Pierre De Mathelin

MD Simone Manfredelli
email: simone.manfredelli@gmail.com

MD Jean-Baptiste Delhorme

PhD Aina Venkatasamy

PhD Serge Rohr

PhD Christian Gaiddon

Sarcopenia remaining after intensive nutritional prehabilitation could be a criterion for the selection of patients for surgery for oesogastric junction adenocarcinoma

Keywords: PdM, Sarcopenia, Esophagectomy, Jejunostomy, Prehabilitation, Esogastric junction adenocarcinoma

 Sarcopenia is recognized as a negative prognostic factor in several cancers.

 Prehabilitation with nutritional support with feeding jejunostomy is a protective factor for sarcopenia occurrence in patients with esogastric junction adenocarcinoma.

 Sarcopenia occurrence was an independent risk factor for overall survival in patients who underwent surgery.

 Overall survival was similar between sarcopenic patients after nutritional prehabilitation with a FJ and patients excluded from surgery in palliative situations.

 The persistence of sarcopenia despite renutrition program could be a selection factor for surgery.

Introduction

Management of oesogastric junction adenocarcinoma (OJA) remains challenging, even with various advances in surgery, chemotherapy, targeted therapy and immunotherapy. Overall survival of OJA remains poor, with a 5-year survival rate below 30%. Oesogastric junction malignancy is among the diseases with the highest association with cancer-related malnutrition [START_REF] Anandavadivelan | Cachexia in patients with oesophageal cancer[END_REF]. This state of malnutrition can be explained by tumor-induced anorexia and by an obstructive mechanism, which may lead to dysphagia and further deteriorate the nutritional status [START_REF] Boshier | Assessment of body composition and sarcopenia in patients with esophageal cancer: a systematic review and meta-analysis[END_REF]. Furthermore, in advanced OJA, aggressive combined chemotherapies involving cytotoxic agents (e.g., cisplatin, 5-fluorouracil, epirubicin) are known to be associated with gastrointestinal side effects and may worsen an already deteriorated nutritional condition.

Nutritional management is crucial for patient care. Preoperative feeding jejunostomy (FJ) is frequently used when denutrition is diagnosed or in cases of dysphagia, as it allows more patients to complete all cycles of preoperative chemotherapy [START_REF] Manfredelli | Could a Feeding Jejunostomy be Integrated into a Standardized Preoperative Management of Oeso-gastric Junction Adenocarcinoma?[END_REF][START_REF] Burden | Pre-operative Nutrition Support in Patients Undergoing Gastrointestinal Surgery[END_REF]. Additionally, the main goal of nutritional management is to prevent the onset of sarcopenia, which is characterized by a progressive and generalized loss of skeletal muscle mass and strength [START_REF] Anandavadivelan | Cachexia in patients with oesophageal cancer[END_REF].

Sarcopenia is a recognized negative prognostic factor after colorectal, pancreatic, hepatic and gastric surgeries [START_REF] Engremont | Additive Value of Preoperative Sarcopenia and Lymphopenia for Prognosis Prediction in Localized Pancreatic Ductal Adenocarcinoma[END_REF][START_REF] Fukushima | Prognostic Significance of Sarcopenia in Patients with Metastatic Renal Cell Carcinoma[END_REF][START_REF] Koch | Sarcopenia as a prognostic factor for survival in patients with locally advanced gastroesophageal adenocarcinoma[END_REF][START_REF] Kawamura | Long-Term Outcomes of Gastric Cancer Patients with Preoperative Sarcopenia[END_REF]. Furthermore, in patients with gastric cancer, its presence at diagnosis is associated with significantly lower overall survival. Moreover, sarcopenia seems to influence postoperative outcomes. For instance, studies have shown that changes in body composition secondary to neoadjuvant chemotherapy for esophageal cancer could correlate with the occurrence of complications after esophagectomy [START_REF] Ida | Changes in Body Composition Secondary to Neoadjuvant Chemotherapy for Advanced Esophageal Cancer are Related to the Occurrence of Postoperative Complications After Esophagectomy[END_REF]. However, the exact origin of sarcopenia remains debated, as it cannot be reverted with complementary feeding [START_REF] Robinson | Prevention and optimal management of sarcopenia: a review of combined exercise and nutrition interventions to improve muscle outcomes in older people[END_REF] and can be induced by anticancer drugs [START_REF] Miyata | Clinical Assessment of Sarcopenia and Changes in Body Composition During Neoadjuvant Chemotherapy for Esophageal Cancer[END_REF][START_REF] Hydock | Characterization of the effect of in vivo doxorubicin treatment on skeletal muscle function in the rat[END_REF]. Interestingly, some studies point toward particular tumors markers or gene variation in cancer patients that correlates with sarcopenia, suggesting that the tumor itself or the patients' genetic background may play a role [START_REF] Argilés | The pivotal role of cytokines in muscle wasting during cancer[END_REF]. Therefore, the question remains whether sarcopenia is caused by malnutrition, anticancer treatment and/or the tumor itself. Similarly, the molecular mechanisms leading to sarcopenia are complex and involve multiple steps, including the activity of transcription factors that regulate the expression of muscle catabolic enzymes, such as TAP63-TRIM63 cascades [START_REF] Von Grabowiecki | Transcriptional activator TAp63 is upregulated in muscular atrophy during ALS and induces the pro-atrophic ubiquitin ligase Trim63[END_REF]. This complexity and the lack of understanding of the etiology of sarcopenia may explain why, thus far, no efficient cure has been established against the loss of skeletal muscle mass, despite some preclinical attempts [START_REF] Von Grabowiecki | Regulation of a Notch3-Hes1 Pathway and Protective Effect by a Tocopherol-Omega Alkanol Chain Derivative in Muscle Atrophy[END_REF]. We previously showed that prehabilitative feeding using FJ helps to prevent sarcopenia following perioperative chemotherapy [START_REF] Voisinet | How to Prevent Sarcopenia Occurrence during Neoadjuvant Chemotherapy for Oesogastric Adenocarcinoma?[END_REF]. The main objective of this study was to investigate the impact of sarcopenia, despite a renutrition program, on patients with OJA.

Patients and Methods

Study Setting and Selection of Participants

This is a single-center retrospective analysis of a prospective database that included consecutive patients diagnosed with OJA from January 2007 to December 2017 at Strasbourg University Hospital (Tertiary center). The Ethics Committee of our institution approved the study, and all patients gave their written consent for their participation in this study. Patients presenting with nonmetastatic OJA, with WHO scores of 0-2, treated with a perioperative chemotherapy protocol were included in our study. All patients underwent a routine abdominal computed tomography (CT) scan before and after chemotherapy. The exclusion criteria were as follows: patients with WHO scores 3-4, patients with missing follow-up or imaging (CT) data either before and/or after preoperative chemotherapy; patients with confirmed metastatic disease at diagnosis, assessed by preoperative imaging; and patients with positive macroscopic peritoneal carcinomatosis or positive peritoneal cytology in staging exploratory laparoscopy. Patients with progressive disease during chemotherapy were used as a comparative group.

All patients were classified by age, sex, BMI at diagnosis, tumor histology, presence or absence of a preoperative feeding jejunostomy before chemotherapy and type of surgery performed.

Management and treatment modalities for each patient were discussed in multidisciplinary meetings.

All patients underwent a complete chemotherapy regimen including EOX (epirubicin, oxaliplatin and capecitabine), ECX (epirubicin, cisplatin, capecitabine), FOLFOX (folinic acid, 5-fluorouracil, oxaliplatin) and FLOT (5-fluorouracil, leucovorin, oxaliplatin, docetaxel). Surgical placement of a feeding jejunostomy was performed during the staging exploratory laparoscopy after multidisciplinary assessment. This was only performed after 2010 according to the evolution of the local practice; after 2010, every patient with dysphagia or insufficient oral intake and/or nutritional risk index (NRI) score < 97.5 benefited from FJ. All patients with preoperative feeding jejnostomy had nutritional support from neoadjuvant period until weight stabilisation after surgery. Surgical resections were performed according to the tumor location after the completion of preoperative chemotherapy. The surgical procedures included total gastrectomy, transhiatal extended gastrectomy and superior polar oesophagogastrectomy (Ivor Lewis esophagectomy), which were all followed by Roux-en-Y reconstruction. Lymphadenectomy was performed according to international standards. Pathology examination was performed in all patients, allowing for classification according to histopathologic findings (i.e., well differentiated, moderately differentiated, isolated cells). Surgery was not performed when there was metastatic disease at diagnosis or appearing during chemotherapy. Patients were classified by ypTNM stage groups (I-IV; AJCC/UICC 8 th edition).

Evaluation of sarcopenia

The evaluation of sarcopenia was performed prospectively according to the literature guidelines on axial abdominal CT images acquired before (T0) and after (T1) chemotherapy for each patient. The third lumbar vertebrae (L3) served as a reference landmark to measure cross-sectional areas of muscles reflecting sarcopenia: the psoas muscle, paraspinal muscles and abdominal wall muscles. Muscles were identified using quantification thresholds from -30 to +110 Housefield units (HU). Muscle areas were assessed using ImageJ VR software. The muscles' cross-sectional areas were automatically segmented, with manual corrections if needed. The muscle surface was defined as the sum of the cross-sectional areas of all these muscles for each patient. The surface muscular index (SMI) in cm 2 /m 2 was obtained by dividing the muscular surface value by the normalized stature body-surface area. The body surface area was calculated using the Mosteller formula [body surface area (m 2 ) = (height (cm) * weight (kg)/3600) * 0.5]) [START_REF] Mosteller | Simplified Calculation of Body-Surface Area[END_REF]. We used literature-based cutoff values for sarcopenia (guidelines by Prado et al.: L3 skeletal SMI < 38.5 cm 2 /m 2 for women and < 52.4 cm 2 /m 2 for men) (18).

Postoperative outcomes and follow-up

Postoperative complications were prospectively recorded according to the Clavien-Dindo classification and Comprehensive Complications Index (CCI) [START_REF] Clavien | The Clavien-Dindo Classification of Surgical Complications: Five-Year Experience[END_REF][START_REF] Slankamenac | The Comprehensive Complication Index: A Novel Continuous Scale to Measure Surgical Morbidity[END_REF]. The CCI was calculated as the sum of all complications, weighted for their severity, and ranked based on the severity of any combination of complications from 0 to 100 in a single patient. Major complications were defined as any complication with a Clavien-Dindo grade ≥ 3. Postoperative stay was also determined for all patients.

Follow-up took place every 3 to 6 months, according to French surgical society (SFCD) guidelines. The diagnosis of recurrence was based on postoperative examination, which included imaging, tumor markers and/or biopsy. Overall survival and disease-free survival rates were calculated for all patients.

The date of initial diagnosis, which was confirmed by the histological examination performed on the endoscopic biopsy of the tumor, was considered the baseline. Follow-up ended for each patient either at the date of death, last follow-up date or at the end of the study.

Statistical Analysis

Statistical calculations were performed using SPSS 10.0 (SPSS Inc., Chicago, IL) and Python Software (version 3). Categorical variables are described as frequencies and percentages, while continuous variables are expressed as the mean and its standard deviation.

Chi-square and Fisher's exact tests were used as appropriate to determine the association between categorical variables. Student's t test and Wilcoxon-rank sum test were used to evaluate differences in means between two groups of continuous variables. ANOVA and logistic regression were used for univariate and multivariate analyses.

Kaplan-Meier survival analysis was used to analyze survival data. Differences were tested with univariate and multivariate Cox proportional hazards regression analyses and the log-rank test. All tests were two-sided, and a difference was considered significant when the p value was < 0.05.

Results

Risk factors for sarcopenia occurrence

Data from 156 patients diagnosed with OJA were reviewed. Among them, 32 (21%) patients with missing follow-up or imaging data were excluded, and the remaining 124 patients were analyzed.

The experimental group was composed of 91/124 patients (74%) who underwent surgery after preoperative chemotherapy. The remaining 33/124 patients (26%) with metastatic disease at the time of diagnosis or with disease progression during neoadjuvant therapy served as controls. In the experimental group, 72/91 patients (79%) underwent a feeding jejunostomy (FJ) before OJA surgical resection. (Fig. 1) Sarcopenic status of all patients before and after surgery and according to the presence or absence of a preoperative feeding jejunostomy are reported in Table A

.1 et Table A.2.
Before preoperative chemotherapy, 13 patients from the experimental group (14.3%) were sarcopenic. Among them, 11/13 patients underwent FJ (vs. 2/13; p=0.603). After preoperative chemotherapy, 21 patients from the experimental group (23%) were sarcopenic (13 with FJ [61.9%] and 8 without FJ [38.1%], p=0.026), while 70 patients (77%) were not.

The demographic and physical characteristics of both sarcopenic and nonsarcopenic populations after preoperative chemotherapy are presented in Table 1. Sex distribution was not significantly different between sarcopenic and nonsarcopenic patients (19 men [90.5%] and 2 women [9.5%] vs. 55 men [78.5%] and 15 women [21.4%], respectively, p= 0.364). Sarcopenic patients presented a significantly smaller mean SMI than nonsarcopenic patients (45.3± 5.4 cm 2 /m 2 vs. 61.1± 9,9 cm 2 /m 2 , p < 0.001). Sarcopenic patients were significantly older than nonsarcopenic patients (mean age 68.6 ± 6.1 years vs. 60.3 ± 12.2 years, p < 0.001) and presented a significantly different mean BMI than nonsarcopenic patients (29.5 ± 5.3 kg/m 2 vs. 26.5 ± 5.0 kg/m 2, respectively, p= 0.024). According to ESPEN guidelines, 39/91 patients (42.9%) were malnourished after preoperative chemotherapy [START_REF] Cederholm | Diagnostic criteria for malnutrition -An ESPEN Consensus Statement[END_REF]. Feeding jejunostomy during chemotherapy does not improve mean BMI (27.4 ± 5.6 before chemotherapy vs. 27.0 ± 5.1 after chemotherapy, p= 0.502).

There were no significant differences between sarcopenic and nonsarcopenic patients regarding the type of surgical resection, histopathology, ypT stages, ypN stages and yp stage (AJCC/UICC) (p = 0.136, p=0.301, p= 0.106, p= 0.441, p = 0.080, respectively).

In the multivariate analysis of factors related to sarcopenia occurrence, age > 65 years old, BMI ≥25 and preoperative feeding jejunostomy were independent risk factors (p = 0.01, p<0.001, p=0.03, respectively, Table 2).

Preoperative sarcopenia and postoperative results

Seventy-one patients out of 91 (78%) presented at least one complication (all severities included) after surgery (Table 3). A major complication (i.e., Clavien-Dindo ≥3) occurred in 6/21 (28.6%) sarcopenic patients vs. 27/70 (38.6%) nonsarcopenic patients (p = 0.564). The mean CCI was not significantly different between sarcopenic and nonsarcopenic patients (27.8  29.0 vs. 23.8  21.4, p= 0.558, respectively). The mean length of stay for sarcopenic and nonsarcopenic patients was not significantly different (21.3  14.3 days vs. 21.3  13.4 days, respectively, p = 0.994).

Impact of preoperative sarcopenia on overall survival and disease-free survival

The mean time of follow-up was 37 months . Sarcopenia after preoperative chemotherapy was identified as a risk factor for lower survival rates in the Cox regression analysis (HR = 3.02; 95% CI [1.55-5.90], p <0.005). Cancer staging was also identified as a negative risk factor for survival (HR = 5.03; 95% CI [2.48-10.20], p <0.005). Age >65, sex, and BMI 25 had no significant influence on survival in the univariate and multivariate Cox regression analyses (p=0.93, p= 0.90, p=0.14, respectively, Table 4).

Patients with sarcopenia had a significantly shorter median overall survival duration than nonsarcopenic patients (33.7 months [95% CI, 18.0-56.5] vs. 58.6 months [95% CI, 31.7-108.2], p = 0.04). (Fig. 2a, part 1). There was no statistically significant difference in disease-free survival between sarcopenic and nonsarcopenic patients, although nonsarcopenic patients had a tendency toward longer disease-free survival (20.4 months [95% CI, 12.2-54.8] vs. 55.2 months [95% CI, 27.4-106.6], p = 0.08) (Fig. 2b, A andB).

Overall survival of sarcopenic patients with or without feeding jejunostomy vs. patients with palliative status

There were no significant differences in the overall survival between sarcopenic patients after a preoperative feeding jejunostomy and patients with progressive or metastatic disease (21.9 months [95% CI, 10.7-48.3] vs. 17.2 months [95% CI, 14.4-22.3], p = 0.46) (Fig. 2, C andD). The causes of death among the 13 sarcopenic patients with FJ were pneumopathy in the postoperative period (n=1) and cancer recurrence (n=7). Five patients were still alive at the end of the follow-up period.

Discussion

Our results showed that 23% of the patients who were eligible for OJA surgery were sarcopenic after their preoperative chemotherapy, according to the CT-based definition of sarcopenia. Age > 65 years old and BMI ≥ 25 kg/m 2 were identified as independent risk factors for sarcopenia occurrence.

Nutrition prehabilitation with a feeding jejunostomy, performed in 79% of the patients, was a significant protective factor against preoperative sarcopenia occurrence. Sarcopenia status negatively influenced overall survival, but its impact on disease-free survival was less obvious, and it did not appear to impact the occurrence of postoperative complications. Overall survival of sarcopenic patients after nutritional prehabilitation with a feeding jejunostomy was not significantly different compared to the overall survival of the control group of patients with a palliative status.

Sarcopenia is more frequently associated with specific types of cancer, notably OJA cancers.

The prevalence of sarcopenia, as defined by Prado's criteria, was 23% in our cohort, which is lower than the reported overall prevalence of 38% of sarcopenic patients in a Caucasian population with oesogastric junction adenocarcinoma in a meta-analysis by Boshier et al. [START_REF] Boshier | Assessment of body composition and sarcopenia in patients with esophageal cancer: a systematic review and meta-analysis[END_REF]. Additionally, sarcopenia is defined as an age-related decline in muscle mass. This association between age and sarcopenia is well described in the literature. Before its importance was considered in the field of oncology, sarcopenia was first described in elderly patients and represented an important element in geriatric care [START_REF] Rangel | Sarcopenia increases risk of long-term mortality in elderly patients undergoing emergency abdominal surgery[END_REF]. Indeed, in this study, patients with sarcopenia were significantly older than nonsarcopenic patients. Nevertheless, age was not an independent predictive factor for survival in our Cox regression analysis. Similarly, patients with sarcopenia had a significantly higher mean BMI than nonsarcopenic patients, which classified them as overweight. We also reported that BMI >25 was associated with sarcopenia. This result hints at sarcopenic obesity which has been demonstrated to be the highest risk factor for poor survival in other studies [START_REF] Prado | Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study[END_REF][START_REF] Rangel | Sarcopenia increases risk of long-term mortality in elderly patients undergoing emergency abdominal surgery[END_REF][START_REF] Batsis | Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies[END_REF][START_REF] Roh | Health Consequences of Sarcopenic Obesity: A Narrative Review[END_REF]. However, our cohort is too small to highlight this result with only 8/21 sarcopenic obese patients (BMI >30). This result highlights the need to pay particular attention to obese or overweight patients and to screen them for sarcopenia.

In the literature, sarcopenia has been shown to be a poor prognostic factor, but unfortunately, there are no clinical standards thus far to treat such patients. Several mechanisms of sarcopenia have been described, mostly pointing toward an impact of chemotherapy on the muscles, the production of chemokines by the tumors, malnutrition (which would be an obvious reason) and/or mechanical obstructive tumors that induce anorexia and dysphagia. Nutritional support by feeding jejunostomy may prevent the occurrence of sarcopenia during neoadjuvant chemotherapy and could be especially helpful in cases of obstructive disease with dysphagia [START_REF] Manfredelli | Could a Feeding Jejunostomy be Integrated into a Standardized Preoperative Management of Oeso-gastric Junction Adenocarcinoma?[END_REF]. In this study, a feeding jejunostomy was placed in 84.3% of nonsarcopenic patients who underwent surgery, and the multivariate analysis suggests that it could protect them from becoming sarcopenic. This result is important because it highlights the possibility that for some patients, attentive feeding could prevent chemotherapyinduced sarcopenia.

In the multivariate analysis, the occurrence of sarcopenia after preoperative chemotherapy and tumor staging were independent predictors of survival. The occurrence of sarcopenia appears to significantly influence overall survival, with no impact on the postoperative complication rate and disease-free survival rate. This result on the disease-free survival may explained by a lack of power of our study which does not allow to highlight a significant difference (55.2 months for sarcopenic patients vs 20.4 months for nonsarcopenic patients).

Sarcopenia was identified as a poor prognostic factor in other cancers and has been shown to negatively impact survival in locally advanced pancreatic adenocarcinoma [START_REF] Engremont | Additive Value of Preoperative Sarcopenia and Lymphopenia for Prognosis Prediction in Localized Pancreatic Ductal Adenocarcinoma[END_REF], gastric cancer (8) and postoperative complications after laparoscopic surgery for colon cancer [START_REF] Oh | Clinical impact of sarcopenia in patients with colon cancer undergoing laparoscopic surgery[END_REF]. However, it was not identified as a pejorative factor for survival in colon cancer [START_REF] Oh | Clinical impact of sarcopenia in patients with colon cancer undergoing laparoscopic surgery[END_REF][START_REF] Maddalena | Prognostic role of sarcopenia in metastatic colorectal cancer patients during first-line chemotherapy: A retrospective study[END_REF]. This difference in the impact of sarcopenia may be explained by the greater malnutrition immediately resulting from dysphagia related to OJA. Hence, FJ appears to be an interesting approach to improve patients' care and overall survival. However, our results also showed that some patients appear resistant to FJ and will still develop sarcopenia. This highlights the influence of other factors (in addition to malnutrition) also involved in sarcopenia occurrence. Some authors have distinguished outcomes according to the histological type of cancer, namely, oesogastric junction adenocarcinoma, squamous cell cancer, or both, but the negative effect on the overall survival of patients undergoing oesophagectomy for OJA has been described in several studies [START_REF] Elliott | Sarcopenia: Prevalence, and Impact on Operative and Oncologic Outcomes in the Multimodal Management of Locally Advanced Esophageal Cancer[END_REF][START_REF] Nakashima | Assessment of Sarcopenia as a Predictor of Poor Outcomes After Esophagectomy in Elderly Patients With Esophageal Cancer[END_REF][START_REF] Tamandl | Markers of sarcopenia quantified by computed tomography predict adverse long-term outcome in patients with resected oesophageal or gastro-oesophageal junction cancer[END_REF][START_REF] Paireder | Impact of sarcopenia on outcome in patients with esophageal resection following neoadjuvant chemotherapy for esophageal cancer[END_REF][START_REF] Levolger | Systematic review of sarcopenia in patients operated on for gastrointestinal and hepatopancreatobiliary malignancies[END_REF]. However, Grotenhuis et al. did not find any difference in survival according to sarcopenic status [START_REF] Grotenhuis | Sarcopenia/Muscle Mass is not a Prognostic Factor for Short-and Long-Term Outcome After Esophagectomy for Cancer[END_REF]. In contrast, Reisinger et al. showed that the amount of muscle mass lost during neoadjuvant CRT was predictive of postoperative mortality in a subgroup of patients presenting with stage 3-4 esophageal tumors [START_REF] Reisinger | Loss of Skeletal Muscle Mass During Neoadjuvant Chemoradiotherapy Predicts Postoperative Mortality in Esophageal Cancer Surgery[END_REF]. We did not find significant differences in the global score of complications according to sarcopenic status. This finding is supported by some studies (32,34) but appears to contradict other studies that found a higher incidence of postoperative global complications in sarcopenic patients [START_REF] Elliott | Sarcopenia: Prevalence, and Impact on Operative and Oncologic Outcomes in the Multimodal Management of Locally Advanced Esophageal Cancer[END_REF][START_REF] Levolger | Systematic review of sarcopenia in patients operated on for gastrointestinal and hepatopancreatobiliary malignancies[END_REF][START_REF] Simonsen | Sarcopenia and Postoperative Complication Risk in Gastrointestinal Surgical Oncology: A Meta-analysis[END_REF][START_REF] Mirkin | Sarcopenia related to neoadjuvant chemotherapy and perioperative outcomes in resected gastric cancer: a multiinstitutional analysis[END_REF], especially pulmonary ones [START_REF] Nakashima | Assessment of Sarcopenia as a Predictor of Poor Outcomes After Esophagectomy in Elderly Patients With Esophageal Cancer[END_REF][START_REF] Nishigori | Sarcopenia as a predictor of pulmonary complications after esophagectomy for thoracic esophageal cancer: Sarcopenia and Esophageal Cancer[END_REF][START_REF] Ida | Sarcopenia is a Predictor of Postoperative Respiratory Complications in Patients with Esophageal Cancer[END_REF].

We performed subgroup analyses to assess overall survival in sarcopenic patients despite a feeding jejunostomy and patients who did not undergo surgical cancer resection due to metastatic or progressive disease. This comparison suggests that survival between these two groups was similar.

Mortality in sarcopenic patients is common and comparable to patients with advanced stages of disease, even though intensive nutrition therapy was performed. These data suggest that sarcopenic patients, despite FJ, do not benefit from curative surgical resection. Persistent sarcopenia despite active renutrition during preoperative chemotherapy could be a key factor in selecting patients who would not benefit from curative surgical intent.

Our study has several limitations, including a retrospective design and a limited cohort of patients from a single institution. The main limit and potential source of bias remains the interpretation of the sarcopenic status by means of a radiologic assessment alone as proposed by Prado et al., however extensively used by many; it could be interesting to use other means to define sarcopenia, such as bioelectrical impedance analysis and/or dual-energy X-ray absorptiometry (2). This could account for the conflicting results with other previously published studies.

CONCLUSION

Intensive nutritional prehabilitation with feeding jejunostomy was a protective factor against preoperative sarcopenia occurrence in patients who were eligible for surgery for oesogastric junction adenocarcinoma. Sarcopenia was an independent poor prognostic factor for overall survival after surgery. Moreover, there was no significant difference in the long-term outcomes between sarcopenic patients who underwent surgery after optimal nutritional care with a feeding jejunostomy and patients without surgery due to metastatic or progressive disease. This result suggests that the response of the patient to nutritional prehabilitation management with a feeding jejunostomy could have a place in patient selection before surgery for oesogastric junction adenocarcinoma.

FIGURE LEGENDS

TABLES

Figure 1 .

 1 Figure 1. Flow diagram

Figure 2 .

 2 Figure 2. Kaplan-Meier analyses of overall survival (A) and disease-free survival (B) of Sarcopenic vs. Nonsarcopenic patients. Kaplan-Meier analyses of overall survival (C) and disease-free survival (D) of patients without surgery due to progressive or metastatic disease vs. Sarcopenic patients with a Feeding Jejunostomy.

Table 1 .

 1 Demographics of the study populationAbbreviations: AJCC/UICC, American Joint Committee on Cancer/Union for International Cancer Control; BMI, body mass index; SMI, surface muscular index.

		All	Sarcopenic	Nonsarcopenic
		n = 91 (%)	n = 21 (23.0%)	n = 70 (77.0%)	p-value
	Age, mean, years SD	62.2 11.6	68.6 6.1	60.3 12.2	<0.001
	Gender, No. (%)				0.364
	Male	74 (81.3)	19 (90.5)	55 (78.6)
	Female	17 (18.7)	2 (9.5)	15 (21.4)
	BMI, mean, kg/m 2 SD	27.2 5.2	29.55.3	26.5 5.0	0.024
	SMI, mean, cm 2 /m 2 SD	57.5 11.2	45.35.4	61.19.9	<0.001
	Surgery, No. (%)				0.136
	Total Gastrectomy	20 (22.0)	2 (9.5)	18 (25.7)
	Extended Gastrectomy	16 (17.6)	5 (23.8)	11(15.7)
	Ivor Lewis Esophagectomy	55 (60.4)	14 (66.7)	41 (58.6)
	Histopathology, No. (%)				0.301
	Well differentiated	52 (57.1)	15 (71.4)	37 (52.9)
	Poorly differentiated	16 (17.6)	2 (9.5)	14 (20.0)
	Isolated cells	23 (25.3)	4 (19.1)	19 (27.1)
	ypT stage, No. (%)				0.106
	T0	14 (15.4)	7 (33.3)	7 (10.0)
	T1	16 (17.6)	3 (14.3)	13 (18.6)
	T2	9 (9.9)	1 (4.8)	8 (11.4)
	T3	42 (46.1)	9 (42.8)	33 (47.1)
	T4	10 (11.0)	1 (4.8)	9 (12.9)
	ypN stage, No. (%)				0.441
	N0	43 (47.2)	13 (61.9)	30 (42.9)
	N1	26 (28.6)	5 (23.8)	21 (30.0)
	N2	12 (13.2)	2 (9.5)	10 (14.2)
	N3	10 (11.0)	1 (4.8)	9 (12.9)
	ypStage (AJCC/UICC), No. (%)				0.080
	0	14 (15.4)	7 (33.4)	7 (10.0)
	1	12 (13.1)	2 (9.5)	10 (14.3)

Table 2 .

 2 Risks factors of sarcopenia occurrence: Multivariate analysis

		Sarcopenic	Nonsarcopenic	Regression
		patients	patients	coefficient
		n = 21 (23.0%)	n = 70 (77.0%)	(log-odds)	p-value
	Age, years, No. (%)			1,70	0.01
	>65	16 (76.2)	29 (41.4)
	<65	5 (23.8)	41 (58.6)
	BMI, kg/m 2 , No. (%)			2,17	<0.001
	≥25	18 (85.7)	34 (48.6)
	<25	3 (14.3)	36 (51.4)
	Feeding Jejunostomy, No. (%)			-1,41	0.03
	Yes	13 (61.9)	59 (84.3)
	No	8 (38.1)	11 (15.7)

Abbreviation: BMI, body mass index

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Conflict of Interest statement


Thomas Crouchet
email: thomas.crouchet@univ-tlse3.fr

Philipp Heeb

Alexis S Chaine

Altitudinal differences in foraging decisions under predation risk in great tits

Keywords: Behavioral plasticity, elevation, foraging, Parus major, predation risk, risk-taking behavior, video playback

Foraging decisions under risk of predation are crucial for survival as predation risk can contribute to a reduction of food intake over time leading to a trade-off between starvation and predation. Environmental variation can provoke changes in food accessibility or predation risk that will in turn affect foraging decisions. Specifically, less predictable or harsher environments, such as those found at high elevation, should lead to more risk-prone foraging in order to prevent risk of starvation, but empirical confirmation of this hypothesis is lacking. In the current study, we used video playbacks combined with an automatic feeder to measure continuous foraging choices between control and predator videos by wild great tits originating from high and low elevations and tested under controlled conditions. Great tits discriminated between two conditions representing differences in predation risk and visited the feeder less frequently when a predator was shown. Moreover, we found that birds from low elevation populations were more risk-averse and visited the feeder significantly less when a predator video playback was broadcasted compared to high elevation individuals. This elevation related contrast was also dependent on the season, body mass and fat reserves of individuals, and was more marked in females. Furthermore, adults visited the feeder less in the presence of a predator compared to yearlings. These results are consistent with predictions from life history theory and starvation-predation trade-off hypotheses and could have implications for individual movements and population dynamics in changing environments.

INTRODUCTION

Foraging decisions in the wild are in part driven by a starvation-predation risk trade-off [START_REF] Houston | General results concerning the trade-off between gaining energy and avoiding predation[END_REF]. While it is vital to find food to survive, the associated risk-taking due to exposure to predators can lead to lethal consequences [START_REF] Lima | Behavioral decisions made under the risk of predation: a review and prospectus[END_REF]. Animals are expected to evolve strategies to limit such risks and models have predicted the existence of behavioral variations in response to changes in predation pressure [START_REF] Lima | Temporal Variation in Danger Drives Antipredator Behavior: The Predation Risk Allocation Hypothesis[END_REF].

For example, birds limit the time spent at foraging patches when the perceived risk of predation increased [START_REF] Krams | Length of feeding day and body weight of great tits in a single-and a twopredator environment[END_REF][START_REF] Verdolin | Meta-analysis of foraging and predation risk trade-offs in terrestrial systems[END_REF][START_REF] Villén-Pérez | Foraging Patch Selection in Winter: A Balance between Predation Risk and Thermoregulation Benefit[END_REF]. They can also rely on indirect cues of predator presence to stop their foraging activity such as mobbing calls [START_REF] Desrochers | Do mobbing calls affect the perception of predation risk by forest birds?[END_REF], alarm calls [START_REF] Lind | Information, predation risk and foraging decisions during mobbing in Great Tits Parus major[END_REF] or odors [START_REF] Amo | Predator odour recognition and avoidance in a songbird[END_REF]. By choosing more exposed foraging patches when hungry, individuals may be more exposed to predators [START_REF] Hilton | Choice of Foraging Area with Respect to Predation Risk in Redshanks: The Effects of Weather and Predator Activity[END_REF]. Likewise, through a dilution effect, foraging in groups can reduce the risk of being predated while foraging [START_REF] Hamilton | Geometry for the selfish herd[END_REF][START_REF] Morse | Structure and Foraging Patterns of Flocks of Tits and Associated Species in an English Woodland During the Winter[END_REF]). As a consequence, birds in large flocks are more risk-prone (i.e. are willing to take more risks) than solitary conspecifics [START_REF] Dolby | Social context affects risk taking by a satellite species in a mixedspecies foraging group[END_REF]. These foraging decisions can have effects on reproductive success and fitness as well as consequences on population dynamics [START_REF] Cresswell | Non-lethal effects of predation in birds[END_REF].

Theory on foraging strategies predicts that the starvation-predation risk trade-off should be impacted by environmental variation [START_REF] Cresswell | How climate change might influence the starvationpredation risk trade-off response[END_REF]. Foraging decisions of passerines in winter should aim to optimize fat reserves in order to avoid starvation in harsher conditions (e.g. shorter days, less and more unpredictable food ;[START_REF] Lima | Predation Risk and Unpredictable Feeding Conditions: Determinants of Body Mass in Birds[END_REF][START_REF] Houston | A Theoretical Investigation of the Fat Reserves and Mortality Levels of Small Birds in Winter[END_REF]. Therefore, individuals with higher metabolic rates [START_REF] Mathot | Does metabolic rate predict risk-taking behaviour? A field experiment in a wild passerine bird[END_REF] or lower body mass [START_REF] Rogers | Predation Risk and Fasting Capacity: Do Wintering Birds Maintain Optimal Body Mass?[END_REF]) are more-risk prone, as their trade-offs tilt towards a higher risk of starvation [START_REF] Pakanen | Northward expanding resident species benefit from warming winters through increased foraging rates and predator vigilance[END_REF]. Similarly, individuals from different elevations face different foraging constraints as colder temperatures increase metabolic requirements and resource availability is more uncertain and scarce as elevation increases [START_REF] Körner | The use of 'altitude' in ecological research[END_REF]. At high elevation, passerines have different daily routines, with foraging peaks in early morning and late afternoon, compared to low elevation birds which prefer to forage during the middle of the day [START_REF] Pitera | Daily foraging routines in food-caching mountain chickadees are associated with variation in environmental harshness[END_REF]). These differences were more marked in harsher periods such as winter, where birds needed to make greater investments in foraging to reach their optimal level of reserves to avoid starvation [START_REF] Pitera | Daily foraging routines in food-caching mountain chickadees are associated with variation in environmental harshness[END_REF]. Foraging decisions are predicted to also depend on predation [START_REF] Mcnamara | Foraging Routines of Small Birds in Winter: A Theoretical Investigation[END_REF]. Predation risk might decrease with elevation in some cases [START_REF] Sasvári | Predation Risk of Tawny Owl Strix aluco Nests in Relation to Altitude, Breeding Experience[END_REF]. For example, European sparrowhawk -a major songbird predator-abundance decreases with increased elevation [START_REF] Newton | Spacing of Sparrowhawk Nesting Territories[END_REF][START_REF] Newton | Spacing of Sparrowhawks in Relation to Food Supply[END_REF]. Reduced predator avoidance behaviors should arise at lower predation risk, a pattern confirmed in a meta-analysis pointing out that high elevation individuals show less intense predator avoidance behaviors on average [START_REF] Stankowich | Fear in animals: a meta-analysis and review of risk assessment[END_REF].

However, not all studies show the same general pattern. In response to a threat, flight initiation distance in dark-eyed juncos (Junco hyemalis) from high elevation was greater than for low elevation individuals (Andrade and Blumstein 2020). Likewise, in a breeding context, latency of mountain chickadees (Poecile gambeli) to go back to their nest box after a predator presentation was longer in high elevation birds (Kozlovsky et al. 2015a). Results from these two studies do not match the prediction that birds from high elevation should show weaker anti-predator responses and suggest that the specific life-history contexts may influence risktaking behavior. Indeed, parameters other than elevation may impact risk avoidance decisions, notably in a foraging context.

Apart from environmental variation, individuals may exhibit different risk-taking foraging behaviors depending on their individual attributes such as physiological state [START_REF] Mcnamara | The value of fat reserves and the tradeoff between starvation and predation[END_REF]). Lighter individuals with fewer fat reserves may suffer faster from food deprivation and thus might forage under higher predation risk to avoid starvation [START_REF] Rogers | Predation Risk and Fasting Capacity: Do Wintering Birds Maintain Optimal Body Mass?[END_REF]. Moreover, females [START_REF] Saitou | Ecological study of social organization in the Great Tit, Parus major L[END_REF]) and smaller or younger individuals [START_REF] Hegner | Dominance and anti-predator behaviour in blue tits (Parus caeruleus)[END_REF][START_REF] Sandell | Dominance, prior occupancy, and winter residency in the great tit (Parus major)[END_REF] are usually subordinates and therefore are expected to have access to lower quality foraging habitats. As a consequence, these categories of individuals might have to forage in less optimal foraging patches with higher predation exposure thus taking more risks [START_REF] Brown | Vigilance, patch use and habitat selection: Foraging under predation risk[END_REF][START_REF] Brodin | Sex differences in learning ability in a common songbird, the great tit-females are better observational learners than males[END_REF]. Furthermore, predator recognition and the ability to react properly to predators might require time to develop and not yet be fully operational in yearlings [START_REF] Putman | The ontogeny of antipredator behavior: age differences in California ground squirrels (Otospermophilus beecheyi) at multiple stages of rattlesnake encounters[END_REF]. Thus, anti-predator behavior could be agedependent. Similarly, consistent behavioral variations among individuals, i.e. personality could impact foraging decisions [START_REF] Sih | Behavioral syndromes: an ecological and evolutionary overview[END_REF]). The fast-slow exploration continuum [START_REF] Dingemanse | Repeatability and heritability of exploratory behaviour in great tits from the wild[END_REF]) is thought to be closely related to optimal foraging decisions [START_REF] Toscano | Personality, foraging behavior and specialization: integrating behavioral and food web ecology at the individual level[END_REF].

The link between exploration and foraging has been experimentally confirmed in different taxa under predation risk [START_REF] Réale | Integrating animal temperament within ecology and evolution[END_REF][START_REF] Jones | Are fast explorers slow reactors? Linking personality type and antipredator behaviour[END_REF][START_REF] Dammhahn | Is risk taking during foraging a personality trait? A field test for cross-context consistency in boldness[END_REF].

In birds, the perception of predation risk is integrated differently by individuals based on their personality [START_REF] Quinn | Personality, Anti-Predation Behaviour and Behavioural Plasticity in the Chaffinch Fringilla coelebs[END_REF]. Shy individuals appear to be more sensitive to the risk of predation while bolder individuals are more risk-prone in their foraging decisions [START_REF] Van Oers | Realized heritability and repeatability of risk-taking behaviour in relation to avian personalities[END_REF][START_REF] Quinn | Personality predicts individual responsiveness to the risks of starvation and predation[END_REF][START_REF] Coomes | Inhibitory control, exploration behaviour and manipulated ecological context are associated with foraging flexibility in the great tit[END_REF]. However, the impact of personality on foraging decisions under predation risk has, to our knowledge, not yet been investigated across contrasting elevations.

In order to understand how elevation influences changes in the starvation-predation trade-off and associated risk-taking behavior, it is necessary to test individuals under controlled conditions [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF]. Bringing individuals into captivity allows standardization of recent exposure to predators, as well as controlling for food availability and energetic needs. In this study, we compared the foraging behavior of wild great tits (Parus major) brought into captivity from high and low elevation populations expected to be facing different ecological and predation pressures. We used video playbacks to test behavioral responses to predator encounters, as birds are known to perceive information provided by videos as realistic information of risk of predation [START_REF] Zoratto | Behavioural response of European starlings exposed to video playback of conspecific flocks: Effect of social context and predator threat[END_REF], and use them to make decisions and modify their behavior accordingly. We expected that birds would forage less during predator presentations compared to control stimuli. Then, we examined whether there were differences in responses towards predators in relation to the elevation of origin of the birds and if age, personality, fat reserves or sex influenced these responses.

METHODS

Study sites and species

We caught wild great tits (N=192) from 4 distinct populations in the French Pyrenees (Aubert,430 m asl.,42°57'51.4"N,1°06'11.3"E; Montjoie, 445 m asl., 42°59'56.0"N, 1°10'09.3"E; Antras, 891 m asl., 42°52'51.7"N, 0°56'44.4"E; Villargein, 898 m asl., 42°54'17.1"N, 1°02'48.0"E). These populations were at least 5 km apart and no movement between them has been detected since the beginning of our group's population monitoring in 2012. In previous work, we studied the variation of ecological factors along this elevational gradient including differences in winter mean temperature (from 8°C at 200 m asl. to -0.5°C at 2000 m asl. with a decrease of ~5°C/1000 m, [START_REF] Buisan | Impact of weather type variability on winter precipitation, temperature and annual snowpack in the Spanish Pyrenees[END_REF], increased variance in temperature at higher elevation, and ensuing impacts on nesting density, the phenology of nesting behavior and fitness (see [START_REF] Bründl | Experimentally induced increases in fecundity lead to greater nestling care in blue tits[END_REF][START_REF] Bründl | Elevational Gradients as a Model for Understanding Associations Among Temperature, Breeding Phenology and Success[END_REF]. Likewise, there are significant differences in vegetation cover and parental behavior across the same gradient [START_REF] Lejeune | Environmental Effects on Parental Care Visitation Patterns in Blue Tits Cyanistes caeruleus[END_REF]. Studies on birds from these populations have also shown contrasts in cognitive traits across this relatively modest elevational shift [START_REF] Cauchoix | Cognition in the field: comparison of reversal learning performance in captive and wild passerines[END_REF][START_REF] Hermer | Elevation-related difference in serial reversal learning ability in a nonscatter hoarding passerine[END_REF][START_REF] Hermer | Great tits who remember more accurately have difficulty forgetting, but variation is not driven by environmental harshness[END_REF]. Based on this previous work, we grouped our capture sites by referring to "high" (Antras and Villargein; mean 894 m asl.) and "low" (Aubert and Montjoie; mean 437 m asl.) populations. At each elevation, we thus had several replicates where elevation was the key difference among experimental groups.

To verify that overall predation risk varied across elevations, we quantified relative predator abundance using camera traps (Busnell Trophy Cam HD Essential E2 ®) set in front of custom made resin great tit models placed 1.5m from the ground across our elevational range (See supplementary materials for images, Figure S1). From February to August of 2016 and 2017, we conducted a total of 232 week-long monitoring sessions. We scored images for avian versus mammalian predators and found that overall predator abundance decreased from lower to higher elevations (Main effect: ANOVA, N=143, F=6.53, P=0.002; 100-600m vs 600-1000m: T=2.32, N=96, Est.=1.92±0.83, P=0.058; 100-600m vs 1000-1500m: T=3.47, N=106, Est.=2.72±0.79, P=0.002; 600-1000m vs 1000-1500m: T=0.95, N=104, Est.=0.81±0.86, P=0.612; Figure 1). Mammal predators are known to attack small passerines on the ground, at feeders and in roosting boxes through fall and winter [START_REF] Orell | Population fluctuations and survival of Great Tits Par us major dependent on food supplied by man in winter[END_REF], creating a world of fear experienced by great tits. Although sparrowhawks were absent from our images, previous studies on similar elevation gradients suggest that sparrowhawks show the same pattern as other predators [START_REF] Newton | Spacing of Sparrowhawk Nesting Territories[END_REF][START_REF] Newton | Spacing of Sparrowhawks in Relation to Food Supply[END_REF]).

Captures using mist nets took place in 8 batches (24 individuals per batch) between mid-October 2020 and mid-March 2022 during fall (10 October -20 December) and winter ( 21December -10 March). We marked each bird with a unique CRBPO (Centre de Recherche sur la Biologie des Populations d'Oiseaux, French bird ringing office) metal band and a colored band containing a PIT-tag (Passive Integrated Transponders, IB Technology, UK) for detection with an RFID (Radio Frequency Identification) reader. We used plumage dimorphism to sex (male or female) and age (adult or yearling) birds [START_REF] Svensson | Identification guide to European Passerines[END_REF]. We quantified fat reserves by scoring fat deposited in the furculum using a 4-point scale (0: no visible fat; 1: base of tracheal pit obscured by fat but less than half full; 2: flat filling the tracheal pit but not bulging; 3: fat filling tracheal pit, bulging and overlying pectoral muscle, modified from [START_REF] Gosler | Environmental and Social Determinants of Winter Fat Storage in the Great Tit Parus major[END_REF].

We also measured body mass at capture and at release using a digital scale (nearest 0.1g) and tarsus length (nearest 0.1mm) at capture as a proxy for body size.

Housing and captivity schedule

Each aviary at the CNRS-SETE facilities (1 x 5 x 3 m, W x L x H) consisted of an indoor (1 x 1 m, W x L) and an outdoor (1 x 4 m, W x L) area (See supplementary materials for images, Figure S2). Birds were housed individually, we alternated cages by elevation so that neighbors came from different elevations, and one empty cage separated neighboring birds who nonetheless remained visually and acoustically connected to others in outdoor areas in order to minimize stress. Birds freely moved in their aviaries between the indoor and the outdoor areas. Cages were enriched with foliage for cover, an artificial tree, horizontal perches, a roosting box, and water. Birds were fed ad libitum with sunflower seeds and mealworms between video playback experiments. In each indoor area of the aviary, an automatic, RFID-based feeder, called the "OpenFeeder" [START_REF] Cauchoix | The OpenFeeder: A flexible automated RFID feeder to measure interspecies and intraspecies differences in cognitive and behavioural performance in wild birds[END_REF]) provided sunflower seeds. Upon release in aviaries, an exploratory score was measured for each bird as the total number of movements (either flights or hops) completed during a 3 min period, following [START_REF] Dingemanse | Repeatability and heritability of exploratory behaviour in great tits from the wild[END_REF].

This measure performed in a novel environment has been found repeatable in other populations of great tits [START_REF] Dingemanse | Variation in personality and behavioural plasticity across four populations of the great tit Parus major[END_REF]. Furthermore, the first measure of exploratory behavior in a novel environment is a good proxy for the personality of an individual [START_REF] Niemelä | On the usage of single measurements in behavioural ecology research on individual differences[END_REF]. The predation risk experiment was performed on the last day of captivity (day 29) at which point birds were used to feeding from the OpenFeeder and had experience with images projected on video screens for other experiments. Previous exposure to screens entailed projection of different numbers of congeners presented on the screens, but our experimental birds had never seen predators on the screens. All birds had experienced the same tests prior to the present experiment. Sample sizes of some batches were lower than the maximum of 24 as a result of technical errors (N=6), a lack of interaction with the OpenFeeder (N=3), or death during a cold snap in January 2021 (N=1; final sample size: 182 birds rather than 192). After the predation risk experiment, we removed the RFID tag, inspected each bird to make sure it was in good condition, and released them at their respective capture sites.

Video playback broadcasting

Perceived risk of predation was simulated with video playbacks [START_REF] Oliveira | Considerations on the use of video playbacks as visual stimuli: the Lisbon workshop consensus[END_REF][START_REF] Rieucau | Video playback and social foraging: simulated companions produce the group size effect in nutmeg mannikins[END_REF][START_REF] Zoratto | Behavioural response of European starlings exposed to video playback of conspecific flocks: Effect of social context and predator threat[END_REF][START_REF] Snijders | Dominance rank and boldness predict social attraction in great tits[END_REF][START_REF] Smit | Personality types vary in their personal and social information use[END_REF] which are an effective tool to test several individuals at the same time whilst controlling the predation risk stimuli. The stimuli were extracted from YouTube videos showing either a blackbird (Turdus merula) for control stimuli or an Eurasian sparrowhawk (Accipiter nisus) for predation stimuli. Only sequences of birds perched and life size at full screen projection were kept. Sound was removed from videos to avoid potential influences of vocalizations. Each stimulus was created with OpenShot Video Editor software (OpenShot Studios, LLC.) and lasted 30s to mimic a realistic encounter with a wild bird (see supplementary materials).

The video presentations were performed during a morning session from 9 am to noon and an afternoon session from 1 pm to 4 pm. Outside of these periods, no video was projected on the screen and birds had ad libitum access to food. During the two experimental sessions, the OpenFeeder was the only source of food available. Every morning, aviaries were swept to remove potential food present on the ground. In each aviary a 22-inch computer monitor (Dell P2219H or P2217) placed next to an OpenFeeder in the indoor section was used to broadcast videos through a Raspberry Pi 3B+ (Raspberry Pi Foundation). Every 30s, the Raspberry Pi played a video randomly selected from 18 different recordings (9 sparrowhawk, 9 blackbird) such that each bird experienced an equal number of predator and control videos but in a different order. The large number of projections (720 total playbacks to each bird) ensured that each bird would see both control and predator stimuli when seeking food. Although video playbacks were broadcasted without interruption, birds freely decided when to forage and were only in close proximity with a video playback for a limited amount of time as birds come to the feeder, take a single seed, and eat it on a perch away from the feeder. All birds of a specific batch were tested simultaneously and could only see the computer screen of their own aviary. The RFID reader on the OpenFeeder recorded when the bird came to feed and during which video. If the bird arrived during the last 5s of a video, the current video stimulus was reread another time to avoid a shift in stimulus while the bird was feeding.

Data analysis

All analyses were performed using R software (R version 4.2.2, R Core Team 2022). Foraging behavior of each individual was quantified by pooling the number of visits to the OpenFeeder during the whole day of the experiment as feeding patterns did not shift throughout the day (i.e. we did not detect habituation to the setup; See supplementary materials). To examine differences in feeder visitation rates between predation and control contexts, we compared the relative number of visits for these two conditions within individuals using a repeated measures ANOVA with season and site of capture as explanatory co-variables. In order to confirm the result of this analysis, we performed a permutation test (permuco package, Frossard and Renaud 2021) with 10 000 iterations following the same structure as the initial ANOVA (season and site of capture as fixed factors, bird identity nested in video playback as a random factor) and following the kennedy method adjusted for exact permutation of repeated measures models (Kherad-Pajouh and Renaud 2015). To examine the effect of individual characteristics (age, sex, exploration, body mass, fat reserves and body size) on predatorcontext dependent foraging, we built generalized linear mixed models (GLMM, lme4 package, [START_REF] Bates | Fitting Linear Mixed-Effects Models using lme[END_REF]) with a binomial distribution and the relative number of visits to predation and control contexts as the response variable. We used the cbind function of the number of visits during sparrowhawk playback ("predator") and number of visits during blackbird playbacks ("control"; 2 column matrix) to account for variation in total number of visits among individuals in the response variable. Individual characteristics (sex, age, elevation of origin, exploration score, body mass, fat reserves, tarsus length, season of experiment; see supplementary materials, Table S1 for metric means and Table S2 for sample sizes by category) were included as explanatory variables. Dominance may impact risk-prone foraging behavior more in harsher, high elevation populations so we added interaction effects between elevation and body mass, fat reserves, tarsus length, sex and age as these variables may impact dominance rank. We also included an interaction between elevation and season as we expected high elevation sites to increase in harshness more with season shifts relative to low elevation populations. Finally, we added an interaction between elevation and exploration score as previous work in a congener has shown elevational differences in exploration of a novel environment (Kozlovsky et al. 2015b). All numeric variables (exploration score, body mass, fat reserves, tarsus length) were centered on the mean and scaled by their standard deviation prior to integration in the model. Batch of capture was added as a random factor to control for variation due to series of capture and year effects. Results were similar when using body mass at capture or at release. Only the former is presented in the results (but see supplementary materials for model with body mass at release, Table S3). Final models included all main effects, but only significant interactions [START_REF] Faraway | Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models[END_REF]. Post-hoc Tukey HSD tests (emmeans package, [START_REF] Lenth | emmeans: Estimated Marginal Means, aka Least-Squares Means[END_REF] were run to assess the significance of differences between levels. Model performance was checked graphically using the DARHMa package [START_REF] Hartig | DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models_. R package version 0[END_REF]. We also checked for multi-collinearity among fixed effects using the performance package to estimate the Variance Inflation Factor (VIF) which was below 5 for all variables [START_REF] Lüdecke | performance: An R Package for Assessment, Comparison and Testing of Statistical Models[END_REF]. While analyses were based on relative number of visits, figures represent the proportion of visits for graphical simplicity. The significance level for P-values was set at 0.05 (two-tailed tests).

RESULTS

Impact of predation risk on foraging preferences

As expected, birds visited the feeder less when a predator (sparrowhawk) was presented on the screen compared to when a control bird (blackbird) was shown on the screen (rmANOVA, F=4.50, df=181, P=0.035; Permutation test, 10 000 iterations, P=0.033; Figure 2).

Correlates of variation in risk-taking foraging strategies

The birds' decision to reduce foraging in predation contexts depended on elevation in interaction with four ecological or individual factors (Table 1). First of all, the pattern was season-dependent (Elevation x Season: Z=2.51, Est.=0.24±0.10, P=0.012; Table 1). In fall, birds from high elevation visited the feeder significantly more during predator playbacks than their low elevation counterparts (post-hoc test: Z=5.42, N=92, Est.=1.46±0.10, P<0.001; Figure 3A) whereas a tendency was detected in winter (post-hoc test: Z=2.17, N=90, Est.=1.15±0.07, P=0.061; Figure 3A). Second, heavier birds visited the feeder less when a predator was present (Elevation x Body mass: Z=-2.00, Est.=-0.12±0.06, P=0.046) but this pattern was only significant for birds from low elevation (post-hoc test: Z=-3.96, N=90, Est.=-0.16±0.04, P<0.001; Figure 3B). Third, birds from high elevation visited the feeder more during predator playbacks when their fat reserves were higher (Elevation x Fat score: Z=-2.10, Est.=-0.11±0.05, P=0.036; posthoc test: Z=2.34, N=92, Est.=0.09±0.04, P=0.019; Figure 3C) whereas feeder visits did not differ between contexts in relation to fat scores for birds from low elevation (post-hoc test: Z=-0.47, N=90, Est.=-0.02±0.04, P=0.636; Figure 3C). Finally, female foraging preferences differed depending on their elevation of origin (Elevation x Sex: Z=2.91, Est.=0.32±0.11, P=0.004) with females from high elevation visiting more often the feeder when a predator was present (posthoc test: Z=4.92, N=79, Est.=1.46±0.11, P<0.001; Figure 3D). Interestingly, males showed an opposite trend in their foraging decisions across elevations (post-hoc test: Z=1.97, N=93, Est.=1.15±0.08, P=0.097; Figure 3D). Overall, when birds from high and low elevation differed in the proportion of visits during predator playbacks, high elevation birds were more riskprone (Figure 3).

DISCUSSION

The trade-off between starvation and predation in foraging decisions should change with environmental conditions [START_REF] Bonter | Daily foraging patterns in freeliving birds: exploring the predation-starvation trade-off[END_REF], for example, when facing less predictable or harsher environments like those encountered at higher elevations [START_REF] Körner | The use of 'altitude' in ecological research[END_REF][START_REF] Pitera | Daily foraging routines in food-caching mountain chickadees are associated with variation in environmental harshness[END_REF]. To control for the short-term influence of various factors interacting with elevation on foraging decisions under the risk of predation (e.g. food availability or thermal conditions) birds in our experiment were tested under standardized, controlled conditions in temporary captivity. As expected, we found that birds from high elevation maintained higher foraging frequency at the feeder under increased perceived predation risk compared to birds originating from lower elevations (Figure 3). This difference between high and low elevation birds was partly dependent on other factors, like the season (Table 1) as it was only significant for birds tested in fall (Figure 3A). In winter, individuals from low elevation became more riskprone, approaching the risk levels during feeding of high elevation conspecifics. This could be linked with an increase in the harshness of foraging conditions (food limitation) from fall to winter at low elevation, leading to a higher risk of starvation under wintering climatic conditions [START_REF] Bejer | Fluctuations of Tits (Paridae) in Denmark and Their Relations to Winter Food and Climate[END_REF][START_REF] Perdeck | GREAT TIT PARUS MAJOR SURVIVAL AND THE BEECH[END_REF]. In contrast, high elevation populations may experience more consistent harsher conditions from fall to winter. If conditions are already harsh in fall, birds may not shift their foraging decisions, which could explain the lack of seasonal difference of foraging decisions in high elevation birds. Food intake is critical to survival through winter and may therefore be the major driver of foraging decisions in our system where birds were kept in safe conditions [START_REF] Houston | A Theoretical Investigation of the Fat Reserves and Mortality Levels of Small Birds in Winter[END_REF]. Likewise, predator abundance could also be one of the factors explaining elevation differences we detected as lower risks of predation at high elevation might lead to lower vigilance and a more risk-prone foraging strategy. In line with previous studies [START_REF] Newton | Spacing of Sparrowhawk Nesting Territories[END_REF][START_REF] Newton | Spacing of Sparrowhawks in Relation to Food Supply[END_REF], we found that the number of predators detected decreased with elevation leading to a more intense "landscape of fear" at low elevation (Figure 1). This could partly explain the more risk-prone foraging detected in high elevation birds.

Interestingly, birds were tested after 28 days spent under controlled conditions with the same food availability and predator risk conditions, yet differences in risk-prone foraging strategies persisted between high and low elevation. While captivity could influence behavior [START_REF] Butler | Time in Captivity, Individual Differences and Foraging Behaviour in Wild-Caught Chaffinches[END_REF], if birds had mostly used recent information on predation risk to make foraging decisions, we should not have detected differences in behavior between high and low elevation birds. Indeed, this captive period with homogenized conditions could have attenuated differences in behavior between high and low elevation birds, implying that our results are conservative. Moreover, it seems likely that reactions to the video playback were weaker than would be during exposure to a real predator as video playbacks do not affect all sensory channels (e.g. no acoustic signals). Finally, birds came from a number of populations at each elevation suggesting that elevation or life history traits tightly linked to elevation per se rather than other peculiarities of the different populations are responsible for the differences we detected. Recent experimental studies have shown that presented with a disturbance or a threat, high elevation dark-eyed juncos flew away from a further distance (Andrade and Blumstein 2020) and high elevation mountain chickadees took longer to come back to their nest box (Kozlovsky et al. 2015a). While these studies show results going in the opposite direction to ours, both were performed during the reproductive period when birds might be facing other life-history trade-offs (e.g. current vs future reproductive success). In contrast, individual survival is the key fitness criteria for birds in winter, thus reducing the number of likely trade-offs impacting foraging decisions. Differences between our results and recent studies highlights the importance of considering a variety of ecological factors and life-history trade-offs to understand risk sensitive foraging decisions.

Starvation risk is influenced by the reliability of access to food and being dominant generally provides such access [START_REF] Ficken | Dominance Rank and Resource Access in Winter Flocks of Black-Capped Chickadees[END_REF]). In our study, we found that at low elevation, heavier birds were more risk-averse than lighter conspecifics (Figure 3B). This result agrees with previous studies where body mass and risky decisions are negatively related [START_REF] Rogers | Predation Risk and Fasting Capacity: Do Wintering Birds Maintain Optimal Body Mass?[END_REF][START_REF] Macleod | Body mass change strategies in blackbirds Turdus merula: the starvation-predation risk trade-off[END_REF]. Interestingly, lighter birds from both elevations appeared to be similarly risk-prone. These individuals may be facing similar high constraints to meet their nutritional needs since dominant individuals are usually heavier and have better access to food by competitively excluding lighter birds from food sources [START_REF] Hegner | Dominance and anti-predator behaviour in blue tits (Parus caeruleus)[END_REF][START_REF] Kullberg | Dominance and anti-predator behaviour of Great Tits Parus major: a field study[END_REF][START_REF] Ficken | Dominance Rank and Resource Access in Winter Flocks of Black-Capped Chickadees[END_REF]). Despite ample food availability during captivity, differences in foraging behavior related to mass at capture were apparent and similar to the relationship with mass at release (Table S3). Birds can also modulate their metabolic effort to deal with starvation [START_REF] Broggi | Individual response in body mass and basal metabolism to the risks of predation and starvation in passerines[END_REF] which could reinforce the effects of weight we detected. In addition, fat reserves can influence foraging decisions as with more fat reserves available, an individual is less susceptible to face starvation risks [START_REF] Houston | A Theoretical Investigation of the Fat Reserves and Mortality Levels of Small Birds in Winter[END_REF]. We found that for low elevation birds, the level of fat reserves did not impact risk-taking during foraging (Figure 3C).

In our controlled system, food access is relatively predictable and available ad libitum so birds may not need to rely strongly on fat reserves [START_REF] Gosler | Environmental and Social Determinants of Winter Fat Storage in the Great Tit Parus major[END_REF]. However, we still found that high elevation birds presenting a higher level of fat reserves were more risk-prone in their foraging decisions (Figure 3C). As starvation risk decreases with more fat reserves, we would expect birds with higher fat reserves to be more risk-averse [START_REF] Mcnamara | The value of fat reserves and the tradeoff between starvation and predation[END_REF]. We can speculate that with higher fat reserves, metabolic expenditure increased [START_REF] Brodin | Mass-dependent predation and metabolic expenditure in wintering birds: is there a trade-off between different forms of predation?[END_REF]) and so birds from harsher conditions need to be more risk prone to meet increased foraging needs.

Fat reserves and metabolic rates likely interact in complex ways and influence how risk taking during foraging shifts with environmental harshness.

We also showed that females -who are often subordinate to male great tits [START_REF] Carrascal | Interactions among Environmental Stress, Body Condition, Nutritional Status, and Dominance in Great Tits[END_REF]) -are sensitive to environmental contrasts (Figure 3D). Since males have priority access to food, females need to develop alternative strategies to meet their foraging needs, such as better memorization [START_REF] Brodin | Sex differences in learning ability in a common songbird, the great tit-females are better observational learners than males[END_REF]. In harsher environments such as those found at high elevations where finding food is expected to be harder, adult males should be able to have priority access to food resources and thus should be more risk-averse than females [START_REF] Lahti | Social dominance and survival in flocking passerine birds: a review with an emphasis on the Willow Tit Parus montanus[END_REF]. Finally, yearlings were more risk-prone regarding their foraging decisions, although we found no significant effect of elevation on their responses (Figure 4A). Juveniles often have less access to food as they are subordinates to adults [START_REF] Sandell | Dominance, prior occupancy, and winter residency in the great tit (Parus major)[END_REF] and occupy less optimal habitats. Thus, adopting risk-prone foraging strategies could be the "best of a bad job" solution for non-dominant individuals allowing them to reach their minimal food intake and fat reserves [START_REF] Krams | Fattening strategies of wintering great tits support the optimal body mass hypothesis under conditions of extremely low ambient temperature[END_REF]. Alternatively, in some mammal and bird species, yearlings do not consistently discriminate between a predator and a non-predator [START_REF] Kullberg | Dominance and anti-predator behaviour of Great Tits Parus major: a field study[END_REF] and predator recognition improves with age [START_REF] Putman | The ontogeny of antipredator behavior: age differences in California ground squirrels (Otospermophilus beecheyi) at multiple stages of rattlesnake encounters[END_REF]. Learning allows individuals to adjust their behavior according to the local risks they face and thus limit the costs associated with excessive vigilance behaviors [START_REF] Blanchard | Induced or Routine Vigilance while Foraging[END_REF]. Our experiment took place during the winter season where yearlings were about 6-9 months post hatch. Due to their younger age, these birds could have had fewer encounters with predators in their life to date than adults and thus might be less likely to consistently identify sparrowhawks as a potential threat. However, [START_REF] Kullberg | Dominance and anti-predator behaviour of Great Tits Parus major: a field study[END_REF] showed that four months after fledging juvenile great tits were able to responded to a predator stimuli. It is possible that the reaction of yearlings towards sparrowhawks in our populations takes longer to develop than the one found by [START_REF] Kullberg | Dominance and anti-predator behaviour of Great Tits Parus major: a field study[END_REF] and is thus driven by naivety towards predators. As an alternative, yearlings could be slower to react to a predator's appearance or disappearance on the screen. A fast response relies on a high level of behavioral plasticity [START_REF] Dingemanse | Between-individual differences in behavioural plasticity within populations: causes and consequences[END_REF], which has been shown to be lower for young individuals [START_REF] Bonamour | Age-dependent phenological plasticity in a wild bird[END_REF]. Our study provides further evidence that age plays a critical role in driving behavioral foraging plasticity and suggests a mechanism to explain lower survival of first year birds compared to older ones [START_REF] Martin | Patterns and Mechanisms for Age-dependent Reproduction and Survival in Birds1[END_REF]. Nestlings face high rates of predation after fledging [START_REF] Naef-Daenzer | Differential post-fledging survival of great and coal tits in relation to their condition and fledging date[END_REF], and risk-prone yearlings might face higher rates of predation thus becoming proportionally less present in populations. Albeit difficult to quantify, future studies should examine the factors that underlie the ontogeny of risk-taking in juveniles and their effects on their first year survival.

Personality has been identified as a key component of individual decisions [START_REF] Mathot | Adaptive strategies for managing uncertainty may explain personality-related differences in behavioural plasticity[END_REF][START_REF] Rojas-Ferrer | Is exploration a metric for information gathering? Attraction to novelty and plasticity in black-capped chickadees[END_REF]) and could have an important impact on foraging. However, we found no clear impact of exploration score in a novel environment on foraging decisions (Figure 4B).

Previous studies showed that fast explorers decreased their foraging activity more under predation risk (Abbey-Lee et al. 2016) and that they are more flexible in their reaction to predation risk by switching food preferences [START_REF] Coomes | Inhibitory control, exploration behaviour and manipulated ecological context are associated with foraging flexibility in the great tit[END_REF] or returning to feed faster following a stressful disturbance [START_REF] Van Oers | Realized heritability and repeatability of risk-taking behaviour in relation to avian personalities[END_REF][START_REF] Quinn | Personality, Anti-Predation Behaviour and Behavioural Plasticity in the Chaffinch Fringilla coelebs[END_REF]. Moreover, high elevation birds were identified as being slower explorers compared to low elevation birds [START_REF] Kozlovsky | Elevation-related differences in novel environment exploration and social dominance in food-caching mountain chickadees[END_REF]. We therefore expected fast explorers to be more risk averse, especially for birds from low elevation, but this was not the case. In our experiment, we did not measure the rapidity to react to a change in the predation risk but overall preferences in foraging decisions. Such decisions are known to also depend on dominance status [START_REF] Quinn | Personality predicts individual responsiveness to the risks of starvation and predation[END_REF].

Future studies should directly examine whether exploration behavior is really associated with risk-prone strategies, in interaction with dominance status, and linked to other fitness traits.

Overall, we found that foraging decisions under predation risk vary among elevations in association with individual characteristics, in accordance with starvation-predation trade-off theory. The differences in risk-taking during foraging we detected were still persistent despite the fact that birds spent 28 days under standardized conditions where all individuals had the same levels of food, competition, ambient temperature, and risk of predation. The contrast between adults and juveniles raises the question of how and when individuals learn the proper responses to be adopted. It would also be important to determine how long individuals retain information about predation risk in their habitat and use that information to modulate their foraging behavior. This question is not trivial as anthropogenic induced climate change is causing range shifts including movements towards higher elevations [START_REF] Parmesan | A globally coherent fingerprint of climate change impacts across natural systems[END_REF][START_REF] Campos-Cerqueira | Have bird distributions shifted along an elevational gradient on a tropical mountain?[END_REF], which brings individuals adapted to their population of origin into areas with different risks and constraints. If such areas include a higher risk of predation, these "immigrant" birds may take more risks and suffer higher mortality. On the other hand, if individuals avoid risk-taking when moving to areas with lower risk, they may miss out foraging opportunities and not get enough food. Further experiments should examine interns (Elodie, Marie, Alexandre, Antoine, Raphaëlle, Audrey, Amélie, Clara, Juliette) who participated in aviaries maintenance. Special thanks to Jérôme Briot for writing code to randomly read video playbacks on the Raspberry Pi. We also would like to thank Ralph Hancock, Gunnar Fernqvist and Luka Hercigonja for giving us the permission to use their video sequences to create our video stimuli and four anonymous reviewers for their constructive comments on an earlier version that helped us to improve the manuscript.

DATA AVAILABILITY STATEMENT

Analyses reported in this article can be reproduced using the data provided by (Crouchet et al. XXXX).

ANIMAL WELFARE NOTE

  Experiments were performed in accordance with guidelines of the Association for the Study of Animal Behaviour. Captures were conducted under permits to ASC from the French bird ringing office (CRBPO, project 576; permit 13619). Housing was approved by the Région Midi-Pyrénées veterinary services (DIREN, n°2012-07) in the SETE experimental aviaries (Préfecture de l'Ariège, institutional permit n°SA-12-MC-054; Préfecture de l'Ariège veterinary services, Certificat de Capacité n°09-321). The French Ministry of Education, Research and Innovation's Ethical comitee approved the experimental protocol under the permit 30185-2021030410167866 v5.

Table 1 :

 1 The relative number of visits to the OpenFeeder during predator (vs. control) video stimuli is influenced by a bird's elevation of origin and individual characteristics. A positive value for the estimate means that the bird is more likely to visit the OpenFeeder during predator playbacks. For each categorical variable, the reference level is presented in brackets.

	Values in bold are significant at P<0.05.
	Model	Estimate	SE	95% conf int Z value P value
	(Intercept)	-0.09	0.14	-0.37; 0.19	-0.64	0.524
	Elevation (Low)	-0.50	0.09	-0.68; -0.33	-5.59	< 0.001
	Body mass	-0.05	0.05	-0.14; 0.05	-0.99	0.321

ACKNOWLEDGMENTS

We would like to warmly thank Maxime Cauchoix for help with OpenFeeders, Nory El Ksaby and Marine Bely for bird trapping, Sandrine Lardennois and Emil Isaksson for their valuable help in monitoring birds in captivity, Antoine Bel for camera trap's photos analysis and all

FUNDING

This project was funded by the Ministère de l'Enseignement Supérieur et de la Recherche (PhD studentship) to TC, by the Human Frontiers Science Program (RGP0006/2015) to ASC and the Agence National pour la Recherche (ANR-SoCo) to ASC and PH. ASC and PH were in part supported by the Laboratoire d'Excellence (LABEX) entitled TULIP (ANR-10-LABX-41) and IAST (ANR-17-EURE-0010 Investissements d'Avenir program).

the mechanisms underlying risk-taking and whether there is a link between the ontogeny of risk-taking and the ability to move to new habitats. Such results would help us understand how individuals might cope with changing climates.

AUTHOR CONTRIBUTIONS

TC, PH and ASC designed the study. TC collected, analyzed the data and wrote the first version of the manuscript. ASC and PH helped capture and house birds, provided input on analyzes, and corrected and improved the manuscript. All the authors gave final approval for publication. There were no conflicts of interest in this study.

LIST OF FIGURES

LIST OF TABLES

Table 1: The relative number of visits to the OpenFeeder during predator (vs. control) video stimuli is influenced by a bird's elevation of origin and individual characteristics. A positive value for the estimate means that the bird is more likely to visit the OpenFeeder during predator playbacks. For each categorical variable, the reference level is presented in brackets.

Values in bold are significant at P<0.05.