Improving the assessment of species compositional dissimilarity in a priori ecological classifications: evaluating map scale,sampling intensity and improvement in a hierarchical classification

Question: Can species compositional dissimilarity analyses be used to assess and improve the representation of biodiversity patterns in a priori ecological classifications? Location: The case study examined the northern‐half of the South‐east Queensland Bioregion, eastern Australia. Methods: Site‐based floristic presence–absence data were used to construct species dissimilarity matrices (Kulczynski metric) for three levels of Queensland's bioregional hierarchy – subregions (1:500 000 scale), land zones (1:250 000 scale) and regional ecosystems (1:100 000 scale). Within‐ and between‐class dissimilarities were compiled for each level to elucidate species compositional patterns. Randomized subsampling was used to determine the minimum site sampling intensity for each hierarchy level, and the effects of lumping and splitting illustrated for several classes. Results: Consistent dissimilarity estimates were obtained with five or more sites per regional ecosystem, 10 or more sites per land zone, and more than 15 sites per subregion. On average, subregions represented 4% dissimilarity in floristic composition, land zones approximately 10%, and regional ecosystems over 19%. Splitting classes with a low dissimilarity increased dissimilarity levels closer to average, while merging ecologically similar classes with high dissimilarities reduced dissimilarity levels closer to average levels. Conclusions: This approach demonstrates a robust and repeatable means of analysing species compositional dissimilarity, determining site sampling requirements for classifications and guiding decisions about ‘lumping’ or ‘splitting’ of classes. This will allow more informed decisions on selecting and improving classifications and map scales in an ecologically and statistically robust manner.