Semi-automated assignment of vegetation survey plotswithin anapriori classification of vegetation types

Assignment of large numbers of vegetation plots to a priori vegetation classifications is increasingly being required to support natural resource management, monitoring and conservation at regional scales. Several automated systems have been developed that use quantitative synoptic tables and algorithm-based plot-to-type assignment. However, where synoptic tables do not exist, and qualitative species lists characterise vegetation type classifications, existing systems may not apply. In these situations, vegetation experts may resort to manual assignment processes that can be slow, subjective and fraught with difficulties. This study combines repeatable and objective quantitative analyses, with new software, to deliver a semi-automated plot-to-type assignment process appropriate for a priori classifications based on qualitative species lists. The flexible semi-automated assignment program (SAAP) calculates a quantitative goodness-of-fit score between plots and types, based on the species that characterise each a priori vegetation type, and the species that characterise groups of plots derived from quantitative analyses. We applied the SAAP to a case-study of 630 native vascular plant species from 930 plots, and an a priori classification of 99 vegetation types. We varied vegetation data set transforms [cover per cent (0–100%), cover score (0–6) and presence–absence (1, 0)] and analysis settings and tested the degree to which the SAAP provided plot-to-type assignment concordant with manual expert assignment. Results provided clear evidence supporting the choice of particular data set transformations and analysis settings to maximise concordance. The SAAP allocated up to 50% of plots to the same expert-assigned vegetation type, and more than 70% of plots to an expert-assigned vegetation type ranked in the top five by the SAAP. When coupled with repeatable and objective quantitative analyses, the SAAP provides vegetation experts with a new semi-automated and quantitative decision support tool to assist with the assignment of vegetation plots within a priori vegetation classifications defined by characteristic species lists