Article Regeneration in the Understory of Declining Overstory Trees Contributes to Soil Respiration Homeostasis along Succession in a Sub-Mediterranean Beech Forest Jesús Rodríguez-Calcerrada 1, * , Roberto Salomón 2 , Josep Barba 3 , Guillermo G. Gordaliza 1 , Jorge Curiel Yuste 4,5 , Carlos Magro 1 and Luis Gil 1 1 Forest History, Physiology and Genetics Research Group, School of Forestry Engineering, “Universidad Politécnica de Madrid”, 28040 Madrid, Spain 2 Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium 3 Plant and Soil Sciences Department, University of Delaware, Newark, DE 19711, USA 4 BC3-Basque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain 5 IKERBASQUE-Basque Foundation for Science, María Díaz de Haro 3, 6 solairua, 48013 Bilbao, Spain * Correspondence: jesus.rcalcerrada@upm.es; Tel.: +34-91-06-71783  Received: 12 July 2019; Accepted: 22 August 2019; Published: 24 August 2019  Abstract: Research Highlights: Tree decline can alter soil carbon cycling, given the close relationship between primary production and the activity of roots and soil microbes. Background and Objectives: We studied how tree decline associated to old age and accelerated by land-use change and increased drought in the last decades, affects soil properties and soil respiration (Rs ). Materials and Methods: We measured Rs over two years around centennial European beech (Fagus sylvatica L.) trees representing a gradient of decline in a sub-Mediterranean forest stand, where the number of centennial beech trees has decreased by 54% in the last century. Four replicate plots were established around trees (i) with no apparent crown dieback, (ii) less than 40% crown dieback, (iii) more than 50% crown dieback, and (iv) dead. Results: Temporal variations in Rs were controlled by soil temperature (Ts ) and soil water content (SWC). The increase in Rs with Ts depended on SWC. The temperature-normalized Rs exhibited a parabolic relationship with SWC, suggesting a reduced root and microbial respiration associated to drought and waterlogging. The response of Rs to SWC did not vary among tree-decline classes. However, the sensitivity of Rs to Ts was higher around vigorous trees than around those with early symptoms of decline. Spatial variations in Rs were governed by soil carbon to nitrogen ratio, which had a negative effect on Rs , and SWC during summer, when drier plots had lower Rs than wetter plots. These variations were independent of the tree vigor. The basal area of recruits, which was three times (although non-significantly) higher under declining and dead trees than under vigorous trees, had a positive effect on Rs . However, the mean Rs did not change among tree-decline classes. These results indicate that Rs and related soil physico-chemical variables are resilient to the decline and death of dominant centennial trees. Conclusions: The development of advanced regeneration as overstory beech trees decline and die contribute to the Rs homeostasis along forest succession. Keywords: successional stage; forest decline; tree old-age; old-growth forest; soil carbon; soil nitrogen; rhizosphere; root biomass Forests 2019, 10, 727; doi:10.3390/f10090727 www.mdpi.com/journal/forests Forests 2019, 10, 727 2 of 18 1. Introduction Soil respiration (Rs ) has been measured in most terrestrial biomes, from deserts, grasslands and shrublands, to forests in temperate, boreal, tropical and Mediterranean regions [1]. From forest studies where carbon (C) fluxes have been partitioned among their components, it has been shown that Rs typically accounts for more than half of the total annual ecosystem respiration [2–4]. Tree ageing and forest succession involve changes in the overstory and, in turn, in water, nutrient and C fluxes between the atmosphere and the rhizosphere that can translate into different rates of Rs [5,6]. Global change-related episodes of forest decline and die off are accelerating forest succession worldwide, particularly in forests weakened by tree ageing, overcrowding and sub-optimal climate conditions where ancient land uses have changed [7]. To date, however, relatively little is known on how forest succession [8,9] and decline affect Rs [10]. In general, the impact of forest decline on Rs is likely to vary among ecosystems depending on how sensitive the roots of dominant species are to defoliation, and how fast microbes are in the decomposing litter and other plant species in colonizing the canopy gaps opened by defoliation and tree death. However, complex plant-soil-climate interactions mediate tree mortality impacts on Rs [10]. For example, Ávila et al. [11] found that the effect of tree decline on soil variables differed in woodlands and closed forests, varied between seasons, and was higher as declining trees were larger. The net impact of forest decline on Rs is not straightforward, due to direct and indirect effects of the decline on soil C and nutrient inputs and icroclimate. Soil areas close to vigorous trees with a high foliage volume might exhibit higher Rs than those close to declining trees with smaller crowns due to higher supply rates of respiratory substrates to roots and their mycorrhizal symbionts [12,13]. Moreover, the microbial respiration could increase around vigorous trees as a result of higher root exudation rates and leaf and fine root litter supply to the soil [14]. In support of this reasoning it has been observed that variables related with photosynthetic C uptake, such as the leaf area index, sap flow, tree size, or tree proximity to and live tree basal area around Rs sampling locations are positively related to Rs [11,15–20]. Nevertheless, Rs can be relatively homeostatic across successional stages or after perturbations [8,21–23]. A progressive decline in the vigor (and crown and root volume) of mature trees may prompt space colonization by woody recruits and herbaceous species, so that the Rs remains similar across successional stages. Further, because Rs results to a large extent from the activity of decomposing microorganisms–contributing 40 to almost 100% of the total Rs at some periods of the year [6,21,24]–it can also be envisaged that Rs is higher close to declining than vigorous trees due to higher litter input and soil metabolic activity [19]. In fact, the plant litter quality and amount affect the soil microclimate and microbial community composition and activity [25,26]. Reducing the leaf area associated to the tree decline may affect Rs via increasing the soil temperature due to enhanced penetration of solar radiation through the canopy. Temperature is one of the main drivers of temporal and spatial variations in Rs , e.g., [27]. Given the positive effect of temperature on enzymatic reactions of all living organisms, Rs can increase in warmer soils of declining stands under non-limiting soil C and nitrogen (N) conditions. Similarly, the soil water content (SWC) has a profound effect on the metabolic activity of roots, associated microorganisms (e.g., mycorrhiza) and soil microbial decomposers [28,29], which is reflected in the positive correlations between Rs and SWC, over time [30–32] and space [31,33,34] at multiple scales. The limiting effect of low SWC on Rs is observed in many studies in seasonally dry ecosystems where, below a threshold of SWC, Rs exhibits a weak sensitivity to temperature. For example, in Mediterranean-type ecosystems where the dry period occurs at the warmer time of the year, low SWC results in the saturation of the response of Rs to temperature [35]. On the contrary, a high SWC can restrict CO2 diffusion from soils, and also make soil oxygen concentrations to drop to limiting thresholds for Rs [36,37]. The effects of vegetation die-off on soil water availability are not easy to predict. Tree dieback might result in lower water consumption, but also in higher soil water evaporation due to canopy defoliation [38–40]. tree decline and understory colonization with recruits (Figure 1). We hypothesized that tree decline would result in a reduction in Rs that would be partly related to a change in the soil physico-chemical properties and a reduction in the fine root density. As a second objective, we explored the influence of soil temperature and water availability on Rs seasonality in two years of contrasted climate conditions, and whether soil temperature, SWC or their influence on Rs changed Forests across the10,tree 2019, 727 decline stages. We expected that Rs was driven by temperature across periods of high 3 of 18 SWC, and by SWC below a certain threshold of SWC. Figure 1. Examples of centennial beech trees around which soil respiration measurements were conducted. Tree decline classes were established in relation to the crown dieback: Vigorous (a; no apparent crown dieback), affected (b; less than 40% crown dieback), severely affected (c; more than 50% crown dieback), and dead (d) trees. Studying the interactions between environmental conditions and forest decline on Rs helps to understand and predict the C balance of forest ecosystems. We measured Rs in a marginal beech (Fagus sylvatica L.) forest stand in south Europe during two consecutive years. This population is suffering an accelerated loss of centennial trees. Among the probable causes are (i) the 21% reduction Forests 2019, 10, 727 4 of 18 in annual rainfall occurred in the last decade relative to the previous one; (ii) the ancient practice of pollarding, which has favored the development of large horizontal branches, prone to fall from mechanical instability, and being infected from open wounds; and (iii) competition with recruits, mainly for water. One objective of this work was to compare Rs among sites of different degrees of decline, from mature climax stages where vigorous centennial trees dominate, to stages of increasing tree decline and understory colonization with recruits (Figure 1). We hypothesized that tree decline would result in a reduction in Rs that would be partly related to a change in the soil physico-chemical properties and a reduction in the fine root density. As a second objective, we explored the influence of soil temperature and water availability on Rs seasonality in two years of contrasted climate conditions, and whether soil temperature, SWC or their influence on Rs changed across the tree decline stages. We expected that Rs was driven by temperature across periods of high SWC, and by SWC below a certain threshold of SWC. 2. Materials and Methods 2.1. Area and Design of the Study The study was conducted in a 1-ha beech stand located within a 120-ha mixed forest in central Spain (3◦ 300 W, 41◦ 070 N; at 1240–1550 m a.s.l.). Trees of beech and oak (Quercus petraea and Quercus pyrenaica) formed an open overstory during most of the 19th and 20th centuries when the forest was managed as an open woodland used for sheep and cattle grazing, resting and breeding [41]. In the 1960s, domestic livestock was banned, and the understory was rapidly colonized by the progeny of overstory trees and other woody species from surrounding forests (hereafter recruits). The climate of the area is sub-Mediterranean, with a mean annual temperature of 9.7 ◦ C and a mean annual rainfall of 873 mm (data from 1994 to 2017, recorded in a meteorological station within the forest). Both variables exhibit a strong seasonality: Minimum and maximum temperatures are reached in January and July, respectively, and a dry period (i.e., 131 mm rainfall on average) typically extends from June through September. Soils are formed from mica schists and mica gneiss substrates. In 2015, sixteen circular plots of 10 m radius were established around centennial beech trees with different degrees of vigor, or dead approximately 15–25 years ago (Figure 1; Table 1). The slope ranged from 15% to 22%, and the orientation from 40◦ N to 87◦ N across the study area. Four plots were selected for each of the four tree-decline classes: Healthy trees with no symptoms of decay (vigorous; V), trees with less than 40% of the crown dead (affected; A1), trees with severe symptoms of decay and most of the crown dead (severely affected; A2), and finally snags, i.e., dead trees with part of the main trunk still standing up (dead; D). The stem density and basal area were computed within the plots, including all stems taller than 1.5 m. Given the high contribution of the central tree to the basal area, the data were also given for the recruits only. Beech and occasionally holly (Ilex aquifolium) were the main recruits in the understory, while shrubs (mostly Genista florida) and herbaceous species (mostly Melica uniflora) were rare. Table 1 summarizes the structural and edaphic properties of the plots. 2.2. Soil Respiration On each plot, four 19-cm diameter polyvinyl chloride (PVC) collars were inserted 2 cm deep into the soil and fixed with brackets and nails. Soil respiration (Rs ) was measured in situ with a close-path infrared gas analyzer (LI-6400, Li-Cor, Inc., Lincoln, NE, USA) coupled to a closed dynamic soil chamber (LI-6400-09). The collars were larger than those supplied by the manufacturer to sample a larger soil surface area (284 cm2 ). A custom-made PVC lid with a central hole slightly larger than the soil chamber was fit to the collar, and the soil chamber was inserted in the hole with a foam gasket to ensure an appropriate seal. The four collars were placed forming a square of 5–8 m side around the centennial beech tree/snag, avoiding fallen branches and rare rock outcrops. If at the time of measuring there was evidence that the collar had been moved by animals, the location was not measured, and the collar was re-installed for subsequent dates. Despite the tree rhizosphere is laterally expansive and Forests 2019, 10, 727 5 of 18 belowground delimitation of individual trees is challenging, previous studies in denser forests than the one surveyed here have shown that single trees are determinant of the surrounding Rs [19,21] and soil microbial communities [42]. Table 1. Characterization of the four types of plots established around centennial beech trees of different declines–vigorous, affected, severely affected–and dead. Crown dieback of the centennial beech tree (%); cover of herbaceous (Herb.) species (%) and cover of shrub species (%); stem density (stems ha−1 ) and basal area (m2 ha−1 ) of either all trees in the plot (ALL) or only saplings >1.5 m height and <0.5 m diameter at breast height (i.e., of recruits established after the woodland was banned to cattle grazing in the 1960s; REC); fine litter depth (i.e., leaves and small twigs; cm); soil water content over 0–20 cm topsoil (SWC; %) averaged for all or only summer measurement dates, and soil bulk density (g cm−3 ), organic matter (OM; %), total carbon (C, %), total nitrogen (N, %), C to N ratio, and fine root density (<2 mm; g m−2 ) of 0–10 cm topsoil; average soil respiration measured across two years (Rs ; µmol m−2 s−1 ), and modeled at mean annual soil temperature (11 ◦ C), and constant temperature and soil water content (20%). Data are means (±SE) from four replicate plots per tree decline class. Different letters separate significantly different means of crown dieback, the only variable for which significant differences among tree classes were found at p < 0.05. Vigorous Affected Severely Affected Dead Crown dieback 1 6.3 ± 3.8 a 25 ± 5.4 a 68.8 ± 8.3 b 100 ± 0 c Herb. cover 1 3.7 ± 1.3 3.2 ± 2.0 11.1 ± 4.4 12.7 ± 4.7 Shrub cover 1 4.2 ± 3.2 10.4 ± 4.9 13.3 ± 8.4 12.3 ± 3.9 Stem density (ALL) 1003 ± 486 2045 ± 543 2690 ± 592 2355 ± 185 Basal area (ALL) 48.8 ± 9.7 36.9 ± 5.7 37.7 ± 6.6 27.4 ± 9.5 Stem density (REC) 963 ± 491 2021 ± 538 2658 ± 592 2340 ± 183 Basal area (REC) 5.3 ± 1.8 12 ± 4.1 15.6 ± 6.6 18 ± 1.3 Soil litter depth 5.3 ± 0.3 5.3 ± 0.3 5.3 ± 0.8 4.1 ± 0.6 SWC 18.8 ± 0.2 19.4 ± 0.1 18.5 ± 0.1 21.4 ± 0.3 SWC summer 10.7 ± 0.2 11.0 ± 0.2 9.6 ± 0.2 10.7 ± 0.4 Soil bulk density 0.77 ± 0.01 0.78 ± 0.01 0.76 ± 0.01 0.73 ± 0.01 Soil OM 9.7 ± 1.0 8.3 ± 0.6 8.9 ± 0.4 8.8 ± 0.6 Soil total carbon 7.7 ± 0.8 6.5 ± 0.5 7.2 ± 0.4 7.4 ± 0.7 Soil total nitrogen 0.48 ± 0.05 0.41 ± 0.04 0.45 ± 0.04 0.49 ± 0.06 Soil C/N 15.8 ± 0.1 16.1 ± 0.2 16.1 ± 0.1 15.2 ± 0.2 Root density 232 ± 4 208 ± 11 229 ± 6 224 ± 8 Rs 4.67 ± 0.08 3.95 ± 0.06 4.49 ± 0.14 4.29 ± 0.05 Rs _11 ◦ C 4.51 ± 0.07 3.92 ± 0.07 4.31 ± 0.15 4.15 ± 0.07 Rs _11 ◦ C, 20% 5.19 ± 0.06 4.41 ± 0.07 5.20 ± 0.12 4.81 ± 0.04 1: Variables visually estimated by the same observer. Fourteen measurement campaigns were conducted between May 2015 and November 2016, at 1–3 months intervals. On each campaign, all measurements were conducted in one day, typically from 10:00 am to 17:00 pm, alternating V, A1, A2, and D plots to avoid differences in soil temperature among tree-decline classes due to the sampling time. Three measurement cycles were recorded per collar and campaign. The topsoil temperature (Ts ) was measured at the same time than Rs with a 6000-09TC temperature probe 5–10 cm away from the collar. 2.3. Soil Physico-Chemical Properties and Root Density A soil pit excavated in one location of the study area revealed a 46 cm deep A horizon (29.3% coarse sand, 51.4% fine sand, 7.3% silt, 12% clay), a 61 cm deep B horizon (53.5% coarse sand, 27.8% fine sand, 8.3% silt, 10.4% clay) and an 18 cm deep C horizon (33.5% coarse sand, 38.5% fine sand, 12.8% silt, 15.2% clay). Moreover, on each plot and campaign, the volumetric soil water content (SWC) was measured in the top 20 cm with a quasi-time-domain-reflectometry system (TDR; TRIME-FM, IMKO Micromodultechnik GmbH, Germany) coupled to a TRIME-T3 tube probe. The frequency of measurements was the same Forests 2019, 10, 727 6 of 18 than for Rs . To account for soil physical properties specific of the forest, TDR measurements were calibrated with independent gravimetric SWC measurements, transformed to volumetric SWC values using the soil bulk density (r2 = 0.97, p < 0.001, n = 13). In November 2015, one sample from the 0–10 cm topsoil per plot was taken for analyses of organic matter (OM) and total C and N content using the Dumas method [43] with an elemental analyzer (Elementar N/CN; VarioMax, Hanau, Germany). Two 10 cm deep soil samples per plot were also taken with a corer and used to measure the bulk density, as the ratio of soil sample mass dried at 105 ◦ C to the core volume. Litter depth was measured with a ruler in four locations per plot, near the soil collars. At the end of the study, two cores of 19 cm in diameter and 10 cm depth were taken per plot to collect small roots. In the laboratory, roots were separated from successive small volumes of soil with the help of a 2-mm sieve, first, and a 200 µm sieve afterwards, using water to carefully disaggregate numerous soil aggregates holding fine roots. Soil-free roots were oven-dried at 70 ◦ C and weighted separately according to their diameter: <2 mm (fine) or 2–5 mm (small). We are confident that roots belonged to beech, given the low abundance of herbaceous and shrub species in the study plots (Figure 1; Table 1) and the same morphology of roots across soil samples. 2.4. Data Analyses For each single date, cycles higher than 1.5 times the 25th to 75th interquartile range were considered outliers and thus removed from the data set. These outliers accounted for 6.1% of the total data set. Thereafter, we calculated the mean Rs per collar, and the mean Rs per plot after averaging the four (occasionally 2–3) collars per plot on each of the 14 field campaigns. Mixed-effects models were used to analyze the temporal relationship between the plot-based mean Rs and tree-decline classes (i.e., V, A1, A2, and D), seasons, Ts and SWC. We evaluated all possible models combining the tree-decline classes, season, Ts , SWC and their first order interactions (saturated model) in order to achieve the minimum adequate model according to the corrected Akaike information criterion (AICc). Once we had the minimum adequate models, we calculated the adjusted r2 . Before running the model selection, we tested the variance inflation factor (VIF) in the saturated model to assess collinearity between Ts and SWC; VIF was lower than two, indicating low collinearity [44]. Since the magnitude of the mixed effects models is dependent on the units of each variable, we scaled and centered all variables to improve the performance and interpretability of the output models [45]. A plot identity and correlation structure was included in the random part of the model to control for the temporal autocorrelation between measurements. The spatial variation in Rs across the 16 plots was studied for the mean Rs measured over the two-year study period, the mean Rs measured on each season, and standardized Rs at constant Ts , or constant Ts and SWC (i.e., at the mean annual or seasonal annual Ts and SWC values). The standardization allowed to account for small differences in Ts and SWC among plots (Figure 2) when studying the influence of OM, C, N, C/N, soil density, and fine and small root density on spatial variations in Rs . To do so, we first model Rs as a linear function of Ts over dates with SWC not limiting to Rs (i.e., >15%; Figure 3a). The 15% SWC threshold was determined as the SWC value below which Rs was best explained by SWC (r2 = 0.44, p < 0.001, n = 73). Second, the residuals between the observed and modeled values of Rs from Ts across the entire range of SWC were modeled as a quadratic function of SWC (Figure 4b). Finally, the standardized Rs was derived from the following equation using the observed Ts and SWC: Rs = (a + b Tsoil ) + (c + d SWC + e (SWC)2 ) (1) where a through e were coefficients specific to each tree-decline class (see Table 2). Differences in all soil physico-chemical variables among the tree-decline classes were tested with general linear models, using a repeated measures analysis for Ts and SWC. Post-hoc Tukey’s HSD tests were conducted to separate the mean values among the tree classes and measurement dates when these factors were significant at p < 0.05. Analyses were performed with Statistica (StatSoft, Tulsa, OK, USA), and R using the packages SMATR [46], nlme [47] and MuMIn [48]. Forests 2019, 10, x FOR PEER REVIEW 7 of 18 Forests 2019, 10, 727 3. Results 7 of 18 3.1. Environmental Conditions 3. Results The two years of study were climatically anomalous: Compared to the 25-year averages of the 3.1. Environmental Conditions study site, the spring through summer period of 2015 was 1.4 °C warmer, while the summer rainfall in 2016 was 67% lower. The two years of study Thewere temperature andanomalous: climatically rainfall followed the typical Compared to theseasonal 25-year pattern averagesofof Mediterranean climates, with warmer temperatures being registered during ◦ the drier the study site, the spring through summer period of 2015 was 1.4 C warmer, while the summer period in the summer (Figure 2a). This resulted in an inverse relationship between Ts and SWC (r2 = 0.59, p < 0.001, rainfall in 2016 was 67% lower. The temperature and rainfall followed the typical seasonal pattern of n = 214; Figure 2a,b). Mediterranean climates, with warmer temperatures being registered during the drier period in the Neither Ts nor SWC, nor their temporal variations were significantly different among the summer (Figure 2a). This resulted in an inverse relationship between Ts and SWC (r2 = 0.59, p < 0.001, tree-decline classes. Still, in some dates of spring and autumn, SWC was consistently higher near the n = 214; Figure 2a,b). dead trees (Figure 2b). Figure 2. Temporal changes in (a) soil temperature and daily rainfall (blue line), (b) soil water content at 0–20 cm depth, and (c) soil respiration (Rs ) around centennial beech trees of different decline classes Forests 2019, 10, 727 8 of 18 – vigorous (V), affected (A1), severely affected (A2) – and dead (D). Values are means (±SE) of n = 4 replicate plots, except for rainfall, recorded in a meteorological station in the study area. Asterisks indicate dates in which significantly different means among tree-decline classes were found at p = 0.05. Neither Ts nor SWC, nor their temporal variations were significantly different among the tree-decline classes. Still, in some dates of spring and autumn, SWC was consistently higher near the dead trees (Figure 2b). Table 2. Mixed effects model of temporal variations in soil respiration as a function of soil temperature (Ts ), soil water content (SWC), tree-decline class (vigorous [V], affected [A1], severely affected [A2] and dead [D]) and season. The model explained 83% of the variation in Rs and was significant at p < 0.001. Estimate SE DF t-Value p-Value Intercept 0.226 0.184 186 1.225 0.222 Ts 0.829 0.163 186 5.088 <0.001 SWC 0.103 0.154 186 0.665 0.507 Ts × SWC 0.233 0.088 186 2.646 0.009 Tree-decline V - - - - -a A1 −0.376 0.245 12 −1.536 0.151 a A2 −0.026 0.245 12 −0.105 0.918 a D −0.131 0.249 12 −0.525 0.609 a Season Spring - - - - -a Summer 0.796 0.189 186 4.211 <0.001 b Fall 1.828 0.478 186 3.821 <0.001 c Winter 0.231 1.931 186 0.12 0.905 abc Ts × season Ts × spring - - - - -a Ts × summer 0.093 0.225 186 0.411 0.681 a Ts × fall 1.017 0.393 186 2.589 0.01 b Ts × winter 0.188 1.237 186 0.152 0.879 ab SWC × season SWC × spring - - - - -a SWC × summer 1.009 0.198 186 5.09 <0.001 b SWC × fall −0.507 0.378 186 −1.341 0.182 a SWC × winter 0.117 0.43 186 0.273 0.786 ab Ts × tree-decline Ts × V - - - - -a Ts × A1 −0.252 0.092 186 −2.738 0.007 b Ts × A2 −0.105 0.091 186 −1.157 0.249 ab Ts × D −0.147 0.097 186 −1.517 0.131 ab 3.2. Temporal Changes in Rs The mean values of Rs varied by 4-fold over the course of the experiment (Figure 2c), and significantly among seasons (Table 2). The lowest values were registered in winter/early spring and the highest in early summer, when Ts was high and storms prevented SWC to drop (Figure 2). This reflects the interaction effect of Ts and SWC on Rs : At a low SWC, the effect of Ts on Rs was lower than at a high SWC (Table 2). The lower sensitivity of Rs to Ts during the summer drought resulted in an asymptotic relationship between Rs and Ts (Figure 3a). Similarly, a lower Ts during periods of high SWC (from fall though spring), resulted in a parabolic relationship between SWC and Rs across the entire range of SWC observed (Figure 3b). Exploring the relationship between both variables at increasing SWC intervals revealed that a positive significant relationship existed between SWC and Rs the highest in early summer, when Ts was high and storms prevented SWC to drop (Figure 2). This reflects the interaction effect of Ts and SWC on Rs: At a low SWC, the effect of Ts on Rs was lower than at a high SWC (Table 2). The lower sensitivity of Rs to Ts during the summer drought resulted in an asymptotic relationship between Rs and Ts (Figure 3a). Similarly, a lower Ts during periods of high Forests SWC2019, 10, fall (from 727 though spring), resulted in a parabolic relationship between SWC and Rs across 9 ofthe 18 entire range of SWC observed (Figure 3b). Exploring the relationship between both variables at increasing SWC intervals revealed that a positive significant relationship existed between SWC and below 15% SWC (Figure 4a), i.e., mostly during the summer (Table 2). This explains that the sensitivity Rs below 15% SWC (Figure 4a), i.e., mostly during the summer (Table 2). This explains that the of Rs to SWC was highest in the summer (when a low SWC was limiting Rs ), and the sensitivity of Rs sensitivity of Rs to SWC was highest in the summer (when a low SWC was limiting Rs), and the to Ts was highest in fall (when a high SWC did not limit Rs ; Table 2). The only significant effect of the sensitivity of Rs to Ts was highest in fall (when a high SWC did not limit Rs; Table 2). The only factor “tree decline” was an interaction with Ts , which indicated that Rs increased with increasing Ts significant effect of the factor “tree decline” was an interaction with Ts, which indicated that Rs faster near vigorous trees than near trees with early symptoms of decline (Table 2, Figure 3). The best increased with increasing Ts faster near vigorous trees than near trees with early symptoms of regression model explaining the relationship between Ts and Rs excluding values with SWC <15% was decline (Table 2, Figure 3). The best regression model explaining the relationship between Ts and Rs a linear model (Figure 3b), which contrasts with the logarithmic relationship between both variables excluding values with SWC <15% was a linear model (Figure 3b), which contrasts with the when all data were included (Figure 3a). The residuals of the Rs vs Ts linear regression model were logarithmic relationship between both variables when all data were included (Figure 3a). The related with SWC, with larger deviations of observed values from the predicted Rs values occurring at residuals of the Rs vs Ts linear regression model were related with SWC, with larger deviations of a low SWC (Figure 4b). The Rs standardized at the mean annual Ts (11) continued to be non-linearly observed values from the predicted Rs values occurring at a low SWC (Figure 4b). The Rs related with SWC (Figure 4c). standardized at the mean annual Ts (11) continued to be non-linearly related with SWC (Figure 4c). Figure3.3. Soil Figure Soil respiration respiration(R (Rss)) as as aa function function of of soil soil temperature temperature(T (Tss)) throughout throughout the the entire entirestudy studyperiod period (a) or after excluding observations above 15% soil water content (b); non-limiting SWC for Rs ; see text), (a) or after excluding observations above 15% soil water content (b); non-limiting SWC for Rs; see observed around centennial beech trees of different decline classes–vigorous (V; squares), affected (A1; text), observed around centennial beech trees of different decline classes–vigorous (V; squares), diamonds), severely affected (A2; triangles), and dead (D; circles). Data points are mean values per plot affected (A1; diamonds), severely affected (A2; triangles), and dead (D; circles). Data points are mean and sampling campaign. In (a) open and closed symbols account for Rs values above and below 15% values per plot and sampling campaign. In (a) open and closed symbols account for Rs values above SWC, respectively; non-linear models are fitted to all data points, separately among tree-decline classes. and below 15% SWC, respectively; non-linear models are fitted to all data points, separately among tree-decline classes. Modeled Rs as a function of both Ts and SWC exhibited a good agreement with observed Rs values (Figure 5). However, the slope and intercept of the linear relationship between both variables differed significantly from one and zero, respectively. Forests 2019, Forests 2019, 10, 10, 727 x FOR PEER REVIEW 10 10 of 18 18 Figure 4. Observed soil respiration (Rs ; a) and derived parameters (b,c) modeled as a function of soil Figurecontent water 4. Observed (SWC)soil in respiration (Rs; a) around plots established and derived parameters centennial beech(b, c) modeled trees as adecline of different function of soil– classes water content vigorous (SWC) in (V; squares), plots established affected (A1; diamonds),around centennial severely beech affected trees (A2; of different triangles), decline and dead (D;classes – circles). vigorous Data points (V; aresquares), affected mean values (A1;and per plot diamonds), severely affected sampling campaign. (A2; In (a) the triangles), inset depicts theand dead (D; relationship circles).15% below Data points SWC. b,c)are mean values relationship of SWCper with plot and sampling residuals campaign. between observed In R(a) the inset s values anddepicts Rs valuesthe relationship below 15% SWC. b, c) relationship of SWC with ◦ residuals between modeled from Ts above 15% SWC (b), and standardized Rs at 11 C (mean annual Ts ; c). Non-linear observed R s values and Rs values regression modeled models were from fittedTpooling s above 15% SWC (b), and all tree-decline standardized classes Rs atamong or separately 11 °C (mean annualclasses tree-decline Ts; c). Non-linear to account for regression models the significant were fitted relationship of Tpooling all tree-decline s with factor “Decline” (seeclasses or separately among Table 2). tree-decline classes to account for the significant relationship of Ts with factor “Decline” (see Table 2). Forests 2019, 10, x FOR PEER REVIEW 11 of 18 Modeled Rs as a function of both Ts and SWC exhibited a good agreement with observed Rs Forests 2019, 10, 727 11 of 18 values (Figure 5). However, the slope and intercept of the linear relationship between both variables differed significantly from one and zero, respectively. Figure5.5.Relationship Figure Relationship between betweenobserved soil respiration observed soil respiration (RRs)s modeled (Rs ) and from temporal and Rs modeled fromvariations temporal in soil waterincontent variations and soil soil water temperature. content and soilDifferent symbols temperature. are usedsymbols Different to distinguish are usedbetween vigorous to distinguish (squares), between slightly vigorousaffected (diamonds), (squares), slightly severely affectedaffected (triangles) (diamonds), and dead severely (circles) affected trees, but (triangles) a single and dead regression model is given for all tree-decline classes pooled for clarity. The major axis regression (circles) trees, but a single regression model is given for all tree-decline classes pooled for clarity. The analysis major axiswasregression used to analyze analysisthewas relationship betweenthe used to analyze therelationship observed and the modeled between Rs . Dotted the observed andline the depicts the 1:1 line. modeled Rs. Dotted line depicts the 1:1 line. 3.3. Spatial Changes in Soil Physico-Chemical Variables and Rs 3.3. Spatial Changes in Soil Physico-Chemical Variables and Rs Spatial patterns of most variables analyzed were not related to the degree of beech decline. Spatialtopatterns Compared of most large changes variables in the analyzed percentage weredieback of crown not related of eachto plot’s the degree centraloftree, beech decline. changes in TCompared s and SWC to large (Figure changes 2a,b), in soil the percentage physico-chemical of crown variables,dieback fine of root each plot’s density, andcentral Rs weretree, changes insignificant in T s and SWC (Figure 2a,b), soil physico-chemical variables, fine root density, and Rs were among tree-decline classes, irrespective of the season, or whether Rs was standardized at a given Ts insignificant and SWC or not among tree-decline (Tables 1 and 2). classes, irrespective of the season, or whether Rs was standardized at a given Ts and SWC Nonetheless, spatialor not (Tablesin variations 1 and 2). considerable and partly explained by soil physico- Rs were Nonetheless, spatial variations chemical variables, mostly C/N and SWC during in R s were considerable and partly explained by soil summer. Litter depth, which was not significantly physico-chemical variables, mostly C/N related to aboveground canopy features (e.g., basal and SWC areaduring or stemsummer. density),Litter depth,with was related which soilwas not ground significantly area covered by related to aboveground herbaceous species (r =canopy −0.54; pfeatures = 0.032), (e.g., soil basal density area (r =or0.52; stem p=density), 0.039) and wassummer related with soil ground area covered by herbaceous species (r = −0.54; p = 0.032), SWC (Figure 6a) across the 16 studied plots. SWC controlled spatial variability in Rs during summer, soil density (r = 0.52; p= 0.039)wetter when and summer SWC plots had (Figure higher 6a) across the standardized Rs 16 at studied o 17 C (the plots. SWC T average controlled spatial variability in s during summer) than drier o R s during summer, when wetter plots had higher standardized Rs at 17 C (the average Ts during ones (Figure 6b). On the other hand, wetter plots during summer had higher soil N concentrations and summer) lower C/Nthan thandrier drier ones plots(Figure (Figure 6b). On the other 6c), whereas, hand, in turn, C/Nwetter plots during was marginally summer had significantly higher related soil to the N concentrations mean and lower C/N annual Rs standardized than at the meandrier plotsT(Figure annual 6c), whereas, in turn, C/N was marginally s and SWC (i.e., taking differences in T s and SWC significantly related to the mean annual R s standardized at the mean annual Ts and SWC (i.e., taking among plots into account; Figure 6d). Soil total C and N were positively correlated (r = 0.92; p < 0.001). differences in Ts and SWC among Of the vegetation-related plots features into account; examined, only theFigure basal6d). areaSoil total C and of recruits was N were positively negatively related correlated (r = 0.92; p < 0.001). to the soil C/N (r = −0.51; p = 0.046), which is consistent with the trend of Rs (standardized at the mean annual OfTthe vegetation-related features examined, only the basal area of recruits was negatively s and SWC) to increase in plots with a higher basal area of recruits (r = 0.48; p = 0.06). related to the soil C/N (r = - 0.51; p = 0.046), which is consistent with the trend of Rs (standardized at the mean annual Ts and SWC) to increase in plots with a higher basal area of recruits (r = 0.48; p = 0.06). Forests 2019, 10, 727 12 of 18 Forests 2019, 10, x FOR PEER REVIEW 12 of 18 Figure 6. Relationships between soil variables across 16 plots established around centennial beech Figure 6. Relationships between soil variables across 16 plots established around centennial beech trees of different decline classes – vigorous (squares), affected (diamonds), severely affected (triangles) – trees of different decline classes – vigorous (squares), affected (diamonds), severely affected and dead (circles): (a) Litter depth versus soil water content (SWC) during summer; (b) summer SWC (triangles) – and dead (circles): (a) Litter depth versus soil water ◦content (SWC) during summer; (b) versus standardized soil respiration (Rs ) during summer at 17 C (the averageo soil temperature (Ts ) summer SWC versus standardized soil respiration (Rs) during summer at 17 C (the average soil during summer); (c) summer SWC versus soil nitrogen concentration (open symbols) and C to N ratio temperature (Ts) during summer); (c) summer SWC versus soil nitrogen concentration (open (C/N; closed symbols); and (d) C/N versus mean Rs during the entire study period standardized at symbols) and C to N ratio (C/N; closed symbols); and (d) C/N versus mean Rs during the entire study 11 ◦ C (the mean annual Ts over the study period; open symbols) and at 11 ◦ C and 20% SWC (the mean period standardized at 11 oC (the mean annual Ts over the study period; open symbols) and at 11 oC annual SWC over the study period; closed symbols). The coefficient of determination (r2 ) and p-value and 20% SWC (the mean annual SWC over the study period; closed symbols). The coefficient of of linear regression models fitted to all tree-decline classes pooled are shown. determination (r2) and p-value of linear regression models fitted to all tree-decline classes pooled are 4. Discussion shown. 4. 4.1. Does Rs Change with Tree Decline in a Mature Beech Forest? Discussion The 4.1. Does Rsspatial Changevariation Rs was in with TreeofDecline controlled a Matureby the soil Beech C/N, basal area of recruits and, during summer, Forest? by SWC. However, the spatial variation in Rs was independent of the overstory tree decline. Previous The have studies spatial variation reported of Rs was response a homeostatic controlled of by thesuccessional Rs to soil C/N, basal changesareainof recruits forest and, involving structure during summer, by SWC. However, the spatial variation in R s was independent of the overstory tree either unchanged forest species composition [8] or species replacement [21]. Here, a minor influence decline. of tree Previous decline onstudies have reported Rs is consistent a homeostatic changes with non-significant responseinofsoil Rs properties to successional acrosschanges in tree-decline forest structure involving either unchanged forest species composition [8] or classes (Table 1). Large differences in the living crown volume among beech trees contrasted with species replacement [21]. Here, a minor non-significant influence changes in theofsoil tree declinelitter density, on R s is consistent with non-significant changes in soil depth, C, N, C/N or fine root density across tree-decline properties across classes. Only SWCtree-decline tended to classes (Table be higher 1). Large near snags differences from autumn in the living (Figure to spring crown volume 2b), butamong this did beech not translate into higher Rs than in the other tree-decline classes because, at this time period, fine trees contrasted with non-significant changes in the soil density, litter depth, C, N, C/N or SWC root diddensity across not control Rs tree-decline classes. (Figure 4a). The Only impact ofSWC tended the tree to be dieback onhigher above-nearand snags from autumn below-ground to processes spring depends(Figure on the2b), but at speed this did not which translate different into higher processes Rs than in The are co-occurring. the other tree-decline progressive classesof degradation because, at this time period, SWC did not control R s (Figure 4a). The impact of the tree dieback on ancient beech trees in the studied forest might have been lately enhanced by (i) competition from their above- and below-ground own recruits, starting to fillprocesses depends in the open woodland on1960s the speed at which when grazing different livestock by domestic processesceased, are co-occurring. The progressive degradation of ancient beech trees in the studied forest might have been lately enhanced by (i) competition from their own recruits, starting to fill the open woodland in Forests 2019, 10, 727 13 of 18 and (ii) drought, exacerbated by a 21% reduction in the summer rainfall in this decade relative to the previous one. Still, several decades can elapse between the incipient to large decline and eventually death of centennial beech trees in the studied forest. During this time, regeneration under declining dominant beech trees is evidenced by the increasing understory population and size of beech seedlings (and more rarely holly recruits) that benefit from the increase in available resources (i.e., irradiance, soil water and nutrients) resulting from the progressive decline of overstory trees [10,21]. Reductions in the leaf biomass, root biomass, nutrients uptake and litter fall from these trees are compensated for by the development of understory trees, as discussed by others [10,21,49]. In support of this, there was a 3-fold increment in the density and basal area of recruits under declining trees than under vigorous ones, although not significant at p < 0.05 (Figure 1; Table 1), and Rs tended to increase as the basal area of recruits was higher. Consistently with these results, Søe and Buchmann [16] found that the stand structure was not related to the vegetation area index in another unmanaged, uneven-aged beech stand; Barba et al. [19] measured a similar root biomass in areas nearing dead and live trees; and Pereira-Blanco et al. [50] measured similar respiration rates in fine roots near dead and living trees. The progressive decline of dominant trees favors a gradual filling of the gaps left by the branch dieback that buffer its impact on the soil microclimate, root biomass and rate of decomposition processes [51]. Moreover, the late-successional character of beech allows trees of this species to establish in the understory for decades “waiting” for the canopy to open, and precludes that other species replace it, which contributes to moderate changes in the soil climate, chemistry and Rs compared to successional changes involving species replacement [19]. Future research should address how the impact of the sudden tree death on soil processes compares with a progressive process of decline. Other studies have discussed that minor changes in Rs associated to stress- or perturbation-induced forest decline can be related to a balancing contribution of autotrophic and heterotrophic respiration to total Rs [10,52]. A reduced activity of roots and mycorrhiza associated to a reduced phloem carbohydrate transport below the ground near declining trees observed in some cases [12,20], (but see [50]) can be counteracted by an increased activity of decomposing microorganisms associated to an increased litterfall [8,53]. Unfortunately, using the trenching method in a parallel experiment did not allow us to separate the autotrophic and heterotrophic components of Rs . The higher Rs temperature sensitivity near vigorous trees than near trees with incipient symptoms of decline (Table 2) could reflect a higher contribution of autotrophic respiration to Rs near vigorous trees, given the higher temperature sensitivity of the autotrophic than heterotrophic respiration [10,24,54]. However, it could also reflect a higher seasonality in plant and soil activity, co-varying with Ts , near vigorous trees [55,56]. The fact that Rs temperature sensitivity did not gradually vary with the degree of tree decline suggests that it was not simply related to the health status of the dominant trees [56]. Independently of the degree of dominant tree decline, there were considerable variations in the mean Rs across plots (3.25–6.35 µmol m−2 s−1 ), that were partly controlled by the microclimate, basal area of beech recruits under the dominant tree, and soil C/N. As already observed, the spatial variability of Rs across plots was controlled by the spatial variability in SWC rather than Ts [16,57]. During summer, Rs was higher in wetter plots (Figure 6b), likely due to low-SWC limitations on the respiratory metabolism of roots and soil microorganisms [28,29,58]; whereas spatial variations in Ts were probably too small (0.86 ◦ C in autumn and 2.74 ◦ C in spring) to cause noticeable changes in Rs . The maintenance of relatively high SWC during the dry summer period favored soil N (but not soil C) concentration and resulted in a reduced C/N (Figure 6c). A higher soil total N concentration in areas of higher SWC [59] or higher precipitation [60] has been previously reported. Here, this result might be related with a higher litter and N input in sites of high SWC (Figure 6a), although soil N and litter were not significantly related. The data suggest that litter accumulation helped to retain the soil water during summer (probably by preventing soil evaporation; Figure 6a), and that a high SWC had a direct effect on Rs by stimulating the soil activity. At comparable Ts and SWC conditions, Rs was negatively related with C/N across plots (Figure 6d). This suggests that N was a better proxy of the soil metabolic activity than C, and/or that the N substrates were more limiting than the C substrates for Forests 2019, 10, 727 14 of 18 the respiratory metabolism of roots and microorganisms. Studies in northern European beech forests have reported the opposite result, which could be explained by lower soil C/N ratios in these forests (9.6–11.7 in the organo-mineral layer [17] and 11.1–13.2 in the mineral layer; Søe and Buchmann [16]) than in our case (13.7–17.8). Higher soil C/N in the studied sub-Mediterranean stand can be related to drier conditions favoring higher plant and litter C/N [61], and/or slowing down soil organic matter decomposition [58,62,63], relative to northern European beech stands. 4.2. How SWC and Ts Govern Temporal Changes in Rs ? The soil temperature drove intra-annual changes in Rs . The positive effect of temperature on the activity of respiratory enzymes of roots and soil microorganisms results in the increase of Rs as Ts rises. This has worried managers and scientists about the impact that climate warming may have on the release of C from soils, which, in forests, stock 383 ± 30 Pg C [64]. Projecting how Rs is going to respond to on-going warming is hampered, among other factors, by the interaction of Ts with many biotic and abiotic factors, including SWC [9,20,21,27,58]. Here, a progressively lower SWC registered during summer had a negative effect on Rs (Figure 4a), attenuated the increase in Rs as Ts increased (Table 2), and resulted in a logarithmic fit of Rs and Ts across the two years of study, given that higher temperatures were registered during the summer drought (Figure 3a). The limiting effect of low SWC on the enzymatic activity of roots and soil microorganisms [29,58] is likely the main factor resulting in reduced Rs rates below certain SWC thresholds in the tropical [65], temperate [16,17] and Mediterranean-type [5,6] forests. Here, the threshold below which SWC controlled Rs was 15%, which is within the wide range but in the lower end of thresholds that have been reported in literature (between 10% to 35% SWC [5,16,35,65]), suggesting a variable sensitivity of soil activity to SWC across systems. The soil water excess also appeared to exert a small negative effect on Rs (Figure 4c). The filling of a large proportion of soil pores with water could reduce the CO2 diffusion from the soil, and also reduce oxygen diffusion to mitochondria of roots and soil microorganisms [37,65]. In temperate forests with clay-rich soils, transient periods of waterlogging can result in the suppression of Rs [16]. Moderate soil clay proportions (12% in the A horizon) and relatively high slopes in the studied forest make waterlogging less relevant to Rs suppression than recurrent summer droughts. The mean Rs normalized at a constant Ts of 11 ◦ C and non-limiting SWC (20%)–the mean annual values–was 4.90 µmol m−2 s−1 , which is higher than Rs at a similar Ts reported in temperate beech forests [16,17]. This is in spite of the higher long-term influence of drought on reducing Rs in the studied sub-Mediterranean stand. It is possible that the high soil N concentration (4.6 g K−1 , equivalent to 368 g m−2 in the mineral topsoil), partly associated to the high basal area and litterfall across plots, contributes to the high Rs in this old forest (with dominant trees >200 years and suppressed trees often >50 years; Gil et al. [41]), compared to younger beech forests. 5. Conclusions The decline of centennial dominant beech trees in a mature forest has only minor effects on the soil microclimate, physico-chemical variables, fine root density and Rs . The gradual process of decline of these trees, even if accelerated in the last years probably by competition with their own recruits and enhanced drought, is probably contributing to the homeostasis in soil processes. Shade tolerant beech recruits establish in the understory and develop as crown dieback advances, self-perpetuating the beech stand and maintaining above- and below-ground characteristics relatively constant. Increased duration and intensity of droughts projected for the study area [66] could exacerbate the control of SWC on the soil activity seen here over time and space; however, it could also have more profound effects on soil processes by altering the successional direction and velocity. Further research oriented to separate the autotrophic and heterotrophic contribution to Rs will help to better understand how tree decline affects soil biological processes, and to eventually delineate management practices aiming at increasing soil organic carbon stocks and reducing soil heterotrophic respiration [67]. Forests 2019, 10, 727 15 of 18 Author Contributions: J.R.-C., R.S., J.B. and J.C.Y.; Methodology, J.R.-C., R.S., J.B., G.G.G., J.C.Y. and C.M.; Software, J.R.-C. and J.B.; Validation, J.R.-C. and J.B.; Formal Analysis, J.R.-C. and J.B.; Investigation, J.R.-C., R.S., J.B., G.G.G., J.C.Y. and L.G.; Resources, J.R.-C., R.S., G.G.G., C.M. and L.G.; Data Curation, J.R.-C., R.S., G.G.G. and C.M.; Writing – Original Draft Preparation, J.R.-C.; Writing – Review and Editing, J.R.-C., R.S., J.B., G.G.G., J.C.Y. and L.G.; Visualization, J.R.-C.; Supervision, J.R.-C., R.S., J.B., G.G.G., J.C.Y. and L.G.; Project Administration, J.R.-C. and L.G.; Funding Acquisition, J.R.-C. and L.G. Funding: This project was possible thanks to the support of the Regional Government of Madrid (P2013/MAE-2760). Acknowledgments: We are thankful to Nicolas Martin-St.Paul and Pepa Aroca for insightful comments on data analyses, and Eva Miranda for technical assistance. J.R.-C. acknowledges the support of the Ministry of Economy, Industry and Competitiveness through the “Ramón y Cajal” Programme. J.C.Y. is indebted to the Spanish projects IBERYCA (CGL2017-84723-P), the BC3 María de Maeztu excellence accreditation (MDM-2017-0714) funded by the Ministry of Economy and Competitiveness (MINECO) and the Basque Government through the BERC 2018-2021 program. R.S. is supported by the FWO and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie fellowship (grant agreement no. 665501). Conflicts of Interest: The authors declare no conflict of interest. References 1. Bond-Lamberty, B.P.; Thomson, A.M. Global Database of Soil Respiration Data, Version 3.0. Data Set; Oak Ridge National Laboratory Distributed Active Archive Center: Oak Ridge, TN, USA, 2014. Available online: http://daac.ornl.gov (accessed on 9 July 2018). [CrossRef] 2. Curiel Yuste, J.; Nagy, M.; Janssens, I.A.; Carrara, A.; Ceulemans, R. 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