Franciska T De Vries
email: f.t.devries@uva.nl

Cécile Thion

Michael Bahn

Benoît Bergk Pinto

Sébastien Cécillon

Helen Grant

Graeme W Nicol

Wolfgang Wanek

James I Prosser

Richard D Bardgett

Glacier forelands reveal fundamental plant and microbial controls on ecosystem N retention

Keywords: nitrogen, carbon, plant-soil interactions, soil microbial community, global change, nitrogen deposition, glacier foreland, succession

The retention of nitrogen (N) is a crucial ecosystem function of both managed and natural ecosystems, and there is a long history of experimental, observational, and conceptual studies identifying its major controls. Yet, the plant and soil microbial controls on the retention of added N remain elusive. Here, we used three ecosystem chronosequences in front of retreating Alpine glaciers to test our hypothesis that the retention of reactive N increases with succession, and to identify the plant and microbial controls of ecosystem N retention. We found that the uptake and retention of N was not affected by succession, despite consistent changes in plant, soil, and microbial properties with increasing time since deglaciation. Instead, we found that plant and microbial properties that did not change with succession controlled 15 N uptake and retention: low root and microbial C/N ratios, as well as high root biomass, increased plant and microbial uptake of added N. In addition, high soil concentrations of nitrate and ammonium reduced the uptake of N in microbes and roots, respectively. These results indicate that plant and microbial N demand, as well as N limitation, are driving ecosystem N retention in glacier forelands. Our findings advance our understanding of the fundamental controls on ecosystem N retention and the role of plant-microbial interactions in this process. This understanding is crucial for predicting and mitigating the response of terrestrial ecosystems to the everincreasing amounts of reactive N in the biosphere.

Significance statement

Human activities have massively increased the amount of reactive nitrogen in the biosphere, which is leading to increased N inputs in terrestrial ecosystems. This excess N can affect plant and soil communities and reduce their diversity, affect the functioning of ecosystems, and exacerbate climate change. The retention of N is an important ecosystem function, yet we have little understanding of the role of plants and soil microbes in this process. We show that plants and soil microbes together take up a significant part of added N, and that this uptake is determined by their N demand and by the availability of soil N. These findings might help predict and mitigate the effects of N enrichment of the terrestrial biosphere.

Introduction

Nitrogen (N) retention is a fundamental and important ecosystem function, particularly in the face of global nitrogen enrichment. The ability of terrestrial ecosystems to retain reactive N is an indicator of the extent to which they are N-limited, as well as being a key property of natural and managed ecosystems, where it regulates N losses that cause eutrophication of aquatic ecosystems and exacerbate climate change [START_REF] Schlesinger | On the fate of anthropogenic nitrogen[END_REF]. Ecosystem N retention and its counterpart N loss are regulated by both abiotic factors, such as parent material and climate, and biotic factors, such as plant and soil microbial properties [START_REF] Templer | Sinks for nitrogen inputs in terrestrial ecosystems: a metaanalysis of N-15 tracer field studies[END_REF]. While many empirical and theoretical studies have examined the role of plant and soil properties, and their nutrient stoichiometry, in regulating ecosystem N retention, our understanding of the underpinning mechanisms remains elusive. In particular, despite widespread acknowledgement of the importance of soil microbes in immobilising reactive N and reducing ecosystem N leaching losses, it is still unresolved which soil microbial community attributes are responsible for regulating ecosystem N retention, and how these are linked to plant communities and soil physicochemical properties [START_REF] Lajtha | Nutrient retention and loss during ecosystem succession: revisiting a classic model[END_REF].

Early studies on terrestrial ecosystem N retention demonstrated that old-growth forests have much larger N-leaching losses than early-successional forests [START_REF] Vitousek | ECOSYSTEM SUCCESSION AND NUTRIENT RETENTION -HYPOTHESIS[END_REF]. This work led to the classic conceptual model of Vitousek and Reiners, who proposed that actively growing plants in early to mid-successional ecosystems have high rates of N uptake, and, as such, these systems have high N retention [START_REF] Vitousek | ECOSYSTEM SUCCESSION AND NUTRIENT RETENTION -HYPOTHESIS[END_REF]. In contrast, late-successional systems, with slower plant growth and biomass accumulation dominated by mature trees or plant communities consisting of slowgrowing species, are more 'leaky' of N. However, many observational studies do not support this classic model [START_REF] Lajtha | Nutrient retention and loss during ecosystem succession: revisiting a classic model[END_REF][START_REF] Lovett | Nutrient retention during ecosystem succession: a revised conceptual model[END_REF]. Recent work for instance has shown that ecosystems can differ strongly in their retention of reactive N and that soil, litter, and microbial biomass pools retain more N than aboveground plant biomass, and are thus more important controls on ecosystem N retention than plant N uptake [START_REF] Templer | Sinks for nitrogen inputs in terrestrial ecosystems: a metaanalysis of N-15 tracer field studies[END_REF]. In particular, the accumulation of soil organic matter and higher C/N ratios of soil and litter have been linked to greater ecosystem N retention through increased microbial N immobilisation [START_REF] Lajtha | Nutrient retention and loss during ecosystem succession: revisiting a classic model[END_REF][START_REF] Lovett | Nutrient retention during ecosystem succession: a revised conceptual model[END_REF][START_REF] Vitousek | AND NITROGEN LOSSES IN AN INTENSIVELY MANAGED LOBLOLLY-PINE PLANTATION[END_REF], although the microbial community attributes associated with these changes are poorly understood.

Recent studies have demonstrated the importance of interactions between plants and soil microbial communities in determining ecosystem N retention. For example, field studies in temperate grasslands show that N retention is greatest, and N leaching losses lowest, in grasslands characterised by plant communities dominated by slow-growing, nutrientconservative species, and soil microbial communities dominated by fungi [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF][START_REF] De Vries | Fungal/bacterial ratios in grasslands with contrasting nitrogen management[END_REF][START_REF] Grigulis | Relative contributions of plant traits and soil microbial properties to mountain grassland ecosystem services[END_REF][START_REF] Bardgett | Soil microbes compete effectively with plants for organic-nitrogen inputs to temperate grasslands[END_REF]. The general mechanism underpinning this observation is that soils with low N availability select for N-conservative plant species and fungal-dominated microbial communities [START_REF] De | Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities[END_REF], which compete for and immobilise significant amounts of N, thereby enhancing N retention [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF]. Moreover, Nconservative plant species and fungi have slower growth and lower turnover rates than fastgrowing plants and bacteria, and thus the N taken up in their tissues is retained for longer, again reducing the availability and mineralisation of N in the soil [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF]. In contrast, controlled common garden experiments have shown that fast-growing plant species and bacteria-dominated microbial communities have the highest N uptake and retention [START_REF] Myrold | Potential importance of bacteria and fungi in nitrate assimilation in soil[END_REF][START_REF] Grassein | Relationships between functional traits and inorganic nitrogen acquisition among eight contrasting European grass species[END_REF][START_REF] De Vries | Plant community controls on short-term ecosystem nitrogen retention[END_REF], potentially indicating that different mechanisms might underlie ecosystem N retention under controlled conditions compared to field conditions. In particular, under field conditions, other factors than plant and microbial N uptake rates might explain ecosystem N retention, such as high soil C stocks and soil C/N ratios [START_REF] Manning | Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks[END_REF], with reduced availability of N for plant and microbial uptake. Under such conditions, plants might employ other strategies to acquire their nitrogen, for example reducing the availability of N by releasing polyphenols, and by taking up organic N forms directly or through associations with mycorrhizal fungi [START_REF] Northup | Polyphenol control of nitrogen release from pine litter[END_REF][START_REF] Wurzburger | Plant litter chemistry and mycorrhizal roots promote a nitrogen feedback in a temperate forest[END_REF]. Thus, the factors affecting ecosystem N retention are complex and highly interlinked, which complicates identifying the plant and microbial properties that underlie increased ecosystem N retention in the real world.

Glacier forelands are excellent model systems for studying how fundamental ecological processes change during ecosystem succession, and studies on these systems have led to major advances in our understanding of the role of plant-soil interactions in ecosystem development [START_REF] Chapin | Mechanisms of primary succession following deglaciation at Glacier Bay, Alaska[END_REF][START_REF] Crocker | Soil development in relation to vegetation and surface age at Glacier Bay, Alaska[END_REF][START_REF] Walker | The use of chronosequences in studies of ecological succession and soil development[END_REF]. As glaciers retreat, they expose barren parent material, which is then colonised by microbes and plants and undergoes primary succession, thus creating a successional gradient with increasing distance from the glacier front. These space-for-time substitutions show predictable changes with increasing distance from the glacier forefront: plant and soil microbial biomass increase, soil organic C and N accumulate, and microbial communities become more dominated by fungi [START_REF] Bernasconi | Chemical and Biological Gradients along the Damma Glacier Soil Chronosequence, Switzerland[END_REF][START_REF] Ohtonen | Ecosystem properties and microbial community changes in primary succession on a glacier forefront[END_REF][START_REF] Tscherko | Functional diversity of the soil microflora in primary succession across two glacier forelands in the Central Alps[END_REF]. In line with the increase in soil C and N with age since deglaciation, the abundance of N-cycling genes related to the decomposition of organic material [START_REF] Zeng | Primary Succession of Nitrogen Cycling Microbial Communities Along the Deglaciated Forelands of Tianshan Mountain, China[END_REF] and ammonia oxidation (25) increases, while that of genes involved in denitrification decreases [START_REF] Brankatschk | Abundances and potential activities of nitrogen cycling microbial communities along a chronosequence of a glacier forefield[END_REF][START_REF] Kandeler | Abundance of narG, nirS, nirK, and nosZ genes of denitrifying bacteria during primary successions of a glacier foreland[END_REF]. Moreover, while vegetation in the early stages of glacier foreland sites is limited by N and in later stages by P [START_REF] Chapin | Mechanisms of primary succession following deglaciation at Glacier Bay, Alaska[END_REF], findings on nutrient limitation of soil microbes are inconsistent, and seem to depend on variations in soil properties within and between glacier forelands [START_REF] Goransson | Soil bacterial growth and nutrient limitation along a chronosequence from a glacier forefield[END_REF][START_REF] Yoshitake | Carbon and nitrogen limitation of soil microbial respiration in a High Arctic successional glacier foreland near Nyangstrom lesund, Svalbard[END_REF][START_REF] Castle | Nutrient limitation of soil microbial activity during the earliest stages of ecosystem development[END_REF]. Thus, these systems provide a unique opportunity to study how ecosystem N retention changes during primary succession, while at the same time displaying enough variation within successional stages to identify the major plant and microbial controls of ecosystem N uptake and retention [START_REF] Walker | The use of chronosequences in studies of ecological succession and soil development[END_REF][START_REF] Bradley | Microbial community dynamics in the forefield of glaciers[END_REF].

Here, we aimed to identify the fundamental plant and microbial controls on ecosystem N uptake and retention using three distinct glacier forelands in the European Alps as model systems. We hypothesised that ecosystem N retention increases with ecosystem succession as a result of increased abundance of slow-growing, N conservative plants which associate with fungaldominated soil microbial communities, and the build-up of soil organic matter. Specifically, we hypothesised that plant communities dominated by N-conservative species would select for Nconservative microbial communities, which, together with an increase in soil C/N ratio, would increase microbial immobilisation of N and decrease N leaching losses. We also identified changes in taxonomic and functional microbial community composition associated with this hypothesised increase in microbial N immobilisation. In particular, we assessed changes in the abundance of soil fungi, bacteria and ammonia-oxidising archaea and bacteria, which play a key role in nitrification. This was achieved by collecting intact soil cores from five locations in each of five successional stages [START_REF] Northup | Polyphenol control of nitrogen release from pine litter[END_REF][START_REF] Sun | Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories[END_REF][START_REF] Zeileis | Measuring Inequality, Concentration, and Poverty. R package version 0.2[END_REF]112, and 157 years old) and one reference site (deglaciated around 2,000 years ago) in three well-documented, geographically distinct glacier forelands in the European Alps: the Damma Glacier in Switzerland [START_REF] Bernasconi | Chemical and Biological Gradients along the Damma Glacier Soil Chronosequence, Switzerland[END_REF], and the Ödenwinkelkees and Rotmoosferner glaciers in Austria [START_REF] Philippot | Distribution of High Bacterial Taxa Across the Chronosequence of Two Alpine Glacier Forelands[END_REF] (see Fig. S1 for an overview of sampling scheme, sites, and site characteristics). A stable isotope approach was used to trace the fate and retention of added N in these intact soil cores: we added the equivalent of 20 kg N ha -1 (as a 0.05 M 15 NH4 15 NO3 solution) to each core and assessed the effect of soil age on the uptake and retention of 15 N in soil, roots, shoots, and microbial biomass and the amount leached after a simulated rainfall event (30 mm) 48 h after 15 N addition [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF]. Detailed analyses of soil, vegetation, and microbial community properties of paired soil samples were used to explain ecosystem N uptake and retention (i.e. the total amount of 15 N retained in soil, plant, and microbial pools after a rainfall event) across glaciers and soil age. This approach enabled us to identify the fundamental plant and microbial drivers of ecosystem N retention, and how these change with ecosystem succession in glacier forelands.

Results

Soil and plant properties

Measured soil properties changed predictably across the three glacier forelands. Soil pH decreased with soil age, while total soil C and N stocks and dissolved organic C (DOC) concentration increased (Table 1, Table S1). In contrast, soil C/N ratio and the availability of inorganic (NH4 + and NO3 -) and organic (DON) N forms did not change significantly with soil age.

Of the plant properties measured, only root N increased significantly with soil age. Other than soil pH, which was overall lowest in the Damma and highest in the Rotmoosferner glacier foreland, none of the soil and plant properties differed between the three glacier forelands (Table 1, Table S1, S2).

Microbial communities

Microbial community properties showed strong and consistent changes across primary succession gradients. Microbial biomass C and N, the total amount of soil DNA, bacterial (16S rRNA), fungal (18S rRNA), and ammonia-oxidising archaea (AOA) gene abundances (assessed using qPCR) all increased with soil age (Table 1, Table S3, S4). While the fungal-to-bacterial gene abundance ratio did not change, the ratio of ammonia-oxidising archaea to ammoniaoxidising bacteria (AOB) increased with soil age. In addition to measuring changes in gene abundance, we used amplicon sequencing of the 16S rRNA gene to assess taxonomic changes in bacterial community composition, and used the resulting taxonomic assignments to predict abundances of bacterial traits and assess changes in bacterial functional community composition, using the beta version of the R package Tax4Fun [START_REF] Asshauer | Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data[END_REF]. In contrast to the absolute abundance of AOA genes, predicted relative abundances of ammonia-oxidising and ammoniatransporter genes did not increase with soil age (Fig. S2). Predicted relative abundances of genes associated with free-living and symbiotic N-fixation genes and chitin degradation increased with soil age (Fig. S2). Because our qPCR analysis of AOB gene abundances (see Methods) resulted in many missing values, and because AOB gene abundances were positively correlated with predicted ammonia-oxidising gene abundances and negatively correlated with ammonia-transporter gene abundance (P < 0.0001 and R-squared = 0.316, and P = 0.018 and Rsquared = 0.119, respectively, Fig. S3), we used these predicted gene abundances for our followup models (see Methods).

Both taxonomic and functional bacterial community composition changed with soil age and patterns were similar across the three glaciers (Fig. 1). We compared the variance explained in taxonomic and functional community composition through running a PCA on arcsinetransformed relative abundances of bacterial OTUs and KEGG orthologs. The first two axes of PCA for functional community composition explained more variance than those for taxonomic community composition -46.5% vs. 13.8% -and soil age, glacier, and their interaction all explained more variance in functional community composition than in taxonomic community composition (Fig. 1). The PCA of functional community composition clearly discriminated young soils from the oldest for both Damma and Ödenwinkelkees glaciers. The functional profile also changed with soil age in the Rotmoosferner glacier foreland, but here discrimination was less marked. Damma and Ödenwinkelkees microbial communities followed very similar shifts in taxonomic composition with soil age, converging to similar structures in the oldest soils, while the Rotmoosferner community was clearly distinct from the other two glacier forelands (Fig. 1). Despite these clear changes in microbial community structure, neither Shannon diversity nor the Gini coefficient of taxonomic bacterial community composition changed with soil age, and OTU richness increased with soil age only in the Rotmoosferner glacier foreland (Fig. S4). The relative abundance of several phyla changed significantly with soil age (Fig. S5). In particular, the relative abundance of Proteobacteria and Actinobacteria decreased with soil age, while that of Acidobacteria, Planctomycetes, Verrucomicrobia and Chloroflexi increased. 15 N uptake, retention, and leaching Roots and microbial biomass were the ecosystem N pools that took up and retained the largest amount of added 15 N; across all soil ages and glaciers they retained on average 0.56 g and 0.62 g 15 N m -2 , respectively, or 28% and 31% of the 15 N added (Fig. 2). These amounts varied strongly across soil age and glacier, though the apparent increase in 15 N uptake by roots with soil age was not significant (P = 0.094; Fig. 2). Similarly, microbial 15 N uptake and shoot 15 N uptake were not affected by soil age, although the latter tended to decrease with soil age (P = 0.079; Fig. 2).

On average, 0.13 g, or 6%, of the added 15 N leached from the soil with the simulated rainfall event, but the amount of 15 N leached did not change with ecosystem succession. As a result, ecosystem 15 N retention was not affected by soil age.

Despite the absence of an effect of soil age on ecosystem 15 N uptake and retention, a significant part of the variation in 15 N uptake in plant and microbial pools was explained by ecosystem properties that did change with soil age. Microbial 15 N uptake increased with a larger microbial biomass N pool and with decreasing microbial C/N ratio (Table 2). In addition, microbial 15 N uptake decreased with greater soil nitrate concentrations. The model for shoot 15 N uptake explained more variation than the model for microbial 15 N uptake (40% vs. 28%); shoot 15 N uptake increased with a larger shoot N pool and decreased with soil total N. The model for root 15 N uptake included more variables and explained the largest amount of variation (48%) of 15 N uptake in biotic ecosystem pools. Root 15 N uptake increased with higher root C/N ratio and root biomass, but the interaction between the two meant that high root C/N ratios increased root 15 N uptake at low root biomass and decreased root 15 N uptake at high root biomass (Fig. S6). In addition, root 15 N uptake decreased with increasing soil ammonium concentrations. Total ecosystem 15 N retention increased with increasing microbial biomass N and was also explained by PC2-axis scores of functional bacterial community composition; this model explained 25% of variation. Finally, 32% of the variation in 15 N leaching was explained by a combination of soil C/N ratio and taxonomic and functional bacterial community composition, where leaching increased in soils with increasing soil C/N ratio.

The volume leached from the intact soil cores increased slightly with higher soil moisture content and the amount of 15 N leached weakly increased with the volume leached (P = 0.013 and R-squared = 0.068 and P = 0.063 and R-squared = 0.041, respectively; Fig. S7). However, the amount of 15 N leached decreased when soil moisture was higher (P = 0.004 and R-squared = 0.095, Fig. S7). The total recovery of 15 N in plants, soil, and leachates together was variable and not affected by soil age and glacier, and, in general, did not amount to the full 2 g m -2 of 15 N added (Fig. S8). The average recovery of added 15 N was 78.4 ± 29.9 (mean ± SD) % across all soil ages and glaciers, and therefore, for 15 N tracer experiments, relatively complete. 15 N enrichment of ecosystem N pools Similar to total 15 N uptake in ecosystem N pools, 15 N enrichment (i.e. the percentage of 15 N enrichment in each ecosystem N pool, which is independent of the size of the pool) of ecosystem N pools varied little with soil age. Neither microbial, root, nor shoot 15 N enrichment changed with successional stage (Fig. S9). In contrast, soil 15 N enrichment decreased with soil age across the foreland of all three glaciers. Microbial 15 N enrichment increased with decreasing microbial biomass C/N ratio and with a decreasing predicted relative abundance of ammonia oxidisers (Table S5). The model for 15 N enrichment of shoot N only explained 8.3% of the variation and showed that shoot 15 N enrichment increased with higher root C/N ratio. In contrast, 68% and 69% of variation in root and soil 15 N enrichment was explained by the selected models, respectively. 15 N enrichment of the root N pool decreased with a larger root N pool size and increasing root biomass. Similar to total root 15 N uptake, root 15 N enrichment also decreased when soil ammonium concentrations increased. Soil 15 N enrichment decreased with an increasing size of the soil N pool.

Direct and indirect controls on plant and microbial 15 N uptake and 15 N leaching Structural equation modelling (SEM) is a robust statistical method to test how well experimental data fit a hypothesised causal structure that is based on prior knowledge [START_REF] Grace | Structural equation modeling and natural systems[END_REF]. To test our hypothesis that N-conservative plants (with high tissue C/N ratio) select for Nconservative microbial communities (with high microbial biomass C/N ratio), which would in turn increase microbial immobilisation, we fitted our data to a previously hypothesised and tested SEM [START_REF] De Vries | Plant community controls on short-term ecosystem nitrogen retention[END_REF]. Importantly, in a previous common garden experiment, this SEM showed that low microbial biomass C/N ratios increased microbial N immobilisation, which contrasts with our current hypothesis [START_REF] De Vries | Plant community controls on short-term ecosystem nitrogen retention[END_REF]. The data fitted this model well and confirmed that plant C/N ratio and root biomass determine plant and microbial 15 N uptake and leaching (Fig. 4). Shoot C/N ratio, root C/N ratio, and root biomass were all correlated. Roots with a high tissue C/N ratio were linked to microbial communities with a high C/N ratio, which, in contrast to our hypothesis, in turn immobilised less 15 N. In addition, high root biomass increased plant 15 N uptake, which decreased 15 N leaching. 15 N leaching was only controlled by plant 15 N uptake, not by microbial 15 N immobilisation.

Discussion

Using the forelands of three geographically distinct retreating glaciers in the European Alps as a model system, we were able to disentangle the plant and microbial properties that regulate ecosystem N uptake and retention during ecosystem succession. Many ecosystem properties changed in a predictable way with soil age across the three glaciers but, in contrast to our hypothesis, neither microbial or plant 15 N uptake, nor ecosystem 15 N retention increased with soil age. However, total ecosystem 15 N retention and microbial 15 N immobilisation, the second largest sink for added 15 N, increased with a greater microbial biomass N pool, which itself increased with soil age across all three glacier forelands. Plant 15 N uptake, rather than microbial 15 N uptake, decreased the amount of 15 N lost via leaching and, in contrast to our expectation, 15 N leaching increased with higher soil C/N ratios. Moreover, we found that microbial 15 N uptake, a measure of microbial immobilisation of N, was primarily determined by the C/N stoichiometry and biomass of soil microbial communities, with only a minor role for changes in taxonomic or functional composition of bacterial communities.

Both taxonomic and functional bacterial community composition, and many soil microbial community properties known to influence soil N availability, were strongly affected by soil age.

The AOA:AOB gene abundance ratio increased with soil age, as previously reported in a salt marsh successional gradient [START_REF] Salles | Successional patterns of key genes and processes involved in the microbial nitrogen cycle in a salt marsh chronosequence[END_REF], possibly as a consequence of a preference of AOA for acidic soils [START_REF] Nicol | The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria[END_REF]. Of the predicted relative abundances of genes involved in the N cycle, only symbiotic and free-living N fixation increased with soil age, which is in line with legumes being more abundant at late-successional stages (Fig. S1). These findings again indicate increased N limitation with succession (36, but see 37). However, in contrast to our expectation, the ratio of fungi-to-bacteria, which has been shown to increase during ecosystem succession [START_REF] Ohtonen | Ecosystem properties and microbial community changes in primary succession on a glacier forefront[END_REF], and has been linked to increased microbial N immobilisation [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF], did not increase. Moreover, none of these microbial community properties explained microbial 15 N immobilisation or the 15 N enrichment of microbial biomass. Only the predicted relative abundance of ammonia-oxidisers, which did not change with ecosystem succession, explained microbial 15 N enrichment; 15 N enrichment of microbial biomass N decreased with an increasing abundance of ammoniaoxidisers, suggesting that their abundance is indicative for microbial communities with low N affinity. While we found a strong correlation between measured AOB gene abundances (which had too many missing values to use in our predictive models) and predicted ammonia-oxidiser abundance, functional gene inference by tools like Tax4Fun that we used here has limited reliability in environmental samples [START_REF] Sun | Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories[END_REF]. Still, taken together, these findings are in line with our observation that microbial 15 N immobilisation and 15 N enrichment did not increase during ecosystem succession.

Rather than taxonomic and functional microbial community properties, whole-community stoichiometry explained microbial uptake and enrichment of 15 N, irrespective of glacier foreland and soil age. Microbial 15 N uptake increased with a greater microbial biomass N pool and with lower microbial biomass C/N ratio, indicating that microbial communities with high N demand immobilised most added 15 N. This finding is consistent with observations in controlled experiments [START_REF] Myrold | Potential importance of bacteria and fungi in nitrate assimilation in soil[END_REF][START_REF] De Vries | Plant community controls on short-term ecosystem nitrogen retention[END_REF], but contradicts field observations that fungal-dominated microbial communities of high C/N ratio immobilise more added N than bacterial-dominated microbial communities of low C/N ratio [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF][START_REF] De Vries | Nitrogen losses from two grassland soils with different fungal biomass[END_REF]. And while soil C stocks or C/N ratios did not explain microbial 15 N immobilisation, microbial 15 N immobilisation decreased with increasing soil nitrate concentrations, indicating that N limitation did drive microbial 15 N uptake (Table 2). A similar pattern was found for root and shoot 15 N uptake. Shoot 15 N uptake increased with a larger shoot N pool, and root 15 N uptake was highest with high root biomass and decreased with higher soil ammonium concentrations, again indicating that 15 N uptake was driven both by N demand and N limitation. This was confirmed by our structural equation model, which showed that across these distinctly different glacier forelands, ecosystem N uptake was determined by plant and microbial N demand. Thus, our results indicate that high microbial N immobilisation by fungal-dominated microbial communities of high C/N ratio [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF][START_REF] De Vries | Nitrogen losses from two grassland soils with different fungal biomass[END_REF] are a result of ecosystem N limitation, rather than of an innate high affinity of fungi for N.

Despite the finding that both plant and microbial 15 N uptake were driven by their N demand, we found no evidence for competition between plants and microbes for 15 N, as indicated by the lack of a significant negative effect of microbial 15 N uptake on plant 15 N uptake (Fig. 4). Microbes have been shown to be stronger competitors for N than plant roots in N-limited systems on short time scales [START_REF] Bardgett | Soil microbes compete effectively with plants for organic-nitrogen inputs to temperate grasslands[END_REF][START_REF] Harrison | Preferences for different nitrogen forms by coexisting plant species and soil microbes[END_REF], and it has been suggested that through selecting for specific soil microbial communities, plants can alter the microbial sink strength for N [START_REF] De Vries | Disentangling plant and soil microbial controls on carbon and nitrogen loss in grassland mesocosms[END_REF]. We did indeed find that high root C/N ratios, indicative of conservative plant nutrient acquisition strategies (Fig. S4), were associated with microbial communities with a high biomass C/N ratio that, however, immobilised less 15 N. Still, high root C/N ratios were only linked to reduced root N uptake when root biomass was high, which suggests that when both root and microbial C/N ratios were low, plants might have increased their N uptake through investing more in root biomass. In alpine, N-limited systems like the glacier forelands studied here, the preferential uptake of different forms of N can alleviate N competition between plants and microbes. This chemical niche partitioning has primarily been found between inorganic and organic N forms, but also between various forms of organic N (amino acids) [START_REF] Bardgett | Soil microbes compete effectively with plants for organic-nitrogen inputs to temperate grasslands[END_REF][START_REF] Harrison | Preferences for different nitrogen forms by coexisting plant species and soil microbes[END_REF][START_REF] Raab | Soil amino acid utilization among species of the Cyperaceae: Plant and soil processes[END_REF]. Our results point to a partitioning of the uptake of inorganic N forms: high soil concentrations of nitrate were associated with lower microbial 15 N uptake, while high soil ammonium concentrations were associated with lower root 15 N uptake, respectively. This finding suggests that microbes were limited by nitrate and plants by ammonium, thus when more of these forms were present, microbial and root uptake of added 15 N was reduced. This is consistent with the knowledge that subalpine grassland plants, especially more conservative species, have higher ammonium than nitrate uptake rates, [START_REF] Legay | Studies of NH4+ and NO3-uptake ability of subalpine plants and resource-use strategy identified by their functional traits[END_REF], and suggests coordination between plant and microbial N uptake strategies beyond broad-scale tissue N requirements, as has been found before [START_REF] Moreau | Plant traits related to nitrogen uptake influence plant-microbe competition[END_REF][START_REF] Theodose | Uptake and allocation of N-15 in alpine plants: Implications for the importance of competitive ability in predicting community structure in a stressful environment[END_REF].

As expected, roots and microbes together accounted for the largest proportion of 15 N retention.

After rapid initial uptake by microbes, N is gradually released through microbial turnover and taken up by roots, while root N is translocated to aboveground biomass [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF][START_REF] Harrison | Preferences for different nitrogen forms by coexisting plant species and soil microbes[END_REF], and eventually transferred to soil organic matter pools via shoot, root, and microbial turnover [START_REF] Zogg | Microbial immobilization and the retention of anthropogenic nitrate in a northern hardwood forest[END_REF]. Despite these shifts in ecosystem N allocation, the total amount of N retained in plants and microbes has been shown to remain constant for two months after 15 N addition, indicating that initial ecosystem N retention is indicative of longer-term N retention [START_REF] Vitousek | AND NITROGEN LOSSES IN AN INTENSIVELY MANAGED LOBLOLLY-PINE PLANTATION[END_REF]. The average total amount of 15 N retained in our systems was relatively high (73%) compared to N retention in temperate grasslands [START_REF] Templer | Sinks for nitrogen inputs in terrestrial ecosystems: a metaanalysis of N-15 tracer field studies[END_REF][START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF][START_REF] Wang | Ecosystem nitrogen retention is regulated by plant community trait interactions with nutrient status in an alpine meadow[END_REF], and comparable to shrubland N retention [START_REF] Templer | Sinks for nitrogen inputs in terrestrial ecosystems: a metaanalysis of N-15 tracer field studies[END_REF]. However, it is important to note that we only collected intact soil cores that included plants, whereas plant cover was sparse during early successional stages. Thus, our method will have resulted in an overestimation of ecosystem N retention per square meter in the field, in particular for the most recently deglaciated soils. Because of the large contribution of plant and microbial 15 N uptake to ecosystem N retention, it is therefore plausible that whole-ecosystem N retention increases with succession.

Our study identifies fundamental plant and microbial controls on ecosystem N retention, and establishes that plant and microbial N demand, as well as N limitation, are driving ecosystem N retention in glacier forelands. Importantly, these findings are in line with experimental studies in grasslands and forests, as well as with recent adaptations to Vitousek and Reiners conceptual model of nutrient retention [START_REF] Lajtha | Nutrient retention and loss during ecosystem succession: revisiting a classic model[END_REF][START_REF] Vitousek | ECOSYSTEM SUCCESSION AND NUTRIENT RETENTION -HYPOTHESIS[END_REF][START_REF] Lovett | Nutrient retention during ecosystem succession: a revised conceptual model[END_REF]. While increasing (predicted) abundances of free-living and symbiotic N fixation genes indicated increasing N limitation with soil age, we did not find an increase in N retention with ecosystem succession, likely because the main determinants of plant and microbial 15 N uptake were not affected by soil age. Our findings highlight the importance of plant and microbial N uptake for ecosystem N retention and advance our understanding of the fundamental controls on ecosystem N retention. This understanding is crucial for predicting the effects of increasing atmospheric deposition of reactive N on terrestrial ecosystems, as well as for increasing the retention of N in natural and managed ecosystems.

Materials and Methods

Sample collection

Soil was collected in geographically distinct glacier forelands located in Ödenwinkelkees (Austria, 47°12' E, 12°64' N, 2,060-2,161 m altitude), Rotmoosferner (Austria, 46°83' E, 11°04' N, >2,296 m altitude) and Damma (Switzerland, 46°64' E, 8°46' N, 1,939-2,050 m altitude).

Mean annual temperatures are 0.2 ± 0.6 °C, -1.0 ± 0.5 °C and 2.2 ± 0.5 °C and mean annual precipitation 2,258 ± 665 mm, 904 ± 150 mm and 2,400 ± 385 mm for the Ödenwinkelkees and Rotmoosferner (both: mean of 2000-2017), and Damma, respectively. Intact soil cores (6.4 cm diam., 15 cm depth, vegetation intact, bottom covered with mesh) and adjacent vegetation and soil samples ('field soil' hereafter, 15 cm depth) were collected from five transects at each site, at distances from the glacier forefront corresponding to an average time since deglaciation of [START_REF] Northup | Polyphenol control of nitrogen release from pine litter[END_REF][START_REF] Sun | Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories[END_REF][START_REF] Zeileis | Measuring Inequality, Concentration, and Poverty. R package version 0.2[END_REF]112 and 157 years, as well as from plots located in each glacial valley but outside the foreland sensu stricto, and deglaciated more than 1,000 year ago (reference soils). This resulted in five replicates for each of five successional sites, plus one reference site for each glacier, i.e. 90 sample units. Soils (not intact cores) were sieved to 4 mm, shipped and stored at 4 °C until further analysis. Intact cores and collected vegetation were immediately shipped and stored under the same conditions (see below) until the start of the 15 N addition experiment and vegetation sample processing. 15

N addition experiment

After shipping to The University of Manchester, intact cores (with vegetation intact) were placed in controlled growth chambers (Percival USA, 9 °C, which is the average September temperature at the sites, 8 h/16 h day/night cycle, at constant moisture content) in a randomised block design and left to acclimatise for one week. Thereafter 4 x 1 ml of a 0.05 M 98.2% enriched 15 NH4 15 NO3 solution was injected in the top 5 cm of each soil core in 4 evenly spaced locations (total of 6.43 mg 15 N per core, equivalent to 20 kg N ha -1 or 2 g m -2 ). Forty-eight hours after 15 N injection, the cores were leached with 97 ml demineralised water (equivalent to a 30 mm rainfall event). Past work has shown that the amount retained in the system 48 h after cores were dismantled, and soil and aboveground and belowground vegetation were separated, and the volume of leachates recorded.

Leachate, soil, and vegetation analyses

Gravimetric soil moisture contents of core and field soils were determined by drying for 24 h at 105 °C, and for the core soils with recorded weight and volume of stones larger than 4 mm, used to determine core bulk density and fine soil (all material <4 mm) content. Leachates and field soil were analysed for dissolved inorganic and organic nitrogen (DON) and dissolved organic C (DOC) as described in De Vries et al. [START_REF] De | Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland[END_REF]. Microbial biomass C and N in field soil and column soil were determined by chloroform-fumigation extraction as described by Brookes et al. [START_REF] Brookes | Chloroform fumigation and the release of soil-nitrogen -a rapid direct extraction method to measure microbial biomass nitrogen in soil[END_REF]. Root and shoot biomass from field samples and intact cores were separated, weighed, dried (60°C for 48 h), weighed again and analysed for total C and N on an Elementar Vario EL elemental analyser. Dried and ground field and core soil samples were also analysed for total C and N on the Elementar Vario EL elemental analyser. Leachates and microbial extracts from soil cores (enriched in 15 N) and field soil (natural abundance of 15 N) were freeze-dried, and, with dried and ground root, shoot, and soil material from both cores and field samples, analysed for 15 N/ 14 N ratios at the Life Sciences Mass Spectrometry Facility, Lancaster node, CEH Lancaster.

Enough sample to provide as near as possible to 84 µg elemental nitrogen was weighed using a high precision micro-balance, (Sartorius Ltd) and then sealed into a 6 x 4 mm tin capsule.

Samples were then combusted using an automated Carlo Erba NA1500 elemental analyser (Carlo erba, part of Thermo Scientific, USA) coupled to a Dennis Leigh Technology Isotope Ratio Mass-Spectrometer (Dennis Leigh Technologies, Sandbach, Cheshire, UK.). A working standard of n-carbobenzyloxy-l-aspartic acid (CBA -Sigma-Aldrich Co. Ltd.) which is calibrated against the certified reference material IAEA-N1 (Ammonium sulphate -NIST number 8547, Bureau of Analysed Samples Ltd, Middlesbrough, UK) was analysed after every 12 th sample resulting in an analytical precision of 0.63‰ for the natural abundance samples. Results are expressed in δ notation; i.e. δ 15 N = [(Rsample -Rstandard)/Rstandard] x 1000 (‰) where R is the ratio of 15 N to 14 N in the sample and standard, accordingly. All δ 15 N results are expressed relative to the international standard of atmospheric air. δ 15 N values from 15 N enriched (core soil) and natural abundance (field soil) samples were then used to calculate 15 N excess atom percent values in the 15 N enriched samples, after which total amounts of 15 N in ecosystem pools (microbial biomass, shoots, roots, soil, and leachates) were calculated using their total N pool sizes, which were then scaled to g m -2 using the bulk density and the surface area of the cores. 15 N retention was calculated as the sum of 15 N in soil, microbial biomass, and aboveground and belowground vegetation.

DNA extraction, qPCR, preparation of amplicons, and sequencing Nucleic acids were extracted from 0.5 g field soil as previously described [START_REF] Griffiths | Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA-and rRNA-based microbial community composition[END_REF] with modifications [START_REF] Nicol | Primary succession of soil Crenarchaeota across a receding glacier foreland[END_REF] and raw extracts were stored at -20 °C. The abundances of fungi, bacteria and ammonia oxidising archaea and bacteria were estimated by real-time quantitative PCR (qPCR). Fungal abundance was assessed using 18S ribosomal gene primers (Fung5F and FF390R, [START_REF] Lueders | Stable isotope probing of rRNA and DNA reveals a dynamic methylotroph community and trophic interactions with fungi and protozoa in oxic rice field soil[END_REF]) [START_REF] Lueders | Stable isotope probing of rRNA and DNA reveals a dynamic methylotroph community and trophic interactions with fungi and protozoa in oxic rice field soil[END_REF] rather than the ITS region to avoid potential bias from the variable length of ITS amplicons. Bacterial abundance was assessed using 16S rRNA gene primers 968F/1401R [START_REF] Felske | Quantification of 16S rRNAs in complex bacterial communities by multiple competitive reverse transcription PCR in temperature gradient gel electrophoresis fingerprints[END_REF]. Archaeal and bacterial ammonia oxidiser abundances were assessed using amoA primers Crenamo23F/Crenamo616R (53) and amo1F/amo2R [START_REF] Rotthauwe | The ammonia monooxygenase structural gene amoA as a functional marker: Molecular fine-scale analysis of natural ammonia-oxidizing populations[END_REF], respectively.

Illumina paired-end sequencing of the hypervariable V3-V4 region of bacterial 16S rRNA gene amplicons was performed at the Centre for Genome-Enabled Biology and Medicine (Aberdeen, UK). DNA quantity, measured fluorometrically using the Qubit dsDNA BR Assay Kit (ThermoFisher, UK), increased with soil age and varied from 1.3 to 190 ng g -1 soil (Fig. S1). We removed samples with less than 5 ng DNA; for each of the remaining samples, PCR (25 cycles each) was performed using 5 ng template DNA, using the forward and reverse primer sequences 5'-CCTACGGGNGGCWGCAG-3' (Bakt_341F) and 5'-GACTACHVGGGTATCTAATCC-3' (Bakt_805R) [START_REF] Klindworth | Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies[END_REF] and Illumina overhang adapters 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3' and 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3', respectively. Replicate amplification products were pooled and processed with AMPure purification system (Agencourt Bioscience Corporation, US). Pure pooled amplicons were then indexed with 8 PCR cycles and fragment size determined on a 2200 TapeStation (Agilent, Germany). Equimolar concentrations of samples were pooled and sequenced on an Illumina MiSeq sequencer with V2 2x150 bp pairedend chemistry including PhiX Control V3 (Illumina, US) and generated 7,576,266 qualitychecked reads.

Sequence cleaning and clustering

Sequence analysis was performed using a custom pipeline. Briefly, Illumina-generated forward and reverse reads were first paired using the PANDAseq Assembler and trimmed to remove primer sequences [START_REF] Masella | PANDAseq: PAired-eND Assembler for Illumina sequences[END_REF]. Trimmed assembled sequences were dereplicated by de novo clustering at 97% similarity [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF] and remaining singletons (1 read in the total dataset) were removed. This generated 14,622 OTUs, with an average of 68,000 reads per sample. Average read number increased significantly with soil age (from 38,909 ± 7,129 to 76,545 ± 9,350). Read numbers in 5 samples of young soils were very low (<4,632) and were discarded from the analysis. A reference V3-V4 database was generated by performing in silico PCR on the SILVA 115 database with the same set of primers using the TaxMan webserver [START_REF] Brandt | TaxMan: a server to trim rRNA reference databases and inspect taxonomic coverage[END_REF]. Double-annotated sequences and sequences with no species annotation were removed from the database, leading to a reference sequence set containing 172,576 unique, fully annotated sequences. Taxonomic assignment was achieved by clustering the 14,622 representative sequences against the reference database using the Uclust algorithm (57) in combination with a custom Linux script considering all possible hits at 97% similarity, dividing the number of reads of each OTU in a sample by the number of potential hit sequences, to distribute the uncertainty of the annotation.

OTU number was used as measure of bacterial richness and computed by custom R scripts using the vegan (59) and ineq (60) packages.

Inference of the functional profile using Tax4Fun

We selected 46 functional traits, grouped into 7 functional categories and 16 functions, related to trophic niche, growth strategy, versatility, adaptation to abiotic stress and biotic interactions (Table S6). For each trait, 1 to 50 functional indicators (i.e. enzymes catalysing that particular functional trait) were selected, based on literature review and exploration of web-based KEGG and MetaCyc resources (versions available online in May 2017) [START_REF] Klindworth | Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies[END_REF][START_REF] Masella | PANDAseq: PAired-eND Assembler for Illumina sequences[END_REF]. Finally, for each indicator, 1 to 56 KEGG ortholog genes, encoding that particular indicator, were listed for 1,849 KEGG orthologs (Table S7). The known biological functions of peptides and enzymes encoded by each of these KEGG proxies was examined individually. KEGG pathways often include orthologs that are not directly linked to the function to which they refer; e.g. the KEGG Quorum Sensing pathway contains many genes that actually encode chemotaxis or pathogen virulence.

In addition, individual KEGG pathways related to organic compound metabolism, often contain both synthesis and degradation enzymes, which were discriminated in the present analysis.

Functional profiles, consisting of the relative abundance of KEGG orthologs, were inferred for each sample from the processed 16S rRNA amplicon sequences, using the beta version of the R package Tax4Fun (http://tax4fun.gobics.de/), which uses available genome annotation of the nearest neighbour of each OTU, based on 16S rRNA gene sequence similarity in the SILVA database [START_REF] Asshauer | Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data[END_REF]. Tax4Fun used 73.1 ± 0.1% of the nucleotide sequences within a sample for prediction (i.e. 26.9 ± 0.1% of the OTUs were not used for functional inference).

Statistical analyses

All variables were scaled to g m -2 using the bulk density, stone content, and area of the intact cores. All statistical analyses were done in R version 3.5.2 (61) and all data were checked for normality and log-transformed where necessary. The effect of individual glacier and time since deglaciation (soil age, this excluded the never-glaciated Reference sites), and their interaction, on soil, vegetation, microbial properties, and 15 N pools was tested using linear mixed effects models with an error term for block to account for the non-independence of the 5 replicate samples per soil age. This was done using the function lme in the R package nlme [START_REF] Pinheiro | nlme: Linear and Nonlinear Mixed Effects Models[END_REF]. To compare the drivers of taxonomic and functional community composition, principal component analyses (PCA) of inferred Tax4Fun KEGG functional and taxonomic profiles were followed by between class analysis (BCA), calculating the between-and within-group inertia percentages and testing the significance of between-and within-group variances using a Monte-Carlo test with 999 iterations using R package ade4 [START_REF] Dray | The ade4 package: Implementing the duality diagram for ecologists[END_REF]. PC scores of taxonomic and functional bacterial community composition were used in subsequent analyses to explain 15 N uptake and retention in ecosystem N pools.

We used a manual model selection procedure, again using the lme function in nlme, to identify the main drivers of 15 N uptake in, and enrichment of, individual ecosystem N pools, entering terms individually first, retaining significant terms, and testing them in combination and in interaction, following De Vries et al. [START_REF] De | Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities[END_REF]. In these models, an error term was included where soil age (including Reference sites) was nested in glacier. Model selection was done based on AIC using maximum likelihood estimation and the final most parsimonious model was run using restricted maximum likelihood estimation. Finally, structural equation modelling was done using the package piecewiseSEM [START_REF] Lefcheck | PIECEWISESEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics[END_REF], and regressions included an error term of soil age (including Reference sites) nested in glacier (as above). Model fit was assessed using Fisher's C statistic.

Tables and figures

Fig. 1 .

 1 Fig. 1. PCA of taxonomic (A) and functional (B) microbial community composition as affected by soil age and glacier.

Fig. 2 .

 2 Fig. 2. Uptake, leaching, and retention of 15 N 48h after addition in different ecosystem N pools as affected by soil age and glacier.

Fig. 3 .

 3 Fig. 3. Structural equation model explaining plant and microbial 15 N uptake and 15 N leaching across all glaciers and soil ages.

Table 1 .

 1 ANOVA table of lme models for the effect of soil age, glacier, and their interaction, on soil, plant and microbial properties. Significant effects (P<0.05) are highlighted in bold, significant trends (P>0.1) in italics.

		Soil age			Glacier		Glacier x soil age
		F1,9	P	Direction of change	F2,9	P	F2,9	P
				with soil age
	Soil moisture (g g -1 )	0.96	0.353		1.04	0.391	0.62	0.559
	Soil pH	21.75	<0.0001	¯	65.04	<0.0001 2.69	0.121
	Soil C (g m -2 )	16.18	0.003		4.12	0.054	2.15	0.172
	Soil N (g m -2 )	31.30	0.0003		1.74	0.229	1.20	0.345
	Soil C/N	0.76	0.405		1.51	0.273	0.31	0.739
	NH4 + concentration (g m -2 )	0.66	0.439		3.98	0.058	1.58	0.258
	NO3 -concentration (g m -2 )	1.67	0.229		1.89	0.207	1.68	0.240
	DON concentration (g m -2 )	1.33	0.279		0.04	0.957	1.21	0.341
	DOC concentration (g m -2 )	15.86	0.003		0.41	0.675	0.12	0.884
	Shoot biomass (g m -2 )	3.15	0.110		1.09	0.377	1.27	0.327
	Root biomass (g m -2 )	3.51	0.093	()	2.17	0.170	0.31	0.738
	Shoot N (g m -2 )	1.39	0.267		0.45	0.652	1.49	0.276
	Root N (g m -2 )	10.10	0.011		2.12	0.176	1.90	0.205
	Shoot C/N	0.75	0.409		0.22	0.808	0.74	0.505
	Root C/N	1.17	0.307		2.32	0.153	0.28	0.760
	Microbial biomass C (g m -2 )	25.39	0.0007		1.70	0.236	0.05	0.946
	Microbial biomass N (g m -2 ) 10.46	0.010		1.97	0.196	0.04	0.965
	Microbial C/N	1.39	0.267		1.15	0.358	0.24	0.793
	DNA (ng m -2 )	6.57	0.031		0.60	0.571	0.26	0.778
	16S (gene copies m -2 )	20.78	0.001		0.09	0.910	0.13	0.880
	18S (gene copies m -2 )	18.98	0.002		0.64	0.548	0.28	0.765
	18S/16S	1.39	0.269		6.02	0.022	2.30	0.156
	AOA (gene copies m -2 )	48.21	0.0001		7.94	0.010	3.70	0.067
	AOB (gene copies m -2 )	2.58	0.147		15.02	0.002	0.90	0.446
	AOA/AOB	14.41	0.009		0.21	0.819	0.37	0.707

Table 2 .

 2 Selected linear mixed effects models for 15 N uptake, retention, and leaching. All biomass, C, and N parameters are expressed in g m -2 .

ln

Microbial

15 N uptake (g m -2 ) ln Shoot 15 N uptake (g m -2 ) Root 15 N uptake (g m -2 ) 15 N retention (g m -2 ) ln 15 N leaching (g m -2 )

		Parameter value	P	Parameter value	P	Parameter value	P	Parameter value	P	Parameter value	P
	Intercept	+0.143	0.138	+0.129	<0.0001	-0.053	<0.0001	+0.948	<0.0001	-3.355	<0.0001
	Soil C and N			-0.028 * ln Soil N	<0.0001					+0.316 * ln Soil C/N 0.017
	pools
	Microbial C	+0.056 * Micr N	<0.0001					+0.088 * Micr N	<0.0001
	and N pools	-0.076 * ln Micr
		C/N	0.0033
	Plant C and			+0.028 * ln Shoot N 0.006	+0.011 * Root C/N	<0.0001
	N pools					+0.0005 * Root	<0.0001
						biom
						-0.000007 * Root	0.0007
						C/N * Root biom
	Soil	-0.127 * ln NO3 -	0.0031			-0.282 * ln NH4 +	0.0077
	dissolved C
	and N pools
	Microbial							+0.007 * Func PC2	0.013	+0.068 * Tax PC2	0.008
	community									+0.008 * Func PC2	0.080
	properties									-0.026 * Tax PC2 *	0.006
										ln Soil C/N
	df	59		65		68		61		59
	AIC	50.4		-286.9		57.5		157.5		246.4
	R-squared	0.280		0.401		0.481		0.248		0.318

Acknowledgements

This work was funded by a British Ecological Society Early Career Project Grant to Franciska de Vries. Franciska de Vries was supported by a BBSRC Fellowship (BB/L02456X/1) and Cécile Thion was funded by NERC NE/L006286/1 and the AXA Research Fund. Benoît Bergk Pinto was funded by the EU Horizon 2020 research and innovation program under the Marie Skłodowska-Curie ITN 675546 -MicroArctic. We thank Victor van Velzen and Caley Brown for help in the laboratory, Bernd Wemheuer for allowing us to perform functional profile predictions using a then unpublished version of Tax4Fun, and Matthew Gemmel and Elaina Collie-Duguid, the Centre for Genome Enabled Biology and Medicine, University of Aberdeen, UK, for advice and bioinformatics support.

Data Availability

Data and code are available from 10.6084/m9.figshare.13913024. This Targeted Locus Study project has been deposited at DDBJ/ENA/GenBank under the accession KCJZ00000000. The version described in this paper is the first version, KCJZ01000000.


Sarah Flèche
email: s.fleche@lse.ac.uk

Cep

Andrew E Clark
email: andrew.clark@ens.fr

The Long-Lasting Effects of Family and Childhood on Adult Wellbeing: Evidence from British Cohort Data

Keywords: life satisfaction, cohort data, childhood, adult outcomes. JEL Classification Codes: A12, D60, I31

To what extent do childhood experiences continue to affect adult wellbeing over the life course? Previous work on this link has been carried out either at one particular adult age or for some average of adulthood. We here use two British birth-cohort datasets (the 1958 NCDS and the 1970 BCS) to map out the time profile of the effect of childhood on adult outcomes, including life satisfaction. We find that the effect of many aspects of childhood do not fade away over time, but are rather remarkably stable. In both birth cohorts child non-cognitive skills are the strongest predictors of adult life satisfaction at all ages. Of these, emotional health is the strongest. Childhood cognitive performance is more important than good conduct in explaining adult life satisfaction in the earlier cohort, whereas this ranking is inverted in the more recent BCS.

Introduction

Previous work has established that the childhood environment plays a powerful role in shaping both socioeconomic success and adult wellbeing. In particular, the analysis of British Cohort Study (BCS) data in [START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF] revealed that a substantial proportion of the variation across individuals in life satisfaction at age 34 results from childhood cognitive and non-cognitive skills, family economic characteristics and parental psychosocial background. 1 Analogous findings appear in [START_REF] Frijters | Does childhood predict adult life satisfaction? Evidence from British Cohort Surveys[END_REF] using National Child Development Study (NCDS) data, for both average life satisfaction over time and life satisfaction at age 50.

This existing work has considered adulthood as a single period, focusing on a single adult year or the average over adult years. The implicit assumption is thus that the effects of childhood on adult life satisfaction are constant over adult ages. However, a growing body of evidence has argued that the formation of skills and/or capabilities (for example education, health status and social wellbeing) is, in fact, dynamic (see [START_REF] Heckman | The Economics of Human Development and Social Mobility[END_REF], for a recent literature review). And as individual life satisfaction is thought to reflect these dynamically-accumulated capabilities (e.g. [START_REF] Oreopoulos | Priceless: The nonpecuniary benefits of schooling[END_REF][START_REF] Oreopoulos | Priceless: The nonpecuniary benefits of schooling[END_REF][START_REF] Powdthavee | What's the good of education on our overall quality of life? A simultaneous equation model of education and life satisfaction for Australia[END_REF], we might expect the determinants of life satisfaction to change by adult age.

In the conventional model of skill formation, cognitive and non-cognitive skills are accumulated over time, and earlier stocks of skills depreciate with age [START_REF] Grossman | On the concept of health capital and the demand for health[END_REF]. The influence of childhood on later outcomes may then be attenuated over time (Todd and[START_REF] Todd | On the specification and estimation of the production function for cognitive achievement[END_REF]2007). A relatively happy childhoodin terms of financial resources, parental employment and education, emotional support, intellectual performance and healthmay yield satisfaction in early adulthood, but with an effect that fades away at older ages. However, recent work on early-childhood development has emphasised the non-linear technology of skill formation (Cunha and[START_REF] Cunha | The Technology of Skill Formation[END_REF][START_REF] Cunha | Estimating the technology of cognitive and noncognitive skill formation[END_REF]. 2 Here the effects of childhood at different adult ages may persist.

We contribute to this literature by estimating the life-cycle effects of family environment and child outcomes on adult wellbeing at a number of different adult ages: this has not been taken into account in the existing life-satisfaction literature. Following [START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF], we also establish which of the intermediate adult outcomes (income, education, employment, health, etc.) are the most important channels for the effect of childhood on adult life-satisfaction. We can thus establish which early childhood circumstances cast the longest shadow over both adult outcomes and life satisfaction at later ages.

We use data from two British birth cohorts (the 1958 NCDS andthe 1970 BCS) to analyse the determinants of a successful adult life. The 1970 BCS follows approximatively 17,000 individuals from birth to up to age 42 at the most recent data sweep. BCS respondents are interviewed roughly every five years, on a variety of topics including health, the labour market, individual behaviours and family formation. As the BCS is a birth cohort, we also have information collected during the respondent's childhood. Adult life-satisfaction is recorded when the respondent is aged 26, 30, 34 and 42. The 1958 NCDS also contains measures of both childhood experiences and outcomes during adulthood, including life satisfaction at ages 33, 42 and 50. The comparison of these two cohort datasets will allow us to look for differences in the age profile of the effect of childhood on adult wellbeing across birth cohorts.

The remainder of the paper is organised as follows. Section 2 presents a brief overview of the literature on the impact of childhood on adult wellbeing. Section 3 describes the datasets that we use, and Section 4 discusses our main results. Section 5 then looks at the various pathways from childhood experiences to adult wellbeing. Last, Section 6 concludes.

Background

There is by now a substantial literature on the relationship between childhood and later adult outcomes (see for example, Keane and Wolpin, 1997, Currie and Almond, 2011 and, Huggett et al., 2011). 3 This literature has clearly shown that early-life characteristics and circumstances predict adult outcomes, including wages, schooling, participation in crime, and success in many aspects of social and economic life. In recent years, economists have included life satisfaction in the list of adult variables that may be determined by childhood circumstances [START_REF] Frijters | Does childhood predict adult life satisfaction? Evidence from British Cohort Surveys[END_REF][START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF].

Programme interventions with long-term follow-ups have confirmed that childhood conditions do affect adult outcomes (see the recent review of [START_REF] Heckman | Fostering and measuring skills interventions that improve character and cognition[END_REF]. The success of the Perry Preschool Program 4 in the US in the early 1960s is widely-cited in support of early public investment in disadvantaged children. The Perry program, along with other similar contemporary projects were found to significantly improve adult outcomes, including education, employment, earnings, marriage and participation in healthy behaviours, and to have reduced participation in crime (Heckman et al., 2010 and García et al., 2016). 5 Parental influence and family environment are well-documented as key childhood circumstances affecting both cognitive and non-cognitive skills (Cuhna and Heckman, 2008;Cuhna et al., 2010;Heckman and[START_REF] Heckman | The Economics of Human Development and Social Mobility[END_REF][START_REF] Conti | Economics of child well-being[END_REF]Heckman, 2014). These former include financial resources and financial strain, parental education and employment, parenting style and the family psychosocial environment. These cognitive and non-cognitive skills in turn have direct effects on wages, schooling, participation in crime, and many other aspects of social and economic life (see e.g. Heckman, 2005;Borghans et al., 2010;Heckman and Rubinstein, 2011).

Taking these observations as established, child development is conventionally considered as a cumulative function of past family background and skill acquisition (see Becker, 1967 and[START_REF] Grossman | On the concept of health capital and the demand for health[END_REF]. In these models, skill is accumulated via a weighted linear technology, so that a skill acquired in a given period may have a declining effect as the individual ages.

However, the recent literature on the economics of child development takes an alternative approach.

There is growing evidence that the technology of skill formation is, in fact, non-linear with dynamic complementarity [START_REF] Cunha | Estimating the technology of cognitive and noncognitive skill formation[END_REF]. There are two key characteristics of this technology: family environment in childhood remains an important contributor to adult success, and the long-run influence of family environment can be as large as that earlier in adult life. Strong evidence is shown for the effects of early health conditions, for example low birth weight or parental health conditions, on later physical health status (see [START_REF] Case | The lasting impact of childhood health and circumstance[END_REF][START_REF] Smith | The impact of childhood health on adult labor market outcomes[END_REF][START_REF] Currie | Child health and young adult outcomes[END_REF].

We here contribute to the literature on the life-cycle impact of childhood variables by focusing on adult life satisfaction. Our contribution is two-fold. We first model the effects of childhood, through family economic and parental psychosocial background and investment in cognitive and non-cognitive skills, on adult life satisfaction at different ages. Second, we ask which intermediate adult outcomes (income, education, employment and health) are the most important channels for the effect of childhood on adult life-satisfaction at different ages.

Data and Methods

We use two British cohort datasets. The first is the 1970 British Cohort Study (BCS), which follows around 17,000 individuals from birth up to age 42 (at the latest data release). The data here provide a broad range of information on socioeconomic characteristics, subjective wellbeing, family environment and individual life circumstances. Information is available on respondents at birth and ages 5, 10, 16, 26, 30, 34 and 42. Subjective wellbeing is assessed using the life-satisfaction question "How dissatisfied or satisfied you are about the way your life has turned out so far", with responses on a 0-10 scale, from age 26 onwards. The descriptive statistics for adult life satisfaction and the main BCS childhood variables we use in our analysis appear in the left-hand panel of Table 1.

The second cohort dataset is the National Child Development Study (NCDS), which covers 17,000 children born in England, Scotland and Wales in a single week in 1958. Following the birth survey in 1958, there have been nine public-use sweeps of the survey members at ages 7, 11, 16, 23, 33, 42, 46, 50 and 55 (as of 2017). At ages 33, 42 and 50, cohort members answered the same life-satisfaction question as in the BCS above, with answers on the scale from 0 "completely dissatisfied" to 10 "completely satisfied". The right-hand panel of Table 1 describes the main NCDS variables, which mirror closely those from the BCS.

Figures 1A and 1B depict the kernel distribution of life satisfaction in the BCDS (at ages 26, 30, 34 and 42) and the NCDS (at ages 33, 42 and 50). The modal life satisfaction score is 8 in both cohorts and at all ages. Just under 10% of BCS respondents reported being completely satisfied and around 5% dissatisfied (which we define as scores from 0 to 3). Average life satisfaction rises slightly from 26 to 34 and then falls from 34 to 42, but these movements are not statistically significant. Life satisfaction in early and middle adulthood is also flat in the NCDS. 6 Average NCDS life satisfaction does fall slightly in the later waves (i.e. at older ages) 7 but remains between seven and eight.

We estimate four specifications of the relationship between childhood variables and adult wellbeing.

The first, simplest, one considers the direct relationship between childhood variables and adult subjective well-being:

𝐿𝑆 𝑖.𝑡+𝑠 = 𝛼 1 + 𝛽 𝐹 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖,𝑡 + 𝛽 𝐶 𝐶𝐻𝐼𝐿𝐷 𝑖,𝑡 + 𝜀 𝑖,𝑡+𝑠 (1)
where 𝐿𝑆 𝑖.𝑡+𝑠 refers to the reported life satisfaction of individual i at period s after the childhood period of t. 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖,𝑡 and 𝐶𝐻𝐼𝐿𝐷 𝑖,𝑡 are vectors of family-background and childhood characteristics respectively (and so are measured at time t). If the error term 𝜀 𝑖𝑡+𝑠 is orthogonal to 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖,𝑡 and 𝐶𝐻𝐼𝐿𝐷 𝑖,𝑡 then the estimated values of 𝛽 𝐹 and 𝛽 𝐶 will show us how family and childhood circumstances affect adult life satisfaction. Identification in equation ( 1) comes from between-person variation in the childhood and family variables.

We carry out this analysis for each of the different adult ages at which we have life-satisfaction information (26, 30, 34 and 42 for the BCS; 33, 42, and 50 for the NCDS). This allows us to test whether the estimated coefficients 𝛽 𝐹 and 𝛽 𝐶 in equation ( 1) are stable over different adult ages. We use balanced samples of respondents, so that the comparison across adult ages will always be carried out for the same individuals. 8 This produces balanced observations on 5,046 individuals in the BCS and 6,875 individuals in the NCDS. The summary statistics for the balanced sample appear in Appendix Table A1, and can be compared to those for the full sample in Table 1. In the NCDS, the individuals in the balanced sample are similar to those in the full sample with respect to many characteristics.

Nevertheless, household income and the measure of intact families (both natural parents living in the household) are both higher in the balanced sample. The differences between the balanced and full sample in the BCS seem smaller. In both datasets, the child outcomes in the balanced sample reveal somewhat better intellectual performance, but similar levels of childhood emotional health and conduct.

We have a number of variables that characterise childhood and the family environment, and we may worry about multi-collinearity. We circumvent this problem by following the approach in Layard et al.

(

The specification of equation ( 2) assumes that current adult outcomes (𝐴𝐷𝑈𝐿𝑇 𝑖,𝑡+𝑠 ) reflect all adultlife outcomes that matter. We may alternatively consider that some types of life-course transitions continue to matter in their own right, even conditional on the final adult outcomes at period t+s. In 8 Note that both BCS and NCDS face issues of attrition as their samples become older. 9 In [START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF] (Figure 3), skills during childhood are calculated as a weighted sum over three childhood periods (aged 5, 10 and 16). Family background variables include family economic (father's social class, family income, family size, father's employment and parents' education attainment) and family psycho-social conditions. [START_REF] Lekfuangfu | Long or short arm? Childhood characteristics and human capital accumulation[END_REF] consider the effect of childhood on later cognitive skills, and put the childhood variables into five age categories instead of summing them from ages 0 to 16. 10 We address missing observations via mean imputation with missing indicator dummy variables.

particular, [START_REF] Switek | Explaining Wellbeing over the life cycle: A look at life transitions during young adulthood[END_REF] emphasises the importance of transitions between the ages of 16 and the 20s to early 30s in defining the later adult.

Our third regression then adds a set of life-transition variables up to a time s' after childhood to the mediated analysis in equation ( 2). Note that the definition of success during young adulthood may differ from that at other ages. Adults in their 40s are considered successful if they are married with children, but this is not necessarily the case for teens or those in their early 20s [START_REF] Marini | The order of events in the transition to adulthood[END_REF][START_REF] Hogan | The Transition to Adulthood[END_REF][START_REF] Billari | The Analysis of Early Life courses: Complex Descriptions of the Transition to Adulthood[END_REF]

In equation ( 3 The variables in all regressions are coded so that higher numbers represent more positive outcomes.

All variables, including the composite variables (for family background and childhood), are standardised, with means of 0 and standard deviations of 1.

The impact of family background and childhood on adult life satisfaction

Baseline results

Table 2 shows the results from the estimation of equation ( 1) at a number of different ages, t+s (26, 30, 34 and 42 for the BCS in the top panel, and 33, 42 and 50 for the NCDS in the bottom panel). In the BCS, the childhood family economic, family psychosocial, emotional health, good conduct and intellectual performance variables are all significant predictors of adult life satisfaction at all four adult ages. The same holds in the NCDS, apart for childhood conduct.

The last column of Table 2 estimates the average adult life satisfaction score over all available years.

Childhood emotional health is the strongest predictor of adult life satisfaction in both the BCS and the NCDS (at around 0.2 -0.3 of a standard deviation). Childhood conduct is second in the BCS, followed by all of the other three background variables (family economic, family psychosocial and intellectual performance), which have similar effect sizes. In the NCDS, it is rather intellectual performance that is in second place, followed by family economic and family psychosocial tied in third place, with childhood conduct being insignificant. There has thus been a notable evolution in the role of child conduct between the two cohort sweeps. For the 1958 cohort, conduct was unimportant, with intellectual performance taking second place only to emotional health in determining adult well-being.

Twelve years later, in the BCS cohort, good conduct as a child was just behind emotional health as a determinant of adult life satisfaction; intellectual performance remained significant but was now less important than child behaviour.

We can also read Table 2 horizontally, instead of vertically, to see how these regression coefficients change at different adult ages; these estimated coefficients are depicted visually in Figures 2A and2B for the BCS and the NCDS respectively. Overall, the results suggest that the link between childhood characteristics and adult wellbeing is remarkably stable, and we find no evidence that influence of childhood experiences on adult life satisfaction fades away as the years go by.

Note that the R-squareds are around 4% in all of the BCS sweeps. Hence, without controlling for other adult outcomes, childhood and family characteristics account for 4% of the between-person variation in adult life-satisfaction from ages 26 to 42. The explanatory power is smaller in the NCDS (at 3%).

Table 3 asks whether the effects of childhood and family background on adult life satisfaction differ by gender. In both the NDCS and the BCS, family psychosocial matters more for female than for male life satisfaction. On the contrary, the effect of child intellectual performance is larger for men than for women in the BCS. Overall, the gender differences here are not particularly striking.

Adding contemporaneous adult outcomes

We now add the proximal adult outcomes to the specification in Table 2 above. The coefficients 𝛾 𝐹 and 𝛾 𝐶 in equation ( 2) reveal the residual influence of the family and childhood variables respectively on adult life satisfaction, independent of the effect that they may have on the seven adult outcomes that we consider. Table 4 shows the results from the BCS (in columns 1-4) and the NCDS (columns 5-7).

Adult emotional health exhibits the strongest correlation with life satisfaction across all ages and in both cohorts (with an effect of around 0.2 to 0.3 standard deviations). Contemporaneous marital status and physical health are also consistently correlated with life satisfaction. The correlations with labourforce status and log income are almost as consistent, but are smaller in size. Last, most of the correlations with contemporaneous education and conduct are insignificant.

Controlling for adult outcomes for the most part (unsurprisingly) reduces the estimated coefficients on the childhood variables: part of the effect of family and childhood works through the adult outcomes.

The coefficients on child emotional health fall substantially in both the BCS and the NCDS (but remain significant), while those on intellectual performance are now small or zero. Childhood conduct remains an important determinant of adult life satisfaction in the BCS, even conditional on adult outcomes, but not in the NCDS.

Adding young adulthood life transitions

We here turn to a potentially independent role for life transitions in early adulthood, conditional on the final adult outcomes that are observed contemporaneously with life satisfaction: this is the specification in equation ( 3) above. The life transitions we consider here are early parenthood, lone parenthood, the age at which the individual left full-time education, the first job being full-time, early marital breakdown and the age at which the individual left home.

Table 5 shows the results when we include these young adult transition variables in addition to the adult outcomes and child and family background variables that already appeared in Table 4. A number of these transitions are significant predictors of adult life satisfaction, even controlling for contemporaneous adult outcomes. In particular, early lone parenthood and marital breakdown continue to cast a shadow on life satisfaction many years later in both datasets: the effect here is found for life satisfaction at younger adult ages. Perhaps more interestingly, the introduction of these transition variables has very little effect on the estimated coefficients on the childhood variables, which are very similar to those in Table 4.

The indirect effects of childhood

Via contemporaneous adult outcomes

The estimated coefficients on the family and childhood variables in Table 2 were reduced in size with the introduction of contemporaneous adult outcomes into the life-satisfaction regressions in Table 4 and of young adult transitions in Table 5. We thus suspect that the family and childhood variables themselves should help predict both the young adult transitions and the adult outcomes. We check for this mediation via contemporaneous adult outcomes by estimating equation (4.1) and that via young adult transitions by estimating equation (4.2).

The comparison of the BCS childhood determinants of well-being in Tables 2 and4 reveals that almost all of the effect of childhood intellectual performance works via adult outcomes, with analogous figures of over one half for childhood emotional health and one quarter to one half for childhood behaviour. The mediation in the NCDS is of similar size.

Figures 3A-3G and 4A-4G plot the effect of each of our five family and childhood composite variables on the seven contemporaneous adult outcomes in the BCS and the NCDS, respectively. The bars on the Y-axis depict the estimated coefficients at the different adult ages, with the associated 95% confidence intervals.

In the NCDS data, childhood intellectual performance is an important predictor of practically all adult outcomes, and is the largest predictor of adult income, employment and education. Remarkably, it is also just as important as childhood emotional health in predicting adult physical and emotional health. Childhood behaviour also has quite consistent effects on adult outcomes, but with an effect size that is much smaller. Childhood emotional health predicts the "social" outcomes of adult marital status, and physical and mental health. Family psychosocial background is correlated with adult marital status and good conduct. Last, the effect of family economic background is much like that of childhood intellectual performance, but of smaller size. 11

The results in the more recent BCS data are to some extent similar. However, although childhood intellectual performance continues to be an important predictor of practically all adult outcomes, its role in predicting adult physical and emotional health is now much reduced compared to that of childhood emotional health. Childhood behaviour in the BCS predicts every adult outcome, and with an effect size that is notably larger than that in the NCDS cohort twelve years earlier. Last, the effect of family economic background seems more pronounced in the BCS than in the NCDS cohort.

Through young adult transitions

We now turn to mediation effects via the young adult transitions in Table 5. The results are shown in Figures 5A and5B for the BCS and NCDS respectively. Unsurprisingly, childhood intellectual performance is the best predictor of age left full-time education in both datasets, and is in general a very consistent predictor of all young adult transitions. Childhood behaviour is also a consistent predictor of transitions, with an effect size that is notably larger in the (more recent) BCS than the NCDS. Childhood emotional health is not a strong predictor of transitions.

The comparison of the estimated coefficients on family and childhood background in Tables 4 and5 however reveal that the inclusion of these young adult transitions does not change our broad conclusions regarding the mediating effect of adult outcomes in Section 5.1 above.

11 Apart from a striking negative correlation with adult marital status, at least in earlier adult years , this probably reflects later marriage by those with more advantaged backgrounds.

Conclusions

There is now increasing interest in not only the contemporaneous correlates of subjective well-being, but also the distal correlates. We here use two UK birth cohorts, the 1958 NCDS and the 1970 BCS to show how family background and childhood variables are related to life satisfaction measured at a variety of adult ages.

There are first a number of similar findings across the two cohorts. Perhaps the most important one is that there is little evidence that the distal determinants of adult well-being change over time: the childhood factors that predict life satisfaction in the 20s predict it just as well in the 40s and beyond.

The effect of childhood and family does not then fade away over time. In both cohort datasets, it is childhood emotional health that is the strongest predictor of adult life satisfaction.

The predictors of adult life satisfaction are not entirely the same in the BCS and NCDS, however.

In particular, the role of childhood intellectual performance is weaker in the later cohort, while the effect of childhood behaviour is stronger (childhood behaviour is not significantly correlated with adult life satisfaction in the NCDS).

When we add adult outcomes, we find that adult emotional health has the largest correlation with adult life satisfaction at all ages in both datasets, but there is little independent role for education. There are again some notable differences: family is more important in the NCDS than in the BCS (although the family effect is notably larger in the latter for respondents in their 30s). Physical health is less important in general in the NCDS, but its coefficient does increase sharply for the respondents at age 50.

The adult outcomes mediate the effect of childhood. Almost all of the effect of childhood intellectual performance works via these adult outcomes, and over half that of childhood emotional health. The figure for childhood behaviour is smaller.

Our results underline the importance of emotional health, both in adulthood and childhood, in determining adult life satisfaction. More broadly, they show that interventions that affect adult outcomes, given childhood and family background, can improve adult well-being, and so can interventions that target the childhood outcomes themselves. There is thus a role for policy all through the lifetime.

The correlations that we find here are similar for our two UK cohorts. But we still only know how to predict the life satisfaction of middle-aged British respondents. That the correlations are similar over adult ages is a useful finding, but one that we would like to extend to older ages. Equally, these results refer to only one country, and their replication elsewhere is part of a current broad international effort to use cohort datasets to inform policy about the causes of well-being throughout life.

Figures

Figure 1A and1B

  ), 𝛿 𝐹 and 𝛿 𝐶 denote the direct effects of family and childhood background on adult life satisfaction, conditional on adult outcomes and transitions. The indirect effects of these variables via adult outcomes are then 𝛿 𝐴 * 𝜋 𝐹 and 𝛿 𝐴 * 𝜋 𝐶 . Analogously, the indirect effects via the early-life transitions 𝑇𝑅𝐴𝑁 𝑖,𝑡+𝑠′ are 𝛿 𝑅 * 𝜌 𝐹 and 𝛿 𝑅 * 𝜌 𝐶 .

Figures

  Figures 2A and 2B: The predictors of adult-life satisfaction at different ages. Balanced sample (BCS and NCDS)

Figure

  Figure 3A-3G: Predictors of adult outcomes at different ages. (BCS -Balanced sample) Fig 3A Fig 3B

Figure

  Figure 4A-4G: Predictors of adult outcomes at different ages. (NCDS -Balanced sample) Fig 4A Fig 4B

Figure 5 :Figure

 5 Figure 5: Predictors of young adult transitions (BCS and NCDS)

  𝐿𝑆 𝑖,𝑡+𝑠 = 𝛼 2 + 𝛾 𝐹 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖𝑡 + 𝛾 𝐶 𝐶𝐻𝐼𝐿𝐷 𝑖𝑡 + 𝛾 𝐴 𝐴𝐷𝑈𝐿𝑇 𝑖,𝑡+𝑠 + 𝜖 𝑖,𝑡+𝑠

2014)

, where the childhood and family variables are aggregated into five composite indicators: family economic, family psychosocial, emotional health, good conduct and intellectual performance (see

[START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF]

, for a detailed description of the method used, and Appendix B for more details). 9

10

The second regression asks to what extent the direct effects of family and childhood identified in equation (

1

) are mediated by adult outcomes. We thus extend equation (1) to include seven contemporaneous objective adult outcomes (education, income, labour-force status, marital status, emotional health, physical health, and good conduct). Equation (2) then describes the production technology of life satisfaction, as a function of current adult achievements and childhood background.

We again estimate this equation at a number of different ages, to see how this production technology changes at different respondent ages. The analysis of BCS data at age 34 in

[START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF]

revealed adult emotional health to be the dominant factor: equation (2) will check whether this is the case at all adult ages.

  . Our young-adult success transitions (𝑇𝑅𝐴𝑁 𝑖,𝑡+𝑠′ ) include that from school to work (age left full-time education, and the characteristics of the first job), partnership (early marital breakdown), parenthood (early parenthood and lone parenthood), and independence (age left home). 𝐿𝑆 𝑖,𝑡+𝑠 = 𝛼 3 + 𝛿 𝐹 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖𝑡 + 𝛿 𝐶 𝐶𝐻𝐼𝐿𝐷 𝑖𝑡 + 𝛿 𝐴 𝐴𝐷𝑈𝐿𝑇 𝑖,𝑡+𝑠 + 𝛿 𝑅 𝑇𝑅𝐴𝑁 𝑖,𝑡+𝑠′ + 𝜏 𝑖,𝑡+𝑠 (3) Last, we directly estimate the indirect effect of childhood circumstances by seeing how they are related to both adult outcomes and younger-adult transition variables. The equations are as follows: 𝐴𝐷𝑈𝐿𝑇 𝑖,𝑡+𝑠 = 𝜃 𝐴 + 𝜋 𝐹 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖𝑡 + 𝜋 𝐶 𝐶𝐻𝐼𝐿𝐷 𝑖𝑡 + 𝜐 𝑖,𝑡+𝑠 (4.1) 𝑇𝑅𝐴𝑁 𝑖,𝑡+𝑠′ = 𝜃 𝑅 + 𝜌 𝐹 𝐹𝐴𝑀𝐼𝐿𝑌 𝑖𝑡 + 𝜌 𝐶 𝐶𝐻𝐼𝐿𝐷 𝑖𝑡 + 𝜔 𝑖,𝑡+𝑠′

: The distribution of life satisfaction by age in the BCS and NCDS

	Figure 1A: BCS at ages 26, 30, 34 and 42
			Life satisfaction (BCS)
		.25
		.2
	Density	.1 .15
		.05
		0
		0	2	4	6	8	10
			Age 42	Age 34
			Age 30	Age 26
	Figure 1B: NCDS at ages 33, 42 and 50
			Life satisfaction (NCDS)
		.25
		.2
	Density	.1 .15
		.05
		0
		0	2	4	6	8	10
			Age 50	Age 42
			Age 33
		Note: The vertical lines refer to mean life satisfaction by age.

Table 1 : Descriptive statistics for life satisfaction and childhood variables

 1

	Tables
			BCS			NCDS
	Variables	No. obs Mean SD	No. obs Mean	SD
	Life satisfaction at 26 (BCS)	8,250	7.16 1.958	NA	NA	NA
	Life satisfaction at 30 (BCS)	10,251	7.29	1.84	NA	NA	NA
	Life satisfaction at 33 (BCS), 33 (NCDS)	8,869	7.41	1.79	10,629	7.42	1.72
	Life satisfaction at 42 (BCS), 42 (NCDS)	8,943	7.36	1.99	11,269	7.29	1.92
	Life satisfaction at 50 (NCDS)	NA	NA	NA	9,632	7.29	1.85
	Social class of the father (5 categories)	11,390	2.55	1.25	9,323	2.24	1.25
	Log of family weekly income	11,676	4.74	0.52	7,376	1.60	1.83
	Total number of siblings	16,362	1.75	1.36	17,415	2.25	1.51
	Average employment rate of father	9,760	0.95	0.14	17,825	0.69	0.42
	Age mother left full-time education	17,128 15.55	2.11	16,114 15.82	0.41
	Age father left full-time education	16,669 15.74	2.31	15,424 16.34	1.52
	Mother's average mental health	10,873	0.78	0.13	NA	NA	NA
	Post-marital conception	16,827	0.91	0.27	18,558	0.96	0.19
	Both natural parents live in household	8,398	0.84	0.36	18,558	0.66	0.26
	Emotional health at 5 (BCS), 7 (NCDS)	12,743	0.62	0.13	14,632	0.82	0.12
	Emotional health at 10 (BCS), 11 (NCDS)	12,681	0.6	0.12	13,819	0.81	0.13
	Emotional health at 16	4,101	0.74	0.11	14,653	0.92	0.09
	Good conduct at 5 (BCS), 7 (NCDS)	12,654	0.79	0.16	14,608	0.75	0.18
	Good conduct at 10 (BCS), 11 (NCDS)	12,581	0.79	0.13	13,805	0.76	0.18
	Good conduct at 16	8,172	0.91	0.16	11,653	0.90	0.13
	Intellect at 5 (BCS), 7 (NCDS)	12,641	0.58	0.24	15,028	0.58	0.15
	Intellect at 10 (BCS), 11 (NCDS)	11,551	0.59	0.12	14,134	0.53	0.16
	Intellect at 16	8,303	0.71	0.45	12,705	0.51	0.25

Table 2 : The family and childhood predictors of adult life -satisfaction at different ages (balanced sample)

 2 Robust standard errors in parentheses. *** 1%, ** 5%, * 10%. All columns include the following variables: female, family economic, family psychosocial, emotional health, good conduct and intellectual performance, measured during childhood. For the BCS, childhood ages are 5, 10 and 16; for the NCDS they are 7, 11 and 16. All continuous variables are standardised. In the last column, average life satisfaction is calculated as the sample mean of life satisfaction across ages for each cohort.

	Life Satisfaction at Age:

Notes:

Table B .2. Data description (childhood and transitional year variables)

 B

		BCS (age)	NCDS (age)
	Childhood: Family Economic
	Father's socio-economic group	10	16
	Log of family weekly income	10	16
	Total number of siblings	10	16
	Average employment rate of	0, 5, 10, 16 average	11, 16 average
	father
	Mother's and father's age on	One answer during age 0, 5,	One answer during age 0, 7,
	leaving full-time education	16	11, 16
	Childhood: Family Psychosocial
	Mother average mental health	0, 5, 10, 16 average	NA
	Whether post-marital conception	0	NA
	Whether both natural parents live	16	7, 11, 16 average
	in household
	Transition years
	Whether early motherhood	26,30,34 (any)	23, 33 (any)
	(pregnancy before 21)
	Whether have any period of lone	26,30,34 (any)	23, 33 (any)
	parenthood
	Any marital breakdown	26,30,34 (any)	23, 33 (any)
	(separated, divorced or widow)
	from 26 to 34
	Age left full time education	Responded at age 30	Responded at age 33
	Whether left home by aged 21 (no	Responded at age 26	Responded at age 33
	longer live at parents' home)
	First job is a full-time	Responded at age 30	Responded at age 23, 33

The childhood variables in[START_REF] Layard | What predicts a successful life? A life-course model of wellbeing[END_REF] are grouped into family economic, family psychosocial, emotional health, good conduct and intellectual performance variables.

[START_REF] Cunha | Estimating the technology of cognitive and noncognitive skill formation[END_REF] find that the elasticity of substitution for cognitive skills and investment is smaller, while that for non-cognitive skills and investment is larger and constant at any age over the life cycle.

For further details of each itemised childhood factor (for example birthweight, early health conditions, family income and environmental-risk exposure), see[START_REF] Ahlburg | Intergeneration transmission of health[END_REF] Currie and Thomas, 1995, 1999;[START_REF] Mcleod | Childhood emotional and behavioural problems and educational attainment[END_REF][START_REF] Case | The lasting impact of childhood health and circumstance[END_REF][START_REF] Black | From the cradle to the labor market? The effect of birth weight on adult outcomes[END_REF][START_REF] Blanden | Explaining intergenerational income persistence: Noncognitive skills, ability and education[END_REF][START_REF] Mensah | Childhood deprivation, health and development: Associations with adult health in the 1958 and 1970 British prospective birth cohort studies[END_REF][START_REF] Currie | Healthy, wealthy and wise: Socioeconomic status, poor health in childhood, and human capital development[END_REF][START_REF] Smith | The impact of childhood health on adult labor market outcomes[END_REF][START_REF] Contoyannis | The role of child health and economic status in educational, health and labor market outcomes in young adulthood[END_REF][START_REF] Currie | Child health and young adult outcomes[END_REF][START_REF] Frijters | Childhood economic conditions and length of life: Evidence from the UK Boyd Orr Cohort, 1937-2005[END_REF][START_REF] Currie | Inequality at birth: Some cases and consequences[END_REF][START_REF] Goodman | The long shadow cast by childhood physical and mental problems on adult life[END_REF][START_REF] Salm | The relationship between child health, development gaps, and parental education: Evidence from administrative data[END_REF][START_REF] Conti | Economics of child well-being[END_REF]

The Perry Preschool Program was a randomised trial that targeted disadvantaged low-IQ African-American children aged between 3 and 4. After two years, all participants left the programme and entered the same public school. Data were collected on the treatment and control groups up to age 40.

Examples of early-childhood programmes are the Carolina Abecedarian Project (ABC) and the Carolina Approach to Responsive Education (CARE). Both programmes were launched in the 1970s and have longterm follow-ups through the mid-30s. The programmes started with early interventions (begun at the age of 8 weeks) and engaged participants from disadvantaged backgrounds up to age 5.

In the 2008 sweep,

7.8% of respondents were completely satisfied and 4% dissatisfied with their lives.7 This fall is significant at the ten per cent level, but not at the five per cent level.

The authors thank Richard Layard and Nick Powdthavee for many useful discussions. Support from the US National Institute on Aging (Grant R01AG040640), the John Templeton Foundation and the What Works Centre for Wellbeing is gratefully acknowledged. For the BCS and NCDS data access, we are grateful to The Centre for Longitudinal Studies, UCL Institute of Education for the use of these data and to the UK

Data

Archive and UK Data Service for making them available. However, they bear no responsibility for the analysis or interpretation of these data.