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Towards Rainy Arctic Winters: Experimental Icing

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Towards Rainy Arctic Winters: Experimental Icing Supplementary material to: Towards rainy Arctic winters: experimental icing impacts tundra plant productivity and reproduction Mathilde Le Moullec, Anna-Lena Hendel, Matteo Petit Bon, Ingibjörg Svala Jónsdóttir, Øystein Varpe, René van der Wal, Larissa Teresa Beumer, Kate Layton-Matthews, Ketil Isaksen and Brage Bremset Hansen. Table of content: Table S1. Soil volumetric water content. Table S2. Sampling dates and number of repeated measurements. Table S3. Correlation coefficients between maximum NDVI and relative abundance. Table S4. Summary statistic of Salix polaris phenophases. Figure S1. Mesic habitat species composition. Figure S2. Estimated NDVI curves across treatments and years. Figure S3. Annual relative abundance. Figure S4. Ordination of the mesic community species composition. Figure S5. Leaf size traits of Salis polaris across years and treatments. Figure S6. Flower counts per year, treatment and species/group. Figure S7. Annual probability curves of Salix polaris phenophases. Table S1. Soil volumetric water content (i.e., soil moisture in %) measured with a ML3 ThetaProbe Sensor (HH2 Soil Moisture Meter from Delta-T Devices Ltd, UK, 1% accuracy, 5-10 cm depth) via repeated measurements at five points per plot, 3-15 times per summer (rounds). a) Soil moisture estimates and 95% CI per treatment and year, b) Pearson correlation coefficients between soil moisture and sub-surface (5 cm depth) temperature for years with repeated measurements across the growing season (2018-2019, df = 18). To compute these correlation coefficients we first fitted separate models for moisture and temperature , with treatment × day-of-year (factor) included as fixed effect, and repeated point measurements nested within plots nested within blocks included as random intercept structure. Then, the extracted fixed-effect estimates for each variable were correlated with each other. C = control, I = icing, IW = icing × warming, W = warming. a) Predictors Estimates CI (Intercept) [C, 2016] 37.85 29.39 – 46.31 treatment [I] -0.01 -4.34 – 4.32 treatment [IW] -3.56 -7.89 – 0.77 treatment [W] -0.11 -4.44 – 4.22 year [2017] 0.40 -8.08 – 8.87 year [2018] -5.52 -13.31 – 2.28 year [2019] -7.37 -15.85 – 1.10 year [2020] -1.39 -11.36 – 8.59 Random Effects Residuals 47.11 Blocks/Plots/Points 19.47 Blocks/Plots 17.77 Year/Round 37.57 Year 0.15 Block 9.14 N square 5 N plot 12 N block 3 N round 15 N year 5 Observations 5808 b) Treatment Correlation coefficient [95% CI] C -0.69 [-0.87:-0.35] I -0.79 [-0.92:-0.49] IW -0.71 [-0.88:0.40] W -0.60 [0.82:-0.21] Table S2. Sampling dates and number of measurement repeats (n). Abundance measurements represent the relative abundance measured by the point intercept method. For the Salix polaris leaf collection, the protocols varied between years: ‘Random’ indicates that S. polaris leaves were gathered from five shoots collected at random (i.e., at five sub-square intercepts of the frame) and in 2018, the leaf’s respective shoot ID was available and used in the linear mixed-effect modelling; ‘Largest’ indicates that the biggest S. polaris leaf was collected in each 16 sub-squares per plot. 2016 2017 2018 2019 2020 Date of icing 3 – 4 Feb 22 – 23 Jan 4 – 5 Jan 24 – 25 Feb 18 – 19 Feb Date OTC setup 23 May 31 May 20 May 4 Jun 20 May NDVI rounds 23 Jun – 11 21 Jun – 30 01 Jun – 20 11 Jun – 17 10 Jun – 18 Aug (n = 9) Jul (n = 7) * Aug (n = 15) Aug (n = 9) Aug (n = 10) Phenology rounds 23 Jun – 13 22 Jun – 31 02 Jun – 21 07 Jun – 10 NA Aug (n = 12) Jul (n = 7) Aug (n = 14) Aug (n = 5) Flower counts 14 Jul 25 July 17 Jul 16 Jul 9 Jul Abundance 2 – 4 Aug 4 – 6 Aug 30 – 31 Jul 23 – 24 Jul NA measurements S. polaris leaf 5 Aug 2 Aug 30 Jul 30 Jul 28 Jul collection S. polaris leaf Random Random Random Largest Largest collection protocol * too short summer period to be included in the analysis Table S3. Pearson’s correlation coefficients between plot-specific maximum NDVI and relative abundance estimates (assessed via point intercept method), separately for the two dominant vascular plant species (Salix polaris, Alopecurus borealis), different groups of vascular plant species, and bryophytes. 2016 (df = 34) 2018 (df = 34) 2019 (df = 34) Total (df = 106) S. polaris 0.45 [0.14,0.68] 0.30 [-0.03,0.57] 0.46 [0.16,0.69] 0.40 [0.23,0.55] A. borealis 0.32 [-0.01,0.58] 0.23 [-0.11,0.52] -0.10 [0.42,0.24] 0.15 [-0.04,0.33] Vascular plants 0.42 [0.11,0.66] 0.38 [0.06,0.63] 0.36 [0.04,0.62] 0.36 [0.19,0.52] Vascular plants 0.15 [-0.19;0.46] 0.07 [-0.27;0.39] 0.04 [-0.29;0.36] 0.09 [-0.10;0.28] without S. polaris and A. borealis Bryophytes -0.40 [-0.64;-0.08] -0.22 [-0.51;0.11] -0.00 [-0.33;0.33] -0.19 [-0.37:-0.00] Table S4. Summary statistic of Salix polaris phenophases in 2018, the summer with the highest number of monitoring rounds (n = 14) and complete vegetative and reproductive phenophases. Two separate models were fitted, with 1) fixed-effects including all treatments (as categories; C = control, I = icing, W = warming, IW = icing and warming combined), and 2) fixed-effects including icing and warming treatments as binomial variables and their interaction (I × W). For both model outputs, the columns ‘intercept’ (Int.), ‘I’ and ‘W’ are identical. Estimates and associated 95% CIs are on the logit-scale obtained from generalized linear mixed-effect models with phenophases following a binomial distribution. The random intercept structure was 16 sub-squares (S), nested within plots (P), nested within blocks (B). Estimates in bold have CIs non-overlapping with 0. Res. = residuals, Obs. = number of observations. Leaves Female flowers Male flowers Unfurling Expended Senescence Fully Flower Stigma Stigma Stigma Seed Anther Pollen Anther leaves leaves start senesced visible visible receptive senesced dispersal visible released senesced Int. 2.65 1.45 -0.35 -0.73 2.36 -0.11 -0.30 -0.43 -2.27 0.17 -0.25 -0.63 (2.10 – 3.19) (1.34 – 1.56) (-0.47 – -0.23) (-0.90 – -0.55) (1.89 – 2.83) (-1.08 – 0.87) (-1.15 – 0.56) (-1.13 – 0.26) (-3.12 – -1.42) (-0.12 – 0.46) (-0.56 – 0.05) (-0.88 – -0.37) I -0.69 -0.14 -0.01 -0.06 -0.51 -0.95 -0.77 -0.64 -0.82 -0.01 0.12 0.22 (-1.10 – -0.29) (-0.29 – 0.02) (-0.13 – 0.12) (-0.19 – 0.08) (-1.18 – 0.16) (-2.23 – 0.32) (-1.87 – 0.34) (-1.55 – 0.26) (-1.49 – -0.15) (-0.33 – 0.31) (-0.27 – 0.51) (-0.12 – 0.56) W 0.71 0.37 -0.08 -0.10 -0.48 0.08 -0.02 -0.19 0.10 0.24 0.39 0.18 (0.24 – 1.17) (0.19 – 0.55) (-0.22 – 0.05) (-0.24 – 0.04) (-1.18 – 0.23) (-1.24 – 1.40) (-1.16 – 1.12) (-1.12 – 0.74) (-0.56 – 0.77) (-0.13 – 0.60) (-0.03 – 0.82) (-0.19 – 0.56) IW 0.95 0.37 -0.08 -0.01 0.10 0.84 0.77 0.49 1.18 0.36 0.45 0.04 (0.48 – 1.41) (0.20 – 0.54) (-0.20 – 0.05) (-0.14 – 0.12) (-0.59 – 0.78) (-0.44 – 2.11) (-0.33 – 1.86) (-0.40 – 1.39) (0.58 – 1.79) (0.02 – 0.69) (0.06 – 0.85) (-0.30 – 0.39) I × W 0.93 0.14 0.01 0.15 1.09 1.71 1.55 1.33 1.90 0.13 -0.06 -0.36 (0.29 – 1.58) (-0.11 – 0.38) (-0.17 – 0.20) (-0.05 – 0.34) (1.08 – 1.09) (-0.13 – 3.55) (-0.04 – 3.15) (0.02 – 2.63) (0.96 – 2.84) (-0.36 – 0.63) (-0.64 – 0.52) (-0.87 – 0.15) Random Effects Res. 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.29 B/P/S <0.01 <0.01 <0.01 <0.01 0.95 0.59 0.39 0.13 0.15 <0.01 <0.01 <0.01 B/P 0.129 <0.01 <0.01 <0.01 0.32 1.72 1.27 0.84 0.31 0.07 0.12 0.08 \ B 0.16 <0.01 <0.01 0.02 <0.01 0.13 0.11 0.07 0.40 0.02 0.01 <0.01 Obs. 7566 7566 7566 7566 3638 3638 3638 3638 3638 2820 2820 2820 Figure S1. Mesic habitat species composition. Percentage of species or species groups recorded during point intercept monitoring (i.e., average number of hits per plot). Estimates and their 95% confidence intervals were obtained from generalized linear mixed-effects model (Poisson distribution) with species/groups as fixed-effect and a random intercept structure including plot nested within block and year (2016-2019, 3 blocks ,12 plots, 1584 observations). Figure S2. Estimated NDVI curves (with 95% CIs) across treatments and years. C = control, I = icing, IW = icing × warming, W = warming.
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