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Identifying hotspots for biodiversity management using rank abundance distributions

Citation

Dunstan, PK and Bax, NJ and Foster, SD and Williams, A and Althaus, F, Identifying hotspots for biodiversity management using rank abundance distributions, Diversity and Distributions, 18, (1) pp. 22-32. ISSN 1366-9516 (2012) [Refereed Article]

Copyright Statement

Copyright 2011 Blackwell Publishing Ltd

DOI: doi:10.1111/j.1472-4642.2011.00838.x

Abstract

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 30S and depths of 1001500m.

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 30S to depths of 1500m 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.

Item Details

Item Type:Refereed Article
Keywords:biodiversity, hotspot, prediction, rank abundance distribution
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Conservation and Biodiversity
Objective Division:Environment
Objective Group:Flora, Fauna and Biodiversity
Objective Field:Marine Flora, Fauna and Biodiversity
Author:Bax, NJ (Professor Nicholas Bax)
Author:Foster, SD (Dr Scott Foster)
Author:Williams, A (Dr Alan Williams)
ID Code:119718
Year Published:2012
Web of Science® Times Cited:8
Deposited By:Centre for Ecology and Biodiversity
Deposited On:2017-08-04
Last Modified:2017-10-05
Downloads:0

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