Predicting seagrass decline due to cumulative stressors

Adams, Matthew P., Koh, Edwin J.Y., Vilas, Maria P., Collier, Catherine J., Lambert, Victoria M., Sisson, Scott A., Quiroz, Matias, McDonald-Madden, Eve, McKenzie, Len J., and O'Brien, Katherine R. (2020) Predicting seagrass decline due to cumulative stressors. Environmental Modelling & Software, 130. 104717.

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Abstract

Seagrass ecosystems are increasingly subjected to multiple interacting stressors, making the consequent trajec-tories difficult to predict. Here, we present a new process-based model of seagrass decline in response to cu-mulative 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 in-dicates 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.

Item ID: 63781
Item Type: Article (Research - C1)
ISSN: 1873-6726
Keywords: Cumulative stress, Dynamic model, Ecological forecasting, Seagrass, Sequential, Monte Carlo sampling, Variational inference
Copyright Information: © 2020 Elsevier Ltd. All rights reserved.
Funders: Great Barrier Reef Marine Park Authority (GBRMPA), National Environmental Science Program (NERP), Tropical Water Quality Hub, Australian Research Council (ARC), ARC Centre of Excellence for Mathematical and Statistical Frontiers, University of Queensland’s Research Computing Centre (RCC)
Projects and Grants: ARC LP160100496, ARC FT170100140, ARC FT170100079, ARC CEMS CE140100049
Date Deposited: 08 Jul 2020 07:36
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050202 Conservation and Biodiversity @ 25%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050205 Environmental Management @ 50%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050206 Environmental Monitoring @ 25%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 50%
96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960808 Marine Flora, Fauna and Biodiversity @ 50%
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