Fungi Associated with Common Buckthorn (Rhamnus cathartica) in Southern Ontario

Common buckthorn (Rhamnus cathartica) is a competitive Eurasian woody shrub currently invading North America. Buckthorn thickets reduce native diversity and may reduce mycorrhizal diversity through the release of allelochemicals. Two aspects of buckthorn’s invasional biology are explored: 1) identifying fungi associating with buckthorn, and 2) determining buckthorn’s allelochemical impacts on arbuscular mycorrhizae in forest soils and an open-greenhouse experiment. Twenty-three fungi were found growing on buckthorn, including Armillaria mellea s.l., Hypoxylon fuscum, H. perforatum, Nectria cinnabarina, and Cylindrobasidium evolvens. Data from invaded and uninvaded sugar maple (Acer saccharum) soils revealed that arbuscular mycorrhizal fungi (AMF) diversity fluctuated as a function of season or potting disturbance, but the presence of buckthorn had little effect on AMF development in maple roots. Buckthorn may be a mycorrhizal generalist, and changes in AMF abundance may be more influenced by underlying stochastic soil processes and aboveground plant composition than by buckthorn and its allelochemicals

Corn microbial diversity and its relationship to yield

This study aimed to identify possible relationships between corn (Zea mays L.) productivity and its endosphere microbial community. Any insights would be used to develop testable hypotheses at the farm level. Sap was collected from 14 fields in 2014 and 10 fields in 2017, with a yield range of 10.1 to 21.7 tonnes per hectare (t/ha). The microbial sap communities were analyzed using terminal restriction fragment length polymorphism (TRFLP) and identified using an internal pure culture reference database and BLAST. This technique is rapid and inexpensive and is suitable for use at the grower level. Diversity, richness, and normalized abundances of each bacterial population in corn sap samples were evaluated to link the microbiome of a specific field to its yield. A negative trend was observed (r = –0.60), with higher-yielding fields having lower terminal restriction fragment (TRF) richness. A partial least square regression analysis of TRF intensity and binary data from 2014 identified 10 TRFs (bacterial genera) that positively, or negatively, correlated with corn yields, when either absent or present at certain levels or ratios. Using these observations, a model was developed that accommodated criteria for each of the 10 microbes and assigned a score for each field out of 10. Data collected in 2014 showed that sites with higher model scores were highly correlated with larger yields (r = 0.83). This correlation was also seen when the 2017 data set was used (r = 0.87). We were able to conclude that a positive significant effect was seen with the model score and yield (adjusted R2 = 0.67, F[1,22] = 46.7, p < 0.001) when combining 2014 and 2017 data. The results of this study are being expanded to identify the key microbes in the corn sap community that potentially impact corn yield, regardless of corn variety, geographic factors, or edaphic factors.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Ribosomal RNA primer map and alignment of LSU200-F/LSU481-R and LSU200A-F/LSU476A-R primers developed in this study.

<p><b>A</b>, Approximate location of LSU200-F/LSU481-R and LSU200A-F/LSU476A-R primers in relation to the D1/D2 variable domains within the LSU of <i>Saccharomyces cerevisiae</i> J01355 in relation to ITS1, ITS2, ITS4 from White et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref035" target="_blank">35</a>], NS31 from Simon et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref036" target="_blank">36</a>], AM1 from Helgason et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref037" target="_blank">37</a>], AMV4.5N-F and AMDG-R from Sato et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref038" target="_blank">38</a>], AML1 and AML2 from Lee et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref039" target="_blank">39</a>], WANDA from Dumbrell et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref040" target="_blank">40</a>], ITS3_KYO2 from Toju et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref041" target="_blank">41</a>], and ITS7o from Kohout et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref042" target="_blank">42</a>]. <b>B</b>, LSU200-F/LSU481-R and LSU200A-F/LSU476A-R alignments made using a custom reference sequence set, aligned using Muscle v 3.8.31 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref031" target="_blank">31</a>] and visualized using CLC Sequence Viewer (<a href="http://www.clcbio.com/" target="_blank">http://www.clcbio.com/</a>), with <i>S</i>. <i>cerevisiae</i> J01355 included as positional reference.</p

New Primers for Discovering Fungal Diversity Using Nuclear Large Ribosomal DNA

<div><p>Metabarcoding has become an important tool in the discovery of biodiversity, including fungi, which are the second most speciose group of eukaryotes, with diverse and important ecological roles in terrestrial ecosystems. We have designed and tested new PCR primers that target the D1 variable region of nuclear large subunit (LSU) ribosomal DNA; one set that targets the phylum Ascomycota and another that recovers all other fungal phyla. The primers yield amplicons compatible with the Illumina MiSeq platform, which is cost-effective and has a lower error rate than other high throughput sequencing platforms. The new primer set LSU200A-F/LSU476A-R (Ascomycota) yielded 95–98% of reads of target taxa from environmental samples, and primers LSU200-F/LSU481-R (all other fungi) yielded 72–80% of target reads. Both primer sets have fairly low rates of data loss, and together they cover a wide variety of fungal taxa. We compared our results with these primers by amplifying and sequencing a subset of samples using the previously described ITS3_KYO2/ITS4_KYO3 primers, which amplify the internal transcribed spacer 2 (ITS2) of Ascomycota and Basidiomycota. With approximately equivalent read depth, our LSU primers recovered a greater number and phylogenetic diversity of sequences than the ITS2 primers. For instance, ITS3_KYO2/ITS4_KYO3 primers failed to pick up any members of Eurotiales, Mytilinidiales, Pezizales, Saccharomycetales, or Venturiales within Ascomycota, or members of Exobasidiomycetes, Microbotryomycetes, Pucciniomycetes, or Tremellomycetes within Basidiomycota, which were retrieved in good numbers from the same samples by our LSU primers. Among the OTUs recovered using the LSU primers were 127 genera and 28 species that were not obtained using the ITS2 primers, although the ITS2 primers recovered 10 unique genera and 16 species that were not obtained using either of the LSU primers These features identify the new primer sets developed in this study as useful complements to other universal primers for the study of fungal diversity and community composition.</p></div

Summary data for all sample sets using LSU200-F/LSU481-R, and LSU200A-F/LSU476A-R (Ascomycota) primers developed in this study, and ITS3_KYO2/ITS4_KYO3 primers developed by Toju et al. [41].

<p>Summary data for all sample sets using LSU200-F/LSU481-R, and LSU200A-F/LSU476A-R (Ascomycota) primers developed in this study, and ITS3_KYO2/ITS4_KYO3 primers developed by Toju et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159043#pone.0159043.ref041" target="_blank">41</a>].</p

Fungal nuclear large ribosomal primer targets (nu-LSU), with positions numbered relative to <i>Saccharomyces cerevisiae</i> (GenBank Accession #: J01355).

<p>Fungal nuclear large ribosomal primer targets (nu-LSU), with positions numbered relative to <i>Saccharomyces cerevisiae</i> (GenBank Accession #: J01355).</p

List of genera and species recovered by LSU200-F/LSU481-R, LSU200A-F/LSU476A-R and ITS3_KYO2/ITS4_KYO3 primers.

<p>Taxa were included only if the total read count across the three subsamples were >3, and are listed alphabetically by higher taxa (A = Ascomycota, B = Basidiomycota, C = Chytridiomycota, E = Entomophthoromycota, G = Glomeromycota, K = Kickxellomycotina, M = Mucoromycotina, Mi = Microsporidia, Z = Zoopagomycotina). (#) = Number of OTUs identified to that taxon.</p

Biodiversity inventories in high gear: DNA barcoding facilitates a rapid biotic survey of a temperate nature reserve

International audienc

Biodiversity inventories in high gear:DNA barcoding facilitates a rapid biotic survey of a temperate nature reserve

Abstract Background: Comprehensive biotic surveys, or ‘all taxon biodiversity inventories’ (ATBI), have traditionally been limited in scale or scope due to the complications surrounding specimen sorting and species identification. To circumvent these issues, several ATBI projects have successfully integrated DNA barcoding into their identification procedures and witnessed acceleration in their surveys and subsequent increase in project scope and scale. The Biodiversity Institute of Ontario partnered with the rare Charitable Research Reserve and delegates of the 6th International Barcode of Life Conference to complete its own rapid, barcode-assisted ATBI of an established land trust in Cambridge, Ontario, Canada. New information: The existing species inventory for the rare Charitable Research Reserve was rapidly expanded by integrating a DNA barcoding workflow with two surveying strategies — a comprehensive sampling scheme over four months, followed by a one-day bioblitz involving international taxonomic experts. The two surveys resulted in 25,287 and 3,502 specimens barcoded, respectively, as well as 127 human observations. This barcoded material, all vouchered at the Biodiversity Institute of Ontario collection, covers 14 phyla, 29 classes, 117 orders, and 531 families of animals, plants, fungi, and lichens. Overall, the ATBI documented 1,102 new species records for the nature reserve, expanding the existing long-term inventory by 49%. In addition, 2,793 distinct Barcode Index Numbers (BINs) were assigned to genus or higher level taxonomy, and represent additional species that will be added once their taxonomy is resolved. For the 3,502 specimens, the collection, sequence analysis, taxonomic assignment, data release and manuscript submission by 100+ co-authors all occurred in less than one week. This demonstrates the speed at which barcode-assisted inventories can be completed and the utility that barcoding provides in minimizing and guiding valuable taxonomic specialist time. The final product is more than a comprehensive biotic inventory — it is also a rich dataset of fine-scale occurrence and sequence data, all archived and cross-linked in the major biodiversity data repositories. This model of rapid generation and dissemination of essential biodiversity data could be followed to conduct regional assessments of biodiversity status and change, and potentially be employed for evaluating progress towards the Aichi Targets of the Strategic Plan for Biodiversity 2011–2020