Predicting seagrass decline due to cumulative stressors
Autor: | Victoria Lambert, Eve McDonald-Madden, Matias Quiroz, Scott A. Sisson, Len J. McKenzie, Edwin J.Y. Koh, Katherine R. O'Brien, Catherine J. Collier, Matthew P. Adams, Maria P. Vilas |
---|---|
Rok vydání: | 2020 |
Předmět: |
0106 biological sciences
Environmental Engineering biology 010604 marine biology & hydrobiology Ecological Modeling Stressor Ecological forecasting biology.organism_classification Atmospheric sciences Bayesian inference 010603 evolutionary biology 01 natural sciences Temperature stress Great barrier reef Seagrass Environmental science Ecosystem 14. Life underwater Carbon loss Software |
Zdroj: | Environmental Modelling & Software. 130:104717 |
ISSN: | 1364-8152 |
DOI: | 10.1016/j.envsoft.2020.104717 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |