In areas of poor plot data, limited funding and expertise, alternate approaches are needed to create elements of a hierarchical classification schema to assist in landscape planning. This is especially important for vulnerable systems under pressure from human activities. Within this paper we introduce an approach to help create a consistent classification section for riparian vegetation at a subcontinental scale, within the context of low plot data availability. We collated occurrence data for selected dominant plants known to occur within riparian environments from electronic databases and our own unpublished survey data. We used generalised dissimilarity modelling (GDM), which models species turnover between pairs of 0.01° grid cells as a function of environmental differences between those cells. Eight climatic and landscape variables were derived for each grid cell. Average temperature and average rainfall had the greatest contribution to species turnover followed by elevation. A model incorporating eight climatic, physiognomic and spatial variables accounted for 48% of the turnover of species. Six ecoregions were defined and used to circumscribe the equivalent number of interim Macrogroups based on the GLM outputs and diagnostic species.

在地块数据较差，资金和专业知识有限的地区，需要使用替代方法来创建分层分类方案的元素，以帮助进行景观规划。这对于在人类活动压力下的脆弱系统尤其重要。在本文中，我们介绍了一种方法，可在低地块数据可用性的背景下，帮助在次大陆规模上为河岸植被创建一致的分类部分。我们从电子数据库和我们自己未发布的调查数据中整理了已知在河岸环境中发生的选定优势植物的发生数据。我们使用了通用异种建模（GDM），该模型可将成对的0.01°网格单元之间的物种转换建模为这些单元之间环境差异的函数。为每个网格单元导出了八个气候和景观变量。平均温度和平均降雨量对物种更新的影响最大，其次是海拔。包含八个气候，地理和空间变量的模型占物种营业额的48％。根据GLM的输出和诊断物种，定义了六个生态区，并用于界定同等数量的临时宏观群。