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Study area and environmental variables
The data set used in this study included 192 sites in which lepidochronological data and shoot density were acquired between 1994 and 2003. Clearly, the rhizome primary production of P. oceanica was estimated as defined by Pergent-Martini et al.12.
The spatial coverage of the data set was not uniform across the Italian Seas. In fact, the sampling sites were mainly concentrated in five Italian regions, i.e. Liguria, Tuscany, Lazio, Basilicata and Apulia (Fig. 1).
Figure 1

Sampling sites from which field data and indirect measurements have been collected (red circles). Data about several sampling stations are available at each site (N = 6 to 15).

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The environmental variables were all acquired from maps and other related information sources (Table 1), according to the main aim of the study. A detailed explanation of these variables and of the methodology for their acquisition is given in the supplementary materials.
Table 1 Environmental factors used as predictive variables for developing P. oceanica models.
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Since these environmental factors were used as predictive variables in the modeling procedure, their selection was based on the ecological nature of the modelled processes, taking into account their influence on the latter. For instance, it is well known that depth plays a crucial role in determining the properties of P. oceanica meadows, such as density and productivity, as it is strictly related to other fundamental environmental factors, e.g. light. Therefore, both depth and gradient were considered as predictive variables, as well as the profile of the isobaths, described as either linear, convex or concave. The presence of sources of disturbance, such as sewage discharge or similar pollution, was also taken into account, as an increase in turbidity following an excessive enrichment from nutrient inputs might entail a reduction of water transparency and light penetration, which in turn can alter the ecological proprieties of a P. oceanica meadow. As for the sea floor typologies, i.e. sand, rock and matte, sources of disturbance have been represented as binary variables because of the intention of using only indirect methods for data acquisition, e.g. maps. Clearly, with such types of data source it was possible to perform, with good confidence, only a qualitative assessment. A quantitative coding of those predictive variables would indeed require expensive and time-consuming efforts for field activities, leading to a major drawback of the proposed approach.
The data set was partitioned into two subsets, i.e. training and test sets, for modeling purposes. Data partitioning represents a critical step in modeling, whose aim is obtaining two subsets that are as much as possible independent from each other, while simultaneously representative of the modelled problem, in order to avoid modeling artifacts and to ensure the applicability of the resulting models18.
Accordingly, the partitioning was not based on random selection of the data, rather the subsets were obtained on the basis of the following approach. The data were stratified according to depth, i.e. they were sorted on the basis of their depth and assigned to one of the following bathymetric classes, i.e.[0,5] m, (5,10] m, (10,15] m, (15,20] m, (20,25] m, (25,35] m. These classes comprised 16.67%, 23.96%, 27.08%, 17.71%, 9.90% and 4.69% of the total number of records, respectively. Subsequently, within each bathymetric class, about 70% of the data, i.e. n = 136, were assigned to the training set, while the remaining ones, i.e. n = 56, to the test set. While the former subset comprising the majority of the data was used for the training procedure of the Machine Learning algorithm, i.e. Random Forest19, the test subset was only used a posteriori to evaluate model performance.
The rationale behind the aforementioned approach is that the depth has a paramount ecological role in regulating both P. oceanica shoot density and rhizome primary production, as previously noted. In fact, a wide range of environmental conditions are related to depth, such as light, water movement and sedimentation flows, which in turn strictly affected the structure, the functioning and the ecological condition of P. oceanica meadows. Therefore, using the abovementioned strategy in the data allocation, the inherent variability of the ecological patterns was properly distributed among the subsets, thus ensuring the possibility of obtaining ecologically sound models.
Random Forest
The Random Forest (RF) is a Machine Learning technique which fits an ensemble of Classification Trees and combines their predictions into a single model19.
RF has proven effective in a wide range of applications as it is able to address, for example, both regression and classification problems20, to perform cluster analysis and missing values imputation21,22.
RF has been used for predicting current and potential future spatial distribution of plant species23, as well as for estimating the marine biodiversity on the basis of the sea floor hardness24. RF has been also applied in ecological applications as a classification tool for the assessment of the vulnerability of P. oceanica meadows over a large spatial scale25, and for land cover classification using remote sensing data26,27.
This method relies upon one of the main features of Machine Learning methods, namely that an ensemble of ‘weak learners’ usually outperforms a single ‘strong learner’19. As a matter of fact, each Classification Tree in the forest represents a weak learner, i.e. a single model, trained on a partly independent data subset, i.e. on a bootstrap sample. Each Classification Tree provides predictions based on the data contained in its bootstrap sample, and many trees are combined into an ensemble model, i.e. into a ‘forest’. The overall output of a RF is obtained by averaging the outcomes of all the trees for regression applications, while it is based on majority voting for classification problems.
The diversity of the trees in the forest is ensured by the use of random subsets of data for the tree-building process, i.e. bootstrap samples, as well as by making a random subset of predictive variables available for the tree splitting procedure. These features allow the RF to reduce the correlation among its Classification Trees, while keeping the variance relatively small, thus leading to a more robust model19.
The selection of a random subset of predictive variables at each split ensures maintaining a certain level of randomness during the tree construction process28, and is necessary for the proper functioning of RF. As a matter of fact, the size of the random subset of predictive variables available for the tree splitting procedure represents a tuning parameter, defined as mtry. The latter together with the minimum number of records to be contained in each leaf, called nodesize, are the main tuning parameters that deeply affect RF performance21,29.
In its original work, Breiman19 suggested to set the mtry value equal to p/3 for regression applications, being p is the total number of predictors, and tuning it from half to twice its original value. On the other hand, nodesize and ntree (the latter parameter is the total number of Classification Trees in the forest) are more related to the generalization ability of the RF, and to the overall complexity of the model. Growing a very large forest, e.g. ntree  > 500, or growing the trees to achieve a high degree of purity at their leaves, e.g. nodesize  More

Main features of mycorrhizal genomes
We compared 62 draft genomes from mycorrhizal fungi, including 29 newly released genomes, and predicted 9344–31,291 protein-coding genes per species (see “Methods”, Supplementary Information and Supplementary Data 1). This set includes new genomes from the early diverging fungal clades in the Russulales, Thelephorales, Phallomycetidae, and Cantharellales (Basidiomycota), and Helotiales and Pezizales (Ascomycota). We combined these mycorrhizal fungal genomes with 73 fungal genomes from wood decayers, soil/litter saprotrophs, and root endophytes (Fig. 1 and Supplementary Data 2). There was little variation in the completeness of the gene repertoires, based on Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis (coefficient of variation, c.v. = 7.98), despite variation in assembly contiguity (Fig. 1). Genome size varied greatly within each phylum, with genomes of mycorrhizal fungi being larger than those of saprotrophic species (Figs. 1 and 2, and Supplementary Data 2; P  More

Chemical properties of submicrometer aerosols
Figure 1a presents the time series of the mass concentrations of OC and water-soluble OC (WSOC) in the submicrometer aerosol particles during the cruise. Overall, the patterns of the temporal OC and WSOC variations were similar to each other (r = 0.632, p  More