Ecology

COVID-19 vs. MoMoThe COVID-19 official deaths and MoMo ED time series overlaped for the period from 3 March 2020 to 1 January 2021 for Spain and its 19 regions (Fig. 2). In general, there was good agreement between both datasets, meaning that most of MoMo ED were related to COVID-19 deaths. During the first wave, the most important differences were observed in Spain, Madrid, Cataluña, Castilla-La Mancha, and Castilla y León. Before 22 June in Spain, MoMo ED showed 15,445 accumulated deaths more than the official COVID-19 deaths, which is beyond the error band. That difference comes basically from the four regions with the largest numbers of deaths (Madrid, Cataluña, Castilla-La Mancha, and Castilla y León). Table 1 shows the accumulated values before 22 June, which were used to estimate the CFR for Spain and its 19 regions according to the third phase of the National Seroprevalence Study4,5. For all regions, the CFR estimated from MoMo ED was larger than the CFR estimated from COVID-19 deaths. In particular, Asturias, Canarias, and Murcia were twice as large. Ceuta and Melilla dramatically increased their CFR from MoMo ED, although that may be biased due to their small populations and numbers of deaths.Similarly, the same variables for the period from 23 June to 29 November 2020 are reported in Table 2. In Spain, MoMo ED showed 6173 accumulated deaths more than the official COVID-19 deaths. This difference is a third of the difference observed prior to 22 June; because this is within the error band, there was a significant improvement in the detection of COVID-19 deaths in this period. Figure 2 also shows a general agreement between MoMo ED and official COVID-19 deaths time series after the first wave, with the exception of late July and early August. These differences were due to two heat waves that were responsible for at least 25% of the MoMo ED16.Infections estimated from COVID-19 deathsTo illustrate the delay between official daily infections data and REMEDID estimated daily infections, we applied REMEDID from COVID-19 deaths assuming CFR = 100%. Figure 3 shows the current IO21 and the infections associated with COVID-19 deaths for the first wave. The latter in Spain reached a maximum on 13 March 2020 (Table 3), the day before the national government decreed a state of emergency and national lockdown. Thus, the adopted measures had an immediate effect, which was observed in the official data IO21 7 days later (20 March). This delay is similar to the incubation period (mean 5.78 days2), which could be explained because official infections were reported when symptoms appeared. This delay reached 16 days when we compared with earlier version IO20 (not shown), which highlights the usefulness of the methodology to reinterpret official data from very early stages of the pandemic. On the other hand, the maximum number of deaths was reached on 1 April, which was 19 days after the inferred infection maximum, bringing this delay close to the 20 days expected between infection and death (Figs. 1, 3).Figure 3Official COVID-19 infections and deaths, and estimated infections with case fatality ratio (CFR) of 100% in Spain during the first wave. Left y-axis: COVID-19 daily infections IO21 (blue curve). Right y-axis: COVID-19 deaths (orange curve) and its REMEDID-estimated infections with CFR = 100% (red curve). All curves are for Spain. Thin blue and orange curves are daily data, and thick curves are smoothed by 14-day running mean. Arrows show delays between the maximum of inferred infections and maxima from COVID-19 deaths (orange arrows) and COVID-19 infections (blue arrows). Solid arrows are expected delays, dotted while arrows are observed delays.Full size imageTable 3 Date of first infection for REMEDID estimated daily infections from COVID-19 deaths (IRO) and from MoMo Excess Deaths (ED) (IRM), and for official COVID-19 daily infections released on June 2020 (IO20) and on February 2021 (IO21).Full size tableWe applied REMEDID to the official COVID-19 deaths with the corresponding estimated CFRs (see “Data” section) to obtain the time series of estimated daily infections, hereafter referred to as IRO. Figure 4 shows IRO and the accumulated infections for Spain and its 19 regions. Note that in Spain, IRO are amplified versions of inferred infections in Fig. 3. In Spain, the first infection, according to IRO,, is on 8 January 2020 (Table 3), 43 days before the first infection was officially reported on 20 February 2020 according to IO20. By contrast, IO21 places the first infection on 1 January 2020. Spain reached the maximum number of IRO on 13 March, a day before the state of emergency and lockdown were enforced (Table 4). On 14 March, IO20 = 1832, and IO21 = 7478; however, IRO = 63,727 (CI 95% 60,050–67,403), 35 and 9 times IO20 and IO21, respectively (Table 5). This implies that on that day, IO20 and IO21 only reported 2.9% (CI 95% 2.7–3.1%) and 11.7% (CI 95% 11.1–12.5%) of new infections, respectively. Although detection of infections clearly improved from IO20 to IO21, almost 90% of the infections are still not documented in the peak of the first wave. The situation is similar for the accumulated infections before 22 June 2020, as reported by the National Seroprevalence Study4,5.Figure 4Daily and accumulated infections for official COVID-19 daily infections (IO21), and daily infections estimated from COVID-19 deaths (IRO). Lines are daily infections and refer to the y-axis on the right; bars are accumulated infections and refer to the y-axis on the left. Red lines and cyan bars are official COVID-19 data; orange lines and blue bars are inferred infections with case fatality ratio (CFR) in Table 1. Thin orange lines correspond to the CFR confidence interval.Full size imageTable 4 Date of the most prominent relative maxima, for Spain and the 19 regions, of the REMEDID estimated daily infections from COVID-19 deaths (IRO) and from MoMo excess deaths (ED) (IRM), and official COVID-19 daily infections (IO21).Full size tableTable 5 REMEDID estimated daily infections from COVID-19 deaths (IRO) and from MoMo excess deaths (ED) (IRM) on 14 March, and for official COVID-19 daily infections released on June 2020 (IO20) and released on February 2021 (IO21).Full size tableIn almost all regions, IO20 showed a delay of 1 month or more between the first infection and IRO (Table 3). No delay in IO21 occurred in Islas Baleares, Castilla-León, and Galicia, while in three regions (Cataluña, Madrid, and La Rioja), the first case occurred earlier than the first case of IRO. However, 6 regions had delays of 15 days and other 6 regions had delays of 1 month. According to IRO, all regions except Ceuta and Melilla had some infections in January, but in IO21 only 6 regions had infections in that month. In all scenarios, the first infections were in Madrid and Cataluña.During the first wave, according to IRO most of the regions had maximum daily infections around 14 March. In Madrid, the maximum was reached on 11 March, coinciding with the educational centres closing and an official warning by the regional government (Table 4). Asturias was the last region to reach peak infections (25–26 March). The maximum percentage of documented cases (12.6%, CI 95% 9.2–18.4%) occurred in Asturias on 14 March, but in the other regions, only between 1.2 and 8% of the infections were documented (Table 5).Figure 4 shows how the IO21 and IRO curves of Spain and the 19 different regions fluctuated following the same pattern until the middle of June 2020, but thereafter, they showed different patterns. This reflects the fact that the Spanish government had decreed the control measures for the whole nation until June, but thereafter, each regional government implemented its own control measures. For example, some regions (e.g., Aragón, Islas Baleares, Cantabria, Comunidad Valenciana, Extremadura, Galicia, Murcia, País Vasco, and La Rioja) had two peaks, but others had only one. An apparent maximum on 22 June in Islas Baleares is an artifact produced by the interpolation for transition from the two CFRs. Although beyond the scope of this work, it would be very interesting to investigate the effects of the different control measures implemented on the corresponding IRO for the 19 regions.The Spanish COVID-19s wave reached a maximum of daily infections on 22 October from IRO and on 26 October from IO21. The delay of 4 days is similar to the mean incubation period (5.78 days2). The estimated number of new infections is still larger than the documented cases, but the shapes of the two curves are more similar in the second wave than in the first wave (Fig. S1). The same is true for the 19 regions, most of which had the largest peak around 22–26 October, with the exceptions of Canarias and Madrid, which reached maxima in late August and early September, respectively.Infections from MoMo excess deathsAssuming that MoMo ED accounts for both recorded and non-recorded COVID-19 deaths, negative deaths are meaningless, and they were set to zero. Then, the associated daily infections can be estimated, as in “Infections estimated from COVID-19 deaths” section, with a CFR of 100% from MoMo ED for Spain (Fig. 5). Note two main differences between this time series and that estimated from official COVID-19 deaths: (1) MoMo data present an error band that was inherited by the estimated infections; (2) MoMo ED estimated infections reached a maximum of 1443 (CI 99% 1329–1547), doubling the 776 inferred daily infections from official COVID-19 deaths in Fig. 3. This is because maximum MoMo ED was 1,584 (CI 99% 1468–1686) and maximum COVID-19 official deaths was 828, both estimated from the 14-day running mean time series. The maximum of inferred infections was reached on 13 March, just one day prior to the state of emergency and lockdown. The expected and observed delays with respect to official infections and MoMo ED were similar to those observed for estimated infections from official COVID-19 deaths. Error bounds of the estimated infections in Fig. 5 were computed from the MoMo ED error bounds. However, it should be highlighted that the combination of the error bounds from MoMo ED and the estimated CFRs might lead to unrealistic error estimates. To avoid this, the error estimates in Fig. 6 were estimated from the MoMo ED time series (no error bounds) and the error bounds of the estimated CFRs.Figure 5Official COVID-19 infections, MoMo Excess Deaths (ED), and estimated infections with case fatality ratio (CFR) of 100% in Spain during the first wave. Left y-axis: COVID-19 daily infections IO21 (blue curve). Right y-axis: MoMo ED (orange curve) and its REMEDID-estimated infections with CFR = 100% (red curve). All curves are for Spain. Thin blue and orange curves are daily data, and thick curves are smoothed by 14-days running mean. Dashed curves represent the error estimate of MoMo ED (orange) and inferred infections (red). Arrows show delays between the maximum of inferred infections and maxima from MoMo ED (orange arrows) and COVID-19 infections (blue arrows). Solid arrows are expected delays, dotted while arrows are observed delays.Full size imageFigure 6Daily and accumulated infections for official COVID-19 daily infections (IO21), and daily infections estimated from MoMo Excess Deaths (ED) (IRM). Lines are daily infections and refer to the y-axis on the right; and bars are accumulated infections and refer to the y-axis on the left. Red lines and cyan bars are for official COVID-19 data; and orange lines and blue bars are for inferred infections with case fatality ratio (CFR) in Table 1. Thin orange lines represent the error estimate of inferred infections.Full size imageThe REMEDID was applied to the MoMo ED with the corresponding CFRs (see “Data” section) to obtain the estimated daily infections, which will be referred hereafter as IRM. The IRM were calculated for Spain and its 19 regions and are depicted in Fig. 6, as well as the accumulated IRM. In Spain, the first infection shown by IRM happened on 9 January, with an error estimate from 9 to 10 January, 41 to 42 days before the first documented infection of IO20 on 20 February 2020 (Table 3). The maximum IRM was 77,855 (CI 95% 73,364–82,347) reached on 13 March. On 14 March, IRM showed 14,128 infections more than IRO (Table 5). Notice that the CFR used with MoMo ED data was larger than the one used with official COVID-19 deaths data, which makes this difference even more remarkable, because the larger the CFR the lower the estimated infections. Therefore, if the true CFRs, which are unknown, were used in both cases, IRM would double IRO on 14 March, as happened when a CFR of 100% was used (Figs. 3, 5). Notice that with the CFRs used, the IRM and IRO resulted in the same accumulated infections on 22 June and 29 November, matching the results of the seroprevalence study. Nevertheless, IRM showed 42 times more cases than IO20 and 10 times more than IO21 on 14 March, detection of official cases of only 2.4% (2.2–2.5%) and 9.6% (9.1–10.2%), respectively.Table 3 shows the estimated date of first infection for Spain and by region. Note that the first cases of IRM in Spain were on 9 January and in Aragón, Canarias, and Navarra on 8 January, which is possible because significant excess deaths in a region may not become significant for the whole country. In general, the maxima of daily infections were closer to those on 14 March in IRM than in IRO. During the first wave, all regions showed a single maximum, except for Ceuta, Melilla, and Murcia, which showed two maxima (Fig. 6). In general, the IRM time series in all regions were similar during that period. The official data clearly under-detected infections during the first wave. On 14 March, IRM were comparable to IRO, overlapping CI in all regions, but not in Spain as a whole (Table 5). During the second wave, there was improved detection of cases with differences among regions. More

Study site and speciesThe sub-tropical forest analysed in this study is located in China, Yunnan, Xishuangbanna, Mengla county, Village Quingyanzhai (#94) N 21.802068, E 101.380214, geodetic datum WGS84 (Fig. 6). The site is characterized by a rocky outcrop rising 30–50 m over surrounding rubber plantations, harbouring about 20 ha of relict dry tropical forest. The outcrop sides are steep and mainly covered with bamboo. The top area is colonized by shrubs and 10–15 m high trees (a few trees on the slopes are much higher). The most conspicuous species is Quercus yiwuensis Y.C. Hsu & H.W. Jen. In March 2017 we selected four individual trees of Q. yiwuensis, and an equal number of Pistacia weinmannifolia Franch, and Beilschmiedia percoriacea C.K. Allen that were also numerous on the site. Plants were identified by the authors in the field and labelled. Botanical specimens were deposited in the School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.Figure 6Map of the study site with approximate position of analysed trees (aerial perspective from Google Maps 2018). GPS positions were obtained less than 1 m from the tree trunks. The distance from N3 to B1 is approximately 60 m.Full size imageQ. yiwuensis was designated P-tree (P1, P2, P3, P4) because we consistently found the orchid Panisea uniflora Lindl. growing on this phorophyte species. P. weinmannifolia was designated B-tree (B1, B2, B3, B4) because it harboured the orchid species Bulbophyllum odoratissimum (Sw) Lindl. (Supplementary Fig. S2 a-d). On P. weinmannifolia trees no P. uniflora was observed, while B. odoratissimum was never found on Q. yiwuensis trees. Both tree species were richly colonized by several other orchid species. Beilschmiedia percoriacea trees were designated neutral tree, N-tree (N1, N2, N3, N4), because neither of the two target orchid species grew on them. The latter tree species carried several lichens and a single fern species (Lepisorus sp.), but only in one instance was observed to carry an orchid epiphyte (Coelogyne sp.).GPS positions of investigated trees were obtained less than 1 m from the trunk (Fig. 6). Accuracy is about 3 m. Accuracy in altitude readings is about 100 m. Distance between degrees of latitude is 111 km. At N 21.78978 the distance between degrees of longitude is 103 km, which means that the last digit in the 5-digit decimal degrees corresponds to 1.11 m in latitude and 1.03 m in longitude.The trees were labelled with different colours as follows:

P-trees (carrying P. uniflora and other epiphytes, but not B. odoratissimum), identified as Q. yiwuensis, with red labels (P1 N 21.79880, E 101.37909, 1073; P2 N 21.79882, E 101.37923, 1072; P3 N 21.79878, E 101.37947, 1074; P4 N 21.79878, E 101.37904, 1073).

B-trees (carrying B. odoratissimum and other epiphytes, but not P. uniflora), identified as P. weinmannifolia, with blue labels (B1 N 21.79878, E 101.37950, 1074; B2 N 21.79880, E 101.37938, 1078; B3 N 21.79881, E 101.37931, 1083; B4 N 21.79884, E 101.37923, 1076).

N-trees (carrying epiphytes, but neither B. odoratissimum nor P. uniflora), identified as B. percoriacea, with yellow labels (N1 N 21.79873, E 101.37905, 1064; N2 N 21.79868, E 101.37908, 1072; N3 N 21.79883, E 101.37893, 1071; N4 N 21.79879, E 101.37895, 1071).

The point of access to the outcrop top area was located at the Western edge (N 21.79880, E 101.37827, 1058, Fig. 6).

SamplingFor each of the twelve selected trees, breast height circumference (BH = 130 cm above ground) was measured. Approximate total height was determined by Nikon Laser Forestry Pro or estimated if sighting lines were interfered by other vegetation.The lowermost individual of the target orchid species was recorded in relation to BH. Bark samples were collected, and bark features recorded at BH, by target orchid, and 50 cm above target orchid or BH, whichever was highest point. In N-trees, where there were no target orchids, sampling was thus at BH, BH + 50 cm, and BH + 100 cm.Sampling on each tree involved approximately 12 cm2 bark cut out with a sterile knife and rubber gloves to prevent cross-contamination, for pH-analysis, metabarcoding, fungal isolation and chemical analysis. Besides, 3 bark cores were taken by trephor sampler (16 mm, 2 mm diam., Costruzioni Meccaniche Carabin Carlo) for water holding measurement.Roots of target orchids were sampled, from three adult individual plants on each P- and B-tree. No permissions were necessary to collect plant samples, using a protocol that avoided plant damages. All plants were left in the exact location where they were found in the sampling site, after collecting the small portions of bark and root material for the study. All experiments including the collection of plant material in this study are in compliance with relevant institutional, national, and international guidelines and legislation.All fresh material collected from the sampling site was first kept in cool boxes, brought to the laboratory, and processed within three days.Fungal isolation from barkFor each sample, half of the bark material and orchid roots were kept at − 80 °C for subsequent metabarcoding analysis. The rest of bark (about 2 g for each sample) was immediately processed for fungal isolation. The large bark portions were ground into powder using a sterile mortar and pestle; 5 ml were reserved for pH measurement, while the rest was suspended in a final volume 50 ml sterile water solution in a sterile centrifuge tube. The tube was shaken with Vortex vibration meter thoroughly and solution aliquots were spread homogenously onto isolation medium plates. For each bark sample, aliquots of 500, 300, 200, and 100 μl, were spread per triplicate to one plate each of PDA (Potato Dextrose Agar) medium, containing ampicillin (50 μg/mL) and streptomycin (50 μg/ml) to inhibit bacterial growth49,50. A diluted solution was also made by mixing 1 ml of the original solution with 9 ml sterile water and plated. Petri dishes were incubated at room temperature (23–25 °C) in the dark for up to 2 months to allow the development of slow-growing mycelia. Fast growing fungal strains started to grow after about two days. Colonies showing different morphology and appearance were transferred to fresh plates to obtain pure cultures. In the following days, other slower growing mycelia were available in the Petri dishes and were also regularly picked up and isolated onto new PDA plates every 2 days. All isolated fungal strains were stored at 4 °C for further analysis. All strains were deposited in the LP Culture Collection (personal culture collection held in the laboratory of Prof. Lorenzo Pecoraro), at the School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.Molecular and morphological analysis of bark culturable fungiThe identification of isolated fungal colonies was performed using DNA sequencing combined with microscopy. Total genomic DNA from isolated fungi was extracted following the cetyltrimethyl ammonium bromide (CTAB) method modified from Doyle and Doyle51. Fungal ITS regions were PCR-amplified using the primer pair ITS1F/ITS452 following the procedure described in Pecoraro et al.37 for PCR reaction, thermal cycling, and purification of PCR products. Controls with no DNA were included in every amplification experiment in order to test for the presence of laboratory contamination from reagents and reaction buffers. Purified DNA amplicons were sequenced with the same primer pair used for amplification. DNA sequencing was performed at the GENEWIZ Company, Tianjin, China.Sequences were edited to remove vector sequences and to ensure correct orientation and assembled using Sequencher 4.1 for MacOsX (Genes Codes, Ann Arbor, MI). Sequence analysis was conducted with BLAST searches against the National Center for Biotechnology Information (NCBI) sequence database (GenBank; http://www.ncbi.nlm.nih. gov/BLAST/index.html) to determine the closest sequence matches that enabled taxonomic identification. DNA sequences were deposited in GenBank (Accession Nos. MW603206 – MW603451). Fungal morphological characters (hyphae, pseudohyphae, conidiophores, conidia, poroconidia, arthroconidia, etc.) were examined using a Nikon ECLIPSE Ci microscope for the identification of isolates following the standard taxonomic keys53,54,55,56,57.Assessment of bark and orchid associated fungal community using Illumina sequencingBark and orchid root samples were pulverized in a sterile mortar, and genomic DNA was extracted using the FastDNA® Spin Kit as described by the manufacturer (MP Biomedicals, Solon, OH, USA)58,59. In total, this resulted in 60 DNA samples, including 36 from bark (3 sampling points for each tree × 12 trees) and 24 from orchid roots (3 orchid individuals sampled on each P- and B-tree × 8 trees; the 4 individual N-trees were not used for orchid sampling because they did not carry the study orchid species). Subsequently, amplicon libraries were created using two primer combinations targeting the internal transcribed spacer 2 (ITS-2): ITS7F and ITS4R60 was used as universal fungal primer pair to target nearly all fungal species, while ITS361 and ITS4OF62 was used to more specifically target orchid mycorrhizal fungi. Previous research has shown that most universal fungal primers have multiple mismatches to many species of the orchid-associating basidiomycetes, in particular in Tulasnellaceae family46,58,63. Since the goal of the present work was to analyse the total fungal community in the orchid-phorophyte environment (bark and orchid roots), as well as more specifically detect the orchid mycorrhizal fungi in the studied samples, it was necessary to combine two different primer pairs to characterise the whole investigated fungal diversity47,64,65,66. Polymerase chain reaction (PCR) amplification was performed in 50 μl reaction volume, containing 38 μl steril distilled water, 5 μl 10 × buffer (100 mM Tris–HCl pH 8.3, 500 mM KCl, 11 mM MgCl2, 0.1% gelatin), 1 μl of dNTP mixture of 10 mM concentration, 0.25 μM of each primer, 1.5 U of RED TaqTM DNA polymerase (Sigma) and approximately 10 μg of extracted genomic DNA. PCR conditions were as follows: 1 cycle of 95 °C for 5 min initial denaturation before thermocycling, 30 cycles of 94 °C for 40 s denaturation, 45 s annealing at various temperatures following Taylor and McCormick62, 72 °C for 40 s elongation, followed by 1 cycle of 72 °C for 7 min extension. To minimize PCR bias, three PCRs were pooled for each sample. The resulting PCR products were electrophoresed in 1% agarose gel with ethidium bromide and purified with the QIAEX II Gel Extraction Kit (QIAGEN). Amplicon libraries were generated using the NEB Next Ultra DNA Library Prep Kit for Illumina (New England Biolabs, USA) following the manufacturer’s instructions to add index codes. Samples were sequenced using the Illumina MiSeq PE 250 sequencing platform (Illumina Inc., San Diego, CA) at Shanghai Majorbio Bio‐Pharm Technology Co., Ltd. (Shanghai, China).Bioinformatics of fungal sequencesSequences originated from the total (ITS7F and ITS4R primers) and orchid-associated (ITS3 and ITS4OF primers) fungi datasets were processed separately. Raw reads were merged with a minimum overlap of 30 nucleotides, and the primer sequences were trimmed off. Subsequently, reads were filtered by discarding all sequences with expected error  > 1. The quality-filtered reads were denoised using the UNOISE3 algorithm67 to create zero-radius operational taxonomic units (zOTUs), with chimera removal. All the steps were performed using USEARCH v.1168. Raw sequences have been deposited in the Sequences Read Archive (SRA) of NCBI as BioProject ID PRJNA702612. The fungal zOTUs were assigned to taxonomic groups using the Blast algorithm by querying against the UNITE + INSD fungal ITS database (version 7.2, released on 10 October 2017)69 using the sintax algorithm with 0.8 cutoff70. The zOTUs originated with the orchid-associated fungal primers were manually screened for possible orchid-associated mycorrhizal families based on the information provided in Table 12.1 in Dearnaley et al.71, and only these were retained for further analysis in this dataset.To attempt removing spurious counts due to cross-talk (assignment of reads to a wrong sample) we removed all the zOTUs represented by less than 0.02% of reads in each sample, which is more conservative than previous error estimates72. The datasets were rarefied to the minimum sequencing depth (23,419 for total fungi and 13,074 for orchid-associated fungi), zOTUs present in less than three samples and low abundant zOTUs (with relative abundance  More

Whole genome resequencing of large yellow croaker populationsWe collected a total of 198 large yellow croaker individuals (Table S1). Of these, 50 individuals were captured in the Zhoushan Sea (the red dot in Fig. 1a) and 48 individuals had been farmed in Zhoushan (the orange dot in Fig. 1a). A further 38 individuals were captured in the Ningde Sea (the blue dot in Fig. 1a). and 62 individuals had been farmed in Ningde (the green dot in Fig. 1a). We performed whole-genome resequencing of these 198 large yellow croaker individuals. We obtained 1.42 Penta base-pairs of genomic DNA, representing about 11 × sequencing depth of the genome per individual. Raw reads were trimmed and aligned to the genome sequence. After variant calling and filtering, a total of 6,302,244 single nucleotide polymorphisms (SNPs) were identified. Using this SNP information, we performed the following population genomic analyses.Figure 1Population structure and relationship of large yellow croaker. (a) Geographic map indicating the sample origins of the large yellow croaker in this study. The gross appearance of a large yellow croaker individual is shown in the picture. The sampling area is highlighted by the red broken line. The dots of different color stand for different population. The number of individuals is given in parentheses after the population name. The geographical maps were generated by using R packages of maps v3.3.0 (https://cran.r-project.org/web/packages/maps) and mapdata v2.3.0 (https://cran.r-project.org/web/packages/mapdata). (b) PCA plot (PC1 and PC2) showing the genetic structure of the 198 large yellow croaker individuals. The degrees of explained variance is given in parentheses. Colors reflect the geographic regions in (a). (c) UMAP of the 198 large yellow croaker individuals. Colors reflect the geographic regions in (a).Full size imageGenetic population structure of the large yellow croaker individualsIn order to examine the genetic population structure of the large yellow croaker individuals, we performed principal component analysis (PCA). In the first component of the PCA, the Zhoushan farmed population separated from the other three populations (Fig. 1b). In the second component of the PCA, the Zhoushan sea-captured population formed a cluster. Also, the Ningde farmed population formed a cluster. The Ningde sea-captured population had a wider distribution than the other populations. Then, we performed uniform manifold approximation and projection (UMAP), a non-linear dimensionality method (Fig. 1c). The result of UMAP is similar to the result of PCA. UMAP showed that the Zhoushan farmed population formed a distinct cluster, and the Zhoushan sea-captured population and Ningde farmed population formed more scattered clusters. UMAP also showed that the Ningde sea-captured population had a wider distribution than the other populations.The evolutionary history of the individuals was inferred with the neighbour-joining (NJ) tree. The NJ tree contains two large groups (Fig. 2a). The first group was formed by the individuals of the Zhoushan farmed population plus several individuals of the Zhoushan sea-captured population. The other group was formed by the individuals in the other three groups. In this group, individuals of the Zhoushan sea-captured formed a distinct cluster from the individuals of the Ningde sea-captured population and those of the Ningde farmed population. The individuals of the Ningde sea-captured population and those of the Ningde farmed population together formed several small groups.Figure 2Neighbor-joining tree and admixture analysis using whole-genome SNP data. (a) Neighbor-joining tree of the 198 large yellow croaker individuals. The color scheme follows Fig. 1. The scale bar represents pairwise distances between different individuals. (b) Cross-validation error in the admixture analysis. The x-axis represents K values and the y-axis represents the corresponding cross-validation error. The cross-validation error was lowest at K = 3. (c) Admixture plot (K = 2, 3, 4) for the 198 large yellow croaker individuals. Each individual is shown as vertical bar divided into K colors. The color scheme follows Fig. 1. Individuals are arranged by population.Full size imageWe performed unsupervised clustering analysis with ADMIXTURE to evaluate the relatedness of the populations. Cross-validation error was lowest at K = 3, suggesting that the population genetic structure of our samples is best modelled by considering the admixture of the three genetic components (Fig. 2b). The individuals of the Zhoushan farmed population are composed of relatively uniform genetic components (Fig. 2c). The individuals of the Ningde farmed population are composed of genetic components that are also relatively uniform but different from those of the Zhoushan farmed population. Both the individuals of the Zhoushan sea-captured population and those of the Ningde sea-captured population were a mixture of the three genetic components.Trends of effective population sizeWe evaluated the extents of linkage disequilibrium for SNP pairs. The average r2 values of linkage disequilibrium decreased by increasing the marker distance between pairwise SNPs, with a rapidly declining trend observed over the first 500 kb (Fig. 3a). Using this information, we estimated the change of the effective population size over the past 1000 generations (Fig. 3b). All the four populations showed decreasing trends of effective population sizes, suggesting that their genetic diversities remain at a low level.Figure 3Trends of effective population sizes. (a) LD decay (r2) from 0 to 4000 kb for four populations. The x-axis represents marker distances between pairwise SNPs. The y-axis represents r2 values of linkage disequilibrium. (b) Effective population sizes of four populations over the past 1000 generations. All of the four populations showed decreasing trends.Full size imageDetection of putative genes associated with the adaptation to different sea environments of the Zhoushan Sea and Ningde SeaTo identify the genetic markers to differentiate individuals of the Zhoushan sea-captured and Ningde sea-captured, we calculated fixation index (Fst) values for each SNP. We identified total 819 SNPs as genetic markers (Table S2). To identify the genes associated with adaptation to the different living environments between these two regions, we calculated average Fst values in 40 kb windows with 10 kb steps (Fig. 4). We identified 47 regions with significant Fst values. The total size of these regions is 3.6 Mb. The sizes of the significant regions were between 40 kb to 0.31 Mb. These regions contained 88 genes (Table S3). We categorised the functions of these genes based on their gene ontology (GO) term annotations (Table S4). These genes include those involved in muscle structure development (GO:0061061) such as pdlim3a (pdz and lim domain 3). This gene is located in the region from 26,673,301 to 26,662,947 bp on chromosome 10, and is reported to be highly expressed in muscle and involved in the crosslinking of actin filaments15. We identified three upstream variants of this gene which are located at 26,675,034 bp, 26,675,134 bp, and 26,678,221 bp on chromosome 10 (Fig. 4). We also identified one downstream variant located at 26,660,973 bp on chromosome 10. Besides muscle structure development (GO:0061061), there are also some enriched GO terms such as regulation of response to external stimulus (GO:0032101) and cell–cell signalling (GO:0007267).Figure 4Genomic regions associated with regional differentiation of large yellow croaker between Zhoushan sea and Ningde sea. Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Zhoushan sea-captured population and Ningde sea-captured population. The x-axis represents chromosomal positions and the y-axis represents the average Fst values.Full size imageDetection of putative genes under selective sweep between the Zhoushan sea-captured population and farmed populationTo identify the genes under selective sweep in the domestication process, we analysed single Fst values for single SNPs and average Fst values in 40 kb windows with 10 kb steps separately both in the Zhoushan and Ningde regions. Between the Zhoushan sea-captured population and farmed population, we identified 23,862 SNPs with significant Fst values by single SNP analysis (Table S5). In the sliding window analysis, the number of significant regions was 317, and the total size of significant regions was 59 Mb (Fig. 5a). The sizes of significant regions were between 40 kb to 8.1 Mb. These regions contain 1709 genes (Table S6). We identified the strong peak of Fst signal on chromosome 11, which contains 423 genes such as hsp90ab1 (heat shock protein 90 alpha family class B member 1). GO analysis showed that genes involved in the regulation of fatty acid oxidation (GO:0031998), the steroid hormone mediated signalling pathway (GO:0043401), fatty acid metabolic processes (GO:0006631), membrane lipid metabolic processes (GO:0006643), regulation of fatty acid metabolic processes (GO:0019217), and long-chain fatty acid transport (GO:0015909). These GO terms include plenty of lipid metabolism-related genes such as ppara (peroxisome proliferator activated receptor alpha), pnpla2 (Patatin like phospholipase domain containing 2). It is worth mentioning that there were plenty of genes related to carbohydrate derivative metabolic processes (GO:1901135) with differences between the Zhoushan sea-captured population and farmed populations (Table S7). Additionally, a number of the growth relative genes include the developmental growth involved in morphogenesis (GO:0060560). Genes were found related to embryo development ending in birth or egg hatching (GO:0009792). Additionally, 47 genes related immune system development (GO:0002520) were obtained, such as taf3 (tata-box binding protein associated factor 3), irf7 (interferon regulatory factor 7) and rps7 (ribosomal protein s7) (Table S7).Figure 5Genomic regions associated with domestication of large yellow croaker between Zhoushan sea or Ningde sea. (a) Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Zhoushan sea-captured and Zhoushan farmed. (b) Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Ningde sea-captured and Ningde farmed. The x-axis represents chromosomal positions and the y-axis represents the average Fst values.Full size imageMoreover, we found that anxa2a (annexin a2a; from 16,718,332 bp to 16,713,531 bp on chromosome 21) have a splice donor site variant at 16,715,408 bp on chromosome 21. This mutation is located at the fifth intron of anxa2a, and is predicted to lead to a premature truncation. The anxa2a gene encodes a phospholipid-binding protein, and is involved in variety of intracellular processes including endocytosis, exocytosis, membrane domain organisation, actin remodelling, signal transduction, protein assembly16. This batch of samples came from breeding selection for a freeze-resistant population. We identified nine downstream mutations (16,713,395 bp, 16,713,442 bp, 16,713,443 bp, 16,713,593 bp, 16,715,408 bp, 16,715,741 bp, 16,716,027 bp, 16,716,216 bp and 16,717,363 bp on chromosome 21) of ice2 (interactor of little elongation complex ELL subunit 2) gene, which is located in the region from 16,727,361 to 16,718,192 bp on chromosome 21. This gene is involved in cold acclimation and determines freezing tolerance17.Detection of putative genes under selective sweep between the Ningde sea-captured and farmed populationFor the Ningde farmed population, we identified 660 SNPs with significant Fst values (Table S8). In the sliding window analysis, the number of significant regions was 42, and the total size of significant regions was 7.8 Mb (Fig. 5b). The sizes of significant regions were between 40 kb to 2.0 Mb. These regions contain 238 genes (Table S9). GO analysis showed identified genes related to the reproduction process such as female gonad development (GO:0008585), i.e. esr1 (estrogen receptor 1), foxo3 (forkhead box O3); the development of primary female sexual characteristics (GO:0046545) and embryonic appendage morphogenesis (GO:0035113), such as mbnl1 (muscle blind like splicing regulator 1); as well as embryonic limb morphogenesis (GO:0030326) and the response to steroid hormones (GO:0048545). Additionally, genes related to digestive tract development (GO:0048565) were enriched, such as hnf1b (hnf1 homeobox b) (Table S10). As per the results of SNPs with the highest Fst analysis between the Ningde sea-captured and farmed population, we identified a downstream variant of esr1, which is located at 9,103,629 bp on chromosome 11. This gene is located in the region from 9,129,853 and 9,108,464 bp on chromosome 11 and encodes estrogen receptor 1, which plays a critical role in responding to steroid hormones (Fig. 5b). Genes involved in visual system development (GO:0150063) such as prox1 (prospero-related homeobox1), nr2e1 (nuclear receptor subfamily 2 group e member 1) and znf513a (zinc finger protein 513a) were also enriched. The znf513a gene is located in the region from 11,664,515 to 11,657,703 bp on chromosome 11 and has a downstream variant located at 11,652,743 bp on this chromosome (Fig. 5b). More

Study sitesTo obtain a cross-section of land-use types through the Eastern Alps (Fig. 2), rooting samples were taken from Tyrol (Austria) and from South Tyrol and northern Trentino (both in Italy), which include two climatic regions—the central European climatic region in the northern part and the sub-Mediterranean climatic region in the southern part of the research area47. The average annual precipitation at the 13 study sites ranges from 400 to 2000 mm, with maximum rainfall observed from June to July47. Mean annual temperature ranges from 0 °C to 9 °C. Additional climatic variability was added by sampling at elevations from 650 to 2680 m a.s.l. The bedrock in the research area is composed of calcareous sedimentary rock in the northern and southern regions and of crystalline rock in the main chain of the Alps, sometimes with superimposed calcareous isles: Stubai Valley (North Tyrol) is geologically dominated by silicate with transitions to limestone; Ötz Valley, Ziller Valley and Igls/Patsch (all North Tyrol), Passeier Valley, Mühlbach, Matsch, Ritten and Jenesien (South Tyrol) are geologically dominated by silicate; and Leutasch (North Tyrol), St. Vigil and Toblach (both South Tyrol) and Monte Bondone (near Trento) are geologically dominated by limestone. The pH of the topsoil (0–10 cm), which ranges from 3.7 to 7.832, is determined by bedrock and land use48. For more details on the study region, see Supplementary Appendix S1.Figure 2Site, sample number and analyzed land-use types in the Eastern Alps. Study sites: B = Monte Bondone; I = Igls/Patsch; J = Jenesien; L = Leutasch; M = Mühlbach; M2 = Matsch; O = Ötz Valley; P = Passeier Valley; R = Ritten; S = Stubai Valley; T = Toblach; V = St. Vigil; and Z = Ziller Valley. The map was created using ArcGIS 10.2.2 (ESRI Inc.) and edited in Microsoft PowerPoint 365 MSO (Map data: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community).Full size imageTo be representative, the most widespread vegetation communities in the 13 study sites for all land-use types (arable land, intensively used hay meadow, lightly managed hay meadow, pasture, agriculturally unused grasslands, and forest) were analyzed (Supplementary Appendix S2). Overall, a total of 171 soil samples were taken, with 15 samples from arable land, 56 samples from intensively used hay meadows, 15 samples from extensively managed hay meadows, 16 samples from lightly stocked pastures, 32 samples from agriculturally unused grasslands, and 37 samples from forests. Meadows that are mown and fertilized with slurry and/or manure at least twice a year were defined as intensively used hay meadows. An extensively managed hay meadow was not fertilized and mown only once a year. Pastures were extensively grazed by cattle and/or sheep (annual average stocking intensity: 0.15–0.4 livestock units (LU) ha−1 year−1) but not mown. As arable land, we defined different crops typical for the region, especially maize and bread cereal crops, as well as vegetables and potatoes. Agriculturally unused grasslands included all grassland areas that were abandoned for at least five years or have never been used for agriculture, such as alpine grasslands. Finally, all permanent deciduous, coniferous or mixed forests were combined into the forest land-use type (thus, no energy forests).Data collection and analysisVegetation and site variables depending on land-use types were used to explain the rooting parameters. As Fig. 1 shows, dependencies between explanatory variables and rooting parameters are not always strictly unidirectional. For example, vegetation composition influences rooting; however, rooting patterns can also influence vegetation composition. We considered as many different dependencies as possible in the applied methods and interpreted discovered statistically significant relationships as associations rather than causal (unidirectional) impacts.Rooting parameters: root mass, root length and rooting depthOverall, 171 rooting samples (Appendices S1 and S2) were taken between 1994 and 2017 in the field with core samplers of 6.8–7.7 cm diameter and a maximum core depth of 70 cm. Before coring, the vegetation was characterized with the standard phytosociological method of Braun-Blanquet49 to directly connect rooting and vegetation characteristics. The size of the vegetation survey areas was determined by the minimal area of a plant community as the area with 90% or more of all plant species within this ecosystem. The survey area ranged between 1 m × 1 m in homogenous meadows and 10 m × 10 m in forests. Even though we chose the rooting survey areas to be homogeneous regarding vegetation composition, it was possible that the rooting measured in the soil cores was affected by species other than those located above the core area due to large heterogeneity within plant communities50. Nevertheless, this error should be negligible.As the data for this analysis were derived from a collection of rooting analyses from different research projects in the past 20 years using the same methodological approach, the number of samples per land-use type and per site was unbalanced (Supplementary Appendix S2). For example, some land-use types were represented only at one site (e.g., all agriculturally unused areas were at site I), while others were represented at three or even more than 10 sites. In addition, the number of samples within each land-use type was also unbalanced: 15 samples for arable land, 32 for agriculturally unused grasslands or 56 samples for intensively used hay meadows. The original data collection included the most common and important plant communities in the project areas except for arable land. Thus, the rooting of the most common crops (maize: n = 3; barley: 3; oat: 3; wheat: 3; and vegetables: 3) was analyzed near Innsbruck in an area specially selected for this purpose.In the laboratory, the soil cores were split into the O-horizon (if present) and mineral soil layers of various thicknesses (0–3 cm, 3–8 cm, 8–13 cm, 13–23 cm, 23–38 cm, 38–53 cm, and  > 53 cm). Root extraction was performed manually with the roots cleared of soil in sieving cascades under running water51. Afterwards, the roots were sorted into three size categories18: (1) very fine roots (diameter between 0 and 1 mm); (2) fine roots (diameter between 1 and 5 mm); and (3) coarse roots (diameter between 5 and 20 mm). Roots of woody species with a diameter larger than 20 mm were not taken into account, as the distribution and diameter of coarse roots (especially trees) in the soil vary greatly spatially; therefore, a single survey cannot be representative of the rooting of an ecosystem50,52. The reason for this classification was due to the different functions of the classes. Very fine roots have a dominant role in the uptake of water and nutrients and may be the main source of stabilized carbon input to soil1. Fine roots are mainly responsible for the transport, anchoring and storage of carbohydrates and are also able to take up water. Coarse roots are important for water transfer and the stabilization of plants. To account for the different specific root lengths (SRLs) of very fine and coarse roots from herbaceous and woody species29, we classified the single samples according to the cover of herbaceous and woody species from the phytosociological surveys into pure grassland samples, mixed grassland samples (dominance of woody species:  50%)18. The conversion of root mass to rooting length was carried out using previously published Eqs. 19 (Table 1). Finally, the maximum depth (RD90%), above which 90% of the total root mass was found, was calculated for each root sample using the equation:$$RD_{90% } = RM_{tot} frac{{arctan left( {frac{{RM_{90% } }}{{RM_{tot} }}} right)}}{{m_{max } }},$$
(1)
where RM90% is 90% of the total root mass (kg m-2) and mmax is the maximum slope of the saturation curve. In the same way, the depths above which 50% (RD50%) and 95% (RD95%) of the total root mass occurred were calculated. In forests and in dwarf shrub-rich communities, the rooting depths and distributions could be biased by the fact that the sampling depth was very shallow, which could lead to underestimating the 50%, 90% and 95% rooting depths53. In grassland ecosystems, croplands and in dwarf shrub rich communities, however, the 70 cm sampling depth is sufficient because most roots are within the top 30 cm18.Table 1 Linear functions to calculate the root length on the basis of root dry weight for different vegetation communities: grassland communities (G), mixed grassland communities (M), and dwarf shrub-rich or tree-rich vegetation communities (W). y = root length (mm m-2) and x = root dry mass (g m-2).Full size tableEnvironmental variablesFor every root sample, we collected 79 potential impact variables on rooting, including 19 site variables, six land-use variables and 53 vegetation variables (see Table 2 and Appendices S1, S3 and S4).Table 2 Groups of variables used to explain rooting parameters, including information on the type (V, vegetation variable; S, site variable; and LU, land-use variable), the number of variables of each group (no.) and examples (for details, see Appendices S1, S3 and S4).Full size tableVegetation variablesIn total, 53 vegetation variables were collected and divided a priori into four groups (Table 2, Supplementary Appendix S3). Variables included in the richness group were ‘number of plant species’, ‘number of taxonomic groups’ and ‘functional types’ (after38). All variables that displayed information on the mean species cover, plant cover variance or dominance of species, the Shannon–Wiener and Evenness indices (both after54) and the total vegetation cover were allocated to the community composition group. We calculated the Shannon–Wiener and Evenness indices54 for species composition, functional types and functional traits.The cover of functional types group included variables that provide information on the abundance, dominance and composition of single plant functional types (see Supplementary Appendix S3). Finally, the community-level trait group (see Supplementary Appendix S3) contained leaf, plant height and root traits (effect traits in sensu55) used to assess the relative effects of aboveground and root trait turnover at the community level. They were calculated for each sample using trait values taken from the literature and the measured abundance of each species within the single community (i.e., community weighted mean56). We used mean root density and main rooting depth for the single species57,58,59. The rooting density of the species was classified into sparse, medium dense, dense, and very dense roots59. The mean leaf size and plant height of the species (sources:60,61; http://www.floraweb.de/; own observations) were classified according to the following thresholds. Plant height was divided into small (mean plant height  90 cm) species. Leaf size was classified as small (mean leaf area  70 cm2) species. In accordance with other authors62,63, most plant species showed clear allometric allocation trends between leaves, stems and root biomass for different groups of plant species. In particular, a trend towards a decreased root mass fraction with plant size was detected.Site characteristicsImportant meteorological parameters were measured at eight study sites at a distance of  1%). We investigated whether all these species were summarized into PCA components, i.e., into species groups with similar habitat requirements. Species not included in any component were treated as their own component (however, in our study, all species were included in a component). The multiple correlation coefficient (R2) of each component with the vegetation and site components/variables was computed. A high R2 denotes that the information of the key species is covered by the vegetation and site variables.All technical details and further detailed descriptions of the methods can be found in Supplementary Appendix S13. Statistical analyses were conducted with Stata/MP 13.1 for Windows. More

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