Management thresholds shift under the influence of multiple stressors: Eelgrass meadows as a case study

Abstract

As human activities increase in intensity and extent, ecosystems face growing threats from multiple stressors. Successful management requires identifying measurable targets, which is challenging because of data limitations, nonlinear ecosystem responses, and potentially shifting targets under multiple stressors. To identify critical management values and determine whether these values shift in the presence of multiple stressors, we use eelgrass (Zostera marina) meadows as a model system. We reviewed 20 studies that measured the effects of light and temperature on eelgrass performance, providing 109 unique study–site–treatment combinations. We modeled the interactive effect of temperature and light on eelgrass population growth rate (i.e., lateral shoot production rates) using a hierarchical generalized additive model and predicted population growth rates across a range of light levels and temperatures. We found that two critical performance metrics of population growth, zero-growth and maximum growth rates, shifted across a gradient of light and temperature, suggesting that fixed management targets linked to population growth rates might be unsuitable for managing meadows under multiple stressors. Our approach bridges the gap between data from laboratory and field studies and could be developed into an interactive management tool.