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Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

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dc.contributor Australian Institute Of Marine Science
dc.contributor Queensland Univ Technol
dc.contributor University Of California Santa Barbara
dc.contributor University Of California System
dc.contributor Queensland University Of Technology (qut)
dc.contributor Sch Math Sci
dc.contributor Bren Sch Environm Sci & Management
dc.contributor Australian Inst Marine Sci
dc.contributor Univ Calif Santa Barbara
dc.contributor.author MENGERSEN, KERRIE
dc.contributor.author VERCELLONI, JULIE
dc.contributor.author CALEY, M. JULIAN
dc.contributor.author KAYAL, MOHSEN
dc.contributor.author LOW-CHOY, SAMANTHA
dc.date.accessioned 2017-03-21T00:55:50Z
dc.date.accessioned 2014-11-10T04:51:51Z
dc.date.accessioned 2014-11-10T04:51:51Z
dc.date.accessioned 2019-05-09T01:09:32Z
dc.date.available 2014-11-10T04:51:51Z
dc.date.available 2017-03-21T00:55:50Z
dc.date.available 2017-03-21T00:55:50Z
dc.date.available 2019-05-09T01:09:32Z
dc.date.issued 2014-11-03
dc.identifier.citation Vercelloni J, Caley MJ, Kayal M, Low-Choy S, Mengersen K (2014) Understanding uncertainties in non-linear population trajectories: A Bayesian semi-parametric hierarchical approach to large-scale surveys of coral cover. PLoS ONE 9(11): e110968 en_US
dc.identifier.issn 1932-6203
dc.identifier.uri http://epubs.aims.gov.au/11068/10624
dc.description.abstract Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.
dc.description.uri http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0110968 en_US
dc.language English
dc.language.iso en_AU en_US
dc.publisher PLoS OPEN en_US
dc.relation.ispartof Null
dc.rights Attribution 3.0 Australia *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/au/ *
dc.subject Resilience
dc.subject Decline
dc.subject Species Richness
dc.subject Science & Technology - Other Topics
dc.subject Models
dc.subject Future
dc.subject Reef Fishes
dc.subject Multidisciplinary Sciences
dc.subject Ecological-systems
dc.title Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover
dc.type journal article en_US
dc.identifier.doi 10.1371/journal.pone.0110968
dc.identifier.wos WOS:000345558100039


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