{"title":"幼虫连通性和水质解释了刺冠海星在大堡礁爆发的空间分布。","authors":"S A Matthews, C Mellin, Morgan S Pratchett","doi":"10.1016/bs.amb.2020.08.007","DOIUrl":null,"url":null,"abstract":"<p><p>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 importance=13.1%) 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.</p>","PeriodicalId":50950,"journal":{"name":"Advances in Marine Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/bs.amb.2020.08.007","citationCount":"5","resultStr":"{\"title\":\"Larval connectivity and water quality explain spatial distribution of crown-of-thorns starfish outbreaks across the Great Barrier Reef.\",\"authors\":\"S A Matthews, C Mellin, Morgan S Pratchett\",\"doi\":\"10.1016/bs.amb.2020.08.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 importance=13.1%) 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.</p>\",\"PeriodicalId\":50950,\"journal\":{\"name\":\"Advances in Marine Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/bs.amb.2020.08.007\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Marine Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/bs.amb.2020.08.007\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/10/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Marine Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/bs.amb.2020.08.007","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/10/8 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Larval connectivity and water quality explain spatial distribution of crown-of-thorns starfish outbreaks across the Great Barrier Reef.
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 importance=13.1%) 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.
期刊介绍:
Advances in Marine Biology was first published in 1963 under the founding editorship of Sir Frederick S. Russell, FRS. Now edited by Charles Sheppard, the serial publishes in-depth and up-to-date reviews on a wide range of topics which will appeal to postgraduates and researchers in marine biology, fisheries science, ecology, zoology and biological oceanography. Eclectic volumes in the series are supplemented by thematic volumes on such topics as The Biology of Calanoid Copepods.