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.
A data frame with 197 observations on the following 28 variables.
Depth from bathymetry
Slope derived from bathymetry
Aspect of slope derived from bathymetry
Seabed current stress
Sediment % carbonate composition
Sediment % gravel grainsize fraction
Sediment % sand grainsize fraction
Sediment % mud grainsize fraction
Nitrate bottom water annual average
Nitrate seasonal range
Phosphate bottom water annual average
Phosphate seasonal range
Oxygen bottom water annual average
Oxygen seasonal range
Salinity bottom water annual average
Salinity seasonal range
Temperature bottom water annual average
Temperature seasonal range
Silicate bottom water annual average
Silicate seasonal range
Chlorophyll annual average
Chlorophyll seasonal range
Attenuation coefficient at 490nm annual average
Attenuation coefficient seasonal range
Sea surface temperature annual average
Sea surface temperature seasonal range
Relative benthic irradiance, annual average
Benthic irradiance seasonal range
A data frame with 8682 observations with the following 2 variables as well as
the same 28 variables as in
northing in scaled units
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
species abundance (where
c is the minimum positive abundance
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.
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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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