Data · dataset · 2021
Data from: Over the hills, but how far away? estimates of mushroom geographic range extents
Listed in Borealis and Agri-environmental Research Data Dataverse — shown once because both records carry DOI 10.5683/sp2/masafu
Description
Abstract
Aim: Geographic distributions of mushroom species remain poorly understood despite their importance for advancing our understanding of the habitat requirements, species interactions and ecosystem functions of this key group of organisms. Here, we estimate geographic range extents (maximum within-species geographic distance) of genetically defined operational taxonomic units (OTUs). Location: World-wide, with emphasis on the American Pacific Northwest Taxa: Amanita, Agaricus, Cortinarius, Galerina, Hebeloma, Hydnum, Hygrocybe, Hygrophorus, Inocybe, Lepiota, Pholiota, and Russula+Lactarius; other genera in Agaricomycotina
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Method
We used publicly available OTUs from ribosomal internal transcribed spacer (ITS) sequences (n=15,373) from 12 mushroom genera with worldwide distributions. For each of 2,324 ~species-level OTUs, we estimated the maximum within-species range extent based on sample locality records. In parallel, we estimated range extents for species in four tree genera.
Contrasting estimates from well-studied trees allowed us to test for potential biases in our range estimates of less well inventoried mushrooms. Results: The median range extents across the 2,324 mushroom OTUs varied from ~1,200 km to 4,039 km, depending on assumptions. These extents were significantly lower than estimates from permuted or randomized data.
Mushroom ranges were comparable to the median natural range extent of tree species (1,613 km). In contrast, the tree median species range increased to 16,581 km when anthropogenic range extensions were included. At least ten mushroom species were similarly broadly distributed, eight of which have been associated with human activity.
Main conclusions: Overall, like tree species, mycorrhizal and saprotrophic fungi show evidence of biogeographic structure rather than global distributions. This reconstruction of geographic range extents drew upon investments into ITS barcoding of extensive herbarium collections. Large scale analyses such as ours can yield estimates of fungal geographic range extents that are a prerequisite to a deeper understanding of the diverse roles of fungi in ecosystems.
Usage notes raw and output files from geographic range extent study Input and intermediate files. Scripts can also be found here: github.com/abazzical/mushroomRanges mushroomRangesBazzicalupo.zip
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP2%2FMASAFU ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp2/masafu ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP2%2… ↗
metadata API · from borealisdata ca
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Mathematics & Statistics · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Evolutionary biology 73%
Provenance · 2 source records, 20 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP2/MASAFU | 8 d ago | JSON v1 |
| Agri-environmental Research Data Dataverse | doi:10.5683/SP2/MASAFU | 7 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3104 | enrichment · borealisdata ca | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].dataverse_subject:other | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:other | source · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:chemistry | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:economics-finance | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:humanities | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:mathematics-statistics | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:ocean-atmospheric | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |