Unraveling the interplay of the soil microbiome and (poly)phenol content in blueberry in response to disturbances

Unraveling the interplay of the soil microbiome and (poly)phenol content in blueberry in response to disturbances | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unraveling the interplay of the soil microbiome and (poly)phenol content in blueberry in response to disturbances Maxime Thomas, Mebarek Lamara, Yves Desjardins, Hugo Asselin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4433091/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Disturbances exert direct and indirect effects on plants through alterations of soil properties and microbiota composition. This can induce stress, resulting in modifications of plants’ phytochemical profile. This in turn can affect the possibility for Indigenous people to engage in cultural activities depending on wild plants used as food or medicine. As a case study, we evaluated correlations between (poly)phenols in Vaccinium angustifolium fruits, disturbances from mining and hydroelectric activities, soil properties, and soil microbiome composition. Methods We collected fruit and soil samples in the territories of three Indigenous communities in eastern Canada. Fruits were analyzed for their concentrations in anthocyanins, proanthocyanidins and other (poly)phenols. Soil microbial DNA was extracted to reconstruct bacterial and fungal communities. A secondary subset of soil samples was used to measure soil properties. Relationships between soil, disturbances and (poly)phenols were investigated using multivariate analyses. Results Disturbances affected soil properties and microbiome, but not fruit (poly)phenol content. Two soil bacterial classes unaffected by disturbances, Bacilli and Desulfitobacteriia, were positively correlated with levels of proanthocyanidines and delphinidin-, cyanidin-, and petunidin-3-glucoside in fruits. Conclusion Disturbances did not affect (poly)phenol content in V. angustifolium fruits. However, mine disturbances may contaminate fruits with pollutants detrimental to human health, which should be evaluated before drawing conclusions about the effect of disturbances on plant nutritional and medicinal properties. Some soil bacterial classes seem to enhance the (poly)phenolic content of V. angustifolium fruits, suggesting that a strategy could be developed for enhancing the nutritional and medicinal properties of this culturally salient species. Blueberry Indigenous people microbiome (poly)phenols mine hydroelectric power lines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Mining and hydroelectric development are two of the main industrial development activities taking place in the Canadian boreal forest (Venier et al. 2014 ; Gauthier et al. 2015 ; Bélisle and Asselin 2021 ). These activities result in disturbances that affect the boreal landscape, including animal and plant species. The establishment of a mining site or a hydroelectric power line entails the clearance of forests and the destruction of wildlife habitats to make room for infrastructure construction (Sonter et al. 2018 ; Haddaway et al. 2019 ; Li and Lin 2019 ). Mining also generates heavy metal pollution that can contaminate the soil, water, and plants surrounding a mining site (Yin et al. 2023a , b ; Yu and Zahidi 2023 ). Exposure to heavy metal pollution poses significant risks to plants, as heavy metals can disrupt metabolic processes, germination rates, and reproductive capabilities (Adeel et al. 2019 ; Yin et al. 2021 ). The creation of hydroelectric power lines can also induce stress in plants. Indeed, the removal of trees for the creation of a power transmission line increases the exposure of understory plants to light, including ultraviolet (UV) radiation. Elevated exposure to UV radiation can impair plant metabolic functions and lead to tissue damage (Bergamini et al. 2004 ). Plants adapt to disturbances, notably by synthesizing secondary metabolites, such as (poly)phenols (Sytar et al. 2013 ; Kumar and Pandey 2013 ; Thakur et al. 2019 ). Secondary metabolites in plants play a crucial role in mitigating the detrimental effects of stresses, including exposure to heavy metals and increased levels of UV radiation (Agati and Tattini 2010 ; Naing and Kim 2021 ). Disturbances can also affect plants indirectly by modifying soil properties and microbiome composition. For instance, mining activities have been shown to lower soil pH (Dudka and Adriano 1997 ; Johnson and Hallberg 2005 ), a change to which the soil microbiome is highly sensitive (Fierer and Jackson 2006 ; Ali et al. 2017 ). Disturbances can also indirectly influence plant health and development through their effects on the soil microbiome, as the interactions between soil microorganisms and roots can have either beneficial or detrimental effects on plants (Ali et al. 2017 ; Trivedi et al. 2020 ). Moreover, the influence of the soil microbiome extends beyond the roots to affect aerial plant parts as well (Salla et al. 2014 ; Rahman et al. 2018 ). For instance, the majority of plants engage in symbiotic relationships with mycorrhizal fungi to enhance nutrient uptake from the soil, illustrating a critical interaction that benefits plant growth and health (Jeffries et al. 2003 ; Brundrett 2009 ). Bacteria too can have beneficial effects on plants, notably by improving nutrition and response to stresses (Franche et al. 2009 ; Ali et al. 2017 ; Rahman et al. 2018 ). The soil microbiome also harbors bacterial and fungal pathogens capable of reducing plant growth (Štraus et al. 2023 ). Thus, by altering soil characteristics, environmental disturbances can provoke shifts in the composition of the soil microbiome (Seitz et al. 2021 ), which in turn can influence a range of plant processes including growth, nutrition, and the production of secondary metabolites (Treutter 2006 ; Kumar and Pandey 2013 ; Trivedi et al. 2020 ; Thomas et al. 2023 ). Disturbances can also affect plant nutritional and medicinal properties, since these attributes are closely linked to the presence and concentration of secondary metabolites (Del Rio et al. 2013 ; Durazzo et al. 2019 ). For example, anthocyanins, a group of secondary metabolites, provide numerous health benefits to humans; notably helping in the prevention of cardiovascular disease, cancer, and diabetes (de Pascual-Teresa and Sanchez-Ballesta 2008 ). Proanthocyanidins are another example of compounds conferring health benefits, such as anti-inflammatory effects and kidney protection (Ivey et al. 2013 ; Yokota et al. 2016 ; Dasiman et al. 2022 ). Modifications in plant properties can affect the livelihood and culture of different populations, notably Indigenous peoples. Indeed, certain plant species are central to cultural activities, for example being used as food or medicine, underscoring the profound connection between Indigenous cultures and specific flora (Garibaldi and Turner 2004 ; Parlee et al. 2005 ; Ladle et al. 2019 ; Boulanger-Lapointe et al. 2019 ). Investigating how such culturally salient species respond to disturbances is thus key to the preservation of Indigenous ways of life. The early lowbush blueberry ( Vaccinium angustifolium (Aiton)) is a culturally salient species for several Indigenous peoples in eastern Canada. This species is culturally salient because of its importance as a key food item (Arnason et al. 1981 ; Batal et al. 2021 ). V. angustifolium contributes to reinforcing the connection of Indigenous peoples with their territories and traditions, through blueberry picking outings and the preparation of traditional food such as blueberry paste (Boulanger-Lapointe et al. 2019 ; Basile et al. 2022 ; Pelletier 2022 ). Blueberries are also rich in phenolic acids, anthocyanins and proanthocyanidins, among other secondary metabolites, which possess antioxidant and antidiabetic properties (Uprety et al. 2012 ; Norberto et al. 2013 ; Grace et al. 2019 ; Weber 2022 ). Disturbances, soil properties, soil microbiome composition, and plant secondary metabolism are intricately linked. Although each of these elements have been the subject of individual studies (Lahdesmaki 1990 ; Tahkokorpi et al. 2010 ; Francioli et al. 2021 ; Zaborowska et al. 2021 ), their interrelations are seldom examined collectively, highlighting a gap in comprehensive understanding of their synergistic effects on ecosystem and plant health. Thus, the objective of this study was to assess the complex interplay between two disturbances (mining and hydroelectric power lines), (poly)phenols in the fruits of V. angustifolium , soil properties and the soil microbiome in the eastern Canadian boreal forest. More specifically, we asked the questions: i) Do disturbances directly affect fruit (poly)phenol content, and if so, how? ii) Which soil microbial taxa, if any, affect fruit (poly)phenol content and how? iii) Do disturbances indirectly affect plant (poly)phenol content through their effect on soil properties and bacterial and fungal communities? Material and Methods Study area This study took place in western Quebec (Canada) on the traditional territories of the Abitibiwinni (Anishnaabe), Mistissini (Cree), and Nemaska (Cree) Indigenous communities (Fig. 1 ). The Abitibiwinni First Nation’s territory is located near the Ontario border, mainly in the black spruce – feather moss bioclimatic domain, except for its southern part located in the balsam fir – paper birch bioclimatic domain (Bélisle and Asselin 2021 ). The Mistissini Cree Nation’s territory is located further north and is very large, with its southern part in the black spruce – feather moss bioclimatic domain and its northern part in the spruce – lichen woodland bioclimatic domain. The territory of the Nemaska Cree Nation is located at the northern boundary of the black spruce – feather moss bioclimatic domain. This territory has experienced frequent fires in the past decades, which led to younger and more open stands (Eeyou Planning Commission 2017 ). Fruit and soil sampling Samples were collected on the territories of the Abitibiwinni, Mistissini and Nemaska communities on August 2nd, 16-17th, and 20th 2022, respectively. The difference in timing between territories was planned to collect fruits at the same phenological stage, accounting for latitude differences. All samples were collected in the black spruce – feather moss bioclimatic domain (Fig. 1 ), more specifically in black spruce stands, to limit the influence of potential confounding environmental variables. A total of 24 sites were sampled, that is 8 in each territory: 3 under hydroelectric power lines, 3 near a mine (200 m or less), and 2 control sites at least 1 km away from these disturbances. The selection of the distance around mining sites for analysis was informed by findings from prior studies, which determined that the effect of mining activities on vegetation extended to an average distance of 200 m (Boisvert et al. 2021 ; Yin et al. 2023a ). All sites were located at least 100 m from roads to limit edge effects, trampling, and disturbances due to traffic. At each site, four fruit samples were collected from different plant individuals for analysis of (poly)phenol compounds. Three organic soil samples were also collected with an auger in a triangular fashion around the fruit sampling locations, for analysis of soil microbiome and soil properties. The three soil samples were combined into a composite sample representative of the soil around the sampled plants. All samples (fruit and soil) were preserved in a cooler immediately after collection, then transferred to a -80°C freezer after each sampling day until extraction. Extraction and analysis of (poly)phenol compounds Fruit samples were sent to INAF’s chemical analysis laboratory (Institute of Nutrition and Functional Foods, Laval University, Quebec City, Canada) where they were extracted for analysis of flavonoids and total (poly)phenol compounds. Flavonoids The proanthocyanidins (PACs) content of the samples was measured by phloroglucinolysis followed by a UPLC-UV-MS/MS analysis. Phloroglucinolysis is a process in which PACs are cleaved into their base units, flavan-3-ols, using phloroglucinol (Kennedy and Jones 2001 ). This allows to quantify PACs and to determine their polymerization degree. A catechin standard was used to quantify PACs by UPLC-UV. The identification of detected compounds was then confirmed by triple quadrupole mass spectrometry. The flavonol content of the samples was measured using the same procedure as for PACs, albeit with the omission of phloroglucinolysis and the use of a quercetin-3-glucoside standard for the purpose of quantification. Total (poly)phenols Total (poly)phenolic content was determined according to the Folin-Ciocalteu method as described in Dudonné et al. ( 2015 ) using gallic acid as a standard. Extract solutions (20 mL in 20% methanol 0.1% TFA) were mixed with 100 mL of 10-fold diluted Folin-Ciocalteu reagent and 80 mL of sodium carbonate solution (75 g/L). After 1 h of incubation at room temperature, the absorbance was measured at 765 nm using a BMG Labtech Fluostar Omega microplate reader (Offenburg, Germany). Physicochemical analyses Before proceeding to physicochemical analyses, soil samples were oven-dried at 35°C for 3 days, then passed through a 5.6 mm sieve to remove large debris, and stored in a -80°C freezer until analysis. Soil samples were analyzed for mineral contents (calcium, aluminum, potassium, phosphorus, magnesium, boron, copper, iron, manganese, zinc, sodium), pH, cation exchange capacity (CEC), organic matter content, as well as nitrogen and carbon content. Minerals were extracted with a Mehlich III solution and quantified by ICP-Optical Emission Spectrometry with the appropriate standard for each mineral (Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques du Québec 2014 ). Soil pH was determined in water with a pH-meter (Ministère de l’Environnement, de la Lutte contre les Changements Climatiques, de la Faune et des Parcs 2023 ). The organic matter percentage in the soil was calculated by loss on ignition (Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques du Québec 2017 ). Microbiome analysis DNA Extraction The DNA of the soil samples was extracted from 250 mg of sampled soil using the Qiagen DNeasy Powersoil Pro kit (QIAGEN 2022 ) following the manufacturer’s protocol. Two negative extraction controls with no soil were also processed following the same protocol. The extracted DNA was stored immediately in a -80°C freezer until further processing. DNA samples were then sent to Genome Quebec Innovation Center (Montreal, Canada) for amplification and sequencing. Amplification and sequencing The metabarcoding method was used to detect and quantify bacteria and fungi in the organic soil. For bacteria, the extracted DNA samples were amplified using the primers 515b-FwR1 forward (GTGYCAGCMGCCGCGGTAA) and 926-RvR2 reverse (CCGYCAATTYMTTTRAGTTT) (Parada et al. 2016 ). For fungi, the primers used were ITS-9F forward (GAACGCAGCRAAIIGYGA) and ITS4R reverse (TCCTCCGCTTATTGATATGC) (White et al. 1990 ; Ihrmark et al. 2012 ). PCR amplifications were performed with 5 minutes of initial denaturation at 95°C, 34 cycles (bacteria) or 40 cycles (fungi) of 30 seconds at 94°C, 30 seconds at 50°C, and 1 minute at 72°C, then a final elongation step of 10 minutes at 72°C. Prior to amplification and sequencing, DNA quality was checked using 1% agarose gel electrophoresis. Amplicons were sequenced on the Illumina MiSeq platform for paired-end reads. A negative control was included in the sequencing for both bacteria and fungi, to ensure that the extraction step did not result in contamination of the samples. Bioinformatic workflow The DADA2 R-package was used to build amplicon sequence variants (ASVs) from the raw sequences (Callahan et al. 2016 ). For bacteria, sequence primers were removed by trimming the first 19 nucleotides of forward reads and first 20 nucleotides of reverse reads in DADA2. After checking reads quality, 16S reads were also truncated at position 260 for forward reads and 190 for reverse reads, as there was a drop in read quality after these points. For fungi, ITS sequences primers were removed with cutadapt (Martin 2011 ) prior to assembly with DADA2. As the ITS sequence length is variable, ITS reads of lesser quality were not truncated to ensure that forward and reverse read could merge, but ITS read quality was decent overall. For 16S and ITS analyses, reads were pseudo-pooled during the ASVs assembly step in order to allow for the detection of rare ASVs. External contaminants were then removed using the decontam R-package using the prevalence method (Davis et al. 2018 ). Taxonomy was assigned using the Silva v138.1 database formatted for DADA2 for the bacterial sequences (McLaren and Callahan 2021 ) and the UNITE v.9.0 database for the fungal sequences (Abarenkov et al. 2022 ). Once ASVs were built, data was transferred into a phyloseq object with the phyloseq R-package for handling (McMurdie and Holmes 2013 ). As a quality check, we removed ASVs that were found in only one sample, and those that were found less than 10 times across all samples. A phylogenetic heat tree of the taxa found in the samples was built using the metacoder R-package for visualization of taxonomic diversity across all samples (Foster et al. 2017 ). Before downstream analyses, the library of each sample was repeatedly rarefied to the library size of the smallest sample by drawing random ASVs without replacement with 1000 repetitions with the mirlyn R-package (Cameron et al. 2021 ), to control for biases in richness induced by differences in library sizes between samples. As the analyses available in the mirlyn package are limited, the multiple libraries created by the repeated rarefaction were then condensed into a single phyloseq object for compatibility with further packages. To do so, a table of ASV abundance was constructed by rounding the mean abundance of each ASV after 1000 rarefactions for each sample. Statistical analyses All analyses were performed using the R software version 4.3.1 (R Core Team 2023 ). Bacteria and fungi were treated separately for all analyses. Effect of disturbances and territory on (poly)phenol concentrations The effect of disturbance type, territory, and their interaction on (poly)phenol concentrations was evaluated with a PERMANOVA using the adonis2 function of the vegan R-package (Oksanen et al. 2022 ). The random effect of the sampling sites was accounted for by constraining permutations: sampling sites could be permuted, but not samples between sites. A total of 9999 permutations were performed using Euclidean distances. Effect of disturbances and territory on soil properties and microbiome The effect of disturbance type, territory, and their interaction on soil properties was also analyzed with a PERMANOVA using the adonis2 function from the vegan R-package (Oksanen et al. 2022 ). Variables were normalized by scaling prior to the PERMANOVA, and 9999 permutations were performed using Euclidean distances. To further study the response of soil properties to disturbances, an ANOVA followed by a Tukey test was performed. Differences in alpha diversity between disturbance types and territories were plotted using the plot_richness function of the phyloseq R-package. Non-metric multidimensional scaling (NMDS) ordination plots were also produced, using the vegan package (Oksanen et al. 2022 ) on Hellinger transformed data to visualize the relation between phylum abundance, soil properties and sampling sites. Finally, a linear discriminant analysis effect size analysis (LEfSe) was performed to detect taxa that were differentially abundant between disturbance types (Segata et al. 2011 ). Since this analysis has a high false discovery rate (Nearing et al. 2022 ), the LDA threshold was conservatively set to 3.5. The LEfSe was computed in R using the microbiomeMarker package (Cao et al. 2022 ). Effect of soil properties on microbiome The effect of soil properties on soil microbiome was evaluated using a distance-based redundancy analysis (db-RDA) using the vegan R-package (Oksanen et al. 2022 ). The explanatory variables used in the model were the following: pH, contents of nitrogen, carbon, phosphorus, magnesium, potassium, iron, copper, manganese, zinc, aluminum, calcium, and sodium, and CEC. Explanatory variables were scaled prior to analysis, and the dissimilarity matrix of microbiome abundance was calculated using the Hellinger distance. Significance of the db-RDA model, axis, and terms was then evaluated by permutation, using the anova function of the vegan R-package with 9999 permutations (Borcard et al. 2018 ). P -values were adjusted with the Benjamini-Hochberg correction (Benjamini and Hochberg 1995 ). Effect of microbiome on (poly)phenol concentrations (Poly)phenol concentrations were not scaled prior to analyses, as concentration was the best available proxy for (poly)phenol bioavailability. The effect of soil bacterial abundance on (poly)phenol concentrations was evaluated. For each site, the average concentration of each (poly)phenol compound was calculated. The rarefied ASV abundances were summed to the phylum level, and phyla with less than 500 observations across all samples were removed from the analysis, as they were unlikely to meaningfully affect plant (poly)phenols. Then, a redundancy analysis (RDA) was used to investigate the effect of the abundance of each phylum on (poly)phenol concentrations using the vegan R-package. Significance of the model, axis and explanatory variables was then evaluated by permutation, with the anova function of the vegan R-package using 9999 permutations (Borcard et al. 2018 ). P -values were adjusted with the Benjamini-Hochberg correction (Benjamini and Hochberg 1995 ). If a significative effect was found at the phylum level, another RDA was conducted with the abundance of the classes of this phylum, and so on with inferior taxonomic levels. In addition, the Proteobacteria and Firmicutes phyla were further explored as they contain plant growth promoting bacteria (Bulgarelli et al. 2013 ; Youseif 2018 ; Getahun et al. 2020 ). The same procedure was followed to evaluate the effect of fungi abundance on (poly)phenols, except with a threshold of 300 observations across all samples, as fungi were less abundant in general. In addition, the Ascomycota and Basidiomycota phyla were also explored as they contain ericoid mycorrhiza (Dong et al. 2022 ). FUNGuild v1.1 (accessed on august 3rd 2023) was used to get fungi putative functional assignations in order to help discuss the results (Nguyen et al. 2015 ). When an RDA highlighted a taxon as having a significant effect on the (poly)phenol profile, the effect of this taxon on individual (poly)phenol was further evaluated by constructing linear models. Results (Poly)phenol content The majority of the (poly)phenols quantified in the samples consisted of PACs, and to a lesser degree, anthocyanins featuring a 3-glucoside moiety, including delphinidin, malvidin, and cyanidin-3-glucoside (Fig. 2 ). No differences were observed in the concentrations of (poly)phenols among the various environmental disturbances examined. Interestingly, concentrations had a lower standard deviation for control sites than for disturbances, which may indicate a higher variability of environmental conditions near disturbed sites. Effect of disturbances Effect of disturbances on (poly)phenol content The content of (poly)phenols was not significantly influenced by disturbance type, territory, or the interaction between these two factors, although the effect of territory was marginally notable ( P = 0.0573; Table A1 ). Effect of disturbances on soil properties Disturbance type and territory had a significant effect on soil properties, while the effect of their interaction was not significant (Table 1 ). Table 1 PERMANOVA of the variation in soil properties as a function of disturbance type, territory, and their interaction. Df Sum of Squares R 2 F Pr(> F) Disturbance 2 71.38 0.19 3.12 0.0004 Territory 2 64.8 0.18 2.83 0.0009 Disturbance:Territory 4 60.1 0.16 1.31 0.1403 Residual 15 171.71 0.47 Total 23 368 1 Samples collected from mining sites exhibited significantly higher levels of copper and iron compared to other locations, and also contained significantly more carbon, nitrogen, and organic matter than sites associated with hydroelectric power lines. They also had a higher pH than control sites. Regarding differences between territories, samples from Nemaska were more concentrated in aluminum and sodium than those from Mistissini, and less concentrated in magnesium and zinc than those from Abitibiwinni. Samples from Mistissini contained more copper than those from other territories. Effect of disturbances on soil bacteria Control site samples tended to have a lower bacterial abundance in general, and a lower abundance of bacteria from the Xanthobacteraceae family in particular (Fig. 3 ). Samples from mine sites were associated with bacteria from the Fibrobacterota, Desulfobacterota and Spirochaetota phyla (Fig. 4 ). Trends regarding bacterial phyla were less obvious for hydro sites, as they were clustered closer to the center of the ordination graph. Samples from control sites tended to have a lower abundance of all bacterial phyla, as suggested by the lower abundance in all bacteria families seen in Fig. 3 . Only hydro sites had significant differentially abundant bacterial taxa (Fig. 5 ). Soil from these sites contained significantly more bacteria from the Verrucomicrobiae class, especially of the Chthoniobacterales order, and more bacteria from the Ktedonobacteria class, especially of the Ktedonobacterales order and Ktedonobacteriaceae family. Effect of disturbances on soil fungi Samples near a mining site tended to contain more Myxotrichaceae (Fig. 6 ). The fungal composition of mining sites within the Abitibiwinni territory differed from the other mining sites, which could be due to differences in mine operation stage or processed ores between the mines. Interestingly, samples from control sites in Mistissini contained a large proportion of Pilodermataceae . The majority of ASVs belonged to the Ascomycota phylum. Abundances of Ascomycota, Basidiomycota, Mortierellomycota, and Mucoromycota were correlated (Fig. 7 ). These phyla tended to be more abundant in sites near mines and sites from Abitibiwinni. Most of the differentially abundant taxa were found in sites under hydroelectric power lines (Fig. 8 ). Fungi from the Geoglossomycetes class, especially from Geoglossales order, the Geoglossaceae family, and the Sarcoleotia genus were more abundant in hydro sites. They also contained more fungi from several taxa of the Helotiales order, specifically from the Dermateaceae family, and from the Humicolopsis genus in the Sordariomycetes class. Fungi from the Myxotrichaceae family, especially from the Oidiodendron genus, which also belong to the Heliotales order were more abundant in mine sites. One specific species, Brahmaculus moonlighticus was more abundant in control sites. Effect of soil properties on the soil microbiome Effect of soil properties on bacteria Soil pH emerged as the sole soil property exerting a significant influence on the composition of soil bacteria ( P = 0.0014, Table 2 ). An increase in soil pH was associated with a rise in the abundance of bacteria belonging to the WD260 order and several species within the Bradyrhizobium genus. Conversely, higher pH levels led to a decrease in the abundance of bacteria from the Acidobacteriae order and various genera within the Acidobacteriaceae family (Fig. 9 ). Table 2 Distance-based redundancy analysis (RDA) of the effect of soil properties on soil bacterial community. Bacteria dissimilarity matrix was calculated using the Hellinger distance. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to p -values, and significant ( F) Model 14 7.2889 1.2987 0.0018 Residual 9 3.6079 Axes dbRDA1 1 1.3336 3.3267 0.0098 dbRDA2 1 1.09 2.719 0.5474 dbRDA3 1 0.7978 1.9902 1 dbRDA4 1 0.6264 1.5625 1 Terms pH 1 1.219 3.0409 0.0014 Nitrogen 1 0.6562 1.6369 0.1794 Carbon 1 0.5865 1.463 0.385 Phosphorus 1 0.3412 0.8511 1 Magnesium 1 0.6340 1.5816 0.2196 Potassium 1 0.386 0.963 1 Iron 1 0.5552 1.385 0.552 Copper 1 0.4962 1.2377 1 Manganese 1 0.3873 0.9662 1 Zinc 1 0.3799 0.9477 1 Aluminum 1 0.431 1.0751 1 Calcium 1 0.4493 1.1207 1 Sodium 1 0.3266 0.8148 1 CEC 1 0.4405 1.0989 1 Effect of soil properties on fungi Soil fungal composition was affected significatively by pH ( P = 0.0195), but also by nitrogen content ( P = 0.0028) (Table 3 ). Nitrogen content was correlated with carbon, magnesium, and calcium content, and mainly associated with increased abundances of fungi from the Piloderma genus, and from the Oidiodendron pilicola species, and with decreased abundances of fungi from the Scytalidium vaccinii species (Fig. 10 ). The pH was correlated with copper content and was associated with increased abundances of fungi from the Piloderma and Oidiodendron genera, and with decreased abundances of fungi from the Mycosymbioces genus (Fig. 10 ). Table 3 Distance-based redundancy analysis (RDA) of the effect of soil properties on soil fungal community. Bacteria dissimilarity matrix was calculated using the Hellinger distance. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to p -values, and significant ( F) Model 14 11.2182 1.364 0.0001 Residual 9 5.2873 Axes dbRDA1 1 1.7237 2.9341 0.0266 dbRDA2 1 1.2332 2.0991 0.0469 dbRDA3 1 1.0541 1.7942 0.9492 dbRDA4 1 1.0443 1.7777 0.9996 Terms pH 1 1.0493 1.7861 0.0195 Nitrogen 1 1.2123 2.0635 0.0028 Carbon 1 0.8902 1.5153 0.0890 Phosphorus 1 0.5908 1.0057 1 Magnesium 1 0.9514 1.6195 0.0768 Potassium 1 0.6803 1.1580 0.9840 Iron 1 0.6436 1.0955 1 Copper 1 0.8704 1.4815 0.0890 Manganese 1 0.8087 1.3766 0.2597 Zinc 1 0.5897 1.0038 1 Aluminum 1 0.9173 1.5614 0.0768 Calcium 1 0.8322 1.4166 0.1880 Sodium 1 0.6696 1.1399 1 CEC 1 0.5123 0.7253 1 Effect of microbiome on (poly)phenol concentrations The abundance of bacteria phyla in the soil did not have a significant effect on (poly)phenol concentrations (Table A2 ). However, when breaking down the Firmicutes phylum into its classes, we found a significative effect of the abundance of Bacilli ( P = 0.0356) and Desulfitobacteriia ( P = 0.0056) on (poly)phenol concentrations (Table 4 and Fig. 11 ). Increased abundances of the Bacilli and Desulfitobacteriia were associated to higher concentrations of PACs (Fig. 11 ). Increased abundances of Desulfitobacteriia were also associated with higher concentrations of delphinidin-3-glucoside, cyanidin-3-glucoside, and petunidin-3-glucoside (Fig. 11 ). We did not find a significant effect of classes of Proteobacteria on (poly)phenol concentrations (Table A3 ). We also explored the Acidobacteriota, RCP2-54, WPS-2, Bacteroidota, Chloroflexi and Myxococcota phyla as they were near significant (Table A2 ), but it was inconclusive. Table 4 Redundancy analysis (RDA) of the effect of Firmicutes Classes abundance on the concentration in (poly)phenolics of V. angustifolium fruits. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to p -values, and significant ( F) Model 4 46486 2.8798 0.0028 Residual 19 76676 Axes RDA1 1 31213 7.7346 0.0300 RDA2 1 11954 2.9622 0.4092 RDA3 1 2035 0.5044 0.9523 RDA4 1 1284 0.3181 0.9523 Terms Bacilli 1 14987 3.7138 0.0356 Negativicutes 1 8951 2.2179 0.1732 Desulfitobacteriia 1 19654 4.8702 0.0056 Clostridia 1 2895 0.7174 0.5566 The abundance of fungi phyla did not have a significative effect on (poly)phenol concentrations (Table A4 ). Abundances of classes from the Ascomycota and Basidiomycota phyla did not have a significant effect on (poly)phenol concentrations either (Table A5 and Table A6 ). We further investigated classes from the Chytridiomycota phylum, as they approached significance (Table A4 ), but the findings remained inconclusive. Discussion Disturbances affected the composition of soil microorganisms, primarily due to alterations in soil characteristics. However, these changes did not influence plant (poly)phenol content. Indeed, the microbiome taxa responding to disturbances were not the same as the taxa that affected plant (poly)phenols. Effect of disturbances on (poly)phenol content Disturbances had no significant effect on the (poly)phenol content of V. angustifolium . This outcome is unexpected given that plants commonly react to stressors, including heavy metal contamination or ultraviolet (UV) radiation, by increasing the synthesis of (poly)phenols (Šamec et al. 2021 ; Jańczak-Pieniążek et al. 2023 ). This observation could be attributed to the specific organ examined in the current study (fruits), as the response of plant secondary metabolism to stress factors can vary depending on the organ (Schreiner et al. 2009 ; Larbat et al. 2012 ; de Miguel et al. 2016 ; Smirnov et al. 2021 ). Indeed, various plant organs may not experience stressors to the same degree and may produce different compounds to counteract stress (Larbat et al. 2012 ; Simek et al. 2016 ). For instance, tomato plants ( Solanum lycopersicum L.) infected by Alternaria solani , a pathogenic fungus, exhibit reduced flavonoid levels in the leaves, whereas the concentrations in the fruits remain unaffected (Quiterio-Gutiérrez et al. 2019 ). Similarly, in the olive tree ( Olea europaea L.), water stress leads to diverse and occasionally contradictory changes in the concentrations of (poly)phenols across different organs, such as leaves and fruits (Jiménez-Herrera et al. 2019 ). Therefore, it is conceivable that the studied disturbances affected other parts of V. angustifolium but did not affect its fruits. Effect of disturbances on soil microbiome The construction of hydroelectric lines and the subsequent vegetation management under the lines creates distinctive soil conditions, particularly as wood from trimmed or felled trees is frequently left on site to decompose naturally (Hydro-Québec 2023 ). According to our analyses, soil from sites under hydroelectric lines was relatively poor in carbon and nitrogen. It is thus possible that woody debris were the main carbon source at these sites. This may explain the higher abundance of saprotroph taxa or taxa able to degrade cellulose and lignin. Bacterial taxa such as Verrucomicrobiae and Ktedonobacteria at the class level, Chthoniobacterales and Ktedonobacterales at the order level, and Ktedonobacteriaceae at the family level, possess the capability to degrade lignin, cellulose, and other complex forms of carbon (Köberl et al. 2020 ; Zhao et al. 2020 ; Li et al. 2021 ; Zheng et al. 2021 ; Rachmania et al. 2022 ). The abundance of the Ktedonobacteria class and its descendant taxa is higher under elevated levels of UV radiation (Maccario et al. 2019 ; Bañeras et al. 2022 ). This phenomenon could account for their higher abundance in sites under hydroelectric lines, where the absence of tree cover results in greater exposure to UV radiation. Regarding fungi, members of the Sordariomycetes class, Dermateaceae family, and Humicolopsis genus are known for their ability to break down complex carbon structures (Osono and Hirose 2009 ; Elíades et al. 2015 ; Zhou et al. 2016 ; Li et al. 2020 ; Miao et al. 2022 ; Xing et al. 2022 ; Rao et al. 2023 ). Fungi from the Geoglossomycetes class and their children taxa have also been described as saprotrophic (Tedersoo et al. 2014 ). However, more recent studies found that they are rather mutualistic and able to form ericoid mycorrhizae (Baba et al. 2021 ; Melie et al. 2023 ). During our field observations, we noted that areas beneath hydroelectric lines exhibited a high abundance of ericaceous shrubs. This environmental characteristic may account for the observed increase in the abundance of ericoid mycorrhizal fungi, which are known to form symbiotic associations with the roots of ericaceous plants. Therefore, marker taxa found under hydroelectric lines could be the result of particular carbon conditions and of the high abundance of ericaceous shrubs. Mining also affects soil conditions, notably by increasing metal concentrations. According to our analyses, soil from mining sites contained higher copper and iron concentrations. This may explain the higher abundance of fungi from the Oidiodendron genus, which are metal-tolerant (Vallino et al. 2009 ; Chiapello et al. 2015 ). However, these sites also had high abundance of fungi from the Myxotrichaceae family, which includes species forming mycorrhizal relationships (Kernaghan and Patriquin 2011 ) as well as functioning as saprotrophs (Dalpé 1989 ; Sigler et al. 2000 ; Rice et al. 2006 ). Yet, it is noteworthy that these fungi are not documented as being tolerant to metals. Regarding bacteria, Sumerlaeota tended to be more abundant near mining sites, although this was not significant. This phylum is relatively unexplored, but is known to be extremophile, thus probably adapted to the particular soil conditions of mining sites (Fang et al. 2021 ). Taxa associated with mining sites are thus partly explained by the specific edaphic conditions generated by mining activity. Finally, the only taxon with higher abundance in control sites, the fungi Brahmaculus moonlighticus , has unfortunately not been studied with regards to its functions or habitat requirements. Interestingly, control sites also had less bacteria from the Xanthobacteraceae family than mining and hydroelectric sites. This is consistent with the characteristics of Xanthobacteraceae , as they are tolerant to polluted soil, and are able to degrade various pollutants including metals (Petrus et al. 2015 ; Martínez et al. 2022 ; Li et al. 2023 ), which explains their lower abundance in undisturbed sites. Effect of soil properties on the microbiome Some microbial taxa were not associated with a particular disturbance type, but responded to variations in soil properties. Concerning soil properties, only pH was found to significantly affect the abundance of bacterial taxa. On the one hand, Acidobacteriae at the class level and Acidobacteriaceae at the family level decreased in abundance with increasing pH, reflecting their acidophilic nature (Campbell 2014 ; Bartram et al. 2014 ; De Jonge et al. 2021 ). On the other hand, the genus Bradyrhizobium increased in abundance with rising pH levels. While Bradyrhizobium species can tolerate a broad spectrum of pH conditions (Meghvansi et al. 2005 ), their optimal pH for growth varies depending on species and strain (Graham et al. 1994 ; Indrasumunar et al. 2012 ). Given that the pH of the study sites was relatively acidic, ranging from 3.4 to 4.7, it is plausible that the Bradyrhizobium strains encountered in this study possess an optimal growth pH closer to neutral conditions. This adaptation could explain their increased abundance in conjunction with rising pH levels within the observed range. The abundance of fungal taxa was influenced not only by soil pH, but also by nitrogen, and marginally by carbon, magnesium, aluminum, and copper content. Unfortunately, the current body of research concerning the optimal edaphic conditions and recognized functions of the majority of the studied taxa has only provided sufficient information to elucidate the response of a single taxon to soil properties. The Piloderma genus increased in abundance with magnesium and other metal concentrations in the soil. This particular genus is documented to thrive in soils with a high magnesium content (Glowa et al. 2003 ). Effect of microbiome on plant (poly)phenols (Poly)phenols in V. angustifolium fruits were not affected by any of the microbial taxa shown to vary with disturbances or soil properties, but were nevertheless affected by other taxa. (Poly)phenols were significatively affected by the abundance of two classes from the Fimicutes bacterial phylum: Bacilli and Desulfitobacteriia. Both these bacterial classes contain plant growth-promoting rhizobacteria (PGPR). Bacilli within the soil, particularly the Bacillus genus, are recognized for their diverse range of functions that are vital to plants (Hrynkiewicz et al. 2010 ; Saxena et al. 2020 ). Among others, they promote plant nutrition through nitrogen fixation, phosphorus and potassium solubilization (Sharma et al. 2013 ; Verma et al. 2015 ; Asari et al. 2017 ; Yousuf et al. 2017 ), and they help plants combat pathogens and mitigate the effects of metal pollution (Ramadoss et al. 2013 ; Goswami et al. 2014 , 2016 ; Borriss et al. 2019 ). Desulfitobacteriia, specifically of the genus Desulfitobacterium increase in abundance following NH 4+ addition, thus could have a role in nitrogen cycling (Xiao et al. 2023 ), and can also detoxify mycotoxins (He et al. 2020 ). In addition, PGPR can enhance plant (poly)phenol production under various stresses, notably by activating the phenylpropanoid pathway (Ait Barka et al. 2006 ; Zhang et al. 2023 ). Therefore, it is likely that these two classes of bacteria, Bacilli and Actinobacteria, influenced the (poly)phenolic content of V. angustifolium fruits through their multifaceted effects on plant metabolism and their role in pathogen control. Conclusion Disturbances influence soil properties and the composition of the soil microbiome. However, intriguingly, these changes did not result in discernible differences in the (poly)phenolic compounds found in the fruits of V. angustifolium . Thus, disturbances due to mining and hydroelectric lines do not appear to affect the nutritional and medicinal properties of blueberries associated with (poly)phenols. However, this does not imply that disturbances have no effect on the overall nutritional and medicinal properties of the fruits. For example, presence of heavy metals in the fruits (due to mining activities) could lead to deleterious consequences for those who consume them (Okereafor et al. 2020 ; Yin et al. 2021 ). Hence, it is imperative to conduct assessments for the presence of pollutants in V. angustifolium fruits before making definitive conclusions regarding the effect of disturbances on the innocuity of these fruits and their nutritional and medicinal properties. Aside from the effects of disturbances and soil properties, two bacterial classes, Bacilli and Desulfitobacteriia, were associated with increased abundance of fruit (poly)phenols. Identifying the environmental conditions that are conducive to the growth of Bacilli and Desulfitobacteriia and actively promoting these conditions could represent a potential strategy for enhancing the nutritional and medicinal properties of V. angustifolium . Declarations Funding This study was funded by the Natural Science and Engineering Research Council of Canada (NSERC) – Université du Québec en Abitibi-Témiscamingue (UQAT) Industrial Chair on northern biodiversity in a mining context. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Maxime Thomas, Mebarek Lamara, Hugo Asselin and Nicole J. Fenton contributed to the study conceptualization and to the design of the methodology. Maxime Thomas and Mebarek Lamara wrote the first draft of the manuscript and performed formal analyses. Yves Desjardins validated research outputs regarding (poly)phenols. Nicole J. Fenton contributed to funding acquisition. All authors reviewed and edited the manuscript and approved its final version. Data Availability The datasets generated during and/or analyzed during the current study contain sensitive information as the samples were collected on the traditional territories of Indigenous communities. They are available from the corresponding author on reasonable request. References Abarenkov, K., Zirk, A., Piirmann, T., Pöhönen, R., Ivanov, F., Nilsson, R.H., and Kõljalg, U. 2022, October 16. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4433091","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305818213,"identity":"77edf38e-5c3f-475c-b256-eac1dba1d000","order_by":0,"name":"Maxime Thomas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJCCAyDCAMysIEGLBETLGSBmI1IbRAtjGxFadNvPGB66UcFQZ87enfjx57zDsv3zGxg//MCjxexMWsLhnDMMEpY9ZzdL8247bDzjGAOzZA8+LQeSDxzObQM67EbuBmnGbYcTG44BncaDT8v5hw2Hc/+BtWz++XPO4cT5QC2Mf/BpuQGypQGsZZsEb8PhxA1ALcx4bbnxDOiXYxKSG86c3WbNcyzdeOOxxGZpGbwOyzH+nFNjw29wvHfzzR811rLzDh8++PENHi1QIAFnMTaAEEmAVPWjYBSMglEwAgAA1oJU8eZfKkIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6266-1256","institution":"UQAT: Universite du Quebec en Abitibi-Temiscamingue","correspondingAuthor":true,"prefix":"","firstName":"Maxime","middleName":"","lastName":"Thomas","suffix":""},{"id":305818214,"identity":"023eab98-81ab-4d22-8da6-55a9a0f6f8ea","order_by":1,"name":"Mebarek Lamara","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mebarek","middleName":"","lastName":"Lamara","suffix":""},{"id":305818215,"identity":"b5418932-6abe-4c2f-a5b4-7c118ca3c1e4","order_by":2,"name":"Yves Desjardins","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yves","middleName":"","lastName":"Desjardins","suffix":""},{"id":305818216,"identity":"825c12de-821e-41bb-9b0b-d6c6fede980f","order_by":3,"name":"Hugo Asselin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"","lastName":"Asselin","suffix":""},{"id":305818217,"identity":"4d2ecc4c-c0b5-4648-b742-f9b004b04c71","order_by":4,"name":"Nicole J. Fenton","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"J.","lastName":"Fenton","suffix":""}],"badges":[],"createdAt":"2024-05-16 20:39:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4433091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4433091/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57943436,"identity":"058ff71c-bc8a-464d-b220-a2ce5de9c34d","added_by":"auto","created_at":"2024-06-07 19:05:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":788221,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area in Quebec, eastern Canada. The red inserts around the main map indicate the locations of sampling sites in relation to hydroelectric power lines, mines, and roads.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/99d8e0ccfcd9e16a4ecea8d1.png"},{"id":57943463,"identity":"aa1362d3-5f33-4719-a20d-896d90e1041e","added_by":"auto","created_at":"2024-06-07 19:06:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":302289,"visible":true,"origin":"","legend":"\u003cp\u003eAverage concentration of the different (poly)phenols measured in the samples for each disturbance type in milligram per 100 grams of dry matter with the associated standard deviation. PACs: proanthocyanidins.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/352c5cfce16ca9dae4a84638.png"},{"id":57943398,"identity":"355b7300-c6e5-4edd-80e8-9b45c221aaf9","added_by":"auto","created_at":"2024-06-07 19:05:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132700,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative abundance of the 20 most abundant bacteria taxa (stacked boxes) classified by family (colors) in samples subjected to different disturbances and from different territories.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/b2f44be8d1ba68b1b88a6318.png"},{"id":57943440,"identity":"674178c0-0c59-497b-993f-fa920aeacc45","added_by":"auto","created_at":"2024-06-07 19:06:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115605,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling of the abundance of bacterial Phyla. Stress = 0.16. A) Points represent the sampling sites. Ellipses represent the standard deviation around each disturbance type. Abd: Abditibacteriota, Aci: Acidobacteriota, Act: Actinobacteriota, Arm: Armatimonadota, Bac: Bacteroidota, Bde: Bdellovibrionota, Chl: Chloroflexi, Cya: Cyanobacteria, Dep: Dependentiae, Des: Desulfobacterota, Elu: Elusimicrobiota, FCP: FCPU426, Fib: Fibrobacterota, Fir: Firmicutes, GAL: GAL15, Gem: Gemmatimonadota, MBN: MBNT15, Met: Methylomirabilota, Myx: Myxococcota, Nit: Nitrospirota, Pat: Patescibacteria, Pla: Planctomycetota, Pro: Proteobacteria, RCP: RCP2-54, SAR: SAR324 clade(Marine group B), Spi: Spirochaetota, Sum: Sumerlaeota, Ver: Verrucomicrobiota, WPS: WPS-2. B) Soil properties and composition in metals and oligo-elements. Cu: copper, Fe: iron, C: carbon, N: nitrogen, Mg: magnesium, Ca: calcium, CEC: Cation Exchange Capacity, Mn: manganese, K: potassium, Zn: zinc, P: phosphorus, Al: aluminum.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/b3d59e82922a5b9c8936debd.png"},{"id":57943434,"identity":"f0648fbe-53b5-4a5a-9900-3480e168e6c6","added_by":"auto","created_at":"2024-06-07 19:05:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59517,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial taxa differentially abundant for different disturbance types. The letter before the name of the taxa indicates the taxonomic level: o: order, c: class, f: family.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/9bb6a89c6b3d25168fca0774.png"},{"id":57943403,"identity":"e8708176-96ab-4d25-9082-37212a64b672","added_by":"auto","created_at":"2024-06-07 19:05:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":122848,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative abundance of the 20 most abundant fungi taxa (stacked boxes) classified by family (colors) in samples subjected to different disturbances and from different territories.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/9e4fe8821f61d510cb9f1430.png"},{"id":57943479,"identity":"f112bb5b-6fe4-4559-855e-3b11058c0996","added_by":"auto","created_at":"2024-06-07 19:06:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":109938,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling of the abundance of fungal Phyla. Stress = 0.17. A) Points represent the sampling sites. Ellipses represent the standard deviation around each disturbance type. Asc: Ascomycota, Bas: Basidiomycota, Chy: Chytridiomycota, Ent: Entorrhizomycota, Fun: Fungi Phylum Incertae sedis, Mor: Mortierellomycota, Muc: Mucoromycota, Roz: Rozellomycota. B) Soil properties and composition in metals and oligo-elements. Cu: copper, Fe: iron, C: carbon, N: nitrogen, Mg: magnesium, Ca: calcium, CEC: Cation Exchange Capacity, Mn: manganese, K: potassium, Zn: zinc, P: phosphorus, Al: aluminum.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/182bc22fc389563aec81a330.png"},{"id":57943480,"identity":"31aecfba-4e4c-489b-a822-1d517ff8a864","added_by":"auto","created_at":"2024-06-07 19:06:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":66673,"visible":true,"origin":"","legend":"\u003cp\u003eFungal taxa differentially abundant for different disturbance types. Different colors indicate different disturbance types. The letter before the name of the taxa indicates the taxonomic level: o: order, c: class, f: family, g: genus, s: species.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/afa111ffc15b94370368b49b.png"},{"id":57943507,"identity":"1bf55c23-86ba-409a-9e33-854d8b824dc0","added_by":"auto","created_at":"2024-06-07 19:06:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":71296,"visible":true,"origin":"","legend":"\u003cp\u003eDistance-based redundancy analysis (RDA) of the effect of soil properties (blue arrows) on soil bacterial community (red). Cu: copper, Fe: iron, C: carbon, N: nitrogen, Mg: magnesium, Ca: calcium, CEC: Cation Exchange Capacity, Mn: manganese, K: potassium, Zn: zinc, P: phosphorus, Al: aluminum. Bacteria dissimilarity matrix was calculated using the Hellinger distance. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to \u003cem\u003ep\u003c/em\u003e-values. Only the first axis and the pH variable are significant. Model adjusted R² = 0.15. To improve readability, bacterial taxa further from the center of the ordination are represented by red text, while the remaining taxa are represented by red points. When a labeled taxa is found in more than one ASV, the label is placed at their centroid. Sampling sites are in black; the first letter indicates the territory (A: Abitibiwinni, M: Mistissini, N: Nemaska), the second letter indicates the disturbance (c: control, h: hydroelectric line, m: mine), while the last letter indicates the replicate.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/f248c2f34bd672b9e259d00f.png"},{"id":57943437,"identity":"081db500-acab-4a79-9090-aef0d6c31514","added_by":"auto","created_at":"2024-06-07 19:05:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":58563,"visible":true,"origin":"","legend":"\u003cp\u003eDistance-based redundancy analysis (RDA) of the effect of soil properties (blue arrows) on soil fungal community (red). Cu: copper, Fe: iron, C: carbon, N: nitrogen, Mg: magnesium, Ca: calcium, CEC: Cation Exchange Capacity, Mn: manganese, K: potassium, Zn: zinc, P: phosphorus, Al: aluminum. Fungi dissimilarity matrix was calculated using the Hellinger distance. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to \u003cem\u003ep\u003c/em\u003e-values. The two axes, and the pH and nitrogen variables are significant. Model adjusted R² = 0.18. To improve readability, fungal taxa further from the center of the ordination are represented by red text, while the remaining taxa are represented by red points. When a labeled taxa is found in more than one ASV, the label is placed at their centroid. Sampling sites are in black; the first letter indicates the territory (A: Abitibiwinni, M: Mistissini, N: Nemaska), the second letter indicates the disturbance (c: control, h: hydroelectric line, m: mine) while the last letter indicates the replicate.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/8ec76c5b9708fe9b0183e0a4.png"},{"id":57943439,"identity":"3b445608-efb6-453f-aacd-946002177d71","added_by":"auto","created_at":"2024-06-07 19:05:59","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":56760,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) ordination representing the effect of the abundance of classes from the Firmicutes Phylum (blue arrows) on the phenolic profile of samples (red). Only the first axis was statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.0300). The abundance of Bacili and Desulfitobacteriia had a significant effect on the phenolic profile (\u003cem\u003ep\u003c/em\u003e = 0.0356 and \u003cem\u003ep\u003c/em\u003e=0056 respectively). (Poly)phenolic compounds are displayed in red; Dga: delphinidin-3-galactoside, Dgl: delphinidin-3-glucoside, Cga: cyanidin-3-galactoside, Dar: delphinidin-3-arabinoside, Cgl: cyanidin-3-glucoside, Ptga: petunidin-3-galactoside, Car: cyanidin-3-arabinoside, Ptgl: petunidin-3-glucoside, Pnga: peonidin-3-galactoside, Ptar: petunidin-3-arabinoside, Pngl: peonidin-3-glucoside, Mga: malvidin-3-galactoside, Mgl: malvidin-3-glucoside, Mar: malvidin-3-arabinoside, D36agl: delphinidin-3-(6”-acetoylglucoside), Pn36aga: peonidin-3-(6”-acetoylgalactoside), C36agl: cyanidin-3-(6”-acetoylglucoside), M36aga: malvidin-3-(6”-acetoylgalactoside), Pt36agl: petunidin-3-(6”-acetoylglucoside), Pn36agl: peonidin-3-(6”-acetoylglucoside), M36agl: malvidin-3-(6”-acetoylglucoside), Cat: catechin, Epicat: epicatechin, PACs: proanthocyanidins. Sampling sites are in black; the first letter indicates the territory (A: Abitibiwinni, M: Mistissini, N: Nemaska), the second letter indicates the disturbance (c: control, h: hydroelectric line, m: mine) while the last letter indicates the replicate.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/2014316c9d7bfff2e53a4bc1.png"},{"id":59739828,"identity":"a8307274-07d7-4f79-82ec-4db6bac6ff1a","added_by":"auto","created_at":"2024-07-05 14:45:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2843173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/12f2004a-a9ea-4609-8ad4-323f1ae8e842.pdf"},{"id":57943435,"identity":"68ae0e8a-e3b1-417a-9a1c-5047b69df6b2","added_by":"auto","created_at":"2024-06-07 19:05:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40688,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-4433091/v1/1595e6e67ad72e7d27afca75.docx"}],"financialInterests":"","formattedTitle":"Unraveling the interplay of the soil microbiome and (poly)phenol content in blueberry in response to disturbances","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMining and hydroelectric development are two of the main industrial development activities taking place in the Canadian boreal forest (Venier et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gauthier et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; B\u0026eacute;lisle and Asselin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These activities result in disturbances that affect the boreal landscape, including animal and plant species. The establishment of a mining site or a hydroelectric power line entails the clearance of forests and the destruction of wildlife habitats to make room for infrastructure construction (Sonter et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Haddaway et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li and Lin \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMining also generates heavy metal pollution that can contaminate the soil, water, and plants surrounding a mining site (Yin et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e,\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Yu and Zahidi \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Exposure to heavy metal pollution poses significant risks to plants, as heavy metals can disrupt metabolic processes, germination rates, and reproductive capabilities (Adeel et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yin et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The creation of hydroelectric power lines can also induce stress in plants. Indeed, the removal of trees for the creation of a power transmission line increases the exposure of understory plants to light, including ultraviolet (UV) radiation. Elevated exposure to UV radiation can impair plant metabolic functions and lead to tissue damage (Bergamini et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlants adapt to disturbances, notably by synthesizing secondary metabolites, such as (poly)phenols (Sytar et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kumar and Pandey \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thakur et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Secondary metabolites in plants play a crucial role in mitigating the detrimental effects of stresses, including exposure to heavy metals and increased levels of UV radiation (Agati and Tattini \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Naing and Kim \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisturbances can also affect plants indirectly by modifying soil properties and microbiome composition. For instance, mining activities have been shown to lower soil pH (Dudka and Adriano \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Johnson and Hallberg \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), a change to which the soil microbiome is highly sensitive (Fierer and Jackson \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Disturbances can also indirectly influence plant health and development through their effects on the soil microbiome, as the interactions between soil microorganisms and roots can have either beneficial or detrimental effects on plants (Ali et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Trivedi et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the influence of the soil microbiome extends beyond the roots to affect aerial plant parts as well (Salla et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, the majority of plants engage in symbiotic relationships with mycorrhizal fungi to enhance nutrient uptake from the soil, illustrating a critical interaction that benefits plant growth and health (Jeffries et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Brundrett \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Bacteria too can have beneficial effects on plants, notably by improving nutrition and response to stresses (Franche et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The soil microbiome also harbors bacterial and fungal pathogens capable of reducing plant growth (Štraus et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, by altering soil characteristics, environmental disturbances can provoke shifts in the composition of the soil microbiome (Seitz et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which in turn can influence a range of plant processes including growth, nutrition, and the production of secondary metabolites (Treutter \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kumar and Pandey \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Trivedi et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisturbances can also affect plant nutritional and medicinal properties, since these attributes are closely linked to the presence and concentration of secondary metabolites (Del Rio et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Durazzo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, anthocyanins, a group of secondary metabolites, provide numerous health benefits to humans; notably helping in the prevention of cardiovascular disease, cancer, and diabetes (de Pascual-Teresa and Sanchez-Ballesta \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Proanthocyanidins are another example of compounds conferring health benefits, such as anti-inflammatory effects and kidney protection (Ivey et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yokota et al. \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dasiman et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModifications in plant properties can affect the livelihood and culture of different populations, notably Indigenous peoples. Indeed, certain plant species are central to cultural activities, for example being used as food or medicine, underscoring the profound connection between Indigenous cultures and specific flora (Garibaldi and Turner \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Parlee et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ladle et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boulanger-Lapointe et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Investigating how such culturally salient species respond to disturbances is thus key to the preservation of Indigenous ways of life.\u003c/p\u003e \u003cp\u003eThe early lowbush blueberry (\u003cem\u003eVaccinium angustifolium\u003c/em\u003e (Aiton)) is a culturally salient species for several Indigenous peoples in eastern Canada. This species is culturally salient because of its importance as a key food item (Arnason et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Batal et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eV. angustifolium\u003c/em\u003e contributes to reinforcing the connection of Indigenous peoples with their territories and traditions, through blueberry picking outings and the preparation of traditional food such as blueberry paste (Boulanger-Lapointe et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Basile et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pelletier \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Blueberries are also rich in phenolic acids, anthocyanins and proanthocyanidins, among other secondary metabolites, which possess antioxidant and antidiabetic properties (Uprety et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Norberto et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Grace et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Weber \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisturbances, soil properties, soil microbiome composition, and plant secondary metabolism are intricately linked. Although each of these elements have been the subject of individual studies (Lahdesmaki \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Tahkokorpi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Francioli et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zaborowska et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), their interrelations are seldom examined collectively, highlighting a gap in comprehensive understanding of their synergistic effects on ecosystem and plant health. Thus, the objective of this study was to assess the complex interplay between two disturbances (mining and hydroelectric power lines), (poly)phenols in the fruits of \u003cem\u003eV. angustifolium\u003c/em\u003e, soil properties and the soil microbiome in the eastern Canadian boreal forest. More specifically, we asked the questions: i) Do disturbances directly affect fruit (poly)phenol content, and if so, how? ii) Which soil microbial taxa, if any, affect fruit (poly)phenol content and how? iii) Do disturbances indirectly affect plant (poly)phenol content through their effect on soil properties and bacterial and fungal communities?\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eThis study took place in western Quebec (Canada) on the traditional territories of the Abitibiwinni (Anishnaabe), Mistissini (Cree), and Nemaska (Cree) Indigenous communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Abitibiwinni First Nation\u0026rsquo;s territory is located near the Ontario border, mainly in the black spruce \u0026ndash; feather moss bioclimatic domain, except for its southern part located in the balsam fir \u0026ndash; paper birch bioclimatic domain (B\u0026eacute;lisle and Asselin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Mistissini Cree Nation\u0026rsquo;s territory is located further north and is very large, with its southern part in the black spruce \u0026ndash; feather moss bioclimatic domain and its northern part in the spruce \u0026ndash; lichen woodland bioclimatic domain. The territory of the Nemaska Cree Nation is located at the northern boundary of the black spruce \u0026ndash; feather moss bioclimatic domain. This territory has experienced frequent fires in the past decades, which led to younger and more open stands (Eeyou Planning Commission \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFruit and soil sampling\u003c/p\u003e \u003cp\u003eSamples were collected on the territories of the Abitibiwinni, Mistissini and Nemaska communities on August 2nd, 16-17th, and 20th 2022, respectively. The difference in timing between territories was planned to collect fruits at the same phenological stage, accounting for latitude differences. All samples were collected in the black spruce \u0026ndash; feather moss bioclimatic domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), more specifically in black spruce stands, to limit the influence of potential confounding environmental variables. A total of 24 sites were sampled, that is 8 in each territory: 3 under hydroelectric power lines, 3 near a mine (200 m or less), and 2 control sites at least 1 km away from these disturbances. The selection of the distance around mining sites for analysis was informed by findings from prior studies, which determined that the effect of mining activities on vegetation extended to an average distance of 200 m (Boisvert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). All sites were located at least 100 m from roads to limit edge effects, trampling, and disturbances due to traffic.\u003c/p\u003e \u003cp\u003eAt each site, four fruit samples were collected from different plant individuals for analysis of (poly)phenol compounds. Three organic soil samples were also collected with an auger in a triangular fashion around the fruit sampling locations, for analysis of soil microbiome and soil properties. The three soil samples were combined into a composite sample representative of the soil around the sampled plants. All samples (fruit and soil) were preserved in a cooler immediately after collection, then transferred to a -80\u0026deg;C freezer after each sampling day until extraction.\u003c/p\u003e \u003cp\u003eExtraction and analysis of (poly)phenol compounds\u003c/p\u003e \u003cp\u003eFruit samples were sent to INAF\u0026rsquo;s chemical analysis laboratory (Institute of Nutrition and Functional Foods, Laval University, Quebec City, Canada) where they were extracted for analysis of flavonoids and total (poly)phenol compounds.\u003c/p\u003e \u003cp\u003eFlavonoids\u003c/p\u003e \u003cp\u003eThe proanthocyanidins (PACs) content of the samples was measured by phloroglucinolysis followed by a UPLC-UV-MS/MS analysis. Phloroglucinolysis is a process in which PACs are cleaved into their base units, flavan-3-ols, using phloroglucinol (Kennedy and Jones \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This allows to quantify PACs and to determine their polymerization degree. A catechin standard was used to quantify PACs by UPLC-UV. The identification of detected compounds was then confirmed by triple quadrupole mass spectrometry.\u003c/p\u003e \u003cp\u003eThe flavonol content of the samples was measured using the same procedure as for PACs, albeit with the omission of phloroglucinolysis and the use of a quercetin-3-glucoside standard for the purpose of quantification.\u003c/p\u003e \u003cp\u003eTotal (poly)phenols\u003c/p\u003e \u003cp\u003eTotal (poly)phenolic content was determined according to the Folin-Ciocalteu method as described in Dudonn\u0026eacute; et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) using gallic acid as a standard. Extract solutions (20 mL in 20% methanol 0.1% TFA) were mixed with 100 mL of 10-fold diluted Folin-Ciocalteu reagent and 80 mL of sodium carbonate solution (75 g/L). After 1 h of incubation at room temperature, the absorbance was measured at 765 nm using a BMG Labtech Fluostar Omega microplate reader (Offenburg, Germany).\u003c/p\u003e \u003cp\u003ePhysicochemical analyses\u003c/p\u003e \u003cp\u003eBefore proceeding to physicochemical analyses, soil samples were oven-dried at 35\u0026deg;C for 3 days, then passed through a 5.6 mm sieve to remove large debris, and stored in a -80\u0026deg;C freezer until analysis. Soil samples were analyzed for mineral contents (calcium, aluminum, potassium, phosphorus, magnesium, boron, copper, iron, manganese, zinc, sodium), pH, cation exchange capacity (CEC), organic matter content, as well as nitrogen and carbon content. Minerals were extracted with a Mehlich III solution and quantified by ICP-Optical Emission Spectrometry with the appropriate standard for each mineral (Minist\u0026egrave;re du D\u0026eacute;veloppement durable, de l\u0026rsquo;Environnement et de la Lutte contre les changements climatiques du Qu\u0026eacute;bec \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Soil pH was determined in water with a pH-meter (Minist\u0026egrave;re de l\u0026rsquo;Environnement, de la Lutte contre les Changements Climatiques, de la Faune et des Parcs \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The organic matter percentage in the soil was calculated by loss on ignition (Minist\u0026egrave;re du D\u0026eacute;veloppement durable, de l\u0026rsquo;Environnement et de la Lutte contre les changements climatiques du Qu\u0026eacute;bec \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicrobiome analysis\u003c/p\u003e \u003cp\u003eDNA Extraction\u003c/p\u003e \u003cp\u003eThe DNA of the soil samples was extracted from 250 mg of sampled soil using the Qiagen DNeasy Powersoil Pro kit (QIAGEN \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) following the manufacturer\u0026rsquo;s protocol. Two negative extraction controls with no soil were also processed following the same protocol. The extracted DNA was stored immediately in a -80\u0026deg;C freezer until further processing. DNA samples were then sent to Genome Quebec Innovation Center (Montreal, Canada) for amplification and sequencing.\u003c/p\u003e \u003cp\u003eAmplification and sequencing\u003c/p\u003e \u003cp\u003eThe metabarcoding method was used to detect and quantify bacteria and fungi in the organic soil. For bacteria, the extracted DNA samples were amplified using the primers 515b-FwR1 forward (GTGYCAGCMGCCGCGGTAA) and 926-RvR2 reverse (CCGYCAATTYMTTTRAGTTT) (Parada et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For fungi, the primers used were ITS-9F forward (GAACGCAGCRAAIIGYGA) and ITS4R reverse (TCCTCCGCTTATTGATATGC) (White et al. \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Ihrmark et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). PCR amplifications were performed with 5 minutes of initial denaturation at 95\u0026deg;C, 34 cycles (bacteria) or 40 cycles (fungi) of 30 seconds at 94\u0026deg;C, 30 seconds at 50\u0026deg;C, and 1 minute at 72\u0026deg;C, then a final elongation step of 10 minutes at 72\u0026deg;C. Prior to amplification and sequencing, DNA quality was checked using 1% agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eAmplicons were sequenced on the Illumina MiSeq platform for paired-end reads. A negative control was included in the sequencing for both bacteria and fungi, to ensure that the extraction step did not result in contamination of the samples.\u003c/p\u003e \u003cp\u003eBioinformatic workflow\u003c/p\u003e \u003cp\u003eThe DADA2 R-package was used to build amplicon sequence variants (ASVs) from the raw sequences (Callahan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For bacteria, sequence primers were removed by trimming the first 19 nucleotides of forward reads and first 20 nucleotides of reverse reads in DADA2. After checking reads quality, 16S reads were also truncated at position 260 for forward reads and 190 for reverse reads, as there was a drop in read quality after these points. For fungi, ITS sequences primers were removed with cutadapt (Martin \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) prior to assembly with DADA2. As the ITS sequence length is variable, ITS reads of lesser quality were not truncated to ensure that forward and reverse read could merge, but ITS read quality was decent overall. For 16S and ITS analyses, reads were pseudo-pooled during the ASVs assembly step in order to allow for the detection of rare ASVs. External contaminants were then removed using the decontam R-package using the prevalence method (Davis et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Taxonomy was assigned using the Silva v138.1 database formatted for DADA2 for the bacterial sequences (McLaren and Callahan \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the UNITE v.9.0 database for the fungal sequences (Abarenkov et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce ASVs were built, data was transferred into a phyloseq object with the phyloseq R-package for handling (McMurdie and Holmes \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As a quality check, we removed ASVs that were found in only one sample, and those that were found less than 10 times across all samples. A phylogenetic heat tree of the taxa found in the samples was built using the metacoder R-package for visualization of taxonomic diversity across all samples (Foster et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Before downstream analyses, the library of each sample was repeatedly rarefied to the library size of the smallest sample by drawing random ASVs without replacement with 1000 repetitions with the mirlyn R-package (Cameron et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), to control for biases in richness induced by differences in library sizes between samples. As the analyses available in the mirlyn package are limited, the multiple libraries created by the repeated rarefaction were then condensed into a single phyloseq object for compatibility with further packages. To do so, a table of ASV abundance was constructed by rounding the mean abundance of each ASV after 1000 rarefactions for each sample.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eAll analyses were performed using the R software version 4.3.1 (R Core Team \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bacteria and fungi were treated separately for all analyses.\u003c/p\u003e \u003cp\u003eEffect of disturbances and territory on (poly)phenol concentrations\u003c/p\u003e \u003cp\u003eThe effect of disturbance type, territory, and their interaction on (poly)phenol concentrations was evaluated with a PERMANOVA using the \u003cem\u003eadonis2\u003c/em\u003e function of the vegan R-package (Oksanen et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The random effect of the sampling sites was accounted for by constraining permutations: sampling sites could be permuted, but not samples between sites. A total of 9999 permutations were performed using Euclidean distances.\u003c/p\u003e \u003cp\u003eEffect of disturbances and territory on soil properties and microbiome\u003c/p\u003e \u003cp\u003eThe effect of disturbance type, territory, and their interaction on soil properties was also analyzed with a PERMANOVA using the \u003cem\u003eadonis2\u003c/em\u003e function from the vegan R-package (Oksanen et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Variables were normalized by scaling prior to the PERMANOVA, and 9999 permutations were performed using Euclidean distances. To further study the response of soil properties to disturbances, an ANOVA followed by a Tukey test was performed.\u003c/p\u003e \u003cp\u003eDifferences in alpha diversity between disturbance types and territories were plotted using the \u003cem\u003eplot_richness\u003c/em\u003e function of the phyloseq R-package. Non-metric multidimensional scaling (NMDS) ordination plots were also produced, using the vegan package (Oksanen et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on Hellinger transformed data to visualize the relation between phylum abundance, soil properties and sampling sites. Finally, a linear discriminant analysis effect size analysis (LEfSe) was performed to detect taxa that were differentially abundant between disturbance types (Segata et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Since this analysis has a high false discovery rate (Nearing et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the LDA threshold was conservatively set to 3.5. The LEfSe was computed in R using the microbiomeMarker package (Cao et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffect of soil properties on microbiome\u003c/p\u003e \u003cp\u003eThe effect of soil properties on soil microbiome was evaluated using a distance-based redundancy analysis (db-RDA) using the vegan R-package (Oksanen et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The explanatory variables used in the model were the following: pH, contents of nitrogen, carbon, phosphorus, magnesium, potassium, iron, copper, manganese, zinc, aluminum, calcium, and sodium, and CEC. Explanatory variables were scaled prior to analysis, and the dissimilarity matrix of microbiome abundance was calculated using the Hellinger distance. Significance of the db-RDA model, axis, and terms was then evaluated by permutation, using the \u003cem\u003eanova\u003c/em\u003e function of the vegan R-package with 9999 permutations (Borcard et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eP\u003c/em\u003e-values were adjusted with the Benjamini-Hochberg correction (Benjamini and Hochberg \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffect of microbiome on (poly)phenol concentrations\u003c/p\u003e \u003cp\u003e(Poly)phenol concentrations were not scaled prior to analyses, as concentration was the best available proxy for (poly)phenol bioavailability.\u003c/p\u003e \u003cp\u003eThe effect of soil bacterial abundance on (poly)phenol concentrations was evaluated. For each site, the average concentration of each (poly)phenol compound was calculated. The rarefied ASV abundances were summed to the phylum level, and phyla with less than 500 observations across all samples were removed from the analysis, as they were unlikely to meaningfully affect plant (poly)phenols. Then, a redundancy analysis (RDA) was used to investigate the effect of the abundance of each phylum on (poly)phenol concentrations using the vegan R-package. Significance of the model, axis and explanatory variables was then evaluated by permutation, with the \u003cem\u003eanova\u003c/em\u003e function of the vegan R-package using 9999 permutations (Borcard et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eP\u003c/em\u003e-values were adjusted with the Benjamini-Hochberg correction (Benjamini and Hochberg \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). If a significative effect was found at the phylum level, another RDA was conducted with the abundance of the classes of this phylum, and so on with inferior taxonomic levels. In addition, the Proteobacteria and Firmicutes phyla were further explored as they contain plant growth promoting bacteria (Bulgarelli et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Youseif \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Getahun et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The same procedure was followed to evaluate the effect of fungi abundance on (poly)phenols, except with a threshold of 300 observations across all samples, as fungi were less abundant in general. In addition, the Ascomycota and Basidiomycota phyla were also explored as they contain ericoid mycorrhiza (Dong et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). FUNGuild v1.1 (accessed on august 3rd 2023) was used to get fungi putative functional assignations in order to help discuss the results (Nguyen et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen an RDA highlighted a taxon as having a significant effect on the (poly)phenol profile, the effect of this taxon on individual (poly)phenol was further evaluated by constructing linear models.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e(Poly)phenol content\u003c/p\u003e\n\u003cp\u003eThe majority of the (poly)phenols quantified in the samples consisted of PACs, and to a lesser degree, anthocyanins featuring a 3-glucoside moiety, including delphinidin, malvidin, and cyanidin-3-glucoside (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). No differences were observed in the concentrations of (poly)phenols among the various environmental disturbances examined. Interestingly, concentrations had a lower standard deviation for control sites than for disturbances, which may indicate a higher variability of environmental conditions near disturbed sites.\u003c/p\u003e\n\u003cp\u003eEffect of disturbances\u003c/p\u003e\n\u003cp\u003eEffect of disturbances on (poly)phenol content\u003c/p\u003e\n\u003cp\u003eThe content of (poly)phenols was not significantly influenced by disturbance type, territory, or the interaction between these two factors, although the effect of territory was marginally notable (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0573; Table \u003cspan\u003eA1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eEffect of disturbances on soil properties\u003c/p\u003e\n\u003cp\u003eDisturbance type and territory had a significant effect on soil properties, while the effect of their interaction was not significant (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePERMANOVA of the variation in soil properties as a function of disturbance type, territory, and their interaction.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerritory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance:Territory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSamples collected from mining sites exhibited significantly higher levels of copper and iron compared to other locations, and also contained significantly more carbon, nitrogen, and organic matter than sites associated with hydroelectric power lines. They also had a higher pH than control sites. Regarding differences between territories, samples from Nemaska were more concentrated in aluminum and sodium than those from Mistissini, and less concentrated in magnesium and zinc than those from Abitibiwinni. Samples from Mistissini contained more copper than those from other territories.\u003c/p\u003e\n\u003cp\u003eEffect of disturbances on soil bacteria\u003c/p\u003e\n\u003cp\u003eControl site samples tended to have a lower bacterial abundance in general, and a lower abundance of bacteria from the \u003cem\u003eXanthobacteraceae\u003c/em\u003e family in particular (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSamples from mine sites were associated with bacteria from the Fibrobacterota, Desulfobacterota and Spirochaetota phyla (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). Trends regarding bacterial phyla were less obvious for hydro sites, as they were clustered closer to the center of the ordination graph. Samples from control sites tended to have a lower abundance of all bacterial phyla, as suggested by the lower abundance in all bacteria families seen in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eOnly hydro sites had significant differentially abundant bacterial taxa (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e). Soil from these sites contained significantly more bacteria from the Verrucomicrobiae class, especially of the Chthoniobacterales order, and more bacteria from the Ktedonobacteria class, especially of the Ktedonobacterales order and \u003cem\u003eKtedonobacteriaceae\u003c/em\u003e family.\u003c/p\u003e\n\u003cp\u003eEffect of disturbances on soil fungi\u003c/p\u003e\n\u003cp\u003eSamples near a mining site tended to contain more \u003cem\u003eMyxotrichaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e). The fungal composition of mining sites within the Abitibiwinni territory differed from the other mining sites, which could be due to differences in mine operation stage or processed ores between the mines. Interestingly, samples from control sites in Mistissini contained a large proportion of \u003cem\u003ePilodermataceae\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe majority of ASVs belonged to the Ascomycota phylum. Abundances of Ascomycota, Basidiomycota, Mortierellomycota, and Mucoromycota were correlated (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e). These phyla tended to be more abundant in sites near mines and sites from Abitibiwinni.\u003c/p\u003e\n\u003cp\u003eMost of the differentially abundant taxa were found in sites under hydroelectric power lines (Fig.\u0026nbsp;\u003cspan\u003e8\u003c/span\u003e). Fungi from the Geoglossomycetes class, especially from Geoglossales order, the \u003cem\u003eGeoglossaceae\u003c/em\u003e family, and the \u003cem\u003eSarcoleotia\u003c/em\u003e genus were more abundant in hydro sites. They also contained more fungi from several taxa of the Helotiales order, specifically from the \u003cem\u003eDermateaceae\u003c/em\u003e family, and from the \u003cem\u003eHumicolopsis\u003c/em\u003e genus in the Sordariomycetes class. Fungi from the \u003cem\u003eMyxotrichaceae\u003c/em\u003e family, especially from the \u003cem\u003eOidiodendron\u003c/em\u003e genus, which also belong to the Heliotales order were more abundant in mine sites. One specific species, \u003cem\u003eBrahmaculus moonlighticus\u003c/em\u003e was more abundant in control sites.\u003c/p\u003e\n\u003cp\u003eEffect of soil properties on the soil microbiome\u003c/p\u003e\n\u003cp\u003eEffect of soil properties on bacteria\u003c/p\u003e\n\u003cp\u003eSoil pH emerged as the sole soil property exerting a significant influence on the composition of soil bacteria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014, Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). An increase in soil pH was associated with a rise in the abundance of bacteria belonging to the WD260 order and several species within the \u003cem\u003eBradyrhizobium\u003c/em\u003e genus. Conversely, higher pH levels led to a decrease in the abundance of bacteria from the Acidobacteriae order and various genera within the \u003cem\u003eAcidobacteriaceae\u003c/em\u003e family (Fig.\u0026nbsp;\u003cspan\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDistance-based redundancy analysis (RDA) of the effect of soil properties on soil bacterial community. Bacteria dissimilarity matrix was calculated using the Hellinger distance. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to \u003cem\u003ep\u003c/em\u003e-values, and significant (\u0026lt;\u0026thinsp;0.05) adjusted \u003cem\u003ep\u003c/em\u003e-values are shown in bold. Model adjusted R\u0026sup2; = 0.15. For the sake of brevity, only the first 4 of the 15 axes are presented.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAxes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0098\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"14\"\u003e\n \u003cp\u003eTerms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAluminum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEffect of soil properties on fungi\u003c/p\u003e\n\u003cp\u003eSoil fungal composition was affected significatively by pH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0195), but also by nitrogen content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0028) (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). Nitrogen content was correlated with carbon, magnesium, and calcium content, and mainly associated with increased abundances of fungi from the \u003cem\u003ePiloderma\u003c/em\u003e genus, and from the \u003cem\u003eOidiodendron pilicola\u003c/em\u003e species, and with decreased abundances of fungi from the \u003cem\u003eScytalidium vaccinii\u003c/em\u003e species (Fig.\u0026nbsp;\u003cspan\u003e10\u003c/span\u003e). The pH was correlated with copper content and was associated with increased abundances of fungi from the \u003cem\u003ePiloderma\u003c/em\u003e and \u003cem\u003eOidiodendron\u003c/em\u003e genera, and with decreased abundances of fungi from the \u003cem\u003eMycosymbioces\u003c/em\u003e genus (Fig.\u0026nbsp;\u003cspan\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDistance-based redundancy analysis (RDA) of the effect of soil properties on soil fungal community. Bacteria dissimilarity matrix was calculated using the Hellinger distance. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to \u003cem\u003ep\u003c/em\u003e-values, and significant (\u0026lt;\u0026thinsp;0.05) adjusted \u003cem\u003ep\u003c/em\u003e-values are shown in bold. Model adjusted R\u0026sup2; = 0.18. For the sake of brevity, only the first 4 of the 15 axes are presented in the table.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAxes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0266\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0469\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edbRDA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"14\"\u003e\n \u003cp\u003eTerms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0195\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAluminum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEffect of microbiome on (poly)phenol concentrations\u003c/p\u003e\n\u003cp\u003eThe abundance of bacteria phyla in the soil did not have a significant effect on (poly)phenol concentrations (Table \u003cspan\u003eA2\u003c/span\u003e). However, when breaking down the Firmicutes phylum into its classes, we found a significative effect of the abundance of Bacilli (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0356) and Desulfitobacteriia (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0056) on (poly)phenol concentrations (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e11\u003c/span\u003e). Increased abundances of the Bacilli and Desulfitobacteriia were associated to higher concentrations of PACs (Fig.\u0026nbsp;\u003cspan\u003e11\u003c/span\u003e). Increased abundances of Desulfitobacteriia were also associated with higher concentrations of delphinidin-3-glucoside, cyanidin-3-glucoside, and petunidin-3-glucoside (Fig.\u0026nbsp;\u003cspan\u003e11\u003c/span\u003e). We did not find a significant effect of classes of Proteobacteria on (poly)phenol concentrations (Table \u003cspan\u003eA3\u003c/span\u003e). We also explored the Acidobacteriota, RCP2-54, WPS-2, Bacteroidota, Chloroflexi and Myxococcota phyla as they were near significant (Table \u003cspan\u003eA2\u003c/span\u003e), but it was inconclusive.\u003c/p\u003e\n\u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRedundancy analysis (RDA) of the effect of Firmicutes Classes abundance on the concentration in (poly)phenolics of \u003cem\u003eV. angustifolium\u003c/em\u003e fruits. Statistical significance was evaluated through a permutation test with 9999 permutations. A Benjamini-Hochberg correction was applied to \u003cem\u003ep\u003c/em\u003e-values, and significant (\u0026lt;\u0026thinsp;0.05) adjusted \u003cem\u003ep\u003c/em\u003e-values are shown in bold. Model adjusted R\u0026sup2; = 0.25.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAxes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0300\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eTerms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacilli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0356\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegativicutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesulfitobacteriia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0056\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClostridia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe abundance of fungi phyla did not have a significative effect on (poly)phenol concentrations (Table \u003cspan\u003eA4\u003c/span\u003e). Abundances of classes from the Ascomycota and Basidiomycota phyla did not have a significant effect on (poly)phenol concentrations either (Table \u003cspan\u003eA5\u003c/span\u003e and Table \u003cspan\u003eA6\u003c/span\u003e). We further investigated classes from the Chytridiomycota phylum, as they approached significance (Table \u003cspan\u003eA4\u003c/span\u003e), but the findings remained inconclusive.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDisturbances affected the composition of soil microorganisms, primarily due to alterations in soil characteristics. However, these changes did not influence plant (poly)phenol content. Indeed, the microbiome taxa responding to disturbances were not the same as the taxa that affected plant (poly)phenols.\u003c/p\u003e \u003cp\u003eEffect of disturbances on (poly)phenol content\u003c/p\u003e \u003cp\u003eDisturbances had no significant effect on the (poly)phenol content of \u003cem\u003eV. angustifolium\u003c/em\u003e. This outcome is unexpected given that plants commonly react to stressors, including heavy metal contamination or ultraviolet (UV) radiation, by increasing the synthesis of (poly)phenols (Šamec et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jańczak-Pieniążek et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This observation could be attributed to the specific organ examined in the current study (fruits), as the response of plant secondary metabolism to stress factors can vary depending on the organ (Schreiner et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Larbat et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; de Miguel et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Smirnov et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, various plant organs may not experience stressors to the same degree and may produce different compounds to counteract stress (Larbat et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Simek et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For instance, tomato plants (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e L.) infected by \u003cem\u003eAlternaria solani\u003c/em\u003e, a pathogenic fungus, exhibit reduced flavonoid levels in the leaves, whereas the concentrations in the fruits remain unaffected (Quiterio-Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, in the olive tree (\u003cem\u003eOlea europaea\u003c/em\u003e L.), water stress leads to diverse and occasionally contradictory changes in the concentrations of (poly)phenols across different organs, such as leaves and fruits (Jim\u0026eacute;nez-Herrera et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, it is conceivable that the studied disturbances affected other parts of \u003cem\u003eV. angustifolium\u003c/em\u003e but did not affect its fruits.\u003c/p\u003e \u003cp\u003eEffect of disturbances on soil microbiome\u003c/p\u003e \u003cp\u003eThe construction of hydroelectric lines and the subsequent vegetation management under the lines creates distinctive soil conditions, particularly as wood from trimmed or felled trees is frequently left on site to decompose naturally (Hydro-Qu\u0026eacute;bec \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to our analyses, soil from sites under hydroelectric lines was relatively poor in carbon and nitrogen. It is thus possible that woody debris were the main carbon source at these sites. This may explain the higher abundance of saprotroph taxa or taxa able to degrade cellulose and lignin. Bacterial taxa such as Verrucomicrobiae and Ktedonobacteria at the class level, Chthoniobacterales and Ktedonobacterales at the order level, and \u003cem\u003eKtedonobacteriaceae\u003c/em\u003e at the family level, possess the capability to degrade lignin, cellulose, and other complex forms of carbon (K\u0026ouml;berl et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rachmania et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe abundance of the Ktedonobacteria class and its descendant taxa is higher under elevated levels of UV radiation (Maccario et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ba\u0026ntilde;eras et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This phenomenon could account for their higher abundance in sites under hydroelectric lines, where the absence of tree cover results in greater exposure to UV radiation.\u003c/p\u003e \u003cp\u003eRegarding fungi, members of the Sordariomycetes class, \u003cem\u003eDermateaceae\u003c/em\u003e family, and \u003cem\u003eHumicolopsis\u003c/em\u003e genus are known for their ability to break down complex carbon structures (Osono and Hirose \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; El\u0026iacute;ades et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miao et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xing et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rao et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fungi from the Geoglossomycetes class and their children taxa have also been described as saprotrophic (Tedersoo et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, more recent studies found that they are rather mutualistic and able to form ericoid mycorrhizae (Baba et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Melie et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). During our field observations, we noted that areas beneath hydroelectric lines exhibited a high abundance of ericaceous shrubs. This environmental characteristic may account for the observed increase in the abundance of ericoid mycorrhizal fungi, which are known to form symbiotic associations with the roots of ericaceous plants. Therefore, marker taxa found under hydroelectric lines could be the result of particular carbon conditions and of the high abundance of ericaceous shrubs.\u003c/p\u003e \u003cp\u003eMining also affects soil conditions, notably by increasing metal concentrations. According to our analyses, soil from mining sites contained higher copper and iron concentrations. This may explain the higher abundance of fungi from the \u003cem\u003eOidiodendron\u003c/em\u003e genus, which are metal-tolerant (Vallino et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chiapello et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, these sites also had high abundance of fungi from the Myxotrichaceae family, which includes species forming mycorrhizal relationships (Kernaghan and Patriquin \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) as well as functioning as saprotrophs (Dalp\u0026eacute; \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Sigler et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rice et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Yet, it is noteworthy that these fungi are not documented as being tolerant to metals.\u003c/p\u003e \u003cp\u003eRegarding bacteria, Sumerlaeota tended to be more abundant near mining sites, although this was not significant. This phylum is relatively unexplored, but is known to be extremophile, thus probably adapted to the particular soil conditions of mining sites (Fang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Taxa associated with mining sites are thus partly explained by the specific edaphic conditions generated by mining activity.\u003c/p\u003e \u003cp\u003eFinally, the only taxon with higher abundance in control sites, the fungi \u003cem\u003eBrahmaculus moonlighticus\u003c/em\u003e, has unfortunately not been studied with regards to its functions or habitat requirements. Interestingly, control sites also had less bacteria from the \u003cem\u003eXanthobacteraceae\u003c/em\u003e family than mining and hydroelectric sites. This is consistent with the characteristics of \u003cem\u003eXanthobacteraceae\u003c/em\u003e, as they are tolerant to polluted soil, and are able to degrade various pollutants including metals (Petrus et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which explains their lower abundance in undisturbed sites.\u003c/p\u003e \u003cp\u003eEffect of soil properties on the microbiome\u003c/p\u003e \u003cp\u003eSome microbial taxa were not associated with a particular disturbance type, but responded to variations in soil properties. Concerning soil properties, only pH was found to significantly affect the abundance of bacterial taxa. On the one hand, Acidobacteriae at the class level and \u003cem\u003eAcidobacteriaceae\u003c/em\u003e at the family level decreased in abundance with increasing pH, reflecting their acidophilic nature (Campbell \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bartram et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; De Jonge et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, the genus \u003cem\u003eBradyrhizobium\u003c/em\u003e increased in abundance with rising pH levels. While \u003cem\u003eBradyrhizobium\u003c/em\u003e species can tolerate a broad spectrum of pH conditions (Meghvansi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), their optimal pH for growth varies depending on species and strain (Graham et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Indrasumunar et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given that the pH of the study sites was relatively acidic, ranging from 3.4 to 4.7, it is plausible that the \u003cem\u003eBradyrhizobium\u003c/em\u003e strains encountered in this study possess an optimal growth pH closer to neutral conditions. This adaptation could explain their increased abundance in conjunction with rising pH levels within the observed range.\u003c/p\u003e \u003cp\u003eThe abundance of fungal taxa was influenced not only by soil pH, but also by nitrogen, and marginally by carbon, magnesium, aluminum, and copper content. Unfortunately, the current body of research concerning the optimal edaphic conditions and recognized functions of the majority of the studied taxa has only provided sufficient information to elucidate the response of a single taxon to soil properties. The \u003cem\u003ePiloderma\u003c/em\u003e genus increased in abundance with magnesium and other metal concentrations in the soil. This particular genus is documented to thrive in soils with a high magnesium content (Glowa et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffect of microbiome on plant (poly)phenols\u003c/p\u003e \u003cp\u003e(Poly)phenols in \u003cem\u003eV. angustifolium\u003c/em\u003e fruits were not affected by any of the microbial taxa shown to vary with disturbances or soil properties, but were nevertheless affected by other taxa. (Poly)phenols were significatively affected by the abundance of two classes from the Fimicutes bacterial phylum: Bacilli and Desulfitobacteriia. Both these bacterial classes contain plant growth-promoting rhizobacteria (PGPR). Bacilli within the soil, particularly the \u003cem\u003eBacillus\u003c/em\u003e genus, are recognized for their diverse range of functions that are vital to plants (Hrynkiewicz et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Saxena et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among others, they promote plant nutrition through nitrogen fixation, phosphorus and potassium solubilization (Sharma et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Verma et al. \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Asari et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yousuf et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and they help plants combat pathogens and mitigate the effects of metal pollution (Ramadoss et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Goswami et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Borriss et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Desulfitobacteriia, specifically of the genus \u003cem\u003eDesulfitobacterium\u003c/em\u003e increase in abundance following NH\u003csub\u003e4+\u003c/sub\u003e addition, thus could have a role in nitrogen cycling (Xiao et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and can also detoxify mycotoxins (He et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, PGPR can enhance plant (poly)phenol production under various stresses, notably by activating the phenylpropanoid pathway (Ait Barka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, it is likely that these two classes of bacteria, Bacilli and Actinobacteria, influenced the (poly)phenolic content of \u003cem\u003eV. angustifolium\u003c/em\u003e fruits through their multifaceted effects on plant metabolism and their role in pathogen control.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDisturbances influence soil properties and the composition of the soil microbiome. However, intriguingly, these changes did not result in discernible differences in the (poly)phenolic compounds found in the fruits of \u003cem\u003eV. angustifolium\u003c/em\u003e. Thus, disturbances due to mining and hydroelectric lines do not appear to affect the nutritional and medicinal properties of blueberries associated with (poly)phenols. However, this does not imply that disturbances have no effect on the overall nutritional and medicinal properties of the fruits. For example, presence of heavy metals in the fruits (due to mining activities) could lead to deleterious consequences for those who consume them (Okereafor et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yin et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, it is imperative to conduct assessments for the presence of pollutants in \u003cem\u003eV. angustifolium\u003c/em\u003e fruits before making definitive conclusions regarding the effect of disturbances on the innocuity of these fruits and their nutritional and medicinal properties.\u003c/p\u003e \u003cp\u003eAside from the effects of disturbances and soil properties, two bacterial classes, Bacilli and Desulfitobacteriia, were associated with increased abundance of fruit (poly)phenols. Identifying the environmental conditions that are conducive to the growth of Bacilli and Desulfitobacteriia and actively promoting these conditions could represent a potential strategy for enhancing the nutritional and medicinal properties of \u003cem\u003eV. angustifolium\u003c/em\u003e.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the Natural Science and Engineering Research Council of Canada (NSERC) \u0026ndash; Universit\u0026eacute; du Qu\u0026eacute;bec en Abitibi-T\u0026eacute;miscamingue (UQAT) Industrial Chair on northern biodiversity in a mining context.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eMaxime Thomas, Mebarek Lamara, Hugo Asselin and Nicole J. Fenton contributed to the study conceptualization and to the design of the methodology. Maxime Thomas and Mebarek Lamara wrote the first draft of the manuscript and performed formal analyses. Yves Desjardins validated research outputs regarding (poly)phenols. Nicole J. Fenton contributed to funding acquisition. All authors reviewed and edited the manuscript and approved its final version.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study contain sensitive information as the samples were collected on the traditional territories of Indigenous communities. They are available from the corresponding author on reasonable request.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbarenkov, K., Zirk, A., Piirmann, T., P\u0026ouml;h\u0026ouml;nen, R., Ivanov, F., Nilsson, R.H., and K\u0026otilde;ljalg, U. 2022, October 16. 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Soil Biology and Biochemistry \u003cstrong\u003e95\u003c/strong\u003e: 135\u0026ndash;143. doi:10.1016/j.soilbio.2015.12.012.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blueberry, Indigenous people, microbiome, (poly)phenols, mine, hydroelectric power lines","lastPublishedDoi":"10.21203/rs.3.rs-4433091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4433091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eDisturbances exert direct and indirect effects on plants through alterations of soil properties and microbiota composition. This can induce stress, resulting in modifications of plants\u0026rsquo; phytochemical profile. This in turn can affect the possibility for Indigenous people to engage in cultural activities depending on wild plants used as food or medicine. As a case study, we evaluated correlations between (poly)phenols in \u003cem\u003eVaccinium angustifolium\u003c/em\u003e fruits, disturbances from mining and hydroelectric activities, soil properties, and soil microbiome composition.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe collected fruit and soil samples in the territories of three Indigenous communities in eastern Canada. Fruits were analyzed for their concentrations in anthocyanins, proanthocyanidins and other (poly)phenols. Soil microbial DNA was extracted to reconstruct bacterial and fungal communities. A secondary subset of soil samples was used to measure soil properties. Relationships between soil, disturbances and (poly)phenols were investigated using multivariate analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDisturbances affected soil properties and microbiome, but not fruit (poly)phenol content. Two soil bacterial classes unaffected by disturbances, Bacilli and Desulfitobacteriia, were positively correlated with levels of proanthocyanidines and delphinidin-, cyanidin-, and petunidin-3-glucoside in fruits.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDisturbances did not affect (poly)phenol content in \u003cem\u003eV. angustifolium\u003c/em\u003e fruits. However, mine disturbances may contaminate fruits with pollutants detrimental to human health, which should be evaluated before drawing conclusions about the effect of disturbances on plant nutritional and medicinal properties. Some soil bacterial classes seem to enhance the (poly)phenolic content of \u003cem\u003eV. angustifolium\u003c/em\u003e fruits, suggesting that a strategy could be developed for enhancing the nutritional and medicinal properties of this culturally salient species.\u003c/p\u003e","manuscriptTitle":"Unraveling the interplay of the soil microbiome and (poly)phenol content in blueberry in response to disturbances","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 19:05:30","doi":"10.21203/rs.3.rs-4433091/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6f05826-af0a-4777-bf7a-0cc11d7145c4","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-05T14:36:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 19:05:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4433091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4433091","identity":"rs-4433091","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}