Partitioning plant spectral diversity into alpha and beta components.

Plant spectral diversity - how plants differentially interact with solar radiation - is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (ß; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ-, ß-, and α-diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify 'hotspots' of spectral α-diversity within a region, and discover spectrally unique areas that contribute strongly to ß-diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.

Moderate disturbances accelerate forest transition dynamics under climate change in the temperate-boreal ecotone of eastern North America.

Several temperate tree species are expected to migrate northward and colonize boreal forests in response to climate change. Tree migrations could lead to transitions in forest types, but these could be influenced by several non-climatic factors, such as disturbances and soil conditions. We analysed over 10,000 forest inventory plots, sampled from 1970 to 2018 in meridional Québec, Canada, to identify what environmental conditions promote or prevent regional-scale forest transitions. We used a continuous-time multi-state Markov model to quantify the probabilities of transitions between forest states (temperate, boreal, mixed, pioneer) as a function of climate (mean temperature and climate moisture index during the growing season), soil conditions (pH and drainage) and disturbances (severity levels of natural disturbances and logging). We further investigate how different disturbance types and severities impact forests' short-term transient dynamics and long-term equilibrium using properties of Markov transition matrices. The most common transitions observed during the study period were from mixed to temperate states, as well as from pioneer to boreal forests. In our study, transitions were mainly driven by natural and anthropogenic disturbances and secondarily by climate, whereas soil characteristics exerted relatively minor constraints. While major disturbances only promoted transitions to the pioneer state, moderate disturbances increased the probability of transition from mixed to temperate states. Long-term projections of our model under the current environmental conditions indicate that moderate disturbances would promote a northward shift of the temperate forest. Moreover, disturbances reduced turnover and convergence time for all transitions, thereby accelerating forest dynamics. Contrary to our expectation, mixed to temperate transitions were not driven by temperate tree recruitment but by mortality and growth. Overall, our results suggest that moderate disturbances could catalyse rapid forest transitions and accelerate broad-scale biome shifts.

Chronic patients' satisfaction and priorities regarding medical care, information and services and quality of life: a French online patient community survey.

BACKGROUND: The French healthcare system is evolving to meet the challenges of an aging population, the growing prevalence of chronic diseases, the development of new technologies and the increasing involvement of patients in the management of their disease. The aim of this study is to assess the satisfaction and priorities of chronic patients regarding medical care, information and services and their quality of life. METHODS: A cross-sectional study was conducted from February to March 2018 via the French Carenity platform. Adult patients enrolled in type 1 diabetes, heart failure or obesity communities were invited to answer an online questionnaire. A numeric scale from 0 (meaning not satisfied) to 5 was used to evaluate patients' satisfaction. Patients' priorities were assessed using a ranking question. Patients' satisfaction and priorities have been combined in a matrix to identify patients' expectations. RESULTS: Sixty-seven respondents of each condition answered a questionnaire. The most important and least satisfactory items about medical care are availability and active listening from healthcare providers, as well as access to coordinated and multidisciplinary care. Regarding information and services, respondents mostly expect connected medical devices, in addition to lifestyle and dietary measures. As for the quality of life, respondents fear that their chronic condition will keep impacting their daily mood and ability to do physical activities. CONCLUSIONS: This study shows that chronic patients want to be more actively involved in their care pathway. Patient training and therapeutic patient education programs could help them manage their chronic conditions within a patient-centred healthcare system.

Astronomical and atmospheric impacts on deep-sea hydrothermal vent invertebrates.

Ocean tides and winter surface storms are among the main factors driving the dynamics and spatial structure of marine coastal species, but the understanding of their impact on deep-sea and hydrothermal vent communities is still limited. Multidisciplinary deep-sea observatories offer an essential tool to study behavioural rhythms and interactions between hydrothermal community dynamics and environmental fluctuations. Here, we investigated whether species associated with a Ridgeia piscesae tubeworm vent assemblage respond to local ocean dynamics. By tracking variations in vent macrofaunal abundance at different temporal scales, we provide the first evidence that tides and winter surface storms influence the distribution patterns of mobile and non-symbiotic hydrothermal species (i.e. pycnogonids Sericosura sp. and Polynoidae polychaetes) at more than 2 km depth. Local ocean dynamics affected the mixing between hydrothermal fluid inputs and surrounding seawater, modifying the environmental conditions in vent habitats. We suggest that hydrothermal species respond to these habitat modifications by adjusting their behaviour to ensure optimal living conditions. This behaviour may reflect a specific adaptation of vent species to their highly variable habitat.

Human and natural controls of the variation in aboveground tree biomass in African dry tropical forests.

Understanding the anthropogenic and natural controls that affect the patterns, distribution, and dynamics of terrestrial carbon is crucial to meeting climate change mitigation objectives. We assessed the human and natural controls over aboveground tree biomass density in African dry tropical forests, using Zambia's first nationwide forest inventory. We identified predictors that best explain the variation in biomass density, contrasted anthropogenic and natural sites at different spatial scales, and compared sites with different stand structure characteristics and species composition. In addition, we evaluated the effects of different management and conservation practices on biomass density. Variation in biomass density was mostly determined by biotic processes, linked with both species richness and dominance (evenness), and to a lesser extent, by land use, environmental controls, and spatial structure. Biomass density was negatively associated with tree species evenness and positively associated with species richness for both natural and human-modified sites. Human influence variables (including distance to roads, distance to town, fire occurrence, and the population on site) did not explain substantial variation in biomass density in comparison to biodiversity variables. The relationship of human activities to biomass density in managed sites appears to be mediated by effects on species diversity and stand structure characteristics, with lower values in human-modified sites for all metrics tested. Small contrasts in carbon density between human-modified and natural forest sites signal the potential to maintain carbon in the landscape inside but also outside forestlands in this region. Biodiversity is positively related to biomass density in both human and natural sites, demonstrating potential synergies between biodiversity conservation and climate change mitigation. This is the first evidence of positive outcomes of protected areas and participatory forest management on carbon storage at national scale in Zambia. This research shows that understanding controls over biomass density can provide policy relevant inputs for carbon management and on ecological processes affecting carbon storage.

Thirty-year recovery of mollusc communities after nuclear experimentations on Fangataufa atoll (Tuamotu, French Polynesia).

A 30-year study of temporal changes in gastropod community structure on the reefs of a Pacific Ocean atoll (Fangataufa, Tuamotu Archipelago) subjected to atmospheric nuclear tests during the 1960s offered the opportunity for an otherwise impossible field experiment that could help ecologists understand mollusc primary succession. Reef molluscs were partly or entirely wiped out by the heat of the nuclear tests and the reefs were recolonized by ocean larvae. On all reefs, community composition before the tests was very different from what it evolved to afterwards. A new method of analysis was developed to study the temporal variation in community composition before versus after the tests (temporal beta diversity). Analyses showed that community compositions diverged through time among the reefs. Only some species can survive the harsh conditions of supralittoral zones, so the same species recolonized them; environmental filtering controlled the development of the new communities. In the reef flat and edge zones, differences in community composition seem to be the result of neutral stochastic colonization by larvae coming from the open ocean. All reefs developed a community composition quite different from that before the nuclear tests.

Oxidative stress modulates the expression of genes involved in cell survival in ΔF508 cystic fibrosis airway epithelial cells.

Although cystic fibrosis (CF) pathophysiology is explained by a defect in CF transmembrane conductance regulator (CFTR) protein, the broad spectrum of disease severity is the consequence of environmental and genetic factors. Among them, oxidative stress has been demonstrated to play an important role in the evolution of this disease, with susceptibility to oxidative damage, decline of pulmonary function, and impaired lung antioxidant defense. Although oxidative stress has been implicated in the regulation of inflammation, its molecular outcomes in CF cells remain to be evaluated. To address the question, we compared the gene expression profile in NuLi-1 cells with wild-type CFTR and CuFi-1 cells homozygous for ΔF508 mutation cultured at air-liquid interface. We analyzed the transcriptomic response of these cell lines with microarray technology, under basal culture conditions and after 24 h oxidative stress induced by 15 µM 2,3-dimethoxy-1,4-naphtoquinone. In the absence of oxidative conditions, CuFi-1 gene profiling showed typical dysregulated inflammatory responses compared with NuLi-1. In the presence of oxidative conditions, the transcriptome of CuFi-1 cells reflected apoptotic transcript modulation. These results were confirmed in the CFBE41o- and corrCFBE41o- cell lines as well as in primary culture of human CF airway epithelial cells. Altogether, our data point to the influence of oxidative stress on cell survival functions in CF and identify several genes that could be implicated in the inflammation response observed in CF patients.

Statistical methods for temporal and space-time analysis of community composition data.

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran's eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space-time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.

Using phylogenetic information and chemical properties to predict species tolerances to pesticides.

Direct estimation of species' tolerance to pesticides and other toxic organic substances is a combinatorial problem, because of the large number of species-substance pairs. We propose a statistical modelling approach to predict tolerances associated with untested species-substance pairs, by using models fitted to tested pairs. This approach is based on the phylogeny of species and physico-chemical descriptors of pesticides, with both kinds of information combined in a bilinear model. This bilinear modelling approach predicts tolerance in untested species-compound pairs based on the facts that closely related species often respond similarly to toxic compounds and that chemically similar compounds often have similar toxic effects. The three tolerance models (median lethal concentration after 96 h) used up to 25 aquatic animal species and up to nine pesticides (organochlorines, organophosphates and carbamates). Phylogeny was estimated using DNA sequences, while the pesticides were described by their mode of toxic action and their octanol-water partition coefficients. The models explained 77-84% of the among-species variation in tolerance (log10 LC50). In cross-validation, 84-87% of the predicted tolerances for individual species were within a factor of 10 of the observed values. The approach can also be used to model other species response to multivariate stress factors.

Beta diversity as the variance of community data: dissimilarity coefficients and partitioning.

Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a dissimilarity matrix obtained using any dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova. Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within- and among-group components by manova, into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.

Potential changes in forest composition could reduce impacts of climate change on boreal wildfires.

There is general consensus that wildfires in boreal forests will increase throughout this century in response to more severe and frequent drought conditions induced by climate change. However, prediction models generally assume that the vegetation component will remain static over the next few decades. As deciduous species are less flammable than conifer species, it is reasonable to believe that a potential expansion of deciduous species in boreal forests, either occurring naturally or through landscape management, could offset some of the impacts of climate change on the occurrence of boreal wildfires. The objective of this study was to determine the potential of this offsetting effect through a simulation experiment conducted in eastern boreal North America. Predictions of future fire activity were made using multivariate adaptive regression splines (MARS) with fire behavior indices and ecological niche models as predictor variables so as to take into account the effects of changing climate and tree distribution on fire activity. A regional climate model (RCM) was used for predictions of future fire risk conditions. The experiment was conducted under two tree dispersal scenarios: the status quo scenario, in which the distribution of forest types does not differ from the present one, and the unlimited dispersal scenario, which allows forest types to expand their range to fully occupy their climatic niche. Our results show that future warming will create climate conditions that are more prone to fire occurrence. However, unlimited dispersal of southern restricted deciduous species could reduce the impact of climate change on future fire occurrence. Hence, the use of deciduous species could be a good option for an efficient strategic fire mitigation strategy aimed at reducing fire Propagation in coniferous landscapes and increasing public safety in remote populated areas of eastern boreal Canada under climate change.

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation.

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran's eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps.

Disentangling invasion processes in a dynamic shipping-boating network.

The relative importance of multiple vectors to the initial establishment, spread and population dynamics of invasive species remains poorly understood. This study used molecular methods to clarify the roles of commercial shipping and recreational boating in the invasion by the cosmopolitan tunicate, Botryllus schlosseri. We evaluated (i) single vs. multiple introduction scenarios, (ii) the relative importance of shipping and boating to primary introductions, (iii) the interaction between these vectors for spread (i.e. the presence of a shipping-boating network) and (iv) the role of boating in determining population similarity. Tunicates were sampled from 26 populations along the Nova Scotia, Canada, coast that were exposed to either shipping (i.e. ports) or boating (i.e. marinas) activities. A total of 874 individuals (c. 30 per population) from five ports and 21 marinas was collected and analysed using both mitochondrial cytochrome c oxidase subunit I gene (COI) and 10 nuclear microsatellite markers. The geographical location of multiple hotspot populations indicates that multiple invasions have occurred in Nova Scotia. A loss of genetic diversity from port to marina populations suggests a stronger influence of ships than recreational boats on primary coastal introductions. Population genetic similarity analysis reveals a dependence of marina populations on those that had been previously established in ports. Empirical data on marina connectivity because of boating better explains patterns in population similarities than does natural spread. We conclude that frequent primary introductions arise by ships and that secondary spread occurs gradually thereafter around individual ports, facilitated by recreational boating.

Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study.

The Mantel correlogram is an elegant way to compute a correlogram for multivariate data. However, recent papers raised concerns about the power of the Mantel test itself. Hence the question: Is the Mantel correlogram powerful enough to be useful? To explore this issue, we compared the performances of the Mantel correlogram to those of other methods, using numerical simulations based on random, normally distributed data. For a single response variable, we compared it to the Moran and Geary correlograms. Type I error rates of the three methods were correct. Power of the Mantel correlogram was nearly as high as that of the univariate methods. For the multivariate case, the test of the multivariate variogram developed in the context of multiscale ordination is in fact a Mantel test, so that the power of the two methods is the same by definition. We devised an alternative permutation test based on the variance, which yielded similar results. Overall, the power of the Mantel test was high, the method successfully detecting spatial correlation at rates similar to the permutation test of the variance statistic in multivariate variograms. We conclude that the Mantel correlogram deserves its place in the ecologist's toolbox.

Variation partitioning involving orthogonal spatial eigenfunction submodels.

When partitioning the variation of univariate or multivariate ecological data with respect to several submodels of spatial eigenfunctions (e.g., Moran's eigenvector maps, MEM) acting as explanatory data, a problem occurs: although the submodels are constructed to be orthogonal to one another, the partitioning based on adjusted R2 statistics produces nonzero values in the intersections between spatial submodels. This phenomenon is described and two solutions are proposed. The first solution is to apportion the intersection fractions proportionally to the variation explained by each submodel. The second solution consists in creating a hierarchy among the spatial submodels, in accordance with hierarchy theory. These solutions lead to new partitioning equations that are described in the Appendix. R functions are provided to carry out partitioning with respect to environmental variables and spatial eigenfunction submodels. This development is important for the correct interpretation of spatial modeling results implying explanatory environmental data as well as submodels of spatial eigenfunctions involving two or more spatial scales.

Patient knowledge of gut microbiota and acceptability of fecal microbiota transplantation in various diseases.

BACKGROUND: Fecal microbiota transplantation (FMT) is now evaluated in various diseases. However, large-scale population treatment may encounter feasibility issues in terms of acceptance. We aim to evaluate patient knowledge of gut microbiota and the acceptability of FMT in various diseases. METHODS: Patients of Carenity's French online community were invited by email to participate in a questionnaire. The following parameters were assessed: patient's principal illness and duration, demographic data, therapeutics, dietary habits, knowledge of gut microbiota, probiotics and FMT, and its acceptability. KEY RESULTS: In total, 877 patients participated in the online questionnaire: 156 with inflammatory bowel disease (17.8%), 127 with rheumatoid arthritis (14.5%), 222 with ankylosing spondylitis (25.3%), 52 with lupus (5.9%), 64 with psoriasis (7.3%), 61 with obesity (7%), and 195 with type 2 diabetes (22.2%). Characteristics of participating patients were similar to those of the entire cohort (n = 23084). Overall, 47.1% (n = 413/877) of patients knew what the microbiota is with no difference among diseases. Knowledge was reported to be developed by patients themselves (203/413; 49.2%) without involving a healthcare professional. If proposed by a healthcare professional, 37.2% (326/877) reported being interested or very interested in undergoing FMT. Factors associated with good acceptability of FMT were the male sex (OR: 1.63, CI95% [1.14 to 2.32]), previous knowledge of FMT (OR: 4.16, CI95% [2.92 to 5.96]), and previous knowledge of gut microbiota (OR: 1.54, CI95% [1.05 to 2.24]). CONCLUSION AND INFERENCES: Knowledge of gut microbiota is still limited in patients' communities and mainly developed by patients themselves, impacting FMT acceptability.

The performance of the Congruence Among Distance Matrices (CADM) test in phylogenetic analysis.

BACKGROUND: CADM is a statistical test used to estimate the level of Congruence Among Distance Matrices. It has been shown in previous studies to have a correct rate of type I error and good power when applied to dissimilarity matrices and to ultrametric distance matrices. Contrary to most other tests of incongruence used in phylogenetic analysis, the null hypothesis of the CADM test assumes complete incongruence of the phylogenetic trees instead of congruence. In this study, we performed computer simulations to assess the type I error rate and power of the test. It was applied to additive distance matrices representing phylogenies and to genetic distance matrices obtained from nucleotide sequences of different lengths that were simulated on randomly generated trees of varying sizes, and under different evolutionary conditions. RESULTS: Our results showed that the test has an accurate type I error rate and good power. As expected, power increased with the number of objects (i.e., taxa), the number of partially or completely congruent matrices and the level of congruence among distance matrices. CONCLUSIONS: Based on our results, we suggest that CADM is an excellent candidate to test for congruence and, when present, to estimate its level in phylogenomic studies where numerous genes are analysed simultaneously.

Relationships between species feeding traits and environmental conditions in fish communities: a three-matrix approach.

Understanding the relationships between species biological traits and the environment is crucial to predicting the effect of habitat perturbations on fish communities. It is also an essential step in the assessment of the functional diversity. Using two complementary three-matrix approaches (fourth-corner and RLQ analyses), we tested the hypothesis that feeding-oriented traits determine the spatial distributions of littoral fish species by assessing the relationship between fish spatial distributions, fish species traits, and habitat characteristics in two Laurentian Shield lakes. Significant associations between the feeding-oriented traits and the environmental characteristics suggested that fish communities in small lakes (displaying low species richness) can be spatially structured. Three groups of traits, mainly categorized by the species spatial and temporal feeding activity, were identified. The water column may be divided in two sections, each of them corresponding to a group of traits related to the vertical distribution of the prey coupled with the position of the mouth. Lake areas of low structural complexity were inhabited by functional assemblages dominated by surface feeders while structurally more complex areas were occupied by mid-water and benthic feeders. A third group referring to the time of feeding activity was observed. Our work could serve as a guideline study to evaluate species traits x environment associations at multiple spatial scales. Our results indicate that three-matrix statistical approaches are powerful tools that can be used to study such relationships. These recent statistical approaches open up new research directions such as the study of spatially based biological functions in lakes. They also provide new analytical tools for determining, for example, the potential size of freshwater protected areas.

Modelling the effect of directional spatial ecological processes at different scales.

During the last 20 years, ecologists discovered the importance of including spatial relationships in models of species distributions. Among the latest developments in modelling how species are spatially structured are eigenfunction-based spatial filtering methods such as Moran's eigenvector maps (MEM) and principal coordinates of neighbour matrices (PCNM). Although these methods are very powerful and flexible, they are only suited to study distributions resulting from non-directional spatial processes. The asymmetric eigenvector map (AEM) framework, a new eigenfunction-based spatial filtering method, fills this theoretical gap. AEM was specifically designed to model spatial structures hypothesized to be produced by directional spatial processes. Water currents, prevailing wind on mountainsides, river networks, and glaciations at historical time scales are some of the situations where AEM can be used. This paper presents three applications of the method illustrating different combinations of: sampling schemes (regular and irregular), data types (univariate and multivariate), and spatial scales (metres, kilometres, and hundreds of kilometres). The applications include the distribution of a crustacean (Atya) in a river, bacterial production in a lake, and the distribution of the copepodite stages of a crustacean on the Atlantic oceanic shelf. In each application, a comparison is made between AEM, MEM, and PCNM. No environmental components were included in the comparisons. AEM was a strong predictor in all cases, explaining 59.8% for Atya distribution, 51.4% of the bacterial production variation, and 38.4% for the copepodite distributions. AEM outperformed MEM and PCNM in these applications, offering a powerful and more appropriate tool for spatial modelling of species distributions under directional forcing and leading to a better understanding of the processes at work in these systems.

Diversity and composition of ectomycorrhizal community on seedling roots: the role of host preference and soil origin.

As the main source of inocula, ectomycorrhizal (ECM) fungal propagules are critical for root colonization and seedling survival in deforested areas. It is essential to know factors that may affect the diversity and composition of ECM fungal community on roots of seedlings planted in deforest areas during reforestation. We quantitatively evaluated the effect of host plant and soil origin on ECM fungal propagule community structure established on roots of Castanopsis fargesii, Lithocarpus harlandii, Pinus armandii, and Pinus massoniana growing in soils from local natural forests and from sites deforested by clear-cut logging in the 1950s and 1960s. ECM root tips were sampled in April, July, and October of 2006, and ECM fungal communities were determined using ECM root morphotyping, internal transcribed spacer (ITS)-RFLP, and ITS sequencing. A total of 36 ECM fungal species were observed in our study, and species richness varied with host species and soil origin. Decreased colonization rates were found in all host species except for L. harlandii, and reduced species richness was found in all host species except for P. armandii in soil from the deforested site, which implied the great changes in ECM fungal community composition. Our results showed that 33.3% variance in ECM fungal community composition could be explained by host plant species and 4.6% by soil origin. Results of indicator species analysis demonstrated that 14 out of 19 common ECM fungal species showed significant preference to host plant species, suggesting that the host preference of ECM fungi was one of the most important mechanisms in structuring ECM fungal community. Accordingly, the host plant species should be taken into account in the reforestation of deforested areas due to the strong and commonly existed host preference of ECM fungi.