This is the accepted version of the article: Sardans i Galobart, Jordi; Urbina Barreto, Ifigenia; Grau Fernández, Oriol; [et al.]. «Long-term drought decreases ecosystem C and nutrient storage in a Mediterranean holm oak forest». Environmental and Experimental Botany, Vol. 177 (Sep. 2020), art. 104135. DOI 10.1016/j.envexpbot.2020.104135 This version is avaible at https://ddd.uab.cat/record/232202 under the terms of the license SUPPLEMENTARY MATERIAL 1 Table S1. Leaf and leaf litter biomasses (mean ± S.E.) in the three studies species in control and drought treatment stands before treatment application (1999) and during treatment application (in 2004 and 2014). The letters between brackets indicate marginally significant statistical differences between control and treatment plots (P<0.1). Biomass Species Treatment Year fraction 1999 2004 2014 Leaf Quercus ilex Control 85880±20771 86208±22326 86677±23443 Drought 90648±23109 88014±24748 85742±25671 Phillyrea Control 13364±6481 14086±6876 15719±7735 latifolia Drought 12088±4847 13022±5321 14479±6029 Arbutus Control 10605±4657 12046±5198 13727±2803 (a) unedo Drought 5598±3204 6049±3291 6142±2745 (b) All Control 109850±15864 112340±16953 118980±17394 Drought 108334±20561 107085±21936 110306±23025 Leaf-litter Quercus ilex Control 694.5±231.3 995.9±280.0 1147±405.8 Drought 702.7± 247.2 955.4±280.0 1070±284.7 Phillyrea Control 211.4±117.0 584.8±310.1 339.7±185.1 latifolia Drought 327.2±113.4 838.1±429.2 346.4±180.6 Arbutus Control 258.2±100.4 478.7±212.2 320.8±155.7 unedo Drought 206.5±95.2 353.3±201.2 103.2±22.9 All Control 1164±202.7 2059±111.4 1808±272.0 Drought 1236±224.5 2147±78.2 1519±96.0 2 Table S2. Best linear mixed models with season and treatment as independent fixed factors, plot as random factor and the different leaf studied variables. When the interaction between season x treatment was not significant, it was not taken into account in the model. The means ± S.E. of drought and control plots of different seasons along the studied period and the mean ± S.E. of control and drought plots in each season are provided with the statistical data. Bold font means statistically significant differences (P<0.05). Species Foliar Best Model R2m variable R2c Arbutus d13C model <- lme(d13c ~ season + treatment, random=~1|plot, data=dades, method="REML") 0.0487 unedo 0.316 Treatment Season Treatment in each season Spring Summer Autumn Control=-26.8±0.2 Autumn=-27.3±0.28 Control=-26.55±0.30 Control=-26.6±0.3 Control=-27.3±0.42 Drought=-26.9±0.19 Spring=-26.7±0.21 Drought=-26.8±0.30 Drought=-26.6±0.3 Drought=-27.3±0.4 F=0.14 Summer=-26.6±0.21 F=0.66 F=0.03 F=0.01 P=0.71 F=2.31 P=0.42 P=0.87 P=0.92 P=0.11 d15N model <- lme(d15n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.309 0.569 Treatment Season Treatment in each season Spring Summer Autumn Control=-4.07±0.13 Autumn=-3.05±0.24 Control=-4.11±0.24 Control=-4.44±0.19 Control=-3.68±0.21 Drought=-2.49±0.20 Spring=-3.42±0.21 Drought=-2.73±0.26 Drought=-2.29±0.39 Drought=-2.43±0.38 F=65.5 Summer=-3.37±0.29 F=11.6 F=24.4 F=8.18 P<0.0001 F=1.36 P=0.0019 P<0.0001 P=0.0076 P=0.26 [C] model <- lme(c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.194 0.194 Treatment Season Treatment in each season Spring Summer Autumn Control=49.7±0.3 Autumn=50.7±0.4a Control=49.7±0.29 Control=48.5±0.1 Control=50.7±0.7 Drought=49.7±0.16 Spring=49.7±0.15ab Drought=40.7±0.8 Drought=48.8±0.02 Drought=50.6±0.3 F=0.004 Summer=48.7±0.34b F=0.18 F=0.11 F=0.06 P=0.95 F=11.4 P=0.67 P=0.74 P=0.81 P<0.0001 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.463 0.540 Treatment Season Treatment in each season Spring Summer Autumn Control=1.51±0.05 Autumn=1.41±0.04b Control=1.81±0.10 Control=1.28±0.05 Control=1.44±0.05 Drought=1.46±0.06 Spring=1.84±0.07a Drought=1.88±0.10 Drought=1.12±0.04 Drought=1.38±0.06 3 F=3.22 Summer=1.20±0.04c F=1.11 F=7.56 F=0.614 P=0.076 F=40.8 P=0.30 P=0.025 P=0.44 P<0.0001 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.678 0.785 Treatment Season Treatment in each season Spring Summer Autumn Control=0.145±0.008 Autumn=0.124±0.006b Control=0.205±0.013 Control=0.105±0.002 Control=0.125±0.009 Drought=0.155±0.009 Spring=0.217±0.008a Drought=0.230±0.008 Drought=0.111±0.005 Drought=0.124±0.007 F=3.12 Summer=0.108±0.003c F=2.57 F=1.38 F=0.0085 P=0.081 F=149 P=0.12 P=0.25 P=0.93 P<0.0001 [K] model <- lme(k ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.389 0.591 Treatment Season Treatment in each season Spring Summer Autumn Control=1.22±0.046 Autumn=0.924±0.054b Control=1.45±0.06 Control=1.268±0.048 Control=0.944±0.070 Drought=1.16±0.056 Spring=1.477±0.039a Drought=1.50±0.05 Drought=1.084±0.067 Drought=0.903±0.086 F=1.46 Summer=1.176±0.051ab F=0.21 F=4.47 F=2.62 P=0.23 F=44.5 P=0.65 P=0.042 P=0.12 P<0.0001 [C:N] model <- lme(cn ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.447 0.545 Treatment Season Treatment in each season Spring Summer Autumn Control=34.5±1.0 Autumn=36.8±1.01b Control=28.7±1.5 Control=38.7±1.3 Control=36.0±1.4 Drought=36.6±1.4 Spring=28.3±1.15c Drought=27.8±1.8 Drought=44.5±1.9 Drought=37.6±1.5 F=0.76 Summer=41.6±1.27a F=0.88 F=6.03 F=0.66 P=0.39 F=47.4 P=0.36 P=0.020 P=0.42 P<0.0001 [C:P] model <- lme(cp ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.596 0.729 Treatment Season Treatment in each season Spring Summer Autumn Control=387±18 Autumn=432±19a Control=258±16 Control=468±13 Control=435±28 Drought=368±19 Spring=239±9b Drought=220±7.8 Drought=454±22 Drought=430±26 F=1.86 Summer=461±12a F=3.22 F=0.29 F=0.021 P=0.18 F=104 P=0.083 P=0.59 P=0.89 P<0.0001 4 [C:K] model <- lme(ck ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.368 0.554 Treatment Season Treatment in each season Spring Summer Autumn Control=44.4±2.3 Autumn=60.8±3.6a Control=35.2±1.4 Control=39.1±1.5 Control=58.8±5.0 Drought=48.6±2.8 Spring=34.4±0.9c Drought=33.6±1.07 Drought=49.6±4.0 Drought=62.8±5.2 F=2.89 Summer=44.4±2.34b F=0.44 F=5.73 F=0.030 P=0.093 F=37.8 P=0.51 P=0.023 P=0.59 P<0.0001 [N:P] model <- lme(np ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.368 0.515 Treatment Season Treatment in each season Spring Summer Autumn Control=11.2±0.4 Autumn=11.8±0.46a Control=9.01±0.38 Control=12.37±0.58 Control=12.21±0.74 Drought=9.95±0.3 Spring=8.57±0.24b Drought=8.13±0.27 Drought=10.26±0.32 Drought=11.47±0.56 F=0.556 Summer=11.3±0.37a F=2.10 F=10.2 F=0.43 P=0.46 F=78.4 P=0.157 P=0.0033 P=0.43 P<0.0001 [N:K] model <- lme(nk ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.302 0.484 Treatment Season Treatment in each season Spring Summer Autumn Control=1.31±0.06 Autumn=1.66±0.09a Control=1.25±0.05 Control=1.035±0.055 Control=1.647±0.132 Drought=1.36±0.06 Spring=1.26±0.04b Drought=1.26±0.07 Drought=1.130±0.081 Drought=1.677±0.128 F=1.7 Summer=1.08±0.05c F=0.054 F=0.93 F=0.026 P=0.20 F=11.6 P=0.82 P=0.34 P=0.87 P<0.0001 [P:K] model <- lme(pk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.477 0.517 Treatment Season Treatment in each season Spring Summer Autumn Control=0.120±0.005 Autumn=0.141±0.006a Control=0.140±0.005 Control=0.083±0.002 Control=0.138±0.010 Drought=0.136±0.005 Spring=0.147±0.005a Drought=0.153±0.005 Drought=0.109±0.006 Drought=0.145±0.007 F=9.60 Summer=0.096±0.004b F=3.18 F=14.4 F=0.33 P=0.0026 F=42.2 P=0.085 P=0.00067 P=0.57 P<0.0001 Phillyrea d13C model <- lme(d13c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0438 latifolia 0.277 Treatment Season Treatment in each season Spring Summer Autumn 5 Control=-28.3±0.22 Autumn=-28.16±0.21b Control=-27.7±0.4 Control=-28.84±0.39 Control=-28.34±0.34 Drought=-27.9±0.17 Spring=-27.81±0.23a Drought=-27.9±0.3 Drought=27.86±0.350 Drought=-27.99±0.25 F=0.715 Summer=-28.35±0.27b F=0.12 F=3.46 F=0.70 P=0.40 F=3.43 P=0.73 P=0.73 P=0.41 P=0.037 d15N model <- lme(d15n ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.270 0.448 Treatment Season Treatment in each season Spring Summer Autumn Control=-5.41±0.13 Autumn=-5.06±0.18 Control=-5.61±0.30 Control=-5.55±0.18 Control=-5.065±0.183 Drought=-4.34±0.19 Spring=-4.86±0.23 Drought=-4.12±0.25 Drought=-3.87±0.33 Drought=-5.048±0.33 F=21.5 Summer=-4.71±0.24 F=15.0 F=20.1 F=0.0022 P<0.0001 F=1.56 P=0.00050 P=0.00010 P=0.96 P=0.22 [C] model <- lme(c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.180 0.180 Treatment Season Treatment in each season Spring Summer Autumn Control=50.0±0.3 Autumn=50.8±0.2a Control=50.1±0.7 Control=49.3±0.10 Control=50.6±0.3 Drought=49.9±0.18 Spring=49.8±0.4ab Drought=49.6±0.3 Drought=49.1±0.10 Drought=51.0±0.3 F=0.048 Summer=49.2±0.06b F=0.27 F=2.4 F=0.88 P=0.82 F=10.4 P=0.61 P=0.13 P=0.36 P<0.0001 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.525 0.584 Treatment Season Treatment in each season Spring Summer Autumn Control=1.654±0.069 Autumn=1.503±0.046b Control=2.139±0.118 Control=1.357±0.034 Control=1.465±0.077 Drought=1.662±0.074 Spring=2.12±0.08a Drought=2.109±0.117 Drought=1.337±0.031 Drought=1.540±0.051 F=0.43 Summer=1.35±0.02c F=0.032 F=0.20 F=0.664 P=0.51 F=27.8 P=0.86 P=0.66 P=0.42 P<0.0001 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.623 0.649 Treatment Season Treatment in each season Spring Summer Autumn Control=0.134±0.009 Autumn=0.098±0.004b Control=0.196±0.016 Control=0.101±0.004 Control=0.104±0.006 Drought=0.123±0.008 Spring=0.194±0.010a Drought=0.192±0.013 Drought=0.085±0.002 Drought=0.093±0.002 F=2.07 Summer=0.093±0.003b F=0.030 F=13.2 F=2.94 P=0.15 F=83.3 P=0.86 P=0.0010 P=0.097 6 P<0.0001 [K] model <- lme(k ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.511 0.524 Treatment Season Treatment in each season Spring Summer Autumn Control=1.276±0.057 Autumn=0.893±0.031c Control=1.566±0.098 Control=1.364±0.054 Control=0.899±0.051 Drought=1.161±0.051 Spring=1.540±0.057a Drought=1.514±0.064 Drought=1.083±0.071 Drought=0.887±0.037 F=4.58 Summer=1.223±0.051b F=0.19 F=9.94 F=0.035 P=0.035 F=48.7 P=0.66 P=0.0037 P=0.85 P<0.0001 [C:N] model <- lme(cn ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.359 0.391 Treatment Season Treatment in each season Spring Summer Autumn Control=32.8±1.6 Autumn=35.5±1.99a Control=24.2±1.1 Control=37.7±1.1 Control=37.4±3.8 Drought=31.8±1.0 Spring=24.4±0.87b Drought=24.6±1.4 Drought=37.1±1.0 Drought=33.7±1.1 F=0.018 Summer=36.9±0.72a F=0.048 F=0.062 F=0.863 P=0.89 F=59.9 P=0.83 P=0.80 P=0.36 P<0.0001 [C:P] model <- lme(cp ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.632 0.637 Treatment Season Treatment in each season Spring Summer Autumn Control=433±21 Autumn=535±17a Control=290±34 Control=449±17 Control=510±29 Drought=475±23 Spring=284±19b Drought=277±19 Drought=587±19 Drought=560±15 F=4.80 Summer=543±15a F=0.12 F=12.0 F=2.40 P=0.031 F=80.2 P=0.73 P=0.0016 P=0.12 P<0.0001 [C:K] model <- lme(ck ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.273 0.273 Treatment Season Treatment in each season Spring Summer Autumn Control=44.8±3.2 Autumn=59.2±2.2a Control=38.4±4.4 Control=37.04±1.51 Control=59.0±3.1 Drought=47.1±2.1 Spring=36.0±4.0b Drought=38.1±7.9 Drought=48.2±3.1 Drought=59.4±3.0 F=0.0066 Summer=42.6±2.0b F=0.34 F=10.7 F=0.011 P=0.94 F=9.55 P=0.56 P=0.0026 P=0.92 P=0.0002 [N:P] model <- lme(np ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.238 0.238 Treatment Season Treatment in each season 7 Spring Summer Autumn Control=13.56±0.63 Autumn=15.81±0.59a Control=12.11±1.48 Control=13.65±0.44 Control=14.90±1.03 Drought=14.6±0.43 Spring=11.65±0.75b Drought=11.19±0.35 Drought=15.87±0.43 Drought=16.71±0.51 F=2.68 Summer=14.76±0.36a F=0.37 F=13.1 F=2.47 P=0.11 F=94.2 P=0.55 P=0.0011 P=0.126 P<0.0001 [N:K] model <- lme(nk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.111 0.124 Treatment Season Treatment in each season Spring Summer Autumn Control=1.46±0.14 Autumn=1.746±0.077a Control=1.655±0.373 Control=1.022±0.051 Control=1.696±0.109 Drought=1.50±0.58 Spring=1.529±0.187ab Drought=1.403±0.063 Drought=1.303±0.078 Drought=1.795±0.111 F=1.17 Summer=1.163±0.052c F=0.44 F=9.07 F=0.41 P=0.28 F=21.7 P=0.51 P=0.0052 P=0.53 P<0.0001 [P:K] model <- lme(pk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.375 0.442 Treatment Season Treatment in each season Spring Summer Autumn Control=0.108±0.005 Autumn=0.113±0.005a Control=0.129±0.010 Control=0.075±0.003 Control=0.119±0.007 Drought=0.106±0.004 Spring=0.128±0.006a Drought=0.127±0.008 Drought=0.083±0.005 Drought=0.108±0.006 F=1.62 Summer=0.079±0.003c F=0.028 F=1.52 F=1.52 P=0.21 F=20.5 P=0.87 P=0.23 P=0.23 P<0.0001 Quercus ilex d13C model <- lme(d13c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0368 0.316 Treatment Season Treatment in each season Spring Summer Autumn Control=-27.8±0.22 Autumn=-28.22±0.23 Control=-27.82±0.42 Control=-27.55±0.29 Control=-28.0±0.41 Drought=-28.4±0.20 Spring=-28.08±0.31 Drought=-28.33±0.47 Drought=-28.48±0.35 Drought=-28.3±0.2 F=4.67 Summer=-28.01±0.24 F=0.66 F=4.27 F=0.28 P=0.033 F=0.221 P=0.42 P=0.048 P=0.60 P=0.80 d15N model <- lme(d15n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.292 0.562 Treatment Season Treatment in each season Spring Summer Autumn Control=-3.45±0.16 Autumn=-2.611±0.216 Control=-3.35±0.26 Control=-3.74±0.28 Control=-3.28±0.29 Drought=-2.00±0.156 Spring=-2.712±0.216 Drought=-2.08±0.27 Drought=-1.99±0.32 Drought=-1.95±0.23 F=62.1 Summer=-2.863±0.263 F=11.6 F=16.7 F=13.2 8 P<0.0001 F=0.63 P=0.0019 P=0.00030 P=0.0010 P=0.53 [C] model <- lme(c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.108 0.108 Treatment Season Treatment in each season Spring Summer Autumn Control=48.6±0.2 Autumn=51.3±1.5 Control=47.8±0.1 Control=48.0±0.1 Control=49.9±0.2 Drought=49.7±1.0 Spring=47.7±0.2 Drought=47.6±0.3 Drought=48.6±0.2 Drought=52.8±2.9 F=2.92 Summer=48.3±0.1 F=0.18 F=4.7 F=0.993 P=0.09 F=0.87 P=0.67 P=0.038 P=0.33 P=0.42 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.363 0.480 Treatment Season Treatment in each season Spring Summer Autumn Control=1.27±0.03 Autumn=1.54±0.06 Control=1.343±0.054 Control=1.056±0.028 Control=1.411±0.051 Drought=1.44±0.05 Spring=1.39±0.04 Drought=1.428±0.06 Drought=1.217±0.051 Drought=1.669±0.099 F=15.9 Summer=1.14±0.03 F=1.11 F=7.55 F=5.40 P<0.0001 F=41.7 P=0.30 P=0.010 P=0.027 P<0.0001 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.185 0.241 Treatment Season Treatment in each season Spring Summer Autumn Control=0.120±0.009 Autumn=0.116±0.013 Control=0.144±0.009 Control=0.095±0.003 Control=0.122±0.026 Drought=0.126±0.005 Spring=0.153±0.006 Drought=0.164±0.009 Drought=0.103±0.005 Drought=0.109±0.004 F=0.30 Summer=0.099±0.003 F=2.57 F=1.85 F=0.25 P=0.58 F=11.4 P=0.12 P=0.18 P=0.62 P<0.0001 [K] model <- lme(k ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.480 0.565 Treatment Season Treatment in each season Spring Summer Autumn Control=0.879±0.039 Autumn=0.599±0.034 Control=1.071±0.060 Control=0.914±0.045 Control=0.635±0.049 Drought=0.777±0.047 Spring=1.088±0.038 Drought=1.106±0.050 Drought=0.678±0.061 Drought=0.546±0.045 F=6.39 Summer=0.796±0.043 F=0.21 F=9.64 F=2.62 P=0.013 F=49.1 P=0.65 P=0.0041 P=0.12 P<0.0001 [C:N] model <- lme(cn ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.352 0.664 9 Treatment Season Treatment in each season Spring Summer Autumn Control=39.5±1.0 Autumn=34.1±1.0 Control=36.35±1.34 Control=46.0±1.3 Control=36.1±1.5 Drought=38.8±1.1 Spring=35.4±1.1 Drought=34.37±1.65 Drought=41.1±1.9 Drought=32.0±1.0 F=10.6 Summer=43.6±1.2 F=0.87 F=4.49 F=5.00 P=0.0016 F=11.4 P=0.36 P=0.043 P=0.033 P<0.0001 [C:P] model <- lme(cp ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.357 0.365 Treatment Season Treatment in each season Spring Summer Autumn Control=456±18 Autumn=496±26 Control=351±21 Control=515±19 Control=500±33 Drought=428±22 Spring=327±14 Drought=303±16 Drought=490±28 Drought=492±42 F=0.039 Summer=503±17 F=3.22 F=0.58 F=0.021 P=0.85 F=10.7 P=0.083 P=0.45 P=0.89 P=0.0001 [C:K] model <- lme(ck ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.553 0.623 Treatment Season Treatment in each season Spring Summer Autumn Control=61.2±3.1 Autumn=92.7±4.6 Control=47.0±3.0 Control=54.7±2.9 Control=82.0±5.3 Drought=75.9±4.7 Spring=45.7±1.8 Drought=44.5±2.2 Drought=79.8±6.4 Drought=103±6.7 F=11.6 Summer=67.3±4.1 F=0.44 F=12.8 F=6.27 P=0.0010 F=17.1 P=0.51 P=0.0012 P=0.018 P<0.0001 [N:P] model <- lme(np ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.310 0.323 Treatment Season Treatment in each season Spring Summer Autumn Control=11.66±0.48 Autumn=14.86±0.82 Control=9.62±0.45 Control=11.4±0.7 Control=13.95±0.95 Drought=12.3±0.70 Spring=9.23±0.27 Drought=8.84±0.30 Drought=12.4±1.1 Drought=15.77±1.33 F=6.28 Summer=11.9±0.6 F=2.10 F=0.64 F=1.24 P=0.014 F=29.6 P=0.16 P=0.43 P=0.27 P<0.0001 [N:K] model <- lme(nk ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.545 0.669 Treatment Season Treatment in each season Spring Summer Autumn Control=1.617±0.102 Autumn=2.827±0.179 Control=1.299±0.076 Control=1.207±0.075 Control=2.34±0.18 Drought=2.24±0.17 Spring=1.312±0.053 Drought=1.325±0.077 Drought=2.078±0.222 Drought=3.31±0.26 10 F=0.925 Summer=1.642±0.139 F=0.054 F=13.9 F=9.13 P=0.34 F=12.2 P=0.82 P=0.00082 P=0.0051 P=0.056 [P:K] model <- lme(pk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.140 0.207 Treatment Season Treatment in each season Spring Summer Autumn Control=0.150±0.018 Autumn=0.213±0.027 Control=0.135±0.004 Control=0.107±0.005 Control=0.208±0.051 Drought=0.179±0.009 Spring=0.142±0.004 Drought=0.150±0.007 Drought=0.169±0.016 Drought=0.219±0.018 F=2.56 Summer=0.138±0.010 F=3.18 F=13.9 F=0.045 P=0.11 F=7.08 P=0.085 P=0.00080 P=0.83 P=0.0014 11 Table S3. Best linear mixed models with season and treatment as independent fixed factors, plot as random factor and the different leaf –litter studied variables. When the interaction between season x treatment was not significant, it was not taken into account in the model. The means ± S.E. of drought and control plots of different seasons along the studied period and the mean ± S.E. of control and drought plots in each season are provided with the statistical data. Bold font means statistically significant differences (P<0.05). Species Litter Best Model R2m variable R2c Arbutus d13C model <- lme(d13c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0559 unedo 0.207 Treatment Season Treatment in each season Spring Summer Autumn Control=-27.2±0.16 Autumn=-27.6±0.2 Control=-27.15±0.23 Control=-27.28±0.39 Control=-27.23±0.19 Drought=-27.4±0.13 Spring=-27.0±0.1 Drought=-26.9±0.18 Drought=-27.43±0.13 Drought=-27.89±0.26 F=0.96 Summer=-27.4±0.2 F=0.82 F=0.13 F=2.18 P=0.33 F=2.87 P=0.37 P=0.72 P=0.15 P=0.062 d15N model <- lme(d15n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.193 0.537 Treatment Season Treatment in each season Spring Summer Autumn Control=-3.16±0.18 Autumn=-2.77±0.28b Control=-3.60±0.22 Control=-2.61±0.36 Control=-3.32±0.28 Drought=-2.04±0.11 Spring=-2.83±0.21b Drought=-2.06±0.25 Drought=-1.83±0.23 Drought=-2.22±0.46 F=32.2 Summer=-2.22±0.22a F=21.3 F=3.35 F=4.25 P<0.0001 F=3.73 P<0.0001 P=0.077 P=0.048 P=0.028 [C] model <- lme(c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.137 0.137 Treatment Season Treatment in each season Spring Summer Autumn Control=51.3±1.1 Autumn=58.0±3.1a Control=50.1±0.2 Control=49.8±0.2 Control=54.0±3.0 Drought=53.9±2.0 Spring=49.7±0.2b Drought=49.2±0.2 Drought=50.3±0.3 Drought=62.1±5.4 F=1.48 Summer=50.0±0.2ab F=9.0 F=1.79 F=1.66 P=0.23 F=6.80 P=0.0054 P=0.191 P=0.21 P=0.0018 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.307 0.331 Treatment Season Treatment in each season Spring Summer Autumn 12 Control=0.47±0.02 Autumn=0.629±0.040b Control=0.429±0.020 Control=0.389±0.016 Control=0.597±0.045 Drought=0.53±0.029 Spring=0.474±0.021a Drought=0.519±0.034 Drought=0.411±0.018 Drought=0.661±0.066 F=3.74 Summer=0.400±0.012b F=5.13 F=0.794 F=0.64 P=0.056 F=19.9 P=0.031 P=0.38 P=0.43 P<0.0001 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.00996 0.261 Treatment Season Treatment in each season Spring Summer Autumn Control=0.066±0.007 Autumn=0.074±0.008 Control=0.059±0.010 Control=0.072±0.016 Control=0.068±0.011 Drought=0.072±0.005 Spring=0.066±0.007 Drought=0.072±0.009 Drought=0.063±0.007 Drought=0.081±0.011 F=0.42 Summer=0.067±0.009 F=0.86 F=0.31 F=0.65 P=0.52 F=0.43 P=0.36 P=0.58 P=0.43 P=0.65 [K] model <- lme(k ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0196 0.199 Treatment Season Treatment in each season Spring Summer Autumn Control=0.420±0.020 Autumn=0.424±0.022 Control=0.405±0.039 Control=0.467±0.032 Control=0.387±0.031 Drought=0.443±0.023 Spring=0.414±0.025 Drought=0.423±0.034 Drought=0.444±0.052 Drought=0.461±0.029 F=0.69 Summer=0.456±0.030 F=0.11 F=0.14 F=3.04 P=0.41 F=0.82 P=0.74 P=0.72 P=0.092 P=0.44 [C:N] model <- lme(cn ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.367 0.405 Treatment Season Treatment in each season Spring Summer Autumn Control=115±3.5 Autumn=95.8±3.2b Control=120±5 Control=130±4 Control=95.0±5.2 Drought=107±3.2 Spring=110±4b Drought=100±6 Drought=125±4 Drought=96.6±3.7 F=0.061 Summer=128±3a F=6.90 F=0.814 F=0.062 P=0.81 F=15.7 P=0.013 P=0.37 P=0.81 P<0.0001 [C:P] model <- lme(cp ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0401 0.371 Treatment Season Treatment in each season Spring Summer Autumn Control=1311±126 Autumn=1272±204 Control=1166±143 Control=1569±287 Control=1199±200 Drought=1036±129 Spring=995±87 Drought=823±82 Drought=93992± Drought=1345±363 F=3.28 Summer=1254±159 F=4.32 F=4.37 F=0.125 P=0.073 F=1.39 P=0.046 P=0.045 P=0.73 13 P=0.25 [C:K] model <- lme(ck ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0312 0.211 Treatment Season Treatment in each season Spring Summer Autumn Control=138±9 Autumn=150±12 Control=145±16 Control=112±6 Control=156±18 Drought=138±9 Spring=139±11 Drought=133±15 Drought=136±14 Drought=145±17 F=0.0016 Summer=124±8 F=0.26 F=2.38 F=0.189 P=0.97 F=1.88 P=0.62 P=0.13 P=0.67 P=0.16 [N:P] model <- lme(np ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0574 0.413 Treatment Season Treatment in each season Spring Summer Autumn Control=11.4±1 Autumn=13.1±1.9a Control=10.0±1.23 Control=11.52±2.0 Control=12.7±1.9 Drought=9.78±1.2 Spring=9.11±0.72b Drought=8.18±0.71 Drought=7.65±0.78 Drought=13.5±3.4 F=1.63 Summer=9.59±1.11b F=1.70 F=3.25 F=0.041 P=0.20 F=3.83 P=0.20 P=0.082 P=0.84 P=0.025 [N:K] model <- lme(nk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.123 0.239 Treatment Season Treatment in each season Spring Summer Autumn Control=1.27±0.10 Autumn=1.62±0.14a Control=1.20±0.12 Control=0.898±0.088 Control=1.71±0.22 Drought=1.37±0.10 Spring=1.31±0.11ab Drought=1.43±0.19 Drought=1.14±0.15 Drought=1.53±0.19 F=0.62 Summer=1.02±0.09b F=1.10 F=1.91 F=0.36 P=0.43 F=7.37 P=0.30 P=0.18 P=0.55 P=0.0011 [P:K] model <- lme(pk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.00620 0.178 Treatment Season Treatment in each season Spring Summer Autumn Control=0.163±0.017 Autumn=0.175±0.017 Control=0.152±0.021 Control=0.161±0.038 Control=0.177±0.026 Drought=0.170±0.012 Spring=0.168±0.016 Drought=0.183±0.023 Drought=0.153±0.014 Drought=0.173±0.024 F=0.11 Summer=0.157±0.020 F=1.0 F=0.034 F=0.016 P=0.74 F=0.30 P=0.33 P=0.85 P=0.90 P=0.74 Phillyrea d13C model <- lme(d13c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.169 latifolia 0.533 Treatment Season Treatment in each season 14 Spring Summer Autumn Control=-28.5±0.15 Autumn=-27.9±0.1a Control=-28.9±0.3 Control=-28.7±0.2 Control=-28.0±0.1 Drought=-28.2±0.1 Spring=-28.7±0.2b Drought=-28.5±0.2 Drought=-28.3±0.1 Drought=-27.8±0.2 F=5.67 Summer=-28.5±0.1b F=1.42 F=1.59 F=0.58 P=0.019 F=14.4 P=0.24 P=0.22 P=0.45 P<0.0001 d15N model <- lme(d15n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.318 0.494 Treatment Season Treatment in each season Spring Summer Autumn Control=-4.58±0.16 Autumn=-3.42±0.15a Control=-5.22±0.25 Control=-4.89±0.17 Control=-3.62±0.25 Drought=-3.73±0.15 Spring=-4.64±0.22b Drought=-4.05±0.29 Drought=-3.90±0.29 Drought=-3.22±0.16 F=23.3 Summer=-4.39±0.19b F=9.24 F=9.03 F=1.70 P<0.0001 F=18.0 P=0.0049 P=0.0052 P=0.20 P<0.0001 [C] model <- lme(c ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.157 0.157 Treatment Season Treatment in each season Spring Summer Autumn Control=49.0±0.21 Autumn=49.6±0.2a Control=48.3±0.5 Control=48.9±0.2 Control=49.8±0.3 Drought=49.1±0.12 Spring=48.7±0.3b Drought=49.1±0.2 Drought=48.7±0.2 Drought=49.4±0.2 F=0.08 Summer=48.8±0.1b F=2.35 F=0.9 F=1.07 P=0.78 F=5.92 P=0.14 P=0.35 P=0.31 P=0.0039 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.733 0.735 Treatment Season Treatment in each season Spring Summer Autumn Control=0.745±0.025 Autumn=0.958±0.022a Control=0.685±0.020 Control=0.612±0.012 Control=0.938±0.038 Drought=0.752±0.026 Spring=0.667±0.013b Drought=0.648±0.015 Drought=0.627±0.017 Drought=0.979±0.024 F=0.12 Summer=0.620±0.011b F=2.16 F=0.49 F=0.84 P=0.73 F=131 P=0.15 P=0.49 P=0.37 P<0.0001 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.528 0.598 Treatment Season Treatment in each season Spring Summer Autumn Control=0.038±0.002 Autumn=0.056±0.003a Control=0.030±0.001 Control=0.028±0.0009 Control=0.056±0.005 Drought=0.038±0.02 Spring=0.030±0.0008b Drought=0.030±0.001 Drought=0.029±0.001 Drought=0.056±0.005 F=0.0022 Summer=0.029±0.0007b F=0.075 F=0.20 F=0.0003 15 P=0.96 F=62.4 P=0.79 P=0.66 P=0.99 P<0.0001 [K] model <- lme(k ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.473 0.625 Treatment Season Treatment in each season Spring Summer Autumn Control=0.504±0.026 Autumn=0.324±0.013b Control=0.592±0.046 Control=0.609±0.018 Control=0.310±0.017 Drought=0.443±0.025 Spring=0.592±0.033a Drought=0.592±0.049 Drought=0.402±0.029 Drought=0.338±0.020 F=0.49 Summer=0.506±0.025a F=0.001 F=35.7 F=1.12 P=0.49 F=35.0 P=0.99 P<0.0001 P=0.30 P<0.0001 [C:N] model <- lme(cn ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.736 0.753 Treatment Season Treatment in each season Spring Summer Autumn Control=68.6±1.8 Autumn=52.6±1.2b Control=71.2±1.7 Control=80.4±1.4 Control=54.2±1.9 Drought=68.6±2.1 Spring=73.8±1.4a Drought=76.4±2.0 Drought=78.5±2.1 Drought=51.0±1.3 F=1.76 Summer=79.4±1.3a F=3.94 F=0.53 F=1.93 P=0.19 F=60.8 P=0.057 P=0.47 P=0.17 P<0.0001 [C:P] model <- lme(cp ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.573 0.622 Treatment Season Treatment in each season Spring Summer Autumn Control=1465±62 Autumn=1003±63b Control=1637±65 Control=1752±55 Control=1005±85 Drought=1458±62 Spring=1656±42a Drought=1674±56 Drought=1700±56 Drought=1001±95 F=0.015 Summer=1726±39a F=0.18 F=0.45 F=0.001 P=0.90 F=72.1 P=0.67 P=0.51 P=0.98 P<0.0001 [C:K] model <- lme(ck ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.416 0.529 Treatment Season Treatment in each season Spring Summer Autumn Control=114±7 Autumn=161±7a Control=89.8±7.3 Control=81.9±3.5 Control=169±11 Drought=126±6 Spring=90.3±4.9b Drought=90.9±6.8 Drought=133±11.1 Drought=153±8 F=2.33 Summer=107±7b F=0.014 F=19.2 F=1.43 P=0.13 F=42.1 P=0.87 P=0.00013 P=0.24 P<0.0001 [N:P] model <- lme(np ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0936 0.259 16 Treatment Season Treatment in each season Spring Summer Autumn Control=21.3±0.8 Autumn=19.1±1.2b Control=23.0±0.9 Control=21.8±0.6 Control=18.9±2.0 Drought=21.0±0.6 Spring=22.5±0.5a Drought=22.0±0.6 Drought=21.7±0.4 Drought=19.3±1.4 F=0.13 Summer=21.8±0.4ab F=0.96 F=0.058 F=0.02 P=0.72 F=5.94 P=0.33 P=0.81 P=0.89 P=0.0038 [N:K] model <- lme(nk ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.643 0.689 Treatment Season Treatment in each season Spring Summer Autumn Control=1.84±0.18 Autumn=3.12±0.2a Control=1.28±0.12 Control=1.02±0.04 Control=3.23±0.30 Drought=1.97±0.13 Spring=1.24±0.07b Drought=1.19±0.09 Drought=1.72±0.16 Drought=3.01±0.1 F=1.02 Summer=1.37±0.10b F=0.31 F=17.9 F=0.44 P=0.32 F=61.1 P=0.58 P=0.00020 P=0.51 P<0.0001 [P:K] model <- lme(pk ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.648 0.648 Treatment Season Treatment in each season Spring Summer Autumn Control=0.086±0.011 Autumn=0.178±0.012a Control=0.055±0.004 Control=0.047±0.002 Control=0.187±0.019 Drought=0.101±0.009 Spring=0.055±0.003b Drought=0.055±0.004 Drought=0.079±0.007 Drought=0.170±0.015 F=0.30 Summer=0.063±0.005b F=0.001 F=17.0 F=0.52 P=0.58 F=81.4 P=0.99 P=0.00027 P=0.47 P<0.0001 Quercus ilex d13C model <- lme(d13c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0949 0.338 Treatment Season Treatment in each season Spring Summer Autumn Control=-28.0±0.1 Autumn=-28.5±0.2b Control=-27.8±0.1 Control=-27.8±0.1 Control=-28.5±0.3 Drought=-28.2±0.1 Spring=-28.0±0.1ab Drought=-28.3±0.2 Drought=-28.0±0.2 Drought=-28.4±0.3 F=0.08 Summer=-27.9±0.1a F=3.55 F=1.78 F=0.09 P=0.88 F=14.4 P=0.069 P=0.19 P=0.76 P<0.0001 d15N model <- lme(d15n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.319 0.480 Treatment Season Treatment in each season Spring Summer Autumn Control=-3.48±0.10 Autumn=-2.86±0.19a Control=-3.67±0.12 Control=-3.56±0.17 Control=-3.21±0.23 Drought=-2.40±0.12 Spring=-3.12±0.14b Drought=-2.57±0.15 Drought=-2.13±0.17 Drought=-2.50±0.20 17 F=23.7 Summer=-2.84±0.18a F=32.2 F=34.9 F=3.99 P<0.0001 F=18.0 P<0.0001 P=0.018 P=0.044 P<0.0001 [C] model <- lme(c ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.153 0.153 Treatment Season Treatment in each season Spring Summer Autumn Control=49.4±0.9 Autumn=54.4±2.6a Control=50.4±2.8 Control=48.5±0.1 Control=49.3±0.2 Drought=51.8±1.8 Spring=49.4±1.4ab Drought=48.3±0.4 Drought=47.7±0.3 Drought=59.4±5.06 F=0.09 Summer=48.1±0.2b F=0.56 F=6.22 F=3.96 P=0.78 F=5.92 P=0.46 P=0.018 P=0.056 P=0.0039 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.402 0.496 Treatment Season Treatment in each season Spring Summer Autumn Control=0.908±0.026 Autumn=1.11±0.04a Control=0.965±0.049 Control=0.757±0.023 Control=1.00±0.03 Drought=1.032±0.037 Spring=1.01±0.04a Drought=1.06±0.05 Drought=0.825±0.021 Drought=1.21±0.07 F=0.12 Summer=0.791±0.016b F=1.74 F=4.66 F=7.89 P=0.73 F=131 P=0.20 P=0.039 P=0.0087 P<0.0001 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.128 0.128 Treatment Season Treatment in each season Spring Summer Autumn Control=0.057±0.004 Autumn=0.059±0.002b Control=0.069±0.009 Control=0.046±0.003 Control=0.057±0.003 Drought=0.066±0.002 Spring=0.070±0.005a Drought=0.070±0.004 Drought=0.065±0.003 Drought=0.061±0.003 F=0.0021 Summer=0.055±0.003b F=0.014 F=21.7 F=0.80 P=0.96 F=64.4 P=0.91 P<0.0001 P=0.38 P<0.0001 [K] model <- lme(k ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.165 0.177 Treatment Season Treatment in each season Spring Summer Autumn Control=0.382±0.030 Autumn=0.268±0.014b Control=0.429±0.082 Control=0.441±0.015 Control=0.278±0.019 Drought=0.294±0.012 Spring=0.378±0.043a Drought=0.328±0.023 Drought=0.299±0.014 Drought=0.258±0.020 F=0.49 Summer=0.369±0.017ab F=1.42 F=47.3 F=0.50 P=0.49 F=35.0 P=0.24 P<0.0001 P=0.48 P<0.0001 [C:N] model <- lme(cn ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.344 18 0.426 Treatment Season Treatment in each season Spring Summer Autumn Control=56.0±1.5 Autumn=49.4±1.4b Control=53.2±2.4 Control=64.9±1.8 Control=50.0±1.6 Drought=51.5±1.4 Spring=50.3±1.8b Drought=47.4±2.5 Drought=58.3±1.3 Drought=48.7±2.4 F=1.76 Summer=61.6±1.3a F=2.90 F=8.37 F=0.18 P=0.19 F=60.8 P=0.099 P=0.0070 P=0.67 P<0.0001 [C:P] model <- lme(cp ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.241 0.268 Treatment Season Treatment in each season Spring Summer Autumn Control=947±40 Autumn=958±55a Control=812±64 Control=1131±73 Control=897±45 Drought=835±42 Spring=770±40b Drought=727±49 Drought=758±32 Drought=1019±99 F=0.015 Summer=944±52a F=1.13 F=22.0 F=1.26 P=0.90 F=72.1 P=0.30 P<0.0001 P=0.27 P<0.0001 [C:K] model <- lme(ck ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.303 0.303 Treatment Season Treatment in each season Spring Summer Autumn Control=152±9 Autumn=216±12a Control=153±19 Control=112±4 Control=192±14 Drought=188±10 Spring=156±11b Drought=159±11 Drought=166±8 Drought=240±21 F=2.33 Summer=139±6b F=0.077 F=36.9 F=3.53 P=0.13 F=10.8 P=0.78 P<0.0001 P=0.070 P=0.0001 [N:P] model <- lme(np ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.295 0.379 Treatment Season Treatment in each season Spring Summer Autumn Control=16.9±0.5 Autumn=19.2±0.7a Control=15.5±1.1 Control=17.3±0.8 Control=18.0±0.8 Drought=16.3±0.7 Spring=15.4±0.6b Drought=15.3±0.7 Drought=13.0±0.5 Drought=20.4±1.2 F=0.13 Summer=15.2±0.6b F=0.014 F=18.6 F=2.73 P=0.72 F=5.94 P=0.91 P=0.00016 P=0.11 P=0.0038 [N:K] model <- lme(nk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.397 0.446 Treatment Season Treatment in each season Spring Summer Autumn Control=2.91±0.22 Autumn=4.43±0.26a Control=3.04±0.42 Control=1.75±0.09 Control=3.92±0.34 19 Drought=3.76±0.2 Spring=3.28±0.26b Drought=3.51±0.32 Drought=2.84±0.12 Drought=4.94±0.35 F=1.02 Summer=2.30±0.13c F=0.78 F=50.8 F=4.39 P=0.32 F=61.1 P=0.38 P<0.0001 P=0.045 P<0.0001 [P:K] model <- lme(pk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.274 0.300 Treatment Season Treatment in each season Spring Summer Autumn Control=0.171±0.011 Autumn=0.239±0.016a Control=0.187±0.017 Control=0.105±0.007 Control=0.223±0.020 Drought=0.236±0.011 Spring=0.208±0.013ab Drought=0.229±0.019 Drought=0.224±0.014 Drought=0.256±0.024 F=1.35 Summer=0.164±0.014c F=2.72 F=56.3 F=1.16 P=0.25 F=54.9 P=0.11 P<0.0001 P=0.29 P<0.0001 20 Table S4. Best linear mixed models with season and treatment as independent fixed factors, plot as random factor and the different soil studied variables. When the interaction between season x treatment was not significant, it was not taken into account in the model. The means ± S.E. of drought and control plots of different seasons along the studied period and the mean ± S.E. of control and drought plots in each season are provided with the statistical data. Bold font means statistically significant differences (P<0.05). Soil variable Best Model R2m R2c d13C model <- lme(d13c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0448 0.531 Treatment Season Treatment in each season Spring Summer Autumn Control=-26.2±0.07 Autumn=-26.1±0.08 Control=-26.1±0.1 Control=-26.2±0.1 Control=-26.3±0.1 Drought=-25.9±0.06 Spring=-26.1±0.07 Drought=-26.1±0.1 Drought=-25.7±0.1 Drought=-26.0±0.1 F=9.48 Summer=-26.0±0.08 F=0.0001 F=5.32 F=3.5 P=0.0027 F=0.95 P=0.99 P=0.028 P=0.073 P=0.39 d15N model <- lme(d15n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.239 0.274 Treatment Season Treatment in each season Spring Summer Autumn Control=-0.206±0.134 Autumn=0.194±0.193 Control=0.019±0.260 Control=-0.419±0.227 Control=-0.313±0.226 Drought=0.925±0.136 Spring=0.326±0.189 Drought=1.20±0.26 Drought=1.32±0.19 Drought=0.543±0.269 F=38.5 Summer=0.559±0.181 F=9.59 F=27.5 F=5.91 P<0.0001 F=1.37 P=0.0042 P<0.0001 P=0.021 P=0.26 21 [C] model <- lme(c ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.157 0.235 Treatment Season Treatment in each season Spring Summer Autumn Control=7.65±0.37 Autumn=6.95±0.42 Control=7.56±0.50 Control=8.18±0.55 Control=7.53±0.76 Drought=5.50±0.28 Spring=6.25±0.46 Drought=5.55±0.44 Drought=5.05±0.50 Drought=6.09±0.61 F=23.7 Summer=6.54±0.43 F=4.20 F=15.1 F=2.17 P<0.0001 F=0.849 P=0.045 P=0.00053 P=0.15 P=0.43 [N] model <- lme(n ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.112 0.291 Treatment Season Treatment in each season Spring Summer Autumn Control=0.482±0.022 Spring=0.443±0.027 Control=0.492±0.047 Control=0.508±0.029 Control=0.456±0.042 Drought=0.378±0.018 Summer=0.445±0.024 Drought=0.376±0.028 Drought=0.372±0.038 Drought=0.411±0.039 F=41.7 Autumn=0.401±0.026 F=3.26 F=8.04 F=0.63 P<0.0001 F=0.981 P=0.081 P=0.0081 P=0.43 P=0.38 [P] model <- lme(p ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0611 0.0792 Treatment Season Treatment in each season Spring Summer Autumn Control=0.058±0.001 Autumn=0.059±0.001 Control=0.058±0.003 Control=0.057±0.002 Control=0.059±0.003 Drought=0.056±0.001 Spring=0.057±0.002 Drought=0.049±0.002 Drought=0.052±0.002 Drought=0.063±0.002 F=1.51 Summer=0.054±0.002 F=1.64 F=2.92 F=0.98 P=0.22 F=0.16 P=0.24 P=0.098 P=0.33 P=0.85 [K] model <- lme(k ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.0337 0.1618 Treatment Season Treatment in each season Spring Summer Autumn Control=1.21±0.02 Spring=1.24±0.02 Control=1.22±0.034 Control=1.21±0.03 Control=1.22±0.04 Drought=1.26±0.02 Summer=1.25±0.02 Drought=1.31±0.038 Drought=1.25±0.04 Drought=1.23±0.04 F=3.25 Autumn=1.22±0.02 F=3.15 F=0.47 F=0.015 P=0.07 F=0.84 P=0.086 P=0.50 P=0.90 P=0.43 [C:N] model <- lme(cn ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.178 0.599 Treatment Season Treatment in each season Spring Summer Autumn 22 Control=15.8±0.2 Spring=14.7±0.27 Control=14.5±0.4 Control=16.1±0.4 Control=16.3±0.40 Drought=14.5±0.2 Summer=15.5±0.23 Drought=14.7±0.3 Drought=13.7±0.5 Drought=14.8±0.25 F=17.0 Autumn=15.3±0.3 F=1.88 F=15.7 F=10.4 P=0.0001 F=1.31 P=0.18 P=0.00043 P=0.0031 P=0.28 [C:P] model <- lme(cp ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.218 0.299 Treatment Season Treatment in each season Spring Summer Autumn Control=129±5 Spring=117±5 Control=125±9 Control=143±8 Control=124±8 Drought=98.1±4.0 Summer=118±7 Drought=111±6 Drought=96.3±0.5 Drought=96.7±8.6 F=27.0 Autumn=106±6 F=1.16 F=14.5 F=5.42 P<0.0001 F=1.62 P=0.29 P=0.00065 P=0.027 P=0.20 [C:K] model <- lme(ck ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.201 0.254 Treatment Season Treatment in each season Spring Summer Autumn Control=6.38±0.31 Spring=5.29±0.35 Control=6.29±0.58 Control=6.85±0.46 Control=6.28±0.66 Drought=4.38±0.21 Summer=5.75±0.34 Drought=4.32±0.40 Drought=4.02±0.32 Drought=4.92±0.43 F=31.1 Autumn=5.10±0.38 F=5.90 F=19.2 F=2.98 P<0.0001 F=1.14 P=0.021 P=0.00014 P=0.095 P=0.32 [N:P] model <- lme(np ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.18284 62 0.377 Treatment Season Treatment in each season 3881 Spring Summer Autumn Control=8.14±0.28 Spring=7.98±0.33 Control=8.12±0.58 Control=8.88±0.44 Control=7.57±0.42 Drought=6.75±0.26 Summer=7.53±0.38 Drought=7.55±0.33 Drought=7.13±0.69 Drought=6.50±0.54 F=1.80 Autumn=6.83±0.31 F=0.52 F=5.02 F=2.41 P=0.18 F=0.76 P=0.48 P=0.033 P=0.13 P=0.47 [N:K] model <- lme(nk ~ treatment + season, random=~1|plot, data=dades, method="REML") 0.15146 69 0.308 Treatment Season Treatment in each season 5485 Spring Summer Autumn Control=0.403±0.019 Spring=0.359±0.023ab Control=0.411±0.039 Control=0.427±0.027 Control=0.381±0.037 Drought=0.301±0.013 Summer=0.369±0.020a Drought=0.291±0.023 Drought=0.297±0.026 Drought=0.332±0.028 F=23.6 Autumn=0.328±0.021b F=5.08 F=10.6 F=1.13 P<0.0001 F=1.33 P=0.032 P=0.0028 P=0.30 23 P=0.40 [P:K] model <- lme(pk ~ treatment*season, random=~1|plot, data=dades, method="REML") 0.16 0.21 Treatment Season Treatment in each season Spring Summer Autumn Control=0.049±0.001 Spring=0.313±0.404 Control=0.049±0.003 Control=0.048±0.002 Control=0.049±0.003 Drought=0.045±0.001 Summer=0.328±0.410 Drought=0.038±0.002 Drought=0.042±0.002 Drought=0.053±0.002 F=2.86 Autumn=0.284±0.371 F=7.87 F=5.10 F=0.60 P=0.094 F=0.13 P=0.0087 P=0.031 P=0.44 P=0.88 24 Table S4. Statistical contribution and significance of the foliar variables used as independent variables in the general discriminant analysis with the different leaf samples of different treatments and years as categorical groups (Figure 3). Variable Wilk’s Lambda F P-value C% 0.823 3.43 <0.0001 N% 0.765 4.93 <0.0001 K 0.824 3.42 <0.0001 P 0.901 1.76 0.033 Ca 0.905 1.68 0.046 Mg 0.871 2.38 0.0019 C:N 0.785 4.37 <0.0001 N:P 0.849 2.85 0.00019 C:K 0.716 6.35 <0.0001 C:P 0.851 2.81 0.00023 N:K 0.697 6.96 <0.0001 N:Mg 0.789 4.29 <0.0001 N:Ca 0.905 1.68 0.046 P:Mg 0.850 2.82 0.00021 P:Ca 0.897 1.84 0.024 Mg:Ca 0.884 2.09 0.0076 25 Table S5. Squared Mahalanobis distances among all groups in the GDA analysis of the leaf concentrations of the three dominant species and pairwise ratios as variables and the samples of each species in the control and drought plots collected in 1999, 2004 and 2014 as grouping factors. The unique grouping factors without significant squared Mahalanobis distances are highlighted in red. 26 27 Species Year 1999 2004 2014 Arbutus Phillyrea Phillyrea Quercus Arbutus Arbutus Phillyrea Phillyrea Quercus Quercus ilex Arbutus Arbutus Phillyrea Phillyrea Quercus ilex Quercus Quercus ilex unedo latifolia latifolia ilex unedo unedo latifolia latifolia ilex Drought unedo unedo latifolia latifolia Drought ilex Control Drought Drought Control Drought Control Control Drought Control Drought Control Control Drought Control Drought Arbutus M=7.19 M=11.3 M=16.7 M=22.9 M=35.3 M=21.1 M=29.5 M=12.8 M=9.22 M=12.3 M=12.8 M=13.1 M=8.25 M=22.0 M=21.4 M=7.77 M=14.0 unedo F=1.14 F=2.68 F=3.95 F=5.42 F=8.00 F=7.04 F=9.97 F=4.86 F=3.49 F=4.68 F=4.82 F=4.12 F=2.61 F=6.95 F=6.75 F=2.45 F=4.43 Control P=0.32 P=0.0006 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P=0.0001 P<0.0001 P<0.0001 P<0.0001 P=0.00084 P<0.0001 P<0.0001 P=0.0017 P<0.0001 1999 Arbutus M=18.4 M=25.3 M=34.9 M=43.3 M=26.5 M=32.0 M=19.5 M=16.8 M=20.6 M=19.6 M=22.3 M=19.1 M=261 M=279 M=19.6 M=29.3 unedo F=2.91 F=3.99 F=5.51 F=6.53 F=5.19 F=6.31 F=4.11 F=3.49 F=4.34 F=4.10 F=4.23 F=3.61 F=4.83 F=6.28 F=3.71 F=5.55 Drought P=0.0002 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 1999 Phillyrea M=1.62 M=7.10 M=16.6 M=20.0 M=31.5 M=8.59 M=6.59 M=4.82 M=10.3 M=12.9 M=12.2 M=11.6 M=10.9 M=7.26 M=12.7 latifolia F=0.383 F=1.68 F=3.68 F=6.69 F=10.7 F=3.26 F=2.50 F=1.84 F=3.90 F=4.07 F=3.84 F=3.66 F=3.43 F=2.29 F=4.02 Control P=0.98 P=0.049 P<0.0001 P<0.0001 P<0.0001 P=0.0003 P=0.004 P=0.028 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P=0.0001 P=0.0036 P<0.0001 1999 Phillyrea M=4.38 M=15.2 M=22.9 M=34.7 M=10.1 M=9.09 M=5.12 M=12.3 M=16.9 M=16.2 M=14.2 M=12.4 M=9.91 M=14.2 latifolia F=0.490 F=3.36 F=7.60 F=11.8 F=3.82 F=3.45 F=1.94 F=4.63 F=5.33 F=5.12 F=4.48 F=3.93 F=3.13 F=4.48 Drought P=0.22 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P=0.017 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 1999 Quercus M=11.9 M=30.9 M=43.1 M=18.0 M=16.7 M=9.58 M=15.8 M=26.3 M=23.2 M=26.4 M=21.0 M=11.0 11.9 ilex F=2.64 F=10.3 F=14.6 F=6.81 F=6.31 F=3.63 F=5.95 F=8.32 F=7.34 F=8.35 F=6.64 F=3.48 F=3.76 Control P=0.00073 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 1999 Quercus M=40.6 M=51.0 M=27.6 M=26.4 M=17.1 M=23.1 M=37.5 M=35.9 M=34.0 M=31.7 M=27.9 M=27.4 ilex F=12.3 F=15.7 F=9.35 F=8.93 F=5.81 F=7.82 F=10.8 F=10.3 F=9.81 F=9.14 F=6.04 F=7.90 Drought P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 1999 Arbutus M=3.61 M=27.4 M=27.4 M=27.4 M=27.4 M=27.4 M=27.4 M=27.4 M=27.4 M=27.4 M=27.4 unedo F=2.08 F=7.90 F=7.90 F=7.90 F=7.90 F=7.90 F=7.90 F=7.90 F=7.90 F=7.90 F=7.90 Control P=0.0094 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2004 Arbutus M=15.5 M=14.9 M=25.9 M=21.8 M=22.3 M=22.6 M=34.4 M=35.5 M=30.5 M=37.8 unedo F=11.6 F=10.9 F=18.9 F=15.7 F=11.7 F=11.9 F=18.0 F=18.7 F=16.1 F=19.9 Drought P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2004 Phillyrea M=1.21 M=5.50 M=8.31 M=10.4 M=10.04 M=17.7 M=16.7 M=9.88 M=16.3 latifolia F=1.15 F=5.21 F=7.75 F=6.56 F=6.34 F=11.2 F10.5= F=6.24 F=10.3 Control P=0.31 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2004 Phillyrea M=5.31 M=6.38 M=6.65 M=5.97 M=14.9 M=14.1 M=6.91 M=13.4 latifolia F=5.03 F=5.95 F=4.20 F=3.77 F=9.39 F=8.93 F=4.37 F=8.47 Drought P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2004 28 Quercus M=4.75 M=115.0 M=13.6 M=17.6 M=17.1 M=10.7 M=14.5 ilex F=4.43 F=9.39 F=8.56 F=11.1 F=10.8 F=6.79 F=9.17 Control P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2004 Quercus M=14.1 M=12.2 M=24.4 M=22.5 M=10.5 M=13.2 ilex F=8.82 F=7.62 F=15.3 F=14.1 F=6.63 F=8.27 Drought P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2004 Arbutus M=1.53 M=7.29 M=6.86 M=9.45 M=13.8 unedo F=0.725 F=3.45 F=3.25 F=4.48 F=6.56 Control P=0.77 P<0.0001 P<0.0001 P<0.0001 P<0.0001 2014 Arbutus M=11.8 M=10.7 M=6.25 M=9.75 unedo F=5.59 F=5.09 F=2.96 F=4.62 Drought P<0.0001 P<0.0001 P<0.0001 P<0.0001 Phillyrea M=2.22 M=20.0 M=25.4 latifolia F=1.05 F=9.47 F=12.1 Control P=0.40 P<0.0001 P<0.0001 2014 Phillyrea M=14.6 M=17.1 latifolia F=6.92 F=8.12 Drought P<0.0001 P<0.0001 2014 Quercus M=3.03 ilex F=1.44 Control P=0.12 2014 29 Table S6. Increases in leaf litter carbon, nitrogen, phosphorus and potassium stocks (Mean ± S.E., kg ha-1) after 5 and 15 years of experiment beginning. Species Treatments Increases of stocks in leaf litter (kg ha-1) 1999-2004 1999-2014 Carbon Nitrogen Phosphorus Potassium Carbon Nitrogen Phosphorus Potassium Quercus ilex Control 121±41 -0.922±1.255 0.115±0.097 -0.191±0.380 826±315 10.9±4.9 0.681±0.448 6.30±3.31 Drought 132±86.7 1.66±1.99 0.237±0.205 0.0083±0.947 1739±648 30.1±9.7 1.91±0.63 9.45±3.59 Statistics F=0.012 F=1.21 F=0.292 F=0.038 F=1.61 F=3.09 F=2.54 F=0.415 P=0.92 P=0.314 P=0.61 P=0.85 P=0.25 P=0.13 P=0.16 P=0.54 Phillyrea Control 145±81.2 2.06±1.25 -0.807±0.441 0.683±0.503 -100±54.9 4.03±2.31 -0.916±0.421 -0.916±0.421 latifolia Drought 288±167 4.61±2.92 -0.810±0.123 1.98±1.40 -158±54.9 5.77±4.60 -1.08±0.21 -1.08±0.21 Statistics F=0.599 F=0.645 F=0.00005 F=0.754 F=0.566 F=0.114 F=0.119 F=0.119 P=0.468 P=0.452 P=0.995 P=0.418 P=0.480 P=0.747 P=0.742 P=0.742 Arbutus Control 165±70.1 2.86±1.14 -0.045±0.157 7.64±0.85 552±476 4.99±4.13 0.727±0.899 -0.157±0.291 unedo Drought 68.7±55.3 2.24±1.46 -0.042±0.12 2.21±0.95 112± 83 0.9870.817 -0.054±0.125 0.0249±0.075 Statistics F=1.20 F=0.097 F=0.00036 F=16.8 F=1.15 F=1.25 F=1.04 F=0.49 P=0.32 P=0.77 P=0.98 P=0.0094 P=0.33 P=0.32 P=0.35 P=0.51 30 31 Table S. Leaf and leaf litter biomasses (mean S.E.) in the three studies species in control and drought treatment stands before treatment application (1999) and during treatment application (in 2004 and 2014). The letters between brackets means marginal statistical differences between control and treatment plots (P<0.1). Biomass Species Treatment Year fraction 1999 2004 2014 Leaf Quercus ilex Control 85880±20771 86208±22326 86677±23443 Drought 90648±23109 88014±24748 85742±25671 Phillyrea Control 13364±6481 14086±6876 15719±7735 latifolia Drought 12088±4847 13022±5321 14479±6029 Arbutus Control 10605±4657 12046±5198 13727±2803 (a) unedo Drought 5598±3204 6049±3291 6142±2745 (b) All Control 109850±15864 112340±16953 118980±17394 Drought 108334±20561 107085±21936 110306±23025 Leaf-litter Quercus ilex Control 694.5±231.3 995.9±280.0 1147±405.8 Drought 702.7± 247.2 955.4±280.0 1070±284.7 Phillyrea Control 211.4±117.0 584.8±310.1 339.7±185.1 latifolia Drought 327.2±113.4 838.1±429.2 346.4±180.6 Arbutus Control 258.2±100.4 478.7±212.2 320.8±155.7 unedo Drought 206.5±95.2 353.3±201.2 103.2±22.9 All Control 1164±202.7 2059±111.4 1808±272.0 Drought 1236±224.5 2147±78.2 1519±96.0 32