Description Usage Details References Examples
Biodiversity survey data sets from the far north Great Barrier Reef including site physical and species data and fine-scale physical data interpolated onto a grid on latitude-longitude.
1 |
A data frame with 197 observations on the following 28 variables.
BATHY
Depth from bathymetry
SLOPE
Slope derived from bathymetry
ASPECT
Aspect of slope derived from bathymetry
BSTRESS
Seabed current stress
CRBNT
Sediment % carbonate composition
GRAVEL
Sediment % gravel grainsize fraction
SAND
Sediment % sand grainsize fraction
MUD
Sediment % mud grainsize fraction
NO3_AV
Nitrate bottom water annual average
NO3_SR
Nitrate seasonal range
PO4_AV
Phosphate bottom water annual average
PO4_SR
Phosphate seasonal range
O2_AV
Oxygen bottom water annual average
O2_SR
Oxygen seasonal range
S_AV
Salinity bottom water annual average
S_SR
Salinity seasonal range
T_AV
Temperature bottom water annual average
T_SR
Temperature seasonal range
Si_AV
Silicate bottom water annual average
Si_SR
Silicate seasonal range
CHLA_AV
Chlorophyll annual average
CHLA _SR
Chlorophyll seasonal range
K490_AV
Attenuation coefficient at 490nm annual average
K490_SR
Attenuation coefficient seasonal range
SST_AV
Sea surface temperature annual average
SST_SR
Sea surface temperature seasonal range
BIR_AV
Relative benthic irradiance, annual average
BIR_SR
Benthic irradiance seasonal range
A data frame with 8682 observations with the following 2 variables as well as
the same 28 variables as in Phys_site
.
NORTH
northing in scaled units
EAST
easting in scaled units
A matrix of 197 rows corresponding to the sites in Phys_site
and 110
columns corresponding to species. The values are log(y+c)
-transformed
species abundance (where c
is the minimum positive abundance y
).
A gradientForest
object built from the site data.
Ellis, N., Smith, S.J., and Pitcher, C.R. (2011) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.
1 2 3 4 5 6 7 8 | data(GBRexample)
# transform the predictors using predict() on a fine-scale grid
gf.GBR
predictors <- names(importance(gf.GBR))
gf.pred <- predict(gf.GBR, Phys_grid[,predictors])
plot(gf.pred, Phys_grid[,c("EAST")], Phys_grid[,c("NORTH")], asp=1, palette="gr", pch=15,
main="Biological composition in Far North Great Barrier Reef")
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