Predicting seagrass decline due to cumulative stressors

Adams, MP; Koh, EJY; Vilas, MP; Collier, CJ; Lambert, VM; Sisson, SA; Quiroz, M; McDonald-Madden, E; McKenzie, LJ; O'Brien, KR

Predicting seagrass decline due to cumulative stressors

Adams, MP Koh, EJY Vilas, MP Collier, CJ Lambert, VM Sisson, SA Quiroz, M

McDonald-Madden, E McKenzie, LJ O'Brien, KR

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Environmental Modelling and Software, 2020, 130
Issue Date:: 2020-08-01

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Field	Value	Language
dc.contributor.author	Adams, MP
dc.contributor.author	Koh, EJY
dc.contributor.author	Vilas, MP
dc.contributor.author	Collier, CJ
dc.contributor.author	Lambert, VM
dc.contributor.author	Sisson, SA
dc.contributor.author	Quiroz, M https://orcid.org/0000-0002-4400-9184
dc.contributor.author	McDonald-Madden, E
dc.contributor.author	McKenzie, LJ
dc.contributor.author	O'Brien, KR
dc.date.accessioned	2021-01-13T02:04:48Z
dc.date.available	2021-01-13T02:04:48Z
dc.date.issued	2020-08-01
dc.identifier.citation	Environmental Modelling and Software, 2020, 130
dc.identifier.issn	1364-8152
dc.identifier.issn	1873-6726
dc.identifier.uri	http://hdl.handle.net/10453/145367
dc.description.abstract	© 2020 Elsevier Ltd Seagrass ecosystems are increasingly subjected to multiple interacting stressors, making the consequent trajectories difficult to predict. Here, we present a new process-based model of seagrass decline in response to cumulative light and temperature stress. The model is calibrated to laboratory datasets for Great Barrier Reef seagrasses using Bayesian inference. Our model, which is fit to both physiological and morphological data, supports the hypothesis that physiological carbon loss rate controls the shoot density decline rate of seagrasses. The model predicts the time to complete shoot loss, and a new, generalisable, cumulative stress index that indicates the potential seagrass shoot density decline based on the time period of cumulative stress. All model predictions include uncertainty estimates based on uncertainty in the model fit to the data. The calibrated model is packaged into a computer program that can forecast the potential declines of seagrasses due to cumulative light and temperature stress.
dc.language	en
dc.publisher	Elsevier BV
dc.relation	http://purl.org/au-research/grants/arc/CE140100049
dc.relation.ispartof	Environmental Modelling and Software
dc.relation.isbasedon	10.1016/j.envsoft.2020.104717
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Environmental Engineering
dc.title	Predicting seagrass decline due to cumulative stressors
dc.type	Journal Article
utslib.citation.volume	130
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-13T02:04:42Z
pubs.publication-status	Published
pubs.volume	130

Abstract:

© 2020 Elsevier Ltd Seagrass ecosystems are increasingly subjected to multiple interacting stressors, making the consequent trajectories difficult to predict. Here, we present a new process-based model of seagrass decline in response to cumulative light and temperature stress. The model is calibrated to laboratory datasets for Great Barrier Reef seagrasses using Bayesian inference. Our model, which is fit to both physiological and morphological data, supports the hypothesis that physiological carbon loss rate controls the shoot density decline rate of seagrasses. The model predicts the time to complete shoot loss, and a new, generalisable, cumulative stress index that indicates the potential seagrass shoot density decline based on the time period of cumulative stress. All model predictions include uncertainty estimates based on uncertainty in the model fit to the data. The calibrated model is packaged into a computer program that can forecast the potential declines of seagrasses due to cumulative light and temperature stress.

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http://hdl.handle.net/10453/145367