Identifying hotspots for biodiversity management using rank abundance distributions
Article first published online: 5 SEP 2011
© 2011 Blackwell Publishing Ltd
Diversity and Distributions
Volume 18, Issue 1, pages 22–32, January 2012
How to Cite
Dunstan, P. K., Bax, N. J., Foster, S. D., Williams, A. and Althaus, F. (2012), Identifying hotspots for biodiversity management using rank abundance distributions. Diversity and Distributions, 18: 22–32. doi: 10.1111/j.1472-4642.2011.00838.x
- Issue published online: 6 DEC 2011
- Article first published online: 5 SEP 2011
- rank abundance distribution
Aim Identification of biodiversity hotspots has typically relied on species richness. We extend this approach to include prediction to regional scales of other attributes of biodiversity based on the prediction of Rank Abundance Distributions (RADs). This allows us to identify areas that have high numbers of rare species and areas that have a rare assemblage structure.
Location Continental slope and shelf of south-western Australia, between 20.5 and 30° S and depths of 100–1500 m.
Methods We use a recently developed method to analyse RADs from biological surveys and predict attributes of RADs to regional scales from spatially abundant physical data for demersal fish and invertebrates. Predictions were made for total abundance (N), species richness (S) and relative evenness at 147,996 unsampled locations using data from two spatially limited surveys. The predictions for S and relative evenness were then independently split into categories, creating a bivariate distribution. The RAD categories are mapped spatially between 20.5 and 30° S to depths of 1500 m to allow identification of areas with rare species and assemblage structure across this region.
Results Rank abundance distributions for demersal fish vary with large scale oceanographic patterns. Peaks in abundance and unevenness are found on the shelf break. The bivariate distributions for richness and evenness for both fish and invertebrates show that all assemblage structures are not equally likely. The RAD categories identify regions that have high numbers of rare species and areas with unique assemblage structure.
Main conclusions Predicted RADs over large regions can be used to identify biodiversity hotspots in more detail than richness alone. Areas of rare species and rare assemblage structure identified from fish and invertebrates largely overlap, despite the underlying data coming from two different data sets with two different collection methods. This approach allows us to target conservation management at species that would otherwise be missed.