BUSCADOR RECOLECTA

These data were generated to investigate how N addition affect SOC accural and chemical composition through the root-pathway and mycelium-pathway in an alpine coniferous forest. Samples of plant and soil were collected from each treatment plots (non-N addition and N-addition) in 2019 and 2020. Therefore, each parameter has 6 replicates (n = 3 replicates for each treatment * 2 sampling date =6)，except for the plant-derived C in different soil size fractions (only measured the samples collected in 2019)., [Methods] Isolation of roots and mycelia using ingrowth cores: To isolate roots and mycelia, we adopted an ingrowth-core technique modified from Zhang et al. (2018) and Keller et al. (2021). Ingrowth cores (6 cm inner diameter and 15 cm depth) were wrapped with a mesh with different pore sizes: mesh size of 2000 µm allowed the ingrowth of fine roots and mycelia (both roots and mycelia accessible); 48-µm mesh permitted the growth of mycelia but not of fine roots (only mycelia accessible), and 1-µm mesh excluded the growth of both roots and mycelia (only the soil) (Fig. 2). The C source in the 2-mm mesh cores was mainly derived from roots, mycelia and litter leachates, that of the 48-µm mesh cores was derived from mycelia and litter leachates, while the 1-µm mesh cores received C only from litter leachates. The soil was collected from the mineral layer (0-15cm) at each plot. After removing the visible roots, the soil from the same plot was homogenized and sieved through a 5-mm mesh. The sieved soil was filled into ingrowth cores corresponding to the soil bulk density at 0-15 cm depth (0.796 g cm-3, approximately 337 g per core). Six sets of ingrowth cores with different mesh-size (1-µm, 48-µm and 2000-μm) were installed in each treatment plot. In total, 108 ingrowth cores (2 N levels * 3 replicates * 6 sets * 3 mesh-sizes) were installed in this coniferous forest. Ingrowth cores were randomly placed in the topmost mineral horizon (0-15cm depth) in each plot in July 2017. The bottom of the ingrowth cores was covered with the corresponding size of the mesh to prevent inputs of roots and mycelia, respectively, and the top was covered by multiple layers of the corresponding size of the mesh to block the entry of coniferous litter but to allow gas and water exchange. When the cores were retrieved, we did not detect any external litter in the cores. To block the influx of new C derived from the saprophytic mycelia outside the cores, we spread a 2 mm-thick layer of silica sand around the cores. Silica sand as a growth substrate effectively reduces the disturbance of saprophytic hyphae (Hagenbo et al., 2017). Ingrowth cores were harvested in August 2019 and August 2020, respectively. Two sets of ingrowth cores were collected in each plot at each sampling date. Cores were transported to the laboratory within the icebox. After the removal of roots, soils inside the cores were sieved through a 2-mm mesh and divided into two subsamples: one subsample stored in -4 °C was used for the analyses of enzyme activities and microbial community composition; the second subsample was air-dried to perform soil aggregate fractionation, SOC determination, and soil biomarkers analysis. Root and mycelium biomass: Roots inside the 2000-µm mesh cores were manually picked out, washed thoroughly, oven-dried at 60°C for 48 hours and then weighed to determine the total root biomass. The ectomycorrhizal mycelium biomass was estimated using mesh bags (2 cm inner diameter, 15 cm depth; mesh size: 48 µm) filled with different particle sizes of HCl-washed silica sand (60 g, 0.36-2 mm) (Wallander et al., 2001). The mesh bags were randomly buried into the 0-15 cm soil depth in each plot in July 2017, and recovered at the same time as the ingrowth cores. The concentration of ergosterols was measured to characterize the biomass of ectomycorrhizal mycelia in the mesh bags (see details in the Supplementary Methods) (Parrent & Vilgalys, 2007). Soil aggregate fractionation and SOC concentration: To understand the physico-chemical protection of SOC in the RP and MP under N addition, soils were physically fractionated into three size fractions to examine the allocation of C and biomarkers among macroaggregates (Macro: 250~2000 µm), microaggregates (Micro: 53~250 µm) and slit-clay (< 53 µm) by using the wet-sieving technique (Six et al., 1998). The proportions of SOC and the concentrations of biomarkers in the three fractions were measured to characterize the role of physical protection by aggregates. The SOC and total N (TN) concentrations in bulk soil and size fractions were analyzed using an elemental analyzer (Vario MACRO, Elementar Analysensysteme GmbH, Hanau, Germany). To assess the protection of SOC by minerals, two forms of Fe and Al oxides, oxalate-extractable Fe/Al oxides (Feo + Alo) and dithionite-extractable Fe/Al (Fed + Ald) were measured by using the extraction method proposed by Gentsch et al (2018). The Fed + Ald indicates the amount of pedogenic Fe and Al within oxides, silicates and organic complexes, whereas Feo + Alo represents poorly crystalline oxyhydroxides (Gentsch et al., 2018). The concentrations of Fe and Al oxides in extracts were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 8300, Perkin Elmer, USA). SOC chemical composition: A range of major biomarkers, which are widely accepted to trace plant-derived and microbial-derived C, respectively, were selected to reveal the changes of the chemical composition of SOC in two pathways under N addition (Barré et al., 2018; Liang et al., 2019). Air-dried soil (1 g) was sequentially extracted (solvent extraction, base hydrolysis, and CuO oxidation) to isolate solvent-extractable free lipids (long-chain fatty acids), cutin- and suberin-derived compounds and lignin-derived phenols (vanillyls, syringyls and cinnamyls), respectively, according to standard protocols (Otto & Simpson, 2007; Tamura & Tharayil, 2014). Since the direct contribution of microbial living biomass to soil amino sugars is negligible, amino sugars are good indicators of microbial necromass (Liang et al., 2017, Joergensen, 2018). Four types of amino sugars, including glucosamine, galactosamine, manosamine, and muramic acid, were tested in this study. By assessing them in soils, we can investigate microbial necromass dynamics at the community-level (i.e., fungi and bacteria) and evaluate the contributions of necromass to SOC storage under different environmental conditions (Joergensen, 2018; Liang et al., 2019). The detailed chemical extractions and analyses of plant and microbial biomarkers are provided in Supplementary Methods. Microbial community composition: Soil microbial community composition was characterized using the phospholipid fatty acids (PLFAs) methods (see details in Supplementary Methods) (Bossio & Scow, 1998). The identification of the extracted fatty acid was based on a MIDI peak identification system (Microbial ID Inc., Newark, DE, USA). The PLFAs i15:0, α15:0, i16:0, i17:0, α17:0 were used to indicate the relative biomass of Gram-positive (G+) bacteria. The PLFAs 16:1ω9c, 16:1ω7c, 18:1ω7c, cy17:0, cy19:0 were used to indicate the relative biomass of Gram-negative (G-) bacteria. The PLFA 18:2ω6c was used as an indicator of saprotrophic fungal biomass. The PLFAs 10Me16:0, 10Me17:0 and 10Me18:0 were used to indicate actinomycete (AC) biomass. Microbial community composition was assessed by the ratio of saprotrophic fungal biomass to bacterial biomass (F/B ratio). Extracellular enzyme activity: The activities of three extracellular enzymes involved in the decomposition of lignin and fungal residues were measured as described by Saiya-Cork et al. (2002) (see details in Supplementary Methods). The β-N-acetyl-glucosaminidase（NAG）participates in chitin and peptidoglycan degradation, hydrolyzing chitobiose to glucosamine (Sinsabaugh et al., 2009). NAG activity was measured fluorometrically using 4-methylumbelliferyl N-acetyl-β-D-glucosaminide as the substrate. Phenol oxidases (POX) and peroxidases (PER) play an important role in degrading polyphenols, and their activities were measured colorimetrically using L-dihydroxyphenylalanine (DOPA) as the substrate. Data calculation and statistical analysis: To isolate the effects of root and mycelium on the SOC dynamics and associated microbial characteristics (i.e., SOC, biomarkers concentrations, fungal and bacterial biomass, and enzymes activities), net changes of the observations mediated by the root-pathway and mycelium-pathway were quantified by the difference of corresponding variables between the 2-mm mesh cores and 48-µm mesh cores, or between the 48-µm cores and 1-µm mesh cores, respectively (Fig. 2). The recent concept proposed by Zhu et al (2020) highlighted the contribution of microbial necromass to the SOC pool (i.e., MCP efficacy). Based on this concept, the changes of MCP efficacy (i.e., the contribution of increased microbial residual C to the increased SOC) under N addition were calculated as follow: Changes of MCP efficacy (% SOC) under N addition = , where MRCN, SOCN, MRCCK, and SOCCK represent the concentration of microbial residual C and SOC in the N-addition plots and the non-N addition plots, respectively. Additionally, the contribution of increased plant-derived C to the increased SOC induced by N addition was calculated using Eq. 1 but replacing microbial residual C with plant-derived C., Plant roots and associated mycorrhizae exert a large influence on soil carbon (C) cycling. Yet, little was known whether and how roots and ectomycorrhizal extraradical mycelia differentially contribute to soil organic C (SOC) accumulation in alpine forests under increasing nitrogen (N) deposition. Using ingrowth cores, the relative contributions of the root-pathway (RP) (i.e., roots and rhizosphere processes) and mycelium-pathway (MP) (i.e., extraradical mycelia and hyphosphere processes) to SOC accumulation were distinguished and quantified in an ectomycorrhizal-dominated forest receiving chronic N addition (25 kg N ha-1 yr-1). Under the non-N addition, the RP facilitated SOC accumulation, while the MP reduced SOC accumulation. Nitrogen addition enhanced the positive effect of RP on SOC accumulation from +18.02 mg C g-1 to +20.55 mg C g-1 but counteracted the negative effect of MP on SOC accumulation from -5.62 mg C g-1 to -0.57 mg C g-1, as compared to the non-N addition. Compared to the non-N addition, the N-induced SOC accumulation was 1.62~2.21 mg C g-1 and 3.23~4.74 mg C g-1, in the RP and the MP, respectively. The greater contribution of MP to SOC accumulation was mainly attributed to the higher microbial C pump (MCP) efficacy (the proportion of increased microbial residual C to the increased SOC under N addition) in the MP (72.5%) relative to the RP (57%). The higher MCP efficacy in the MP was mainly associated with the higher fungal metabolic activity (i.e., the greater fungal biomass and N-acetyl glucosidase activity) and greater binding efficiency of fungal residual C to mineral surfaces than those of RP. Collectively, our findings highlight the indispensable role of mycelia and hyphosphere processes in the formation and accumulation of stable SOC in the context of increasing N deposition., National Natural Science Foundation of China, Award: 32171757. The Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team, Award: xbzg-zysys-202112. The Second Tibetan Plateau Scientific Expedition and Research, Award: 2019QZKK0301. European Research Council Synergy project, Award: SyG-2013-610028 IMBALANCE-P. The Spanish Government, grant, Award: PID2019-110521GB-I00. National Natural Science Foundation of China, Award: 31901131. National Natural Science Foundation of China, Award: 42177289. The Spanish Government, grant, Award: PID2020-115770RB-I00., Peer reviewed

[Methods See the Materials and methods section in the original paper., [Usage Notes] Microsoft Excel are required to open the data files., This dataset is the data used to create figures in paper of Global change biology entitled "Data on Winter warming offset one half of the spring warming effects on leaf unfolding", we constructed a phenological model based on the linear or exponential function between the chilling accumulation (CA) and forcing requirements (FR) of leaf-out. We further used the phenological model to quantify the relative contributions of chilling and forcing on past and future spring phenological change. The results showed that the delaying effect of decreased chilling on the leaf-out date was prevalent in natural conditions, as more than 99% of time series exhibited a negative relationship between CA and FR. The reduction in chilling linked to winter warming from 1951-2014 could offset about one half of the spring phenological advance caused by the increase in forcing. In future warming scenarios, if the same model is used and a linear, stable correlation between CA and FR is assumed, declining chilling will continuously offset the advance of leaf-out to a similar degree. Our study stresses the importance of assessing the antagonistic effects of winter and spring warming on leaf-out phenology., National Key R&D Program of China, Award: 2018YFA0606102. National Natural Science Foundation of China, Award: 41871032. Youth Innovation Promotion Association, CAS, Award: 2018070. National Natural Science Foundation of China, Award: 42125101., Peer reviewed

Z1 (Fell field-Bare ground-Plot 1) Z1.fastq Z2 (Fell field-Salix-Plot 1) Z2.fastq Z3 (Fell field-Dryas-Plot 1) Z3.fastq Z4 (Fell field-Bare ground-Plot 2) Z4.fastq Z5 (Fell field-Salix-Plot 2) Z5.fastq Z6 (Fell field-Dryas-Plot 2) Z6.fastq Z7 (Fell-field-Bare ground-Plot 3) Z7.fastq Z8 (Fell field-Dryas-Plot 3) Z8.fastq Z9 (Fell field-Salix-Plot 3) Z9.fastq Z10 (Heath-Bare ground-Plot 2) Z10.fastq Z11 (Snowbed-Bare ground-Plot 1) Z11.fastq Z12 (Snowbed-Salix-Plot 1) Z12.fastq Z13 (Snowbed-Bare ground-Plot 3) Z13.fastq Z14 (Snowbed-Salix-Plot 3) Z14.fastq Z15 (Snowbed-Dryas-Plot 3) Z15.fastq Z16 (Snowbed-Bare ground-Plot 2) Z16.fastq Z17 (Snowbed-Salix-Plot 2) Z17.fastq Z18 (Snowbed-Dryas-Plot 2) Z18.fastq Z19 (Snowbed-Dryas-Plot 1) Z19.fastq Z20 (Heath-Dryas-Plot 2) Z20.fastq Z21 (Heath-Salix-Plot 2) Z21.fastq Z22 (Heath-Bare ground-Plot 1) Z22.fastq Z23 (Heath-Dryas-Plot 1) Z23.fastq Z24 (Heath-Salix-Plot 1) Z24.fastq Z25 (Heath-Bare ground-Plot 3) Z25.fastq Z26 (Heath-Salix-Plot 3) Z26.fastq Z27 (Heath-Dryas-Plot 3) Z27.fastq, Fungi play a key role in soil-plant interactions, nutrient cycling, and carbon flow and are essential for the functioning of arctic terrestrial ecosystems. Some studies have shown that the composition of fungal communities is highly sensitive to variations in environmental conditions, but little is known about how the conditions control the role of fungal communities (i.e. their ecosystem function). We used DNA metabarcoding to compare taxonomic and functional composition of fungal communities along a gradient of environmental severity in Northeast Greenland. We analysed soil samples from fell fields, heaths, and snowbeds, three habitats with very contrasting abiotic conditions. We also assessed within-habitat differences by comparing three widespread microhabitats (patches with high cover of Dryas, Salix, or bare soil). The data suggest that, along the sampled mesotopographic gradient, the greatest differences in both fungal richness and community composition are observed among habitats, while the effect of microhabitat is weaker, although still significant. Furthermore, we found that richness and community composition of fungi are shaped primarily by abiotic factors and to a lesser, though still significant extent, by floristic composition. Along this mesotopographic gradient, environmental severity is strongly correlated with richness in all fungal functional groups: positively in saprotrophic, pathogenic, and lichenised fungi, and negatively in ectomycorrhizal and root-endophytic fungi. Our results suggest complex interactions amongst functional groups, possibly due to nutrient limitation or competitive exclusion, with potential implications on soil carbon stocks. These findings are important in light of the environmental changes predicted for the Arctic., Peer reviewed

[Methods] The field experiments were conducted in the growing (July) and non-growing seasons (January) in both the freshwater and brackish C. malaccensis wetlands. Three 1 × 1 m quadrats (5 m apart) were randomly established at each site, and three soil cores (0–20 cm) were randomly collected in each quadrat and pooled into one sample. All samples were then stored in a portable refrigerator and immediately transported to the laboratory. The samples were homogenized and then split into two subsamples: one subsample was air-dried for the determination of P fractions and physicochemical parameters, and the other subsample was frozen at −80°C for DNA extraction. Plant biomasses were also collected during each season. We used the Hedley scheme of sequential extraction to estimate the fractions and availabilities of soil P (Hedley et al., 1982), which can effectively distinguish between Pi and Po. Briefly, soil samples were successively extracted using an anion-exchange resin (resin-P), 0.5 M NaHCO3 (NaHCO3-Pi and NaHCO3-Po), 0.1 M NaOH (NaOH-Pi and NaOH-Po), 0.1 M NaOH with sonication (NaOHs-Pi and NaOHs-Po), and 1 M HCl (HCl-Pi). The residual soils were then digested with 4 mL of H2SO4 and 1 mL of HClO4 (residual-P). The concentration of P was measured using a spectrophotometer. The P was further classified as labile P (resin-P, NaHCO3-Pi, and NaHCO3-Po), moderately labile P (NaOH-Pi and NaOH-Po), and stable P (NaOHs-Pi, NaOHs-Po, HCl-P, and residual-P) based on its availability to plants and microbes (Rodrigues et al., 2016). The salinity of the water was measured in situ using a salinometer (Oakton Instruments, Springfield, USA). Soil electric conductivity (EC) and pH were determined using a 2265FS EC meter (Spectrum Technologies Inc., Aurora, USA) and a pH meter (IQ Scientific Instruments, Carlsbad, USA), respectively. Soil moisture was evaluated by determining the amount of water lost at 105°C. Soil organic C (SOC) was analyzed using the dichromate oxidation method. Soil concentrations of total C (TC) and N (TN) were measured using an elemental analyzer (Elementar, Frankfurt, Germany). Soil concentrations of ammonium-N (NH4+-N) and nitrate-N (NO3−-N) were determined using flow-injection analysis (Skalar Analytical SAN++, Lachat, Netherland) and extraction with 2 M KCl. The soil texture was determined using a Mastersizer 2000 particle-size analyzer (Malvern Panalytical Ltd., Melvin, UK). Plant biomasses were measured by drying samples to constant weight at 70°C. Soil microbial DNA was extracted using an OMEGA DNA Kit following the manufacturer’s instructions. The quality and quantity of the extracted DNA were determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, USA) and agarose gel electrophoresis, respectively. The extracted microbial DNA was processed, and metagenomic shotgun sequencing libraries were constructed with insert sizes of 400 bp using an Illumina TruSeq Nano DNA LT Library Preparation Kit. Each library was sequenced on an Illumina HiSeq X-ten platform (Illumina, San Diego, USA) using the PE150 strategy at Personal Biotechnology Co., Ltd. (Shanghai, China). Please refer to the Supporting Information for more detailed descriptions (Appendix I). We obtained a total of 931 million qualified sequences from 12 metagenomes, ranging from 69 million to 88 million sequences per sample for downstream analyses (Table S1). [Usage Notes] The dataset can be opened using regular Office software., Accelerated sea-level rise is expected to cause the salinization of freshwater wetlands, but the responses to salinity of the availability of soil phosphorus (P) and of microbial genes involved in the cycling and transformation of P remain unexplored. Our results suggest that the P-cycling microbial community abundance and P availability respond positively to moderate increases in salinity by promoting the microbial solubilization and mineralization of soil P in brackish wetlands. Changes in microbial communities and microbially mediated P cycling may represent microbial strategies to adapt to moderate salinity levels, which in turn control soil function and nutrient balance., National Natural Science Foundation of China. Natural Science Foundation of Fujian Province. Fundación Ramón Areces Project. Spanish Government. Catalan Government., Peer reviewed

[Usage Notes] Oriol_fasta_files_for_submission Oriol fasta files for submission.zip, The encroachment of shrubs into grasslands is common in terrestrial ecosystems dominated by grass. Land abandonment and favourable climatic trends in recent decades have favoured the expansion of shrubs into subalpine grasslands in many mountainous regions across Europe. The advance of the succession from grassland to shrubland is expected to have a major impact on ecosystem functioning. We used DNA metabarcoding to assess whether the structure of soil fungal communities varied along the succession from subalpine grassland to shrubland in the Pyrenees, and investigated whether shrub encroachment was associated with changes in soil properties. The expansion of shrubs increased the soil C:N ratio and/or reduced the N, P, or K contents. Plant-driven changes in soil properties were strongly associated with the compositional turnover of fungi, including arbuscular mycorrhizal, ectomycorrhizal, ericoid, root endophytic, saprotrophic, lichenised, and pathogenic fungi. Total richness and the richness of most functional groups were correlated with soil P, N, and the C:N or N:P ratios. We show that the interplay between abiotic factors (changes in soil properties) and biotic factors (occurrence and identity of shrubs) played a key role in the structure and uniqueness of soil fungal communities along the succession., Peer reviewed

Soil acidification as a global change factor can devastatingly affect plant growth and productivity. In frequently disturbed ecosystems, plant resprouting ability strongly determines biomass reconstruction and resilience after aboveground damage. However, how plant regrowth responds to soil acidification remains largely unknown, especially regarding the role of the rhizosphere in mediating this response. We manipulated a soil-acidification gradient via adding purified elemental sulfur powder at various rates (0-50 g S m−2 year−1) in a frequently mown meadow. Shoot regrowth of functional groups were measured after clipping and supporting roles of rhizosphere versus bulk soils were disentangled using isotope labelling along the acidification gradient. Regrowth of grasses and sedges increased while forbs decreased along the acidification gradient. The results suggest that grasses were competitors capable of taking up nutrients from both rhizosphere and bulk soils, while sedges were acid-tolerators with lower sensitivity to decreased nitrogen-mineralization rates. Forbs, as typical ruderals, were vulnerable to N competition with microbes, particularly in the rhizosphere soil. Therefore, biomass regrowth of forbs was explained more by physicochemical and biological parameters from the rhizosphere than bulk soil Synthesis. Divergent interplay between plant functional groups and rhizosphere soils was the prominent driver for biomass regrowth responding to soil acidification., Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDA23080400. National Natural Science Foundation of China, Award: 32071563. National Natural Science Foundation of China, Award: 31870441. National Natural Science Foundation of China, Award: 32101320., Peer reviewed

This study was conducted at the ForHot research site in Iceland (Sigurdsson et al., 2016) between August 2017 and June 2018 (64°0′N, 21°11′W). Soil type was a Brown Andosol (Arnalds, 2015). Mean annual temperature at the site was 5.1 °C. The coldest and warmest temperatures in the neighboring village of Eyrarbakki in 2016 were -12.3 °C and 21.6 °C, respectively. Average annual precipitation for the same year was 1153 mm (Icelandic Meteorological Office, 2016). The vegetation was an unmanaged grassland dominated byAgrostis capillarisL.,Galium borealeL. andAnthoxantum odoratumL. Vascular plants cover 46% of the area over a moss mat which covers up to 88% of the ground. This grassland has been geothermally warmed since 29 May 2008, when an earthquake transferred geothermal energy from hot groundwater to previously unheated soils (Sigurdsson et al., 2016). Belowground temperatures at 10 cm depth now display a permanent warming gradient reaching +10 °C, with a discreet increase in aboveground temperature of +0.2 °C. The warming has only been mildly disruptive, with seasonality remaining unchanged. Soil humidity was only marginally affected, with volumetric water content changing from 40% to 38%, and water pH increased from 5.6 in unheated soil to up to 6.3 after warming. Geothermal groundwater has remained in the bedrock and has not reached the root zone, thus avoiding direct eco-toxicological effects (Sigurdsson et al., 2016). The resulting stable conditions and lack of artifacts provide a realistic natural belowground experiment on soil warming under climate change. Natural N deposition in the area is 1.3 ± 0.1kg N ha-1 y-1 (Leblans et al., 2014). Five transects were established, each one consisting of three 2 x 2 m plots, and each plot at different temperature: an unheated control, a low warming level of ca. +3 °C and a higher warming level of ca. +6 °C above the ambient reference in the control (henceforth referred as “+3 °C” and “+6 °C”).
Soil cores were collected using an auger to a depth of ~10 cm, excluding the O horizon. Soil cores were sampled seasonally four times: August 2017, corresponding to late growing season; November 2017, at start of winter and initial soil freezing; April 2018, with the first soil thaw in un-warmed soils, and June 2018, in the early part of the growing season. We thus collected a total of 20 core samples for each warming treatment (5 replicates in 4 seasons for 3 temperature levels = 60 samples). All samples were immediately sieved to remove roots and stones larger than 2 mm. Fifteen grams of each sample were then frozen in plastic bags in liquid N in the field to immediately stop all biological processes. All frozen samples were freeze-dried in the laboratory. eDNA was extracted from 15 g soil samples belonging to DNA remains (i.e. no alive fauna) as previously described (Taberlet et al., 2012; Zinger et al., 2016).
The soil hexapod communities were genetically characterized based on Molecular Operational Taxonomic Units (MOTUs) using the retrieved eDNA and applying a metabarcoding approach. We amplified the 16S mitochondrial rDNA region using the Ins16S_l primer pair (Ins16S_1-F: 5′-TRRGACGAGAAGACCCTATA-3′; Ins16_1-R: 5′-TCTTAATCCAACATCGAGGTC-3′; Clarke et al. 2014). This primer pair, specifically designed for hexapod metabarcoding, introduces a very limited taxonomic bias and performs very well for identifications at the species level throughout the Hexapoda subphylum (e.g. Kocher et al., 2017; Talaga et al., 2017). PCR amplification was performed in triplicate in 20-μL mixtures consisting of 10 μL of AmpliTaq Gold Master Mix (Life Technologies, Carlsbad, USA), 5.84 μL of nuclease-free Ambion water (Thermo Fisher Scientific, Waltham, USA), 0.25 μM each primer, 3.2 μg of bovine serum albumin (Roche Diagnostic, Basel, Switzerland) and 2 μl of DNA template that was diluted 10-fold to reduce PCR inhibition by humic substances. The thermal profile of the PCR amplification was 40 cycles of denaturation at 95 °C (30 s), annealing at 49 °C (30 s) and elongation at 72 °C (60 s), with a final elongation step at 72 °C for 7 min. Tags had at least five differences between them to minimize ambiguities (Coissac et al., 2012). The sequenced multiplexes comprised extractions/PCR blank controls, unused tag combinations and positive controls (Kocher et al., 2017). The PCR products were then sequenced using the MiSeq platform (Illumina Inc., San Diego, USA), with the expected sequencing depth set at 400 000 reads per sample. The sequences were processed using OBITOOLS software (Boyer et al., 2016). Low-quality sequences (containing Ns, alignment scores <50, lengths <140 bp or >320 bp and singletons) were excluded. The remaining sequences were clustered into MOTUs using SUMACLUST (Mercier et al., 2013) at a threshold of sequence similarity of 97%. The hexapod MOTUs were taxonomically assigned using Blast. MOTUs showing <80% similarity with either the local or the EMBL reference databases were removed, leading to 219 MOTUs. These retained MOTUs included taxa from classes Insecta and Entognatha, which both belong to the subphylum Hexapoda. We then applied a post-processing pipeline (Zinger et al., 2021) to minimize PCR and sequencing errors, contaminations and false-positive sequences, and by detailed curation of ecologically incongruent assignments (i.e. taxa with distributions outside the palearctic and neartic ecozones). This conservative approach retained a total of 33 identified species. We then used checklists of Icelandic hexapod species and information from previous studies at the same study site (Fjellberg, 2007; Holmstrup et al., 2018) to assess the performance of our eDNA metabarcoding protocol to properly describe the hexapod communities in the soil., Peer reviewed

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data. Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau / Third Pole Environment Data Center (http://data.tpdc.ac.cn)., 1. It is challenge to scale-up from simplified proxies to ecosystem functioning since the inherent complexity of natural ecosystems hinders such an approach. One way to address this complexity is to track ecosystem processes through the lens of plant functional traits. Elevational gradients with diverse biotic and abiotic conditions offer ideal settings for inferring functional trait responses to environmental gradients globally. However, most studies have focused on differences in mean trait values among species and little is known on how intraspecific traits vary along wide elevational gradients and how this variability reflects ecosystem productivity., 2. We measured functional traits of the sub-shrub Koenigia mollis (Basionym: Polygonum molle) (a widespread species) in 11 populations along a wide elevational gradient (1515-4216 m) considering from subtropical forest to alpine biomes treeline in the central Himalayas. After measuring different traits (plant height, specific leaf area, leaf area, length of flowering branches, leaf carbon isotope – δ 13C, leaf carbon and leaf nitrogen concentrations), we investigated drivers on changes of these traits and also characterized their relationships with elevation, climate and net primary productivity (NPP)., 3. All trait values decreased with increasing elevation, except for δ 13C that increased upwards. Likewise, most traits showed strong positive relationships with potential evapotranspiration (PET), while δ 13C exhibited a negative relationship. In this context, elevation-dependent water-energy dynamics is the primary driver of trait variations. Further, five key traits (plant height, specific leaf area, leaf carbon, leaf nitrogen and leaf δ 13C) explained 90.45% of variance in NPP., 4. Synthesis. Our study evidences how elevation-dependent climate variations affect ecosystem processes and functions. Intraspecific variability in functional traits is strongly driven by changes in water-energy dynamics, and reflects changes in community productivity over elevation. K. mollis, with one of the widest elevational ranges known to date, could be a model species to infer functional trait responses to environmental gradients globally. This study sheds new insight on how plants modify their basic ecological strategies to cope with changing environments., National Science and Technology Major Project of China: Second National Science and Technology and Research Programme (STEP)（2019QZKK0000）, Peer reviewed

Location in the climatic niche over several years of plant comunities from a field manipulation experiment of climate change carried on in Garraf (Spain). 1. Description of methods used for collection-generation of data: Field data: vegetation surveys based on transects within experimental plots Species distribution data: species occurrences along their geographical distributions were obtained from the database of the Global Biodiversity Information Facility GBIF 2019,) Climate data: climatic variables were obtained for the period 1979-2013 at 1-km2 resolution from CHELSA v.1.2 database Karger et al. (2018) DOI: 10.1038/sdata.2017.122; Karger et al., (2017) DOI: 10.16904/envidat.228.v2.1 See details in DOI: 10.1111/1365-2745.14233 2. Methods for processing the data: A principal component analysis (PCA) was applied to reduce the dimensionality of the 13 climatic variables to the first two PCA axes. The species occurrences from the geographic space were translated into the two-dimensional climatic space defined by the two PCA axes. A two-dimensional kernel density function was applied to the species occurrences in the climatic space. The centroid of each species niche was obtained as the center of gravity of the niche. The observed climate at the study site for the average of the experimental period 1999-2014 and for each year were translated into the two-dimensional climatic space. The Euclidean distance in the climatic space between community location (based on species centroid averaged weighed by species's abundance) and the location of the observed climate in the respective years was calculated.