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

Lawson, BE, Wardell-Johnson, G, Beeton, RJS and Pullar, DV (2010) Improving the assessment of species compositional dissimilarity in a priori ecological classifications: evaluating map scale, sampling intensity and improvement in a hierarchical classification. Applied Vegetation Science, 13 4: 473-484. doi:10.1111/j.1654-109X.2010.01085.x


Author Lawson, BE
Wardell-Johnson, G
Beeton, RJS
Pullar, DV
Title Improving the assessment of species compositional dissimilarity in a priori ecological classifications: evaluating map scale, sampling intensity and improvement in a hierarchical classification
Journal name Applied Vegetation Science   Check publisher's open access policy
ISSN 1402-2001
1654-109X
Publication date 2010-10-01
Sub-type Article (original research)
DOI 10.1111/j.1654-109X.2010.01085.x
Volume 13
Issue 4
Start page 473
End page 484
Total pages 15
Place of publication Malden, MA, United States
Publisher Wiley-Blackwell Publishing
Collection year 2011
Language eng
Subject C1
Formatted abstract
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. © 2010 International Association for Vegetation Science.
Keyword Biodiversity
Ecological Mapping
Ecosystem classification
Land-use planning
Multivariate statistics
Sample size
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Sun, 26 Sep 2010, 10:09:14 EST