Ecology

Sampling strategyAll sampling sites were located in the Eifel National park, situated in the south-western part of Germany close to the Belgian border (Supplementary Fig. 1, Supplementary Table 1).In this study the sampling site comprised a forest conversion gradient from a Norway Spruce (Picea abies) monoculture to a European Beech forest (Fagus sylvatica). To reflect the different stages of conversion from spruce to beech, four forest types were defined: pure beech (PB), old beech (OB), young beech (YB) and pure spruce (PS) (Supplementary Table 1, Supplementary Fig. 2).The forest types differed in tree species composition and tree age. The pure beech and pure spruce forest types were monoculture stands. The pure beech stands were approximately 180 years old and partly under special protection through North-Rhine Westphalia (Naturwaldzelle) (Sampling site 01). The spruce monocultures were substantially younger with ca. 60 years old. Spruces of the same age dominated the young beech sampling sites that had only recently been underplanted with beeches. At the old beech sampling sites, beeches had already reached a height of more than 3 m and actions to remove spruces from the forest were conducted.A total of 12 Townsend Malaise traps (three per forest type) were set up in the Eifel National Park, North-Rhine Westphalia, Germany, during July 2016. To ensure that the orientation of the Malaise traps was consistent and to minimize potential biases caused by wind direction and position of sun, the highest point of each trap was set facing south. The traps were left in the field for the full duration of the experiment until April 2017, ensuring that insects were collected from exactly the same locations. In October 2016, two additional traps (Malaise Trap 13, pure spruce and Malaise Trap 14, old beech) were installed at two further sampling sites (Sample Site 13 and Sample Site 14). All traps were equipped with a bottle filled with approximately one litre of absolute ethanol (99,96%) over a 2-week period in July 2016 (13.07–27.07), October 2016 (13.10–27.10), January 2017 (11.01–25.01) and April 2017 (12.04–26.04) (Supplementary Table 2). The ethanol was replaced every week to ensure that the concentration of the preservative ethanol was stable and to avoid loss of insects a mesh filter was used (MICROFIL V Filter White Gridded 0.45 µm-diameter 47 mm & 100 ml Funnel Sterilized). Due to heavy snow during the winter period, new traps were set at the start of the new sampling season in January 2017.Three soil samples were collected around each Malaise trap, from the organic horizon of the top 10 cm layer (excluding the litter layer). Soil sample sites were located 4 m and 5 m away from the trap, forming a triangle in the centre of which the Malaise trap was located (Supplementary Fig. 3). One corner of the sampling triangle was pointing south, while both remaining corners were pointing north west and north east, respectively.Each sampling site was sampled four times in the course of a 1-year period. Soil sampling and Malaise trapping were synchronized and soil sampling was done on day 14 of each Malaise trapping period, when the last bottles were collected (Supplementary Table 3). Each soil sample consisted of approximately twenty 44 mm × 100 mm cores, taken 5 cm apart. A total of 162 soil samples were collected and stored at − 20 °C until further processing.DNA extractionBulk samples from Malaise trapsNon-destructive DNA extraction was performed by overnight incubation in lysis buffer, using a modified protocol of Aljanabi and Martinez (1997). The arthropods were sieved from the collecting ethanol using a mesh filter (MICROFIL V Filter White Gridded 0.45 µm-diameter 47 mm and 100 ml Funnel Sterilized), which was processed with the specimens. The insects were dried for 10 min at room temperature. Depending on biomass, between 15 and 25 ml of extraction buffer (0.4 M NaCl, 10 mM Tris-HCl pH 8.0, 2 mM EDTA pH 8.0) and 2% Sodium dodecyl sulphate (SDS) were added to each bulk sample. Finally, 400 µg Proteinase K was added per ml of lysis buffer and samples were lysed overnight at 52 °C on an orbital shaker at 30 rpm. The next day, the lysate was poured out of the bottles using the MICROFIL V Filter (White Gridded 0.45 µm-Dia 47 mm and 100 ml Funnel Sterilized) and a 6 M NaCl solution was added to the lysate to a concentration of 4 mmol. The samples were vortexed for 30 s, centrifuged at 4700 rpm for 30 s and the supernatant was transferred to a falcon tube and an equal volume of isopropanol was added. After careful mixing by inversion, the tubes were left at − 20 °C for 1 h and subsequently centrifuged at 4700 rpm for 60 min. The supernatant was discarded and the resulting pellet was washed with 20 ml of ice cold 70% ethanol, by centrifuging at 4700 rpm for 15 min. The remaining ethanol was discarded and the pellet was left to dry at 20 °C overnight. The pellet was then resuspended in 1 ml of sterile H2O and stored at − 20 °C until further processing.Soil samplesDNA extraction from the soil samples was conducted using two different extraction methods: a commercial (lysis-based) DNA extraction kit (Macherey-Nagel NucleoSpin Soil) and a (no lysis) phosphate buffer protocol from Taberlet et al. 201240. Each of the triplicated samples were processed individually. After defrosting the soil overnight at 4 °C, the samples were thoroughly mixed, DNA was extracted from 0.5 g of soil per sample using the Macherey-Nagel NucleoSpin Soil Kit following the manufacturer’s protocol.The second DNA extraction method allowed extracellular DNA to be extracted from larger amounts of starting material using a phosphate buffer and did not include a lysis step. Each of the three samples taken per sample site and season were treated individually. Soil samples were removed from the − 20 °C chamber approximately 12 h before DNA extraction and stored at   4 °C overnight. The next morning, each sample was thoroughly mixed and an equal weight of saturated phosphate buffer solution (Na2HPO4; 0.12 M; pH 8)40 was added. Samples were placed in an orbital shaker at 120 rpm for 15 min. Thereafter, duplicates were processed, where two 2 ml Eppendorf tubes were filled with 1.7 ml of the resulting mixture and centrifuged for 10 min at 10,000 g. Four hundred microliters of the resulting supernatant were transferred to a new 2 ml collection tube and 200 μl of SB binding buffer from the Macherey-Nagel NucleoSpin Soil Kit was added. Duplicate lysates were merged by loading onto a single NucleoSpin Soil Column and centrifuged at 10,000 g for 1 min. From this step onwards, the standard manufacturer’s protocol for the Macherey-Nagel NucleoSpin Kit was followed from step 8. DNA was eluted with 50 μl of SE Buffer (Macherey-Nagel). Ten microliters of the resulting DNA eluate was diluted in 90 μl of pure H2O (Sigma), followed by DNA purification using the PowerClean Pro DNA Clean-Up Kit (MO Bio Laboratories, Inc.) following the manufacturer’s protocol. For the purposes of this study, results from the two types of soil extraction were merged.Choice of primers and amplicon library preparationFor amplicon library preparation a primer pair targeting the 313 bp ‘mini barcode’ region of the mitochondrial Cytochrome c Oxidase subunit I gene (COI) was used41. The ‘mini barcode’ primer pair consisted of the forward primer mlCOIintF 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGWACWGGWTGAACWGTWTAYCCYCC -3′41 and the reverse primer dgHCO2198 5′- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTAAACTTCAGGGTGACCAAARAAYCA-3′41 (Illumina overhang in regular font and primer in bold). Library preparation was carried out using a two-step PCR approach42,43 , whereby PCR1 amplifies the gene region of interest and PCR2 adds the sample index together with the Illumina overhang (indexed primers). For each sample, a unique combination of indexes was chosen.DNA extracts were quantified using the Quantus Fluorometer (Promega). Ten nanograms of template DNA was used for PCR1. PCR1 consisted of 7.5 µl Q5 Hot Start High-Fidelity 2 × Master Mix (New England BioLabs), 1 μl Sigma H2O, 0.5 µl forward Primer (10 µM), 0.5 µl reverse primer (10 µM), 0.5 μl Bovine Serum Albumin (Thermo Scientific) and 1 µl template DNA, making up a total of 15 µl. PCR1 cycling conditions were as follows: 2 min at 98 °C (1 ×); 40 s at 98 °C, 40 s at 45 °C, 30 s at 72 °C (20 ×); 3 min at 72 °C (1 ×). PCR products were purified with 4 µl of HT ExoSAP-IT (Applied Biosystems) to each sample, following the manufacturers’ protocol. For PCR2 (index PCR) the purified PCR products were split into two PCR tubes.For PCR2, each tube contained 12.5 µl Q5 Hot Start High-Fidelity 2X Master Mix (New England BioLabs), 3 µl Sigma H2O, 1.2 µl of index forward primer (10 µM) (AATGATACGGCGACCACCGAGATCTACAC NNNNNNNN ACACTCTTTCCCTACACGACGC TC), 1.2 µl of index reverse primer (10 µM) CAAGCAGAAGACGGCATACGAGAT NNNNNNNN GTGACTGGAGTTCAGACGTGTGCTC) and 8 µl of purified PCR1 product. PCR2 cycling conditions were as follows: 2 min at 98 °C (1 ×); 40 s at 98 °C, 30 s at 55 °C, 30 s at 72 °C (20 ×); 3 min at 72 °C (1 ×). All tagged PCR products were visualised by gel electrophoresis and PCR bands with the expected size were excised and purified using the QIAquick Gel Extraction Kit (Qiagen). Purified PCR products were quantified using the Quantus Fluorometer (Promega) and pooled in equal concentrations. The resulting purified amplicon library pool (3 ng/µl) was sequenced on an Illumina MiSeq (MiSeq Reagent Kit v3, 2 × 300 bp) sequencing platform at Liverpool University’s Centre for Genomic Research (Liverpool, UK). Raw sequence data were deposited in the GenBank short read archive (SRA) under accession numberPRJNA681091 and PRJNA706915.Bioinformatics and data analysisThe raw fastq files were trimmed for the presence of Illumina adapter sequences using Cutadapt version 1.2.1. at the Centre for Genomic Research (Liverpool, UK). Sequences were trimmed using Sickle version 1.200 with a minimum window quality score of 20 and reads shorter than 20 bp were removed after trimming.The fastq sequences were then checked for the presence of the COI primers with Cutadapt version 1.1844 using the following settings: maximum error rate (-e): 0.1, minimum overlap (-O): 20, minimum sequence length (-m): 50. Sequences lacking either the forward or reverse primer were removed and primer pairs were trimmed off from the remaining sequences. Subsequently, paired-end reads were merged with vsearch version 2.7.045. Merged sequences with a length of 293–333 bp were retained for further analysis and filtered with a maxEE threshold of 1.0 using vsearch (version 2.7.0)45 before demultiplexing the fastq sequences using the script split_libraries_fastq.py implemented in QIIME146 using a phred quality threshold of 19. Dereplication, size sorting, denovo chimera detection as well as Operational Taxonomic Unit (OTU) clustering with a 97% cutoff was conducted with vsearch 2.7.045. Finally, an OTU table was built by using the –usearch_global function in vsearch 2.7.045 followed by the python script “uc2otutab.py” (https://drive5.com/python/uc2otutab_py.html). For taxonomy assignment, representative sequences were blasted against the GBOL database (https://www.bolgermany.de/gbol1/identifications downloaded on 2nd of July 2019) using blastn 2.9.0+47.The resulting OTU table was curated with LULU48. Curation started with an initial blasting of OTU representative sequences against each other using blastn (version 2.9.0). The following parameter settings were chosen: ‘query coverage high-scoring sequence pair percent’ (-qcov_hsp_perc) was set to 80, meaning that a sequence was reported as a match when 80% of the query formed an alignment with an entry of the reference file. Secondly, minimum percent identity (-perc_identity) was set to 84, requiring the reference and query sequence to match by at least 84% to be reported as a match. The format of the output file was customized using the –outfmt settings ‘6 qseqid sseqid pident’. The output file included the name of the query sequence and the name of the reference sequence next to the percentage match. The resulting OTU match list was uploaded into R (version 3.5)49 and the R-package ‘lulu’ (version 0.1.0)48 was used to perform post clustering curation using standard settings. The LULU algorithm filters the dataset for artificial OTUs and these were either classified as “daughter OTU” and merged with the corresponding “parent OTU” or were discarded from the dataset.The resulting curated OTU table was loaded into Excel where data was formatted to upload into R (R studio running R version 3.5). Only OTUs with an assignment at species level (blastID ≥ 99%) were used for subsequent analysis. Furthermore, results from the two types of soil extraction were merged.UpsetR plots were prepared using the R package UpSetR (version 1.4.0)50 for visualization of shared arthropod OTUs between sample types in each season. Differences in number of OTU proportions are shown in a Marioko plot prepared with the R package ggplot251. To analyze dissimilarities between communities depending on season and sample type, Permutational Multivariate Analysis of Variance (PERMANOVA) using Jaccard distance matrices for incidence data of detected arthropod species (blastID ≥ 99%) were performed using dplyr (version 0.8.3)52, betapart (version 1.5.1)53 and vegan (version 2.5–6)54. In order to analyse dissimilarity differences in arthropod community composition between the different forests and seasons the Jaccard similarity index (J) was used on a presence-absence matrix based on arthropod species. Calculated Jaccard indices were visualized on a heatmap using the R package ggplot251. Sample completeness curves and sample-size-based R/E curve with extrapolations of Hill numbers for incidence data based on the combined dataset for all forests and seasons were prepared using the R-package iNEXT55 at default settings (40 knots, 95% confidence intervals generated by the bootstrap procedure (50 bootstraps)).To correlate community structure and diversity levels with the different seasons and forest types a Permutational Multivariate Analysis of Variance (PERMANOVA) based on the Jaccard similarity index for a presence-absence matrix of detected arthropod species (blastID ≥ 99%) was performed with the adonis function in R. Differences in arthropod community composition between the different forests and seasons was assessed using the Jaccard similarity index (J), where the higher the index, the more similar the communities. More

Host and siteB. ermanii Chamiss, a deciduous broad-leaved tree species, occurs naturally in open spaces within subalpine and boreal forests in Northeast China, Japan and the Russian Far East.40,41,42 B. ermanii is also a typical tree species of the timberline, because it can resist to severe frost, strong wind and low temperature by ecophysiological and ecomorphological flexiblity.43,44,45 In addition, a rich mycobiome of B. ermanii has been reported,16,46,47 which may facilitate the survival and spread of the host plant.On the northern slope of Changbai Mountain, Northeast China, B. ermanii grows over a broad elevation range from ca. 1700 to 2100 m a.s.l., and forms pure stands along >300 m vertical belt below the timberline.42,48 The establishment of Changbai Nature Reserve (CNR) in 1960 gave strict protection to the Erman’s birch (B. ermanii) forests.49 All our sampling was located within the core region of the CNR. The region has a typical continental temperate monsoon climate, with higher elevations experiencing lower temperature and greater precipitation.50 Soils of the Erman’s birch forests are Permi-Gelic Cambosols, whereas the soils beyond the upper and lower limits of B. ermanii zone are Permafrost cold Cambosols and Umbri-Gelic Cambosols, respectively. Climate change, particularly rising temperature, threatens the populations of B. ermanii and the whole Erman’s birch forest ecosystem in Changbai Mountain.50,51Field sample collectionWe sampled fine roots and neighboring soils for B. ermanii individuals at six elevation-related habitats of B. ermanii on 2–8 September, 2018 (Fig. 1a). These six elevation habitats include the upper limit (2069–2116 m), tree islands (1997–2042 m), treeline (1949–1992 m), pure stands (including two sub-sites isolated by over 2 km; 1900–1926 m), ecotone of dark coniferous forests and Erman’s birch forests (1742–1765 m) and lower limit (sparse individuals in coniferous forests; 1688–1706 m), respectively. In each habitat, 14 trees were randomly selected, and all trees were located more than 20 m apart from other sampled trees to ensure independence of each sample. Neighboring soils and fine roots were collected according to the protocols of Yang et al.28 and Lankau and Keymer,52 respectively. Briefly, with the trunk as center and the DBH as distance from the stem, we collected four soil cores (diameter = 3.5 cm, depth = 10 cm) after removal of litter and mixed them as a single composite soil sample (Fig. 1b). We excavated surface soils near the base of trees and traced roots from the base to terminal fine roots in three directions. The fine roots of three directions were combined as a composite fine root sample, and each raw sub-fine-root section was nearly 6 cm wide and 8 cm long (Fig. 1c, d). All the samples were brought back to the laboratory with ice bags within 8 h. Soil was sieved through a 2-mm mesh and divided into two subsamples: one was stored at 4 °C to determine the soil properties, whereas the other was stored at −40 °C for subsequent DNA extraction. Fine roots were rinsed with sterile water and cut into 1.5-cm segments: one subsample (ca. 80%) was stored at 4 °C to determine root biochemical traits, and the other subsample (ca. 20%) was stored at −40 °C for subsequent DNA extraction. In the field, tree height, canopy diameter, DBH, elevation, slope, latitude, and longitude of each sampled tree were recorded. In total, 84 fine roots and 84 neighboring soils were collected.Fig. 1: Sampling map and procedures in this study.a Sampling map in the core region of CNR: the icons with different colors represent the tree individuals of B. ermanii. Contours were fitted in a map of Google Earth. b The sampling procedure of neighboring soils: each red point represent one soil core with depth 0–10 cm and diameter 3.5 cm. c The sampling procedure of fine roots: each red square (nearly 6 × 8 cm) represent a sub-fine-root system (namely, d).Full size imageMeasurement of soil properties and root traitsWe measured 28 soil properties, including soil pH, moisture, conductivity, dissolved organic carbon, dissolved organic nitrogen (DON), ammonium nitrogen, nitrate nitrogen, total carbon, total nitrogen, total phosphate, total potassium, total calcium, total magnesium, total manganese, total iron, total aluminum, available phosphate, available potassium, available calcium, available magnesium, available manganese, available iron, available aluminum, C/N ratio, C/P ratio and the proportions of clay, silt and sand. The measurement methods of soil pH, moisture, ammonium nitrogen, nitrate nitrogen, total carbon, total nitrogen and total content of other elements followed our recent study.28 In addition, soil conductivity was determined with a soil to water ratio of 1:5 by conductivity meter (Mettler Toledo FE30, Shanghai, China). Mehlich 353 and three-acid-system (nitric acid, perchloric acid, and hydrofluoric acid) were used to extract the available and total content of elements, respectively. Total and available content of phosphate, potassium, calcium, magnesium, manganese, iron, and aluminum were measured using an ICP Optima 8000 (Perkin-Elmer, Waltham, MA, USA). The proportions of clay, silt, and sand were measured by Laser Particle Sizer LS13320 (Beckman, Brea, CA, USA).Eighteen root traits, including root total carbon (RTC), root total nitrogen (RTN), root phosphate, root potassium, root calcium, root magnesium, root manganese, root iron, root aluminum, root C/N ratio, root N/P ratio, lignin, cellulose, hemicellulose, soluble sugar, soluble protein, free amino acid (FAA) and free fatty acids (FFA), were also measured. Specifically, RTC and RTN were determined with a carbon–hydrogen–nitrogen (CHN) elemental analyzer (2400 II CHN elemental analyzer; PerkinElmer, Boston, MA, USA). Root phosphate, root potassium, root calcium, root magnesium, root manganese, root iron, and root aluminum were measured in ICP Optima 8000 (Perkin-Elmer, Waltham, MA, USA). Soluble protein was measured by a dye-binding assay.54 FAA was analyzed by the amino acid analyzer L-8800 (Hitachi, Tokyo, Japan) with leucine as the standard sample. FFA was determined by NEFA FS kits (Diasys, Holzheim, Garman) and the automatic biochemical analyzer AU680 (Olympus, Tokyo, Japan). The measurement methods of lignin, cellulose, hemicellulose, and soluble sugar followed that of our previous study.16Calculation of distance to forest edgeThe location of each tree individual was determined by latitude and longitude. A high-resolution map (treecover2000) on global forest cover at a spatial resolution of 30 m was used as a base map.55 In the map, the areas where forest cover was more than 30% were defined as the “mainland” in the IBT framework and shown as the green grids in ArcGIS (Fig. S1). This standard referred to the proposal of Convention on Climate Change Kyoto.56 Then, we calculated the minimum distance of each tree to the neighboring forest edge (i.e., green grids) by using the function Near of the Proximity tool box in ArcGIS 10.0 (ESRI, Redlands, CA, USA).Sequencing and bioinformaticsSoil total DNA was extracted from 0.5 g of soil by using FastDNA® Spin kit for Soil (MP Biomedicals, Solon, Ohio, USA). Total DNA of fine roots was extracted from 0.3 g of plant tissue by using Qiagen Plant DNeasy kits (Qiagen, Hilden, Germany). PCR procedures, including primers (ITS1-F: CTTGGTCATTTAGAGGAAGTAA, ITS2: GCTGCGTTCTTCATCGATGC) and conditions were described in our previous studies.16,28 The PCR products of all samples were normalized to equimolar amounts and sequenced on the Illumina MiSeq PE300 platform of the Majorbio Company, Shanghai, China.We first merged the paired-end reads using FLASH.57 QIIME 1.9.058 and Cutadapt 1.9.159 were applied for quality filtering, trimming, and chimera removal. Altogether 8,238,146 sequences passed quality filtering (parameters: minlength = 240; maxambigs = 0; phred quality threshold = 30). ITSx 1.0.11 was used to remove the flanking small ribosomal subunit (SSU) and 5.8 S genes,60 leaving the ITS1 region for further analyses. The putative chimeric sequences were removed using a combination of de novo and reference-based chimera checking, with the parameter –non_chimeras_rentention = union in QIMME.61 The remaining sequences were then clustered into operational taxonomic units (OTUs) at 97% similarity threshold by using USEARCH.62 Singletons were also removed during the USERCH clustering process. Fungal taxonomy was assigned to each OTU by using the Ribosomal Database Project Classifier with minimum confidence of 0.8.63 The UNITE v.8.0 (http://unite.ut.ee) release for QIIME served as a reference database for fungal taxonomy.64 The OTU table was then curated with LULU, a post-clustering OTU table curation method, to improve diversity estimates.65After removing non-fungal sequences, the final data set included 7,849,126 fungal sequences covering 6663 OTUs in 168 samples (minimum 4662; maximum 70,779; mean 46,721 sequences per sample). The rarefaction curves of the average observed OTU number are shown in Fig. S2. FUNGuild was used to assign each OTU to a putative functional guild, and the assignments with confidence ranking “possible” were assigned as “unknown” as recommended by the authors.66 We further modified the assignment of EcM fungi (the subject in the present study) and their lineages according to.31 For some OTUs that were simultaneously assigned to endophytic, saprotrophic, or pathogenic fungi, we considered these as endophytes in roots and saprotrophs in soil samples.StatisticsAll statistical analyses were conducted in R 3.5.267 and AMOS 21.0 (AMOS IBM, New York, USA). In order to analyze the alpha diversities of soil fungi and the three most dominant guilds (viz., EcM, endophytic and saprotrophic fungi) at the same sequencing depth, the data set was subsampled to 4662 reads with 30 iterations. The mean number of observed OTUs was used to represent the diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi, as previously implemented in.15,68 Numbers of EcM fungal lineages and saprotrophic genera, families, orders, and classes of each sample were also calculated based on the same subsampling.First, linear and quadratic regression models were used to determine the effect of elevation on diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi. The model with lowest Akaike’s information criterion (AIC) value was selected. In order to account for spatial effects, linear mixed-effects models (LMMs) were fitted using the lme4 package69 to analyze the variation in diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi along the elevation gradient with latitude and longitude as random factors. Corrected Akaike Information Criterion (AICc) for small data sets was used to identify the best mixed-effects model from linear and quadratic polynomial models. The significance of each LMM was tested by the function Anova in the car package.70 Marginal (m) and conditional (c) R2 were calculated by the function r.squaredGLMM in the MuMIn package.71 Marginal R2 (R2m) represents the variance explained by fixed effects, whereas conditional R2 (R2c) represents the variance explained by both fixed and random effects.Second, to test the application of IBT on EcM fungal diversity, DBH and RTC of B. ermanii were chosen as proxies of island area and energy, respectively, whereas DFE was chosen as the proxy of island isolation (i.e., island distance to mainland). Linear regression models were used to assess the species-area, species-energy, and species-isolation relationships. In order to account for spatial effects, LMMs were also used for these independent relationships with latitude and longitude as random factors as described above. Classical power-law function models were used to identify the species-area relationship for EcM fungal diversities in roots and soils using z-values in the formula S = CAz 72 to compare EcM fungi with macroorganisms in previous studies. Furthermore, ordinary least squares (OLS) multiple regression models were performed to identify the relative contributions of DBH, RTC, and DFE on pattern of EcM fungal diversity when considering other predictive variables. Here, five spatial vectors (PCNM1-5) with significant positive spatial autocorrelation (Fig. S3) were obtained by the principal coordinates of neighbor matrices (PCNM) method,73 and added into OLS multiple regression models to consider the possible geographic effect. EcM fungal diversities in roots and soils, 28 soil properties, 18 root traits, elevation, slopes, DBH, tree height, canopy diameter, and DFE were standardized (average = 0 and SD = 1) before the OLS multiple regression analysis. AIC was used to identify the best OLS multiple regression model, as implemented in the MASS package.74 Variance inflation factor (VIF) was calculated for each model by the function vif in the car package. We used the criterion VIF  More

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This study aimed to investigate species-specific thermal adaptations of two endemic amphipod species differing in their thermal tolerance and one widespread Holarctic species by comparing long-term responses to cold and warm temperatures beyond their respective preference temperatures. We hypothesize that the Holarctic G. lacustris is limited at the extreme low temperature (i.e. 1.5 °C) preventing this species from establishing a stable population in Lake Baikal, whereas the Baikal endemic amphipods possessing specific thermal adaptations to maintain a high energy metabolism in winter at low temperatures.To test this hypothesis, we measured metabolic markers in long-term acclimated animals exposed to four different temperatures (i.e., 1.5 °C, 6 °C, 12 °C and 15 °C) including the respective preference temperature of each species. More specifically , we studied key metabolic enzyme activities, such as citrate synthase and cytochrome c and oxidase, to estimate aerobic energy production and lactate dehydrogenase as an indicator of anaerobic glycolysis. To better understand the metabolic output of these enzymes we studied metabolite contents such as ATP, lactate, and glycogen. Finally, we investigated activities of three antioxidant enzymes to evaluate the level of cellular stress. Overall, we could demonstrate significant differences of several markers between all three species and their responsiveness to temperature. Common patterns and their implications for the performance of the species in their habitats are discussed in the following.Metabolic properties at optimal temperaturesLong-term acclimation to the preferred (= optimal) temperatures allows maintaining homeostasis and avoids any stress related to the non-optimal thermal conditions. At these optimal temperatures, the aerobic scopes of the species should be at their maxima meaning that maximum excess oxygen is available to fuel processes above maintenance to support e.g., growth and reproduction7,27.Here, we found differences in the capacities of aerobic metabolism, levels of energy equivalents and energy stores between the three species, as indicated by the higher activity of cytochrome c oxidase, ATP and glycogen levels in G. lacustris compared to the two Baikal species. This is in line with our previous findings about routine metabolic rates, measured as oxygen consumption in the studied species21.Constantly low temperatures led to decreased cellular ATP levels and activities of cytochrome c oxidase in Baikal endemics, thereby indicating a reduced rate of basal metabolism. The same trend was observed in Antarctic fish compared to temperate and tropical fish species27,28.However, the activity of another marker of aerobic metabolism—citrate synthase—is similar among the three species. The higher citrate synthase/cytochrome c oxidase ratio in Baikal species compared to G. lacustris may be attributed to the prevalence of lipid over glucose metabolism in Baikal species29. Significantly higher levels of glycogen in G. lacustris support this assumption.Here, we studied one thermally tolerant Baikal endemic species, E. cyaneus, and one cold-stenothermal species, E. verrucosus. Previous studies on the molecular evolution of thermal tolerance revealed significant structural and functional differences in heat shock proteins (Hsp70), a universal molecular protection system24. Significant differences in routine metabolic rate and lethal temperatures were also shown21, supporting both endemic species’ thermal classification. The present results widen our mechanistic understanding of thermotolerance in Baikal amphipods. Particularly, when acclimated to their preferred temperatures, the two Baikal species showed some differences in metabolic fuel usage. Higher glucose, glycogen and lactate levels correspond to a more enhanced glucose metabolism, which may be required to maintain higher metabolic activity of the thermotolerant species E. cyaneus. However, capacities of cytochrome c oxidase and citrate synthase activities are similar, as well as the ATP level, which indicates similar rates of oxygen metabolism under optimal thermal condition in these two species.Oxygen metabolism is tightly connected with the antioxidative defense against reactive oxygen species (ROS) as they are natural byproducts of aerobic metabolism. Therefore, we studied the activity of three antioxidative enzymes. Our results indicate that despite higher aerobic capacities and presumably higher metabolic rates in G. lacustris compared to the two Baikal species, the activities of catalase and glutathione S-transferase were comparable and for peroxidase even higher in the cold stenothermal E. verrucosus than in G. lacustris (Fig. 2). Similarly, Abele and Puntarulo24,30 showed that the basic levels of superoxide dismutase activity in polar mollusks are higher than in temperate ones. Increased ROS generation in cells of ectothermic animals may occur due to higher oxygen solubility in cold water and biological fluids31,32. Thus, higher peroxidase level in E. verrucosus is likely an adaptation to the low temperatures with its high dissolved oxygen content in Baikal water.Glutathione S-transferase activities were found to be higher in both thermotolerant species—G. lacustris and E. cyaneus. Besides protecting against ROS, this enzyme has multiple functions, including xenobiotic detoxification33. Thus, its higher activity may be related to the enhanced tolerance level to various environmental factors in both species besides temperature.Thermal exposure to lower than the preferred temperaturesWe hypothesized that Baikal species have metabolic adaptations to cold temperatures that allow an active lifestyle in the cold. Thus, we expected cold compensation mechanisms (i.e., increased enzyme activities due to higher densities of mitochondria in cold stenotherms). Our results indicate that this is the case only for E. cyaneus (Baikal endemic, thermotolerant), as it showed increased cytochrome c oxidase activities at 1.5 °C. As the citrate synthase activity and ATP level remained unchanged, compensation of the respiratory chain seems to be sufficient to support stable energy production at low temperatures and an alteration of the mitochondrial ultrastructure, i.e., increased cristae density (cytochrome c oxidase) relative to the matrix (citrate synthase) may be involved in cold acclimation.E. cyaneus represents the rather small summer-reproducing complex among Baikal endemic amphipods. Despite its relatively high thermotolerance24, this species is actively moving under the ice cover in winter. In our experiments, animals of this species expressed high locomotor and feeding activity at all exposure temperatures, including 1.5 °C. Our results unravel the metabolic adaptation providing this rather wide thermotolerance window of this species. Already at 6 °C, glycogen started to accumulate, which also reoccurred at 1.5 °C. Glycogen accumulation is a well-known strategy of overwintering ectotherms known as cold hardiness, especially in those surviving in frozen habitats under oxygen-limited conditions34. Similar, we found an accumulation of glycogen above the control level in the Holarctic G. lacustris when exposed to 12 and 6 °C. However, at 1.5 °C the level of glycogen in this species was below the control level, indicating reduced glycogenesis possibly due to metabolic depression in the cold.Instead, E. cyaneus can maintain a high level of glycogenesis at 1.5 °C, and therefore accumulate glycogen in winter, despite its lower metabolic rate compared to G. lacustris. Surprisingly, at 6 °C we found a strong increase in lactate dehydrogenase activity in E. cyaneus, which was not followed by a significant accumulation of lactate or significant depletion of ATP indicating the absence of functional anaerobiosis. This unpredicted reaction of lactate dehydrogenase in this species at 6 °C, which is the Baikal littoral zone’s annual average temperature, requires further studies.Exposure to 1.5 °C and 6 °C caused decreased catalase and peroxidase activities and decreased lactate levels in E. cyaneus, which would be in line with a reduced metabolic rate, following the Q10 rule. Thus, our results indicate that E. cyaneus can maintain a high level of aerobic metabolism within a wide thermal range including the common winter temperature—1.5 °C. Although E. cyaneus is one of the most thermotolerant Baikal amphipod species, differences to the closely-related thermotolerant Holarctic G. lacustris became apparent as the latter failed to maintain high ATP and lactate levels at the lowest exposure temperature likely due to lacking compensation (citrate synthase, cytochrome c oxidase) or even inactivation of enzyme functions (including lactate dehydrogenase). This reduced energy state may likely contribute to the low locomotor activity of G. lacustris at the lowest exposure temperature.Surprisingly, no cold compensation in any of the studied parameters was found for the cold-loving Baikal endemic species E. verrucosus. This species belongs to the winter-reproducing complex, which is most common in the littoral zone. The absence in cold compensation of this species indicates that at 1.5 °C its energy metabolism remains on its physiological maximum.The absence of glycogen accumulation at low temperatures in this species can be explained by the food availability during winter in Lake Baikal’s littoral zone. Presumably, the existing amount of winter nutrition is sufficient for E. verrucosus to maintain maximum aerobic capacities and energy metabolism. The high nutrition allows the species to breed, develop eggs and release juveniles, which occurs during the seasons with lowest annual temperatures. Another explanation can be the usage of lipids instead of carbohydrates as energy source in winter. The lower amount of glycogen and free glucose than in the thermotolerant congener E. cyaneus, supports this hypothesis.Thus, despite the relatively similar aerobic capacities of the thermotolerant E. cyaneus and the winter-reproducing cold stenothermal E. verrucosus at their respective preference temperatures, the latter species shows a different metabolic fuel use, that allows E. verrucosus to maintain its maximum metabolic rate at 1.5 °C.We hypothesized that the Holarctic G. lacustris has disadvantages compared to the two Baikal species regarding energy metabolism at low temperatures. Gammarus lacustris is eurythermal and often overwinters in small ponds, which are nearly completely frozen in winter35. However, it has been shown that temperatures below its optimal range cause decreased respiration rates and activity possibly resulting in torpor21,35. Our observations confirmed these assumptions as at 1.5 °C individuals of G. lacustris significantly decreased their locomotor and feeding activities. The assumption that G. lacustris shows metabolic depression at 6 °C and 1.5 °C is supported by the decreased ATP, lactate, and lactate dehydrogenase activity levels. Moreover, only for this species we observed mortality at 1.5 °C, which indicates that this experimental thermal condition is stressful. This is supported by the increase in catalase activity over the control level and the elevated level of peroxidase activity at 1.5 °C, while at 12 °C and 6 °C, the capacity for this enzyme was decreased compared to the control (following the Q10 rule). In this case, exposure to 1.5 °C could cause cellular damages resulting in the development of oxidative stress. Preparation for such extreme temperatures for G. lacustris like requires enough time to accumulate cryoprotectors25 and adjust the metabolism in its natural habitat. Besides, we observed the accumulation of glycogen, like in the thermotolerant E. cyaneus. For G. lacustris, accumulation of glycogen for the wintertime is essential, as it is often overwintering in nearly completely frozen ponds and therefore experiences periods of hypoxia23. Glycogen can be metabolized via anaerobic glycolysis, in contrast to lipid storages, which require oxygen for their metabolizations34. In comparison to E. cyaneus, accumulation of glycogen over the control level in G. lacustris occurs already at 12 °C, indicating the lower thermal threshold of the zone of preferred temperatures for this species22, but this accumulation disappeared at the lowest temperature indicating that G. lacustris follows a different strategy for winter survival than cold hardiness34.Thermal exposure to higher than the preferred temperaturesPrevious studies indicated that 15 °C is the critical thermal threshold for large adults of the stenothermal E. verrucosus, and it was observed that most of these individuals start to migrate to deeper littoral zones when the temperature in the Baikal littoral surpass about 11 °C21. Thus, E. verrucosus is behaviorally adapted to escape deleterious temperatures. When exposed to gradual temperature increase, individuals of E. verrucosus showed accumulation of lactate and heat shock proteins at temperatures exceeding 12 °C22,24. Our results complement these previous findings, as lactate levels were significantly higher than the control lactate levels at 12 °C. At 15 °C, increases of lactate dehydrogenase capacities were detected, which may foster a higher turnover and remobilization of lactate at this temperature compared to 12 °C, as lactate level did not further increase. As shown earlier lactate dehydrogenase was the only metabolic enzyme exhibited similar kinetic and regulatory properties as the other two amphipod species (following simple Q10 rules)23. The results here confirm the need of E. verrucosus to keep higher anaerobic capacities and turnover rates at the upper limit of the thermal window, where functional hypoxia may appear7,27.In the more thermotolerant E. cyaneus, exposure to 15 °C caused increases of ATP, lactate, and both peroxidase and catalase activities. Increases of both ATP and lactate likely indicate the enhancement of the cellular respiration rather than the onset of anaerobiosis. Activation of peroxidase and catalase may indicate both the development of cellular stress and the general increase in metabolic rate following the Q10 rule. The last explanation is more likely, as antioxidant enzyme activities, lactate and ATP content gradually increased with rising temperature. As E. cyaneus is a sedentary species, that occupies a rather narrow zone of the upper littoral, such a metabolic plasticity can serve as a specific adaptation to its thermal niche. Opposite, for the migrating E. verrucosus, behavioral responses are more important when temperatures gradually increase. More

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