fungi {gllvm} | R Documentation |
Wood-decaying fungi data
Description
Dataset of 1666 binary observations for 215 fungal species, in different 53 European Beech forests spread across 8 regions.
Usage
data(fungi)
Format
- Y
A data frame with the presence-absences of 215 fungal species measured at 1666 logs.
- X
A data frame of 8 predictor variables.
- DBH.CM
Diameter at breast height (cm)
- AVERDP
Decay stage of logs on a 1-5 scale
- CONNECT10
Connectivity of the surrounding forest at 10km scale
- TEMPR
Annual temperature range in degrees Celsius
- PRECIP
Annual precipitation in milimeters
- log.Area
ln(area in hectares) of reserves
- REGION
Site groups identified based on spatial clusters
- RESERVE
Site name
- TR
A data frame of the traits used in Abrego et al. (2022).
- tree
The phylogenetic tree.
- C
The phylogenetic covariance matrix.
- dist
The phylogenetic distance matrix.
Details
Observations of fungi species inhabiting European beech logs, in different European countries. The countries have been grouped in eight different regions. Logs were surveyed in 53 different reserves (or sites). Included environment and trait covariates are limited to those analyzed in the original article, though more are available in the published dataset on datadryad.org.
References
Abrego, N., Bässler, C., Christensen, M., and Heilmann‐Clausen, J. (2022). Traits and phylogenies modulate the environmental responses of wood‐inhabiting fungal communities across spatial scales. Journal of Ecology, 110(4), 784-798.
Abrego, N., Bässler, C., Christensen, M., and Heilmann-Clausen, J. (2022). Data and code from: Traits and phylogenies modulate the environmental responses of wood-inhabiting fungal communities across spatial scales [Dataset]. Dryad. https://doi.org/10.5061/dryad.t76hdr82r
Examples
## Not run:
data(fungi)
Y <- fungi$Y
X <- fungi$X
TR <- fungi$TR
C <- fungi$C
dist <- fungi$dist
#model <- gllvm(y = Y, X = cbind(int = 1, X), TR = TR,
# formula = ~DBH.CM + AVERDP + I(AVERDP^2) + CONNECT10 + TEMPR + PRECIP +
# log.AREA + (DBH.CM + AVERDP + I(AVERDP^2) + CONNECT10 + TEMPR + PRECIP +
# log.AREA):(FB.type + Sp.log.vol.µ3 + Lifestyle),
# family = "binomial", num.lv = 0, studyDesign = X[,c("REGION", "RESERVE")],
# colMat = list(C, dist = dist), colMat.rho.struct = "term",
# row.eff = ~(1 | REGION/RESERVE), sd.errors = FALSE,
# randomX = ~int + DBH.CM + AVERDP + I(AVERDP^2) +
# CONNECT10 + TEMPR + PRECIP + log.AREA,
# beta0com = TRUE, nn.colMat = 10, maxit = 20000)
## End(Not run)