Predicting seagrass decline due to cumulative stressors. (August 2020)

• Record Type: Journal Article
• Authors: 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.; O'Brien, Katherine R.
• Is Part Of: Environmental modelling & software. Volume 130(2020)
• Publication Date: 2020-08
• Subjects: Cumulative stress -- Dynamic model -- Ecological forecasting -- Seagrass -- Sequential Monte Carlo sampling -- Variational inference; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104717
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Easy-to-use program to predict cumulative light and temperature stress on seagrass. Software predictions made from a new process-based model of tropical seagrass decline. Model suggests net carbon loss rate controls shoot density decline rate in seagrass. Model calibrated to data via two posterior-computation methods for Bayesian inference. New generalisable cumulative stress index forecasted by model, including uncertainty.