2018
DOI: 10.1111/mec.14817
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Differential biodiversity responses between kingdoms (plants, fungi, bacteria and metazoa) along an Alpine succession gradient

Abstract: Biological diversities of multiple kingdoms potentially respond in similar ways to environmental changes. However, studies either compare details of microbial diversity across general vegetation or land use classes or relate details of plant community diversity with the extent of microbially governed soil processes, via physiological profiling. Here, we test the hypothesis of shared responses of plant and rhizosphere bacterial, fungal and metazoan biodiversities (especially across-habitat β-diversity patterns)… Show more

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Cited by 34 publications
(22 citation statements)
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References 75 publications
(95 reference statements)
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“…Raw sequencing data were treated with MOTHUR v1.33 [ 27 ] following the pipeline previously used by Montagna et al [ 28 ]. After making contigs using the make.contigs command with default settings, bacterial 16S rRNA raw sequences were filtered based on the following specifications: Phred score > 30, minimum fragment length of 250 bp, absence of ambiguous nucleotides (which are sometimes generated during contig formation when the delta between quality scores of a mismatched base is lower than 6), maximum 10 bp-long homopolymers, and maximum primer mismatch of 1 bp.…”
Section: Methodsmentioning
confidence: 99%
“…Raw sequencing data were treated with MOTHUR v1.33 [ 27 ] following the pipeline previously used by Montagna et al [ 28 ]. After making contigs using the make.contigs command with default settings, bacterial 16S rRNA raw sequences were filtered based on the following specifications: Phred score > 30, minimum fragment length of 250 bp, absence of ambiguous nucleotides (which are sometimes generated during contig formation when the delta between quality scores of a mismatched base is lower than 6), maximum 10 bp-long homopolymers, and maximum primer mismatch of 1 bp.…”
Section: Methodsmentioning
confidence: 99%
“…In a Brazilian agricultural system, for example, geographical distance explained nearly 18 times more variance in soil fungal community composition than environmental factors such as soil and climate characteristics (Gumiere, Durrer, Bohannan, & Andreote, 2016). Biogeography of soil bacteria has been investigated at a range of scales from continental and global scales (e.g., Bahram et al, 2018;Barberán, Bates, Casamayor, & Fierer, 2012;Fierer & Jackson, 2006) to the landscape scale (Bru et al, 2011;Pasternak et al, 2013), and even at the cen- The scale at which similar processes act varies between taxonomic groups due to variation in body size and life history, and is an important consideration when studying spatial patterns of soil communities (Montagna et al, 2018). When studying landscape scale processes, Jackson and Fahrig (2012) highlight the concept of the 'scale of effect' (the scale at which an ecological response is best predicted by the habitat structure) and recommend sampling areas far enough apart to ensure sample points are independent (i.e., taxa in one sample point do not directly interact with those in others).…”
Section: Scale and Structurementioning
confidence: 99%
“…The scale at which similar processes act varies between taxonomic groups due to variation in body size and life history, and is an important consideration when studying spatial patterns of soil communities (Montagna et al., 2018). When studying landscape scale processes, Jackson and Fahrig (2012) highlight the concept of the ‘scale of effect’ (the scale at which an ecological response is best predicted by the habitat structure) and recommend sampling areas far enough apart to ensure sample points are independent (i.e., taxa in one sample point do not directly interact with those in others).…”
Section: Spatio‐temporal Considerationsmentioning
confidence: 99%
“…The DNA-based identification methods represented by DNA taxonomy, applicable also in DNA metabarcoding (Taberlet, Coissac, Pompanon, Brochmann, & Willerslev, 2012), stand out as widely used approaches for biodiversity surveys (e.g., Telfer et al, 2015;Pavan-Kumar, Gireesh-Babu, & Lakra, 2015;deWaard et al, 2019). These methods, using standardized gene regions, allow the speeding up of organism identification and help overcoming issues in morphological taxonomy, for example by making it possible to detect the presence of organisms merely from their DNA, released in the environment (e.g., Ficetola, Miaud, Pompanon, & Taberlet, 2008;Montagna et al, 2018;Ruppert, Kline, & Rahman, 2019).…”
Section: Introductionmentioning
confidence: 99%