Identifying hotspots for biodiversity management using rank abundance distributions


Piers K. Dunstan, CSIRO Wealth from Oceans Flagship, GPO Box 1538, Hobart, 7001 Tasmania, Australia.


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.