Abstract
Context
Ensembles of artificial neural network models can be trained to predict the continuous characteristics of vegetation such as the foliage cover and species richness of different plant functional groups.
Objectives
Our first objective was to synthesise existing site-based observations of native plant species to quantify summed percentage foliage cover and species richness within four functional groups and in totality. Secondly, we generated spatially-explicit, continuous, landscape-scale models of these functional groups, accompanied by maps of the model residuals to show uncertainty.
Methods
Using a case study from New South Wales, Australia, we aggregated floristic observations from 6806 sites into four common plant growth forms (trees, shrubs, grasses and forbs) representing four different functional groups. We coupled these response data with spatially-complete surfaces describing environmental predictors and predictors that reflect landscape-scale disturbance. We predicted the distribution of foliage cover and species richness of these four plant functional groups over 1.5 million hectares. Importantly, we display spatially explicit model residuals so that end-users have a tangible and transparent means of assessing model uncertainty.
Results
Models of richness generally performed well (R2 0.43–0.63), whereas models of cover were more variable (R2 0.12–0.69). RMSD ranged from 1.42 (tree richness) to 29.86 (total native cover). MAE ranged from 1.0 (tree richness) to 20.73 (total native foliage cover).
Conclusions
Continuous maps of vegetation attributes can add considerable value to existing maps and models of discrete vegetation classes and provide ecologically informative data to support better decisions across multiple spatial scales.
Supplementary material to "Simulation of soil carbon dynamics in Australia under a framework
that better connects spatially explicit data with Rᴏᴛʜ C"
