Predicting seagrass decline due to cumulative stressors

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.

Environmental Modelling & Software

Environmental Modelling & Software

130

1873-6726

N/A

N/A

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