Detection of plot‐level changes in ectomycorrhizal communities across years in an old‐growth mixed‐conifer forest

Izzo, Antonio D.; Agbowo, Josephine; Bruns, Thomas D.

doi:10.1111/j.1469-8137.2005.01354.x

Cited by 197 publications

(123 citation statements)

References 51 publications

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“…As in many other studies (e.g. Izzo et al, 2005;Kjøller, 2006;Nara, 2006;, many of the mycorrhizal fungi of the root tips remain unidentified to species level even after DNA sequencing. This may simply be a consequence of the incomplete coverage of fungal species in the international sequence databases; something that is especially true for alpine fungi ).…”

Section: Discussionmentioning

confidence: 75%

“…Since it is rarely possible to sample ectomycorrhizal communities exhaustively, it is hard to compare species richness between studies (Taylor, 2002), but the richness found here seems to be similar to that of many temperate forest ecosystems (Rosling et al, 2003;Izzo et al, 2005;Kjøller, 2006). The dominating fungal taxa were C. geophilum, Thelephoraceae, Cortinarius (mostly from subgenus Telamonia), and Sebacinales.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Ectomycorrhizal Diversity on Dryas octopetala and Salix reticulata in an Alpine Cliff Ecosystem

Ryberg

Larsson

Molau

2009

Arctic, Antarctic, and Alpine Research

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Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Ectomycorrhizal Diversity on Dryas octopetala and Salix reticulata in an Alpine Cliff Ecosystem

Ryberg

Larsson

Molau

2009

Arctic, Antarctic, and Alpine Research

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“…High-quality ITS sequences 4350 bp in length were aligned, manually edited and clustered into molecular operational taxonomic units (hereafter referred to as 'species') at X97% similarity (Izzo et al, 2005;Murata et al, 2013) using ATGC ver. 7 (Genetyx Corp., Tokyo, Japan).…”

Section: Molecular Analysesmentioning

confidence: 99%

The mid-domain effect in ectomycorrhizal fungi: range overlap along an elevation gradient on Mount Fuji, Japan

et al. 2014

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Mid-domain effect (MDE) models predict that the random placement of species' ranges within a bounded geographical area leads to increased range overlap and species richness in the center of the bounded area. These models are frequently applied to study species-richness patterns of macroorganisms, but the MDE in relation to microorganisms is poorly understood. In this study, we examined the characteristics of the MDE in richness patterns of ectomycorrhizal (EM) fungi, an ecologically important group of soil symbionts. We conducted intensive soil sampling to investigate overlap among species ranges and the applicability of the MDE to EM fungi in four temperate forest stands along an elevation gradient on Mount Fuji, Japan. Molecular analyses using direct sequencing revealed 302 EM fungal species. Of 73 EM fungal species found in multiple stands, 72 inhabited a continuous range along the elevation gradient. The maximum overlap in species range and the highest species richness occurred at elevations in the middle of the gradient. The observed richness pattern also fit within the 95% confidence interval of the mid-domain null model, supporting the role of the MDE in EM fungal richness. Deviation in observed richness from the mean of the middomain null estimation was negatively correlated with some environmental factors, including precipitation and soil C/N, indicating that unexplained richness patterns could be driven by these environmental factors. Our results clearly support the existence of microbial species' ranges along environmental gradients and the potential applicability of the MDE to better understand microbial diversity patterns.

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“…As this is logistically difficult it may be more feasible to use mycorrhizas because they can be temporally stable (Cox 2010, Izzo et al 2005, Koide et al 2007). Spatial bias is currently the most detrimental to the use of online databases for creating fungal SDMs and is illustrated by this analysis.…”

Section: Discussionmentioning

confidence: 99%

Mapping fungi from below ground: online genetic resources and ectomycorrhizal geographic distributions

Tse-Laurence¹,

Bidartondo²

2011

iForest

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© iForest -Biogeosciences and Forestry IntroductionEctomycorrhizal (ECM) fungi are obligate plant mutualists and they are among the most functionally important soil organisms in forest ecosystems (Smith & Read 2008). However, as the delimitation and identification of many ECM species is problematic and their life cycles largely subterranean, the geographic ranges for species are unknown. There is a need to establish current distributions in the face of changing environmental conditions, because without them even large changes in mycorrhizal distributions may go undetected.Some ECM fungal species have conspicuous fruiting bodies that can thus be used to generate species distribution maps, e.g., Amanita phalloides (Wolfe et al. 2010). This is often not possible as many ECM species are cryptic and difficult to observe in this fashion, e.g., truffles and resupinate crusts.For these fungi an approach using their mycorrhizas for identification is more practical. DNA sequences of the internal transcribed spacer (ITS) region of the nuclear ribosomal DNA provide a universal genetic marker for fungi. This study makes use of their growing availability in online DNA databases to obtain spatial presence data for ECM species thus far unmapped. Ryberg et al. (2008) studied the strength of GenBank for meta-analysis and identification of ECM fungi with a focus on illustrating the gaps in identification for the genus Inocybe, but they also analysed the location of fungal species from GenBank providing a rough idea of their distribution on a wholecountry basis. This was an early example demonstrating the potential for a DNA sequence method for mycorrhizal mapping. Two recent studies have applied spatial data on fungal presence to generate Species Distribution Models (SDM). Wollan et al. (2008) used herbarium mushroom records to create a fungal SDM for Norway, and Wolfe et al. (2010) gathered mushroom data from Europe to create a powerful predictive SDM for North American Amanita phalloides. The application of MAXENT software shows promising results for niche modelling based on presence-only data (Wollan et al. 2008) which are often the only data available for fungi. Before applying niche modelling software this study sought to test the quality of DNA data and the available environmental layers.Studies by Cox et al. (2010aCox et al. ( , 2010b inferred ECM responses to nitrogen deposition at large geographic scales that differ from those at local scales. Here too the argument was made for using DNA to identify ECM in large-scale spatial analysis, but the problems and methodological incongruences of combining multiple studies were also noted. To enable this new facet of mycorrhizal ecology, Lilleskov & Parrent (2007) called for a unified approach to fungal root sampling. We envision that georeferenced fungal DNA sequence data will continue to accumulate rapidly to eventually reveal fungal species distributions. This study explores what signal indicating the environmental preferences of ECM might be already hidden in the growing onlin...

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Detection of plot‐level changes in ectomycorrhizal communities across years in an old‐growth mixed‐conifer forest

Cited by 197 publications

References 51 publications

Ectomycorrhizal Diversity on Dryas octopetala and Salix reticulata in an Alpine Cliff Ecosystem

Ectomycorrhizal Diversity on Dryas octopetala and Salix reticulata in an Alpine Cliff Ecosystem

The mid-domain effect in ectomycorrhizal fungi: range overlap along an elevation gradient on Mount Fuji, Japan

Mapping fungi from below ground: online genetic resources and ectomycorrhizal geographic distributions

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