscape elements for the pipistrelle Pipistrellus pipistrellus and the serotine bat Eptesicus serotinus[END_REF] or the density of all edge habitats [START_REF] Ancillotto | Effects of free-ranging cattle and landscape complexity on bat foraging: Implications for bat conservation and livestock management[END_REF][START_REF] Mendes | Bat richness and activity in heterogeneous landscapes: guild-specific and scale-dependent?[END_REF]. We also found forest cover to positively affect MRE activity, consistent with [START_REF] Roemer | Influence of landscape and time of year on bat-wind turbines collision risks[END_REF], who showed that bat activity at height decreased with the distance to the forest, and with Boughey et al. (2011a) who found higher bat activity at ground level with an increasing proportion of forest. Unexpectedly, our model revealed a positive effect of impervious habitat proportion on SRE activity, a relationship that is elsewhere described as negative [START_REF] Gili | Bats in urbanising landscapes: habitat selection and recommendations for a sustainable future[END_REF]. Impervious habitat in this dataset corresponds to roads and the buffer scale selected is very local (100m). This is likely an indirect positive effect related to ecotone/hedgerow associated with roads (i.e. road access to a wind turbine), a favourable context for foraging of narrow-and edge-space foragers of the SRE guild [START_REF] Denzinger | Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats[END_REF].

Increasing blade rotation speed logistically reduced LRE activity and SRE occurrence, and linearly decreased MRE activity, in accordance with previous studies [START_REF] Cryan | Behavior of bats at wind turbines[END_REF][START_REF] Horn | Behavioral Responses of Bats to Operating Wind Turbines[END_REF]. It should be noted that it is unlikely that this result fully mirrors the effect of wind speed because wind speed and blade speed are not fully confounded (Fig. S3). In addition, the negative effect of blade rotation speed is preserved at both high and low wind speeds (Fig. S10).

Regarding weather conditions, increasing temperatures promoted the activity of LRE and MRE guilds, while increasing wind speeds and cumulated rain suppressed the activity of all guilds.

These results corroborate studies of bat activity at height, showing very similar patterns [START_REF] Wellig | Mitigating the negative impacts of tall wind turbines on bats: Vertical activity profiles and relationships to wind speed[END_REF][START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF][START_REF] Horn | Behavioral Responses of Bats to Operating Wind Turbines[END_REF][START_REF] Arnett | Patterns of pre-construction bat activity at a proposed wind facility in south-central Pennsylvania[END_REF][START_REF] Redell | Patterns of pre-construction bat activity at a proposed wind facility in south-central Wisconsin[END_REF].

Interestingly, both LRE and MRE guilds exhibited some tolerance to unfavourable weather conditions, with a non-negligible proportion of remaining activity in such conditions (see Fig. S11). For instance, above wind speeds of 8m/s, 9% of MRE activity and 12% of LRE activity remained; below a temperature of 10°C, 2% of MRE activity and 7% of LRE activity remained (Fig. S11), which is highly consistent with findings by [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF] in Germany.

With respect to seasonality, a peak in LRE and MRE activity was detected in August, thus reinforcing previous studies reporting a peak in bat fatalities at wind turbines in this period [START_REF] Schuster | Consolidating the State of Knowledge: A Synoptical Review of Wind Energy's Wildlife Effects[END_REF][START_REF] Arnett | Patterns of Bat Fatalities at Wind Energy Facilities in North America[END_REF].

Assessing the effectiveness of using model equations to limit bat exposure compared to conventional curtailments

The multifactor responses of bat activity and occurrence at wind turbine nacelles reported in this study highlight the crucial need for curtailment strategies based on all possible combinations of the driving factors, while proving that curtailment based on fixed environmental thresholds such as cut-in wind speed and temperature is not fully effective in avoiding bat exposure.

Based on the relationship between the percentage of recorded bat activity or occurrence and the percentage of lost blade rotations entailed by each curtailment threshold (i.e. wind speed and temperature values for blanket curtailment and a predicted bat activity and occurrence value for multicriteria curtailment algorithm), multicriteria curtailment algorithm will save many more bats from exposure to spinning blades (i.e. on average 20 to 29%, 7 to 12% and 24 to 31% less exposure for LRE, MRE and SRE guilds, respectively, depending on temperature threshold considered in blanket curtailment). This result corroborates conclusions from [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF] who performed a similar assessment using the real loss of energy production and curtailment thresholds based on a mean number of fatalities per turbine and per year. The fact that the difference in efficiency is smaller for the MRE than for the LRE guild (both being at high risk of collision; [START_REF] Roemer | Bat flight height monitored from wind masts predicts mortality risk at wind farms[END_REF], is mainly due to the fact that blanket curtailment is significantly less efficient for LREs as they are more tolerant to non-optimal weather conditions (Fig. S11). The increased effectiveness on LRE (the most collision-sensitive guild) reinforces the importance of moving from current blanket curtailments to a multi-criteria algorithm-based approach.

Limitations and recommendations

The study calls for prudence when using data from different recording methods that should be controlled before any modelling as they could strongly bias the algorithm to use for curtailment.

This requires regulatory databases (as is the case with the DEPOBIO tool in France;

https://depot-legal-biodiversite.naturefrance.fr/) to demand the input of metadata related to the methods used, or ideally to harmonise these methods. Thus, in order to be generalised to all types of material and settings, the algorithm should either be adapted to each type using appropriate data, or a ratio of equivalence in activity between pairs of material/settings should be defined in future studies.

To go further in the modelling of bat exposure, the curtailment algorithm method we propose should be adapted on an intra-night scale to account for the variation of bat activity during the night and thus minimise even more production losses [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF]. In addition, our efficiency assessment does not rely on a real loss of energy production as such information is rarely available from wind energy developers. However, as blade speed distributions do not differ between lost blade rotations of both curtailment methods, the relative comparison of lost blade rotations between curtailment methods as a proxy of energy production losses is not biased. Bat activity around nacelles was reported to be a good proxy for fatality risk [START_REF] Peterson | Acoustic Exposure to Turbine Operation Quantifies Risk to Bats at Commercial Wind Energy Facilities[END_REF][START_REF] Korner-Nievergelt | Estimating bat and bird mortality occurring at wind energy turbines from covariates and carcass searches using mixture models[END_REF], but we encourage further research on the relationship between activity and mortality to refine algorithms towards an explicit reduction of the real collision risk, either by giving more weight to conditions in which activity is most strongly correlated with mortality or by using mortality data directly. Acoustic-informed blanket curtailment is another method practised in North America, notably using the Turbine Integrated Mortality Reduction (TIMR) system which, in addition to a wind-speed threshold, integrates a real-time bat activity criterion. Although this system is not directly comparable to our algorithm (intra-night timescale, effectiveness assessed using daily fatality surveys), it seems to show similar effectiveness (i.e. a 37% reduction in exposure compared to blanket curtailment) [START_REF] Rabie | Efficacy and cost of acoustic-informed and wind speed-only turbine curtailment to reduce bat fatalities at a wind energy facility in Wisconsin[END_REF][START_REF] Hayes | A smart curtailment approach for reducing bat fatalities and curtailment time at wind energy facilities[END_REF]. Future studies could therefore compare these two types of curtailment strategies on an equivalent basis to highlight the strengths and weaknesses of each, especially regarding technological constraints. Finally, the baseline data used to train the algorithm should be updated on a regular basis with data from the latest wind turbine models in order to explicitly incorporate their dimensional changes into the modelling.

The strategy of algorithm-based curtailment should be conceived on a large scale to save a global percentage of the bat community from exposure, although on some sites the method may currently be less effective. In the future, to capitalise on large-scale data, algorithms could be developed using national data and applied site by site as it accounts for the landscape context, and could be regularly updated with data from new post-construction monitoring. This will require more years and sites of monitoring to account for the inter-annual stochasticity of the responses and to cover larger landscape gradients, respectively, and would also require updating algorithms with the most recent data to consider climate change and especially the gradual increase in temperature. The large amount of regulatory post-implementation acoustic monitoring performed each year could be included annually to update algorithms so that the exposure threshold defined by the central authority is continuously based on a predictive tool accounting for climate change. Since temperate insectivorous bat species respond to a documented set of landscape characteristics, weather conditions and seasonality, we expect the development of such curtailment algorithms to be efficient and of great relevance in most temperate ecosystems.

Finally, our study calls for the use of multicriteria curtailment algorithms instead of basic blanket curtailments as power production is clearly predicted to be higher and the benefit for bats is high in most situations [START_REF] Behr | Mitigating Bat Mortality with Turbine-Specific Curtailment Algorithms: A Model Based Approach[END_REF].

  75% of wind turbines forLRE and MRE guilds, respectively (Figs. S5 & S6). Finally, when the algorithm was trained on 33 out of 34 wind turbines and predictions made on the remaining wind turbine(i.e. model training and predictions based on independent sites), algorithm curtailment had higher AUC values than blanket curtailment at 81 and 69% of wind turbines for LRE and MRE, respectively (Fig.S7 & S8).

Figure 2 .

 2 Figure 2. Percentage of bat activity and occurrence variance explained by each variable (i.e.

Figure 3 .

 3 Figure 3. Panel A depicts the method to compare blanket (black) and algorithm-based (blue)

Figure 4 .

 4 Figure 4. Predicted number of bat passes or probability of presence from generalized linear

Figure 5 .

 5 Figure 5. Average change in remaining percentage of bat activity exposed to spinning blades

Table 1 .

 1 Estimates, standard errors and p-values from full models testing the effect of landscape, wind turbine and weather/time variables on LRE and MRE activity and SRE occurrence.Missing values indicate that the landscape variable was not selected in full models (only the three best explaining ones per guild were included, see Statistical analysis section for more details) or the no need for quadratic or cubic effects on weather/date variables. Significant effects (P<0.05) are shown in bold.

Acknowledgments

This work was funded by France Energie Eolienne (FEE) with the additional support of the Agence de la transition écologique (ADEME), the region Bretagne (SAD), the Office Français de la Biodiversité (OFB), and the DREAL Pays de la Loire. We thank France Energie Eolienne (FEE) for involving these members in the data collection process and especially Jérôme Dumont and Frédéric Roch. We thank all participants which provided acoustic and weather/wind turbine functioning data. We thank CC-IN2P3 and PCIA-MNHN for providing computing and storage facilities, and Didier Bas for his help in this process. We also thank Léa Mariton for her help in computing rainfall data, and Robin Dixon on behalf of all FEE members Véronique de Billy on behalf of OFB for proofreading the first version of the manuscript.

Data accessibility

Data used for analyses will be available on a dedicated platform.

Author's contribution K.B. with the support of C.K. conceived the ideas, designed the methodology, collected and processed the data, and analysed the data; K.B. led the writing of the manuscript with the support of all authors. JSPF computed landscape variables. All authors critically contributed to the drafts and gave their final approval for publication.

Conflict of Interest

France Energie Eolienne (FEE) is an association of more than 300 members, professionals of the wind energy sector in France, who have built more than 90% of the turbines installed on the French territory and operate more than 85% of them. The scientific question was defined with FEE whose members provided the data. FEE had no role in data preparation and analysis, interpretation and discussion of the results, and decision to publish. The Office Français de la Biodiversité (OFB) is a public institution dedicated to the protection and restoration of biodiversity in metropolitan and overseas France, under the authority of the French Ministry of Ecological Transition and Agriculture and Food. OFB and FEE proofread an early version of the manuscript and this proofreading did not influence the interpretations, discussions and conclusions of the study. Corrections made to the first draft following these proofreadings can be found at https://doi.org/10.5281/zenodo.6818161. Authors take complete responsibility for the accuracy of their analysis and their interpretations and discussions. None of the authors was employed by any of the funders prior to, or at the time of the submission of the article. We declare having no other competing interest.