Multiple Metrics of Temperature, Light, and Water Motion Drive Gradients in Eelgrass Productivity and Resilience

Abstract

Characterizing the response of ecosystems to global climate change requires that multiple aspects of environmental change be considered simultaneously, however, it can be difficult to describe the relative importance of environmental metrics given their collinearity. Here, we present a novel framework for disentangling the complex ecological effects of environmental variability by documenting the emergent properties of eelgrass (Zostera marina) ecosystems across ∼225 km of the Atlantic Coast of Nova Scotia, Canada, representing gradients in temperature, light, sediment properties, and water motion, and evaluate the relative importance of different metrics characterizing these environmental conditions (e.g., means, extremes, variability on different time scales) for eelgrass bioindicators using lasso regression and commonality analysis. We found that eelgrass beds in areas that were warmer, shallower, and had low water motion had lower productivity and resilience relative to beds in deeper, cooler areas that were well flushed, and that higher temperatures lowered eelgrass tolerance to low-light conditions. There was significant variation in the importance of various metrics of temperature, light, and water motion across biological responses, demonstrating that different aspects of environmental change uniquely impact the cellular, physiological, and ecological processes underlying eelgrass productivity and resilience, and contribute synergistically to the observed ecosystem response. In particular, we identified the magnitude of temperature variability over daily and tidal cycles as an important determinant of eelgrass productivity. These results indicate that ecosystem responses are not fully resolved by analyses that only consider changes in mean conditions, and that the removal of collinear variables prior to analyses relating environmental metrics to biological change reduces the potential to detect important environmental effects. The framework we present can help to identify the conditions that promote high ecosystem function and resilience, which is necessary to inform nearshore conservation and management practices under global climate change.