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Rhizosphere soil samplingA total of 20 rhizosphere soil samples (20 tomato plants) were collected at the flowering stage from a tomato field located in Qilin town, Jiangsu province, China, 118°57’ E, 32°03’ N, which had been infested by the pathogen Ralstonia solanacearum for more than 15 years [8]. After uprooting plants, excess soil was first gently shaken from the roots, and the remaining soil attached to roots was considered as rhizosphere soil. Each rhizosphere soil sample was then used for bacterial strain isolation.Isolation and identification of rhizobacteriaIsolationA total of 640 bacterial strains were isolated from the fresh rhizosphere soil samples, according to a previously established protocol [11]. Briefly, 1 g of each rhizosphere sample was mixed with 9 mL MS buffer solution (50 mM Tris-HCl [pH 7.5], 100 mM NaCl, 10 mM MgSO4, 0.01% gelatin) in a rotary shaker at 170 rpm min−1 for 30 min at 30 °C. After serial dilution in MS buffer solution, 100-μl volumes of the diluted soil suspensions were plated on 1/10 tryptone soy agar (1/10 TSA, 1.5 g L−1 tryptone, 0.5 g L−1 soytone, 0.5 g L−1 sodium chloride, and 15 g L−1 agar, pH 7.0). After a 48-h incubation at 30 °C in the dark, 32 isolates were randomly picked per rhizosphere soil sample. To avoid potential fungal contamination, only highly diluted samples were used for isolation. The isolates were then re-streaked on TSA plates for colony purification. Approximately 5.5% (35 isolates) of the bacterial isolates failed to grow on the TSA plates for unknown reasons when we re-streaked them and were therefore omitted from the dataset. The final collection thus consisted of 605 bacterial isolates derived from 20 rhizosphere soil samples. All purified isolates were cultured in 100 μl tryptone soy broth (TSB, liquid TSA) in 96-well microtiter plates at 30 °C with shaking (rotary shaker at 170 rpm) for 18 h before freezing and storing at −80 °C in 15% glycerol.Strain identificationWe sequenced the full 16 S rRNA gene to taxonomically identify all 605 rhizobacterial isolates. The 16 S rRNA gene was sequenced via Sanger sequencing of PCR products from glycerol stocks by Shaihai Songon Biotechnology Co., Ltd, Shaihai Station. The PCR system (25 µl) was composed of 1 µl of bacterial cells (overnight culture), 12.5 µl mixture, 1 µl of forward (27 F: 5-AGA GTT TGA TCA TGG CTC AG-3) and reverse primer (1492 R: 5-TAC GGT TAC CTT GTT ACG ACT T-3) each [17] and 9.5 µl of sterilized water. PCR was performed by initially denaturizing at 95 °C for 5 min, cycling 30 times with a 30-s denaturizing step at 94 °C, annealing at 58 °C for 30 s, extension at 72 °C for 1 min 30 s, and a final extension at 72 °C for 10 min. The 16 S rRNA gene sequences were identified using NCBI databases and homologous sequence similarity. A total of 90 bacterial isolates that were identified as Ralstonia solanacearum were removed from further analyses, resulting in 515 remaining isolates.Direct effect of rhizobacteria on pathogen growth in vitroWe used R. solanacearum strain QL-Rs1115 tagged with the pYC12-mCherry plasmid as a model bacterial pathogen [8, 18]. We first tested the direct effects of the 515 non-R. solanacearum bacterial strains on the growth of R. solanacearum in vitro by using supernatant assays. Briefly, after 48 h of growth in NB (nutrient broth) medium (glucose 10.0 g l−1, tryptone 5.0 g l−1, yeast extract 0.5 g l−1, beef extract 3.0 g l−1, pH 7.0) on a shaker at 170 rpm, 30 °C, all bacterial cultures were filter sterilized to remove living cells (0.22 µm filter). Subsequently, 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of the pathogen (adjusted to OD600 = 0.5 after 12 h growth at 30 °C with shaking) were added into 180 µl of fresh NB medium (5-times diluted, in order to better reflect the effect of the supernatant). Control treatments were inoculated with 20 µl of 5 X diluted NB media instead of the bacterial supernatant. Each treatment was conducted in triplicate. All bacterial cultures were grown for 48 h at 30 °C with shaking (170 rpm) before measuring pathogen density as red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) [9, 11] which was linearly related to the CFU of pathogen R. solanacearum (Fig. S1). To test for significance of growth promotion or inhibition, R. solanacearum densities were log10-transformed prior to analyses of variance (ANOVA) and Bonferroni t test to compare mean differences between each rhizobacterial supernatant treatment and the control treatment, with p values less than 0.05 considered statistically significant. The effect on pathogen growth was defined as the percentage of improvement or reduction in pathogen growth by the supernatant compared to the control treatment. When the effect on pathogen growth was positive, i.e., when the supernatants from strains significantly promoted the growth of the pathogen, they were considered as helpers of the pathogen. If the effect on pathogen growth was negative, i.e., when the supernatants from strains significantly inhibited the growth of the pathogen, they were considered as inhibitors of the pathogen.Assessing strain redundancy among the 515 non-Ralstonia solanacearum bacteriaWe assessed possible redundancy among the 515 strains of the non-Ralstonia solanacearum rhizobacteria. To encompass both taxonomic and functional redundancies, we considered the 16 S rRNA gene sequences as well as the direct effect of their supernatant on Ralstonia solanacearum. Self BLAST searches were performed on the full 515 sequence dataset using the makeblastdb and blastn commands from the BLAST command line tool [19]. Sequences showing >99% identity over >95% of the full length of the 16 S rRNA gene were considered as taxonomically redundant. We then compared the direct effects on pathogen growth of the taxonomically redundant strains, and removed those showing the same patterns of interactions (positive, negative or neutral). Accordingly, (see the dataset “Library of rhizobacterial strains” in the supplementary information), 355 of the 515 strains (68.9%) were removed from the original dataset for further analyses.Phylogenetic tree constructionThe 16 S rRNA gene sequences of the 160 non-redundant bacteria were aligned using MUSCLE [20]. Sequences in the alignment were trimmed at both ends to obtain maximum overlap using the MEGA X software, which was also used to construct taxonomic cladograms [21]. We constructed a maximum-likelihood (ML) tree, using a General Time Reversible (GTR) + G + I model, which yielded the best fit to our data set. Bootstrapping was carried out with 100 replicates retaining gaps. A taxonomic cladogram was created using the EVOLVIEW web tool (https://evolgenius.info//evolview-v2/). To show the relationship between phylogeny and the effects of rhizobacteria on pathogen growth, we added taxonomic status (phylum) of each rhizobacterial strain and its effect on pathogen growth as heatmap rings to the outer circle of the tree separately (Fig. 2B).Fig. 2: Taxonomic characterization of rhizobacterial isolates that inhibited or helped the growth of Ralstonia solanacearum.A Distribution of in vitro effects of 160 rhizobacterial supernatants on R. solanacearum growth. The red vertical line represents no effect on R. solanacearum growth. B Cladogram depicting the phylogenetic relationship among the 160 isolates based on their full-length 16 S rRNA gene sequences. The inner ring depicts the different effect of isolates supernatant on R. solanacearum growth: positive effect (blue), negative effect (red) and no significant effect (gray). The outer ring shows the four phyla to which the isolates belong. C The proportion of rhizobacterial isolates per phylum whose supernatant showed inhibitory, stimulatory or no effect on R. solanacearum growth. The size of the circles represents the number of rhizobacterial isolates in the given phylum. The thickness of lines represents the percentage of rhizobacterial isolates that have the indicated effect on R. solanacearum growth in each phylum.Full size imageEffects of rhizobacteria on pathogen helper strains growth in vitroWe then assessed the potential of different rhizosphere isolates to inhibit helper strains. We first selected two model helper strains (Phyllobacterium ifriqiyense LM1 (Pi) and Microbacterium paraoxydans LM2 (Mp)), which showed strong positive effects on pathogen growth both in co-culture and in supernatant assays (Fig. S2). We defined the effect of rhizobacterial strains on the growth of helpers as the indirect effect on R. solanacearum growth. To study these indirect effects, we first chose a subset of 46 rhizobacterial strains representing a gradient of positive, neutral or negative effect on pathogen growth based on supernatant assays (results in x axis of Figs. 3C and 4A, B, C). We then tested the effects of these 46 rhizobacterial strains on the growth of each of the two helper strains using supernatant assays. Briefly, after 48 h growth in NB media, each of the 46 bacterial monocultures was passed through a 0.22 µm filter to remove living cells. Then 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of Pi or Mp (adjusted to OD600 = 0.5 after 12 h growth at 30 °C with shaking) were added into 180 µl of fresh NB medium (5-times diluted, in order to better reflect the effect of the supernatant). Control treatments were inoculated with 20 µl of 5× diluted NB media instead of a bacterial supernatant. Each treatment was replicated four times. All bacterial cultures were grown for 24 h at 30 °C with shaking (170 rpm) before measuring helper density as optical density (OD600). To test for significance of growth promotion or inhibition, we used analyses of variance (ANOVA) and Bonferroni t test to compare mean differences of helper density between each rhizobacterial supernatant treatment and the control treatment, with p values lower than 0.05 being considered statistically significant. The effect of rhizobacteria on the helpers’ growth (results in y axis of Fig. 3C and x axis of Fig. 4D, E, F) was defined as the percentage of increase or reduction in helper growth by the supernatant compared to the control treatment.Fig. 3: Effect of helper strains on Ralstonia solanacearum growth and plant disease severity.Effects of the two helper strains Phyllobacterium ifriqiyense (Pi) and Microbacterium paraoxydans (Mp) on Ralstonia solanacearum (Rs) growth in vitro (A) and in vivo (B) and on plant disease severity (C). Different letters indicate significant differences based on Tukey post hoc test. Error bars show ±1 SE (n = 3 for in vitro, n = 4 for in vivo). D Effects of 46 rhizobacterial strains on the growth of R. solanacearum and the two model helper strains in vitro. The x-axis shows the direct effect of each rhizobacterial strain on R. solanacearum growth (data from the experiment in which R. solanacearum was grown in the presence of supernatant from each of the 46 rhizobacterial strains—the same data is presented on the x axis of Fig. 4A). The y-axis shows the effect of each rhizobacterial strain on each of the two helper strains (data from the experiment in which each helper was grown in the presence of supernatant from each of the 46 rhizobacterial strains—the same data is presented on the x axis of Fig. 4C). In (C), “−1”, “0” and “1” on the x-axis denote that R. solanacearum growth is completely inhibited, not influenced or increased 2× by supernatant from the rhizobacteria, respectively. Similarly, “−1”, “0” and “1” on the y-axis denote the same growth effects with reference to growth of the helper strains. Black dots indicate results involving interactions with Pi, and red dots indicate results involving interactions with Mp.Full size imageFig. 4: The importance of direct versus indirect effects on Ralstonia solanacearum density and disease severity in the presence of helper strains.In the presence of helper Phyllobacterium ifriqiyense (Pi) or Microbacterium paraoxydans (Mp), respectively, the importance of direct effects on the density of R. solanacearum both (A) in vitro and (B) in vivo, and (C) disease severity (the data on the x axis of (A) are the same data which was presented on the x axis of Fig. 3C, the data on x axis of (B) and (C) are part of the data on x axis of (A)); the importance of indirect effects on the density of R. solanacearum both (D) in vitro and (E) in vivo, and (F) disease severity (the data on the x axis of (D) are the same data which was presented on the y axis of Fig. 3C, the data on x axis of (E) and (F) are part of the data on x axis of (D)). In all panels, “−1”, “0” and “1” on the x-axis denote that R. solanacearum growth (A, B, and C) or helper growth (D, E, and F) is completely inhibited, not influenced or increased 2× by supernatant from the rhizobacteria, respectively.Full size imageIn vitro pathogen growth in the presence of a helper strain and supernatant from rhizobacterial isolatesTo disentangle the direct effects from the indirect effects of rhizobacteria on R. solanacearum growth, we compared their relative effects using in vitro triculture assays comprised of R. solanacearum, one of the two helper strains and supernatant of one of the 46 chosen rhizobacterial strains. Briefly, after 48 h of growth in NB media, each of the 46 bacterial monocultures was passed through a 0.22 µm filter to remove living cells. Then, 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of Pi or Mp (densities were adjusted to ~107 cells per ml) were added to 180 µl of fresh NB medium (5-times diluted). Each treatment was replicated four times. At the same time, 2 µl overnight culture of mCherry-tagged R. solanacearum (density was adjusted to ~106 cells per ml) was added to each treatment in 96-well plates at 30 °C with shaking (170 rpm). After 24-h growth, R. solanacearum density (results in y axis of Fig. 4A, D) was measured as the red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) with a SpectraMax M5 plate reader.In vivo pathogen growth and plant disease development in the presence of a helper strain and a rhizobacterial strainTo validate in vitro results, we set up greenhouse experiments where plants were inoculated with a bacterial consortium consisting of R. solanacearum, one of the two helper strains and a test rhizobacterial strain. Tomato seeds (Lycopersicon esculentum, cultivar “Ai hong sheng”) were surface-sterilized by soaking them in 3% NaClO for 5 min and in 70% ethyl alcohol for 1 min before being germinated on water-agar plates for 2 days. Seeds were then sown into seedling trays containing gamma irradiation-sterilized (to avoid potential effects of the resident community) seedling substrate (Huainong, Huaian Soil and Fertilizer Institute). At the three-leaf stage, tomato plants were transplanted to seedling trays containing 200 g of the same seedling substrate as describe above.To relate our results to practical application conditions, we selected a subset of 12 strains that displayed a range of inhibitions effects on pathogen and helpers (Table S1) out of the 46 rhizobacterial isolates used for the in vitro assays. Each rhizobacterial strain was used in combination with each of the two helper strains and R. solanacearum, resulting in a total of 28 treatments (Table S2), including a water control, R. solanacearum alone, and R. solanacearum with just each of the two helper strains (results in Fig. 3B, C). For each treatment, four replicate seedling trays were used, with each replicate seedling tray containing 4 tomato plants. Three days after transplantation, plants of each treatment were inoculated with one of the two helper strains, alone or in combination with one of the rhizobacterial strains, using the root drenching method at a final concentration of 108 CFU g−1 soil for each bacterial strain [22]. Seven days after inoculation of helper alone or together with rhizobacteria, R. solanacearum was introduced to the roots of all plants at a final concentration of 107 CFU g−1 soil. The positive control treatment with R. solanacearum alone was inoculated only with the pathogen, and the negative control treatment was not inoculated with any bacteria. Tomato plants were maintained under standard greenhouse conditions (i.e., at natural temperature variation ranging from 28 °C to 32 °C, 15/9 h day/night conditions) and watered regularly with sterile water. Seedling trays were rearranged randomly every two days. Forty days after transplantation, plants were destructively harvested. The disease index for each plant was recorded based on a scale ranging from 0 to 4 [23]. Disease severity for each replicate seedling plate was calculated as described by: Disease severity = [∑ (The number of diseased plants in the disease index category × disease index category)/ (Total number of plants used in the experiment × highest disease index category)] ×100% [23, 24]. Simultaneously, we collected rhizosphere soil samples following an established protocol [4]. Briefly, two plants were randomly chosen from each replicate seedling tray to collect rhizosphere soils and further combined to yield one sample, resulting in a total of 112 rhizosphere soil samples for which R. solanacearum population densities were determined.Quantification of R. solanacearum at the end of the in vivo experimentWe determined R. solanacearum densities using quantitative PCR (qPCR). DNA was extracted from rhizosphere soils using a Power Soil DNA isolation kit (Mo Bio Laboratories) following the manufacturer’s protocol. DNA concentrations were determined by using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and extracted DNA was used for R. solanacearum density measurements using specific primers (forward, 5ʹ-GAA CGC CAA CGG TGC GAA CT-3ʹ; reverse, 5ʹ-GGC GGC CTT CAG GGA GGT C-3ʹ) targeting the fliC gene, which encodes the R. solanacearum flagellum subunit [25]. The qPCR analyses were carried out with a StepOnePlus Real-Time RCR Instrument using SYBR green fluorescent dye detection and three technical replicates as described previously [4].Statistical analysesTo meet assumptions of normality and homogeneity of variance, R. solanacearum densities measured in vitro and in vivo were log10-transformed. When comparing mean differences between treatments, we used analyses of variance (ANOVA) and the Tukey Test, where p values lower than 0.05 were considered statistically significant. R. solanacearum densities were explained by two quantitative indices, the direct effect of rhizobacteria on R. solanacearum growth (the effect of rhizobacteria on R. solanacearum growth) and the indirect effect of rhizobacteria on R. solanacearum growth (the effect of rhizobacteria on helper strains’ growth). Nonlinear regression analyses (Sigmoidal, Sigmoid, 3 Parameter) were used to analyze the relationship between the direct effect and pathogen density, as well as the relationship between indirect effects and pathogen density in the presence of helper strains in vitro. The relationships between them, and between direct/indirect effects and disease severity in the presence of helper strains in vivo, were analyzed using linear regressions. These analyses were carried out using the R 3.6.3 program (www.r-project.org) and Sigma Plot (V.12.5).To further consider the growth inhibition of R. solanacearum, and disease suppression, we fitted a linear model to estimate the relative importance of direct effects versus indirect effects on the density of R. solanacearum both in vitro and in vivo, and on disease severity. This model considered the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (see the R script “Model” in the supplementary information). These analyses were performed in R version 3.6.3 [26] in conjunction with the package car, readxl and dplyr, and tidyverse 1.2.1 [27]. Briefly, proportional effects were normalized using a folded cube root transformation as suggested in J.W. Tukey [28] and fitted using a linear model with direct effects, indirect effects, and an interaction between helper strains and indirect effects as fixed factors. Normality of residuals was tested using the Shapiro-Wilk normality test and visual inspection of QQ-plots with standardized residuals. Type-II sum of squares were calculated using the ANOVA function from car 3.0-2 [29]. Subsequent visualization of the model outcome (results in Fig. 5) showed the predicted R. solanacearum densities and disease severity for different values of the inhibition via pathogen (Direct) or helper (Indirect) as estimated from the statistical model. For the Direct effect line, the indirect effect is set to be zero, while for the Indirect effect line, the direct effect is set to be zero.Fig. 5: The relative importance of direct versus indirect effects on Ralstonia solanacearum density and disease severity in the presence of helper strains.Relative importance of direct versus indirect effects on Ralstonia solanacearum density both in vitro (A) and in vivo (B), and disease severity (C) in presence of helper strains on the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (quadrant “H−P−” in Fig. 3C). This shows the predicted R. solanacearum densities and disease incidence for different values of the inhibition via pathogen (Direct) or helper (Indirect) as estimated from the statistical model (Table 1) which with direct effects, indirect effects, and an interaction between helper strains and indirect effects as fixed factors. For the Direct line, the indirect effect was set to zero, while for the indirect line, the direct effect was set to zero.Full size image More

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Study areaThe study area (78 000 km2) is located between 68–71°N and 20–26°E, with strong climatic gradients, ranging from wet maritime to relatively dry continental, over tens of kilometers. The landscape of this climatically sensitive high-latitude region has been affected by multiple glaciations in the past. It includes the Scandes Mountains near the Arctic Ocean and low-relief areas to the south and east. The majority of the region (52%) is underlain by sporadic permafrost. Continuous and discontinuous permafrost are limited to the highest mountains of the study area (2% and 7%, respectively)17,26. This large proportion of sporadic, typically warm and shallow permafrost in the study area indicates that ground thermal response to climate warming can be rapid27. Our data do not cover low-relief plateaus of continuous permafrost (similar to northern Siberia and Alaska), where the generally high ice content of soil may lead to different and enhanced LSP responses under climate warming (e.g., ice wedge degradation and surface ponding) with altered AGB feedbacks43,44.LSP observationsThe data consist of 2917 study sites (each 25 m × 25 m) and includes previously combined observations (both in-situ [n = 581] and remote-sensing [n = 2336]) of the active surface features of three cryogenic LSP common in the area: cryoturbation, solifluction, and nivation. These LSP are mainly associated with seasonal freeze–thaw processes. Cryoturbation (i.e., frost churning) is a general term for soil movement caused by differential heave, and it creates typical surface features such as patterned ground, frost boils and hummocky terrain5. Solifluction is the slow mass wasting of surficial deposits through frost creep and permafrost flow, where gravitation causes frost-heaved soil to settle downwards during the summer thaw, creating features of lobes and terraces50. In addition, solifluction also includes gelifluction which is a mass wasting process caused by high porewater pressure in unconsolidated surface debris creating similar lobes and terraces5,50. We use the term nivation to collectively designate various weathering and fluvial processes which are intensified and depicted by the presence of snowbeds (which in general are melting in mid-July – late-August) and nivation hollows28,51. We expect the presence of such a snowbed to be an indication of active nivation processes, since in these environments the year-to-year spatial snow patters are fairly consistent31.The rationale behind LSP sampling is described in previous geomorphic studies which served as a basis for the used protocol52,53. Due to the large study domain, study objectives (focus on distribution of active surface features, not on activity itself) and modeling resolution (50 m × 50 m), we used a visual method to estimate the presence/absence of the mapped LSP. We used high-resolution aerial photography (spatial resolution of 0.25 m−2) and targeted field surveys (GPS accuracy ~5 m; Garmin eTrex personal navigator) to construct the LSP dataset. A binary variable (1 = presence, 0 = absence) was assigned to each LSP to indicate their evident activity (or absence). The activity/absence of the LSP was visually estimated based on the evidence in ground surface, indicated by e.g., frost-heaving, cracking, microtopography (e.g., erosional and depositional forms), soil displacement indicative to a process form (e.g., solifluction lobes, patterned ground), changes in vegetation cover and late-lying snow. Such indicators average the LSP activity over several years. Even small areas with slight indication of activity were considered active processes. However, such a protocol based on a visual assessment is susceptible for incorrect activity classification; solifluction may be active despite having a complete vegetation cover19 and the presence of late-lying snowbed, although being a good indication28, does not necessarily mean that active nivation processes are present.Remotely sensed vegetation indexFor obtaining remotely sensed vegetation index for the study area, we employed a maximum-value compositing approach. We downloaded all available clear sky (less than 80% land cloud cover) Landsat OLI 8 images overlapping the study area from June to September between 2013 and 2017 (total of 1086 scenes) from the United States Geological Survey (USGS) database (http:\earthexplorer.usgs.gov). Images were USGS surface reflectance products, which were preprocessed (georeferencing, projection, and atmospheric corrections) by USGS54. Landsat-8 satellite is the latest addition to the Landsat mission that has provided repeated land surface information globally since the 1970’s and is the most commonly used fine-scale satellite system for vegetation mapping. The native resolution of the Landsat OLI sensor is 30 m for the spectral bands used in the image processing steps of this study.Normalized difference vegetation index (NDVI), a widely used spectral index to estimate the amount of green vegetation, was calculated as55:$$({{{{{rm{rho }}}}}}{{{{{rm{NIR}}}}}}-{{{{{rm{rho }}}}}}{{{{{rm{red}}}}}})/({{{{{rm{rho }}}}}}{{{{{rm{NIR}}}}}}+{{{{{rm{rho }}}}}}{{{{{rm{red}}}}}})$$
(1)
where ρNIR and ρred are the surface reflectance for their respective Landsat bands, 0.851–0.879 (mu)m and 0.636–0.673 (mu)m.USGS provides pixel-based quality assessment bands for all surface reflectance products. These bands were used to mask clouds, snow, water, and other low-quality pixels from the individual NDVI scenes. Additionally, if the NDVI images still had unphysical values over 1 or under -1, these pixels and their surroundings of 100 m radius were excluded. We determined maximum values for each 30 m resolution pixel of the study area individually. After masking cloud, snow, and water from the scenes, obvious scattered erroneous NDVI values remained in some scenes. Therefore, we excluded the values outside the pixel-based 95% percentile prior to maximum composite.The CFmask cloud detection algorithm that is used to generate the quality assessment band has clear difficulties in distinguishing small snow patches from clouds. As such, a large portion of late-lying snow beds were repeatedly and incorrectly classified as clouds. Moreover, the CFmask algorithm creates buffers around the cloud pixels54, hence much information was lost around the snow patches that were incorrectly identified as clouds. After these processing steps, some pixels around the extreme late-lying snow beds had still too low number of NDVI records to provide reliable NDVI values for the maximum composite. To fill these small and scattered gaps in the initial maximum NDVI composite, we selected 74 mostly cloud-free scenes between August and September. For these 74 scenes, we manually digitized cloud masks to exclude cloud-contaminated pixels with high certainty. Moreover, every pixel must have passed the following quality checks to be included in the gap-filling composite: not classified as water in the USGS quality assessment band; normalized difference snow index (NDSI) value less than 0.4, and blue band reflectance less than 0.1 (to exclude snow); reflectance of red band between 0.03 and 0.4 (second check for water and snow, and deepest shadows); NDVI between 0 and 0.4 (lower threshold to exclude snow and water contamination; higher threshold to exclude erroneous values, as very late snowbed habitats always have very limited vegetation cover). Additionally, if the NDVI images had unphysical values over 1 or under -1, these pixels and their surroundings (200 m radius) were excluded. Pixels in the 74 selected images which passed these checks, were then used to create a secondary maximum NDVI composite that was used to fill the gaps in the initial maximum NDVI composite. The secondary composite comprised 0.4% of the pixels in the final composite. Among all 2917 LSP observation sites, 2.9% were located within the gaps in the initial maximum NDVI composite, and thus received their maximum NDVI values from the secondary NDVI composite.In the used Landsat data, the nivation sites were not covered by snow, but instead were associated with generally lower AGB values as nival processes affect the vegetation’s structure and composition (Supplementary Table 1).Above-ground biomass dataAbove-ground biomass (AGB) reference data were collected from two regions, with a total of 433 sites that represent an area of > 4000 km2 (Supplementary Fig. 9). The first dataset (hereafter BM region 1; centering to ca. 69°N, 21°E) was collected between 2008 and 2011, and the second dataset (BM region 2; centering to ca. 70°N, 26.2°E) between 2015 and 2017. Both study regions are representative of an arctic and alpine treeline ecotone and include data from mountain birch forest to barren oroarctic tundra56,57.The BM region 1 dataset consists of 309 field sites (each 10 m × 10 m), which are located around eight different massifs covering a wide range of environmental conditions (Supplementary Figs. 9–10). Sampling was performed in transects to cover various aspects of the slope (i.e., topoclimatic conditions), starting from the foothill of the mountain, and ending at the summit. A plot was systematically established at every 20 m increase in elevation and recorded with a GPS device. Four clip-harvest biomass samples (20 cm × 20 cm) were taken 5 m from the plot center in every cardinal direction. Two samples were used in bare mountaintops (north, south). The clip-harvest samples were dried for 48 h at +65 °C, and dry weight was recorded. The sample biomass values were converted to g m-2 and the average sample value was calculated for each site (Supplementary Fig. 9). The original BM region 1 dataset contains forest and treeline plots, but these were excluded from the final analyses due to an incomparable tree sampling strategy with BM region 2, which could introduce uncertainty into biomass estimates.The BM region 2 data were collected from three different massifs having an elevation range from 120 m to 1064 m (Supplementary Fig. 9). The biomasses were sampled from 102 sites (each 24 m × 24 m in size) that were chosen using a stratified sampling to cover gradients of thermal radiation (potential incoming solar radiation), soil moisture (topographic wetness index, TWI) and vegetation zone (forest, treeline, and alpine zones). Radiation and TWI were calculated from a 10 m digital elevation model (DEM, provided by the National Land Surveys of Finland and Kartverket, the Norwegian mapping authority), and assigned to one of three classes based on observation percentiles (breaks at 20% and 80%) leading to total of 27 strata. Vegetation zones were digitized based on aerial imagery. After the first field survey, 22 sites were added to account for vegetation types that were not sufficiently represented by the GIS-based stratification. Thus, the total sample size of the BM region 2 dataset is 124 AGB sites.The same clip-harvest sample protocol was used as in BM region 1; additional samples were also taken from 12 m in every cardinal direction, thus each site had eight AGB samples (Supplementary Fig. 9). Trees with diameter at breast height (DBH) greater than 20 mm were measured from a 900 m2 circular plot, which corresponds to the size of the NDVI product resolution. Large stems (DBH  > 80 mm in the forest and 40 mm at the treeline) were measured from the whole plot, whereas smaller stems were measured from five subplots. Specifically, the center subplot was 100 m2, and the four subplots located at 8 m to every cardinal direction were each 12.5 m2. For the subplot observations, we used a plot expansion factor (900/150 = 6) to generalize the observations for the whole plot assuming a homogeneous forest structure i.e., each subplot stem represents six trees within the 900 m2 plot. A total of 98% of the measured stems were mountain birch (Betula pubescens ssp. czerepanovii), making it the most abundant species in the area. For predicting stem biomass, we used the average of three allometric equations58,59,60, in order to reduce the uncertainty related to the transferability of an individual allometric model. In addition, Populus tremula (1% of the observations) were found on low-altitude south-facing slopes, and Salix caprea (1%) in moist, nutrient-rich sites. Species-specific models61,62 were used to estimate their respective stem biomasses. Individual pines (Pinus sylvestris) were scattered in the area but were not present in any of the sampled plots.The plots of above-ground tree biomass were converted to g m−2 and added to the mean clip-harvest AGB to obtain the total vascular plant AGB for each site. The BM region 1 and BM region 2 datasets were combined, and the NDVI value was extracted from the site center coordinates.Spatial autocorrelation (SAC) is a common property of any spatial dataset and means that observations are related to one another by the geographical distance63. SAC in the model residuals violates the independence assumption commonly required by statistical models and can lead to inflated hypothesis testing and biased model estimates64. To investigate whether the plot-scale AGB data are spatially autocorrelated, we calculated semivariogram which describes the spatial dependency between the observations as a function of distance between the point pairs65. Semivariogram were calculated as:$${{{{{rm{gamma }}}}}}(h)=frac{1}{2N(h)}mathop{sum }limits_{i=1}^{{N}_{h}}{left(Zleft({s}_{i}right)-Zleft({s}_{i}+hright)right)}^{2}$$
(2)
where N(h) denotes the number of data pairs within distance h, and (Zleft({s}_{i}right)) is an observation (or model residual) in location i. For the calculation, we used R package gstat66 (version 2.0-0). A visual inspection of the semivariogram indicated spatial autocorrelation at short distances (“AGB” in Supplementary Fig. 11). Therefore, for the NDVI-AGB conversion, we used a generalized least squares modeling (GLS, as implemented in R package nlme67 [version 3.1-137]) that can explicitly account for SAC in the data. For the modeling, the AGB values were log(x+0.1) transformed. The GLS, where AGB was modeled as a function of NDVI, were fitted assuming an exponential spatial correlation structure:$$gamma left(hright)={c}_{0}+cleft(1-{e}^{-h/a}right)$$
(3)
where ({c}_{0}) is the difference between the intercept and origin (i.e., the “nugget” parameter in geostatistics), (c) is the amount of variance (i.e., the “sill”) and a represents the distance of spatial dependency (i.e., the “range”). The fitted GLS was as follows:$${{log }}left({AGB}right)=-1.038629+9.725572times {NDVI}$$
(4)
The estimated spatial correlation parameters were c0 = 0.516, c = 0.484 and a = 260.605, indicating that the distance of spatial autocorrelation extends to ca. 261 m. The semivariogram for the model residuals indicated a notable reduction in the amount of spatial autocorrelation compared to the AGB data (Supplementary Fig. 11). The fitted model explained 70.6% of the deviance in the data. When the predicted values were converted back to the response scale, the model explained 60.5% of the deviance. Therefore, for the subsequent analyses we use the above-ground biomass estimated by the model.Environmental predictorsIn addition to LSP, we used climate, topography, and soil predictors to model AGB. Gridded monthly average temperatures and precipitation data (1981–2010; spatial resolution 50 m × 50 m) based on a large collection ( > 950) of Fennoscandian meteorological stations were used in a spatial interpolation scheme17. Three climate predictors—growing degree days (GDD, °C, base temperature 5 °C), mean February air temperature (Tfeb, °C) and water balance (WAB, mm)—were calculated from the gridded climate data. WAB is the difference between total annual precipitation and potential evapotranspiration (PET), which was estimated from the monthly air temperature and precipitation data68:$${PET}=58.93times {T}_{{above}0^circ C}/12$$
(5)
These climatic predictors were selected to represent different aspects of climate that are critical for tundra vegetation: heat requirements, cold tolerance and moisture availability. In addition, two local scale topographic predictors were calculated from a DEM (spatial resolution of 50 m × 50 m, provided by the National Land Survey Institutes of Finland, Norway, and Sweden): topographic wetness index69 (TWI, a proxy for soil moisture) and potential annual direct solar radiation70 (MJ cm-2 a-1). Slope angle was initially considered as a potential predictor for AGB but was later omitted due to the strong correlation with TWI (-0.93, P ≤ 0.001). We also calculated peat cover (%) from a digital land cover classification71. Here, the native resolution of 100 m was resampled at 50 m to match the resolution of the climatic and topographic predictors, using nearest-neighbor interpolation. The binary peat cover variable was transformed to a continuous scale using a spatial mean filter of 3 × 3 pixels52. Finally, the topmost soil layer of a global gridded soil database72 was used to obtain pH data. Again, the original resolution of 250 m was also resampled to 50 m resolution using bilinear interpolation.Our fine-scale data revealed strong environmental gradients over the 78,000 km2 study area (Supplementary Table 1), most of which were only moderately inter-correlated (Spearman’s correlation coefficient  More