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.

海草生态系统越来越多地受到多种相互作用的压力，这使得随之而来的轨迹难以预测。在这里，我们提出了一种基于新的基于过程的海草下降模型，以响应累积的光和温度应力。使用贝叶斯推断，该模型已针对大堡礁海草的实验室数据集进行了校准。我们的模型既适合生理数据又适合形态数据，支持以下假设：生理碳损失率控制着海草的枝条密度下降率。该模型预测完成芽丢失的时间，以及一个新的，可概括的累积应力指数，该指数指示基于累积应力时间段的潜在海草枝密度下降。所有模型预测都包括基于与数据拟合的模型中的不确定性的不确定性估计。校准后的模型被打包到一个计算机程序中，该程序可以预测由于累积的光照和温度应力而导致的海草潜在下降。