PLoS ONE plos plosone PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0227530 PONE-D-19-35297 Research Article Biology and life sciencesEcologyEcosystemsForests Ecology and environmental sciencesEcologyEcosystemsForests Ecology and environmental sciencesTerrestrial environmentsForests Biology and life sciencesOrganismsEukaryotaPlantsTrees Physical sciencesChemistryPhysical chemistryIonsCations Earth sciencesAtmospheric scienceAtmospheric chemistryAcid deposition Physical sciencesChemistryEnvironmental chemistryAtmospheric chemistryAcid deposition Ecology and environmental sciencesEnvironmental chemistryAtmospheric chemistryAcid deposition Physical sciencesChemistryChemical compoundsNitrates Physical sciencesChemistryEnvironmental chemistrySoil chemistry Ecology and environmental sciencesEnvironmental chemistrySoil chemistry Ecology and environmental sciencesSoil scienceSoil chemistry Biology and life sciencesEcologyForest ecology Ecology and environmental sciencesEcologyForest ecology Physical sciencesChemistryPhysical chemistryChemical deposition Engineering and technologyManufacturing processesSurface treatmentsChemical deposition Soil solution in Swiss forest stands: A 20 year's time series Soil solution in Swiss forest stands http://orcid.org/0000-0003-4268-7659 Braun Sabine Investigation Validation Writing – original draft 1 * http://orcid.org/0000-0003-1159-8071 Tresch Simon Writing – review & editing 1 Augustin Sabine Conceptualization 2 Institute for Applied Plant Biology, Witterswil, Switzerland Federal Office for the Environment, Berne, Switzerland Aherne Julian Editor Trent University, CANADA

The authors have declared that no competing interests exist.

 * E-mail: sabine.braun@iap.ch 14 7 2020 2020 15 7 e0227530 20 12 2019 22 6 2020 2020 Braun et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Soil solution chemistry is influenced by atmospheric deposition of air pollutants, exchange processes with the soil matrix and soil-rhizosphere-plant interactions. In this study we present the results of the long-term Intercantonal Forest Observation Program in Switzerland with soil solution measurements since 1998 on a current total of 47 plots. The forest sites comprise two major forest types of Switzerland including a wide range of ecological gradients such as different nitrogen (N) deposition and soil conditions. The long-term data set of 20 years of soil solution measurements revealed an ongoing, but site-specific soil acidification. In strongly acidified soils (soil pH below 4.2), acidification indicators changed only slowly over the measured period, possibly due to high buffering capacity of the aluminum buffer (pH 4.2–3.8). In contrast, in less acidified sites we observed an increasing acidification rate over time, reflected, for example, by the continuous decrease in the ratio of base cations to aluminum (BC/Al ratio). Nowadays, the main driver of soil acidification is the high rate of N deposition, causing cation losses and hampering sustainable nutrient balances for tree nutrition. Mean nitrate leaching rates for the years 2005–2017 were 9.4 kg N ha-1 yr-1, ranging from 0.04 to 53 kg N ha-1 yr-1. Three plots with high N input had a remarkable low nitrate leaching. Both N deposition and nitrate leaching have decreased since 2000. However, the latter trend may be partly explained due to increased drought in recent years. Nonetheless, those high N depositions are still affecting the majority of the forest sites. Taken together, this study gives evidence of anthropogenic soil acidification in Swiss forest stands. The underlying long-term measurements of soil solution provides important information on nutrient leaching losses and the impact climate change effects such as droughts. Furthermore, this study improves the understanding of forest management and tree mortality regarding varying nitrate leaching rates.

 http://dx.doi.org/10.13039/501100003338 Bundesamt für Umwelt Forestry Departments cantons AG, BE, BL, BS, TG, SO, ZH Environmental departments of Central Switzerland SB and ST: Federal Office for the Environment (measurements and manuscript writing), forest authorities of the cantons AG, BE, BL, BS, TG, SO, ZH and Environmental Offices of Central Switzerland (long-term Intercantonal Forest Observation Program). Data Availability https://doi.org/10.5061/dryad.2z34tmphm. 1 Introduction

Since the 1980s it has been recognized that soil acidification due to anthropogenic input of sulfur and nitrogen compounds [1] poses a serious threat to forest health [2]. An increase in soil acidification, related to atmospheric acid deposition, has been reported in many European countries such as Germany [3], Sweden [4] or France [5]. The deposition of acidifying substances, in particular of sulfur compounds, has decreased in recent years in Europe, due to effective mitigation measures. In consequence, sulfate concentrations in soil solution have decreased significantly [6]. However, the development of nitrogen indicators is more divergent [7]. In Canada, for example, the chemical recovery of streams was slower than expected due to the reduction of acid deposition [8].

In Switzerland, acidification and eutrophication due to the deposition of reactive nitrogen compounds remain a critical issue [9],[10]. Between 1990 and 2017, emissions of sulfur compounds decreased by 85%, those of oxidized nitrogen by 55% and those of reduced nitrogen by 18% [11]. However, the input of nitrogen in forests in the proximity of intensive agriculture is still high with an average of 20.4 kg N ha-1 yr-1 and up to 50 kg N ha-1 yr-1 [12]. Exceedance of the critical loads for acidity and nitrogen is a continuing worldwide environmental problem, also in Switzerland [10]. For instance, Graf Pannatier et al. [14] observed a decrease in the BC/Al ratio in two out of five Swiss long-term forest monitoring sites between 1999 and 2007, which can be interpreted as an ongoing acidification during this time period.

Concerns about forest health led to the initiation of forest monitoring programs in the 1980s, where monitoring of soil solution is an important part [13]. The chemistry of soil solution is affected by atmospheric deposition, exchange processes between the solid and the dissolved phase in the soil and the nutrient uptake by the roots and other rhizosphere processes [14]. Stress indicators based on soil solution have been elaborated by expert groups under the International Cooperative Program on Modelling and Mapping of Critical Loads and Levels and Air Pollution Effects, Risks and Trends (ICP Modelling und Mapping) of the Geneva Air Convention (CLRTAP) of the UNECE [15]. Important chemical criteria for assessing the acidity of soil solution in forest ecosystems are the ratio between base cations (BC = Ca2+ + K+ + Mg2+) and aluminum (BC/Al-ratio) [16], the pH value and the concentration of inorganic aluminum [15]. Aluminum bound in organic complexes is not toxic for plant roots [17]. In addition, critical thresholds have been identified for base saturation (BS) of the solid phase, for the alkalinity and for the acid neutralizing capacity of the soil solution [15].

Elevated nitrate leaching from the rooting zone in form of negatively charged nitrate induces acidification due to the concomitant loss of positively charged base cations from the soil. Acidification may impair root development [18], [19], [20] and nutrient supply to plants [3], [21], [22]. Sustainability calculations revealed that the loss of the cations Ca2+, Mg2+, and K+ through nitrate leaching poses a greater risk to Swiss forest stands than cation exports with whole tree harvest [23]. To protect forests from such negative effects, maximum tolerable values for nitrate leaching have been defined [15]. Not only for forest ecology, but also for drinking water management, increased nitrate leaching is an important issue, as it poses a risk to human health [24].

The eutrophication effects of N have been addressed in another UNECE document [25]. Rihm and Achermann calculated the exceedance of the critical loads of N for Switzerland using a mass balance approach [10]. For productive forests they estimated an exceedance rate of 87% for the year 2015 [12]. Consequences of N excess have been highlighted by Aber [26] who developed a conceptual model for the effects of N saturation in forest ecosystems. This model describes different stages of the saturation process, with soil nitrate leaching occurring at a late stage, when N can no longer be used for growth. It was further developed by Aber [27], then confirmed and improved by Emmett [28]. Lovett & Goodale [29] suggested, based on results from an experiment in oak stands with very high N input, that many processes may occur simultaneously rather than in sequence. Observed N effects in forests differ according to site conditions such as climate, soil quality, and annual N input. In Scandinavia, Binkley & Högberg [30] concluded that increased N deposition led to higher forest growth without causing quantifiable problems. These findings are in contrast to the overview on forest effects by Näsholm et al. [31] who listed numerous N impacts in Northern ecosystems. Data analysis from Swiss forests show that current forest growth is only slightly increased by N and that deposition rates >25 kg N ha-1 yr-1 show rather inhibitory effects [32], [35].

Nitrate leaching is strongly linked to atmospheric N deposition [33], [34]. The C:N of forest soil ratio is known to be a good predictor of the risk of nitrate leaching [35]. A strong increase of nitrate leaching was observed at C:N ratios < 25. At higher ratios nitrogen is immobilized and nitrification is inhibited [36]. In addition, high nitrate leaching rates were observed after strong disturbances such as tree cutting [37].

The aim of the present study is to analyze trends in soil solution data collected over a period of 20 years from currently 47 plots of the Intercantonal Forest Monitoring Program in Switzerland [38]. The observed changes in the element concentration of the soil solution measurements were analyzed with respect to international critical limits and other threshold values in order to assess the risk of acidification and eutrophication effects on forest health in Switzerland. The following research questions were discussed:

Is there an exceedance of critical limits?

Do the reductions in acid depositions lead to corresponding changes in soil solution chemistry?

What are suitable predictors to recognize the risk of high nitrate leaching?

The parameters measured in this monitoring program are based on the Guidelines on Reporting Monitoring and Modelling of Air Pollution Effects of the Geneva Air Convention [39].

 2 Materials and methods 2.1 Plots

The investigated sites are part of the Intercantonal Forest Observation Program in Switzerland [38]. Permission to perform the study was obtained from the local forest authorities and the forest owners. The first soil solution samplers were installed in nine out of 189 plots in 1997, and samples have been collected since 1998. In the following years, additional plots were included, resulting in a current total of 47 plots with soil solution measurements (Table 1, Fig 1). The plots cover a wide range of forest soils. Base saturation was determined using an unbuffered NH4Cl extract [40]. The pH was measured in a suspension with 0.01 N CaCl2 at a ratio of 1:2.5. A detailed description of the forest assessments carried out as well as other soil and foliar analyses is given by [32]. The rates of tree mortality and removed trees were derived from annual observations and combined into one variable referred to as "tree removal rates". This variable was further divided into the four following lagged effects: the tree removal rate of the current year (lag0), the previous year (lag1), the last two years (lag2) and the last three years (lag3).

10.1371/journal.pone.0227530.g001 Forest plots with soil solution samplers (sampled in 2017 and 2018), grouped according to the base saturation of the topsoil (average 0–40 cm). 10.1371/journal.pone.0227530.t001 Site properties of forest plots with soil solution samplers. pH: pH(CaCl<sub>2</sub>) in the uppermost 40 cm of the soil, BS: Base saturation in the uppermost 40 cm of the soil (%).

CN: C:N ratio in the forest floor or the uppermost humus horizon. Prec: precipitation in mm, average 1981–2018. Leaching: leaching water flux in mm, calculated with the hydrological model Wasim-ETH [41], average 1981–2018. Species: tree species: Fa beech, Pic Norway spruce, Ab fir, La larch, Pin pine. Soil types: FAO classification. Weath: weathering rate in keq ha-1 a-1: calculations with SAFE [42] for the rooting zone (0–60 cm). Start: starting year of the soil solution measurements. Site Muri (storm) was cleared in 1999 during the gale “Lothar”.

Site abbr. altitude (m) prec (mm) leaching (mm) species pH BS (%) CN soil type weath keq ha-1 yr-1 start (year)
Aarwangen AW 470 1140 482 Fa 3.99 10 14.5 Dystric Cambisol 1.31 2002
Aeschau AU 940 1512 783 Ab Pic (Fa) 3.67 20 26.0 Dystric Arenosol 0.45 1997
Aeschi AI 510 1160 472 Fa Pic 3.87 15 21.2 Haplic Luvisol 1.24 1998
Allschwil AL 350 896 153 Pic 4.31 88 14.0 Haplic Luvisol 2006
Bachtel BAB 1030 1825 1093 Fa 3.93 36 15.6 Chromic Luvisol 4.62 1999
Bachtel BA 1040 1770 998 Pic 4.01 7 24.8 Chromic Luvisol 0.90 1997
Beromünster BE 640 1220 321 Pic 5.00 90 23.1 Gleyic Cambisol 7.06 2016
Bonfol BO 450 1091 417 Fa 4.26 18 20.3 Dystric Cambisol 0.67 2004
Braunau BRAU 710 1253 400 Pic 4.05 55 19.8 Haplic Luvisol 2006
Breitenbach BB 460 1111 346 Fa 4.53 91 14.3 Haplic Luvisol 0.85 2003
Brislach beech BRB 435 1041 378 Fa 4.09 25 13.3 Haplic Luvisol 0.88 2000
Brislach spruce BR 435 1042 258 Pic 3.93 12 23.3 Haplic Luvisol 0.84 1997
Bürglen BUR 640 1582 572 Pic 4.77 99 22.2 Cambisol 0.36 2016
Busswil BU 600 1195 388 Pic 3.78 3 18.9 Haplic Luvisol 0.99 2004
Diessenhofen DI 520 942 290 Pic 3.77 16 20.8 Dystric Cambisol 2006
Frienisberg FR 725 1209 542 Fa Pic 3.90 21 21.2 Dystric Arenosol 0.64 1997
Gelfingen GE 540 1135 451 Fa 6.55 100 21.9 Calcaric Cambisol 1.59 2016
Giswil GI 540 1306 479 Fa 5.86 100 19.5 Calcaric Cambisol 10.84 2016
Grenchenberg GB 1220 1511 961 Fa Pic 5.64 100 15.1 Calcaric Cambisol 19.05 1997
Grosswangen GW 600 1114 320 Pic 3.52 14 21.9 Stagnic Acrisol 1.25 2016
Habsburg K HA 430 1072 308 Fa 4.17 16 17.1 Haplic Luvisol 0.84 2004
Hinwil HI 650 1456 619 Pic 5.12 95 15.4 Eutric Cambisol 1.33 2002
Le Châtelard LC 1010 1654 811 Pic 3.74 20 29.3 Gleyic Cambisol 1.53 2006
Lurengo spruce LUB 1620 1786 1098 Pic Pin La 3.90 28 26.2 Dystric Arenosol 1999
Lurengo N exp. LU 1600 1786 1123 Pic La 4.17 19 22.5 Podzol 0.59 1997
Möhlin MO 290 1034 267 Pic 3.79 12 17.5 Haplic Luvisol 1.22 1998
Muri beech MUB 490 1110 340 Fa 4.00 24 18.3 Haplic Luvisol 0.56 1999
Muri spruce MUF 490 1104 278 Pic 3.88 10 26.5 Dystric Cambisol 0.74 2001
Muri storm MU 490 1104 588 Pic 4.08 23 18.9 Haplic Luvisol 0.62 1997
Muttenz MUU 375 912 228 Fa 4.06 41 15.7 Stagnic Luvisol 0.50 2004
Oberschrot OS 950 1340 541 Fa Pic 3.61 11 17.2 Gleyic Stagnic Cambisol 2006
Olsberg OL 380 998 240 Fa 4.06 20 15.4 Dystric Planosol 0.48 2004
Pratteln PR 415 966 339 Fa 5.15 100 12.4 Chromic Luvisol 2.24 2002
Rafz RAF 540 995 315 Pic 4.18 16 19.0 Haplic Luvisol 0.70 2004
Riehen RI 470 1005 402 Fa 6.41 100 13.3 Haplic Luvisol 1.26 2002
Rünenberg RU 590 1017 245 Fa 4.13 35 17.2 Haplic Luvisol 0.68 2002
Sagno SA 770 1782 943 Pic 3.83 25 21.8 Eutric Cambisol 0.41 1999
Scheidwald SW 1170 1500 547 Pic 3.41 7 27.9 Dystric Gleysol 0.66 2008
Sempach SE 550 1139 450 Fa 3.71 39 21.6 Gleyic Luvisol 2.32 2016
Stans ST 560 1437 924 Fa 6.40 100 17.4 Calcaric Cambisol 28.30 2016
Wangen WG 500 1143 450 Fa Pic 3.88 24 23.3 Chromic Luvisol 2008
Wangen SZ WSZ 470 1536 634 Fa 4.43 95 14.8 Luvisol 1.77 2016
Wengernalp WA 1870 1605 922 Pic 3.53 28 14.2 Podzol 0.15 1997
Winterthur WI 530 1178 465 Pic 5.25 97 16.0 Vertisol 18.46 2003
Zofingen ZO 540 1130 370 Fa Pic 4.00 17 17.9 Haplic Luvisol 0.92 2004
Zugerberg HG ZBB 980 1569 900 Fa Pic 4.20 37 19.8 Eutric Cambisol 0.63 1999
Zugerberg (N-exp.) ZB 940 1457 821 Fa 3.91 100 18.5 Dystric Cambisol 0.45 1997
Zugerberg VG ZV 900 1457 550 Pic 3.62 24 20.2 Dystric Cambisol 1 2002
2.2 Soil solution

For each site and soil depth, eight soil solution samplers (ceramic suction cups, 0653X01-B0.5M2, Soilmoisture Equipment Corp.) were installed in the topsoil and five in the subsoil. Actual depths varied according to soil condition but a frequent sampling design was 20, 50 and 80 cm. Detailed site-specific information and time series can be found in [43]. Following the monthly sampling of the soil solution, the samples from the same location and from the same depth were pooled. We measured pH (Metrohm pH-meters 716 and 809, with Metrohm Aquatrode), conductivity (Metrohm conductivity meters 712 and 856, with Metrohm cell 6.0916.040) and alkalinity (titration with HCl to a pH of 4.35 (Metrohm 809)) of the soil solution immediately after sampling. Samples were then kept frozen (-20°C) until further analysis. For cation analysis the samples were acidified with 0.5 ml HNO3 in 10 ml solution prior to freezing. For anion analysis the samples were filtered through a 0.45 μm membrane filter. Ca2+, Mg2+, Al3+ and Mn2+ were analyzed using atomic absorption photometry (Varian 640) and K+, Na+ by flame photometry (Varian 640). Inorganic Al was measured as difference before and after passing the samples through an ion exchanger (0.5 ml IC-H, Alltech 30264). NH4+ was measured by photometric determination with indophenol blue [44]. NO3-, SO42- and Cl- were assessed by ion chromatography with suppressed conductivity (Dionex GP50 pump, ED50 electrochemical detector and AS3500 autosampler). Dissolved organic carbon was measured by UV absorption at 280 nm according to [45].

Quality control was achieved by calculation of the ion balance, by comparison of measured and calculated conductivity [46,47] and by analysis of reference samples distributed once a year by the Norwegian Institute for Air Research (NILU).

The relation between base cations and aluminum (BC/Al) was calculated on a molar basis [15], using the concentration of inorganic aluminum. The determination of organic aluminum started in 2005. In order to get a homogenous time series for older data, the average proportion of organic aluminum to total aluminum was calculated for each soil layer. This proportion was then applied to data from 1998 to 2005. It varied between 50% for the uppermost soil water samplers and 25% in the lowest ones (S3 Fig in S1 File).

The amount of leaching water in mm was calculated using the hydrological model Wasim-ETH [41] taking into account soil characteristics (pF curve, texture), current vegetation cover and daily meteorological data interpolated for each site [32]. Leaching fluxes calculated for each sampling period were multiplied with concentrations to calculate element fluxes.

2.2.1 Critical limits in soil solution

The molar ratio between base cations (BC = Ca2+, Mg2+, K+) to aluminum (Al3+), the BC/Al ratio, in the soil solution is an important criterion for evaluating soil acidification. It has been shown to be closely correlated with growth and vitality parameters of the vegetation [16]. Initially, a limit value of 1 has been set for the BC/Al ratio [48], [16]. Since this limit value is not necessarily sufficient to protect forests, Ouimet et al. [49] suggested a BC/Al limit value of 10 for calculations of critical loads in Canada. Based on these findings, the critical limits of the BC/Al ratio were revised [15]. In Switzerland a revised BC/Al limit value of 7 has recently been applied [50].

Other critical limit values for the soil solution are a pH of 4 and a Al concentration of 0.2 eq m-3 [15]. The Acid Neutralizing Capacity (ANC), an indicator of vulnerability to acidification, is defined as sum of the base cations Ca2+, Mg2+, K+ and Na+ minus the sum of the anions nitrate, sulfate and chloride. It relates the two criteria Al and proton concentration, given a pKGibbsit of 8.04 [15]: ANC=−Al−H

By solving this equation for Alcrit of 0.2 eq m-3 and pH 4, the maximum allowable leaching of alkalinity from the rooting zone is -300 μeq l -1 [51].

While the above mentioned acidity indicators refer to soil solution, an indicator widely used in forestry is the base saturation (the cations Ca2+, Mg2+, K+ on the cation exchange complex in BS) of the soil solid phase [52]. The parameter BS reflect the potential supply of cations to the trees. The relation between the cations in the soil solution and the BS can be described with the exchange equations according to Gapon or Gaines-Thomas; [53]. This relation between the soil solid phase and the soil solution has been examined, among others, by Hildebrand [54] and Schall et al. [55] who both stated that the exchange relationships depend on the chemical status of the soil. In the present study, we provide field data of the relation between the exchange complex and soil solution sampled in situ.

Monthly soil solution data were compared with the critical limits listed above. In order to avoid sensitivity to single outliers, an exceedance was indicated when more than 1% of the values were above the limit value.

Eutrophication effects of N input can be evaluated by using the concentrations and total amounts of N-leaching. Critical limits have been set accordingly. A concentration of >0.2 mg N l-1 in soil solution has been related to changes in ground vegetation and in tree nutrition [15]. For temperate deciduous forests, a threshold for a total annual nitrate leaching amount of 2–4 kg N ha-1 yr-1 has been set to avoid excessive base cation leaching and acidification. Absolute limits for nitrate leaching, based on concentrations in the soil solution, are particularly important for areas with high precipitation. Under such conditions, high losses of cation nutrients and thus reduced base saturation can occur as a consequence.

 2.3 Weathering rates

Weathering rates were calculated using the model SAFE [42] for a subset of monitoring plots. The calculations are based on the measured mineralogy of soil samples [56]. The results were summed up for each layer down to a depth of 60 cm, taking into account deeper depths where dense rooting occurred at greater depths. This differentiation has been based on correlation analysis between soil chemistry and foliar analysis suggesting that nutrient uptake is considerably small below 60 cm [23]. Recent analyses of Al concentration in tree rings suggest that the SAFE model gives reasonable estimates of base saturation and thus also of weathering rates (Hopf et al., unpublished results).

 2.4 Atmospheric deposition

Data on atmospheric deposition of reactive nitrogen compounds and base cations were received from the Swiss Federal Office for the Environment [12]. N deposition was modelled in a spatial resolution of 0.1 ha and the deposition of base cations with a spatial resolution of 2 km [57]. Model comparisons of N deposition with observational data showed a strong agreement [58], with the exception of the plots in Southern Switzerland, where the import of N compounds by air from Italy were more difficult to take into account. The deposition of base cations in Southern Switzerland was modelled according to [59].

 2.5 Statistics

In order to test if the BC/Al ratio depends on the degree of acidification, a moving time window of 5 years were formed for the dependent variable and pH in soil solution.The development within these time windows was analyzed using a linear mixed effect model with plot as random effect (R, package lme4 [60]. Including the BC/Al ratio and soil solution pH at the start of the 5 year period, base saturation of the soil in the corresponding soil horizon (%), weathering rate of base cations (keq ha-1 yr-1), proportion of coniferous trees in the plot (%), modelled N deposition (kg N ha 1 yr 1), organic carbon in the corresponding soil horizon (% C), C:N and N:P ratio in the forest floor, clay content (%) and soil depth (binary variable coded as ≤70 (0) and >70 cm (1)).

For the relation between BC/Al in soil solution and the chemistry of the solid phase the properties of the horizon at the depth of the soil solution samplers were used as described above.

Explanatory variables for nitrate leaching were analyzed based on annual means. Linear mixed effect models with plot and year as random effects were used including modelled N deposition (kg N ha-1 yr-1), C:N ratio in the uppermost soil horizon, water holding capacity (0–100 cm in mm), annual minimum of site water balance (mm), rate of seepage water (mm), tree removal rate (current year and 3 lagged effects), shrub cover of the plot from a vegetation survey, proportion of coniferous trees in the plot and altitude (m).

Predictors were selected backwards using the Akaike Information Criterion (AIC). When the number of predictors had to be reduced to avoid oversaturation of the model, the Bayes Information Criterion (BIC) criterion was used. Residuals were examined for normal distribution, homoskedasticity and outliers using diagnostic plots. In the case of BC/Al and nitrate leaching a log transformation was required. Regression plots were produced using the R functions ggpredict [61] and ggplot [62]. The former extracts predictions including 95% confidence intervals from a multivariate model taking the mean value of all other predictors. Pseudo-R2 for mixed regression models were calculated according to Nakagawa and Schielzeth [63]. All R-codes and data for the models including diagnostic and effect plots are provided in the supplementary materials.

 3 Results 3.1 Data description and time trends

There is a significant time trend for the measured indicators of acidity pH, ANC and BC/Al ratio as well as for the cations Ca, Mg, K, Al and the anions NO3- and SO42-, taken together mean concentrations per element and depth in all plots (Table 2). The only exceptions are ANC in <30 cm depth and Al in 30–60 cm depth. The pH has generally increased with time while the BC/Al ratio has decreased. These two parameters give thus conflicting interpretation. However, the base cations have all decreased while Al concentrations have increased.

10.1371/journal.pone.0227530.t002 Summary statistics for the dataset by depth layer.

Mean and confidence interval: estimates corrected for the varying dataset (mixed regression). Minimum and maximum: median values of single years per site and depth. Regression against time: time trend with mixed regression of log transformed predictors (except pH and ANC). n = 20211 monthly samples, 47 plots, 22 years.

element unit depth Statistics 95%-Confidence interval time trend
    (cm) mean min max low high coeff. se p value
Aciditiy indicators                  
pH   <30 5.24 4.04 8.03 4.96 5.51 0.006 0.001 <0.001
pH   30–60 5.52 4.10 8.33 5.16 5.89 0.017 0.001 <0.001
pH   >60 6.05 4.11 8.43 5.74 6.36 0.027 0.001 <0.001
ANC μeq l-1 <30 98 -445 2620 -31 227 0.903 0.556 0.104
ANC μeq l-1 30–60 253 -606 4520 -3 508 1.369 0.580 0.018
ANC μeq l-1 >60 98 -601 5744 -31 227 3.053 1.027 0.003
BC/Al   <30 3.20 0.72 >10000 2.47 4.17 -0.020 0.001 <0.001
BC/Al   30–60 3.71 0.75 >10000 2.63 5.22 -0.014 0.001 <0.001
BC/Al   >60 6.35 1.22 >10000 4.79 8.44 -0.015 0.001 <0.001
Cations                    
Ca mg l-1 <30 2.28 0.08 43.04 1.52 3.42 -0.030 0.002 <0.001
Ca mg l-1 30–60 2.43 0.04 105.2 1.39 4.28 -0.030 0.002 <0.001
Ca mg l-1 >60 4.15 0.11 100.7 2.57 6.71 -0.016 0.001 <0.001
Mg mg l-1 <30 0.54 0.07 8.32 0.41 0.73 -0.022 0.001 <0.001
Mg mg l-1 30–60 0.58 0.08 12.26 0.41 0.81 -0.028 0.001 <0.001
Mg mg l-1 >60 0.89 0.08 17.58 0.64 1.25 -0.021 0.001 <0.001
K mg l-1 <30 0.21 0.01 5.12 0.16 0.30 -0.036 0.002 <0.001
K mg l-1 30–60 0.18 0.01 1.59 0.13 0.25 -0.047 0.002 <0.001
K mg l-1 >60 0.14 0.01 2.06 0.11 0.18 -0.033 0.001 <0.001
Al mg l-1 <30 0.19 0.01 3.77 0.13 0.29 0.018 0.002 <0.001
Al mg l-1 30–60 0.14 0.01 3.80 0.09 0.24 0.003 0.002 0.222
Al mg l-1 >60 0.07 0.01 3.61 0.05 0.10 0.016 0.002 <0.001
Anions                    
NO3- mg N l-1 <30 0.66 0.01 11.83 0.37 1.16 -0.049 0.003 <0.001
NO3 mg N l-1 30–60 0.52 0.01 77.39 0.25 1.06 -0.025 0.003 <0.001
NO3 mg N l-1 >60 0.20 0.01 19.55 0.11 0.38 -0.055 0.003 <0.001
SO42- mg S l-1 <30 0.79 0.06 6.46 0.61 1.03 -0.050 0.001 <0.001
SO42- mg S l-1 30–60 1.12 0.09 11.25 0.82 1.54 -0.047 0.001 <0.001
SO42- mg S l-1 >60 1.73 0.14 23.18 1.36 2.21 -0.038 0.001 <0.001
3.2 Acidification 3.2.1 Acidification status, comparison with thresholds

The exceedance of acidity limits according to the Geneva Air Convention [15] or suggested by [64] are listed in Table 3. A BC/Al ratio lower than 1 in at least one layer was observed in 27% of the plots and a BC/Al ratio lower than 7 was observed in 71% of the plots. The frequency distribution of the BC/Al ratio and ANC are shown in S1 Fig in S1 File.

10.1371/journal.pone.0227530.t003 Frequency of plots with exceedance of various acidity limits in the years 2013–2018.

Number of sites = 47.

Acidity limit Reference Frequency of plots with exceedance (%)
BC/Al <1 [15] 27
BC/Al<7 [15] 71
ANC < -500 μeq l-1 [64] 22
ANC <-300 μeq l-1 [15] 37
ANC <0 μeq l-1 [64] 86
pH <4 [15] 8
Al >0.2 eq m-3 [15] 0

The BC/Al ratio in soil solution was regressed against base saturation in the corresponding soil layer (Fig 2A) and against pH(CaCl2) of the solid phase (Fig 2B). The relation with base saturation is stronger (p = <0.001, Adj.R2 = 0.73, S1 Table in S1 File) than with pH(CaCl2) (p = <0.01, Adj.R2 = 0.55, S1 Table in S1 File). This difference can be attributed to the high buffer capacity of the aluminum buffer (pH 3.8 –pH 4.2; 150 kmol H+ per % clay), reflected by the cluster of points around pH 4 (Fig 2B). For BS = 20 the regression function predicts a BC/Al ratio of 10 (95% CI 7.7–12.6); for BS = 40 a BC/Al of 28 (22.4–33.9). These relationships allow–within certain limits–to link BC/Al ratios with base saturation values which are more often used as acidity indicator in forestry.

10.1371/journal.pone.0227530.g002 <p>Relationship between the BC/Al ratio of the soil solution and base saturation (A) and between the BC/Al ratio and soil pH (B). Points represents measurements from the topsoil (depth 0–70 cm), triangles from the subsoil (>70 cm depth). Model outputs with the corresponding coefficients are given in S1 Table in <xref ref-type="supplementary-material" rid="pone.0227530.s001">S1 File</xref>.</p> </caption> <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0227530.g002" xlink:type="simple"/> </fig> </sec> <sec id="sec013"> <title>3.2.2 Leaching of base cations

The leaching of base cations is relevant for the assessment of acidification and the evaluation of the sustainability of forest nutrition. The relation between the Ca input from weathering and Ca leaching was clearly significant (p = <0.001, Adj.R2 = 0.52). In 35 out of 41 plots (85%), Ca leaching exceeded the Ca input by weathering (Fig 3A). When atmospheric deposition of Ca is added to weathering (Fig 3B), 16 plots (39%) still have a negative Ca balance. The highest Ca losses were observed in weakly buffered sites in the silicate and exchange buffer ranges (pH 4.2–6.2; WI, HI, PR, BB) as well as on one site with very high N input (SA). Leaching of Ca was correlated significantly with weathering rate (p = <0.001, Adj.R2 = 0.59) and soil water holding capacity (p = <0.01), while neither N deposition nor species composition of the tree layer were significant predictors (S6 Table in S1 File). The same analysis with leaching rates at different time periods slightly reduced the proportion of plots with a negative balance at the later time period. For instance, between 2015–2018, Ca leaching exceeded the Ca weathering input in 83% of the plots.

10.1371/journal.pone.0227530.g003 Relation between the leaching of Ca at a depth of 60–80 cm (average 2005–2018) and the weathering rate of base cations cumulated over the uppermost 60 cm.

A: Relation between Ca leaching and weathering rate. B: Relation between Ca leaching and the sum of Ca weathering and Ca deposition. Site abbreviations can be found in Table 1. Only sites with Ca leaching exceeding the weathering input by a factor of 1.1 are labelled in the plot. The line represents the 1:1 line.

 3.2.3 Development of acidification

The BC/Al ratio decreased significantly over time in all depth and base saturation levels (S7 Table in S1 File). Even in base rich soils there was a clear decrease of the BC/Al ratio, i.e. a clear increase in acidification (Fig 4). The decrease of BC/Al ratio over time was strongest in the uppermost soil depth (S7 Table in S1 File). Below the rooting zone this decrease was weaker but still significant.

10.1371/journal.pone.0227530.g004 Development of the BC/Al ratio over time in soils of different soil depths and base saturation levels.

Error bars: 95% confidence interval, extracted from the mixed regression models (S7 Table in S1 File).

The declining trends became weaker the more acidified the soil was. To examine this association, the changes of BC/Al ratio within five years were analyzed in relation to the initial status of the sample in a moving window analysis. The resulting regression (Table 4, Fig 5) supports the hypothesis that the trends are related to acidification status. Significant predictors for the trend were the initial BC/Al ratio, the initial pH, BS and soil depth. Predicted changes of log BC/Al in Fig 5 below zero correspond to an expected decrease of the BC/Al ratio during the following five years. This means that a decrease of BC/Al is expected when the BS is below 48%, the pH below 5.9 or the BC/Al ratio above 16.3. The pH value of 5.9 corresponds well with the lower limit of the Ca buffer range (pH 6.2), according to the equilibrium of CaCO3 with H2CO3 in soil [65], [66]. These values may be regarded as "thresholds” for acidification under the current deposition situation. No interpretation was found for the unexplained part of this regression, addressed as residuals.

10.1371/journal.pone.0227530.g005 Rate of changes of BC/Al ratio within five years in relation to selected predictors from <xref ref-type="table" rid="pone.0227530.t004">Table 4</xref>.

Predicted values including 95% confidence intervals are conditioned on all other fixed effects. Negative changes signify an expected decrease in BC/Al. The linearity of the relations was tested using polynomial functions.

 10.1371/journal.pone.0227530.t004 Changes of BC/Al ratio within five years and various parameters.

Dependent variable: Difference in log transformed BC/Al during five years. Pseudo-R2 fixed effects = 0.37, Pseudo-R2 including random effect = 0.53, n = 994).

coeff SE p value
(Intercept) -0.218 0.024 <0.001
Initial BC/Al-ratio -0.132 0.006 <0.001
Initial soil solution pH 0.057 0.005 <0.001
base saturation (%) 0.0014 0.002 <0.001
depth coded (0: <70 cm, 1:> = 70 cm) 0.046 0.005 <0.001
3.2.4 Nitrate leaching

Mean annual nitrogen leaching rate was 9.4 kg N ha-1 yr-1 for the years 2005–2018 (S2 Table in S1 File). Nitrogen leaching decreased significantly between 1998 and 2018 (Fig 6). The proportion of plots exceeding the leaching limits of the Geneva Air Convention [15] decreased from 83% (1998, 12 plots) to 34% (2018, 47 plots). The two plots with the highest average leaching rates (S2 Table in S1 File) have very different properties. Plot SA in Southern Switzerland has a leaching rate of 52 kg N ha-1 yr-1, resulting from a high N deposition, leading to an average nitrogen concentration of 6.7 mg N l-1, and a high output with the seepage water (>900 mm yr-1). Whereas, the second plot AL in Northwestern Switzerland is characterized by extremely high N concentrations in soil water (average 30 mg N l-1) and a low leaching water flux, resulting in an average leaching rate of 55 kg N ha-1 yr-1. Despite high nitrogen deposition and low base saturation three plots have almost negligible N leaching (GW, SW, BU). Interestingly enough these plots stand out with a very high crown transparency (proportion of Norway spruce with >25% transparency in 2016, 2017 and 2018 was: 81%, 74% and 43%, respectively, while the average proportion in all 76 Norway spruce plots was 23.4%).

10.1371/journal.pone.0227530.g006 Development of N leaching 1998–2018.

The decrease with time is significant with p<0.001. Thick squares and error bars (95% confidence intervals) are estimates corrected for the varying number of plots per year from mixed regression (S3 Table in S1 File). Small dots are raw measurements, n = 586.

In the mixed regression model, significant predictors for N leaching rates were N deposition, drought, tree removal and water holding capacity (Table 5, Fig 7). Drought predictors included potential evapotranspiration, site water balance and the amount of leached water. The effect of tree removal was largest one year after the removal (lag 1, Table 5). The combined effect of annual tree removal rates (Fig 7B) was calculated as weighted average based on the model coefficients of lag 0, 1, 2 and 3 (Table 5; S4 Table in S1 File). The effect of tree removal is particularly well represented one site (LUB) with a considerable high leaching rate with N inputs of approximately 17 kg N ha-1 yr-1 (red points in Fig 7A). The reason for these results is a bark beetle infestation in the years 2015 and 2016. N leaching was reduced in dry years and on soils with a high water holding capacity (S6 Fig in S1 File). The C:N ratio in the forest floor was not a significant predictor for N leaching.

10.1371/journal.pone.0227530.g007 <p>A: Relationship between N leaching and N deposition (<xref ref-type="table" rid="pone.0227530.t005">Table 5</xref>). B: Relationship between N leaching and tree removal averaged over 0–3 years (S4 Table in <xref ref-type="supplementary-material" rid="pone.0227530.s001">S1 File</xref>). Tree removal is in fraction of 1, i.e. 0.4 means that 40% of the trees are removed.</p> </caption> <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0227530.g007" xlink:type="simple"/> </fig> <table-wrap id="pone.0227530.t005" position="float"> <object-id pub-id-type="doi">10.1371/journal.pone.0227530.t005</object-id> <label>Table 5</label> <caption><title>Mixed regression model of N leaching with annual data.

Dependent variable: N leaching in kg N ha-1 yr-1, log transformed. Pseudo-R2 fixed effects = 0.37, Pseudo-R2 including random effect = 0.77, n = 586.

coeff SE p-value
(Intercept) 4.19 1.7 0.013
N deposition (kg N ha-1 yr-1) 0.10 0.02 <0.001
tree removal rates current year (lag 0) 0.95 0.6 0.101
tree removal rates previous year (lag 1) 2.59 0.6 <0.001
tree removal rates two years before (lag 2) 1.96 0.6 <0.001
tree removal rates three years before (lag 3) 1.09 0.5 0.028
potential evapotranspiration (mm) -0.003 0.001 <0.001
minimum site water balance (mm) -2.09 0.8 0.009
rate of seepage water (mm) 0.0011 0.26 <0.001
water holding capacity (mm) -0.01 0.004 0.001

Since both N leaching and N deposition decreased during the observation period a direct causal link between these two parameters seems obvious. This hypothesis was tested by extracting annual predictions for the variations in climate (potential evapotranspiration, minimum site water balance and leaching water), or N deposition based on the mixed regression model (Table 5). These estimates were compared with values predicted from the full model and with observed data. This analysis revealed that changes of N deposition and climatic factors contributed equally to the changing N leaching rates (Fig 8).

10.1371/journal.pone.0227530.g008 Comparison of model predictions for the trend of N leaching over time.

The "full model" included all predictors given in Table 4. The "drought" model included predictions for precipitation (leaching water) and temperature (potential evapotranspiration, site water balance). The "N deposition" model predicted values for the changes in N deposition. The model "observed" included the raw data of N leaching. The points are annual means with a loess smoother (degree of smoothing α = 0.8) as lines.

Although N leaching in plots with coniferous trees was clearly larger than in neighboring plots with deciduous trees (S5 Fig in S1 File), the effect of the proportion of coniferous trees on N leaching revealed not to be significant in the regression analysis. This could be due to a possible confounding effect, since the modeled N deposition itself depends on the tree species.

 4 Discussion

The present findings of the long-term Intercantonal Forest Observation Program indicate that soil acidification continues to be an important issue in Swiss forests, both in terms of extent and ongoing progression. Overall, these results are in accordance with findings reported from ICP Forests in Switzerland [14]. At European level a nonsignificant decrease in the ratio between BC and Altot was observed, but no signs of recovery from the decreasing acid deposition [6]. The authors explain this by delayed responses. For example, they found that NO3- slightly decreased in the subsoil but not in the topsoil.

The thresholds observed for the development of the BC/Al ratio under the current deposition regime (Fig 5) can partially be explained by the soil chemical equilibria of CaCO3 and Al in soils. The pH of 5.9 is rather close to the pH value at which free CaCO3 disappears and the Ca buffer range changes into the cation exchange buffer range (pH<6.2). Between pH 6.2 and 4.2 soils are buffered by the silicate and the cation exchange buffer range which have a lower capacity. At a pH of 4.2 the aluminum buffer range is reached which has a high capacity [66]. The results presented here underline the effectiveness of the various buffer mechanisms. No significant relation was found between N deposition and the speed of acidification indicated by the change in BC/Al ratio. This may be partly explained by the various initial soil acid-base-states (different buffer ranges) of the sites in this study. Under homogeneous conditions a clear relation was found between addition of NH4NO3 and the decrease of BC/Al on a soil with low buffer capacity [43]. The time between changes in deposition and changes in the soil solution depends on the chemical state of the soil and the amount of deposition [7].

Effects of soil acidification on the forest health found in the framework of the long-term Intercantonal Forest Observation Program in Switzerland were reduced rooting depth [20], increased uprooting of trees [67] and increased Mg deficiency [68]. The uprooting of trees was considerably higher on soils with a base saturation <40%. Visible Mg deficiency has strongly increased in the last 10–15 years, indicating the importance of soil acidification processes for forest health. Moreover, it has been shown that the foliar Mg concentrations in beech leaves are related to the Mg concentrations in the soil solution [68].

The reduction of N deposition between 1998 to 2018 could explain the observed decrease in N leaching. However, the statistical analysis revealed that climate, especially potential evapotranspiration and runoff, plays an equally important role in this trend (Table 5). Analysis of the temporal variation in N leaching showed a significant contribution of relative mortality or tree removal, that can be detected up to three years after the event. This is in line with previous studies that found an increase in N leaching after clear cutting in a catchment area [37] or after tree removal [69]. In contrary other findings [35], the C:N ratio was not a significant predictor for N leaching. However, it can be explained by the low C:N ratio of the soils examined. 197 out of 212 plots have a C:N ratio of <25 which was assigned by Gundersen et al. [35] as a threshold for an enhanced leaching risk. Sampling of these soils started when N deposition had been high for a long time.

The higher N leaching under Norway spruce compared to beech, observed on paired plots, is consistent with the observations from Germany [70], [71]. This may be explained by the higher N deposition in Norway spruce stands due to the higher leaf area, the higher surface roughness and the evergreen needles. However, our analyses revealed that tree species was confounded with N deposition as tree species influences deposition modeling.

The present results do not allow general conclusions to be drawn about suitable indicators of N eutrophication. Accepted indicators for eutrophication are increased N leaching, increased nitrate concentration in the soil solution, decreased C:N ratio of the forest floor or increased N in foliage [72], [15]. Based on our results we question the reliability of the N concentration of the leaves as an indicator of eutrophication, since our measurements show that in beech leaves today they are no longer correlated with N deposition, which was the case in the 1980s [38]. The generally low C:N ratios of the soils presented in this study allow no further differentiation. N leaching is elevated on many plots with high N inputs but there are clear outliers: on three plots with a very high N input there is almost no detectable nitrate concentrations in the soil solution and thus no N leaching. None of the commonly accepted eutrophication indicators mentioned above apply to all plots. This conclusion is confirmed by [73]. It must be stated that the confidence interval for the relation between N leaching and N deposition becomes very lager at higher N inputs due to the different site conditions with respect to soils and climate.

 5 Conclusions

The large number of soil solution measurements of the long-term Intercantonal Forest Observation Program has shown an increase in acidification in most sites between 2005 and 2018, even for base rich soils. The progression of acidification depends on the chemical status of the soil, which is reflected in the buffer ranges. Strongly acidified soils lie in the aluminum buffer range and are therefore less susceptible to further changes of the pH value. The main driver of the observed acidification is high N deposition, which leads to a high nitrate leaching and thus to a high cation loss. In nutrient balance calculations, these cation leaching losses were the most important contribution to the budget, exceeding the input of Ca by weathering and deposition and thus endangering forest sustainability [23].

Soil acidification has negative consequences for forest health, such as increased risk of windthrow on soils with low base saturation <40% [67] or decreased rooting depth for soils with a base saturation <20% [20]. Here we present that, based on the relation between BC/Al ratio and base saturation, these base saturation thresholds translate to a BC/Al ratio in soil solution of 51 and 12, respectively. The BC/Al ratio of 12 is close to the ratio of 10 recommended by [49] as critical limit in critical loads calculations. The relation presented here allows realistic estimates of the relation between BS, pH and BC/Al ratio in mineral soils under field conditions.

The high N deposition above the critical loads is still affecting most of the observed plots, although air pollution measures have resulted in a decrease since the 1980s. The current study provides information to disentangle the effect of drought and nitrogen input on the N leaching losses. It furthermore quantifies the effect of forest management and tree mortality on the variation of N leaching over time. Future research on the interaction between soil solution and forest health should take into account the long-term effects of drought on tree nutrient uptake and changes in ground vegetation.

 Supporting information <p>(PDF)</p> </caption> </supplementary-material> </sec> </body> <back> <ack> <p>We would like to thank the field and lab team of the Institute for Applied Plant Biology who performed the monthly sampling and analysis with a lot of patience and care: Delphine Antoni, Dieter Bader, Ute Schröder, Moïse Groelly, Caroline Stritt und Roland Woëffray. Part of the data analysis on N leaching was performed in a project on "Forest and Climate" supported by the Federal Office for the Environment in cooperation with Peter Waldner, WSL.</p> </ack> <ref-list> <title>References Prenzel J (1985) Verlauf und Ursachen der Bodenversauerung. Zeitschrift der deutschen geologischen Gesellschaft 136: 293–302. Ulrich B (1987) Stability, elasticity and resistance of terrestrial ecosystems with respect to matter balance. Ecological Studies 61: 11–49. Ulrich B, Mayer R, Matzner E (1986) Vorräte und Flüsse der chemischen Elemente. In: Ellenberg H, Mayer R, Schauermann J, editors. 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Braun S, Schindler C, Volz R, Flückiger W (2003) Forest damage by the storm "Lothar" in permanent observation plots in Switzerland: the significance of soil acidification and nitrogen deposition. Water Air and Soil Pollution 142: 327–340. Braun S, Schindler C, Rihm B (2020) Foliar nutrient concentrations of European beech Switzerland: relations with nitrogen deposition, ozone, climate and soil chemistry. Frontiers in Forests and Global Change. Weis W, Rotter V, Göttlein A (2006) Water and element fluxes during the regeneration of Norway spruce with European beech: Effects of shelterwood-cut and clear-cut. For Ecol Manage 224: 304–317. Rothe A, Kreutzer K, Küchenhoff H (2002) Influence of tree species composition on soil and soil solution properties in two mixed spruce-beech stands with contrasting history in Southern Germany. Plant Soil 240: 47–56. Rothe A, Mellert KH (2004) Effect of forest management on nitrate concentrations in seepage water of forests in southern Bavaria, Germany. Water Air and Soil Pollution 156: 337–355. Sutton M. A., Howard C. M., Erisman J. W., Billen G., Bleeker A., Grennfelt P., et al. (2011) The European Nitrogen Assessment. Cambridge: Cambridge University Press. 612 p. Flechard CR, Ibrom A, Skiba UM, de Vries W, Van Oijen M, Cameron DR, et al. (2020) Carbon-nitrogen interactions in European forests and semi-natural vegetation—Part 1: Fluxes and budgets of carbon, nitrogen and greenhouse gases from ecosystem monitoring and modelling. Biogeosciences 17: 1583–1620. 10.1371/journal.pone.0227530.r001 Decision Letter 0 Aherne Julian Academic Editor 2020 Julian Aherne This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

13 Feb 2020

PONE-D-19-35297

Soil solution in Swiss forest stands: a 20 year's time series

PLOS ONE

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Additional Editor Comments (if provided):

The two reviewers have very different comments on your manuscript. Reviewer 2 is more critical of the manuscript and raise important concerns regarding the objectives, context, title and presentation of the data. It is an impressive data set, but the objectives of the paper should be clear, and the analysis should support the objectives. I have recommended major revisions.

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Reviewer #1: Yes

Reviewer #2: No

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: No

Reviewer #2: No

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Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a highly relevant and thoroughly elaborated.

It is quite interesting to read that the soil solution monitoring has been continued for 20 years and the future ahead.

The technical description of the approach and the discussion are very well written. The quality of the text is high and it could be published as it is.

Nevertheless, there is always room for improvement:

1/ The introduction of the manuscript is reflecting the state of knowledge of 30 years ago. At that time there was the concern that acidification threatens European forests, and 20 years ago it was stated that N eutrophication is a major concern. There have been many papers since them that put these concerns into perspective. A good source of information of such reviews are the TAMM Reviews of Forest Ecology and Management. - I recommend to either state in the Introduction that the text is reflecting the knowledge when the monitoring programmes were initiated, or (preferred) to expand the Introduction by more recent references.

2/ The methods section is quite complex. The author repeatedly refers to extensive manuals where the single steps are described. Presumingly, very few readers will look up these sources. -- Considering that supplementary material comes with the paper, it would be desirable to show the code that had been used. -- Also a statement on confidence in the invidual steps would be interesting. E.g. how well does SAFE reflect weathering in the field?

3/ The full data sets are not available. - If this is the data policy of the institute, a statement on data ownership could be made.

Reviewer #2: The author has evaluated the chemistry of soil solution at a number of Swiss forest sites with respect to their excedance of critical limits for acidity and nitrate. In addition some models were run to relate the critical limits to site, stand, atmospheric inputs and a number of other factors.

First off, the premise of the paper is not sound. Line 28 states ‘Soil acidification is a serious threat’. This is not the case. Soil acidification is a natural process of soil development in temperate climates where there is a precipitation surplus. The objectives are not clearly stated and the results presented e.g. Ca budgets do not correspond to the stated aim on Line 64-66. Line 66 states that soil solution chemistry will be related to forest health but this is not presented. No evidence is presented to indicate that forests in Switzerland are suffering from the effects of acidification from atmospheric deposition of S and N. Is there forest health data available? Foliar chemistry for example? Evidence of magnesium deficiency or yellowing of needles?

The title of the paper is 'Soil solution in Swiss forest stands: a 20 year's time series' but no time series are presented.

It is not clear what the purpose of the N assessment is and how it relates to acidification and what it contributes to the paper. The author states that ‘N leaching may not always be a good eutrophication indicator’ [L22-23]. This has already been well established in the literature

e.g. Lovett, G.M., Goodale, C.L. A New Conceptual Model of Nitrogen Saturation Based on Experimental Nitrogen Addition to an Oak Forest. Ecosystems 14, 615–631 (2011). https://doi.org/10.1007/s10021-011-9432-z

A lot of the literature cited is quite old e.g. refs 11, 16, 18, 19, 25, 27, 30. A lot of new knowledge has been published in relation to the issue of soil acidification by atmospheric deposition that the author should review.

In addition, a lot of work done on Swiss forest monitoring specifically with respect to this issue, but this work is not cited. I would encourage the author to review these papers and position their work within this context. Sample below

Pannatier, E.G., Thimonier, A., Schmitt, M. et al. A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environ Monit Assess 174, 3–30 (2011). https://doi.org/10.1007/s10661-010-1754-3

Graf Pannatier, E., Walthert, L. and Blaser, P. (2004), Solution chemistry in acid forest soils: Are the BC : Al ratios as critical as expected in Switzerland?. Z. Pflanzenernähr. Bodenk., 167: 160-168. doi:10.1002/jpln.200321281

Thimonier, A., Graf Pannatier, E., Schmitt, M., Waldner, P., Walthert, L., Schleppi, P., et al. (2010a). Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites? European Journal of Forest Research, 129, 443–461.

Waldner, P., Schaub, M., Graf Pannatier, E., Schmitt, M., Thimonier, A., & Walthert, L. (2007). Atmospheric deposition and ozone levels in Swiss Forests: Are critical values exceeded? Environmental Monitoring and Assessment, 128, 5–17.

I question many aspects of the analysis.

Is Bc:Al expressed as mols of charge here? Is aluminium total Al? What is the basis that an exceedance >1% of the observations [Line 156] is biologically significant?

In calcareous soils, the Al concentrations will be negligible, which makes the Bc:Al ratio very large. This has a spurious effect on the statistical analysis e.g. Figure 2 has Bc:Al values up to 10,000. In the same figure Bc:Al for soil depth >70cm is shown, but Bc:Al is an indicator for stress on fine plant root yet the author states that root depth only extended to 60cm [L173]. So I don’t think Bc:Al below this depth is relevant. For a study of acidification, the analysis should be confined to those soils in the Al buffering pH range.

The regression analysis is not appropriate. It is not clear what the objective of this analysis is and many of the relationships have already been established in the literature. For example, why is Bsat a predictor for Bc:Al in soil solution? Bsat should be considered a response variable and not a predictor in this case. Also Bsat to Bc:Al relationships have already been developed and are used in the critical loads modelling e.g. Gaines-Thomas and Gapon equations.

I would also question the data used as an input to the model. The author states that the number of sites ranges from ‘9 to 47’. I don’t think it is appropriate to have uneven numbers of observations for different sites. Also it is not clear how the 5 year time interval [L186] was generated for predictors. Was soil Bsat, Soil C:N measured every 5 years? Was the weathering rate recalculated for every 5 year interval? Time intervals for a particular site are not independent observations – was this accounted for in the model?

The relationship presented in Figure 2, between pHCaCl2 and Bc:Al is spurious.

I don’t think the results presented in Table 3 are valid. The levels of significance are likely a result of the large number of observations used.

If N leaching has decreased and N deposition is the main driver of acidity [L18-19] then why is the rate of soil acidification increasing [L17-18, L264]? Why wasn’t N deposition significant in the model of soil acidification (Table 3)?

In Fig 3 the ca leaching rate is greater than deposition (and weathering + deposition. What is the proposed driver of this Ca loss, if N deposition is decreasing? How do these values relate to other published values for weathering? Was the weathering model calibrated? It has previously been reported that soil solution in acid pseudogleyed horizons can be influenced by underlying clay soils (Graf Pannatier 2004). Was this taken into account in the analysis?

In relation to Fig. 5 it is not clear what this figure is trying to show. Why not simply plot Bc:Al change with time? Again the linear relationships don’t seem valid.

The statistical relationships in figures 6 to 8 don’t seem valid, but seem to be driven by a few outliers. Why does N leaching extend above 150KgN?

The English needs to be improved and there are numerous grammatical errors and formatting errors. This makes the paper difficult to read. some examples..

L374 comma at the end of line

L13; ‘ranging’ instead of ‘growing’

L18: ‘Main driver’ – should be ‘The main driver..’

L36 'concerns on forest health'..should be 'concerns about forest health'

The conclusions are not supported by the findings.

**********

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Reviewer #1: Yes: Robert Jandl

Reviewer #2: No

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Submitted filename: PONE-D-19-35297_reviewer_rja.pdf

 10.1371/journal.pone.0227530.r002 Author response to Decision Letter 0 Submission Version 1

30 Mar 2020

Reviewer #1:

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a highly relevant and thoroughly elaborated.

It is quite interesting to read that the soil solution monitoring has been continued for 20 years and the future ahead.

Reviewer #1: The technical description of the approach and the discussion are very well written. The quality of the text is high and it could be published as it is.

Reviewer #1: Nevertheless, there is always room for improvement:

1/ The introduction of the manuscript is reflecting the state of knowledge of 30 years ago. At that time there was the concern that acidification threatens European forests, and 20 years ago it was stated that N eutrophication is a major concern. There have been many papers since them that put these concerns into perspective. A good source of information of such reviews are the TAMM Reviews of Forest Ecology and Management. - I recommend to either state in the Introduction that the text is reflecting the knowledge when the monitoring programmes were initiated, or (preferred) to expand the Introduction by more recent references.

Author: Thank you for your valuable comments. We changed now the wording in the first sentence to show that soil acidification has been an issue in the 1980's.

Lines 29-32: Since the 1980s it has been recognized that soil acidification due to anthropogenic input of sulfur and nitrogen compounds ([1]) poses a serious threat to forest health ([2]). An increase in soil acidification, related to atmospheric acid deposition, has been reported in many European countries such as Germany ([3]), Sweden ([4] or France ([5]).

However, the later sentences have already stated that mitigation measures have improved the situation. Therefore, it is necessary to refer to older literature as they are the basis of the Critical Loads for Acidity. I do, however, not agree with the conclusions of the proposed Tamm review by Högberg [6] that there is no N problem as they refer to Northern Ecosystems where total N deposition is much lower and the economy of forest growth and thus biomass production are much more important. In Switzerland, forest N eutrophication is a problem, not a fertilization measure.

Reviewer #1: 2/ The methods section is quite complex. The author repeatedly refers to extensive manuals where the single steps are described. Presumingly, very few readers will look up these sources. -- Considering that supplementary material comes with the paper, it would be desirable to show the code that had been used. -- Also a statement on confidence in the invidual steps would be interesting. E.g. how well does SAFE reflect weathering in the field?

Author: We have now added a description on base saturation determination as this is the part which the reader should know without looking up – is this conclusion correct? We have added the R scripts we used to the supplementary material and a remark on validation of SAFE calculations to the methods section.

Reviewer #1: 3/ The full data sets are not available. - If this is the data policy of the institute, a statement on data ownership could be made.

Author: We have uploaded all R codes and a description of data manipulation and models including the raw data on the data base dryad:

Data Availability: The data and an R project including an RMarkdown file with all R codes, model outputs and diagnostic plots have been uploaded to Dryad: https://doi.org/10.5061/dryad.2z34tmphm.

Before acceptance of the paper the data are accessible via:

https://datadryad.org/stash/share/PjiHqj7uwJK13MBFbbbiRVeoO_DI1byCEPpHDD3bDms

 

Reviewer #2:

Reviewer #2: The author has evaluated the chemistry of soil solution at a number of Swiss forest sites with respect to their excedance of critical limits for acidity and nitrate. In addition some models were run to relate the critical limits to site, stand, atmospheric inputs and a number of other factors.

Reviewer #2: First off, the premise of the paper is not sound. Line 28 states ‘Soil acidification is a serious threat’. This is not the case. Soil acidification is a natural process of soil development in temperate climates where there is a precipitation surplus. The objectives are not clearly stated and the results presented e.g. Ca budgets do not correspond to the stated aim on Line 64-66. Line 66 states that soil solution chemistry will be related to forest health but this is not presented. No evidence is presented to indicate that forests in Switzerland are suffering from the effects of acidification from atmospheric deposition of S and N. Is there forest health data available? Foliar chemistry for example? Evidence of magnesium deficiency or yellowing of needles?

Author: Thank you for your comments. According to your suggestions we changed the first sentences of the introduction:

Lines 29-34: Since the 1980s it has been recognized that soil acidification due to anthropogenic input of sulfur and nitrogen compounds ([1]) poses a serious threat to forest health ([2]). An increase in soil acidification, related to atmospheric acid deposition, has been reported in many European countries such as Germany ([3]), Sweden ([4] or France ([5]). Due to mitigation measures, the deposition of acidifying substances in Europe, in particular of sulfur compounds, has decreased in recent years, but eutrophication due to the deposition of reactive nitrogen compounds remains a critical issue ([7], [8]).

The reader can now understand that measures have been taken against acid deposition. It is correct that soil acidification is a natural process but the pH reached with the main natural acidifying substance HCO3- is +/- pH 4.8-5.0, depending on the partial pressure of the CO2. In the uppermost cm of the mineral soil the organic acids can acidify the soil somewhat more, but the organic acids cannot reach the subsoil, because they dissociate in the Bs-horizon of a soil, at the latest (due to the normal pH gradient in soils). In contrast, the acidification due to the input of the mobile anions SO42- and NO3- is more “effective”; SO42- can be stored in the B horizon, but this is not stable, the SO42-- desorbs if the S-input declines. In this case, the deeper soil horizons acidify. This was demonstrated in a lot of intensive monitoring plots. Nitrate cannot be stored in the soil. If it is not taken up by vegetation, the NO3- passes through the profile, together with a concomitant cation (BC or an acid cation like Mn or Al), diminishing the BS% in soils. We have added now a reference to [1] to highlight these processes.

The relations between soil acidification and forest health have been discussed in the submitted manuscript on line 50 ([9]) and line 416 ([10] ). These two references show results from the forest plots presented in this study but we agree that this has not been stated clearly. We have now added a separate paragraph to the Discussion section including information on Mg deficiency which has also increased in recent years, and that foliar Mg has been related to soil solution Mg.

Lines 410-416: Effects of soil acidification on forest health in the forest plots presented in the current study have been observed on rooting depth [9], uprooting of trees [10] and on Mg deficiency [11]. The uprooting of trees was clearly increased on soils with a base saturation <40%. Visible Mg deficiency also has strongly increased within the last 10-15 years, suggesting that soil acidification is still an issue in the Intercantonal forest observation plots. Foliar Mg concentrations in beech leaves have been shown to be related with Mg concentrations in soil solution [11].

Reviewer #2: The title of the paper is 'Soil solution in Swiss forest stands: a 20 year's time series' but no time series are presented.

Author: This is not true. Figure 4 shows a time series of the BC/Al ratio and Figure 6 on N leaching. But with 47 plots it is not feasible to show all plots separately. The graphs for all single plots are, however, accessible through the project report ([12] ).

Reviewer #2: It is not clear what the purpose of the N assessment is and how it relates to acidification and what it contributes to the paper. The author states that ‘N leaching may not always be a good eutrophication indicator’ [L22-23]. This has already been well established in the literature

e.g. Lovett, G.M., Goodale, C.L. A New Conceptual Model of Nitrogen Saturation Based on Experimental Nitrogen Addition to an Oak Forest. Ecosystems 14, 615–631 (2011). https://doi.org/10.1007/s10021-011-9432-z

Author: The reason why N is related with acidification is that the negatively charged nitrate carries along positively charged ions. We have added a remark to the corresponding paragraph. We have also introduced a reference to Lovett & Goodale but their conclusion is not that N leaching is not a eutrophication indicator but that it may occur simultaneously with other stages of N saturation. The sentence that "N leaching may not always be a good eutrophication indicator" is in the abstract and cannot refer to a reference at this place.

Reviewer #2: A lot of the literature cited is quite old e.g. refs 11, 16, 18, 19, 25, 27, 30. A lot of new knowledge has been published in relation to the issue of soil acidification by atmospheric deposition that the author should review.

Author: We introduced a number of newer references ([8], [13], [14],[15], [16] ). However, when referring to the Critical Load manuals it is necessary to cite the old literature which was the basis to setting the limits.

Reviewer #2: In addition, a lot of work done on Swiss forest monitoring specifically with respect to this issue, but this work is not cited. I would encourage the author to review these papers and position their work within this context. Sample below

Pannatier, E.G., Thimonier, A., Schmitt, M. et al. A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environ Monit Assess 174, 3–30 (2011). https://doi.org/10.1007/s10661-010-1754-3

Graf Pannatier, E., Walthert, L. and Blaser, P. (2004), Solution chemistry in acid forest soils: Are the BC : Al ratios as critical as expected in Switzerland?. Z. Pflanzenernähr. Bodenk., 167: 160-168. doi:10.1002/jpln.200321281

Thimonier, A., Graf Pannatier, E., Schmitt, M., Waldner, P., Walthert, L., Schleppi, P., et al. (2010a). Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites? European Journal of Forest Research, 129, 443–461.

Waldner, P., Schaub, M., Graf Pannatier, E., Schmitt, M., Thimonier, A., & Walthert, L. (2007). Atmospheric deposition and ozone levels in Swiss Forests: Are critical values exceeded? Environmental Monitoring and Assessment, 128, 5–17.

Author: We have added the Pannatier 2011 reference..

Reviewer #2: I question many aspects of the analysis.

Is Bc:Al expressed as mols of charge here? Is aluminium total Al? What is the basis that an exceedance >1% of the observations [Line 156] is biologically significant?

In calcareous soils, the Al concentrations will be negligible, which makes the Bc:Al ratio very large. This has a spurious effect on the statistical analysis e.g. Figure 2 has Bc:Al values up to 10,000. In the same figure Bc:Al for soil depth >70cm is shown, but Bc:Al is an indicator for stress on fine plant root yet the author states that root depth only extended to 60cm [L173]. So I don’t think Bc:Al below this depth is relevant. For a study of acidification, the analysis should be confined to those soils in the Al buffering pH range.

Author: The calculation of BC/Al is described in the UNECE manuals but we have now inserted an explanation how we did it. The 1% limit was used to remove outliers. The mapping manual does not define how many values have to exceed the threshold but the roots will react to single values rather than any average. We agree that calculation of BC/Al in calcareous soils is subjected to errors but omitting these values would bias the dataset towards acid soils. Looking at outliers in the graphs is misleading as we have log transformed them before data analysis. We agree that BC/Al at soil depth >70 cm is not relevant for plant roots but we am showing these values for the sake of completeness, to show the trends in soil solution chemistry. We do not draw any plant relevant conclusions on these values.

To confine acidification studies only on soils with pH 3.8 – 4.2 (Al buffer range) would lead to wrong conclusions; to detect trends one should use the whole existing pH-range. Thus, we do not agree with the recommendation that the data analysis should be restricted to the Al buffer range.

Reviewer #2: The regression analysis is not appropriate. It is not clear what the objective of this analysis is and many of the relationships have already been established in the literature. For example, why is Bsat a predictor for Bc:Al in soil solution? Bsat should be considered a response variable and not a predictor in this case. Also Bsat to Bc:Al relationships have already been developed and are used in the critical loads modelling e.g. Gaines-Thomas and Gapon equations.

Author: The aim of the regression analysis was to find out why the decrease in BC/Al is going on fast in some plots and slow in others. We have added now an introducing sentence. The Gaines-Thomas and Gapon equations are useful in soil chemical models to describe the exchange of cations between soil solution and soil solid phase. Here we follow a statistical approach to explain the measured changes in the composition of the soil solution. For comparisons of the critical thresholds of BC/Al in soil solution with the base saturation of the soil solid phase it is important to have field data for validation

Reviewer #2: I would also question the data used as an input to the model. The author states that the number of sites ranges from ‘9 to 47’. I don’t think it is appropriate to have uneven numbers of observations for different sites. Also it is not clear how the 5 year time interval [L186] was generated for predictors. Was soil Bsat, Soil C:N measured every 5 years? Was the weathering rate recalculated for every 5 year interval? Time intervals for a particular site are not independent observations – was this accounted for in the model?

Author: The mixed regression accounts for unequal number of observation and for unequal replication. So from the statistical point of view it is not a problem to compare Bsat and C:N measured once or weathering rate modelled once with monthly measured soil solution data. The mixed regression handles also repeated measures from one site, e.g. time intervals.

The Bsat and soil C:N were not measured every 5 year, because it is not expectable to find measurable changes in such a short time, esp. in a heterogeneous environment like forest soils. The weathering rate is stable, it is enough to measure it once. In contrast, the composition of the soil solution is a fast reacting monitor of changes, that’s why it is measures monthly, and for the annual changes aggregated to annual means.

Reviewer #2: The relationship presented in Figure 2, between pHCaCl2 and Bc:Al is spurious.

Author: Figure 2 shows measured BC/Al ratio in relation to pH and base saturation. Based on this comment it is not clear how this is wrong or how it could be changed or improved.

Reviewer #2: I don’t think the results presented in Table 3 are valid. The levels of significance are likely a result of the large number of observations used.

Author: Figure 5 illustrates the regressions with the confidence intervals and the data points so the reader can check that the regressions are quite good and not just an artefact of the high number of points.

Reviewer #2: If N leaching has decreased and N deposition is the main driver of acidity [L18-19] then why is the rate of soil acidification increasing [L17-18, L264]? Why wasn’t N deposition significant in the model of soil acidification (Table 3)?

Author: The processes leading to soil acidification are not linear so a decrease in N deposition (which is actually small anyway in Switzerland) will not necessarily lead to an immediate decrease in soil acidification (for recovery effects see e.g. Verstraeten 2017). N is still accumulating as long as the deposition of N is larger than the use by the forest stand. The relation between the amount of N deposition and soil acidification can be shown in the experiment but in the field the geological heterogeneity is very large. We have now made a reference to this experiment.

Reviewer #2: In Fig 3 the ca leaching rate is greater than deposition (and weathering + deposition. What is the proposed driver of this Ca loss, if N deposition is decreasing? How do these values relate to other published values for weathering? Was the weathering model calibrated? It has previously been reported that soil solution in acid pseudogleyed horizons can be influenced by underlying clay soils (Graf Pannatier 2004). Was this taken into account in the analysis?

Author: The deposition in the X-axis is Ca-deposition, not N deposition. Thus, Figure 3 does not show N deposition but the Ca leaching is the result of NO3 -leaching.

The weathering model was calibrated in Sweden. Recent analyses with Al in tree rings confirm that the dynamic soil acidification model is performing quite well. We have put this remark to the methods section. – The indicated reference discusses the effect of underlying clay soils on soil solution in 80 cm depth. As the soil solution chemistry at larger depths is not in the focus of the current paper, such processes do not change the main conclusions. Similar situations (soil wetness >2 and calcareous layer present in <100 cm with acid topsoil) occur in just one among 47 plots.

Reviewer #2: In relation to Fig. 5 it is not clear what this figure is trying to show. Why not simply plot Bc:Al change with time? Again the linear relationships don’t seem valid.

Author: A plot of BC/Al change with time is given in Figure 4. Figure 5 illustrates the speed of the change in BC/Al in relation to the predictors in Table 3. Predicted values including 95% confidence intervals are conditioned on all other fixed effects. Negative changes signify an expected decrease in BC/Al. The linearity of the relations was tested using polynomial functions

Reviewer #2: The statistical relationships in figures 6 to 8 don’t seem valid, but seem to be driven by a few outliers. Why does N leaching extend above 150KgN?

Author: The regressions were made with log transformed values (this is stated in the text) in order to meet the assumptions for a liner mixed effect model. The models were tested for robustness. The shaded areas give the 95% confidence interval. For the graphs, the values were backtransformed so the scatter seems larger than it actually was.

The mixed regression model given in Table 4 explains about 77 % of the variation of N leaching. The amount of n=586 seems valid to test the given amount of fixed effects. These fixed effects explained alone 36% of the data. The Pseudo-R2 are now given in the tables.

E.g. Lines 370-372:

Table 4: Mixed regression model of N leaching with annual data (n=586). Dependent variable: N leaching in kg N ha 1 yr 1, log transformed. Pseudo-R2 fixed effects = 0.37, Pseudo-R2 including random effect = 0.77.

All models can now be seen in the Supplementary Materials in the RMarkdown documented R codes in order to achieve the highest standards of statistical reproductivity of the models.

The points with very high N leaching have been plot with disturbances. The points show annual leaching rates.

Reviewer #2: The English needs to be improved and there are numerous grammatical errors and formatting errors. This makes the paper difficult to read. some examples.

Author: A great effort has been invested to improve wordings and eliminate all grammatical errors.

L374 comma at the end of line

Author: Thank you for this note this has been changed.

L13; ‘ranging’ instead of ‘growing’

Author: Thank you for this comment: 'ranging' means that they extend from .. to. 'Growing' means that the number has increased. We have now replaced "growing" by "increasing".

L18: ‘Main driver’ – should be ‘The main driver..’

Author: Thank you for this note this has been changed.

L36 'concerns on forest health'..should be 'concerns about forest health'

Author: Thank you for this note this has been changed.

The conclusions are not supported by the findings.

Author: We cannot comment this as this is a statement without proof.

Submitted filename: reviewer_comments_rev1.docx

 10.1371/journal.pone.0227530.r003 Decision Letter 1 Aherne Julian Academic Editor 2020 Julian Aherne This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

28 Apr 2020

PONE-D-19-35297R1

Soil solution in Swiss forest stands: a 20 year's time series

PLOS ONE

Dear Dr. Braun,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

All reviewers agree that this manuscript is an important contribution to the literature, especially given the long-term data set. Further, it is noted that the revised manuscript is much improved. Nonetheless, two of the reviewers have indicated that further revisions are required, specifically with respect to clarification of the research question and hypothesis, presentation of data, and the description of the data analysis.

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Additional Editor Comments (if provided):

Dear Sabine Braun, please note that your manuscript was sent out for further review as one of the initial reviewers (the more critical of the two) was unavailable. All reviewers agree that this manuscript is an important contribution to the literature, especially given the long-term data set. Further, it is noted that the revised manuscript is much improved. Nonetheless, two of the reviewers have indicated that further revisions are required, specifically with respect to clarification of the research question and hypothesis, presentation of data, and the description of the data analysis. while I have indicated 'Major Revisions' are required in line with the recommendations of the reviewers, I believe that these are minor revisions and that they can be easily addressed. I look forward to seeing your revised manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: No

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Reviewer #1: Yes

Reviewer #3: I Don't Know

Reviewer #4: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I want to congratulate the authors to an exceptional manuscript. The text has a high potential to be used in classrooms. The coverage of the topic is comprehensive. A particular value is the availability of the code of the data evaluation. The description of the data has the same high quality as the main text itself. I am greatly impressed by the quality of the submission and hope that it will find wide recognition.

Reviewer #3: This is an interesting analysis of a 20 year's time series of measured element concentrations in forests soil solution in relation to different environmental variables. The work adds to current knowledge on progressive acidification of forest soils intensified by atmospheric nitrogen and sulphur deposition. The authors elaborated further on a first version of their manuscript taking into account various comments of two reviewers. I think this has substantially improved the overall quality of the paper, but there are still some issues that need to be solved before it can be published, particularly concerning the modeling analysis.

detailed comments

L18: ‘Acidification indicators remained stable at high levels...’ : I think this terminology is too vague for an abstract. Could you please formulate it in more concrete terms?

L23 (and further): The term ‘N leaching’ is used throughout the paper, which I think is not entirely correct, since dissolved organic forms of N (DON) are not included. I suggest to consistently write ‘nitrate leaching’ (assuming that ammonium leaching is negligible).

L25-26: I suggest to drop the following sentence: ‘Therefore, we suggest a restricted use of N leaching as an eutrophication indicator’. Plenty of studies have shown that NO3 leaching is an important indicator of eutrophication/N saturation, but since forests are natural ecosystems with a huge variety in environmental conditions there logically also must be exceptions.

L58-59: as the BC/Al ratio is defined here, I think you should also mention what you mean with base cations (Ca2+ + K+ + Mg2+) in this sentence.

L69-71: I suggest to refer to the groundwater directive here. DIRECTIVE 2006/118/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 12 December 2006 on the protection of groundwater against pollution and deterioration (https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2006:372:0019:0031:EN:PDF).

L137 and L178: it is not clear to me whether and how the alkalinity data were actually used in your analysis. I assume ANC was calculated rather as the balance between anions and cations? If alkalinity itself was not used, the corresponding part of the sentence in L137 should be removed.

L224 and Fig. 2: a quadratic term was used in order to allow modeling of a non-linear relationship. This approach has not been described in the materials and methods section. I actually wonder if a linear mixed additive model (gamm) applied on the untransformed data would not be more appropriate here, as it would probably be more easy to understand. The current graphs are somewhat misleading: it looks as if the curve flattens of at higher BS, but instead it is the opposite: if the Y-axis would be in regular units instead of log-scale, one would see an exponentially increasing curve.

L226: I do not understand this. BC/Al ratio at the start of the 5 year period was included as a predictor for the BC/Al ratio?

Section 3.1.3 and 3.1.4: I have some doubts on the modeling analysis. The number of independent variables in the initial models (about 10) is actually quite large for the number of sites (n = 47). While there is no general rule of restriction, a large number of variables could easily lead to overfitting of the model and thus wrong interpretation and results. I am not entirely convinced that the applied backward selection with AIC/BIC excluded the least important variables. Instead, it might have been a better approach to restrict the initial number of variables (pre-selection) based on present knowledge or to do a forward selection.

Table 2 and 3: The data for the dependent variables BC/Al ratio and N leaching were log-transformed, as mentioned in the caption of both tables. Log-transformation changes the relationship between the dependent and independent variables and may therefore lead to a wrong interpretation of the results. This choice should thus be clearly motivated, which information is currently missing in the materials and methods section.

L253-254: you wrote that normality and homogeneity of variance of the residuals was checked, but it is necessary to show also graphs of normalised residuals vs. fitted values and of normalised residuals against each independent variable in the model, in order to allow the reader to judge the validity of models. In L258-259 it is stated that this information is included in the Supplementary information, but I couldn’t find it there.

Reviewer #4: This manuscript assesses recent soil acidification in Swiss forest stands in response to atmospheric acidic deposition using soil solution chemistry measured over the last 20 years. This is a valuable dataset, due to the large number of plots that have been monitored for many years in different soil types across Switzerland in two major forest types (spruce and beech) and exposed to different levels of atmospheric deposition.

I was not reviewer of the first version of the manuscript. It seems that the authors have made a great deal of effort in improving the quality of the manuscript. However, I have major concerns about the quality of the revised manuscript: 1) there is no research questions and hypothesis, 2) the description of the current state of knowledge is thin and the authors’ results are not integrated in a broader context, 3) the way of presenting some results is questionable. I explain the three points:

1) Missing research question: the authors do not ask any research question and do not postulate any hypotheses. It is therefore difficult to assess whether their statistical analyses allow them to answer their questions! In recent decades, air pollution reduction policies have resulted in a large reduction in sulphur emissions and, to a lesser extent, in nitrogen emissions. However, the authors do not question the effect of these measures on soil acidification in their introduction. It is not clear whether the focus is on trends of soil acidity indicators or on the effects of forest health.

2) Insufficient description of the current state of knowledge: There are few references to recent studies related to the trends of the soil solution chemistry in response to declining acidic deposition. Yet many publications have been published in the last decade about the potential recovery of soil solution in forest soils and of surface waters in Europe and North America (in particular in countries participating in ICP-Forests and ICP-Waters) in response to declining acidic deposition. In addition, a major European study has been recently published (Johnsson et al. 2018, Global Change Biology, DOI: 10.1111/gcb.14156) on the response of soil solution chemistry in European forests. There was no mention of this study in the revised manuscript. Since the current state of the knowledge is insufficiently described, the introduction does not tell which knowledge gap this manuscript aims to fill. Also in the discussion, the results are not integrated in a larger context, not even at the national scale. There are few references to Swiss and European studies related to soil (solution) acidification.

3) Several concerns about the way of presenting results:

3a. I could not see a systematic way in presenting the data. The level of data aggregation for the different graphs and tables seems to be different, which makes the manuscript heterogeneous and difficult to understand (e.g. analysis of different depth intervals in figures). The tables are also difficult to read because units are not included. For instance, it would help the reader to know which year corresponds to the intercept and the units of intercept and changes (relative, absolute slope?) in the tables presenting the linear models.

3b. The topic of the manuscript is soil solution chemistry over the last 20 years, but I have not learnt much about the quality of the soil solution and how it has changed over time in the studied plots. A statistical summary of concentrations of individual cations and anions and the corresponding trends would have been useful. To present only the BC/Al ratio is insufficient to assess changes in soil solution chemistry. Knowing the behavior of sulphur, base cations and aluminum concentrations (or fluxes if available) is essential to better understand the effect of declining atmospheric deposition. The authors refer to a report illustrating time series at each plot. The link in the reference list is, however, not valid. After searching in Internet, I found this interesting report. The graphs show that the temporal trends differ between the plots and depths and, interestingly, that acidity indicators are stabilizing in the last years in some plots after a decrease in the first years of observation.

3c. Table 2: this analysis is not convincing. The authors mean in this graph that even a low percentile of measurements below the critical thresholds at a given plot would have a critical impact, which has not been reported in the literature. A more detailed analysis including information about the distribution of BC/Al values would be more informative.

3d. Figure 2: The calculation of BC/Al ratio at pH > 6.5 is misleading. The aluminum concentrations are very low, close to the detection limit and therefore cannot be quantified precisely. Very small differences in Al concentrations lead to large variations in BC/Al. In that respect, the quantification limit of Al should be reported to assess the uncertainty related to high BC/Al. In addition, inorganic Al was measured as difference before and after passing the samples through an ion exchanger and therefore uncertainties add up. Another tricky point in this figure is the comparison of soil and soil solution parameters using large depth intervals (<70 cm and >70 cm). The authors do not explain how BC/Al, BS and pH measurements were aggregated in this large depth interval. In this analysis, the presence of calcareous parent material in the subsoil might explain the discrepancies between BS (Fig. 2B) and soil pH (Fig. 2C) (see Blaser et al. 2008, https://doi.org/10.1002/jpln.200625213). Acidified forest soils on calcareous parent material in Switzerland usually have a strong base saturation gradient with depth. Also soil water regime (hydromorphy) and reduced drainage play an important role in the chemistry of these soils.

3e. Figure 3 is very interesting, because few studies have assessed Ca weathering rates and compare them to Ca leaching. However, information about temporal trends is missing. It is known from many studies that concentrations of base cations in soil solution have decreased in the last decade. The driver responsible for this decrease is not completely clear (chemical equilibrium due to declining sulphate concentrations in soil solution? declining atmospheric deposition of base cations? increase in root uptake? ). The temporal analysis of BC leaching from the soil is therefore also relevant because the rate of BC loss from the soil might have slowed down in the last decade, which is still a relevant information to assess the effect of air pollution reduction measures.

3f. Figure 5 is interesting. Fig. 5A shows that high BC/Al ratios at a given time are likely to decrease in the following years, while low BC/Al ratios are likely to stabilize or even increase. In lign 312, the authors write “The decreasing trends were getting weaker the stronger the soils are acidified”. I do not understand why the predicted values in 5B and 5C actually show the opposite trend.

Other specific comments:

• l. 17: remove “in Swiss forest soils”, not necessary

• l.18. “remained stable at high level”: formulation is not clear

• l. 20 “an increasing acidification”: not clear. An increasing acidification rate? Acidification is happening anyway.

• l. 28-29: “Taken together, this study provides evidence of increasing soil acidification in Swiss forest stands.” This is not clear. Do the author mean that the rates of acidification are increasing? Fig. 5A illustrates a more balanced view.

• l.31: “climate change”: meteorological parameters were used in the analysis. Climate change was not analysed.

• l.45-48: The link between the exceedance of critical loads for acidity and the decreasing BC/Al ratio in Pannatier et al. (2011) is not clear. Declining BC/Al does not mean that critical loads are exceeded.

• L.51: “is of crucial importance”: why?

• L. 132-133: suction cups? at which depth? Basic information on the type of suction cups used in this study and sampled depths should be available in this section.

• L. 159: what are the vegetation parameters?

• L. 211: reference in prep is not suitable

• L. 289: Reference to buffer ranges is missing

• L. 318, l. 470: not comma before that. Remove everywhere

• L. 401: “The results are confirmed by findings of the long-term forest monitoring of ICP Forests in Switzerland ([66]).” This sentence is not correct since ref 66 is much older.

• L. 445-447: “Based on our results we question the reliability of the N concentration of the leaves as an indicator for eutrophication, since our measurements show that in beech leaves today they are no longer correlated with N deposition, which was the case in the 1980s”. Not shown by data from this manucript.

• L. 476-L. 475. “Soil acidification has negative consequences for forest health (…). This conclusion is not supported by the findings of this manuscript.

• L. 479: no analysis of climate change in this manuscript. Meteorological parameters were used. The manuscript did not show that the droughts were related to climate change. References are needed to make this link.

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Reviewer #1: Yes: Robert Jandl

Reviewer #3: No

Reviewer #4: No

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 10.1371/journal.pone.0227530.r004 Author response to Decision Letter 1 Submission Version 2

10 Jun 2020

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1

Reviewer #1: I want to congratulate the authors to an exceptional manuscript. The text has a high potential to be used in classrooms. The coverage of the topic is comprehensive. A particular value is the availability of the code of the data evaluation. The description of the data has the same high quality as the main text itself. I am greatly impressed by the quality of the submission and hope that it will find wide recognition.

Authors: Thank you for your valuable and precise review work.

 

Reviewer #3

Reviewer #3: This is an interesting analysis of a 20 year's time series of measured element concentrations in forests soil solution in relation to different environmental variables. The work adds to current knowledge on progressive acidification of forest soils intensified by atmospheric nitrogen and sulphur deposition. The authors elaborated further on a first version of their manuscript taking into account various comments of two reviewers. I think this has substantially improved the overall quality of the paper, but there are still some issues that need to be solved before it can be published, particularly concerning the modeling analysis.

Authors: Thank you for your valuable questions and suggestions.

detailed comments

Reviewer #3: L18: ‘Acidification indicators remained stable at high levels...’ : I think this terminology is too vague for an abstract. Could you please formulate it in more concrete terms?

Authors: The sentence was now changed into "... In strongly acidified soils (soil pH below 4.2), acidification indicators changed only slowly"

Reviewer #3: L23 (and further): The term ‘N leaching’ is used throughout the paper, which I think is not entirely correct, since dissolved organic forms of N (DON) are not included. I suggest to consistently write ‘nitrate leaching’ (assuming that ammonium leaching is negligible).

Authors: You are right, we changed now "N leaching" to "nitrate leaching" throughout the document.

Reviewer #3: L25-26: I suggest to drop the following sentence: ‘Therefore, we suggest a restricted use of N leaching as an eutrophication indicator’. Plenty of studies have shown that NO3 leaching is an important indicator of eutrophication/N saturation, but since forests are natural ecosystems with a huge variety in environmental conditions there logically also must be exceptions.

Authors: We removed this sentence and changed the wording in the preceding one.

Reviewer #3: L58-59: as the BC/Al ratio is defined here, I think you should also mention what you mean with base cations (Ca2+ + K+ + Mg2+) in this sentence.

Authors: We inserted the names of the ions included for base cations calculation. Is this what you asked for?

Reviewer #3: L69-71: I suggest to refer to the groundwater directive here. DIRECTIVE 2006/118/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 12 December 2006 on the protection of groundwater against pollution and deterioration (https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2006:372:0019:0031:EN:PDF).

Authors: Switzerland is not in the EU so reference was now made to the Swiss legislation.

Reviewer #3: L137 and L178: it is not clear to me whether and how the alkalinity data were actually used in your analysis. I assume ANC was calculated rather as the balance between anions and cations? If alkalinity itself was not used, the corresponding part of the sentence in L137 should be removed.

Authors: Titrated alkalinity (not ANC) was part of the quality control for ion balance and calculated conductivity so it should stay in.

Reviewer #3: L224 and Fig. 2: a quadratic term was used in order to allow modeling of a non-linear relationship. This approach has not been described in the materials and methods section. I actually wonder if a linear mixed additive model (gamm) applied on the untransformed data would not be more appropriate here, as it would probably be more easy to understand. The current graphs are somewhat misleading: it looks as if the curve flattens of at higher BS, but instead it is the opposite: if the Y-axis would be in regular units instead of log-scale, one would see an exponentially increasing curve.

Authors: A log transformation is quite common for concentration data. Thus, we cannot see the point that we should try to replace the transformation of the dependent variable with a more sophisticated model of the independent variables. Actually, after recalculation the model with base saturation loses the polynomial term.

For answering the question on gamm, we compared two models with BC/Al as dependent variable and pH(CaCl2) as independent variable (Figure 2 b).

The first model uses BC/Al after log transformation and a linear model with the independent variable pH as polynom with 2 degrees of freedom.

model1 <- lm(LBCAL~poly(PHCACL,degree=2),data_BC_AL_ratio1)

The second model uses BC/Al without transformation and a gam model with the independent variable pH as polynom with 3 degrees of freedom.

model2 <- gam(BCAL ~ s(PHCACL,k=3),data=data_BC_AL_ratio1)

The residual plots show a good normal distribution of the residuals in model 1 and an S shaped distribution in model 2. The Tukey-Anscomb plot for model 1 show a rather homogenous distribution of the points whereas model 2 is clearly heteroscedastic. Both problems can be solved with a log transformation of the dependent variable. The R2 is somewhat lower in model 2 (R2 adj=0.527) than in model 1 (R2 adj=0.547), the AIC much higher (722.8 vs. 229.3).

Reviewer #3: L226: I do not understand this. BC/Al ratio at the start of the 5 year period was included as a predictor for the BC/Al ratio?

Authors: The hypothesis tested was that the change of BC/Al depends on the degree of acidification. The initial BC/Al ratio was taken as a measure for this. An introductory sentence was inserted.

Reviewer #3: Section 3.1.3 and 3.1.4: I have some doubts on the modeling analysis. The number of independent variables in the initial models (about 10) is actually quite large for the number of sites (n = 47). While there is no general rule of restriction, a large number of variables could easily lead to overfitting of the model and thus wrong interpretation and results. I am not entirely convinced that the applied backward selection with AIC/BIC excluded the least important variables. Instead, it might have been a better approach to restrict the initial number of variables (pre-selection) based on present knowledge or to do a forward selection.

Authors: In this regression analysis, the initial BC/Al ratio and solution pH have a higher degree of freedom as they vary within 5 year intervals. Base saturation is the predictor with the lowest degrees of freedom. It varies at the level of depth*plot and has 89 observations.

Reviewer #3: Table 2 and 3: The data for the dependent variables BC/Al ratio and N leaching were log-transformed, as mentioned in the caption of both tables. Log-transformation changes the relationship between the dependent and independent variables and may therefore lead to a wrong interpretation of the results. This choice should thus be clearly motivated, which information is currently missing in the materials and methods section.

Authors: Models with and without log transformation were compared and the distribution of the residuals checked for normality. We have added a sentence to the statistics section. However, for concentration data log transformation is very common.

Reviewer #3: L253-254: you wrote that normality and homogeneity of variance of the residuals was checked, but it is necessary to show also graphs of normalised residuals vs. fitted values and of normalised residuals against each independent variable in the model, in order to allow the reader to judge the validity of models. In L258-259 it is stated that this information is included in the Supplementary information, but I couldn’t find it there.

Authors: This information has been included as part of the R documentation which is available in the dryad repository (https://datadryad.org/stash/share/PjiHqj7uwJK13MBFbbbiRVeoO_DI1byCEPpHDD3bDms). No change has been made.

 

Reviewer #4:

Reviewer #4: This manuscript assesses recent soil acidification in Swiss forest stands in response to atmospheric acidic deposition using soil solution chemistry measured over the last 20 years. This is a valuable dataset, due to the large number of plots that have been monitored for many years in different soil types across Switzerland in two major forest types (spruce and beech) and exposed to different levels of atmospheric deposition.

I was not reviewer of the first version of the manuscript. It seems that the authors have made a great deal of effort in improving the quality of the manuscript. However, I have major concerns about the quality of the revised manuscript: 1) there is no research questions and hypothesis, 2) the description of the current state of knowledge is thin and the authors’ results are not integrated in a broader context, 3) the way of presenting some results is questionable. I explain the three points:

Authors: Thank you for your review work.

Reviewer #4: 1) Missing research question: the authors do not ask any research question and do not postulate any hypotheses. It is therefore difficult to assess whether their statistical analyses allow them to answer their questions! In recent decades, air pollution reduction policies have resulted in a large reduction in sulphur emissions and, to a lesser extent, in nitrogen emissions. However, the authors do not question the effect of these measures on soil acidification in their introduction. It is not clear whether the focus is on trends of soil acidity indicators or on the effects of forest health.

Authors: We have now addressed research questions and discussed the air pollution reduction more in detail in the introduction.

Lines 41-46:

due to mitigation measures, the deposition of acidifying substances in Europe, in particular of sulfur compounds, has decreased in recent years. In consequence, sulfate concentrations in soil solution have decreased significantly ([6]). However, the development of nitrogen indicators is more divergent ([7]). In Canada, for example, the chemical recovery of streams was slower than expected due to the reduction of acid deposition ([8]).

Lines 109-122:

The aim of the present study is to analyze trends in soil solution data collected over a period of 20 years from currently 47 plots of the Intercantonal Forest Monitoring Program in Switzerland ([41]). The observed changes in the element concentration of the soil solution measurements were analyzed with respect to international critical limits and other threshold values in order to assess the risk of acidification and eutrophication effects on forest health in Switzerland. The following research questions were addressed:

I. Are critical limits and thresholds exceeded?

II. Do the reductions in acid depositions translate into corresponding changes in soil solution chemistry?

III. What are suitable predictors for the risk of high nitrate leaching?

The parameters measured in this monitoring program are based on the Guidelines on Reporting Monitoring and Modelling of Air Pollution Effects of the Geneva Air Convention ([42]).

Reviewer #4: 2) Insufficient description of the current state of knowledge: There are few references to recent studies related to the trends of the soil solution chemistry in response to declining acidic deposition. Yet many publications have been published in the last decade about the potential recovery of soil solution in forest soils and of surface waters in Europe and North America (in particular in countries participating in ICP-Forests and ICP-Waters) in response to declining acidic deposition. In addition, a major European study has been recently published (Johnsson et al. 2018, Global Change Biology, DOI: 10.1111/gcb.14156) on the response of soil solution chemistry in European forests. There was no mention of this study in the revised manuscript. Since the current state of the knowledge is insufficiently described, the introduction does not tell which knowledge gap this manuscript aims to fill. Also in the discussion, the results are not integrated in a larger context, not even at the national scale. There are few references to Swiss and European studies related to soil (solution) acidification.

Authors: Thank you. The results from ICP Forests are now introduced. We made also reference to the Canadian study.

Reviewer #4: 3) Several concerns about the way of presenting results:

3a. I could not see a systematic way in presenting the data. The level of data aggregation for the different graphs and tables seems to be different, which makes the manuscript heterogeneous and difficult to understand (e.g. analysis of different depth intervals in figures). The tables are also difficult to read because units are not included. For instance, it would help the reader to know which year corresponds to the intercept and the units of intercept and changes (relative, absolute slope?) in the tables presenting the linear models.

Authors: The level of data aggregation depends on the type of data so this cannot be unified. But we have now introduced units into the tables where appropriate. The unit of the intercept has no significance for the interpretation of the results except when all predictors are set to zero. The main reason for the regressions against time was to get estimates for the single years which are corrected for the changing sample size.

Reviewer #4: 3b. The topic of the manuscript is soil solution chemistry over the last 20 years, but I have not learnt much about the quality of the soil solution and how it has changed over time in the studied plots. A statistical summary of concentrations of individual cations and anions and the corresponding trends would have been useful. To present only the BC/Al ratio is insufficient to assess changes in soil solution chemistry. Knowing the behavior of sulphur, base cations and aluminum concentrations (or fluxes if available) is essential to better understand the effect of declining atmospheric deposition. The authors refer to a report illustrating time series at each plot. The link in the reference list is, however, not valid. After searching in Internet, I found this interesting report. The graphs show that the temporal trends differ between the plots and depths and, interestingly, that acidity indicators are stabilizing in the last years in some plots after a decrease in the first years of observation.

Authors: We have corrected the link to the internal report. A new table has been introduced with mean concentrations per depth class and time trends.

Reviewer #4: 3c. Table 2: this analysis is not convincing. The authors mean in this graph that even a low percentile of measurements below the critical thresholds at a given plot would have a critical impact, which has not been reported in the literature. A more detailed analysis including information about the distribution of BC/Al values would be more informative.

Authors: The CCE documentation does not clarify if the critical thresholds are to be exceeded as annual average or as single value. We decided to use the latter as a root will not see the average but an actual value. The use of the percentile was only to avoid single outliers and make thus the analysis more robust. But we agree that a density distribution would be helpful. We added this to the Supplementary Material..

Reviewer #4: 3d. Figure 2: The calculation of BC/Al ratio at pH > 6.5 is misleading. The aluminum concentrations are very low, close to the detection limit and therefore cannot be quantified precisely. Very small differences in Al concentrations lead to large variations in BC/Al. In that respect, the quantification limit of Al should be reported to assess the uncertainty related to high BC/Al. In addition, inorganic Al was measured as difference before and after passing the samples through an ion exchanger and therefore uncertainties add up. Another tricky point in this figure is the comparison of soil and soil solution parameters using large depth intervals (<70 cm and >70 cm). The authors do not explain how BC/Al, BS and pH measurements were aggregated in this large depth interval. In this analysis, the presence of calcareous parent material in the subsoil might explain the discrepancies between BS (Fig. 2B) and soil pH (Fig. 2C) (see Blaser et al. 2008, https://doi.org/10.1002/jpln.200625213). Acidified forest soils on calcareous parent material in Switzerland usually have a strong base saturation gradient with depth. Also soil water regime (hydromorphy) and reduced drainage play an important role in the chemistry of these soils.

Authors: The comparison between soil solution and solid phase was based on the chemical criterial of the solid phase at the depth of the lysimeter. This has been already stated in the Methods section but an additional remark has been made now.

Lines 162-163:

Actual depths vary according to soil condition but a frequent sampling design was 20, 50 and 80 cm.

The large depth intervals were only used as an additional binary indicator of soil depth as a finer discrimination was not significant. The discussion on the presence of calcareous parent material has therefore no relevance for the presented results.

It is correct that the BC/Al ratio at pH>6.5 is subjected to a large error. However, when these data would be omitted the data set would be subjected to a large bias. It is common use to set the detection limit for data analysis when working with concentration data with part of the samples below the detection limit.

Reviewer #4: 3e. Figure 3 is very interesting, because few studies have assessed Ca weathering rates and compare them to Ca leaching. However, information about temporal trends is missing. It is known from many studies that concentrations of base cations in soil solution have decreased in the last decade. The driver responsible for this decrease is not completely clear (chemical equilibrium due to declining sulphate concentrations in soil solution? declining atmospheric deposition of base cations? increase in root uptake? ). The temporal analysis of BC leaching from the soil is therefore also relevant because the rate of BC loss from the soil might have slowed down in the last decade, which is still a relevant information to assess the effect of air pollution reduction measures.

Authors: We tried to include two time periods into Figure 3 but this gets then very confusing. We therefore decided to add a sentence explaining how much the results are changed when only later data are included.

Lines 347-350:

The same analysis with leaching rates at different time periods slightly reduced the proportion of plots with a negative balance. For instance, between 2015-2018, Ca leaching exceeded the Ca weathering input in 83% of the plots.

The differences are actually very small as for almost all the plots the exceedance is remaining.

Reviewer #4: 3f. Figure 5 is interesting. Fig. 5A shows that high BC/Al ratios at a given time are likely to decrease in the following years, while low BC/Al ratios are likely to stabilize or even increase. In lign 312, the authors write “The decreasing trends were getting weaker the stronger the soils are acidified”. I do not understand why the predicted values in 5B and 5C actually show the opposite trend.

The predicted values in Fig. 5 are the result of a multivariate analysis. Fig. 5B and C show the result of changes in base saturation and pH when BC/Al is kept constant (at the mean value). They show the relevance of buffer ranges: at higher pH and/or base saturation soils are in the CaCO3 buffer range where changes have a positive sign.

Other specific comments:

Reviewer #4: • l. 17: remove “in Swiss forest soils”, not necessary

Authors: o.k., removed.

Reviewer #4: • l.18. “remained stable at high level”: formulation is not clear

Authors: The sentence has been changed

Lines 17-20:

In strongly acidified soils (soil pH below 4.2), acidification indicators changed only slowly, possibly due to high buffering capacity of the aluminum buffer (pH 4.2 – 3.8).

Reviewer #4: • l. 20 “an increasing acidification”: not clear. An increasing acidification rate? Acidification is happening anyway.

Authors: O.k., we introduced "acidification rate".

Lines 20-22:

In contrast, in less acidified sites we observed an increasing acidification rate, reflected, for example, by the continuous decrease in the ratio of base cations to aluminum (BC/Al ratio).

Reviewer #4: • l. 28-29: “Taken together, this study provides evidence of increasing soil acidification in Swiss forest stands.” This is not clear. Do the author mean that the rates of acidification are increasing? Fig. 5A illustrates a more balanced view.

Authors: We changed "increased" by "anthropogenic".

Lines 30-31:

Taken together, this study provides evidence of anthropogenic soil acidification in Swiss forest stands.

Reviewer #4: • l.31: “climate change”: meteorological parameters were used in the analysis. Climate change was not analysed.

Authors: We replaced "climate change" by "drought" as this was actually analysed.

Lines 31-33:

The underlying long-term measurements of soil solution provides important information on nutrient leaching losses and their dependence on drought.

Reviewer #4: • l.45-48: The link between the exceedance of critical loads for acidity and the decreasing BC/Al ratio in Pannatier et al. (2011) is not clear. Declining BC/Al does not mean that critical loads are exceeded.

Authors: We changed the sequence of the corresponding sentence.

Lines 54-59:

For instance, Graf Pannatier et al. ([14]) observed low BC/Al ratios in the topsoil in two out of five Swiss long-term forest monitoring sites, i.e. an exceedance of the critical loads for acidity. In addition, a decrease in the BC/Al ratio has been found in two out of five plots between 1999 and 2007, which can be interpreted as an ongoing acidification during this time period.

But there is no doubt that a BC/Al ratio <1 as shown in this study can be interpreted as an exceedance of the critical load for acidity as this was part of the definition.

Reviewer #4: • L.51: “is of crucial importance”: why?

Authors: "Crucial importance " was now replaced by "important part".

Lines 61-62:

Concerns about forest health led to the initiation of forest monitoring programs in the 1980s, where monitoring of soil solution is an important part ([15]).

Reviewer #4: • L. 132-133: suction cups? at which depth? Basic information on the type of suction cups used in this study and sampled depths should be available in this section.

Authors: A sentence has been introduced to explain the type and the depth of the soil solution samplers.

Lines 161-164:

For each site and soil depth, eight soil solution samplers (ceramic suction cups, 0653X01-B0.5M2, Soilmoisture Equipment Corp.) were installed in the topsoil and five in the subsoil. Actual depths vary according to soil condition but a frequent sampling design was 20, 50 and 80 cm.

Reviewer #4: • L. 159: what are the vegetation parameters?

Authors: It is not clear what is meant here but we added a remark that we used current vegetation cover for the hydrological model.

Lines 188-190:

The amount of leaching water in mm was calculated using the hydrological model Wasim-ETH ([44]) taking into account soil characteristics (pF curve, texture), current vegetation cover and daily meteorological data interpolated for each site ([34]).

Reviewer #4: • L. 211: reference in prep is not suitable

Authors: We changed this to "unpublished results"

Reviewer #4: • L. 289: Reference to buffer ranges is missing

Authors: It is not clear what is asked here: a reference to a paper or an indication of the pH ranges. We decided to do the latter and inserted a pH range for the exchange buffer range.

Reviewer #4: • L. 318, l. 470: not comma before that. Remove everywhere

Authors: o.k.

Reviewer #4: • L. 401: “The results are confirmed by findings of the long-term forest monitoring of ICP Forests in Switzerland ([66]).” This sentence is not correct since ref 66 is much older.

Authors: We replaced this reference by Graf Pannatier et al. 2011 which is more recent.

Reviewer #4: • L. 445-447: “Based on our results we question the reliability of the N concentration of the leaves as an indicator for eutrophication, since our measurements show that in beech leaves today they are no longer correlated with N deposition, which was the case in the 1980s”. Not shown by data from this manucript.

Authors: But a reference is given (Braun et al. 2020) therefore we think is well supported.

Reviewer #4: • L. 476-L. 475. “Soil acidification has negative consequences for forest health (…). This conclusion is not supported by the findings of this manuscript.

Authors: It is supported by findings from the the long-term Intercantonal Forest Observation Program. Detailed references are given therefore we think this is well supported.

Reviewer #4: • L. 479: no analysis of climate change in this manuscript. Meteorological parameters were used. The manuscript did not show that the droughts were related to climate change. References are needed to make this link.

Authors: We replaced now "climate change" by "drought". It would lead too far to lead the discussion on the relation between climate change and drought at this place and would actually be not relevant for the conclusions.

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 10.1371/journal.pone.0227530.r005 Decision Letter 2 Aherne Julian Academic Editor 2020 Julian Aherne This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 2

23 Jun 2020

Soil solution in Swiss forest stands: a 20 year's time series

PONE-D-19-35297R2

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This is an important contribution. The authors have done an excellent job in addressing all reviewers’ concerns in the revised manuscript. I recommend that it is accepted for publication.

Reviewers' comments:

 10.1371/journal.pone.0227530.r006 Acceptance letter Aherne Julian Academic Editor 2020 Julian Aherne This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

29 Jun 2020

PONE-D-19-35297R2

Soil solution in Swiss forest stands: a 20 year's time series

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