GBRexample: Great Barrier Reef example data
In gradientForest: Random Forest functions for the Census of Marine Life synthesis project.
GBRexample R Documentation
Great Barrier Reef example data
Description
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.
Usage
data(GBRexample)
Details
- Phys_site
-
A data frame with 197 observations on the following 28 variables.
BATHYDepth from bathymetry
SLOPESlope derived from bathymetry
ASPECTAspect of slope derived from bathymetry
BSTRESSSeabed current stress
CRBNTSediment % carbonate composition
GRAVELSediment % gravel grainsize fraction
SANDSediment % sand grainsize fraction
MUDSediment % mud grainsize fraction
NO3_AVNitrate bottom water annual average
NO3_SRNitrate seasonal range
PO4_AVPhosphate bottom water annual average
PO4_SRPhosphate seasonal range
O2_AVOxygen bottom water annual average
O2_SROxygen seasonal range
S_AVSalinity bottom water annual average
S_SRSalinity seasonal range
T_AVTemperature bottom water annual average
T_SRTemperature seasonal range
Si_AVSilicate bottom water annual average
Si_SRSilicate seasonal range
CHLA_AVChlorophyll annual average
CHLA _SRChlorophyll seasonal range
K490_AVAttenuation coefficient at 490nm annual average
K490_SRAttenuation coefficient seasonal range
SST_AVSea surface temperature annual average
SST_SRSea surface temperature seasonal range
BIR_AVRelative benthic irradiance, annual average
BIR_SRBenthic irradiance seasonal range
- Phys_grid
-
A data frame with 8682 observations with the following 2 variables as well as
the same 28 variables as in Phys_site.
NORTHnorthing in scaled units
EASTeasting in scaled units
- Sp_mat
-
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).
- gf.GBR
-
A gradientForest object built from the site data.
References
Ellis, N., Smith, S.J., and Pitcher, C.R. (2011) Gradient Forests: calculating importance
gradients on physical predictors. Ecology, 93, 156–168.
Examples
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")
gradientForest documentation built on Aug. 24, 2023, 3:03 p.m.
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| GBRexample | R Documentation |
Great Barrier Reef example data
Description
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.
Usage
data(GBRexample)
Details
- Phys_site
-
A data frame with 197 observations on the following 28 variables.
BATHYDepth from bathymetry
SLOPESlope derived from bathymetry
ASPECTAspect of slope derived from bathymetry
BSTRESSSeabed current stress
CRBNTSediment % carbonate composition
GRAVELSediment % gravel grainsize fraction
SANDSediment % sand grainsize fraction
MUDSediment % mud grainsize fraction
NO3_AVNitrate bottom water annual average
NO3_SRNitrate seasonal range
PO4_AVPhosphate bottom water annual average
PO4_SRPhosphate seasonal range
O2_AVOxygen bottom water annual average
O2_SROxygen seasonal range
S_AVSalinity bottom water annual average
S_SRSalinity seasonal range
T_AVTemperature bottom water annual average
T_SRTemperature seasonal range
Si_AVSilicate bottom water annual average
Si_SRSilicate seasonal range
CHLA_AVChlorophyll annual average
CHLA _SRChlorophyll seasonal range
K490_AVAttenuation coefficient at 490nm annual average
K490_SRAttenuation coefficient seasonal range
SST_AVSea surface temperature annual average
SST_SRSea surface temperature seasonal range
BIR_AVRelative benthic irradiance, annual average
BIR_SRBenthic irradiance seasonal range
- Phys_grid
-
A data frame with 8682 observations with the following 2 variables as well as the same 28 variables as in
Phys_site.NORTHnorthing in scaled units
EASTeasting in scaled units
- Sp_mat
-
A matrix of 197 rows corresponding to the sites in
Phys_siteand 110 columns corresponding to species. The values arelog(y+c)-transformed species abundance (wherecis the minimum positive abundancey). - gf.GBR
-
A
gradientForestobject built from the site data.
References
Ellis, N., Smith, S.J., and Pitcher, C.R. (2011) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.
Examples
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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