dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present paper, we further the analysis of the same CEIP-NEU observational datasets through forest ecosystem modelling, with the objective of isolating the N r deposition impact on forest productivity and C sequestration potential from the parallel effects of climate, soil water retention, and forest age and management. A mechanistic modelling framework, driven by environmental forcings, inputs, growth limitations, internal cycling and losses, was required to untangle the relationships in measurement data, because the observed dependence of N r deposition on climate, combined with the large diversity but limited number of flux observation sites, restricted the applicability and validity of multivariate statistical methods. We describe a methodology to derive, through meta-modelling, standardization factors for observation-based forest productivity metrics, in order to factor out the part of the variance that was caused by influences other than N r deposition (climate, soil, stand age).
Carbon and nitrogen datasets from flux tower sites Ecosystem-scale carbon fluxes and atmospheric nitrogen deposition data were estimated within the CEIP and NEU networks at 31 European forests (6 deciduous broadleaf forests, DBF; 18 coniferous evergreen needleleaf forests, ENF, of which 7 were spruce-dominated and 11 were pinedominated; 2 mixed needleleaf-broadleaf forests, MF; 5 Mediterranean evergreen broadleaf forests, EBF) and nine short natural or semi-natural (SN) vegetation sites (wetlands, peatlands, unimproved and upland grasslands) (Table  S1  in the Supplement). In the following we often adopted the terminology "observation-based" rather than simply "measured" to reflect the fact many variables such as GPP or below-ground C pools rely on various assumptions or even empirical models for their estimation on the basis of measured data (e.
Briefly, the C datasets include multi-annual (on average, 5-year) mean estimates of NEP, GPP and R eco (total ecosystem respiration) based on 10-20 Hz EC measurements, post-processing, spectral and other corrections, flux partitioning, and empirical gap-filling (e. g. Lee et al. , 2004; Aubinet et al. , 2000; Falge et al. , 2001; Reichstein et al. , 2005; Lasslop et al. , 2010). The fully analysed, validated, gap-filled and partitioned inter-annual mean CO 2 fluxes (NEP, GPP, R eco ), as well as the meteo-rological data used as ecosystem model inputs (Sect. 2. 2), were retrieved from the European Fluxes Database Cluster (2012) and the NEU (2013) database. Dry deposition of reactive nitrogen was estimated by measuring at each site ambient concentrations of the dominant gas-phase (NH 3 , HNO 3 , NO 2 ) and aerosol-phase (NH + 4 , NO - 3 ) N r concentrations (data available from the NitroEurope database; NEU, 2013), and applying four different inferential models to the concentration and micro-meteorological data, as described in  Flechard et al.
General description of the BASFOR ecosystem model The BASic FORest (BASFOR) model is a process-based, deterministic forest ecosystem model, which simulates the growth and biogeochemistry (C, N and water cycles) of temperate deciduous and coniferous stands at a daily time step  (van Oijen et al. , 2005; Cameron et al. , 2013 Cameron et al. , , 2018)). Model code and documentation are available on GitHub (BAS-FOR, 2016). Interactions with the atmospheric and soil environments are simulated in some detail, including the role of management (thinning or pruning). BASFOR is a onedimensional model, i. e. no horizontal heterogeneity of the forest is captured, and BASFOR does not simulate some variables which are important in forest production, such as wood quality or pests and diseases. Nine state variables for the trees describe (i) C poolsleaves, branches and stems; roots; and reserves (CL, CB and CS or collectively CLBS; CR; CRES; kg C m -2 ); (ii) N pool in leaves (NL; kg N m -2 ); and (iii) stand density (SD, trees m -2 ), tree phenology (only for deciduous trees), accumulated chill days (d) and accumulated thermal time (Tsum; • C d).
Model implementation and calibration BASFOR simulations of forest growth and C, N and H 2 O fluxes were made for all CEIP-NEU forest sites from planting (spanning the interval 1860-2002) until the end of the NEU project (2011). At a few sites, natural regeneration occurred, but for modelling purposes a planting date was assigned based on the age of the trees. Meteorological data measured at each site over several years since the establishment of the flux towers (typically 5-10 years) were replicated backwards in time in order to generate a time series of model inputs for the whole period since planting. Assumptions were made that inter-annual meteorological variability was sufficiently covered in the span of available measurements and that the impact of climate change since planting was small and could be neglected.
The atmospheric CO 2 mixing ratio was provided as an exponential function of calendar year, fitted to Mauna Loa data since the beginning of records in 1958 (NOAA, 2014) and extrapolated backwards to around 1860-1900 for the oldest forests included in this study. The global CO 2 mixing ratio driving the model thus increased from around 290 ppm in 1900 to 315 ppm in 1958 to 390 ppm in 2010 (Fig. 1 ). Similarly, atmospheric N r deposition was a key input to the model and was forced to vary over the lifetimes of the planted forests; N dep was assumed to rise from pan-European levels well below 0. 5 g N m -2 yr -1 at the turn of the 20th century, sharply increasing after World War II to reach an all-time peak around 1980, and decreasing subsequently from peak values by about one-third until 2005-2010, at which point the NEU N dep estimates were obtained.
For the purposes of BASFOR modelling, we attempted to recreate a plausible density and thinning history over the lifetime of the stands. The guiding principle was that after the age of 20 years one could expect a decadal thinning of the order of 20 %, following  Cameron et al. (2013) , while the initial reduction was 40 % during the first 20 years. In the absence of an actual record of planting density (observed range: 1400-15 000 trees ha -1 ), a default initial value of 4500 trees ha -1 was assumed (for around two-thirds of the sites). The general principles of this default scheme were then applied to fit the available density and thinning data for each site, preserving all actual data in the time series while filling in the gaps by plausible interpolation. The density time courses thus obtained, underlying all subsequent model runs, are shown in Fig.
S2. The model was calibrated through a multiple site Bayesian calibration (BC) procedure, applied to three groups of plant functional types (PFTs), based on C/N/H 2 O flux and pool data from the CEIP-NEU databases (see  Cameron et al. , 2018). A total of 22 sites were calibrated, including deciduous broadleaf forests (DB1-6) and evergreen needleleaf forest ENF-spruce (EN1-7) and ENF-pine (EN8-18). The model parameters were calibrated generically within each PFT group; i. e. they were not optimized or adjusted individually for each observation site. In the companion paper  (Flechard et al. , 2020) , baseline BASFOR runs were produced for all 31 forest sites of the network, including also those stands for which the model was not calibrated, such as Mediterranean evergreen broadleaf (EB1 through EB5) and mixed deciduous-coniferous (MF1, MF2), to test the predictive capacity of the model beyond its calibration range (see Fig.
6  in  Flechard et al. , 2020). However, for the analyses and scenarios presented hereafter, these seven uncalibrated sites were removed from the dataset, as were two additional sites: EN9 and EN12 -EN9 because this agrosilvopastoral ecosystem called "dehesa" has a very low tree density (70 trees ha -1 ; Tables S1-S2 in the Supplement to  Flechard et al. , 2020)  and is otherwise essentially dry grassland for much of the surface area, which BASFOR cannot simulate; EN12 because this was a very young plantation at the time of the measurements, also with a very large fraction of measured NEP from non-woody biomass. All the conclusions from BASFOR meta-modelling are drawn from the remaining 22 deciduous, pine and spruce stands (sites highlighted in Table  S1 ).
Meta-modelling as a tool to standardize EC-based productivity data One purpose of BASFOR modelling in this study was to gain knowledge on patterns of C and N fluxes, pools and internal cycling that were not, or could not be, evaluated solely on the basis of the available measurements (for example, SOM mineralization and soil N transfer; retranslocation processes at the canopy level; patterns over the lifetime of a stand). The model results were used to complement the flux tower observations to better constrain elemental budgets and assess the potential and limitations of C sequestration at the European forest sites considered here. Additionally, we used meta-modelling as an alternative to multivariate statistics (e. g. stepwise multiple regression, mixed nonlinear models, residual analysis) to isolate the importance of N r deposition from other drivers of productivity.
This follows from the observations by Flechard et al. (  2020 ) that (i) N r deposition and climate were not independent in the dataset and that (ii) due to the large diversity of sites the limited size of the dataset and incomplete information on other important drivers (e. g. stand age, soil type, management), regression analyses were unable to untangle these climatic and other inter-relationships from the influence of N r deposition. BASFOR (or any other mechanistic model) is useful in this context, not so much to predict absolute fluxes and stocks but to investigate the relative importance of drivers, which is done by assessing changes in simulated quantities when model inputs are modified. Meta-modelling involves building and using surrogate models that can approximate results from more complicated simulation models; in this case we derived simplified relationships linking forest productivity to the impact of major drivers, which were then used to harmonize observations from different sites.
For example, running BAS-FOR for a given site using meteorological input data from another site, or indeed from all other sites of the network, provides insight into the impact of climate on GPP or NEP, all other factors (soil, vegetation structure and age, N r deposition) being equal. Within the boundaries of the network of 22 selected sites, this sensitivity analysis provides relative information as to which of the 22 meteorological datasets is most, or least, favourable to growth for this particular site. This can be repeated for all sites (22 × 22 climate scenario simulations). It can also be done for soil physical properties that affect the soil water holding capacity (texture, porosity, rooting depth), in which case the result is a relative ranking within the network of the different soils for their capacity to sustain an adequate water supply for tree growth.
We define GPP * as GPP obs being modulated by one or several dimensionless factors (f X ): GPP * = GPP obs × f CLIM × f SOIL × f AGE , (10) where the standardization factors f CLIM , f SOIL and f AGE are derived from BASFOR model simulations corresponding to the CEIP-NEU time interval around 2005-2010, as described below. The factors involved in Eq. (  10 ) address commonly considered drivers but not nitrogen, which is later assessed on the basis of GPP * rather than GPP obs. Other potentially important limitations such as non-N nutrients, soil fertility, air pollution (O 3 ), poor ecosystem health and soil acidification are not treated in BASFOR and cannot be quantified here. Further, the broad patterns of the GPP vs. N dep relationships reported in  Flechard et al. (2020) , i. e. a non-linear increase and eventual saturation of GPP as N dep increases beyond a critical threshold, did not show any marked difference between the three forest PFTs (deciduous, pine, spruce), possibly because the datasets were not large enough and fairly heterogeneous.
Values of f CLIM and f SOIL were calculated for each site in several steps, starting with the calculation of the ratios of modelled GPP from the scenarios to the baseline value GPP base such that X(i, j ) = GPP(i, j )/GPP base (i), (11) where i(1. n) denotes the site being modelled and j (1. n) denotes the climate dataset (j CLIM ) or soil parameter set (j SOIL ) used in the scenario being simulated (see Table  S2  for the calculation matrices). The value of the X(i, j ) ratio indicates whether the j th scenario is more (> 1) or less (< 1) favourable to GPP for the ith forest site. For each site, the aim of the f CLIM factor (and similar reasoning for f SOIL ) (Eq. 10) is to quantify the extent to which GPP differs from a standard GPP * value that would occur if all sites were placed under the same climatic conditions.
Discussion 4. 1 A moderate non-linear response of forest productivity to N r deposition The C sequestration response to N dep in European forests was derived using a combination of flux-tower-based C and N exchange data and process-based modelling, while a number of previous studies have been based on forest inventory methods and stem growth rates (e. g. de Vries et al. , 2009; Etzold et al. , 2014). The main differences with previous metaanalyses that were also based on EC flux datasets (e. g. Magnani et al. , 2007; Fleischer et al. , 2013; Fernández-Martínez et al. , 2014 , 2017)  were that (i) we derived total N dep from local measurements of the wet and dry fractions as opposed to regional/global CTM outputs; (ii) we untangled the N dep effect from climatic, soil and other influences by means of a mechanistic model, not through statistical methods; and (iii) in  Flechard et al.
(2020)  we estimated ecosystem-level N, C and greenhouse gas (GHG) budgets calculated through a combination of local measurements, mechanistic and empirical models, and database and literature data mining. Our most plausible estimates of the dC/dN response of net productivity over the lifetime of a forest are of the order of 40-50 g C per g N on average over the network of sites included in the study (Table  2 ). Such values are broadly in line with the recent reviews by  Erisman et al. (2011)  and by  Butterbach-Bahl et al. (2011)  (range 35-65 g C per g N) but slightly larger than estimates given in a number of other studies (e. g. Liu and Greaver, 2009; de Vries et al. , 2009 de Vries et al. , , 2014a)). Given the considerable uncertainty attached to these numbers (Table  2 ), they cannot be considered significantly different from any of those earlier studies.
, 2018) , the forecast increased N r emissions and increased N dep levels may thus not have a positive impact on the continent's land-based CO 2 sink. Data treatment and selection in our dataset (e. g. removal of Nsaturated forests) strongly impacted the plausible range of dC/dN responses (Table  2 ) derived from the original data. The non-linearity of ecosystem productivity relationships to N dep  (Butterbach-Bahl and Gundersen, 2011; Etzold et al. , 2014)  limits the usefulness and significance of simple linear approaches. These data suggest that there is no single dC/dN figure applicable to all ecosystems and that the highly nonlinear response depends on current and historical N dep exposure levels, as well as on the degree of N saturation  (Aber et al. , 1989 (Aber et al. , , 1998)) , although factors other than N, discussed later, may also be involved.
, 2009; ICP, 2019)  or other forest growth databases, or global-scale FLUXNET data (hundreds of sites worldwide; see  Burba, 2019). Thus, the gain in precision of N dep estimates from local measurements was offset by the smaller population sample size. Nonetheless this study does show the added value of the N r concentration monitoring exercise and the need to repeat and extend such initiatives. Understanding, quantifying and reducing all uncertainties leading up to dC/dN estimates are key issues to explore. Apart from measurement uncertainties in N r deposition and losses, and in the C balance based on EC measurements, analysed in the companion paper, the major difficulties that arose when assessing the response to N dep of forest productivity included the following: i. The heterogeneity of the population of forests, climates and soils in the network, and the large number of potential drivers relative to the limited number of sites, hindered the use of a straightforward, regression-based analysis of observational data without a preliminary (model-based) harmonization.
This places severe constraints on the interpretation of productivity data in response to N dep , since most current models, which do not account for other nutrient lim-itations, cannot be called upon to normalize for differences between sites. The impact of the fertility classification on CSE of the sites included in  Fernández-Martínez et al. (2014)  was questioned by  Kutsch and Kolari (2015)  on the basis of unequal qual-ity of the EC flux datasets found in FLUXNET and other databases. By excluding complex terrain sites (and young forests) from the  Fernández-Martínez et al. (2014)  dataset,  Kutsch and Kolari (2015)  calculated a much reduced variability in CSE, with a reasonable mean value of 15 % (range 0 %-30 %), suggesting a much lower influence of nutrient status than claimed by  Fernández-Martínez et al.
(2014). In their reply,  Fernández-Martínez et al. (2015)  reanalysed the same subset of sites selected by  Kutsch and Kolari (2015)  but using the same generalized linear model as used in their original analysis of the whole dataset as opposed to the linear model used by  Kutsch and Kolari (2015). Fernández-Martínez et al. (2015)  then maintained that the findings of the original study were still valid for the restricted dataset, i. e. that the nutrient status had a significant influence on CSE. The smaller European dataset of our study poses a similar dilemma. The much wider variation in CSE obs than modelled CSE 5-year may both point to possible measurement issues if CSE obs values (especially the larger ones) are considered ecologically implausible and/or inform on important ecological processes that are not accounted for in the model.
In fact, based on prior knowledge of this site's acidification history, and since such mechanisms and impacts are not mathematically represented in BASFOR, EN8 was from the start discarded from the calibration dataset for the Bayesian procedure  (Cameron et al. , 2018). The four lowest CSE obs values were found at sites with topsoil pH < 4 (Fig. 10c ), although other forests growing on acidic soils had reasonably large CSE obs ratios. The large variability in CSE obs cannot be explained by any single edaphic factor (Fig. 10a-c ), more likely by a combination of many factors that may include N dep (Fig. 10e ). As noted previously, C flux measurements at all four forest sites with N dep > 2. 5 g N m -2 yr -1 (EN2, EN8, EN15, EN16) indicated lower productivity estimates than those in the intermediate N dep range, or at least smaller than might have been expected from a linear N fertilization effect (Fig.
Forest age Forest age is expected to affect photosynthesis (GPP), growth (NPP), carbon sequestration (NEP) and CSE for many reasons. A traditional view of the effect of stand age on forest NPP  (Odum, 1969)  postulated that R aut increases with age and eventually nearly balances a stabilized GPP, such that NPP approaches zero upon reaching a dynamic steady state. Revisiting the paradigm,  Tang et al. (2014)  found that NPP did decrease with age (> 100 years) in boreal and temperate forests, but the reason was that both GPP and R aut declined, with the reduction in forest growth being primarily driven by GPP, which decreased more rapidly with age than R aut after 100 years. However, the ratio NPP/GPP remained approximately constant within each biome. The effect of age on NEP and CSE is even more complex since this involves not only changing successional patterns of GPP and R aut , but also those of R het over a stand rotation of typically one century or more, which is much longer than the longest available flux datasets.
When N is strongly limiting, and in young forests, N r deposition may well favour SOM decomposition. To examine the potential impact of N dep on R soil , we compiled the soil respiration data available from the literature and databases for the collection of forest sites in our study, which covers the whole N limitation to N saturation spectrum. Sites ranged from highly N limited boreal systems, where an N addition might trigger enhanced tree growth, increased microbial biomass and heterotrophic respiration, to N-saturated, acidified systems (EN2, EN8, possibly also EN15, EN16), in which poor ecosystem and soil health may lead to different ecological responses than those of the below-ground carboncycling scheme in  Janssens et al. (2010). Since the below-ground autotrophic (root and rhizosphere) respiration component is regulated to a large extent by photosynthetic activity  (Collalti and Prentice, 2019) , as well as seasonality in below-ground C allocation  (Högberg et al.
4-0. 5. It is also noteworthy that the largest R soil / GPP ratios (EN5, EN17) are found at sites with relatively large SOC and topsoil C/N ratio compared with the other sites (Figs. 10a  and 11b ). The R het / R soil ratio also tends to decrease with N dep (Fig. 11e ), and although measured by different methods at the different sites, this is arguably a more robust metric than chamber / EC respiration ratios, because the differential respiration measurements on control and treatment plots (root exclusion, trenching, girdling) are made on the same spatial and temporal scales. Many other factors that impact soil respiration (age, soil pH, microbial abundance and diversity, etc. ) are not con-sidered here and are beyond the scope of this paper. In view of these uncertainties, if the assessment within this restricted dataset does not provide full and incontrovertible proof of the negative impact of N r deposition on soil respiration, it at least is not in open contradiction to the prevailing paradigm that both below-ground autotrophic and heterotrophic respiration are expected to decrease as N r deposition increases.
Acknowledgements. The authors gratefully acknowledge financial support by the  European Commission  through the two  FP6  integrated projects CarboEurope- IP  (project no. GOCE-CT-2003-505572 ) and  NitroEurope Integrated Project  (project no. 017841 ), the  FP7   ECLAIRE  project (grant agreement no. 282910 ), and the  ABBA COST Action   ES0804. We are also thankful for funding from the  French GIP-ECOFOR  consortium under the F-ORE-T forest observation and experimentation network, as well as from the MDM-2017-0714 Spanish grant. We are grateful to  Janne Korhonen ,  Mari Pihlatie  and  Dave Simpson  for their comments on the paper. Finalization of the paper was supported by the  UK Natural Environment Research Council  award number  NE/R016429/1  as part of the  UK-SCAPE programme delivering national capability.
We also wish to thank two anonymous referees for their constructive criticism of the paper. Financial support. This research has been supported by the  European Commission's Sixth Framework Programme  (grant nos. 017841  and  GOCE-CT-2003-505572 ) and  European Commission 's  Seventh Framework Programme (ECLAIRE , grant no. 282910 ). Review statement. This paper was edited by  Sönke Zaehle  and reviewed by two anonymous referees.