Relative Importance of Plant Species Composition and Environmental Factors in Affecting Soil Carbon Stocks of Alpine Pastures (NW Italy)

Abstract

Alpine pastures are agricultural systems with a high provision of ecosystem services, which include carbon (C) stocking. Particularly, the soil organic C (SOC) stocks of Alpine pastures may play a pivotal role in counteracting global climate change. Even if the importance of pasture SOC has been stated by several research studies, especially by comparing different land uses, little is known about the role of plant species composition. We studied a wide sample of 324 pastures in the north-western Italian Alps by performing coupled vegetation and soil surveys. Climatic (i.e., mean annual precipitation), topographic (i.e., elevation, slope, southness), vegetation (i.e., the first three dimensions of a non-metric multid imensional scaling—NMDS), and soil (i.e., pH) parameters were considered as independent variables in a generalised linear model accounting for SOC stocks in the 0–30 cm depth. Pasture SOC was significantly affected by precipitation (positively) and by pH (negatively) but not by topography. However, the higher influence was exerted by vegetation through the first NMDS dimension, which depicted a change in plant species along a thermic-altitudinal gradient. Our research highlighted the remarkable importance of vegetation in regulating SOC stocks in Alpine pastures, confirming the pivotal role of these semi-natural agricultural systems in the global scenario of climate change.

1. Introduction

Mountain pastures can provide many ecosystem services, such as provisioning services (e.g., biodiversity, forage), regulation and maintenance services (e.g., water purification, soil retention), and cultural services (e.g., nature-based recreation, eco-tourism) [1,2]. Among regulation services, carbon (C) stocking is of particular relevance [3]. Carbon stocking is a key process, able to reduce the amount of atmospheric CO₂ originated by anthropogenic emissions [4]. Therefore, the role of land uses efficient in C stocking, namely, able to counteract current climate change, is becoming essential worldwide. Indeed, the land sinks represent the main reduction factor in the global C balance by removing about one fourth of the total emitted C [5]. Part of the C is stocked in the above ground biomass (especially in woodlands), but a major portion is allocated in the soil [6]. Soil organic carbon (SOC) mainly derives from the stocking of atmospheric CO₂ fixed by plants through photosynthesis and its amount can vary depending on site conditions, biotic factors, including vegetation composition, and anthropic management [7].

Although the importance of SOC stocking in slowing global warming has been widely studied [4,8], little is known about the role of Alpine pastures and the variability of SOC stocks related to climatic, environmental, and vegetation features [9]. Specifically, several research studies compared different land uses (e.g., grasslands, forests, arable crops) in terms of their ability to stock C in the European Alps, but the importance of botanical composition within pastures has not been explored yet. It is worth mentioning that Alpine pastures in Europe are composed by a huge variety of plant species and habitats, determined by different topographic (elevation, slope, aspect), abiotic (climate, bedrock type), and biotic (pastoral management, first of all, which directly affects soil fertility) conditions [10,11].

The present study aimed at evaluating the relative importance of various abiotic and biotic (i.e., vegetation) drivers in affecting SOC stocks in a wide sample of pastures in the western Italian Alps.

2. Materials and Methods

The study was conducted in a wide number of Alpine valleys within the Piedmont region, north-western Italy (Figure 1), characterised by contrasting climatic, topographic, vegetation, and soil conditions. Between years 2000 and 2007, we surveyed 324 grassland sites, encompassing a wide geographical and ecological range. The survey sites were ascribable to 54 different vegetation types (sensu Cavallero et al. [12]; see Appendix A). All the grasslands were grazed by cattle during summers, generally with lenient stocking rates.

Elevation, slope, and southness of the sites were computed using a digital terrain model at 5-m resolution [13]. Mean annual precipitation was assessed at each site using a 1-km resolution raster obtained by interpolating the long-time data series (1977–2007) of 386 weather stations spread all over the region [14]. Spatial analyses were carried out with QGIS v.3.16 LTR software [15].

At each site, the composition of grassland vegetation was determined with the vegetation point-quadrat method [16] along 25-m transects and at 50-cm intervals. To account for species richness more accurately, the list of all occasional species not recorded along the transect but occurring in a 1-m buffer area around was completed as well [17,18]. Nomenclature followed Landolt et al. [19]. Then, the relative abundance of every species was calculated as the proportion in percentage of the frequency of occurrence of each species on the sum of the frequencies of all the species in each transect. A value of 0.3% was attributed to all occasional species [17]. Species relative abundances were used to perform a non-metric multidimensional scaling (NMDS) to take the vegetation composition of each survey into account in further analyses. The number of dimensions of the NMDS was defined after checking the goodness of stress value, while Bray–Curtis was specified as dissimilarity index and 100 maximum random starts were set. Species relative abundances were also used to compute some plant community variables, namely: Landolt’s indicator values for temperature (T), humus (H), soil moisture (F), and soil nutrients (N) [19], the pastoral value (PV, which is a proxy for forage productivity and quality [16]), and Shannon diversity index [20]. These plant community variables together with species richness, were included in the NMDS biplots as supplementary variables.

A soil pit was dug close to each vegetation transect for soil description and sampling. The volumetric content (%) of coarse fragments, i.e., particles larger than 2 mm and smaller than 25 cm diameter, was visually assessed. Then, a soil sample of each horizon observed within the 0–30 cm depth interval was collected and transported to the laboratory. Samples were analysed for pH (soil:water = 1:2.5) according to standard soil analysis procedures [21] and an average pH value, weighted on the depth (in cm) of each observed horizon, was calculated. Organic C content was determined as well, using Walkley–Black titration [22].

Bulk density was estimated according to the following pedotransfer function, specifically calibrated for ‘permanent grasslands’ land use of the Alpine soil region [23]:

$$BD=1.565081-0.3946467\times \mathrm{SOC}-0.0103851\times Skel$$

where BD is the bulk density derived from the pedotransfer function and SOC and Skel are the % of OC and coarse fragments in the soil samples, respectively. Whenever Skel proportion was above 10%, the following correction was applied [24]:

$$B{D}_c=BD\times \left[1-1.67\times {\left(\frac{Skel}{100}\right)}^{3.39} \right]$$

where BDc is the corrected bulk density, referred to the fine earth fraction, and Skel is the coarse fragment content by mass. The OC, BD, and Skel values were used to assess the SOC stocks at each site as the sum of SOC values of all i horizons found within the first 30 cm, weighted on their relative depth (in cm):

$${\mathrm{SOC}}_{stock}={\displaystyle \sum }_{i=1}^n(O{C}_i\times B{D}_i\times dept{h}_i\times \left(1-Ske{l}_i\right)\times 100)$$

Precipitation among the climatic variables, elevation, slope, and southness among the topographic ones, the components of the NMDS for vegetation, and soil pH were included in a generalized linear model to predict C stock. Previous to run the model, all variables were tested for autocorrelation, and standardised in order to compare the resulting β scores. Being SOC stock a continuous variable, the Gaussian and Gamma distributions were applied and the best fitting one, i.e., that one showing the lowest Akaike Information Criterion [25], was retained. Statistical analyses were carried out in R environment, version 3.5.2 [26], using ‘goeveg’ [27], ‘vegan’ [28], and ‘glmmTMB’ [29] packages.

3. Results and Discussion

3.1. Climate, Topography, and Vegetation Features

Mean annual precipitation of the studied sites ranged from 727 to 1574 mm, thus including dry to wet climatic conditions. The altitude, slope, and aspect ranged, respectively, between 988 and 2688 m a.s.l., between 0.4 and 49.8°, and between 1.1 and 179.7°. Such a wide range of topographic conditions, combined with different soils and varying effects of livestock grazing, determined a huge variability of ecological conditions and consequently a considerably high species richness. Indeed, we recorded more than 685 plant species in total and about 35 species per transect. The descriptive statistics of climatic, topographic, and vegetation features of the sites are reported in

Table 1. Climatic, topographic, and vegetation descriptors of the 324 sites. SE, standard error of the mean; Landolt’s indicators: F, soil moisture; N, soil nutrients; H, humus; T, temperature.

Variable	Min	25%	Median	75%	Max	Mean	SE
Climate
Precipitation [mm y−1]	726.9	900.4	962.3	1103.5	1574.1	1008.2	8.88
Topography
Elevation [m a.s.l.]	988	1813	2094	2329	2688	2041	20.0
Slope [°]	0.4	12.9	20.8	28.8	49.8	20.9	0.57
Southness [°]	1.1	78.7	124.9	155.9	179.7	111.9	2.91
Vegetation
Species richness	9	26	35	44	62	35	0.7
Shannon index	1.3	3.2	3.7	4.1	5.0	3.6	0.04
Landolt’s F	1.6	2.3	2.6	2.9	4.2	2.6	0.02
Landolt’s N	1.6	2.2	2.4	2.7	4.7	2.5	0.02
Landolt’s H	1.9	3.0	3.2	3.4	4.9	3.2	0.02
Landolt’s T	1.9	1.9	2.3	2.7	3.9	2.3	0.03
Pastoral Value	20.9	34.4	40.6	46.3	73.1	41.2	0.51

.

Being 0.16 the stress value of the first three dimensions of the NMDS, i.e., less than 0.20, the fitting was considered satisfactory [30]. The supplementary variables included in the NMDS biplot improved the understanding of such a complex and variable vegetation, by highlighting its ecological trends in terms of plant community indices (Figure 2). Plant species were arranged on the first NMDS dimension according to a thermic-altitudinal gradient (Figure 2a), with thermophilic low-altitude species on the left side (such as Bromus erectus Huds., Brachypodium rupestre (Host) Roem. & Schult., Lathyrus pratensis L., Plantago media L., and Rosa canina aggr.) and those typical of cold, high-altitude environments on the right side (such as Alchemilla pentaphyllea L., Carex curvula All., Leucanthemopsis alpina (L.) Heywood, Phyteuma globularifolium Sternb. & Hoppe, and Salix herbacea L.). The arrow of Landolt’s T confirmed this gradient, being left-directed and close to the horizontal axis. The second dimension was related to the storage of dead organic material (as outlined by Landolt’s H arrow), with species growing on soils poor in humus in the upper part of the graph (such as Anthyllis vulneraria L., Helianthemum oelandicum (L.) Dum. Cours., Helictotrichon sedenense (DC.) Holub, Onobrychis montana DC., and Sesleria caerulea (L.) Ard.) and species found on soils with higher humus content at the bottom (such as Calluna vulgaris (L.) Hull, Potentilla erecta (L.) Raeusch., Carex pallescens L., Agrostis capillaris L., Poa chaixii Vill.). Finally, the distribution of the species on the third dimension showed a positive gradient of soil nutrient and forage quality, as shown by the position of Landolt’s N and PV arrows, respectively. Indeed, in Figure 2b the species typical of nutrient rich environments, such as Taraxacum officinale s.l., Peucedanum ostruthium (L.) W.D.J. Koch, Poa pratensis L., Geranium sylvaticum L., and Silene vulgaris (Moench) Garcke, were in the upper part of the biplot, while those typical of nutrient-poor pastures, such as Festuca paniculata (L.) Schinz & Thell., C. vulgaris, Vaccinium myrtillus L., Chamaecytisus hirsutus (L.) Link, and Gymnadenia conopsea (L.) R. Br., were at the bottom.

3.2. Soil Features

The soil pH encompassed both acidic and basic soil conditions, ranging from 3.3 to 8.3 (

Table 2. Soil descriptors of the 324 sites. SE, standard error of the mean.

Variable	Min	25%	Median	75%	Max	Mean	SE
pH	3.3	4.6	5.0	5.8	8.3	5.3	0.06
Coarse fragment content [%]	0.0	6.8	15.9	25.8	70.0	18.5	0.81
Bulk density [t m−3]	0.2	0.7	0.9	1.0	1.2	0.8	0.01
Soil organic carbon [t ha−1]	1.9	59.2	87.8	112.8	234.9	87.8	2.09

). Soil C stock in the investigated pastures ranged between 1.9 and 234.9 t ha⁻¹, with an average value of 87.8 t ha⁻¹. Such values were higher when compared to those of other land uses (arable lands: 52.6 ± 5.56; permanent crops: 41.4 ± 2.06; woodlands: 71.4 ± 2.10; t ha⁻¹ ± standard error), which were recorded with the same methods in the same region during a previous trial [23]. Rodríguez-Murillo [31] and Hoffmann et al. [32] found similar SOC contents in Spanish and Swiss pastures, respectively. Another recent study conducted by Ferré et al. [33] on Italian alpine grasslands reported lower values of C stocks. However, this trial was carried out in a single 1.5-ha study area characterised by a limited variability of ecological conditions, and the related outcomes should be considered with caution consequently. Canedoli et al. [3] in north-western Italy and Liefeld et al. [34] in Switzerland reported lower C stocks compared to our trial, but at the same time they highlighted higher SOC values in grasslands than in the woodlands and the arable lands, respectively, highlighting a similar trend. This may be due to the accumulation of OC in the upper soil horizons, which is particularly relevant in well-managed alpine pastures if compared to forests [35]. Indeed, the positive role of Alpine grasslands as CO₂ sinks may be exerted only with an active and balanced pastoral management, thus avoiding both overgrazing and abandonment [36,37]. Other research studies located in the European Alps reported SOC amounts characterised by wide variability, but they did not consider the role of differing plant species composition in determining the variations of soil bio-chemical features [38,39].

3.3. Modelling Soil Organic Carbon Stocks

Data analysed through generalised linear model with Gaussian distribution showed a lower Akaike information criterion when compared to Gamma one (3237 vs. 3287) thus the relative model results were retained. Model outputs highlighted the relative importance of each factor in affecting SOC stocks (

Table 3. Results of the generalized linear model accounting for the stock of soil organic carbon. NMDS, non-metric multidimensional scaling; SE, standard error; ***, p < 0.001; **, p < 0.01.

	β Score	SE	p Value	Sig.
(Intercept)	87.787	1.928	<0.001	***
Precipitation	9.994	2.515	<0.001	***
Elevation	7.619	4.206	0.070
Slope	0.241	2.325	0.917
Southness	0.182	2.237	0.935
NMDS1	−11.782	4.068	0.004	**
NMDS2	−3.611	2.897	0.213
NMDS3	−1.991	2.219	0.370
pH	−8.574	2.752	0.002	**

), providing new knowledge through a comprehensive approach concerning the role of vegetation in C bio-cycling of European Alpine pastures, which was scantly focused till present. Among the selected variables, those exerting a significant influence on SOC stocks were precipitation, vegetation (particularly, the first dimension of the NMDS), and soil pH. Conversely, elevation, slope, and southness showed non-significant effects as well as the second and third NMDS dimensions. The limited importance of southness and slope confirmed the outcomes of a previous trial [40], which, however, reported significant negative effects of both elevation and precipitation. In the present study, the precipitation showed a positive influence on SOC, likely due to an indirect effect on biomass production, which is generally associated to higher C stocks [41].

However, vegetation was found to be the most important driver, as highlighted by the highest β score. Its negative sign showed that higher SOC stocks were recorded in pastures with higher proportions of those species distributed on the left side of Figure 2a, i.e., in pastures rich in plants typical of warm, low-altitude, species-rich environments. Similar to precipitation, species typical of warmer pastures (proxied by Landolt’s T value) may be associated to greater biomass production, with positive effects on SOC content [41]. Species richness may exert a positive influence on C stocking as well, since it generally corresponds to a diversity of root systems (characterised by differing depts, biomasses, C storages, etc.) and to an enhanced soil microbial diversity (which improves SOC transformation and degradation), which indirectly influences decomposition processes [42,43]. Surprisingly, a significant effect of the second dimension of NMDS (i.e., a vegetational proxy of soil humus content) on SOC was not observed. This may depend on humus type, which could affect SOC content but is not taken into account by Landolt’s H [19,44]. However, further investigations are needed to clarify this relationship. Finally, the lack of a significant effect of the third dimension of NMDS (related to soil fertility) was likely expected. Indeed, in this study, the pastures with low Landolt’s N and PV, i.e., with low soil fertility due to undergrazing [45], were encroached by shrubs, such as C. vulgaris, V. myrtillus, and C. hirsutus. Likely, the low biochemical quality of shrub litter delayed its decomposition and allowed higher organic matter accumulations in the topsoil [37]. However, the effect of shrub proliferation at a depth greater than the 30 cm considered here was partially unclear since the low root turnover of shrubs compared to grasses should have reduced the C inputs in the soil.

As for pH, larger amounts of SOC were recorded in soils with an acidic reaction, confirming the remarkable importance of pH in affecting SOC stocks in Alpine grasslands [46], probably because low pH is associated to high SOC contents, or mineralisation is reduced at low pH [47,48].

According to our results, the SOC stocking of Alpine pastures, generally managed under extensive grazing regimes, was predominantly influenced by the vegetation rather than by abiotic factors. More specifically, we observed a remarkable role of warm-pasture species (such as B. erectus), which might have a limited interest as fodder resource (in terms of quantity and quality [49]), but which can definitely have a remarkable weight on carbon stocks. Dry pastures, which generally host large proportions of such plants, are widely represented in the Alps. For instance, the dry grasslands dominated by B. rupestre, F. paniculata, or F. ovina aggr. cover more than 30% of the pasture area in Piedmont Region [12]. The importance of alpine pastures in SOC stocking was in general confirmed, as the observed values were generally higher compared to other land uses. Thus, pasture conservation policies should be encouraged, such as through specific PES (payments for ecosystem services) [50]. In the current scenario of climate change, the abundance of warm grassland species will likely increase in the future years [51], and a shift at higher elevations would be expected. Consequently, an increase of SOC stocks in Alpine pastures might be observed but, precipitation being a relevant factor affecting C cycling as well, a targeted monitoring should be carried out to take the complex and spatially heterogeneous patterns of climate change into account [52,53].

Future research should be addressed to monitor the possible effects of management intensity, for instance of different stocking rates or grazing regimes. Moreover, the SOC stocking ability of permanent pasture should be compared with that of mountain hay meadows. An extension would be advisable to lowland grasslands too, where the species richness and diversity are generally lower compared to the mountain ones, and which are generally more intensively managed in terms of number of exploitations per year and fertilisation.

4. Conclusions

The novel results of this study carried out in a huge range of ecological conditions highlighted the relevant importance of grassland species composition in affecting soil C stock of Alpine soils, while topographic attributes had negligible effects. More specifically, dry pastures (which also generally host rare plants and a high species richness) stocked more carbon in the upper soil horizons. Among abiotic factors, precipitation positively affected soil organic carbon stocks, likely through an indirect effect due to the increased herbage biomass. Conversely, lower SOC values were found on acidic soils, where mineralization might be hampered. Future conservation strategies should aim to consider the role of such extensively managed pastures, which can be found in the Alpine region, and of the dry grassland species in enhancing this ecosystem service.