domain: sdv sde

text:
M Chatelain
email: marion.chatelain@uibk.ac.at

François Nold

Jérôme Mathieu

Metal pollution drives earthworm biodiversity in urban lawns

Keywords: urbanization, soil pollution, heavy metals, trace elements, mercury, arsenic, species composition, community assemblage

Urban soils represent hotspots of metallic trace elements (MTEs) pollution. Despite the critical impact of soil organisms on soil ecosystem services, there is limited understanding regarding the effects of MTE levels in urban soils on these organisms. This is particularly surprising considering that earthworms, key organisms for soil ecosystems, are commonly used in MTE toxicity tests. This research investigates the impact of MTE pollution on earthworm communities in lawns within the city of Paris. In this study, we sampled a comprehensive array of earthworm communities, totalling 965 individuals from 13 distinct species belonging to Lumbricus, Aporrectodea, Allolobophora and Octolasion genera. These communities were collected from three different locations within 18 parks.

At these sites, we assessed the concentrations of eight metals and metalloids in the soil (As, Cd, Cr, Cu, Zn, Ni, Pb and Hg), along with selenium concentrations and eight fundamental soil parameters, to examine the association between earthworm communities and soil attributes. Median MTE concentrations exceeded recommended statutory limit values by approximately 20% (0.6 mg/kg Cd), 30% (36.8 mg/kg Cu), 40% (122.0 mg/kg Zn), and up to 90% (0.6 mg/kg Hg and 99.7 mg.kg Pb).

Nevertheless, these concentrations exhibited considerable variability both between and within parks, correlating with variations in earthworm community structures. Specifically, our results highlight that Cu concentrations in the soil explain about 6% of the variation in the assemblage of earthworm species. Our findings underscore the importance of considering MTE pollution levels to enhance our comprehension of earthworm distribution in urban environments and its effects on the ecosystem services provided by urban lawns.

INTRODUCTION

Soil contamination stands out as a major concern when assessing threats to soil resources in both Europe and globally [START_REF] Com | EU Soil Strategy for 2030: Reaping the benefits of healthy soils for people, food, nature and climate[END_REF]FAO, 2020). Metallic trace elements (MTEs), along with mineral oils, are the most common contaminants in European urban soil. While lithogenic sources predominantly influence MTE concentrations in soils, urban areas have become hotspots for MTE pollution due to anthropogenic sources such as vehicle emissions, industrial discharges, construction activities, improper waste disposal, and the use of metal-containing products [START_REF] Alloway | Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability[END_REF][START_REF] Tóth | Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment[END_REF]. Despite this, the ecology of urban soils represents only 2-3% of overall research in the field of soil ecology, and this proportion has not shown significant growth over time. Furthermore, many studies on urban soils exclusively focus on investigating physicochemical conditions and biogeochemical processes, neglecting the exploration of the biota present in those environments [START_REF] Guilland | Biodiversity of urban soils for sustainable cities[END_REF]. Nevertheless, urban soils can host abundant and diverse organisms [START_REF] Joimel | Urban and industrial land uses have a higher soil biological quality than expected from physicochemical quality[END_REF], which provide crucial services such as supporting vegetation development or regulating water flow [START_REF] Oksanen | The ecosystem services of urban soils: A review[END_REF]. Determining the role of MTE pollution in the distribution of soil organisms remains a challenging yet pivotal task for creating safer, more resilient, and sustainable cities and human (United Nations,SDG 11).

Urban areas are the recipient of various MTEs such as lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), copper (Cu), zinc (Zn), and nickel (Ni). MTEs concentrations in urban soils can greatly vary within the urban matrix [START_REF] Appleton | Variation in soil chemistry related to different classes and eras of urbanisation in the London area[END_REF][START_REF] Bityukova | Urban geochemistry: A study of element distributions in the soils of tallinn (Estonia)[END_REF][START_REF] Cachada | Major inputs and mobility of potentially toxic elements contamination in urban areas[END_REF], for instance increasing in densely populated areas, industrial sites and along high-traffic roads [START_REF] Cicchella | Urban soil contamination in Salerno (Italy): Concentrations and patterns of major, minor, trace and ultra-trace elements in soils[END_REF]. However, data on MTE levels in urban environments remain geographically biased and are sometimes inadequately reported (e.g. 45% of the 174 studies on European urban soils did not provide data on standard deviation [START_REF] Binner | Metals in urban soils of Europe: A systematic review[END_REF]). Furthermore, only a limited number of studies have explored the connection between MTE pollution and soil fauna communities, and the results obtained from these investigations vary considerably [START_REF] Fiera | Biodiversity of Collembola in urban soils and their use as bioindicators for pollution[END_REF]Pižl and Josens, 1995;[START_REF] Santorufo | Soil invertebrates as bioindicators of urban soil quality[END_REF][START_REF] Tóth | Urban soil quality deteriorates even with low heavy metal levels: An arthropod-based multi-indices approach[END_REF][START_REF] Yu | Inconsistent Patterns of Soil Fauna Biodiversity and Soil Physicochemical Characteristic Along an Urbanization Gradient[END_REF]. The scarcity of research in this area stands in stark contrast to the abundant literature available on MTE toxicity in soil organisms, particularly earthworms [START_REF] Sivakumar | Effects of metals on earthworm life cycles: a review[END_REF][START_REF] Yadav | Heavy metal toxicity in earthworms and its environmental implications: A review[END_REF].

Earthworms are considered keystone organisms because of their profound impact on soil ecosystems, influencing soil structure, organic matter dynamics, and the activities of other organisms. Their activity affect soil aggregation and porosity, leading to significant consequences for water, nutrients and gas flow, as well as soil erodibility. Additionally, they are associated with positive effects on plant growth and immunity [START_REF] Bertrand | Earthworm services for cropping systems. A review[END_REF][START_REF] Blouin | A review of earthworm impact on soil function and ecosystem services: Earthworm impact on ecosystem services[END_REF]. The importance of earthworms in temperate environments has long been recognized, resulting in a substantial number of studies aimed at identifying the dominant biotic or abiotic factors influencing earthworm distribution (Phillips et al., 2019). These studies consistently demonstrated that earthworms can detect MTEs in soils and escape from contaminated to clean soil (e.g. [START_REF] Chatelain | Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks[END_REF][START_REF] Langdon | Survival, Pb-uptake and behaviour of three species of earthworm in Pb treated soils determined using an OECD-style toxicity test and a soil avoidance test[END_REF][START_REF] Lowe | Assessment of avoidance behaviour by earthworms ( Lumbricus rubellus and Octolasion cyaneum ) in linear pollution gradients[END_REF]Lukkari and Haimi, 2005). However, MTE sensitivity (e.g. survival, reproduction) varies between species [START_REF] Chatelain | Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks[END_REF][START_REF] Langdon | Survival, Pb-uptake and behaviour of three species of earthworm in Pb treated soils determined using an OECD-style toxicity test and a soil avoidance test[END_REF][START_REF] Langdon | Survival and behaviour of the earthworms Lumbricus rubellus and Dendrodrilus rubidus from arsenate-contaminated and non-contaminated sites[END_REF]Lukkari et al., 2005). More specifically, previous research indicates that epigeic species may exhibit lower sensitivity to MTEs [START_REF] Langdon | Survival, Pb-uptake and behaviour of three species of earthworm in Pb treated soils determined using an OECD-style toxicity test and a soil avoidance test[END_REF][START_REF] Lowe | Assessment of avoidance behaviour by earthworms ( Lumbricus rubellus and Octolasion cyaneum ) in linear pollution gradients[END_REF]Lukkari et al., 2005). This could be attributed to their proximity to the soil surface, exposing them to a variety of conditions that may not all be conducive to optimal growth. Consequently, they need to be resilient enough to withstand such environmental variability [START_REF] Tomlin | Behaviour as a source of earthworm susceptibility to ecotoxicants[END_REF]. Furthermore, invertebrates show a reduced sensitivity to xenobiotics as body size increases [START_REF] Baird | Using biological traits to predict species sensitivity to toxic substances[END_REF][START_REF] Ippolito | Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits[END_REF]. Finally, different earthworm species may be more or less exposed to MTEs in the soil, depending on their ecological traits, particularly their position in the soil and dietary habits. Epigeic earthworms, for example, inhabit parts of the soil with abundance organic matter, typically at or near the soil surface, where they feed on leaf litter, decaying plant roots or dung. Consequently, their diet may contain lower concentrations of MTEs compared to the diet of endogeic earthworms, which also consume soil where MTE can accumulate. Moreover, MTEs are expected to be less available at the soil surface than at deeper horizons utilized by endogeic and anecic earthworms [START_REF] Bravin | Rhizosphere pH Gradient Controls Copper Availability in a Strongly Acidic Soil[END_REF][START_REF] Van Herwijnen | The effect of organic materials on the mobility and toxicity of metals in contaminated soils[END_REF]. Supporting this observation, the limited number of studies that have explored the impact of MTE levels on earthworm communities on site have shown a change in community composition, along with a decline in earthworm richness, density, and/or biomass as MTE contamination increases near mining sites [START_REF] Huang | Impact of soil metals on earthworm communities from the perspectives of earthworm ecotypes and metal bioaccumulation[END_REF][START_REF] Lévêque | Effects of historic metal(loid) pollution on earthworm communities[END_REF][START_REF] Nahmani | Effects of heavy metal soil pollution on earthworm communities in the north of France[END_REF]. The potential existence of comparable relationships between soil MTE pollution and earthworm communities in urban environments has yet to be investigated [START_REF] Rutgers | Mapping earthworm communities in Europe[END_REF][START_REF] Tóth | Earthworm assemblages in urban habitats across biogeographical regions[END_REF].

To address this gap, we sampled earthworm communities within the lawns of 18 parks in Paris (France), where we measured soil physicochemical properties, including the concentrations of eight metals and metalloids. Based on earthworm species assemblage in grasslands in the northern part of France, we expected to encounter up to 17 earthworm species and communities to be composed of seven to nine species, dominated by the species Aporrectodea caliginosa, Allolobophora chlorotica, Aporrectodea longa, and Aporrectodea icterica [START_REF] Decaëns | Assembly rules within earthworm communities in North-Western France-A regional analysis[END_REF]. The 18 parks included in this study were created between 1840 and 2007, with soil imported from market-garden or agricultural lands. Due to variations in park age and soil origin, differences in soil physicochemical characteristics were anticipated between parks. Additionally, local renovations and varying proximity to roadways led us to expect variations in physicochemical characteristics within parks as well. Therefore, we hypothesised that (i) earthworm species assemblage differ between and within parks in response to variations in MTE levels, (ii) earthworm abundance and richness decrease with increasing MTEs concentrations in the soil and (iii) the association between earthworm community composition and MTE levels in the soil depends on soil physicochemical characteristics affecting MTE availability in the soil.

METHODS

Study sites

To investigate earthworm response to soil physico-chemical characteristics, earthworms and soils were sampled from lawns at 18 parks in Paris (Figure 1). Three distinct locations, each situated between 40 and 370 meters apart, were sampled at every park, resulting in a total of 54 sampling sites. The selection of parks aimed for a relatively even distribution across the city, intending to encompass a range of park sizes and ages (see Supplementary Material); soil was always sampled from open areas, away from trees and bushes. As parks have been created or renovated, soil has always been imported.

The thickness of the material varies between 0.30m and 2.00m, depending on the type of plant cover (lawns, flowers, shrubs, trees). The oldest gardens have soil imported from the inner suburbs of Paris (often sandy, calcareous market-garden soils, frequently contaminated with MTEs by irrigation with wastewater and/or sewage sludge); while the most recent gardens have soil imported from locations further away from the city (often silty, non-calcareous agricultural soils, only slightly contaminated with MTEs). However, partial renovations or extensions were carried out in certain parks, resulting in the introduction of new soil to specific areas (see Supplementary Material).

Earthworms' communities & soil sampling

Free-living earthworms were collected from February to May 2016, a time when they are particularly active, at each 54 sampling point. At each point, one 30 x 30 x 30 cm block of soil was extracted using a spade. The topsoil (from the surface to 5 cm below the surface) and the subsoil (from 10 to 30 cm below the surface) were separated before being manually searched for earthworms. Earthworms were kept in small terrariums (12 cm x 10 cm x 8 cm) to be transferred to the laboratory for identification.

For the purpose of soil physico-chemical analyses, at least 1 L of soil was sampled from the topsoil and from the subsoil, separately.

Earthworm identification

Earthworms were identified in the lab using morphological characteristics [START_REF] Bouché | Lombriciens de France: écologie et systématique[END_REF][START_REF] Sims | Earthworms: Keys and Notes for the Identification and Study of the Species[END_REF]. The species names used herein are conformed to the Fauna Europaea web site (http://www.faunaeur.org/index.php). Species were categorized within the four functional groups: epigeic (Lumbricus castaneus, Lumbricus rubellus rubellus, and Lumbricus rubellus castanoides), epianecic (Aporrectodea giardi, Lumbricus terrestris, Lumbricus herculeus, and Aporrectodea longa), endogeic (Aporrectodea caliginosa, Aporrectodea rosea, and Aporrectodea icterica,) and intermediate (Allolobophora chlorotica, Octolasion lacteum, and Octolasion cyaneum) [START_REF] Bottinelli | An explicit definition of earthworm ecological categories -Marcel Bouché's triangle revisited[END_REF]. For each 54 sample of 27 dm 3 each, we calculated species abundance (i.e. the number of individuals per species), juvenile abundance, (i.e. the number of juveniles, whatever the species), earthworm abundance (i.e. the total number of individuals, including juveniles), and functional group abundance (i.e. the number of individuals per functional group). We also calculated species richness (i.e. the number of different species, excluding juveniles), and species frequency (i.e. the percentage of sampling sites where each species occurred).

Soil preparation & physicochemical analyses

Each sampling site (n=54) was characterised by 16 physico-chemical characteristics. These encompass the concentrations of eight metals or metalloids (As, Cd, Cr, Cu, Zn, Ni, Pb and Hg), as well as the concentration of selenium (Se)another trace element of health concern. The set of characteristics also includes basic soil parameters, specifically pH, electrical resistivity (i.e. how much the soil conducts electric current, which varies with salt content), total carbonates (CO 3 ), organic nitrogen (ON), soil organic matter (SOM), assimilable phosphorus (P), and the carbon to nitrogen ratio (C/N).

These parameters were measured separately in both the topsoil and subsoil. Analyses were carried out by the Laboratory of Agronomy of the Paris City (France) using the methods summarised in Table 1.

Statistical analyses

Statistical analyses were performed using R software (version 4.1.3). Soil characteristics -To compare soil chemical characteristics between the 54 sampling points, we performed multiple linear models ('lm' function) with one of the 32 soil physico-chemical characteristic as dependant variable and the park as explanatory variable. In addition, we performed a cluster analysis on soil physico-chemical characteristics per sampling site ('kmeans' function in the 'stats' package). The optimal number of clusters was determined using within cluster sums of squares method, aka elbow method ('fviz_nbclust' function in the 'factoextra' package). The results of cluster analysis were visualized using the 'fviz_cluster' function ('factoextra' package). To determine the soil physico-chemical characteristics underlying those clusters, the coordinates of each sampling point on the two-dimensional space were extracted and we performed correlation test between these coordinates and each 32 physico-chemical characteristics ('cor.test' function in the 'rstatix' package).

Earthworm abundance & richness -To investigate the association between earthworm total abundance and soil physico-chemical properties, we performed multiple linear models ('lm' function) with the log-transformed number of earthworms sampled at each sampling point as the dependent variable and one of the 34 soil physicochemical characteristic as the explanatory variable. Because earthworm abundance and richness are expected to vary between parks due to different histories and location, the park was added as covariate. Because soil pH and content in organic matter is known to influence MTE bioavailability [START_REF] Nahmani | A review of studies performed to assess metal uptake by earthworms[END_REF], hence toxicity, pH or SOM and their interaction with the MTE concentrations were added as explanatory variables in models including one of the MTEs as explanatory variable. For those models, we performed a backward stepwise selection using the AIC.

When interaction terms were retained in the models, simple slope analyses using Johnson-Neyman intervals were performed using the 'sim_slopes' function of the 'interactions' package [START_REF] Long | interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions[END_REF].

To identify different responses between species and age categories, we run similar models (i) for each species present in at least 10% of the sampling sites, (ii) on adults only (whatever the species) and (iii) on juveniles only (whatever the species). We used similar models to measure the association between earthworm richness (the number of species at each sampling point) and soil physico-chemical properties. The analysis comparing earthworm responses to soil physico-chemical properties among functional groups was discontinued due to the predominant representation of endogeic species among the sampled earthworms' communities.

Species composition -Differences in the taxonomic composition of earthworm communities were tested using a linear constrained ordination method based on Hellinger distances (tb-RDA) on logtransformed abundance data using the 'rda' function of the 'vegan' package [START_REF] Oksanen | The ecosystem services of urban soils: A review[END_REF].

Only the six species that were present in at least 10% of the 54 sampling sites were included. The global model included the 32 soil physico-chemical characteristics as constraining variables. The soil characteristics that were the most important to explain the compositional changes were identified by forward selection using the 'ordistep' function ('vegan' package). The variance explained by the constrained ordination and by each of the components was tested by Monte Carlo permutation test ('anova' function in the package 'vegan'). Variation partitioning was calculated using the 'rdacca.hp' function of the package of the same name [START_REF] Lai | Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package[END_REF]. The contribution of each selected constraining variable to the components of the ordination was fitted by linear contribution scores using the 'envfit' function. The ordination diagrams were created with the 'ordiplot' function from 'vegan', which coordinates were extracted for use in 'ggplot2' [START_REF] Wickham | Package "ggplot2[END_REF].

RESULTS

Soil characteristics

Soil physico-chemical characteristics varied significantly between the 54 sampling points; the concentrations of assimilable phosphorous (P), total carbonates (CO 3 ), mercury (Hg), lead (Pb), copper (Cu), cadmium (Cd) and zinc (Zn) in both topsoils and subsoils varied the most between sampled soils (Figure 2). Importantly, despite the wide range of Hg, Pb, Cu, Cd and Zn concentrations, the soil of the 18 Parisian parks were in general characterised by high concentrations of these five MTEs, for which median concentrations were ca. 20% (Cd), 30% (Cu), 40% (Zn) and 90% (Hg and Pb) higher than the recommended statutory limit values (Table 3). The majority of the soil physicochemical characteristics were more variable between the 18 Parisian parks than within each park; between 24% and 84% of the variability among sampling points was attributed to variations between parks; only the C/N ratio and resistivity in the subsoil, and chromium concentrations in the topsoil were not significantly different between parks (Table 2). However, the cluster analysis revealed that soil composition within sites can be highly variable: the three samples per park had comparable soil composition in only half of the parks (Figures 1 and3). Variations in Pb, Zn, Hg, Cu, Cd, soil organic matter (SOM) and organic nitrogen (ON) concentrations in the topsoil and subsoils, and in assimilable phosphorous concentrations (P) in the topsoil, mainly accounted for the dissimilarities between the three clusters (Figure 3). Consistently, several chemical characteristics were strongly correlated. This was particularly notable for concentrations of Pb, Zn, Hg, Cu, Cd and P (Figure 4). 3). P: assimilable phosphorous, Cu: copper, Hg: Mercury, Zn: zinc, Pb: lead, As: arsenic, Cd: cadmium, Ni: nickel, SOM: soil organic matter, C:N: carbon/nitrogen ratio, Cr: chromium, ON: nitrogen, Se: selenium, CO3: total carbonates, Res: resistivity.

Earthworm communities

Species composition

A total of 965 earthworms were sampled. From them, 355 were sorted into 13 species; the remaining 610 individuals were juveniles. Overall, A. caliginosa and A. rosea were by far the most abundant and the most frequent species in the 18 Parisian parks, followed by A. chlorotica and A. icterica (Figure 5).

Quite similarly, the variation in earthworm abundance between sampling sites was the highest in A. caliginosa, A. rosea, followed by A. chlorotica and O. lacteum (Figure 5). Endogeic, intermediate, epianecic, and epigeic earthworms accounted for 60%, 24%, 12% and 5% of the adults, respectively.

Variation in earthworm abundance

Earthworm abundance per site ranged from one to 74 individuals per 27dm 3 , with a median of 13.5 individuals. Fifteen out of the 34 models explained a higher proportion of the variation in earthworm abundance than the model accounting for inter-park variations only (i.e. these models explained more than 58.6% of the variation in earthworm abundance between samples). However, the relationship between earthworm abundance and the physico-chemical variables was significant in only ten of the models. Earthworm abundance decreased with increasing P, Cu, Hg, Zn and Pb concentrations in the topsoil and the subsoil; the negative relationship between earthworm abundance and Hg concentrations in the subsoil tended to disappear in soils rich in organic matter (when SOM > 88.46 ‰); these variables explained between 4.8% and 10.4% of the variation in earthworm abundance (Figure 6a). Because P, Cu, Hg, Zn and Pb levels are highly correlated with each other (see cluster analysis in section 4.1), some of the relationships may result from covariations.

The relationships between earthworm abundance and soil physico-chemical properties were largely similar when considering adults only; only the relationships between abundance and Zn, Cu or Hg concentrations in the subsoil were marginally non-significant; the other variables explained between 7.9% and 17% of the variation in the abundance of adults (see Supplementary material). When considering juveniles only, the relationships between abundance and Cu or Hg concentrations in the topsoil and the subsoil, or P concentrations in the topsoil were conserved; they explained between 3.9% and 6.4% of the variation in the abundance of juveniles (see Supplementary material). Finally, the abundance of A. longa tended to decrease with increasing Cu concentrations in the subsoil (p=0.059). None of the relationships were significant when considering the other species alone.

Variation in species richness

Species richness per site ranged from zero to six, with a median of two species. Seventeen out of the 32 models explained a higher proportion of the variation in earthworm richness than the model accounting for inter-park variations only (i.e. these models explained more than 25.8% of the variation in earthworm richness between samples). However, the relationship between earthworm richness and the physico-chemical variable was significant in only six of the models (Figure 6b): earthworm richness decreased when increasing Pb, Hg, Cu, Zn and Cd concentrations in the subsoil but only when the subsoil was relatively poor in organic matter (when SOM < 65.07 ‰, 63.33 ‰, 58.71 ‰, 59.99 ‰ and 47.14 ‰, respectively). On the contrary, earthworm richness decreased with increasing Cr concentrations in the subsoil when the subsoil was relatively rich in organic matter (when SOM > 66.66 ‰). These variables explained 6.2% to 15.8% of the variation in earthworm richness. Moreover, earthworm richness tended to decrease with increasing P concentrations in the topsoil (P=0.074); this variable explained 4.6% of the variation in earthworm richness. (in black) and topsoil (in grey) as explanatory variable. The park name was always added as categorical explanatory variable. We highlight the adjusted R of the model including only the park name as explanatory variable (R = 0.586 and R = 0.258, respectively), as well as the models with an AIC at least two points lower than the model including the park name only (Δ AIC > 2), and in which the association between earthworm abundance and the physico-chemical variable was significant (pvalue < 0.05). P: assimilable phosphorous, Cu: copper, Hg: Mercury, Zn: zinc, Pb: lead, As: arsenic, Cd: cadmium, Ni: nickel, SOM: soil organic matter, C:N: carbon/nitrogen ratio, Cr: chromium, N: nitrogen, Se: selenium, CO3: total carbonates, Res: resistivity.

Variation in community composition

The 16 physico-chemical variables measured in the subsoils and topsoils explained together 59% of the variation in earthworm community structure among the 54 sampling sites. However, only two variables significantly explained such variation, namely copper concentrations in the subsoil (F=3.20, P=0.020), and pH in the subsoil (F=2.43, P=0.030); they explained 5.7% and 4.3% of the variation in earthworm community structure, respectively.

The first and second components of the redundancy analysis explained 6% (F=3.79, P=0.003) and 4.1% (F=2.31, P=0.054) of such variation. RDA1 is strongly associated to Cu concentrations in the subsoil (r=-0.90), while RDA2 is strongly associated to the pH of the subsoil (r=-0.82). The ordination along RDA1 is driven by the variation in the abundances of A. chlorotica, and A. longa, which contributes to 78% and 16% to RDA1, respectively: the relative abundance of A. chlorotica and A. longa increased and decreased with increasing Cu concentrations in the subsoil, respectively (Figure 7). The ordination along RDA2 is driven by the variation in the abundances of A. rosea, A. longa, A. icterica and A. caliginosa, which contribute to 38%, 23%, 22% and 10% to RDA2, respectively: the relative abundance of A. rosea and A. icterica tended to increase, while the relative abundance of A. longa and A. caliginosa tended to decrease with increasing the pH in the subsoil, respectively (Figure 7).

These results are consistent with the distribution of each of the species (Figure 8). More specifically, while the median Cu concentration in the subsoil was 36.5 mg/kg, A. chlorotica were mainly present when concentrations were between 41 and 113 mg/kg. Because Cu, Hg, Pb, Zn, Cd, Ar, P, OM and N concentrations in the soil were positively correlated (see Figure 4), A. chlorotica occurred in soils with relatively high concentrations of these elements. On the contrary, A. longa mainly occurred in soils with low Cu concentrations in the subsoil (with concentrations between 16.5 and 23.5 mg/kg); interestingly, the physico-chemical characteristics of the soils where A. longa mainly occurred varied little (Figure 8). Finally, while the six species mainly occurred in slightly alkaline soils (with pH over 7), A. icterica and A. rosea also frequently occurred in moderately alkaline soils (with pH over 8) (Figure 7 & 8). Concentrations were scaled and centred on the overall mean concentration. Therefore, values lower and higher than zero are relatively low and relatively high, respectively. For each physico-chemical variable, we also highlight the range of concentrations that includes 50% of the sites where the species was present (i.e. the 1 st and the 3 rd quartiles of the distribution). Those were calculated for the six earthworm species that were present in at least 10% of the 54 sampling sites.

Hg: mercury (in mg/kg), Se: selenium (in mg/kg), Pb: lead (in mg/kg), Ni: nickel (in mg/kg), Zn: zinc (in mg/kg), Cu: copper (in mg/kg), Cr: chromium (in mg/kg), Cd: cadmium (in mg/kg), As: arsenic (in mg/kg), P: assimilable phosphorous (in ‰), Res: resistivity (in in ohm.cm), C.N: carbon to nitrogen ratio, ON: organic nitrogen (in ‰), SOM: soil organic matter (in ‰), CO 3 : total carbonates (in ‰).

DISCUSSION

A total of 965 earthworms belonging to 13 different species were sampled at three different sites within lawn of each of the 18 Parisian parks included in this study. At these sites, we measured the concentrations of eight metals and metalloids (As, Cd, Cr, Cu, Zn, Ni, Pb and Hg), along with selenium (Se), alongside basic soil parameters (pH, P, resistivity, C/N, ON, SOM, CO 3 ), and tested the link between earthworm communities and soil characteristics while accounting for variations between parks. Besides documenting on MTE pollution levels in the European city of Paris and their variations at short and very short scale, we were able to identify Cu, Hg, Zn and Pb levels, together with P and pH, as important abiotic parameters shaping earthworm communities in urban lawns.

Variation in MTE levels in urban lawns

Overall, the lawns in Parisian parks exhibit high levels of Hg, Pb, Zn, Cu, and Cd. Actually, median concentrations of these MTEs were 90% (Hg and Pb), 40% (Zn), 30% (Cu) and 20% (Cd) higher than the recommended statutory limit values [START_REF] Mathieu | Proposition de référentiels régionaux en éléments traces métalliques dans les sols : leur utilisation dans les évaluations des risques sanitaires[END_REF]. The mean concentrations observed in our study closely resemble those obtained in 2015 by Foti and colleagues across 30 urban lawns in Paris [START_REF] Foti | Trace element concentrations along a gradient of urban pressure in forest and lawn soils of the Paris region (France)[END_REF]. These elements are characteristic of urban soils [START_REF] References Ajmone-Marsan | Trace Elements in Soils of Urban Areas[END_REF][START_REF] Cachada | Major inputs and mobility of potentially toxic elements contamination in urban areas[END_REF][START_REF] Chatelain | Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks[END_REF][START_REF] Foti | Trace element concentrations along a gradient of urban pressure in forest and lawn soils of the Paris region (France)[END_REF] where they accumulate due to various anthropogenic sources, including the combustion of fossil fuels, waste incineration, or the production of alloys (e.g. brass), paints, batteries, or tires [START_REF] Binner | Metals in urban soils of Europe: A systematic review[END_REF]. While our study did not specifically intend to pinpoint the origin and source of pollution, it is evident that older parks exhibited elevated concentrations of these MTEs. The observed pattern may be attributed, at least in part, to the soil's origin, as older parks were initially established using soil imported from the inner suburbs of Paris, frequently subjected to irrigation with wastewater and/or sewage sludge. Hg, Pb, Zn, Cu and Cd concentrations in the soil exhibiting a strong correlation within the finer scale of the urban environment, our results further suggest a shared emission source. Actually, the robust correlation observed between the concentrations of MTEs, particularly Pb, Cu and Hg, and the levels of assimilable phosphorus (P)primarily emitted by vehicular traffic in urban areas -suggests that road traffic is likely the primary source of MTE pollution in urban lawns [START_REF] Kuoppamäki | Urban snow indicates pollution originating from road traffic[END_REF]. This conclusion is consistent with the findings of a previous study conducted in the same geographical region [START_REF] Foti | Trace element concentrations along a gradient of urban pressure in forest and lawn soils of the Paris region (France)[END_REF].

Hg, Pb, Zn, Cu, and Cd are also the elements whose concentrations varied the most between soils. Generally, these elements exhibited greater variation across the 18 Parisian parks than within individual parks. However, in half of the parks, there were substantial differences in MTE pollution levels among the three sampled locations within each park. For instance, at Square du Temple, two of the sites displayed MTE concentrations (Hg, Pb, Cu, Cd, and Zn) at opposite ends of the spectrum: one site had MTE levels similar to those found in rural lawns, while another showed levels comparable to those measured near smelters [START_REF] Chatelain | Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks[END_REF][START_REF] Lukkari | Effects of heavy metals on earthworms along contamination gradients in organic rich soils[END_REF]; the third site showed intermediate levels of MTEs. This finding underscores the high heterogeneity of urban soils, emphasizing the importance of taking multiple samples when studying them. It also suggests that the soil biota's response to soil characteristics might vary at a very fine scale, leading to a strong structuration of communities within urban lawns.

Earthworm community response to MTE levels

Earthworm abundance per site ranged from one to 74 individuals per 27dm 3 , with a median of 13.5 individuals. Consistent with patterns observed in proximity to MTE-rich mining sites, earthworm total abundance in urban lawns decreased with increasing MTE pollution levels in the soil [START_REF] Huang | Impact of soil metals on earthworm communities from the perspectives of earthworm ecotypes and metal bioaccumulation[END_REF][START_REF] Lévêque | Effects of historic metal(loid) pollution on earthworm communities[END_REF][START_REF] Mariet | Impact assessment of legacy wastes from ancient mining activities on current earthworm community[END_REF][START_REF] Nahmani | Effects of heavy metal soil pollution on earthworm communities in the north of France[END_REF]. More specifically, it was negatively associated with the levels of Cu, Hg, Zn and Pb. Our findings also align with the observations made in Brussels, Belgium (Pižl and Josens, 1995). Due to the positive correlation among the concentrations of these MTEs in the soil, it becomes challenging to ascertain whether the toxicity of these soils stems from the toxicity of individual MTEs or from synergistic effects within the MTE mixture. However, experimental studies demonstrated negative effects of all four MTEs on earthworm survivability and reproduction [START_REF] Sivakumar | Effects of metals on earthworm life cycles: a review[END_REF][START_REF] Yadav | Heavy metal toxicity in earthworms and its environmental implications: A review[END_REF].

On the other hand, our research emphasizes that, among the physicochemical variables measured in this study, Cu concentrations in the soil have the most significant influence on the assemblage of earthworm species. While soils with elevated Cu levels also contained higher amounts of Hg, Pb, Zn, Cd, As, phosphates, organic matter and nitrogen, these additional factors did not enhance the explanatory power of our redundancy analysis. This suggests that the primary role in shaping earthworm communities lies with Cu concentrations. Specifically, the relative abundance of A. chlorotica increased in soils with a high Cu content. Interestingly, while Pižl and Josens concluded in their study that soil pollution levels in Brussels did not significantly shape the composition of earthworm communities, their results clearly show that the earthworm community at the site with the highest concentration of Cu in the soil (28.83 mg/kg) was dominated by A. chlorotica (Pižl and Josens, 1995). Although this outcome might imply that A. chlorotica has a higher tolerance to Cu pollution, earlier research indicated that this species is one of the most susceptible to MTE pollution [START_REF] Chatelain | Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks[END_REF][START_REF] Nahmani | Effects of heavy metal soil pollution on earthworm communities in the north of France[END_REF][START_REF] Spurgeon | The effects of metal contamination on earthworm populations around a smelting works: quantifying species effects[END_REF]. For example, when subjected to soil containing a blend of Zn, Pb, Cu, Cr, and Ni, designed to simulate pollution levels found in urban environments, several A. chlorotica individuals died, while no casualty was measured in the two other earthworm species studied [START_REF] Chatelain | Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks[END_REF]. Nevertheless, it does not exclude the possibility that this species is particularly tolerant to Cu. For instance, the presence of biliverdin contributes to the green coloration of this species [START_REF] Satchell | Colour Dimorphism in Allolobophora chlorotica Sav. (Lumbricidae)[END_REF]). Yet, in vitro studies highlighted the antioxidant and cytoprotective properties of biliverdin, predominantly due to their ability to scavenge free radical species [START_REF] Mcdonagh | Turning green to gold[END_REF]. Moreover, compared to other earthworm species, A. chlorotica is rich in riboflavin-storing eleocytes [START_REF] Mazur | Riboflavin storage in earthworm chloragocytes and chloragocyte-derived eleocytes and its putative role as chemoattractant for immunocompetent cells[END_REF]. Besides playing a key role in earthworm immunity, riboflavin may be involved in the storage and transport of Cu [START_REF] Smith | Riboflavin binding protein contains a type II copper binding site[END_REF]. Alternatively, the tolerance of A. chlorotica to Cu pollution may be attributed to its ecological niche. Specifically, this species predominantly inhabits the upper layer of the soil. In grasslands and lawns, it is situated amidst the roots of grass, where organic matter is abundant and the pH higher. This environment is also the place of intricate interactions among plants, microorganisms, and soil, which collectively influence the availability of MTEs [START_REF] Bravin | Rhizosphere pH Gradient Controls Copper Availability in a Strongly Acidic Soil[END_REF]. Our findings underscore the necessity for additional comprehensive research dedicated to unravelling the intricate nuances of earthworm sensitivity to MTE pollution within urban environments. This inquiry is crucial for a more thorough understanding of earthworm distribution and for shedding light on acclimatization and/or adaptation mechanisms [START_REF] Pauwels | Using biomarkers in an evolutionary context: Lessons from the analysis of biological responses of oligochaete annelids to metal exposure[END_REF]. 4.3. Organic matter and pH-mediated association between soil MTE levels and earthworm communities Earthworms can be exposed to MTEs through both their skin and their digestive tract. Both pathways, dermal absorption and ingestion, contribute to the accumulation of MTEs in earthworm tissues. Soil pH plays a significant role in determining the solubility of MTEs in the soil, which in turn affects their bioavailability and their dermal uptake by earthworms. While experimental findings show variability, theoretically, in acidic soils, specific MTEs such as Pb and Cd tend to exhibit increased solubility, leading to a higher likelihood of absorption through the skin of earthworms [START_REF] Bradham | Effect of soil properties on lead bioavailability and toxicity to earthworms[END_REF][START_REF] Janssen | Equilibrium partitioning of heavy metals in dutch field soils. II. Prediction of metal accumulation in earthworms[END_REF][START_REF] Morgan | Earthworms as biological monitors of cadmium, copper, lead and zinc in metalliferous soils[END_REF][START_REF] Nahmani | A review of studies performed to assess metal uptake by earthworms[END_REF][START_REF] Spurgeon | Effect of pH on metal speciation and resulting metal uptake and toxicity for earthworms[END_REF].

Nonetheless, in our study, the associations between earthworm community composition and MTE concentrations were found to be unrelated to soil pH. This suggests that intestinal absorption, which is less influenced by soil pH, could be an important route of MTE uptake in nature [START_REF] Oste | Cadmium uptake by earthworms as related to the availability in the soil and the intestine[END_REF].

On the other hand, the lack of correlation with soil pH could be attributed to the limited variation in soil pH across the sites (ranging from 7.1 to 8.3), all falling within the classification of slightly alkaline. Nevertheless, our findings corroborated the influence of soil pH in shaping the structuration of earthworm communities [START_REF] Rutgers | Mapping earthworm communities in Europe[END_REF]. More specifically, the relative abundances of the species A. rosea and A. icterica increased with increasing soil pH. On the contrary, in less alkaline soils, A. longa and A. caliginosa dominated the communities.

The presence of organic matter in soils is also known to have a profound influence on MTE bioavailability, altering MTE speciation, pH, microbial interactions, physical properties, and binding competition [START_REF] Hernandez-Soriano | Effects of soil water content and organic matter addition on the speciation and bioavailability of heavy metals[END_REF][START_REF] Nahmani | A review of studies performed to assess metal uptake by earthworms[END_REF]. Hence, increased soil organic matter levels are typically linked to diminished availability of MTEs for soil organisms [START_REF] Van Herwijnen | The effect of organic materials on the mobility and toxicity of metals in contaminated soils[END_REF]. Consistent with these results, our study revealed that adverse impacts of soil concentrations of Pb, Hg, Cu, Zn, and Cd on earthworm richness were evident solely in soils with lower organic matter content (when SOM < 65.07 ‰,63.33 ‰,58.71 ‰,59.99 ‰,and 47.14 ‰,respectively). More specifically, epi-anecic species and the uncommon intermediate species O. cyneum and O. lacteum were absent from soils exhibiting these chemical characteristics. Similarly, the inverse correlation between earthworm total abundance and subsoil Hg concentrations exhibited a tendency to weaken in soils with higher levels of organic matter (when SOM > 88.46 ‰). Surprisingly, earthworm species richness decreased with increasing Cr concentrations in the subsoil when the subsoil was relatively rich in organic matter (when SOM > 66.66 ‰). This finding contradicts the notion that organic matter, which provides electrons to the soil, aids in the conversion of Cr(VI) to the less soluble and less harmful Cr(III) [START_REF] Choppala | Chemodynamics of chromium reduction in soils: Implications to bioavailability[END_REF][START_REF] Han | Distribution, transformation and bioavailability of trivalent and hexavalent chromium in contaminated soil[END_REF]. Despite the interactive effect of MTE concentrations and SOM on earthworm richness, SOM did not significantly explain the structuration in earthworm communities. This outcome could stem from the positive correlation between Cu and SOM, leading to SOM not contributing significantly to the variance explained by the multivariate analysis. Another factor may be that four of the six species most responsive to Pb, Hg, Cu, Zn, and Cd concentrations in low-SOM soil were excluded from the multivariate analysis. This exclusion was due to their relatively low occurrence, being present in less than 10% of the 54 sampling sites.

Conclusion

In this study, we demonstrated that the concentrations of Hg, Pb, Zn, Cu, and Cd in urban soils are key environmental factors influencing earthworm community structure at local levels. Given the widespread and highly variable distribution of these MTEs in soils, we believe that accounting for MTE pollution levels will improve our understanding of earthworm distribution within urbanised landscapes [START_REF] Rutgers | Mapping earthworm communities in Europe[END_REF][START_REF] Tóth | Earthworm assemblages in urban habitats across biogeographical regions[END_REF]. Importantly, our study underscores the significant heterogeneity in soil chemical characteristics within urban lawns, emphasizing the importance of examining these relationships at a very small geographical scale. By doing this, we can also gain insight into the differences in how soils perform essential ecosystem services, including water retention, atmospheric carbon storage, and the appearance of healthy lawns (Pavao-Zuckerman and Pouyat, 2017).

Table 1. Methods used to measure soil chemical characteristics 1 Soil samples were dried at 40°C, 2 mm sieved and 150 µm ground with a ball mill (MM400 Retsch). A total of 0.5 g per sample was then digested simultaneously in 1.2 mL HNO 3 (65%) and 3.5 mL HCl (37%) during 16h at room temperature, then 2h at 100°C. Ultrapure water was added to reach a final volume of 50 mL and left to decant during 8h. Total As (189.042 nm), Cd (228.802 nm), Cr (284.325 nm), Cr (284.325), Cu (324.754 nm), Zn (213.856 nm), Ni (231.604 nm), Pb (220.353 nm), Hg (184.950) and Se (196.090 nm) concentrations were determined using an inductively coupled plasma atomic emission spectrometer (iCAP 7000 ICP-AES, Thermo Electron, USA). The ICP-AES conditions were as follows: plasma power 1150 W, nebulize gaz flow 0.5 L/min, auxiliary gas flow 0.5 L/min. The ICP-OS was calibrated before performing measurements with the use of As, Cd, Cr, Cu, Ni, Zn, Pb and Hg standard solutions (1000 ± 2 mg dm -3 in 5% HNO 3 ) from Chem-Lab. Each MTE was measured three times per sample. Relative standard deviation between the three measurements per sample were all below 10%. The blanks and certified reference materials (Internal Soil Reference Materials ISRM1 and ISRM2) were prepared and analyzed using the same methods as the samples. Calibration and blank samples were analyzed every ten measurements. Quantification limits were as follows: As: 4 mg/kg, Cd, 0.2 mg/kg, Cr: 5 mg/kg, Cu: 10 mg/kg, Zn: 25 mg/kg, Ni: 5 mg/kg, Pb: 10 mg/kg, Hg: 0.2 mg/kg and Se: 0.5 mg/kg. The concentration ranges for which the method is linear were as follows: As: 0-40 mg/kg, Cd: 0-10 mg/kg, Cr: 0-100 mg/kg, Cu: 0-1000 mg/kg, Zn: 0-500 mg/kg, Ni: 0-100 mg/kg, Pb: 0-1000 mg/kg, Hg: 0-40 mg/kg and Se: 0-40 mg/kg.

2 pH was measured using a glass electrode, in a 1:5 (m/V) diluted suspension of soil in water. Measurement with a pH meter was done after 5 min. of agitation and after a minimum contact time of 2 hours but a maximum contact time of 24 hours.

3 Conductivity was measured using a specific electrode, in a 1:5 (m/V) diluted suspension of soil in water. Conductivity was measured immediately after 30 min. of agitation.

4 Hydrochloric acid was added to the soil sample to break down all carbonates. The volume of carbon dioxide released was measured using a Bernard calcimeter.

5 Samples were previously ground to 150 µm, then weighed using a microbalance. They were combusted with oxygen and under helium flow at ca. 930°C. Carbon dioxide and nitrogen were separated by chromatography and quantified using a thermal conductivity detector. The organic matter content was calculated as 1.724 times the content in total organic carbon. 6 Extraction of soluble forms of phosphorus was done by shaking the sample in an aqueous solution of 0.1 M ammonium oxalate (pH 7.0) with a sample/solution ratio of 1/25 (m/V). The solution was stirred during 2h and filtered. The determination of phosphomolybdic acid complex, reduced by ascorbic acid in sulphuric medium, was done by spectrophotometry.

Table 2. Between park variations in soil physicochemical characteristics -Outputs of the models (F-value, P-value and adjusted R²) testing whether the variation in soil physicochemical characteristics was higher between parks than within parks. We highlight in grey the variables that did not significantly differ between parks. As: arsenic, Cd: cadmium, Cr: chromium, Cu: copper, Zn: zinc, Ni: nickel, Pb: lead; Hg: mercury. Median concentrations were calculated on the average concentration per sampling site in the topsoil and the subsoils. Recommended statutory limit values (SLV) are based on a note from the CIRE IdF from the 3 rd of July 2006 [START_REF] Mathieu | Proposition de référentiels régionaux en éléments traces métalliques dans les sols : leur utilisation dans les évaluations des risques sanitaires[END_REF]. The note did not indicate any recommended SLV for arsenic. We highlight in grey the MTEs which median concentration was above the SLV.

Figure 1 .

 1 Figure 1. Sampling sites -Map highlighting the location of the 18 Parisian parks where earthworms

Figure 2 .

 2 Figure 2. Soil physico-chemical composition -Boxplots highlighting the distribution of each of the

Figure 3 .

 3 Figure 3. Physico-chemical composition-based similarities between sampled soils -K-means

Figure 4 .

 4 Figure 4. Covariance between physico-chemical variables -Heatmaps based on Pearson

Figure 5 .

 5 Figure 5. Species abundance and frequency -(a) Aggregate abundance (across all sites combined) and mean ± se abundance per sampling site (i.e. mean number of individuals per 27 dm 3 sample) per

Figure 6 .

 6 Figure 6. Best environmental variables explaining earthworm abundance and species richness -

Figure 7 .

 7 Figure 7. Species composition -Triplot representing the 1 st (RDA1) and 2 nd (RDA2) components of

Figure 8 .

 8 Figure 8. Species distribution -Density plots representing the distribution of the physico-chemical

Table 3 .

 3 MTE concentrations -Range and median concentrations of MTEs (in mg/kg) at the 54 sampling sites within the 18 Parisian parks.

		Topsoils	Subsoils
	pH	F=17.33, P<0.001, R²=0.84	F=9.53, P<0.001, R²=0.73
	Resistivity F=3.08, P=0.002, R²=0.40	F=0.55, P=0.908, R²=-0.17
	CO 3	F=3.44, P=0.001, R²=0.44	F=3.04, P=0.002, R²=0.40
	ON	F=4.60, P<0.001, R²=0.54	F=2.19, P=0.023, R²=0.28
	C/N	F=2.31, P=0.017, R²=0.30	F=1.48, P=0.160, R²=0.13

ACKNOWLEDGEMENTS

This work was carried out with the help of two master's students -Mégane Philippé and Aleksander Miedziejewski, and one bachelor's student -Neyla Turak, to whom we are very grateful. Moreover, we would like to thank the "Direction des Espaces verts et de l'Environnement" for allowing me to sample earthworms and soil in the parks in Paris. This work was supported by the Austrian Science Fund [grant numbers V-953].

During the preparation of this work the author(s) used ChatGPT in order to correct grammar. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.


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GW150914: The Advanced LIGO Detectors in the Era of First Discoveries

Keywords: numbers: 04.80.Nn, 95.55.Ym, 95.75.Kk, 07.60.Ly

Following a major upgrade, the two advanced detectors of the Laser Interferometer Gravitationalwave Observatory (LIGO) held their first observation run between September 2015 and January 2016. With a strain sensitivity of 10 -23 / √ Hz at 100 Hz, the product of observable volume and measurement time exceeded that of all previous runs within the first 16 days of coincident observation. On September 14th, 2015 the Advanced LIGO detectors observed a transient gravitational-wave signal determined to be the coalescence of two black holes [1], launching the era of gravitationalwave astronomy. The event, GW150914, was observed with a combined signal-to-noise ratio of 24 in coincidence by the two detectors. Here we present the main features of the detectors that enabled this observation. At full sensitivity, the Advanced LIGO detectors are designed to deliver another factor of three improvement in the signal-to-noise ratio for binary black hole systems similar in masses to GW150914.

Introduction -On September 14th, 2015, both Advanced LIGO detectors in the USA, H1 in Hanford, Washington and L1 in Livingston, Lousiana, made the first direct measurement of gravitational waves [START_REF] Abbott | Observation of gravitational waves from a binary black hole merger[END_REF]. The event, GW150914, was determined to be the merger of two black holes, with masses of 36 M and 29 M , into a black hole of approximately 62 M [START_REF] Abbott | Properties of the binary black hole merger GW150914[END_REF]. 3.0 solar masses of energy ( 5.4 × 10 47 J) was radiated in gravitational waves. The gravitational waves from this event, which occurred at a distance of 410 Mpc 1.3 ×10 9 light years, changed the separation between the test masses by 4×10 -18 m, about one 200th of a proton radius.

The Advanced LIGO detectors, multi-kilometer Michelson-based interferometers [START_REF] Aasi | Advanced LIGO[END_REF], came online in September 2015, after a major upgrade targeting a factor of 10 sensitivity improvement over initial detectors [START_REF] Abbott | LIGO: the Laser Interferometer Gravitational-wave Observatory[END_REF][START_REF] Accadia | Virgo: a laser interferometer to detect gravitational waves[END_REF].

While not yet at design sensitivity during their first observation run, they have already exceeded the strain sensitivity of the initial detectors across the entire frequency band, significantly surpassing the past discovery potential. This paper describes the Advanced LIGO detectors, as well as their current and final design sensitivity, at the inception of gravitational-wave astronomy [START_REF] Abbott | Astrophysical implications of the binary black hole merger GW150914[END_REF][START_REF] Abbott | Tests of general relativity with GW150914[END_REF][START_REF] Abbott | GW150914: Implications for the stochastic gravitational-wave background from binary black holes[END_REF][START_REF] Abbott | Australian Square Kilometer Array Pathfinder (ASKAP) Collaboration, Dark Energy Survey and the Dark Energy Camera GW-EM Collaborations, The Fermi GBM and LAT Collaborations[END_REF][START_REF] Adrián-Martínez | High-energy neutrino followup search of gravitational wave event GW150914 with ANTARES and IceCube[END_REF].

Astrophysical Reach -In general relativity, a gravitational wave far from the source can be approximated as a time-dependent perturbation of the space-time metric, expressed as a pair of dimensionless strain polarizations, h + and h × [START_REF] Thorne | Gravitational radiation[END_REF]. An interferometric gravitational-wave detector acts as a transducer to convert these space-time perturbations into a measurable signal [START_REF] Weiss | Electromagnetically coupled broadband gravitational antenna[END_REF]. The interferometer mirrors act as 'freely falling' test masses. Advanced LIGO measures linear differential displacement along the arms which is proportional to the gravitationalwave strain amplitude. We define the differential displacement as ∆L = δL x -δL y , where L x = L y = L are the lengths of two orthogonal interferometer arms. The gravitational-wave strain and the interferometer displacement are related through the simple equation ∆L = h L, where h is a linear combination of h + and h × .

The tiny displacements induced by astrophysical events demand that the interferometer mirrors be free from environmental disturbances and require a highly sensitive interferometric transducer-designed to be limited only by disturbances arising from fundamental physics consideration.

Since the interferometer response to displacement, or equivalently gravitationalwave strain, is frequency dependent, it is necessary to represent the limiting detector noises as functions of frequency normalized by the interferometer response.

The left panel of Figure 1 shows the amplitude spectral density of the total strain noise in units of strain per √ Hz during the first observation run (O1 run) and, for comparison, during the final science run of the initial LIGO detectors (S6 run). In the detectors' most sensitive frequency band between 100 Hz and 300 Hz, the O1 strain noise is 3 to 4 times lower than achieved in the S6 run. At 50 Hz, the improvement is about a factor of 30.

The right panel of Figure 1 shows the single detector signal-to-noise ratio (SNR) for an optimally oriented compact binary system consisting of two 30 M black holes as a function of redshift z, and for different interferometer configurations. The observed strain amplitude is largest for a source whose orbital plane is parallel to the detector's plane and is located straight above or below; we refer to such a source as optimally oriented. The SNR is computed in the frequency domain [START_REF] Finn | Binary inspiral, gravitational radiation, and cosmology[END_REF] using standard cosmology [START_REF] Ade | Planck 2013 results. XVI. Cosmological parameters[END_REF] and phenomenological waveforms which account for inspiral, merger and ringdown, but not spins [START_REF] Ajith | Inspiral-merger-ringdown waveforms for black-hole binaries with nonprecessing spins[END_REF].

A Michelson interferometer lacks good directional sensitivity to gravitational waves. The antenna pattern covers approximately half the sky, both above and beneath the plane of the detector. Moreover, the antenna patterns [START_REF] Miller | Prospects for doubling the range of Advanced LIGO[END_REF] are shown to highlight the discovery potential in the coming years. The right plot shows the single detector signal-to-noise ratio (SNR) under optimal orientation as function of redshift z-for two merging black holes with mass 30 M each. GW150914 was not optimally orientated and was detected with a single detector SNR of 13 to 20 at z = 0.09; this event would not have been seen in S6.

of the two LIGO detectors are aligned to maximize the coincident detection of gravitational-wave signals, constrained to the 10 ms inter-site propagation time. The coincidence constraint substantially rejects non-Gaussian noise and vetoes local transients.

The observed strain amplitude is inversely proportional to the luminosity distance. For small redshifts, z < 1, the observable volume, and thus the detection rate, grows as the cube of the detector sensitivity. The number of detected events is expected to scale with the product of observing volume and observing time. Between September 12 and October 20 the H1 and L1 detectors had a duty cycle of 70% and 55%, respectively, while the observing time in coincidence was 48%. After data quality processing [START_REF] Abbott | Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914[END_REF], 16 days of data were analyzed around GW150914, resulting in a time-volume product of 0.1 Gpc 3 yr for binary black hole systems with masses similar to GW150914 [START_REF] Abbott | The rate of binary black hole mergers inferred from Advanced LIGO observations surrounding GW150914[END_REF].

The Displacement Measurement -The current generation of advanced detectors use two pairs of test masses as coordinate reference points to precisely measure the distortion of the space-time between them. A pair of input and end test masses is located in each of the two arms of a Michelson laser interferometer, as shown in Figure 2. The Advanced LIGO test masses are very pure and homogeneous fused silica mirrors of 34 cm diameter, 20 cm thickness and 40 kg mass.

It is critical that the test masses be free from sources of displacement noise, such as environmental disturbances from seismic noise, or thermally driven motion. These noise sources are most relevant at frequencies below 100 Hz, while shot noise of the optical readout is dominant at high frequencies. Figure 3 shows the measured displacement noise of Advanced LIGO during the first observing run, together with the major individual contributions, as discussed below.

To reduce the effects of ground vibrations, the test masses are suspended by multi-stage pendulums [START_REF] Aston | Update on quadruple suspension design for Advanced LIGO[END_REF], thus acting as free masses well above the lowest pendulum resonance frequency of 0.4 Hz. Monolithic fused silica fibers [START_REF] Braginski | Oscillators for free-mass gravitational antennas[END_REF] are incorporated at the bottom stage to decrease suspension thermal noise [START_REF] Cumming | Design and development of the advanced LIGO monolithic fused silica suspension[END_REF], which limits the useful frequencies to 10 Hz and above. The Advanced LIGO test masses require about 10 orders of magnitude suppression of ground motion above 10 Hz. The multistage pendulum system attenuates the ground motion by seven orders of magnitude. It is mounted on an actively controlled seismic isolation platform which provides three orders of magnitude of isolation of its own [START_REF] Matichard | Seismic isolation of Advanced LIGO: review of strategy, instrumentation and performance[END_REF][START_REF] Wen | Hydraulic external pre-isolator system for LIGO[END_REF]. Moreover, these platforms are used to reduce the very large displacements produced by tidal motion and microseismic activity. Tidal forces can produce displacements up to several 100 µm over a multi-kilometer baseline on time scales of hours. The dominant microseismic activity is driven by ocean waves. The resulting ground motion can be as large as several µm at frequencies around 0.15 Hzeven far inland.

The entire test mass assembly including the suspension system and part of the seismic isolation system resides inside an ultra-high vacuum system, with pressures typically below 1 µPa over the 10, 000 m 3 volume, to prevent acoustic shorting of the seismic isolation systems and to reduce Rayleigh scattering in the optical readout.

The test masses are also susceptible to changes in the local gravitational field caused by changing mass distributions in their vicinity. While not limiting presently, at design sensitivity this time-dependent Newtonian noise source possibly becomes relevant below 20 Hz, and might require active cancellation [START_REF] Cella | Off-line subtraction of seismic Newtonian noise[END_REF][START_REF] Driggers | Subtraction of Newtonian noise using optimized sensor arrays[END_REF]. Thermally driven motion is another important source of displacement noise. It includes the Brownian motion of the suspension system [START_REF] Saulson | Thermal noise in mechanical experiments[END_REF] as well as the test masses [START_REF] Levin | Internal thermal noise in the ligo test masses: a direct approach[END_REF], and mechanical loss in the mirror optical coatings [START_REF] Harry | Optical coatings and thermal noise in precision measurement[END_REF]. The mirror coatings, a dielectric multilayer of silica and titania-doped tantala [START_REF] Harry | Titania-doped tantala/silica coatings for gravitational-wave detection[END_REF][START_REF] Granata | Mechanical loss in state-of-the-art amorphous optical coatings[END_REF], were developed to provide the required high reflectivity while minimizing coating thermal noise [START_REF] Flaminio | A study of coating mechanical and optical losses in view of reducing mirror thermal noise in gravitational wave detectors[END_REF][START_REF] Agresti | Optimized multilayer dielectric mirror coatings for gravitational wave interferometers[END_REF][START_REF] Villar | Measurement of thermal noise in multilayer coatings with optimized layer thickness[END_REF]; at design it will eventually limit the sensitivity around 60 Hz [START_REF] Aasi | Advanced LIGO[END_REF].

The predicted levels for seismic, thermal and Newtonian noise sources are summarized in Figure 3 and compared to the total measured displacement noise. They are currently not limiting the sensitivity due to the presence of other technical noise sources, summarized in the next section and detailed in Ref. [START_REF] Abbott | The sensitivity of the Advanced LIGO detectors at the beginning of gravitational wave astronomy[END_REF].

Quantum noise in the interferometer arises from the discrete nature of photons and their Poisson-distributed arrival rate [START_REF] Caves | Quantum-mechanical radiation-pressure fluctuations in an interferometer[END_REF][START_REF] Braginsky | Quantum Measurement[END_REF][START_REF] Mcclelland | Advanced interferometry, quantum optics and optomechanics in gravitational wave detectors[END_REF]. The momentum transfer of individual photons hitting a test mass gives rise to radiation pressure noise. Quantum radiation pressure noise scales as 1/mf 2 , where m is the mass of the mirror and f the frequency, and therefore it is most significant at lower frequencies.

Photon shot noise arises from statistical fluctuations in the photon arrival time at the interferometer output, and it is a fundamental limit of the transducer in sensing changes of the differential arm length. The importance of shot noise decreases as the inverse square-root of the laser power circulating in the interferometer arms. During the first observing run, Advanced LIGO was operating with 100 kW of circulating laser power. The corresponding quantum noise curve, comprising both low frequency radiation pressure noise and high frequency shot noise, is shown in Figure 3; it is limiting at frequencies above 70 Hz. In the upcoming years, we plan to increase the circulating laser power up to 750 kW, and thus reduce the shot noise contribution.

Coincident detection between the two LIGO observatories is used to reject transient environmental disturbances. Both observatory sites deploy seismometers, accelerometers, microphones, magnetometers, radio receivers, weather sensors, AC-power line monitors, and a cosmic ray detector for vetoes and characterization of couplings [START_REF] Effler | Environmental influences on the LIGO gravitational wave detectors during the 6th science run[END_REF].

Interferometric Transducer -The Advanced LIGO detector uses a modified Michelson laser interferometer to translate strain into an optical phase shift [START_REF] Aasi | Advanced LIGO[END_REF]. Similar to an electromagnetic receiver, the optimal antenna length for a gravitational-wave detector is a quarter wavelength. For a gravitational wave at 100 Hz this is 750 km. The Advanced LIGO interferometer arms are 4 km long and employ an optical resonator between the input and end test masses that multiplies the physical length by the effective number of round-trips of the light. However, the physical length cannot be arbitrarily short, because test mass displacement noises are multiplied by the same factor.

The output port of the Michelson interferometer is held at an offset from a dark fringe, resulting in a small amount of light leaving the output port [START_REF] Fricke | DC readout experiment in Enhanced LIGO[END_REF]. A differential optical phase shift will then decrease or increase the amount of light, depending which interferometer arm is momentarily stretched or squeezed by a passing gravitational wave. This light signal is measured by a photodetector, digitized and calibrated [START_REF] Abbott | Calibration of the Advanced LIGO detectors for the discovery of the binary black-hole merger GW150914[END_REF], before being sent to the analysis pipelines [START_REF] Abbott | Observing gravitationalwave transient GW150914 with minimal assumptions[END_REF][START_REF] Abbott | GW150914: First results from the search for binary black hole coalescence with Advanced LIGO[END_REF].

The calibration factor that converts detected laser light power to mirror displacement is measured by applying a known force to a test mass [START_REF] Goetz | Precise calibration of LIGO test mass actuators using photon radiation pressure[END_REF]. An auxiliary 1047nm wavelength laser is reflected off the end test mass and modulated in intensity to generate a varying force. The response of the optical transducer is measured by sweeping the modulation frequency through the entire The sum of all known noise sources accounts for most of the observed noise with the exception of the frequency band between 20 Hz and 100 Hz. This will be the focus of future commissioning to full sensitivity. The quantum noise includes both shot noise and radiation pressure noise. Thermal noise includes terms due to the suspensions, the test masses and the coatings. Seismic noise is the ground displacement attenuated through the seismic isolation system and the suspensions. Cross couplings from the auto-alignment system and from the auxiliary lengths are combined into the trace labelled "other DOF" (degrees-of-freedom). Newtonian gravitational noise is estimated from density perturbations due to surface ground motion. The strong line features are due to the violin modes of the suspension wires, the roll and bounce modes of the suspensions, the AC power line and its harmonics, and the calibration lines. Not shown are numerous noise sources that do not contribute significantly-such as laser frequency, intensity and beam jitter noise, sensor and actuation noise, and Rayleigh scattering by the residual gas [START_REF] Abbott | The sensitivity of the Advanced LIGO detectors at the beginning of gravitational wave astronomy[END_REF].

detection band. It is also tracked by a set of fixed frequency lines. This way, the calibrated strain readout is computed in real-time with less than 10% uncertainty in amplitude. The overall variability of the detector's sensitivity was about ±10%.

The main light source is a pre-stabilized 1064-nm wavelength Nd:YAG laser. It is followed by a high power amplifier stage, capable of generating a maximum output power of 180 W [START_REF] Kwee | Stabilized high-power laser system for the gravitational wave detector Advanced LIGO[END_REF]. During the first observation run, only 20 W were injected into the interferometer. A triangular optical resonator of 32.9 m round-trip length is placed between the laser source and the interferometer to reject higher order transverse optical modes and to stabilize the laser frequency further [START_REF] Mueller | The Advanced LIGO input optics[END_REF]. At the output port, a bow-tie optical resonator of 1.3 m round-trip length is used to reject unwanted spatial and frequency components on the light. Optical curvature mismatch of the interferometer mirrors is caused by manufacturing imperfections and by thermal lensing due to heating from the main laser beam. A thermal compensation sys-tem provides active correction by means of ring heaters arranged around the test masses and a set of CO 2 lasers for central heating [START_REF] Brooks | Direct measurement of absorptioninduced wavefront distortion in high optical power systems[END_REF].

The Advanced LIGO detector uses coupled optical resonators to maximize the sensitivity of the interferometric transducer. These optical resonators enhance the light power circulating in each arm while simultaneously optimizing the effective antenna length and the gravitationalwave signal bandwidth [START_REF] Drever | The Detection of Gravitational Waves[END_REF][START_REF] Drever | Quantum optics[END_REF][49][START_REF] Meers | Recycling in laser-interferometric gravitational-wave detectors[END_REF][START_REF] Mizuno | Resonant sideband extraction: a new configuration for interferometric gravitational wave detectors[END_REF]. As the interferometer is held near a dark fringe, most of the light is reflected back to the laser source. Adding a partially transmissive mirror at the interferometer input forms an optical resonator, leading to a power gain of 35 to 40 at the beamsplitter. The optical resonator in the interferometer arms enhances the circulating power by another factor of 300. Thus, 20 W of laser power entering the interferometer results in nearly 100 kW circulating in each arm. A partially reflective mirror is also placed at the output port to enhance the signal extraction and to increase the detector bandwidth. The resulting differential pole frequency or detector bandwidth is 335 Hz (H1) and 390 Hz (L1) [START_REF] Abbott | The sensitivity of the Advanced LIGO detectors at the beginning of gravitational wave astronomy[END_REF]. The optical mode matching in the output resonator is worse for H1.

All of these coupled optical resonators require servo controls to be brought and held on resonance [START_REF] Staley | Achieving resonance in the Advanced LIGO gravitational-wave interferometer[END_REF]. The lengths of the optical resonators in the interferometer arms are stabilized to less than 100 fm, whereas the lengths of the other coupled resonators are kept within 1 to 10 pm [START_REF] Abbott | The sensitivity of the Advanced LIGO detectors at the beginning of gravitational wave astronomy[END_REF]. Similarly, the interferometer test masses are aligned within tens of nanoradians relative to the optical axis for optimal performance. The noise arising from sensing and control of these extra degreesof-freedom are combined together in the curve labeled "other DOF" in Figure 3. The Pound-Drever-Hall reflection locking technique is used to sense the auxiliary longitudinal degrees-of-freedom [START_REF] Drever | Laser phase and frequency stabilization using an optical resonator[END_REF][START_REF] Fritschel | Readout and control of a powerrecycled interferometric gravitational-wave antenna[END_REF], while an interferometric wavefront sensing scheme is deployed for the alignment system [START_REF] Morrison | Automatic alignment of optical interferometers[END_REF][START_REF] Barsotti | Alignment sensing and control in Advanced LIGO[END_REF]. The differential arm length is controlled by the same technique during lock acquisition, but switched to the offset locking technique described above when observing. Digital servo systems are used to feed control signals back to actuators which steer the relative longitudinal positions and orientations of the interferometer mirrors. To prevent reintroducing ground motion onto the test masses, electrostatic actuators [START_REF] Affeldt | Advanced techniques in GEO 600[END_REF] are mounted to a second quadruple pendulum known as the reaction chain. Only test masses use reaction chains; all other interferometer mirrors use coil actuators mounted on a rigid structure surrounding the suspensions.

Servo controls are also necessary for damping the plethora of normal modes of the pendular suspensions and for stabilizing the seismic isolation system to an inertial reference frame. Moreover, at high laser power, optical springs introduce angular instabilities due to photon radiation pressure-induced torques acting on the mirrors [START_REF] Sidles | Optical torques in suspended Fabry-Perot interferometers[END_REF][START_REF] Dooley | Angular control of optical cavities in a radiation-pressure-dominated regime: the Enhanced LIGO case[END_REF], while the mirror acoustic modes introduce parametric instabilities [START_REF] Braginsky | Analysis of parametric oscillatory instability in power recycled LIGO interferometer[END_REF][START_REF] Evans | Observation of parametric instability in Advanced LIGO[END_REF]. At the current laser power only one acoustic mode is unstable which can be tuned away by the ring heaters. Together with thermal heating, angular optical springs and multiple parametric instabilities are the main challenges that need to be overcome to increase the circulating laser power; both will require active damping for stable operations.

Overall, more than 300 digital control loops with bandwidths spanning from sub-Hz to hundreds of kHz are employed to keep each Advanced LIGO interferometer operating optimally during observation. The digital control computers also serve as the data acquisition system that continuously writes on the order of 10 5 channels of time series data to disk, at a rate of 12 MB/s. It is synchronized to GPS to better than 10 µs [START_REF] Abbott | Calibration of the Advanced LIGO detectors for the discovery of the binary black-hole merger GW150914[END_REF]. A statebased automation controller provides hands-free running during operations.

Outlook -The global gravitational-wave network will be significantly enhanced in the upcoming years. In 2016 Advanced LIGO will be joined by Advanced Virgo, the 3 km detector located near Pisa, Italy [START_REF] Acernese | Advanced Virgo: a second-generation interferometric gravitational wave detector[END_REF]. The Japanese KAGRA interferometer [START_REF] Aso | Interferometer design of the KAGRA gravitational wave detector[END_REF] and a possible third LIGO detector in India [64] will provide a global network that allows for improved parameter estimation and sky localization [START_REF] Abbott | Prospects for observing and localizing gravitational-wave transients with Advanced LIGO and Advanced Virgo[END_REF]. Achieving design sensitivity with the network of current detectors will define earthbound gravitational-wave astrophysics in the near future. Looking further ahead, we can envision current technologies leading to a factor of two improvement over the Advanced LIGO design sensitivity [START_REF] Miller | Prospects for doubling the range of Advanced LIGO[END_REF], so that events such as GW150914 could be detected with SNRs up to 200. More dramatic improvements will require significant technology development and new facilities.

FIG. 2 .

 2 FIG. 2. Interferometer configuration and test mass setup.Each arm of the Michelson interferometer includes two suspended test masses. The two test masses are placed 4 km apart and form an optical resonator with a gain of 300. The suspension system is shown on the right, each test mass is at the bottom of a quadruple pendulum. It provides high isolation above the resonance frequencies which range from 0.4 Hz to 13 Hz. The test mass is attached to the penultimate mass through fused silica fibers providing a high mechanical quality factor which lowers the thermal noise. The other stages use steel wire. The attachment point to the seismic isolation system as well as stages 1 and 2 implement cantilever springs for vertical isolation. Each test mass is accompanied by its own reaction chain to minimize actuation noise. Coil actuators are mounted to the upper stages of the reaction chain and an electrostatic actuator is implemented at the bottom stage. Shown on the left are the other optics of the Michelson interferometer with the beamsplitter and the perpendicular arm. The two optics at the interferometer input and output port comprise the coupled resonator system which amplifies the response of the optical transducer.

FIG. 3 .

 3 FIG. 3. The displacement sensitivity of the Advanced LIGO detector in Hanford during the first observation run O1; the Livingston detector has a similar sensitivity, as shown in Figure 1.The sum of all known noise sources accounts for most of the observed noise with the exception of the frequency band between 20 Hz and 100 Hz. This will be the focus of future commissioning to full sensitivity. The quantum noise includes both shot noise and radiation pressure noise. Thermal noise includes terms due to the suspensions, the test masses and the coatings. Seismic noise is the ground displacement attenuated through the seismic isolation system and the suspensions. Cross couplings from the auto-alignment system and from the auxiliary lengths are combined into the trace labelled "other DOF" (degrees-of-freedom). Newtonian gravitational noise is estimated from density perturbations due to surface ground motion. The strong line features are due to the violin modes of the suspension wires, the roll and bounce modes of the suspensions, the AC power line and its harmonics, and the calibration lines. Not shown are numerous noise sources that do not contribute significantly-such as laser frequency, intensity and beam jitter noise, sensor and actuation noise, and Rayleigh scattering by the residual gas[START_REF] Abbott | The sensitivity of the Advanced LIGO detectors at the beginning of gravitational wave astronomy[END_REF].

Acknowledgement -The authors gratefully acknowledge the support of the United States National Science Foundation (NSF) for the construction and operation of the LIGO Laboratory and Advanced LIGO as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS) and the Foundation for Fundamental Research on Matter supported by the Netherlands Organisation for Scientific Research, for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, Department of Science and Technology, India, Science & Engineering Research Board (SERB), India, Ministry of Human Resource Development, India, the Spanish Ministerio de Economía y Competitividad, the Conselleria d'Economia i Competitivitat and Conselleria d'Educació, Cultura i Universitats of the Govern de les Illes Balears, the National Science Centre of Poland, the European Commission, the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the Lyon Institute of Origins (LIO), the National Research Foundation of Korea, Industry Canada and the Province of Ontario through the Ministry of Economic Development and Innovation, the Natural Science and Engineering Research Council Canada, Canadian Institute for Advanced Research, the Brazilian Ministry of Science, Technology, and Innovation, Russian Foundation for Basic Research, the Leverhulme Trust, the Research Corporation, Ministry of Science and Technology (MOST), Taiwan and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, MPS, INFN, CNRS and the State of Niedersachsen/Germany for provision of computational resources.

This document has been assigned the LIGO Laboratory document number LIGO-P1500237.