Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/131878
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Type: Book chapter
Title: Larval connectivity and water quality explain spatial distribution of crown-of-thorns starfish outbreaks across the Great Barrier Reef
Author: Matthews, S.A.
Mellin, C.
Pratchett, M.S.
Citation: Advances in Marine Biology, 2020 / Sheppard, C. (ed./s), vol.87 (1), Ch.9, pp.223-258
Publisher: Elsevier
Publisher Place: UK
Issue Date: 2020
Series/Report no.: Advances in Marine Biology; 87(1)
ISBN: 0128215291
9780128215296
Editor: Sheppard, C.
Statement of
Responsibility: 
S.A. Matthews, C. Mellin, and Morgan S. Pratchett
Abstract: Outbreaks of the coral eating crown-of-thorns starfish (COTS; Acanthasts cf. solaris) occur in cyclical waves along the Great Barrier Reef (GBR), contributing significantly to the decline in hard coral cover over the past 30 years. One main difficulty faced by scientists and managers alike, is understanding the relative importance of contributing factors to COTS outbreaks such as increased nutrients and water quality, larval connectivity, fishing pressure, and abiotic conditions. We analysed COTS abundances from the most recent outbreak (2010–2018) using both boosted regression trees and generalised additive models to identify key predictors of COTS outbreaks. We used this approach to predict the suitability of each reef on the GBR for COTS outbreaks at three different levels: (1) reefs with COTS present intermittently (Presence); (2) reefs with COTS widespread and present in most samples and (Prevalence) (3) reefs experiencing outbreak levels of COTS (Outbreak). We also compared the utility of two auto-covariates accounting for spatial autocorrelation among observations, built using weighted inverse distance and weighted larval connectivity to reefs supporting COTS populations, respectively. Boosted regression trees (BRT) and generalised additive mixed models (GAMM) were combined in an ensemble model to reduce the effect of model uncertainty on predictions of COTS presence, prevalence and outbreaks. Our results from best performing models indicate that temperature (Degree Heating Week exposure: relative importance¼13.1%) and flood plume exposure (13.0%) are the best predictors of COTS presence, variability in chlorophyll concentration (12.6%) and flood plume exposure (8.2%) best predicted COTS prevalence and larval connectivity potential (22.7%) and minimum sea surface temperature (8.0%) are the best predictors of COTS outbreaks. Whether the reef was open or closed to fishing, however, had no significant effect on either COTS presence, prevalence or outbreaks in BRT results (<0.5%). We identified major hotspots of COTS activity primarily on the mid shelf central GBR and on the southern Swains reefs. This study provides the first empirical comparison of the major hypotheses of COTS outbreaks and the first validated predictions of COTS outbreak potential at the GBR scale incorporating connectivity, nutrients, biophysical and spatial variables, providing a useful aid to management of this pest species on the GBR.
Keywords: Animals
Anthozoa
Starfish
Disease Outbreaks
Larva
Coral Reefs
Water Quality
Rights: © 2020 Elsevier Ltd. All rights reserved.
DOI: 10.1016/bs.amb.2020.08.007
Published version: https://www.sciencedirect.com/bookseries/advances-in-marine-biology/vol/87/issue/1
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