John R Speakman

Jasper M A De Jong

Srishti Sinha

Klaas R Westerterp

Yosuke Yamada

Hiroyuki Sagayama

Philip N Ainslie

Liam J Anderson

Lenore Arab

Kweku Bedu-Addo

Stephane Blanc

Alberto G Bonomi

Pascal Bovet

Soren Brage

Maciej S Buchowski

Nancy F Butte

Stefan G J A Camps

Jamie A Cooper

Richard Cooper

Krupa Sai

Das

Peter S W Davies

Lara R Dugas

Ulf Ekelund

Sonja Entringer

Terrence Forrester

Barry W Fudge

Melanie Gillingham

Santu Ghosh

Annelies H Goris

Michael Gurven

Lewis G Halsey

Catherine Hambly

Hinke H Haisma

Daniel Hoffman

Sumei Hu

Annemiek M Joosen

Jennifer L Kaplan

Peter Katzmarzyk

William E Kraus

Robert F Kushner

William R Leonard

Marie Löf

Corby K Martin

Eric Matsiko

Anine C Medin

Erwin P Meijer

Marian L Neuhouser

Theresa A Nicklas

Robert M Ojiambo

Kirsi H Pietiläinen

Jacob Plange-Rhule

Guy Plasqui

Ross L Prentice

Susan B Racette

David A Raichlen

Eric Ravussin

Leanne M Redman

Susan B Roberts

Michael C Rudolph

Luis B Sardinha

Albertine J Schuit

Analiza M De Silva

Eric Stice

Samuel S Urlacher

Giulio Valenti

Ludo M Van Etten

Edgar A Van Mil

Brian M Wood

Jack A Yanovski

Tsukasa Yoshida

Xueying Zhang

Alexia J Murphy-Alford

Cornelia U Loechl

Anura Kurpad

Amy H Luke

Herman Pontzer

Matthew S Rodeheffer

Jennifer Rood

Dale A Schoeller

William W Wong

Dr Erica J Velthuis-Te Wierik

Dr Wilhelmine W Verboeket-Van De Venne

Dr Jeanine A Verbunt

Dr Jonathan C K Wells

Dr George Wilson

Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Abstract

Obesity is caused by prolonged positive energy balance 1,2 . Whether reduced energy expenditure stemming from reduced activity levels contributes, is debated 3,4 . Here we used the IAEA DLW database on energy expenditure of adults in the USA and Europe (n = 4799) to explore patterns in total (TEE: n=4799), basal (BEE: n = 1432) and physical activity energy expenditure (AEE: n = 1432) over time. In both sexes total energy expenditure (TEE) adjusted for body composition and age declined with time, while adjusted AEE increased over time. In males adjusted BEE decreased significantly, but in females this didn't reach significance. A larger dataset of basal metabolic rate (BMR equivalent to BEE) measurements of 9912 adults across 163 studies spanning 100 years replicated the decline in BEE in both sexes. Increasing obesity in the USA/Europe has probably not been fueled by reduced physical activity leading to lowered TEE. We identify here decline in adjusted BEE, as a previously unrecognized novel factor.

Main text: Obesity is a global health threat 5 . Although excess body fat is a result of prolonged positive energy balance 1,2 , the exact causes of this imbalance remain elusive. Two major potential factors have been suggested. First, food consumption (net energy consumption accounting for losses in feces) may have increased 2 . Alternatively, declines in energy expenditure, due to reduced work-time physical activity 4 , combined with increases in sedentary behavior, partly linked to elevated 'screen time' (TV, computer and phone use) 6,7 may be a key driver. These may be linked in a vicious cycle 8 , where low activity leads to weight gain, which inhibits activity, leading to further weight gain.

Although there is direct evidence, that physical activity levels have declined and sedentary time has increased 4,6,7,8 , these changes do not necessarily translate into alterations in total energy expenditure (TEE). As individuals get larger the energy cost of movement also increases 9 . Thus, the same amount of energy may be utilized even though the actual time spent active has declined. Moreover, increases in one type of activity/behavior may be replaced by decreases in another behavior of equal cost. Consequently, apparently large behavior changes may result in only minor alterations in expenditure. Finally, it has been suggested that we may compensate for changes in physical activity by adjusting expenditure on other physiological tasks 10,11 . Although low TEE is repeatable, and having low TEE is not a risk factor for future weight gain over short timescales 12 , this does not negate a possible impact over longer periods.

In the present paper we address the idea that reduced physical activity leading to reduced activity energy expenditure (AEE) may have fueled the epidemic.

The doubly-labelled water (DLW) method is a validated isotope based methodology for the measurement of free-living energy demands 13 . A previous analysis using this method suggested there had been no change in TEE between 1986 and 2005, calling into question the reduced physical activity hypothesis 14 . Nevertheless, these observations were based on a limited sample (n = 314) from a single European city over a restricted timespan of about 20 years. Here we expanded this analysis using data for 4799 adults living across Europe and the USA drawn from the IAEA DLW database 15 for which we also had BEE measures in 1429 individuals. All estimates of TEE were recalculated using a common equation [START_REF] Lewis | Further considerations of the effect of altitude on basal metabolism: A study of young women residents of Denver[END_REF] that has been shown to perform best in validation studies [START_REF] Lewis | Further considerations of the effect of altitude on basal metabolism: A study of young women residents of Denver[END_REF] .

We split the data by sex, because this may affect the etiology of energy balance 17,18 . This resulted in 1672 measurements of males and 3127 measurements of females. In addition, for 632 of the males and 800 of the females we also had measurements of basal energy expenditure (BEE) from which we derived activity energy expenditure (AEE) and physical activity level (PAL) -for calculations see methods. The data span a period of over 30 We first explored the changes in the unadjusted levels of TEE, BEE and AEE over time (Supplementary Fig S2: Table 1). In males there was no significant relationship between TEE and the date of measurement (date coded as months since Jan 1982) (r 2 = 0.0015, p = 0.14 (ns): Fig In females as well, there was a significant decline in the adjusted TEE over time (Fig 3a: Because there was a small sample of measures in the early 1980s in males these may have exerted undue leverage in the regression models. We therefore repeated the analysis excluding these data. Their removal had no impact on the detected relationships (Supplementary Table S1). Since individual studies may also exert undue leverage we performed additional sensitivity analyses on the BEE effect (post 1987) where the data for each study was systematically removed and the regression recalculated. In males removal of no individual study resulted in the loss of significance (Supplementary Table S2). In females however, the absence of significance was due to inclusion of data from a single study (Supplementary Table S3). We have no reason to exclude these data, but their undue influence may explain the anomalous lack of decline in female BEE when TEE is declining and AEE is rising (Table 1 andfig

2).
Hence, in both males and females there was a decline in the adjusted TEE by 7.7 and 5.6% respectively and in males in the adjusted BEE over time by 14.7% over 30 years (females declined by 2% which was not significant). In both sexes the confidence limits for the decline in adjusted TEE overlapped with the confidence limits for the decline in adjusted BEE, suggesting the decline in adjusted BEE could be sufficient to explain the reduction in adjusted TEE. In both sexes there was in contrast a significant increase over time in adjusted AEE. The comparable declines in adjusted TEE and BEE resulted in a significant increase in PAL (=TEE/BEE) in males To replicate and check our observation of decreasing BEE over time we systematically reviewed data from the literature on mean BMR over the last 100 years, restricted to studies in the USA and Europe, to match the restricted regions included in the time course from the IAEA database (Figs 1,2 and Table 1). For the distinction between BEE and BMR see the methods. The main effect on Loge BMR was Loge BM (Fig 3a), with additional effects of sex and age (total r 2 = 0.88). Including the date of measurement, sex, age and loge body mass as predictors in a weighted regression analysis there was a significant negative effect of date of measurement (R 2 = 0.024, p = 0.022) on the adjusted loge BMR (Fig 3b). On average, BMR adjusted for BM, age and sex has declined by about 0.34 MJ/d over the last 100 years. This decline is consistent with, but at a lower rate, than the data from the IAEA database reported above (Table 1).

Basal metabolism may be influenced by many factors one of which is diet. Human dietary changes during the epidemic have included many things such as changes in the amounts of fiber and fat, and the types of fat consumed. Because evaluating the impacts of long-term diets on human metabolism is difficult, we explored the potential impact of dietary fatty acids on metabolic rate using the mouse as a model. Working with mice has the advantage that diets can be rigorously controlled and maintained constant over protracted periods. We exposed adult male C57BL/6 mice to 12 diets (for details see supplementary Tables S2 andS3) that varied in their fatty acid composition for 4 weeks (equivalent to 3.5 years in a human). Mouse BMR (kJ/d) was strongly related to body weight (regression r 2 = 0.512, p = 3x10 Overall the data we present do not support the idea that lowered physical activity in general, leading to lowered energy expenditure, has contributed to the obesity epidemic during the last 30 years. Unadjusted AEE was higher in individuals with greater BMI (supplementary Fig S4). This is because, as shown previously, despite on average moving less, individuals with greater BMI have higher costs of movement 9 . Rather than adjusted AEE declining, it has significantly increased overtime in both sexes. Yet TEE (adjusted for age and body composition) has declined significantly in both males and females over the past 3 decades. Because adjusted AEE has increased at the same time that TEE has declined there has been a corresponding reduction in adjusted BEE (which only reached significance in males). The observation that adjusted AEE (and PAL in males) has significantly increased over time is counter intuitive given the patterns established in worktime physical activity and the suggested progressive increase in sedentary behavior 4,[6][7][8] . One possibility is that lowered work time physical activity may have been more than offset by increased engagement in leisure time physical activity. For example, sales of home gym equipment in the USA increased from 2.4 to 3.7 Bn US$ between 1994 and 2017 19 . Time spent in leisure time PA in the USA also increased between 1965 and 1995, 20 suggesting leisure time PA has replaced the decline in worktime PA levels 20 . Leisure time PA has also changed in other westernized populaions 21 . Although time spent on computers has increased, much of the increase in this time has largely come at the expense of time spent watching TV. Since these activities have roughly equivalent energy costs 22 this change would not be apparent as a decline in overall adjusted AEE.

The reduction in adjusted BEE is less easily understood but is consistent with the recent observation that body temperatures have also declined over time 23 , over the same interval as the reduction of BMR in the wider data set we analysed (Fig 3b). The magnitude of secular change in BMR is consistent with studies measuring BMR and body temperature in several contexts, including calorie restriction, ovulation, and fever which show a 10-25% increase in BMR per 1 o C increase in core temperature 24,25 . It was recently suggested that changes in both activity and basal metabolism may have contributed to the decline in body temperature (Tb) 26 , but our data suggest this is probably dominated by a BMR effect. The reduction in Tb has been speculated to be a consequence of a reduction in baseline immune function because we have greatly reduced our exposure to many pathogens. However, the links between immune function and metabolism are not straightforward. For example, artificial selection on metabolic rate leads to suppressed innate but not adaptive immune function 27 , and studies of birds point to no consistent relation between immune function and metabolism either within or between subjects 28 . Experimental removal of parasites in Cape ground squirrels (Xerus inauris) led to elevated rather than reduced resting metabolic rate 29 . Nevertheless, some studies in foragerhorticulturalist societies in South America have noted elevated BMR is linked to increased levels of circulating IgG 30 and cytokines 31 , supporting the view that a long term decline in BEE may be mediated by reduced immune function. Whether this has any relevance to changes in the USA/Europe in the past 30 years is unclear. It is also possible that the long-term reduction in BMR represents methodological artefacts. In the early years, measurements of BMR were often made using mouthpieces to collect respiratory gases, and recently such devices have been shown to elevate BMR by around 6% 32 . A second possibility is that early measurements paid less attention to controlling ambient temperature to ensure individuals were at thermoneutral temperatures 33 .

During the past century there have been enormous changes in the diets of US and European populations (USDA and FAO food supply data) 34 . These changes have included alterations in the intake of carbohydrates, fiber and fats, with % protein intake remaining relatively constant 34 . While intake of carbohydrates peaked in the late 1990s the intake of fat has increased almost linearly since the early part of the 1900s. Moreover, the fat composition has changed dramatically with large increases in soybean oil and seed oils from the 1930s onwards (dominated by the polyunsaturated 18:2 linoleic acid and other PUFAs) and reductions in animal fats (butter and lard) (dominated by saturated fatty acids palmitic (16:0) and stearic acid (18:0) and the mono-unsaturated oleic acid (18:1)) 34 . The change has been dramatic, as animal fats comprised >90% of the fatty acid intake in 1910 but are currently less than 15%.

Because linoleic acid is desaturated to form arachidonic acid (ARA) and ARA is linked to endocannabinoids it has been speculated that expanding linoleic acid in the diet may be linked to various metabolic issues. Effects on basal metabolic rate however are disputed, and if anything, PUFAs lead to elevated not reduced metabolism 35,[START_REF] Hommes | Increased resting energy expenditure in human immunodeficiency virus-infected men[END_REF] , although many studies suggest no effect [START_REF] Spicher | Assessment of total energy expenditure in free-living patients with cystic fibrosis[END_REF][START_REF] Vermeij | Assessment of energy expenditure by indirect calorimetry in healthy subjects and patients with liver cirrhosis[END_REF] . This variation in outcome may reflect difficulties in controlling human diet over protracted periods necessary to generate robust changes in metabolism. In mice, where we can rigorously control the diet for prolonged periods (equivalent to many years of human life), we have shown here no effect of PUFAs on metabolic rate, but a clear impact of saturated fat, with greater intake of saturated fat leading to higher metabolic rate (adjusted for body mass). This finding is consistent with earlier reports of relationships between membrane lipids and elevated metabolic rate in mice, particularly a positive effect of palmitic and stearic acids [START_REF] Hommes | Resting energy expenditure and substrate oxidation in human immunodeficiency virus (HIV)-infected asymptomatic men: HIV affects host metabolism in the early asymptomatic stage[END_REF][START_REF] Roberts | Dietary energy requirements of young adult men, determined by using the doubly labeled water method[END_REF] . This suggests that alterations in the intake of saturated relative to unsaturated fat over the last 100 years may have contributed to the decline in BEE reported here, although clearly we should be cautious about extrapolations from males of a single inbred mouse strain and further studies in humans are required. Moreover, other aspects of the diet that impact metabolic rate may also have changed over time, for example intake of fiber which has declined in recent years [START_REF] Schols | Measurement of resting energy expenditure in patients with chronic obstructive pulmonary disease in a clinical setting[END_REF] and has been shown in a randomized controlled trial to affect resting metabolic rate [START_REF] Thorne | Thermogenic response to intravenous nutrition in patients with cirrhosis[END_REF] .

Strengths and limitations

A strength of this study is the large sample of individuals over a restricted geographical area (US and Europe) measured using a complex methodology. This has allowed us to detect a small but nevertheless biologically meaningful signal. However, it is important to be aware that the studies were not designed with the current analysis in mind. Hence while we have adjusted for differences in age and body composition there may be other factors that differed over time that we did not adjust for and that could explain the trends we found. Further, the participants recruited at different time points may not have been representative of the underlying populations, even though the overall distribution seems representative (Fig S1). The data are cross-sectional which limits the inferences that can be made regarding causality in the associations. Finally, while we have speculated on some potential factors that might have contributed to the reduction in BEE (i.e. immune function and diet), these factors were not quantified in most of the participants who had their TEE measured. The mouse work we performed showing potential links of diet to metabolism was only conducted in males of one strain and a single age and may not be more broadly applicable. These potential mechanisms therefore remain speculations until more direct data can be collected.

Conclusion

Overall our data show that there has been a significant reduction in adjusted TEE over the last three decades, which can be traced to a decline in BEE rather than any reduction in AEE linked to declining physical activity levels. Indeed, our data show that AEE has significantly increased over time. Reductions in BEE extend much further back in time (TEE data do not extend further back than 1981 as that was the first year the DLW technique was applied to humans), and mouse data indicated that one of many possible explanations is decreases in the intake of saturated relative to unsaturated fatty acids. If the decline in BEE over time has not been compensated for by a parallel reduction in net energy intake then the energy surplus resulting would be deposited as fat. This study therefore identifies a novel potential contributor to the obesity epidemic, that has not been previously recognized: a decline in adjusted BEE linked to reduction in overall adjusted TEE. Further understanding the determinants of BEE and the cause of this decline over time, and if it can be reversed, are important future goals.

Requests for materials

Correspondence should be directed to Dr John Speakman (j.speakman@abdn.ac.uk). Forms for requesting data from the IAEA DLW database can be found at www.dlwdatabase.org and should be directed to the lead corresponding author j.speakman@abdn.ac.uk or Dr Alexia Alford at the IAEA (a.alford@iaea.org). Requests for the reviewed BMR data should be directed to Dr Anura Kurpad (a.kurpad@sjri.res.in). Requests for the mouse data should be directed to Dr Matthew Rodeheffer (matthew.rodeheffer@yale.edu). the fitted least squares regression fits. For regression details refer to text and Table 1. the fitted least squares regression fits. For regression details refer to text and Table 1. Significance of the relationships is also shown. p > .01 was considered not significant (ns). race), pregnant and lactating females and individuals with specific disease states. In total this resulted in 4799 measurements across both sexes. Submissions to the database did not reveal whether the sex was self-reported or assigned. Although an option was available to designate individuals as trans-sexual, none of the submitted data were identified as such. Gender was not available from the submitted data. Estimates of TEE were recalculated using a common equation [START_REF] Lewis | Further considerations of the effect of altitude on basal metabolism: A study of young women residents of Denver[END_REF] which has been shown to perform best in validation studies. The final data set included 1672 measurements of males and 3127 measurements of females.

Males

For 632 of the males and 800 of the females we also had measurements of basal metabolic rate (BMR) measured by indirect calorimetry. BMR measurements were derived either from hood calorimetry or from minimal metabolic rate determined overnight during chamber calorimetry (strictly sleeping metabolic rates or SMR). We converted these BMR or SMR to estimates of basal energy expenditure (BEE). BMR and SMR are measured for relatively short periods lasting 30 minutes to an hour. BEE is a theoretical value for the energy expenditure that would pertain if this BMR/SMR measurement was sustained for 24h. For those individuals with measurements of both BEE and TEE we estimated activity energy expenditure (AEE = (0.9*TEE)-BEE), and the physical activity level (PAL = TEE/BEE). The value 0.9 in the equation for AEE assumes the thermic effect of food (TEF) is 10% of the total energy expenditure. In practice this varies between individuals and is dependent on the diet. Variation is introduced therefore by imprecision in this value. However, since the TEF is largely dependent on protein in the diet, and protein intakes have remained stable over the last 40 or so years there is unlikely to be any systematic imprecision in the value that could affect the detected trends. It is important to note that TEE and BEE are both measured directly, while AEE is only inferred from the difference between the two. The accuracy and precision of TEE relative to chamber indirect calorimetry for the equation utilized here was estimated at 0.4% (accuracy) and 7.7% (precision) [START_REF] Lewis | Further considerations of the effect of altitude on basal metabolism: A study of young women residents of Denver[END_REF] . The accuracy and precision of estimates of basal metabolic rates of metabolism inferred by indirect calorimetry has been evaluated using alcohol burns and is estimated at around 1-2%. Error in the estimate of AEE by subtraction is considerably higher than the direct estimates of TEE and BEE [START_REF] Franssila-Kallunki | Factors associated with basal metabolic rate in patients with type 2 (non-insulin-dependent) diabetes mellitus[END_REF] .

The DLW method is based on the differential elimination of isotopes of oxygen and hydrogen introduced into the body water 13 . The details of the practical implementation of the method and its theoretical basis have been previously published. We recently derived a new equation for the calculation of CO2 production using the technique [START_REF] Lewis | Further considerations of the effect of altitude on basal metabolism: A study of young women residents of Denver[END_REF] and recalculated the entries in the database using this common equation. These were then converted into energy expenditure using the Weir equation [START_REF] Leenen | Visceral fat accumulation in obese subjects: relation to energy expenditure and response to weight loss[END_REF] with food quotients derived from the original studies.

Additional characteristics of the subjects (body mass (BM), age, and sex) were measured using standard protocols. We estimated the fat-free mass (FFM) of individuals using the estimated total body water and an assumed hydration constant for lean tissue of 0.73 (ref 45) and then calculated fat mass by difference (FM = BM-FFM). The date of the measurement was expressed in months relative to January 1982 which was the first year that the DLW method was applied to human subjects.

In the first set of analyses we used the unadjusted measures of TEE, BEE and AEE as dependent variables in general linear models with time since January 1982 as the predictor. Tests were two-sided and p < .05 was taken as significant. All analyses were performed using Minitab v19.

It is well established that TEE, BEE and AEE depend on body composition, as well as subject age.

Patterns of variation in unadjusted values with time might then reflect biased population sampling with respect to these traits. For example, if more older subjects were sampled later in the time course this might give a spurious indication that TEE was declining since all EE parameters decline after ~60y [START_REF] Schneeweiss | Energy metabolism in acute hepatic failure[END_REF] . We adjusted (logarithmically) TEE, BEE and AEE using loge FFM, loge FM and age as the predictor variables using general linear modelling. Analyses were run separately for each sex therefore no adjustment for sex was necessary. In both sexes, for loge BEE, the predictors age, loge FFM and loge FM were all significant but for loge TEE and loge AEE, only age and Loge FFM were significant. In the latter cases we deleted the non-significant predictor and re-ran the analyses. Following the above procedure we then calculated the residuals to the fitted models and added them back to the mean logged TEE, BEE and AEE across all measurements. These values were then converted back to measures of 'adjusted TEE', 'adjusted BEE' and 'adjusted AEE' by taking the exponent of the derived values. We then checked that the residuals were normally distributed and the adjusted variables were not significantly related to any of the predictor variables to ensure that the adjustment was adequate. Tests applied were two-sided and p < .05 was taken as significant. We then sought relationships between the adjusted variables and date of measurement using linear regression.

The adjusted variables cover a narrower time span from 1990 to 2017.

Sensitivity analyses

We performed several checks on the data to make sure the trends were not being driven by individual studies. First there were some small studies in males prior to 1987 that may have exerted undue leverage in the analysis. We therefore excluded these data and reran all the regressions (Table S1). There were no significant changes in any of the parameters. Since the downward trend in BEE was the most important new finding we directed particular attention to this trend.

To evaluate if the male BEE data would be better fit by a more complex model than the linear model we used, we included higher order terms of the date into a regression analysis. In this analysis the r 2 explained by date, date 2 and date 3 was increased relative to just including date alone. However, the variance inflation factors (VIF) for these more complex models were enormous. When date and date 2 were included the VIF for each variable was 28.9, and when all 3 were included the VIF values were 438 for date, 2084 for date 2 and 663 for date 3 . The usual VIF cut-off for deciding whether to include an extra term into a model is 5. In this case it was clear that higher order terms were not justified relative to a simple linear model.

We performed a general linear model analysis with date as a covariate and study as a factor in the model. In males when we used such a model there was indeed a large study effect (F = 12.97, p < 10 -15 ) but the effect of date remained highly significant (F= 22.87, P < 10 -8 ) and strongly negative (coefficient = -1.85 MJ/d over 30 years), exceeding that in the original analysis. In females there was also a strong study effect (F = 9.54, P < 10-12) but the effect of date remained non-significant (F = 12.9, P = 0.256).

Using the post 1987 data we then systematically removed the data for each study and reran the analyses to see if any particular study exerted undue effects on the regression. The analyses are summarized in Table S2. This analysis showed that no individual study was responsible for the negative relationship. In all cases the relationship between BMR and time remained negative and highly significant. A single study (number 23 in 1991) involved relatively high BMR values and so omitting it reduced the coefficient and the significance. But the p value for the regression when omitting these data was still highly significant P < 10 -5 , and the coefficient still strongly negative and biologically important.

We then turned our attention to the female data for BEE against date to see if the absence of a relationship there might be because of inclusion of any particular study. We used the same leave one out procedure as used for the males. The results are shown in Table S3. In this case the pattern was very different in that the relationship was always not significant (P > 0.1), except when a single study (study 65) was removed from the analysis, and in that case the relationship became significant (P = 0.001) and the negative gradient (extrapolated to over 30 years) increased to -0.39 MJ/day. Omitting a second study (n = 69) has a smaller effect that also resulted in the relationship becoming marginally significant (p = 0.037). If both studies 65 and 69 were omitted (not shown in table) the p value for the relationship fell to P < 10 We have no objective reason to reject these data but it is interesting that the anomalous absence of a negative relationship of BMR to time in the females is dependent only on inclusion of this one study. It is worth noting that excluding this study from the male data strengthened the relationship for males (Table S2).

Systematic review of BMR trends

This systematic review was registered at PROSPERO, 2021 (CRD42021270242) and executed per PRISMA guidelines (see supplementary Fig S6). The details of search strategy, data extraction and analysis were as follows.

Search Strategy

A literature search was carried out on PubMed using the following keywords: "Basal metabolic rate", "BMR", "basal energy expenditure", "BEE", "resting metabolic rate", "RMR", "resting energy expenditure", "REE", "Indirect calorimetry", "open circuit indirect calorimetry", "ventilated hood indirect calorimetry", "healthy", "apparently healthy". Papers published in English from 1900 to 2021 were considered. Cross-references from these papers were also evaluated.

Eligibility and study selection

Papers reporting the measurement of BMR (or BEE) or resting metabolic rate (RMR) on healthy male and female adults, aged 18-65 years, across different body mass index (BMI) were included. Additionally, only studies on either the American or European population were considered. Data were abstracted from cross-sectional studies as well as randomized controlled trials (RCTs). For RCTs, the data collected for control group, which met our inclusion criteria, were included. Studies on infants, children, adolescents, elderly, moderate or heavy activity workers, soldiers and athletes were excluded. The study selection was carried out in two levels: first, articles were included based on their title and abstract; second, the included articles were again filtered based on a reading of their full text. Finally, 163 studies were included into the analysis, as listed in the references below.

Data Extraction

From the selected articles, data on age, sex, geography, population/ethnicity, sample size, weight, height, body mass index, physical activity level, fat mass, fat free mass, method of measuring BMR or RMR, measured value for BMR or RMR, and the instrument error or intra-individual variability were extracted where available. When raw data were available in some papers, the individual data was included in the analysis. When BMR or RMR was reported as kcal or KJ per day, it was converted to MJ per day. When the BMR or RMR data was provided as per kg body weight or fat free mass (FFM) or per square meters surface area, then it was converted to MJ per day by multiplying the respective body weight, FFM or surface area. Finally, assuming that RMR is 10% higher than BMR, any reported RMR data were converted to BMR as 0.9 x RMR.

Where mean for age and weight was not provided in the paper [11,21,23,75,80,93,146,154], but range or dispersion indicators of age and weight (minimum/maximum or 95% CI or IQR) were provided, the middle values of these were transformed to obtain the mean values. In some papers, rather than body weight, FFM and fat mass (FM) values were reported; here, these were added to obtain the body weight [47,84,93,151,159]. When SD or SE of weight was provided, these were converted to 95% CI. If FFM and body weight were reported, the FM was calculated as a difference between body weight and FFM.

The data were extracted, sorted, coded and entered into an Excel workbook; statistical analyses were carried out on R version 4.1.0 (R Core Team, 2021, Vienna, Austria). A weighted regression analysis of loge BMR (MJ) on time was performed where loge (body weight), age and sex were considered as covariates along with regression weights assigned to studies based on their study size (n). added to 50 μL of total lipid extract, and samples were taken to dryness under N2 gas. Dried samples were immediately resuspended in 500 μL of 100% ethanol, saponified with 500 μL of 1 M NaOH at 90 °C for 45 min in Teflon capped tubes, and then acidified by addition of 525 μL of 1 M HCl. Saponified FA were re-extracted using 1 mL of isooctane (twice), dried under N2 gas, and were derivatized as above. The pentafluorobenzyl FA esters were resuspended in 200 μL of isooctane and diluted 1:10 into isooctane into GC/MS autosampler vials for injection. Analyte data were acquired in NICI full scan, the FA-analyte peak area ratio to that of its corresponding stable isotope reference FA was calculated for each analyte, and ratios were converted to absolute amounts relative to regression curves for each chain length and saturation [START_REF] Svendsen | Impact of regional and total body composition and hormones on resting energy expenditure in overweight postmenopausal women[END_REF][START_REF] Hugli | Diet-induced thermogenesis in chronic obstructive pulmonary disease[END_REF] . Total SFA, MUFA and PUFA was the quantitative sum of the nmoles of the class of fatty acid measured.

Quantitative FA data were normalized to the total mass of diet input to the lipid extraction (i.e. mg FA / g diet). Dietary FA amounts are listed in Table S4. Dietary FA intake (in mg) was calculated by multiplying dietary FA amounts (mg/g) by the amount of diet consumed (g). S3: Impact of removing individual studies on the relationship between female BEE and date. Individual studies are identified by the code for the study is that in the database. N is the number of samples omitted. Dates covered are the months since jan-82 that were involved in the particular data set. Coefficient is the fitted linear regression coefficient converted to the change in MJ/d over 30 years. r2x100 is the explained variation (%) by time since 1987. F and p are the regression statistics for the time effect. ns is P > 0.1. when P < 0.1 the exact p is quoted.

Supplementary figures

Study

  years with the first measurements in late 1981 and the latest measurements made in late 2017, with most data obtained between 1990 and 2017. The distribution of BMI in the sample for both males and females is shown in Supplementary Fig S1. Overall females had higher BMI than males. In the pooled sample the distribution was BMI < 18.5 = 2.3%, BMI 18.5 to 25 = 40.3%, BMI 25 to 30 = 35.1% and BMI >30 = 22.2%. Combined overweight and obesity was 57.3%. In both males and females body weight increased over time (Figure S1) reflecting the secular trend in body weight over the same interval.

  S2a) the least squares regression fit gave a gradient of +1.5 kJ/month (95%CI = ±2.06 kJ/month). This gradient leads to an estimated change in average TEE over30 years of + 0.55 MJ/day (95%CI = ±0.727 MJ/day). Contrasting the lack of significant change in TEE, there was a significant decline in BEE over time (Fig S2b) (r 2 = 0.029, p = 0.000018). The gradient of decline (3.3 kJ/month, 95%CI = ±1.4 kJ/month) was equivalent to an average fall in BEE by 1.19 MJ (9.7%) over 30 years (95%CI = ±0.54 MJ/day). As might be anticipated since TEE*0.9 = BEE + AEE, the absence of a change in TEE and declining BEE was reflected by an increase in AEE over time, but this did not reach significance (Fig S2c) (r 2 = 0.003, p = 0.16). The gradient of the change in AEE (1.4 kJ/month, 95%CI = ±1.8 kJ/month) was equivalent over 30 years to an increase by 0.50 MJ/day (95%CI = ±0.69 MJ/day). In females, unadjusted levels of TEE, BEE and AEE did not change significantly over time (supplementary Fig S3, Table1).All the energy expenditure variables (TEE, BEE and AEE) in both sexes were dependent on body mass (BM) and BMI (illustrated for BMI in supplementaryFig 4). Because of these relationships it is necessary to adjust the raw expenditure data over time (FigsS2 and S3) to account for any changes in body composition over time that might generate a biased estimate of the change in expenditure variables. We adjusted the levels of log transformed TEE, BEE and AEE for body size and composition using residuals from general linear models with loge fat-free mass, loge fat mass and age as predictors. In this analysis the data were logged because the relationships between energy expenditure components and body composition follow power law relationships. In males, adjusted TEE significantly declined over the measurement period (Fig 2a: r 2 = 0.0103, p < .0001). The gradient of the fitted regression was -32.5 kJ/month (95%CI = ±1.20 kJ/month) leading to an estimated average change over 30 years of -0.93 MJ/day in adjusted TEE (95%CI = ±0.465 MJ/day), a decline on average of 7.7%. The adjusted BEE showed a highly significant decline over time (Fig 2b: r 2 = 0.064, p < 10 -9 ) with the gradient of 2.67 kJ/month (95%CI = ±0.82 kJ/month) being equivalent to an average fall in BEE of 0.96 MJ/day (14.7%) over 30 years (95%CI = ±0.15 MJ/day). In contrast, the adjusted AEE increased over time (Fig 2c: r 2 = 0.0221, p < .0003). The gradient of +2.8 kJ/month (95%CI = ±1.4 kJ/month) was equivalent to a rise of 1.01 MJ/day over 30 years (95%CI = ±0.53 MJ/day).

r 2

 2 = 0.006, p < .00002). The gradient of the effect 1.42 kJ/month was equivalent to a reduction in TEE over 30 years of 0.51 MJ (95%CI = ±0.22 MJ/day) or 5.6%. This decline was paralleled by a reduction in adjusted BEE of 2.0% but this did not reach significance (Fig 3b: r 2 = 0.0015, p > .05). The gradient of the fall in adjusted BEE was 0.3 kJ/month, equivalent to a reduction in adjusted BEE over 30 years of 0.11 MJ/day (95%CI = ±0.21 MJ/day). In contrast, and again similarly to the males, adjusted AEE significantly increased over time (Fig 3c: r 2 = 0.0063, p = 0.026). The gradient of increase in AEE of 1.16 kJ/month was equivalent to an increase in AEE of 0.42 MJ/day over 30 years (95% CI = ± 0.37 MJ/day).

(

  Males supplementary Fig S5a: r 2 = 0.0215, p < .0003) but in females the change in PAL over time was not significant (females supplementary Fig S5b: r 2 = 0.0037, p = 0.085).

- 11 :

 11 Fig 4A). We included the total intake of different fatty acids (SAT: saturated fatty acids, MUFA: monounsaturated fatty acids and PUFA: poly-unsaturated fatty acids) with body weight into a general linear model. Only intake of saturated fatty acids was significant (SAT: F = 11.05, p = 0.002 (Fig 4B); MUFA: F = 1.38, p = 0.245; PUFA: F = 0.17, p = 0.686) with higher levels of SAT linked to higher energy expenditure (Fig 4B).

Figure 1 :

 1 Figure 1: Trends over time in a) adjusted total energy expenditure, b) adjusted basal energy expenditure, and c) adjusted activity energy expenditure for males. Adjustments were made for body composition (fat and fat-free mass or body mass, and age) -see methods for details. All expenditures are in MJ/d and time is expressed in months since January 1982. Significant years are also indicated. Each data point is a different measurement of expenditure. The red lines are

Figure 2 :

 2 Figure 2: Trends over time in a) adjusted total energy expenditure, b) adjusted basal energy expenditure, and c) adjusted activity energy expenditure for females. Adjustments were made for body composition (fat and lean mass and age) -see methods for details. Significant years are also indicated. All expenditures are in MJ/d and time is expressed in months since January 1982. Each data point is a different individual measurement of expenditure. The red lines are

Figure 3 :

 3 Figure 3: A: effect of loge body mass on the loge basal metabolic rate (BMR) in a systematic review of 165 studies dating back to the early 1900s (first study 1919). Data for males in blue and for females in red. Studies with mixed male and female data not illustrated. B: Bubble plot showing the Residual loge Basal metabolism derived from a weighted regression of loge BMR against sex, age and loge (body mass) plotted against date of measurement in the same 165 studies. Bubbles represent the sample size of the studies. The red line is the fitted weighted regression.

Figure 4 :

 4 Figure 4: A: the relationship between body weight and metabolic rate in the mice fed different diets with variable fatty acid compositions. B: the effect of saturated fatty acid intake on residual metabolic rate -corrected for body weight.

  Figure 1

B

  Figure 3

  -5 and the gradient was -0.59 MJ/d. Study 65 was a study of overweight individuals by Camps et al (2013).

Figure S1 :Figure S2 :

 S1S2 Figure S1: Distribution of BMI in the sample data for a) females and b) males. Trends in body weight over the interval from 1982 to 2017 for c) males and d) females. There was a significant increase in weight over time in both sexes. For males (gradient = 0.015 kg/month F = 7.04, p = 0.009) reflecting an average weight increase of 5.4 kg over 30 years, and for females (gradient = 0.023 kg/month F 20.84, p = 0.000005) reflecting an average increase of 8.3 kg over 30 years Figure S2: Trends over time in a) unadjusted total energy expenditure, b) unadjusted basal energy expenditure, and c) unadjusted activity energy expenditure for males. All expenditures are in MJ/d and time is expressed in months since January 1982. Significant years are also indicated. Each data point is a different measurement of expenditure. The red lines are the fitted least squares regression fits. For regression details refer to text and Table1.

Figure S3 :

 S3 Figure S3: Trends over time in a) unadjusted total energy expenditure, b) unadjusted basal energy expenditure, and c) unadjusted activity energy expenditure for females. All expenditures are in MJ/d and time is expressed in months since January 1982. Significant years are also indicated. Each data point is a different measurement of expenditure. The red lines are the fitted least squares regression fits. For regression details refer to text and Table1.

Figure S4 :

 S4 Figure S4: Relationships between energy expenditure parameters and Body mass index (BMI). In females the relationships were: for TEE vs BMI (F = 559.3, p < 10 -16 ), TEE vs BM (F = 1163, p < 10-16), BEE vs BMI (F = 242.6 , p < 10 -16 ) BEE vs BM (F = 341.1, p < 10 -16 ), AEE vs BMI (F = 45.13, p < 10 -10 ) and AEE vs BM (F = 91.08, p < 10 -16 ). For males the relationships were: for TEE vs BMI (F = 114.6, p < 10 -16 ), TEE vs BM (F = 302.3, p < 10 -16 ), BEE vs BMI (F = 79.4, p < 10 -16 ) BEE vs BM (F = 341.1, p < 10 -16 ), AEE vs BMI (F = 16.28, p = 6 x 10 -5 ) and AEE vs BM (F = 53.19, p < 10 -14 ).

Figure S5 :

 S5 Figure S5: Trends over time in Physical Activity Level (PAL = TEE/BEE). PAL is dimensionless and time is expressed in months since January 1982. Significant years are also indicated. Upper plot is for males and lower is for females. The red lines are the fitted least squares regression fits.For regression details refer to text.

Figure S6 :

 S6 Figure S6: Systematic review strategy. Flow diagram for selection of studies according to PRISMA guidelines.

  Figure S1 a) b)

  36. Van Marken Lichtenbelt, W.D.. Mansink, R.P. and Westerterp, K.R. (1997) The effect of fat composition of the diet on energy metabolism. Z. ernharnungs. 36: 303-305. 37. Katz, M. et al (2009) Dietary n-3-polyunsaturated fatty acids and energy balance in overweight or moderately obese men and women: a randomized controlled trial. Nutr. and metab. 6: e24 38. De Meira, J.E.C., et al (2018) Unsaturated fatty acids do not have a favourable metabolic response in overweight subjects: results of a meta-analysis. J. Functional foods. 43: 123-130. 39. Haggerty, C. et al (2008) Intra-specific variation in resting metabolic rate in MF1 mice is not associated with membrane lipid desaturation in the liver. Mech Ag. Dev 129: 129-137. 40. Brzek et al. (2007) Anatomic and molecular correlates of divergent selection for basal metabolic rate in laboratory mice. Physiological and Biochemical Zoology, 80: 491-99. 41. McGill, C.R. et al (2015) Ten-year trends in Fiber and whole grain intakes and food sources for the United states population: national health and nutrition examination survey 2001-2010. Nutrients 7: 1119-1130. 42. Karl, J.P. et al (2017) Substituting whole grains for refined grains in a 6-wk randomized trial favorably affects energy-balance metrics in healthy men and postmenopausal women. Am. J. Clin. Nut. 105: 589-599. References only in the methods 43. Speakman, J.R. and Pontzer, H. (2023: in review) Quantifying physical activity energy expenditure based on doubly-labelled water and basal metabolism calorimetry: what are we actually measuring? Current opinion in clinical nutrition and metabolic care 44. Weir, J.B. (1949) new methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 109: 1-9. 45. Wang, Z.M., Deurenberg, P., Wang, W., Pietrobielli, P., Baumgartner, R.N. and Heymsfield, S.B. (1999) Hydration of fat-free body mass: review and critique of a classic body composition constant. Am J. Clin Nut. 69: 833-841. 46. Pontzer, H. et al (2021) Daily energy expenditure through the human life course. Science 373: 808+ 47. Rudolph, M. C., Wellberg, E.A., Lewis, A.S., Terrell, K.L., Merz, A.L., Maluf, N.K., Serkova, N.J. and Anderson, S.M. (2014) Thyroid hormone responsive protein Spot14 enhances catalysis of fatty acid synthase in lactating mammary epithelium. J Lipid Res 55: 1052-1065 48. Rudolph, M. C., Karl Maluf, N., Wellberg, E.A., Johnson, C.A., Murphy, R.C. and Anderson, S.M.

(2012)

Mammalian fatty acid synthase activity from crude tissue lysates tracing (1)(3)C-labeled substrates using gas chromatography-mass spectrometry. Anal Biochem 428: 158-166

Table S2 :

 S2 Impact of removing individual studies on the relationship between male BEE and date. Individual studies are identified by the code for the study is that in the database. N is the number of samples omitted. Dates covered are the months since jan-82 that were involved in the particular data set. Coefficient is the fitted linear regression coefficient converted to the change in MJ/d over 30 years. R2x100 is the explained variation (%) by time since 1987. And F and p are the regression statistics for the time effect.

	Table
	Study omitted	n	dates covered	coefficient	r2x100 F	p
	None	0		-0.912	5.47 35.3 < 10 -8
	10	12	191	-0.918	5.57 35.2 < 10 -8
	13	4	61	-0.952	5.81 37.3 < 10 -8
	17	4	97	-0.886	4.90 32.7 < 10 -8
	20	10	85	-0.964	5.82 37.0 < 10 -8
	22	8	92	-0.942	5.63 35.9 < 10 -8
	23	21	109	-0.653	3.02 18.4 < 10 -5
	24	10	96-99	-0.966	5.88 37.4 < 10 -8
	31	38	121	-0.794	3.96 23.6 < 10 -6
	32	26	145	-0.912	5.47 35.3 < 10 -8
	35	12	157	-0.960	6.07 38.6 < 10 -8
	36	10	138-171	-0.886	5.19 32.9 < 10 -8
	37	10	157	-0.892	5.29 33.5 < 10 -8
	40	19	183-185	-0.937	5.82 36.5 < 10 -8
	41	4	184	-0.901	5.38 34.5 < 10 -8
	43	30	193-197	-0.908	5.39 34.6 < 10 -8
	46	7	185-190	-0.875	5.36 34.1 < 10 -8
	49	30	205-214	-0.888	5.54 33.6 < 10 -8
	51	9	199-200	-0.905	5.42 34.4 < 10 -8
	55	10	235-236	-0.902	5.40 34.3 < 10 -8
	57	10	259-269	-0.906	5.40 34.3 < 10 -8
	59	12	241	-0.892	5.34 33.7 < 10 -8
	60	24	290	-0.972	6.03 37.6 < 10 -8
	62	18	191-314	-0.982	6.12 38.6 < 10 -8
	65	22	339-358	-1.140	7.79 49.7 < 10 -12
	69	18	373-380	-1.135	7.32 47.9 < 10 -12
	72	14	322-327	-0.998	6.28 39.9 < 10 -10
	74	190	199-305	-0.577	3.05 13.4 < 10 -4
	79	11	302-321	-0.925	5.52 34.9 < 10 -8
	129	25	266-269	-0.813	4.63 28.4 < 10 -7

Acknowledgements

The authors gratefully acknowledge funding to directly support this work as well as funding for the original studies that contributed to the database that are not listed individually here. In particular direct support grants CAS 153E11KYSB20190045 from the Chinese Academy of Sciences to JRS and grant BCS-1824466 from the National Science foundation of the USA to HP, are gratefully acknowledged. The mouse work was supported by grants from the Swedish Research Council International Postdoctoral Fellowship (VR 2018-06735) to JMAJ, NIH grants K01DK109079 and R03DK122189 to MCR, and grants R01DK090489 and R01DK126447 to MSR. AK is supported by the IA/CRC/19/1/610006 grant from the DBT-Wellcome Trust India Alliance. We are grateful to T. Goodrich for comments on earlier drafts of the manuscript. The DLW database, which can be found at https://www.dlwdatabase.org/, is also generously supported by the IAEA, Taiyo Nippon Sanso and, SERCON. We are grateful to these companies for their support and especially to Takashi Oono of Taiyo Nippon Sanso. The funders played no role in the content of this manuscript. Department of Physiotherapy, Faculty of Allied Health Sciences, General Sir John Kotelawala Defence University, Sri Lanka Dr William Dietz George Washington University, Washington DC, USA Dr Alice E. Dutman TNO Quality of Life, Zeist, The Netherlands Dr Simon D Eaton UCL, Great Ormond Street Institute of Child Health, London, UK Dr Cara Ebbeling Boston Children's Hospital, Boston, Massachusetts, USA. Dr Asmaa El Hamdouchi Unité Mixte de Recherche en Nutrition et Alimentation, CNESTEN-Université Ibn Tofail URAC39 Dr Sölve Elmståhl Lund University, Lund, Sweden Dr Mikael Fogelholm Dept of Food and Nutrition, Helsinki, Finland Dr Tamara Harris Aging, NIH, Bethesda, MD, Dr Marije B. Hoos Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Centre, Maastricht, Netherlands. Dr Rik Heijligenberg Academic Medical Center of Amsterdam University, Amsterdam, The Netherlands Dr Hans U. Jorgensen Bispebjerg Hospital, Copenhagen, Denmark Dr Noorjean Joonas Central health Laboratory, Ministry of Health and Wellness, Mauritius Dr Kitty P. Kempen Maastricht University, Maastricht, The Netherlands Dr Misaka Kimura Institute for Active Health, Kyoto University of Advanced Science, Kyoto, Japan Dr Wantanee Kriengsinyous 61 Institute of Nutrition, Mahidol University, Thailand Dr Rebecca Kuryiyan Division of Nutrition, St. John's Research Institute, Bangalore, India Dr Estelle V. Lambert Health through Physical Activity, Lifestyle and Sport Research Centre, Division of Exercise Science and Sports Medicine (ESSM), FIMS International Collaborating Centre of Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town Dr Pulani Lanerolle Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Colombo, Sri Lanka Dr Chystel L. Larsson University of Gothenburg, Gothenburg, Sweden Dr Nader Lessan Imperial College London Diabetes Centre, Abu Dhabi, United Arab Emirates and Imperial College London, London, United Kingdom Dr David S. Ludwig Boston Children's Hospital, Boston, Massachusetts, USA. Dr Margaret McCloskey Royal Belfast Hospital for Sick Children, Belfast, Northern Ireland Dr Alida Melse-Boonstra Wageningen University, Wageningen, Netherlands Dr Gerwin A. Meijer Maastricht University, Maastricht, The Netherlands Dr James C. Morehen Research Institute for Sport and Exercise, Liverpool John Moores University, Liverpool, UK. Dr James P Morton Research Institute for Sport and Exercise, Liverpool John Moores University, Liverpool, UK. Dr Aviva Must Tufts University School of Medicine, Boston, USA Dr Christine D. Nystrom Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, Sweden Dr Daphne L. Pannemans Maastricht University, Maastricht, The Netherlands

Data Availability

With respect to the IAEA database and the meta-analysis of BMR data this work comprises a secondary analysis of data that are mostly already published and available in the primary literature. These data have been compiled into a database, access to which is free. Forms for requesting data can be found at www.dlwdatabase.org and should be directed to the lead corresponding author j.speakman@abdn.ac.uk or Dr Alexia Alford at the (a.alford@iaea.org). The BMR data are available upon request to co-corresponding author Dr Anura Kurpad (a.kurpad@sjri.res.in). The mouse data described in the paper are available upon request to co-corresponding author Dr Matthew Rodeheffer (matthew.rodeheffer@yale.edu).

Author contributions

JRS, KRW and LH processed and analysed the IAEA data, JMAdJ, JLK, and MCR collected, processed and analysed the mouse data, SS, SG, JRS and AK collected and analysed the retrospective BMR data from the literature. JRS, YY, HS, PNA, LFA, LJA, LA, IB, KBA, EEB, SB, AGB, CVCB, PB, MSB, NFB, SGJAC, GLC, JAC, RC, SKD, LRD, UE, SE, TF, BWF, AHG, MG, CH, AEH, MBH, SH, NJ, AMJ, PK, KPK, MK, WEK, RFK, EVL, AML, WRL, NL, CKM, ACM, EPM, JCM, JPM, MLN, TAN, RMO, HP, KHP, YPP, JPR, GP, RLP, RAR, SBR, DAR, ER, LMR, RMR, JR, SBR, MR, DAS, AJS, AMS, ES, SSU, GV, LMvE, EAvM, JCKW, GW, BMW, WWW, JAY, TY, XYZ contributed data to the database. JRS, YY, HS, SS, AJMM, CU, AHL, HP, JR, DAS and WWW created, curated and administered the database.

Conflict of interest

The authors have no conflicts of interest to declare.

Supplementary materials for

Speakman et al Total daily energy expenditure has declined over the last 4 decades due to declining basal expenditure not reduced activity expenditure.

Contents

1. Materials and Methods 2, Supplementary figures 3. Supplementary tables 4. group authorship details.

Materials and methods

This study involves in part a retrospective analysis of data submitted to the IAEA DLW database (www.dlwdatabase.org). The data stretch back to the late 1980s, however, the clinical trials registry was only launched by the NIH in February 2000, hence, there was no possibility to preregister the work before data collection started. Nevertheless, the analysis performed here was pre-registered on the IAEA DLW database website in 2020 (https://doubly-labelled-waterdatabase.iaea.org/dataAnalysisPlanned).

DLW database study

Data were extracted from the IAEA Doubly Labeled Water (DLW) Database S5). After 4 weeks of HFD feeding, mice were housed in a TSE

PhenoMaster system for 4 days. Data from the final 72 hours were used for calculations. Oxygen (O2) consumption (mL/h), carbon dioxide (CO2) production (mL/h) and food intake (g) were recorded every 30 minutes. Energy expenditure (kJ/h) was calculated using the Weir Equation 44.

Respiratory exchange ratio (RER) was calculated as vCO2/vO2.

Preparation of samples for GCMS

For mouse diets, approximately 40-50 mg of pulverized diet was weighed and dissolved in 0.5 mL of pure water, acidified with 10 μL of 1 M HCl, and 1 mL of 100% methanol was added. Diet samples were mechanically homogenized to a uniform slurry. Total lipid extraction was performed on all samples as previously described [START_REF] Svendsen | Impact of regional and total body composition and hormones on resting energy expenditure in overweight postmenopausal women[END_REF] . 1.5 mL of isooctane/ethyl acetate 3:1 v/v was added, vortexed vigorously, the organic phase was collected, and this step was repeated.

The two volumes of organic phase were combined and taken to dryness by evaporation under nitrogen gas at 40°C. Samples were resuspended in 300 μL of isooctane/ethyl acetate 3:1 v/v.

The diet samples were subsequently diluted 1:200 into isooctane/ethyl acetate 3:1 v/v.

Fatty acid quantification by GCMS

Individual stable isotope fatty acid (FA) stock solutions were made in isooctane/ethyl acetate 3:1 v/v, a mixture containing 1.0 μg/μL of every FA was made in isooctane/ethyl acetate 3:1 v/v that was further diluted to 50 ng/μL, and stable isotope reference FA regression curves were prepared 47.48 . For total FA composition, 500 ng of the blended internal reference standard was

Supplementary tables

Group authorship details

This group authorship contains the names researchers who submitted data to the database or did not assent inclusion to the main authorship because they felt their contribution was not sufficient to merit authorship, or their specific data was not used in the present analysis (eg pediatric data). It also includes some people whose data were contributed into the IAEA DLW database by the analysis laboratory but they later could not be traced, or they did not respond to emails to assent inclusion among the authorship.


Coline Monchanin
email: coline.monchanin@gmail.com

Erwann Drujont

Gaël Le Roux

Philipp Lösel

Andrew B Barron

Jean-Marc Devaud

Arnaud Elger

Mathieu Lihoreau

Environmental exposure to metallic pollution impairs honey bee brain development and cognition

Keywords: Apis mellifera, trace metals, legacy pollution, PER conditioning, morphometry, micro-computed tomography scanning 5, tables: 1, boxes : 0

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Metals and metalloids are naturally found at low levels in the Earth crust and water as a result of volcanic eruptions and soil erosion. However, over the past century, human activities have raised environmental levels far above this baseline [START_REF] Han | Assessment of global industrial-age anthropogenic arsenic contamination[END_REF]. In particular, mining operations [START_REF] Demková | Laboratory bioassays on the impact of cadmium, copper and lead on the development and survival of honeybee (Apis mellifera L.) larvae and foragers[END_REF] and metal smelters have elevated concentrations of toxic pollutants such as arsenic, lead and mercury in superficial soils [START_REF] Su | A review on heavy metal contamination in the soil worldwide: situation, impact and remediation techniques[END_REF] and water [START_REF] Nordstrom | Worldwide occurrences of arsenic in ground water[END_REF] across large areas worldwide, exposing people and the wildlife to hazardous concentrations through inhalation of air and dust, and contaminated food, water, and soil (ATSDR, 2019). In humans, metals bind to proteins and enzymes, disrupting their activity and provoking cellular damage. They can cause neurotoxicity, oxidative stress, and carcinogenesis [START_REF] Azeh Engwa | Mechanism and health effects of heavy metal toxicity in humans[END_REF], leading to sublethal cognitive effects, such as sensory impairments, mood disorders or learning and memory deficits [START_REF] Sankhla | Heavy metals contamination in water and their hazardous effect on human health-a review[END_REF][START_REF] Wright | Metals and neurotoxicology[END_REF]. However, the consequences on the wildlife are much less understood.

Pollinators are key sentinel species that are exposed to trace metals [START_REF] Bromenshenk | Pollution monitoring of Puget Sound with honey bees[END_REF] in plant nectar and pollen [START_REF] Krunić | Honey resistance to air contamination with arsenic from a copper processing plant[END_REF] and water [START_REF] Li | Trends and health risks of dissolved heavy metal pollution in global river and lake water from 1970 to 2017[END_REF]. In honey bees, these elements contaminate honey [START_REF] Bastías | Honey as a bioindicator of arsenic contamination due to volcanic and mining activities in Chile[END_REF], wax [START_REF] Gajger | Variations in lead, cadmium, arsenic, and mercury concentrations during honeybee wax processing using casting technology[END_REF], propolis [START_REF] Maragou | Determination of Arsenic in Honey, Propolis, Pollen, and Honey Bees by Microwave Digestion and Hydride Generation Flame Atomic Absorption[END_REF] and are ultimately ingested by the larvae [START_REF] Cozmuta | Lead traceability along soilmelliferous flora-bee family-apiary products chain[END_REF]. Honey bees seem unable to detect field-realistic concentrations of metals [START_REF] Monchanin | Honey bees cannot sense harmful concentrations of metal pollutants in food[END_REF], and controlled exposure to metallic pollutants (alone or combined) leads to developmental and cognitive deficits [START_REF] Burden | Acute exposure to selenium disrupts associative conditioning and long-term memory recall in honey bees (Apis mellifera)[END_REF]Di et al., 2016;Monchanin et al., 2021aMonchanin et al., , 2021b)). Contact with high environmental levels of metallic pollutants may thus have severe consequences for population growth and the associated pollination services.

Here, we explored the sublethal effects of environmental exposure of honey bee colonies to trace metal pollutants in the field. We sampled foragers from apiaries along a pollution gradient surrounding a former gold mine (Salsigne, France; Fig. 1A-B.) and examined their morphological development, brain organisation and cognition. During its exploitation the site became contaminated mostly with arsenic, but also with lead, cadmium, copper, nickel and zinc. This contamination has persisted even after the closure and partial remediation of the site [START_REF] Khaska | Arsenic and metallic trace elements cycling in the surface water-groundwater-soil continuum down-gradient from a reclaimed mine area: isotopic imprints[END_REF][START_REF] Khaska | Innovative isotopic method to evaluate bioaccumulation of As and MTEs in Vitis vinifera[END_REF]. As the contaminant levels are well above the international permissible limits (for arsenic: up to 76 times higher in irrigation water [START_REF] Guerin | Arsenic speciation in some environmental samples: a comparative study of HG-GC-QFAAS and HPLC-ICP-MS methods[END_REF], 290 times higher in soil [START_REF] Koechler | Constitutive arsenite oxidase expression detected in arsenic-hypertolerant Pseudomonas xanthomarina S11[END_REF]), the Salsigne area is considered as "critically polluted" [START_REF] Oulès | Determination of pollution trends in an abandoned mining site by application of a multivariate statistical analysis to heavy metals fractionation using SM&T-SES[END_REF]. Hence monitoring human populations and wildlife in this area is of great importance.

Results

Bees closer to the mine accumulated higher levels of metals

We sampled bees from five apiaries within 11 km of the former gold mine, at different distances along an expected gradient of environmental metallic pollution (Fig. 1A-B). First, we verified this gradient by measuring trace elements (Al, As, Cd, Cu, Fe, Pb, Zn) in the bodies of bees using Inductively Coupled Plasma -Mass Spectrometry. To provide a single measure of overall pollution for each site, we performed a principal component analysis (PCA) using all metal concentrations (mg.kg -1 fresh matter) in bee bodies (Fig. 1C; Table S1) and used the first principal component (PC1) as a pollution index: high values corresponded to high contamination of bees with all the metals (Fig. 1D). Pollution indices greatly different between sites (F4,30=13.421, p<0.001; Table S1). Bees from site B had the highest metal content (mean score on PC1 ± s.e.m: 2.778 ± 0.632), followed by bees from site A (0.266 ± 0.350), while bees from site E had the lowest content (-1.630 ± 0.338) (Fig. 1D). Because the area is known to be highly polluted with arsenic, we also compared its levels across the sites and again found significant variation (F4,30=46.33, p<0.001).

Bees from sites A (0.350 ± 0.047 mg.kg -1 ) and B (0.977 ± 0.122 mg.kg -1 ) contained higher levels of As than to bees from the three sites further away (Site C: 0.043 ± 0.005 mg.kg -1 ; Site D: 0.052 ± 0.007 mg.kg -1 ; Site E: 0.067 ± 0.016 mg.kg -1 ) (pairwise comparisons: p<0.01; Fig. 1E). This confirms sites A and B were overall more contaminated than sites C-D, and in particular with arsenic.

Bees closer to the mine showed lower learning performances

Next, we conducted cognitive experiments. We trained bees caught in each of the five apiary to associate an odorant to a sucrose reward using olfactory proboscis extension response (PER) conditioning [START_REF] Matsumoto | Revisiting olfactory classical conditioning of the proboscis extension response in honey bees: A step toward standardized procedures[END_REF]. Such associative learning is critical for bees to recognise flowers, specialise on them and forage efficiently [START_REF] Giurfa | Cognition with few neurons: higher-order learning in insects[END_REF].

Prior to conditioning, we tested all bees for an intact proboscis extension reflex upon antennal stimulation with 50% (w/v) sucrose solution. The proportions of responding bees were similar among sites (GLMM: p=0.993) (Site A: 97.8%, N=136; Site B: 98.5%, N=135; Site C: 97.9%, N=140; Site D: 98.3%, N=121; Site E: 100%, N=144). Therefore, site location did not affect appetitive motivation nor sucrose perception by bees.

We then trained 673 bees in a five-trial absolute learning task, during which we recorded conditioned responses to an odour. A similar small proportion of bees from each site spontaneously responded at the first odour presentation and was therefore discarded (N=42; GLMM: p=0.336).

When considering the 631 remaining bees, learning was observed in all sites (Fig. 2A). However, by the last conditioning trial, a lower proportion of bees from sites A (GLMM: -2.130 ± 0.777, p=0.006) and C (-1.572 ± 0.780, p=0.044) had learned the task, as compared to those from the farthest site E (A: 56.45%, B: 73.39%, C: 68.94%, D: 78.90%, E: 91.55%). When analysed individually (Fig. 2B), we found that bees from site A had lower acquisition scores (total number of conditioned responses across all conditioning trials) than bees from site B (GLMM: -1.018 ± 0.477, p=0.033), site D (-1.055 ± 0.489, p=0.031) and site E (-1.287 ± 0.581, p=0.027). There was no effect of the odorant used for conditioning (limonene or eugenol) on bee responses (GLMM: p=0.141). Thus, bees from apiaries closer to the former mine had lower learning performance. and inconsistent or absent responses (white). Letters indicate significant differences between sites for each response pattern.

Bees closer to the mine showed reduced short-term memory specificity

Using the same bees, we next assessed one-hour memory recall by recording conditioned PER response to odorants, without sucrose reward. In addition to the odorant used during training (conditioning stimulus, CS), we tested bees with a novel odorant to evaluate the specificity of memory [START_REF] Urlacher | Measurements of chlorpyrifos levels in forager bees and comparison with levels that disrupt honey bee odor-mediated learning under laboratory conditions[END_REF]. Among bees that had effectively learnt the task (N=468), onehour memory recall of the odour-reward association differed between sites (GLMM: p=0.002).

Bees from sites B and C responded less to the CS (68.13% and 64.85% respectively) than bees from site D (GLMM: -1.591±0.504, p=0.016 and -1.795 ± 0.500, p=0.003 respectively).

Additionally, bees from site A responded more to the novel odour than bees from sites D (GLMM:

1.196 ± 0.411, p=0.036) (Fig. 2C). The individual response patterns (Fig. 2D) showed a clear effect of site location on the proportion of bees displaying CS-specific memory (GLMM: p=0.005) (A: 21.43%, B: 35.16%, C: 30.77%, D: 58.14%, Site E: 47.69%), with significantly fewer specific responses recorded in bees from site A than from sites D and E (GLMM: resp. -1.625 ± 0.477, p<0.001; -1.210 ± 0.477, p=0.011); and in bees from site B than from sites C and D (resp. -1.162 ± 0.441, p=0.008 and -0.898 ± 0.422, p=0.033). Bees from site A also showed higher generalization of their response to the novel odour as compared to those from all other sites (GLMM: p=0.019; A: 58.57%, B: 32.97%, C: 34.07%, D: 33.72%, E: 34.62%). Thus, the closer the bees to the mine, the lower their memory specificity.

Bees closer to the mine showed reduced long-term memory

To further investigate memory impairments, we conducted a second experiment on bees sampled in contrasting conditions of metallic pollution: site A (high contamination) and site E (low contamination). We compared the performances of bees in short-term (1h) and long-term (24h) memories, the latter guiding foraging decisions and participating in communication between bees [START_REF] Farina | Social learning of floral odours inside the honeybee hive[END_REF]. To better capture the effect on olfactory generalization, we used two novel odours in addition to the CS, according to their degree of structural similarity: one perceptually similar (low generalization level) and one dissimilar (high generalization level) [START_REF] Urlacher | Measurements of chlorpyrifos levels in forager bees and comparison with levels that disrupt honey bee odor-mediated learning under laboratory conditions[END_REF].

Like in the first experiment, the proportions of bees initially exhibiting intact PER was similar between both sites (GLMM: p=1; A: 96.17%, E: 100%). After five conditioning trials, a lower proportion of bees from site A had learnt the task compared to site E (GLMM: -2.197 ± 0.866, p=0.011) (A: 62.00%, E: 92.52%). While they also exhibited a lower mean acquisition score (mean ± s.e.m: A: 2.27 ± 0.14, E: 3.22 ± 0.11), the difference was not significant (GLMM: -0.848 ± 0.526, p=0.107).

In the short-term memory retrieval test (Fig. 3A-B), bees from both sites, that had effectively learnt the task (N=192), responded similarly to the CS (A: 67.94%; E: 68.25%) (GLMM: 0.123 ± 0.579, p=0.846). However, bees from site A had more generalization responses to novel odours (Fig. 3A), although only significantly to the similar one (GLMM: 1.3231 ± 0.305, p<0.001) (similar: 69.89% vs. 40.40%; dissimilar: 16.13% vs. 3.03%). Patterns of individual responses confirmed the effect of the proximity to the mine on the selectivity of responses (Fig. 3B). Bees from site A displayed less CS-specific responses (12.90% vs. 37.37%; GLMM: -1.400 ± 0.387, p<0.001), generalised more to other odours, both similar (51.61% vs. 35.35%; GLMM: 0.668 ± 0.295, p=0.024) and dissimilar (16.13% vs. 3.03%; GLMM: 1.848 ± 0.795, p=0.020).

Thus, here also, bees closer to the mine exhibited higher levels of generalization and less specific memory.

When tested for long-term memory (Fig. 3C-D), bees from both sites responded in similar proportions to all three odours (Fig. 3C): the CS (A: 51.72%, E: 66.67%; GLMM: -0.630 ± 0.482, p=0.191), the similar odour (A: 48.28%, E: 39.39%; GLMM: 0.352±0.411, p=0.392) and the dissimilar one (A: 20.69%, E: 11.11%; GLMM: 0.803 ± 0.625, p=0.199). However, bees from site A displayed less CS-specific responses (Fig. 3D; A: 10.34%, E: 29.29%; GLMM: -0.865 ± 0.414, p=0.037) and more inconsistent or absent responses (A: 49.43%, E: 35.35%; GLMM: 0.850 ± 0.412, p=0.039). Generalization responses were equally frequent in bees from both sites, to the similar odour (A: 25.29%, E: 26.26%; GLMM: -0.224 ± 0.339, p=0.508) as well as to the dissimilar one (A: 14.94%, E: 9.09%; GLMM: 0.469 ± 0.696, p=0.501). Therefore, a shorter distance to the mine was associated with lower individual long-term memory specificity, as observed for short-term memory.

Bees closer to the mine had smaller heads

To assess the potential developmental impact of mine proximity on bee development, we measured five morphological parameters (Fig. 4A) on all the bees collected in the two experiments (N=1021). We assessed the effect of site location on overall morphology with a PCA including all parameters (Fig. 4B; Table S2). Two PCs discriminated overall larger bees vs. smaller bees (PC1) and bees with large and wide heads vs. short wing and femur (PC2) (PERMANOVA: Pseudo-F=10.923, p=0.001). Bees from site A were morphologically different from those from all other sites (pairwise PERMANOVA: p=0.01 for all comparisons). Their heads were not longer than those of other bees (F4,12.498=1.224, p=0.354; Fig. 4C), but significantly narrower than in sites D and E (resp. LMM: -0.080 ± 0.034, p=0.037 and -0.084 ± 0.037, p=0.045; Fig. 4D). Head width and length measures were thus collapsed into the first component of another PCA (explaining 68% of the variance) that we used as a proxy of the head size (Table S3). Bees from the closest site (A) had overall smaller heads than bees from the farthest sites (D and E) (resp. LMM: -0.940 ± 0.414, p=0.044 and -1.011 ± 0.455, p=0.049; Fig. 4E). However, bees from all sites exhibited similar femur length (F4,12.565=0.311, p=0.866; Fig. 4F), wing length (F4,12.683=0.336, p=0.849; Fig. 4G) and body weight (F4,12.498=3.13, p=0.054; Fig. 4H). This means the bees closest to the mine had smaller heads but no overall smaller bodies. Letters indicate significant differences between sites (LMM).

Bees closer to the mine had smaller antennal lobes

Given the observed variations in head size among bees from different sites, we finally assessed whether proximity to the mine might alter the volume of the brain or of specific neuropils related to learning and memory deficits (Fig. 5). We quantified volumes from 3D reconstructions of the brain based on microtomography scanning (Fig. 5A) obtained from a subset of bees from sites A and E used in the second behavioural experiment (see Fig. 3). Total brain volume was positively correlated with head size (r=0.37, p=0.023), but did not differ between sites A and E (Fig 5B). However, we found differences at the level of individual neuropils. In particular, the antennal lobes (ALs), i.e. the peripheral brain structures involved for olfactory learning and memory [START_REF] Giurfa | Invertebrate learning and memory: Fifty years of olfactory conditioning of the proboscis extension response in honeybees[END_REF], were significantly smaller in bees from site A than in bees from site E (Fig. 5C).

By contrast, none of the other major neuropils of the bee brain (mushroom bodies MBs, optic lobes OLs, central complex CX) varied between sites (Fig. 5D-G). This suggests the cognitive olfactory deficits observed in bees sampled near the mine are linked to impaired development of their ALs. However, we did not find any influence of overall AL volume on the proportion of learners (GLMM: 1.116 ± 0.855, p=0.192) nor the acquisition score (GLMM: 0.239 ± 0.371, p=0.519) in this small subset of bees (N=38).

Discussion

Recent studies show controlled exposure to heavy metals impacts honey bee health and cognitive performance suggesting severe consequences for populations in the field [START_REF] Burden | Acute exposure to selenium disrupts associative conditioning and long-term memory recall in honey bees (Apis mellifera)[END_REF]Monchanin et al., 2021aMonchanin et al., , 2021b)). However no such assessment had yet been made. Here we demonstrated the relationship between environmental metal contamination and impaired behaviour in the field using cognitive assays on honey bees sampled along a gradient of metallic pollution. Bees exposed to higher levels of metals exhibited poorer olfactory learning abilities and memory specificity. This was associated with decreased head size and reduced volumes of brain regions supporting olfactory learning. Since functional learning and memory are crucial to the behavioural and chemical ecology of bees, individuals exposed to metal pollution may be less efficient at foraging and interacting socially, ultimately jeopardizing the capacity of the colony to feed, grow and survive.

Forager bees closer to the mine, and thus exposed to higher levels of arsenic and other pollutants, developed smaller heads, with smaller antennal lobes, than those from the most distant site. These neurodevelopmental impairments are reminiscent of those observed in mammals following exposure to arsenic [START_REF] Tolins | The developmental neurotoxicity of arsenic: cognitive and behavioral consequences of early life exposure[END_REF][START_REF] Tyler | Sex-dependent effects of the histone deacetylase inhibitor, sodium valproate, on reversal learning after developmental arsenic exposure[END_REF][START_REF] Wu | Effects of high fluoride and arsenic on brain biochemical indexes and learning-memory in rats[END_REF]. They are also consistent with data from other insects. When feeding on arsenic-contaminated food, grasshoppers exhibited a decreased body weight [START_REF] Rathinasabapathi | Arsenic hyperaccumulation in the Chinese brake fern (Pteris vittata) deters grasshopper (Schistocerca americana) herbivory[END_REF], and moths had reduced larval survival and a longer pupal stage [START_REF] Andrahennadi | Arsenic accumulation, biotransformation and localisation in bertha armyworm moths[END_REF]. Oribatid mites collected along a pollution gradient where arsenic was recorded showed increasing incidence and severity of leg deformities towards the pollution source [START_REF] Eeva | Leg deformities of oribatid mites as an indicator of environmental pollution[END_REF]. Ants sampled along a pollution gradient of zinc and cadmium developed smaller heads with increasing pollution levels [START_REF] Grześ | Monomorphic ants undergo within-colony morphological changes along the metal-pollution gradient[END_REF], and so did midges fed with cadmium or copper [START_REF] Martinez | Morphological abnormalities in Chironomus tentans exposed to cadmium-and copper-spiked sediments[END_REF]. Exposure to stressors during development such as infestation by Varroa ectoparasites [START_REF] Belaïd | ) on morphometry and cuticle component of the worker honeybees (Apis mellifera Linnaeus, 1758)[END_REF] or chronic exposure to lead (Monchanin et al., 2021a) can reduce bee head growth, and brain growth, e.g. pesticides [START_REF] Smith | Insecticide exposure during brood or early-adult development reduces brain growth and impairs adult learning in bumblebees[END_REF]. The effects of arsenic exposure on brain development are thus likely to contribute to the reduced cognitive abilities observed in our results, since bees exposed to increasing environmental levels show more severe growth defects as well as with learning and memory deficits. Previous work already pointed out that bees perform less well in olfactory tasks when they have smaller heads, as a result of natural interindividual size variability [START_REF] Gronenberg | Brain composition and olfactory learning in honey bees[END_REF]Monchanin et al., 2021a). The reduced head size, and specifically ALs volume, associated to metal exposure might explain the impaired olfactory learning, as these primary olfactory centres support olfactory discrimination and learning [START_REF] Menzel | Behavioral, neural and cellular components underlying olfactory learning in the honeybee[END_REF]. We found no effect on the size of MBs, known to support olfactory memory retrieval [START_REF] Menzel | Searching for the memory trace in a mini-brain, the honeybee[END_REF], despite the observed memory impairments correlating to contamination exposure. This suggests that the lower memory specificity in more exposed bees might be the consequence of altered coding of odorants in the ALs during learning. Yet, we do not exclude that exposure to higher contamination levels may have a subtler impact on MBs, e.g. by affecting synaptic connectivity [START_REF] Cabirol | Relationship between brain plasticity, learning and foraging performance in honey bees[END_REF] with possible consequences on memory specificity [START_REF] Groh | Analysis of synaptic microcircuits in the mushroom bodies of the honeybee[END_REF], even without changes in overall volume. Overall, arsenic exposure is known to affect neural functions, in particular by altering various neurotransmitter levels in mammals [START_REF] Xi | Prenatal and early life arsenic exposure induced oxidative damage and altered activities and mRNA expressions of neurotransmitter metabolic enzymes in offspring rat brain[END_REF]: it can lead to neurodevelopmental and cognitive disorders, including learning and memory impairments (reviewed in [START_REF] Tolins | The developmental neurotoxicity of arsenic: cognitive and behavioral consequences of early life exposure[END_REF]). Altered GABA signalling might affect odour discrimination [START_REF] Mwilaria | Disruption of GABAA in the insect antennal lobe generally increases odor detection and discrimination thresholds[END_REF] and memory specificity [START_REF] Hosler | Impairment of olfactory discrimination by blockade of GABA and nitric oxide activity in the honey bee antennal lobes[END_REF], and altered amine levels, in particular octopamine, might affect acquisition levels [START_REF] Rein | Octopamine modulates activity of neural networks in the honey bee antennal lobe[END_REF].

Because learning and memory are critical processes used by honey bees to find, identify and exploit environmental resources, bees foraging in this area are exposed to high levels of arsenic that might prove detrimental to their learning and/or memory abilities.

Although the impacts of contamination around the former gold mine of Salsigne have been little studied, our conclusions are consistent with epidemiological data obtained in the area, showing an excessive mortality rate among mine workers [START_REF] Simonato | A retrospective mortality study of workers exposed to arsenic in a gold mine and refinery in France[END_REF] and arsenicspecific cancer attributed to environmental contamination [START_REF] Dondon | Cancer mortality during the 1968-1994 period in a mining area in France[END_REF], but also significant amounts of bioaccumulated arsenic in small mammals [START_REF] Drouhot | Responses of wild small mammals to arsenic pollution at a partially remediated mining site in Southern France[END_REF]. A citizen science project carried between 1994 and 1997 found a significant accumulation of arsenic (up to 0.69 mg.kg -1 ) and lead (up to 0.95 mg.kg -1 ) in the pollen collected by honey bees [START_REF] Oulès | Determination of pollution trends in an abandoned mining site by application of a multivariate statistical analysis to heavy metals fractionation using SM&T-SES[END_REF].

These levels of metals fall within the range of doses that cannot be perceived by honey bees and are readily ingested in laboratory assays [START_REF] Monchanin | Honey bees cannot sense harmful concentrations of metal pollutants in food[END_REF]. Hence, bees could be all the more at risk because they cannot avoid such concentrations. Additionally, here foragers collected around the mine accumulated amount of arsenic (0.350 mg.kg -1 ) below levels reported in other studies, e.g. in Italy (0.367 mg.kg -1 ; [START_REF] Zarić | Arsenic speciation analysis in honey bees for environmental monitoring[END_REF]), in the US (1.707 mg.kg -1 ; [START_REF] Fisher | Apis mellifera L. as a monitor of environmental arsenic contamination from copper smelting[END_REF] or in Serbia (0.996; [START_REF] Krunić | Honey resistance to air contamination with arsenic from a copper processing plant[END_REF]). Yet, our results indicate a marked effect, and call for more studies to include simple behavioural and morphometric indicators as a powerful way to characterize sublethal impacts on pollinators foraging in polluted environments.

Materials and Methods

Field sites

This study was carried out in 2020 in the vicinity of a former gold mine located in Salsigne, France (43°18'41''N, 2°22'44''E; Fig. 1A-B). The ores processing, to extract mainly gold and silver, led to major contamination by arsenic in the surroundings of the industrial plant, in soils [START_REF] Drouhot | Responses of wild small mammals to arsenic pollution at a partially remediated mining site in Southern France[END_REF] and water [START_REF] Guerin | Arsenic speciation in some environmental samples: a comparative study of HG-GC-QFAAS and HPLC-ICP-MS methods[END_REF], far beyond international maximal permissible limits (for arsenic: <0.2ppm in food and <0.01ppm in drinking water (Codex Alimentarius, 2015), <0.1ppm in irrigation water [START_REF] Ayers | Water quality for agriculture, FAO irrigation and drainage paper[END_REF], <20ppm in soil [START_REF] De Vries | Critical limits for cadmium, lead and mercury related to ecotoxicological effects on soil organisms, aquatic organisms, plants, animals and humans[END_REF]). Successive floods [START_REF] Gaume | Hydrological analysis of the river Aude, France, flash flood on 12 and 13 November 1999[END_REF][START_REF] Jo Sénat | [END_REF] contributed to spreading metal pollutants in the valley, raising public health concerns supported by alarming arsenic levels measured from children in the area (ARS, 2019). In collaboration with beekeepers, five apiaries were selected within 11 km of the former gold mine (sites A-E in Fig. 1, Table 1) and have been settled for at least two months before the beginning of the experiment. Site A was the closest to the former ore processing site. It was therefore considered as the most polluted due to release of slag, fumes and dust [START_REF] Drouhot | Responses of wild small mammals to arsenic pollution at a partially remediated mining site in Southern France[END_REF]. Site B was closer to the open mine cast with underground operations, and was also characterized by high arsenic pollution [START_REF] Khaska | Tracking natural and anthropogenic origins of dissolved arsenic during surface and groundwater interaction in a postclosure mining context: Isotopic constraints[END_REF]. Both sites were in the vicinity of main mining deposits (Fig. 1). Sites A to D were located along a north-west transect, following the predominant wind direction of the area (EVADIES, 2021), potentially involving a higher exposure to windborne dusts and particles from the mine, while site E was less under wind influence and would be less impacted, and was therefore considered as less polluted. Land cover within a 3 km radius of each study apiary was extracted from the Theia OSO 2019 product [START_REF] Inglada | Theia OSO Land Cover Map[END_REF], and individual land cover classes were combined into main categories (urban, crop, forest and grassland; [START_REF] Sponsler | Honey bee success predicted by landscape composition in Ohio, USA[END_REF] see details in Fig. 1 and Fig. S1).

Site A was surrounded by more grasslands, sites B, C, D by forests and site E by crops and urban area (Fig. S1).

Table 1: Location of the study sites and details on the hives.

Bees

Between July and August 2020, we collected returning forager honey bees (Apis mellifera, Buckfast strain) at the hive entrance (Table 1) on the day before the behavioural experiments. We housed bees in plastic boxes containing groups of 20 individuals with access to 400 µL of 50% (w/v) sucrose solution (thus ca. 20 µL per bee following trophallaxis) and kept the plastic boxes overnight in an incubator (28 ± 1 °C, 70% humidity) [START_REF] Villar | Redefining single-trial memories in the honeybee[END_REF]. In the morning of the following day, we cooled bees on ice and harnessed them in plastic tubes, secured with tape and a droplet of wax at the back of the head [START_REF] Matsumoto | Revisiting olfactory classical conditioning of the proboscis extension response in honey bees: A step toward standardized procedures[END_REF]. We then fed them 5 µL of 50% sucrose solution and left them to rest for 3 h in the incubator.

Site

Geographical

Conditioning

We first tested the proboscis extension reflex (PER) of all bees by stimulating their antennae with 50% sucrose solution, and only kept those that responded for the conditioning. We performed olfactory absolute conditioning, in which bees must learn to associate an odour (conditioned stimulus, CS) delivered by an automatic stimulus delivery system [START_REF] Aguiar | Can honey bees discriminate between floral-fragrance isomers?[END_REF] with a 50% sucrose reinforcement [START_REF] Matsumoto | Revisiting olfactory classical conditioning of the proboscis extension response in honey bees: A step toward standardized procedures[END_REF]. In the first experiment (Fig. 2), we used pure limonene and eugenol as the CS (Sigma-Aldrich Ltd, Lyon, France) alternately on successive days, so that each combination was used for about half of the bees. In the second experiment (Fig. 3), we used 1-nonanol (Sigma-Aldrich Ltd, Lyon, France). The conditioning included five trials with a ten-minute inter-trial interval. Each conditioning trial (37 s in total) started when a bee was placed in front of the stimulus delivery system, which released a continuous flow of clean air (3,300 mL.min -1 ) to the antennae. After 15 s, the odour was introduced to the airflow for 4 s, the last second of which overlapped with sucrose presentation to the antennae using a toothpick and subsequent feeding for 4 seconds. The bee remained another 15 s under the clean airflow. We recorded the presence or absence of a conditioned PER to each odour at each conditioning trial (1 or 0), and the sum of conditioned responses was used to calculate an individual acquisition score ranging between 0 and 4. After the last conditioning trial, we put the bees back into the incubator for 1 h, before submitting them to memory tests.

Memory tests

Retention tests were performed at 1 h (both experiments) and 24 h (second experiment only) postconditioning. For long-term memory assays, bees were fed 15 µL of 50% sucrose solution after the short-term memory test, left overnight in the incubator, and fed the following morning with 5 µL of sucrose to ensure their survival. This second test was performed using the same procedure as for short-term retrieval, 24h after the end of conditioning.

In addition to the odour used during the conditioning (CS), novel odours were presented, following the same dynamics of the conditioning trial but with no sucrose reward. For novel odours, in the first experiment we used alternatively limonene and eugenol (depending on which was the CS). In the second one, we used alternatively 1-nonanol and nonanal for odours perceived as similar to the CS (1-nonanol), and 1-hexanol for a dissimilar odour, based on different levels of generalization [START_REF] Guerrieri | Perceptual and neural olfactory similarity in honeybees[END_REF]. We recorded the presence or absence of a conditioned PER to each odour at each trial (1 or 0).

We classified bees according to their response during the memory test. In the first experiment, the response patterns were: response to CS only, response to both odours, no or inconsistent (response to novel odour only) response. In the second experiment (Fig. 3), we classified bees as: responding to the CS only, showing generalization toward the similar odorant (i.e. responding to the CS and the similar odour, low generalization level), showing generalization to both the similar and dissimilar odorants (i.e. responding to all odours, high generalization level), no or inconsistent response. Only bees that learnt the task were kept for the analysis of the memory performances.

Morphometry

All bees were frozen after the behavioural assays and stored at -18°C. An experimenter blind to bee identity measured their fresh body weight (±0.001g) (precision balance ME103T, Mettler-Toledo Gmbh, Greifensee, Switzerland) and took measures (±0.01 mm) under a Nikon SMZ 745T dissecting microscope (objective x0.67) with a Toupcam camera model U3CMOS coupled to the ToupView software. We measured the head length, head width, wing length and femur length (Fig. 4A) (Monchanin et al., 2021a).

Brain scanning and volume measurements

We performed micro-computed tomography (micro CT) scanning of 47 foragers from the second experiment (Site A: N= 25; Site E: N=22). We removed the front part of the head (Smith et al.,

Statistics

We analysed the data using R Studio v.1.2.5033 (RStudio Team, 2019). All data are available in Dataset S1.

For the metal analysis, we conducted a PCA (package FactoMineR; [START_REF] Lê | FactoMineR: An R Package for Multivariate Analysis[END_REF]) on the levels of the seven selected metals [START_REF] Szentgyörgyi | Forewing structure of the solitary bee Osmia bicornis developing on heavy metal pollution gradient[END_REF]. Linear mixed-effects models (LMM) (package lmerTest; [START_REF] Kuznetsova | lmerTest Package: Tests in Linear Mixed Effects Models[END_REF]) were run on the pollution index (PC1) and arsenic content considering site as fixed effect, and hive as random factors.

For the conditioning trials, we performed generalized linear mixed-effects models (GLMM) (package lme4; [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]), fitted with binomial family, with hive and conditioning date as random factors and site as fixed effect. Using GLMM, we evaluated whether site location would impact the percentage of initial responses to antennal stimulation, spontaneous responses at the first conditioning trial, conditioned responses at the last conditioning trial and responses to each odour during memory test, as well as the proportion of individual response patterns during retrieval. GLMMs were followed by ANOVAs to test the overall significance of fixed categorical variables using the Anova function (package car; [START_REF] Fox | An {R} companion to applied regression[END_REF].

Acquisition scores were transformed as in [START_REF] Smithson | A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables[END_REF], using the formula ((x * (n -1) + 0.5) / n), to avoid zeros and ones in the normalized variables. They were then compared with GLMM (package Template Model Builder; [START_REF] Brooks | glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling[END_REF]), and fixed categorical variables significance was tested using Anova.glmmTMB function of that package.

For the morphometric analyses, we conducted a PCA on the five parameters measured, and clusters were compared with permutational multivariate analysis of variance (PERMANOVA) (package Vegan; [START_REF] Oksanen | vegan: Community Ecology Package. R package version 2[END_REF]. In addition, head width and length measures were collapsed into the first component of a PCA, which was used as a proxy of the head size. Linear mixed-effects models (LMM) were run for each morphological parameter, considering site as fixed effect, and hive and date as random factors. LMM were followed by F-tests to test the significance of fixed categorical variables.

For the brain analyses, we conducted, for each neuropil, a LMM with hive as random factor and site as fixed effect.

Supporting materials

Figure 1 :

 1 Figure 1: Study sites and metal pollution levels. A) Former gold mine located in the South of

Figure 2 :

 2 Figure 2: Learning and short-term memory. A) Acquisition curves show changes in the

Figure 3 :

 3 Figure 3: Short-term and long-term memory. A) Percentages of responses to the three odours

Figure 4 :

 4 Figure 4: Morphometric measurements of bees. A) Details of the parameters measured: (1)

Figure 5 :

 5 Figure 5: Brain area volumes (mm 3 ) of bees from site A (N=18) and E (N=20). A) Example of

Table S1 :

 S1 Metal contents (mean ± s.e.m, mg.kg 1 fresh matter) and pollution index (PC1outcome of the PCA representing the metal pollution in the study area) of bees collected (N=7 replicates per site) from the five sites and their contribution in the principal component analysis (PCA). Correlation coefficients >0.4 in absolute value are shown in bold.

  grassland (pasture/hay; light green), and urban (grey). Remaining land cover (yellow) consisted 787 of barren land and open water. Surface expressed as percentage.

	Metal	Site A	Site B	Site C	Site D	Site E	PC1	PC2
	content						correlation	correlation
	Al	5.040±0.753	46.283±5.737	4.409±0.712	11.204±3.248	3.841±0.458	0.80	-0.41
	As	0.350±0.047	0.977±0.122	0.043±0.005	0.052±0.007	0.067±0.016	0.85	-0.44
	Cd	0.081±0.015	0.156±0.011	0.079±0.003	0.058±0.007	0.050±0.010	0.83	-0.28
	Cu	9.038±0.486	8.515±0.693	6.666±1.016	7.377±0.850	6.405±0.607	0.69	0.34
	Fe	102.442±6.30	108.26±9.258 104.819±8.401 82.119±9.340 54.285±3.166	0.72	0.26
	Pb	4 0.823±0.221	0.870±0.573	0.652±0.448	0.173±0.040	0.313±0.109	0.62	0.35
	Zn	34.843±1.302 40.823±2.840	41.377±5.367 35.250±4.606 38.775±11.766	0.42	0.67
	Pollution	0.266±0.350	2.778±0.632	-0.464±0.521	-0.952±0.411	-1.630±0.338	/	/
	index
	(PC1)

Acknowledgements

We thank Laurent Drouin and Julien Denoy, for letting us work on their hives and Renaud Lebrun for the help with micro-CT. 3D data acquisitions were performed using the micro-CT facilities of the Montpellier Ressources Imageries platform member of the national infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04, «Investments for the future»), and of the Labex CEMEB (ANR-10-LABX-0004) and NUMEV (ANR-10-LABX-0020). We thank Jérôme Silvestre and Amélie Brocherieux (Laboratoire Ecologie Fonctionelle et Environnement, Toulouse) and Camille Duquenoy (Géosciences Environnement Toulouse and Service ICP-MS OMP Toulouse) for help with trace element measurements in bees. CM received funding by a PhD fellowship from the French Ministry of Higher Education, Research and Innovation. ABB was funded by a Future Fellowship from Australian Research Council (FT140100452) and the Eldon and Anne Foote Trust. AE and ML were funded by the CNRS and grants of the Agence Nationale de la Recherche (project POLLINET ANR-16-CE02-0002-01, project 3DNaviBee ANR-19-CE37-0024, project BEE-MOVE ANR-20-ERC8-0004-01), the European Regional Development Fund (project ECONECT), the Occitanian Regional Council (project DIAGNOSE) and the Agence de la Transition Ecologique (project LOTAPIS). While writing, ML received support from the European Commission (ERC Cog BEE-MOVE GA101002644).