Data from: The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds
Data files
Mar 24, 2023 version files 641.15 KB
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Odriozola_etal_2023_data.zip
637.89 KB
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README.md
3.26 KB
Oct 06, 2023 version files 1.33 MB
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Odriozola_etal_2023_data.zip
1.33 MB
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README.md
3.50 KB
Abstract
Aim
How community composition varies in space and what governs the variation has been extensively investigated in macroorganisms. However, we have only limited knowledge for microorganisms, especially fungi, despite their ecological and economic significance. Based on previous research, we define and test a series of hypotheses regarding the composition of fungal communities, its most influential drivers and their spatial scale dependence.
Location
Czech Republic.
Time period
Present.
Taxa studied
Fungi.
Methods
We analyzed the distance decay relationships, community composition and its drivers (physical distance, litter and soil chemistry, tree composition, climate) in fungi, using multivariate analyses. We compared the results across three fungal ecological guilds (ectomycorrhizal fungi, saprotrophs and yeasts), two forest microhabitats (litter and bulk soil) and six spatial scales (from 5 m to 80 km) that comprehensively cover the Czech Republic.
Results
We found that, similar to macroorganisms, the ectomycorrhizal fungi and saprotrophs showed marked distance-decay relationships, and their community composition was driven mainly by vegetation and dispersal at local scales, but at regional scales, by environmental effects. In contrast, the third fungal guild, the unicellular yeasts, showed little distance decay, suggesting extraordinary spatial homogeneity, as often seen in microorganisms, such as bacteria.
Main conclusions
Our results underscore the remarkable variation in the community ecology of fungi, which seems to range well-known patterns both from the macro- and the microworld. Knowledge of these patterns advances our understanding of the ecology of fungi, rather understudied organisms of significant ecological and economic importance, which our findings identify as a potentially suitable model for bridging the gaps between the biogeography of micro- and macroorganisms.
README: Data from: The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds
Description of the Data and file structure
The zipped file in Dryad contains the data necessary to reproduce the statistical analyses published in the manuscript "The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds" in Global Ecology and Biogeography by Odriozola et al.
To run the R code in Zenodo, the zip containing the dataset should be unzipped to a folder and set that folder as working directory in the beginning of the script.
The four datasets in the folder "Odriozola_etal_2023_data":
OTU_table.csv: Sequence counts of Operational Taxonomic Units of fungi collected in six datasets representing different spatial scales across the Czech Republic.
Environment.csv: Environmental variables measured in six datasets representing different spatial scales across the Czech Republic.
- Sample Unique identifier of each sampling unit.
- Horizon Classification of samples into litter and soil.
- lat Latitude of the sample location.
- long Longitude of the sample location.
- Ntot Total N content of litter and soil samples (%).
- Cox Organic C content of litter and soil samples (%).
- C/N C to N ratio of litter and soil samples.
- pH pH measured at litter and soil samples.
- Herb layer % cover of the herb layer.
- Stand age Years since the last disturbance of the stand.
- BIO01 Mean annual daily mean air temperatures averaged over 1 year (C).
- BIO02 Mean diurnal range of temperatures averaged over 1 year (C).
- BIO03 Ratio of diurnal variation to annual variation in temperatures (C).
- BIO04 Standard deviation of the monthly mean temperatures (C/100).
- BIO05 The highest temperature of any monthly daily mean maximum temperature (C).
- BIO06 The lowest temperature of any monthly daily mean maximum temperature (C).
- BIO07 The difference between the Maximum Temperature of Warmest month and the Minimum Temperature of Coldest month (C).
- BIO08 The wettest quarter of the year is determined (to the nearest month) (C).
- BIO09 The driest quarter of the year is determined (to the nearest month) (C).
- BIO10 The warmest quarter of the year is determined (to the nearest month) (C).
- BIO11 The coldest quarter of the year is determined (to the nearest month) (C).
- BIO12 Accumulated precipitation amount over 1 year (kg m-2).
- BIO13 The precipitation of the wettest month (kg m-2).
- BIO14 The precipitation of the driest month (kg m-2).
- BIO15 The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean) (kg m-2).
- BIO16 The wettest quarter of the year is determined (to the nearest month) (kg m-2).
- BIO17 The driest quarter of the year is determined (to the nearest month) (kg m-2).
- BIO18 The warmest quarter of the year is determined (to the nearest month) (kg m-2).
- BIO19 The coldest quarter of the year is determined (to the nearest month) (kg m-2).
Vegetation.csv: Vegetation survey of the sampled locations with % cover of each species.
Sample Unique identifier of each sampling unit.
Taxonomy_Ecology.csv: Taxonomic and ecological guild assignments of Operational Taxonomic Units of fungi.