John Davison’s research while affiliated with University of Tartu and other places

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Publications (238)


Variation in the soil chemistry (N, P, K, Mg, Ca, and pHKCl) of study sites according to Principal Components Analysis (a). The dependence of AM fungal root colonization on variation in soil chemistry (Soil PC1 and PC2) is shown in panels (b) and (c). Colours indicate different plant mycorrhizal statuses: NM (green)—non‐mycorrhizal; FM (orange)—facultatively mycorrhizal; OM (blue)—obligately mycorrhizal. Each point on the graph represents a plant individual (a–c).
Mean hyphal colonization of plant roots (A) and AM fungal richness (B, C) in plant roots, shown in relation to plant mycorrhizal status (A, B) or plant population mean hyphal colonization (C). The boxes show the median, interquartile range (IQR), and potential outliers, with whiskers extending to values within 1.5 times the IQR. Colours indicate different plant mycorrhizal statuses: NM (green)—non‐mycorrhizal; FM (orange)—facultatively mycorrhizal; OM (blue)—obligately mycorrhizal; each point on the plots represents a plant individual. Lowercase letters indicate statistically significant differences (p < 0.05) between factor levels according to Tukey's HSD post hoc test.
The effect of plant species mycorrhizal status on plant species level network topological parameters: nestedness rank (a), normalised degree (b), resource range (c) and species strength (d). The boxes show the median, interquartile range (IQR), and potential outliers, with whiskers extending to values within 1.5 times the IQR. Colours indicate plants of different mycorrhizal status: NM (green)—non‐mycorrhizal; FM (orange)—facultatively mycorrhizal; OM (blue)—obligately mycorrhizal; each point on the plots represents a plant species. Lowercase letters indicate statistically significant differences (p < 0.05) between factor levels according to Tukey's HSD post hoc test.
Plant mycorrhizal status indicates partner selectivity in arbuscular mycorrhizal interaction networks
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December 2024

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Mycorrhizal symbiosis, specifically arbuscular mycorrhiza, is one of Earth's oldest and most widespread symbiosis. Existing evidence suggests that plant species differ in their associations with mycorrhizal partners, with different species reported to be always (obligately mycorrhizal, OM), sometimes (facultatively mycorrhizal, FM) or never (non‐mycorrhizal, NM) associating with arbuscular mycorrhizal (AM) fungi and this plant reliance on AM fungi is called plant mycorrhizal status. However, very little is known about how host plant mycorrhizal status shapes the network topology of interacting AM fungi. Here, we use a standardized sampling scheme to test whether plant species with different mycorrhizal statuses differ in mean AM fungal hyphal colonization and various indices of the AM fungal networks such as nestedness rank and resource range. We collected the roots and rhizosphere soil of 19 plant species representing five families. Each plant species was sampled from three distinct habitats. We determined AM fungal colonization in the roots and AM fungal community composition in roots and rhizosphere soil using molecular methods. We found that previously reported NM plant species had lower mean AM fungal colonization than FM plant species, but no differences were found between FM and OM plant species. Network analyses indicated that AM fungal communities in the roots of FM plant species had higher nestedness rank and resource range than networks associated with OM plant species, suggesting that OM plant species are more generalist regarding partner selection and interact with a wider range of fungal partners. Our results suggest that plant mycorrhizal status conveys useful information about the characteristics of AM fungal interaction networks, revealing that plant species consistently associated with AM fungi are less selective towards their fungal partners. Read the free Plain Language Summary for this article on the Journal blog.

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Experimental design for imposing extreme climatic events and their effects on soils collected from across Europe
a,b, Sampled site locations (a) and experimental set-up (b) for simulation of extreme climatic events. The 10 circles represent those countries where three replicate grassland sites within 11 km of each other were sampled, resulting in 30 sites in total. Sites represent the diversity of biogeographic regions present in Europe: alpine (AT, Austria), subarctic (SE, Sweden), Arctic (IS, Iceland), Atlantic (Ox, Oxford and La, Lancaster, both UK), boreal (EE, Estonia), continental (DE, Germany), Mediterranean (ES, Spain and GR, Greece) and steppe climate (RU, Russia). c–h, The simulated climate extremes consistently shift soil microbial communities in the same direction despite their contrasting composition. Non-metric multidimensional scaling (NMDS) ordinations of prokaryotic (n = 576) (c), fungal (n = 574) (d) and shotgun metagenome (n = 308) (e) communities, based on Bray–Curtis dissimilarities show that the origin of the sample (country and site) is the main driver of microbial community composition, followed by the type of disturbance and the time elapsed following the disturbance (Extended Data Table 1). The colour of points indicates the country of origin. Partial redundancy analysis (RDA) ordinations show disturbance effects on communities after controlling for site effects (black arrows). Prokaryotic (f), fungal (g) and shotgun metagenome (h) communities exhibit a consistent shift in response to individual disturbances. Percentage of variance explained, having conditioned on country and site, is given on the RDA axes; only the first two axes are shown. Total variance explained by all four constrained RDA axes is 2.7, 3.6 and 8.8% for prokaryotic, fungal and shotgun metagenomes, respectively. Conditional variance (country and site within country), as a proportion of total variance, was 71, 68 and 91% for prokaryotic, fungal and shotgun metagenomes, respectively. 2D, two-dimensional; DW, dry weight. Map in a adapted from ref. ⁴⁵, European Environment Agency CC BY 4.0.
Ecological resistance and resilience strategies associated with the 500 most abundant ASVs
a,b, ASVs associated with prokaryotes (a) and fungi (b). The central tree indicates the taxonomy of the 500 most abundant ASVs, with tips coloured by phylum (more abundant ASVs are more intensely coloured). In the rings surrounding the tree (one per disturbance treatment), purple/blue colours indicate ASVs that perform significantly worse following disturbance (P ≤ 0.05, two-tailed Wald test, relative to other organisms and to the control treatment as fitted by a linear mixed-effects model across all soils, treatments and samplings, without correction for multiple testing; n = 548 and 586 samples for bacteria and fungi, respectively). Orange colours indicate ASVs that perform relatively better following disturbance using the same criteria. The shade of colour indicates the dynamics of the response as shown in the key: the darkest shades indicate a statistically significant divergence from the control at the end of the disturbance, followed by a statistically significant change in the same direction over the following month (that is, not resilient). The palest shades indicate no significant change at the end of the disturbance, but a significant divergence over the following month. ASVs in which a model did not converge are indicated by pale grey tiles across all four perturbation rings.
Changes in abundance of protein functions with extreme climatic event disturbances
a,b, Functional classifications relative to control in metagenomic samples. a, Effects of disturbance on each of the 28 highest-level functions; red indicates an increase in abundance following the perturbation given at the top of the column, blue a decrease. One linear mixed-effects model for the proportion of reads was fitted per function using data from S1 and S4 (n = 280 metagenomic samples; Methods). The change in proportion with disturbance, relative to control (±s.e.m.), as estimated by this model, is shown on the horizontal axis; functions with significant changes among treatments (P ≤ 0.05, F3,280; two-tailed test, following false discovery rate correction for multiple models on the analysis of variance (ANOVA) P value of the relevant mixed-effects model, shown in bold). When a Dunnett’s test of a particular disturbance versus control accounting for multiple testing within a mixed-effects model is significant (P ≤ 0.05, two-tailed test), the point is filled and shown on a coloured background. b, Numbers (horizontal axis) and proportions (figures in bars) of individual proteins with and without significant effects of disturbance in each of the 28 highest-level functions (P ≤ 0.05 following false discovery rate correction for multiple models). In a few cases a linear mixed-effects model did not converge for a particular protein (shown in grey).
Correlations of initial soil and climatic properties with biofunctional responses to perturbation
a, Rank correlations between initial properties and the resistance (left) and resilience (right) of community function following disturbance. Resistance and resilience are quantified as the negative Bray–Curtis dissimilarity of metagenome-derived functional (protein-level) matrices between control and treatment directly following either the disturbance (S1) or 1 month of recovery (S4), respectively. The order of initial properties on the y axis is determined by complete-linkage clustering according to their correlations with resistance–resilience across perturbations. b, Relationship of soil functional measurements (enzyme activities, microbial substrate use, gas fluxes and soil C and N concentrations) and the relative abundance of finest-scale functional categories (proteins) from the metagenomes. Distances among soil samples were calculated for both the relative abundances of all functional categories in the metagenomes and each of the four classes of functional soil measurements. For each treatment and time point, coloured tiles show the rank correlation between these two distance matrices, with asterisks indicating the significance (two-tailed Mantel test, nperm = 999; ***P ≤ 0.001, **P ≤ 0.01, *P ≤ 0.05, ·P ≤ 0.1, uncorrected for multiple testing). Note the different colour scales in a and b. Ammonium, plant available ammonium concentration; C:N ratio, soil carbon:nitrogen ratio; DOC, dissolved organic carbon concentration; DON, dissolved organic nitrogen concentration; MAP, mean annual precipitation; MAT, mean annual temperature; Nitrate, plant available nitrate concentration; Plength, difference between Pmax and Pmin; Pmax, annual precipitation maximum; Pmin, annual precipitation minimum; Tlength, difference between Tmax and Tmin; Tmax, maximum annual temperature; Tmin, minimum annual temperature; Total C, total soil carbon content; Total dissolved N, total dissolved nitrogen concentration; Total N, total soil nitrogen content; WHC, soil moisture content expressed as percentage of moisture content at 100% water-holding capacity; WHC100, soil moisture content at 100% WHC.
Soil microbiomes show consistent and predictable responses to extreme events

November 2024

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940 Reads

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3 Citations

Nature

Increasing extreme climatic events threaten the functioning of terrestrial ecosystems1,2. Because soil microbes govern key biogeochemical processes, understanding their response to climate extremes is crucial in predicting the consequences for ecosystem functioning3,4. Here we subjected soils from 30 grasslands across Europe to four contrasting extreme climatic events under common controlled conditions (drought, flood, freezing and heat), and compared the response of soil microbial communities and their functioning with those of undisturbed soils. Soil microbiomes exhibited a small, but highly consistent and phylogenetically conserved, response under the imposed extreme events. Heat treatment most strongly impacted soil microbiomes, enhancing dormancy and sporulation genes and decreasing metabolic versatility. Microbiome response to heat in particular could be predicted by local climatic conditions and soil properties, with soils that do not normally experience the extreme conditions being imposed being most vulnerable. Our results suggest that soil microbiomes from different climates share unified responses to extreme climatic events, but that predicting the extent of community change may require knowledge of the local microbiome. These findings advance our understanding of soil microbial responses to extreme events, and provide a first step for making general predictions about the impact of extreme climatic events on soil functioning.


Conceptual framework linking symbiont plasticity with local abundance and geographic occupancy.
Abundance, occupancy and mycorrhizal flexibility of species in herbaceous vegetation plots in the sPlotOpen database. Local abundance was calculated as median plot level abundance (in species present in ≥ 10 plots), geographic occupancy–the number of plots occupied, and global abundance–cumulative abundance in the data set. Mycorrhizal flexibility (F, flexible; NF(M), nonflexible mycorrhizal; NF(NM), nonflexible nonmycorrhizal) could be assigned to 2144 species (F = 661 (31%); NF(M) = 1142 (53%); NF(NM) = 334 (16%)). The relationship between species local abundance and geographical occupancy is displayed with different mycorrhizal flexibility types distinguished. Inset panels show the median (point) and interquartile range (bars) of local abundance and occupancy for species of different mycorrhizal flexibility. Triangular symbols identify the 50 species with the highest global abundance.
Manifestation of symbiont plasticity, expressed as root mutualist flexibility. Variation in the rate of root colonization (represented by red points) among (a) flexible (b) nonflexible (nonsymbiont) and (c) nonflexible (obligate symbiont) plant species. Among flexible species, flexibility may be manifested (i) within genotypes, (ii) between genotypes and (iii) between populations. Different colours represent different genotypes within a given mutualism flexibility type.
Symbiont plasticity as a driver of plant success

February 2024

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111 Reads

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6 Citations

We discuss which plant species are likely to become winners, that is achieve the highest global abundance, in changing landscapes, and whether plant‐associated microbes play a determining role. Reduction and fragmentation of natural habitats in historic landscapes have led to the emergence of patchy, hybrid landscapes, and novel landscapes where anthropogenic ecosystems prevail. In patchy landscapes, species with broad niches are favoured. Plasticity in the degree of association with symbiotic microbes may contribute to broader plant niches and optimization of symbiosis costs and benefits, by downregulating symbiosis when it is unnecessary and upregulating it when it is beneficial. Plasticity can also be expressed as the switch from one type of mutualism to another, for example from nutritive to defensive mutualism with increasing soil fertility and the associated increase in parasite load. Upon dispersal, wide mutualistic partner receptivity is another facet of symbiont plasticity that becomes beneficial, because plants are not limited by the availability of specialist partners when arriving at new locations. Thus, under conditions of global change, symbiont plasticity allows plants to optimize the activity of mutualistic relationships, potentially allowing them to become winners by maximizing geographic occupancy and local abundance.


Map of study sites in western Estonia
Each site comprised four dynamic stages reflecting the degree of woody encroachment experienced prior to restoration: unmanaged open, managed open, managed previous scrub, and managed previous forest (see methods for details). The map was made using freely available vector and raster map data from Natural Earth (naturalearthdata.com) and the Estonian Administrative and Settlement Division: Land Board; accessed 01.06.2023.
The richness of prokaryotic (A), fungal (B) and plant (C) communities (VST-transformed data matrices)
Letters above boxes indicate significant differences between groups.
Distance-based redundancy analysis of prokaryotic (A), fungal (B) and plant (C) community composition
Mean relative abundances (%) of the different functional guilds of prokaryotes (A), fungi (B) and plants (C) in different dynamic stages
Letters within boxes indicate significant differences between groups within individual functional groups.
Procrustes correlation between the composition of prokaryotic, fungal and plant communities, and analysis of Procrustes residuals
Soil community composition in dynamic stages of semi-natural calcareous grassland

October 2023

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116 Reads

European dry thin-soil calcareous grasslands (alvars) are species-rich semi-natural habitats. Cessation of traditional management, such as mowing and grazing, leads to shrub and tree encroachment and the local extinction of characteristic alvar species. While soil microbes are known to play a critical role in driving vegetation and ecosystem dynamics, more information is needed about their composition and function in grasslands of different dynamic stages. Here we assess the composition of soil fungal, prokaryotic, and plant communities using soil environmental DNA from restored alvar grasslands in Estonia. The study areas included grasslands that had experienced different degrees of woody encroachment prior to restoration (woody plant removal and grazing), as well as unmanaged open grasslands. We found that, in general, different taxonomic groups exhibited correlated patterns of between-community variation. Previous forest sites, which had prior to restoration experienced a high degree of woody encroachment by ectomycorrhizal Scots pine, were compositionally most distinct from managed open grasslands, which had little woody vegetation even prior to restoration. The functional structure of plant and fungal communities varied in ways that were consistent with the representation of mycorrhizal types in the ecosystems prior to restoration. Compositional differences between managed and unmanaged open grasslands reflecting the implementation of grazing without further management interventions were clearer among fungal, and to an extent prokaryotic, communities than among plant communities. While previous studies have shown that during woody encroachment of alvar grassland, plant communities change first and fungal communities follow, our DNA-based results suggest that microbial communities reacted faster than plant communities during the restoration of grazing management in alvar grassland. We conclude that while the plant community responds faster to cessation of management, the fungal community responds faster to restoration of management. This may indicate hysteresis, where the eventual pathway back to the original state (grazed ecosystem) differs from the pathway taken towards the alternative state (abandoned semi-natural grassland ecosystem).


Correlations between estimates of AM fungal niche differentiation derived from different niche types (a, b) and between niche types and different individual niche axes (c, d). Differentiation is calculated pairwise between taxa in (a) and (c), with correlation between niche categories estimated using Mantel r and P; and as a community‐weighted mean in (b) and (d), with correlation between niche categories estimated using Pearson's r and P. In (c) and (d), red boxes indicate correspondence between individual axes and niche types. AC, abiotic condition; AR, abiotic resource; BC, biotic condition; BR, biotic resource; FN, full niche; FU, fungal composition; MAP, mean annual precipitation; MAT, mean annual temperature; OC, organic carbon; PL, plant composition; PR, prokaryote composition. ***p < 0.001; **p < 0.01; *p < 0.05.
Niche differentiation among AM fungal communities measured using different niche types and the full niche. AC, abiotic condition, AR, abiotic resource, BC, biotic condition, BR, biotic resource, FN, full niche. A standardized effect size (SES) was calculated in relation to a stratified null model, distinguishing different biogeographic realms. Boxes and whiskers indicate interquartile ranges and non‐outlier ranges, respectively. Letters above bars indicate significant differences between groups (p < 0.05; pairwise comparison of estimated marginal means).
Niche differentiation among AM fungal communities measured using different individual niche axes. AC, abiotic condition, AR, abiotic resource, BC, biotic condition, BR, biotic resource, FU, fungal composition, MAP, mean annual precipitation, MAT, mean annual temperature, OC, organic carbon, PL, plant composition, PR, prokaryote composition. A standardized effect size (SES) was calculated in relation to a stratified null model, distinguishing different biogeographic realms. Boxes and whiskers indicate interquartile ranges and non‐outlier ranges, respectively. Letters above bars indicate significant differences between groups (p < 0.05; pairwise comparison of estimated marginal means).
Niche types and community assembly

October 2023

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425 Reads

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3 Citations

Ecology Letters

Studies of niche differentiation and biodiversity often focus on a few niche dimensions due to the methodological challenge of describing hyperdimensional niche space. However, this may limit our understanding of community assembly processes. We used the full spectrum of realized niche types to study arbuscular mycorrhizal fungal communities: distinguishing abiotic and biotic, and condition and resource, axes. Estimates of differentiation in relation to different niche types were only moderately correlated. However, coexisting taxon niches were consistently less differentiated than expected, based on a regional null model, indicating the importance of habitat filtering at that scale. Nonetheless, resource niches were relatively more differentiated than condition niches, which is consistent with the effect of a resource niche‐based coexistence mechanism. Considering niche types, and in particular distinguishing resource and condition niches, provides a more complete understanding of community assembly, compared with studying individual niche axes or the full niche.


GlobalAMFungi: a global database of arbuscular mycorrhizal fungal occurrences from high‐throughput sequencing metabarcoding studies

October 2023

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454 Reads

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32 Citations

Arbuscular mycorrhizal (AM) fungi are crucial mutualistic symbionts of the majority of plant species, with essential roles in plant nutrient uptake and stress mitigation. The importance of AM fungi in ecosystems contrasts with our limited understanding of the patterns of AM fungal biogeography and the environmental factors that drive those patterns. This article presents a release of a newly developed global AM fungal dataset (GlobalAMFungi database, https://globalamfungi.com) that aims to reduce this knowledge gap. It contains almost 50 million observations of Glomeromycotinian AM fungal amplicon DNA sequences across almost 8500 samples with geographical locations and additional metadata obtained from 100 original studies. The GlobalAMFungi database is built on sequencing data originating from AM fungal taxon barcoding regions in: i) the small subunit rRNA (SSU) gene; ii) the internal transcribed spacer 2 (ITS2) region; and iii) the large subunit rRNA (LSU) gene. The GlobalAMFungi database is an open source and open access initiative that compiles the most comprehensive atlas of AM fungal distribution. It is designed as a permanent effort that will be continuously updated by its creators and through the collaboration of the scientific community. This study also documented applicability of the dataset to better understand ecology of AM fungal taxa.


Schematic representation of data preparation and modelling, including feature selection and dataset combinations in different random forest (RF) models. Data preparation started with compilation of a plant mycorrhizal trait database, comprising the response variables, and phylogenetic and environmental information as predictors. The predictor matrices were orthogonally decomposed and included in the form of eigenvectors into RF models. Feature selection was conducted using the Boruta algorithm, and five RF models were built to determine the importance of phylogeny and environmental conditions in explaining plant mycorrhizal traits.
Phylogeny and mycorrhizal traits of 11,770 vascular plant species, and phylogenetic signal for each plant mycorrhizal trait. In the phylogenetic tree, the first ring represents mycorrhizal trait characteristic for each tip (species), the second ring (purple strips) highlights the range of the 15 largest families in our dataset, and the third ring represents the abundance (as a stacked bar) of each trait within each of the largest families. In the violin plot, node entropies were calculated based on the linear version of Shannon entropy. δ, measuring the degree of phylogenetic signal between a trait vector and a phylogeny, is higher in types than statuses. Note that some species (e.g., among Pinaceae) displayed as ECM exhibited dual mycorrhiza (AM + ECM). AM, arbuscular mycorrhizal; ECM, ectomycorrhizal; ERM, ericoid mycorrhizal; FM, facultatively mycorrhizal; NM, non‐mycorrhizal; OM, obligately mycorrhizal; ORM, orchid mycorrhizal. In order to include all species in both trait analyses, NM is presented as a type and a status.
Global distribution of mycorrhizal traits among studied plant species: Arbuscular mycorrhizal (AM), ectomycorrhizal (ECM) and ericoid mycorrhizal (ERM) types, and non‐mycorrhizal (NM), facultatively mycorrhizal (FM) and obligately mycorrhizal (OM) statuses. In order to visualise global patterns of mycorrhizal trait distribution, data for 50 × 50 km grid cells were calculated by aggregating 1 × 1 km plant occurrence data and upscaling environmental variables (see Appendix S1 for details); cells with only one species were excluded. Note that the scales vary between panels to emphasise relative trait variations; figures with a uniform 0%–100% scale are shown in Appendix S6. Inset (bottom‐left): Share of species with particular mycorrhizal traits in relation to edaphic and climatic gradients. For grid cells with complete environmental data (46,957 grids), edaphic and climatic factors were described using their first two principal components. The first two principal components explained 46.1% and 25.1% of variation in climatic variables, and 28.4% and 15.4% of variation in edaphic variables. Climate PC1 and PC2 mainly characterised temperature and precipitation, respectively. Soil PC1 characterised a range of soil properties including soil available water, cation exchange capacity, bulk density, organic carbon, nitrogen and phosphorus content; soil PC2 mainly reflected cation content and base saturation (Appendix S7). Lines represent fitted curves from LOESS. Numbers between brackets in the ×‐axes correspond to the variance explained by each principal component. Relationships with individual environmental variables are shown in Figure S1.
Environmental feature importance for specific plant mycorrhizal types (AM, ECM and ERM) and statuses (NM, OM and FM), and for plant mycorrhizal type and status. Feature importance (as percentages) of the 20 most important predictors belonging to the best model set are shown for each plant mycorrhizal trait, modelled in the absence (RF model 4, Figure 1) of phylogenetic predictors. The importance of each trait was calculated from the mean and standard deviation of accumulation of the impurity decrease within each tree. AM, arbuscular mycorrhizal; ECM, ectomycorrhizal; ERM, ericoid mycorrhizal; FM, facultatively mycorrhizal; NM, non‐mycorrhizal; OM, obligately mycorrhizal.
Environmental modulation of plant mycorrhizal traits in the global flora

September 2023

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577 Reads

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12 Citations

Ecology Letters

Mycorrhizal symbioses are known to strongly influence plant performance, structure plant communities and shape ecosystem dynamics. Plant mycorrhizal traits, such as those characterising mycorrhizal type (arbuscular (AM), ecto‐, ericoid or orchid mycorrhiza) and status (obligately (OM), facultatively (FM) or non‐mycorrhizal) offer valuable insight into plant belowground functionality. Here, we compile available plant mycorrhizal trait information and global occurrence data (∼ \sim 100 million records) for 11,770 vascular plant species. Using a plant phylogenetic mega‐tree and high‐resolution climatic and edaphic data layers, we assess phylogenetic and environmental correlates of plant mycorrhizal traits. We find that plant mycorrhizal type is more phylogenetically conserved than plant mycorrhizal status, while environmental variables (both climatic and edaphic; notably soil texture) explain more variation in mycorrhizal status, especially FM. The previously underestimated role of environmental conditions has far‐reaching implications for our understanding of ecosystem functioning under changing climatic and soil conditions.


Plant-symbiotic fungal diversity tracks variation in vegetation and the abiotic environment along an extended elevational gradient in the Himalayas

August 2023

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195 Reads

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5 Citations

FEMS Microbiology Ecology

Arbuscular mycorrhizal (AM) fungi can benefit plants under environmental stress, and influence plant adaptation to warmer climates. However, very little is known about the ecology of these fungi in alpine environments. We sampled plant roots along a large fraction (1941–6150 m asl) of the longest terrestrial elevational gradient on Earth and used DNA metabarcoding to identify AM fungi. We hypothesized that AM fungal alpha and beta diversity decreases with increasing elevation, and that different vegetation types comprise dissimilar communities, with cultured (putatively ruderal) taxa increasingly represented at high elevations. We found that alpha diversity of AM fungal communities declined linearly with elevation, whereas within-site taxon turnover (beta diversity) was unimodally related to elevation. Composition of AM fungal communities differed between vegetation types and was influenced by elevation, mean annual temperature and precipitation. In general, Glomeraceae taxa dominated at all elevations and vegetation types, however, higher elevations were associated with increased presence of Acaulosporaceae, Ambisporaceae and Claroideoglomeraceae. Contrary to our expectation, the proportion of cultured AM fungal taxa in communities decreased with elevation. These results suggest that, in this system, climate-induced shifts in habitat conditions may facilitate more diverse AM fungal communities at higher elevations but could also favour ruderal taxa.


Correlations between eDNA, GBIF, Kreft & Jetz and Cai datasets. Numbers show correlation strength and direction, red asterisks show significance (*p < 0.05; **p < 0.01; ***p < 0.001).
Global distribution of (A) eDNA family richness, (B) GBIF family richness, (C) Kreft & Jetz estimate of richness and (D) Cai estimates of richness predicted using generalised additive models. Red points indicate sampling locations. The Sahara region was excluded from interpolations because of insufficient sampling.
dbRDA plot (distance-based redundancy analysis) showing effects climatic variables (CHELSA) and biogeographic realm on (A) eDNA and (B) GBIF family level composition (following variance stabilising transformation). Ellipses indicate 1 standard deviation around the centroids for different biogeographic realms.
Relative success of taxonomic assignment of eDNA reads (trnL P6 loop). Values are the logarithm of the ratio between the number of reads (A, B) or OTUs (C, D) getting a match against trnL intron sequence data in INSDC at family (A, C) or species (B, D) level and the number not getting a match. Higher values indicate greater proportions of reads or OTUs getting a match. Red points indicate sampling locations. Global predictions are the result of a generalised additive model. The Sahara region was excluded from interpolations because of insufficient sampling.
Metabarcoding of soil environmental DNA to estimate plant diversity globally

April 2023

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526 Reads

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7 Citations

Introduction Traditional approaches to collecting large-scale biodiversity data pose huge logistical and technical challenges. We aimed to assess how a comparatively simple method based on sequencing environmental DNA (eDNA) characterises global variation in plant diversity and community composition compared with data derived from traditional plant inventory methods. Methods We sequenced a short fragment (P6 loop) of the chloroplast trnL intron from from 325 globally distributed soil samples and compared estimates of diversity and composition with those derived from traditional sources based on empirical (GBIF) or extrapolated plant distribution and diversity data. Results Large-scale plant diversity and community composition patterns revealed by sequencing eDNA were broadly in accordance with those derived from traditional sources. The success of the eDNA taxonomy assignment, and the overlap of taxon lists between eDNA and GBIF, was greatest at moderate to high latitudes of the northern hemisphere. On average, around half (mean: 51.5% SD 17.6) of local GBIF records were represented in eDNA databases at the species level, depending on the geographic region. Discussion eDNA trnL gene sequencing data accurately represent global patterns in plant diversity and composition and thus can provide a basis for large-scale vegetation studies. Important experimental considerations for plant eDNA studies include using a sampling volume and design to maximise the number of taxa detected and optimising the sequencing depth. However, increasing the coverage of reference sequence databases would yield the most significant improvements in the accuracy of taxonomic assignments made using the P6 loop of the trnL region.



Citations (65)


... We observed that the Shannon, Simpson, and Pielou indices of soil bacteria in the KC treatment were significantly higher than those in the SC treatment (Table 2). Considering the differences between the native species Kandelia candel and the exotic species Sonneratia caseolaris, Kandelia candel, as a native species [57,58], has interacted with the local soil environment, climatic conditions, and other biotic populations over a long period, forming stable ecological relationships [59,60]. This long-term coevolution may have enabled Kandelia candel's root exudates and leaf litter to provide a more suitable growth environment and nutrient sources for specific soil microorganisms [61,62], thereby promoting their reproduction and increasing diversity. ...

Reference:

Geographic Distribution Patterns of Soil Microbial Community Assembly Process in Mangrove Constructed Wetlands, Southeast China
Soil microbiomes show consistent and predictable responses to extreme events

Nature

... In accordance with microbial networks in grasslands (Wagg et al., 2019;Luo et al., 2022), we observed that highly complex ECM fungal networks presented fewer taxa explaining enzymatic activity rates in forest soils, i.e., as if increasing network complexity would lead to functional redundancy, likely explained by the functional complementarity among OTUs conforming to the network . This result supporting the ideas of functional redundancy (i.e., enzymatic activity conservation across ECM fungi, Baldrian and Kohout, 2017) fits well into the holobiont theory (Zobel et al., 2024). Indeed, from the holobiont perspective, this redundancy ensures that essential functions carried out by microbiota are maintained even if one or more species are lost or disrupted, providing resilience against environmental changes, disturbances or the loss of specific microbial species (Vandenkoornhuyse et al., 2015). ...

Symbiont plasticity as a driver of plant success

... Given the influence of mycorrhizae on community structure, recent research has pivoted towards examining their role in community assembly (Peay, Garbelotto, and Bruns 2010;Peay 2016;Davison et al. 2023). This assembly is governed by a balance of deterministic processes (Vellend 2010) and stochastic processes (Hubbell 2001). ...

Niche types and community assembly

Ecology Letters

... To this day, 355 species of arbuscular mycorrhizal (AM) fungi are described within the Glomeromycota phylum (Větrovský et al., 2023). While it is generally accepted that AM fungi lack host specificity, our understanding of their functional traits, environmental preferences, and host associations remains incomplete. ...

GlobalAMFungi: a global database of arbuscular mycorrhizal fungal occurrences from high‐throughput sequencing metabarcoding studies

... NM Proteaceae are known to be associated with low-P soils in western Australia and South Africa (Lambers et al. 2015), and a range of other nonmycorrhizal woody plants grow in habitats regarded as inimical to mycorrhizal fungi, including aquatic, arid, saline and very cold environments (Brundrett 2009). Meng et al. (2023) have recently documented global patterns of NM species representation in floras, but we are not aware of any global studies examining the environmental correlates of the proportional abundance of the NM association in woody vegetation. ...

Environmental modulation of plant mycorrhizal traits in the global flora

Ecology Letters

... This could be attributed to the fact that the oak forests have neutral soils and that the bacterial community in oak forests may have an appropriate pH range for activity. Along extensive environmental gradients, studies have reported that changes in bacterial community composition were hump-shaped [49]. The acidic or alkaline pH soils provided stronger physiological constraints on the survival of the bacterial taxa compared with the neutral pH soils, due to greater energy costs associated with homeostasis [50]. ...

Plant-symbiotic fungal diversity tracks variation in vegetation and the abiotic environment along an extended elevational gradient in the Himalayas
  • Citing Article
  • August 2023

FEMS Microbiology Ecology

... Despite being broadly applied in monitoring of animal species, the application of eDNA in weed biosecurity is limited [12,13]. Several studies used a metabarcoding approach for biodiversity analyses to investigate the plant communities using soil [14], dust [15] and honey [16] samples. Additionally, qPCR-based targeted species detection methods have been developed for various aquatic weeds, such as Hydrilla verticillata (L.f.) Royle, Egeria densa Planch. ...

Metabarcoding of soil environmental DNA to estimate plant diversity globally

... . Although these spaces are recognized for their conducive role to the positive overall well-being of urban inhabitants of all ages [5,6], they considerably benefit children [7]. It has been extensively shown that urban planning has to consider child-friendly spaces [8]. ...

Atopic sensitization in childhood depends on the type of green area around the home in infancy
  • Citing Article
  • April 2023

Clinical & Experimental Allergy

... Vol.: (0123456789) fixation (Sepp et al. 2023). Furthermore, factors such as climate, rainfall, and seasonality affect the distribution, diversity, and population density of Bradyrhizobium in African soils, influencing legume nodulation and nitrogen fixation, which in turn determines the critical need for inoculation (Grönemeyer and Reinhold-Hurek 2018). ...

Global diversity and distribution of nitrogen-fixing bacteria in the soil

... Secondly, it is favorable for species richness because the large elevation drop (2240 m) shapes a richer diversity of habitats (Figure 4c) [58]. Thirdly, the abundance of riverine forests in the Crane River, with the highest number of tree species (15), may provide sufficient resources for the shrub and herbaceous species and also favor species richness [59][60][61]. The Kayertes River, on the contrary, is short and located at the headwaters of the Irtysh River Basin, where species are scarce. ...

The multiscale feedback theory of biodiversity
  • Citing Article
  • September 2022

Trends in Ecology & Evolution