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.

Incorporating anthropogenic thresholds to improve understanding of cumulative effects on seagrass beds

“…First, the vulnerability scores used were not specific to Atlantic Canada and instead were previously developed for the New England region (Kappel et al 2012a). Comparisons of the results of the same elicitation approach applied to separate groups of experts in California vs. New England supports the generalizability of applying vulnerability weights across regions (Kappel et al 2012a); however, differences in environmental conditions between or within regions may alter the response of seagrass to stressors (Krumhansl et al 2021). For example, warmer temperatures increase the light requirements of seagrass and can consequently increase their vulnerability to stressors that limit underwater light (Beca-Carretero et al 2018).…”

“…Seagrass bed locations were compiled from field surveys conducted over the past decade (Weldon et al 2009;Schmidt et al 2012;Skinner et al 2013;Cullain et al 2018;Wong 2018;Krumhansl et al 2020). Of the 187 beds, 180 were included in Murphy et al (2019), while seven additional seagrass bed locations were included from Krumhansl et al (2021). The seagrass bed locations span three provinces (Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI)) and two bioregions (Scotian Shelf and Gulf of St. Lawrence).…”

Environment predicts seagrass genotype, phenotype, and associated biodiversity in a temperate ecosystem

“…Since this range of pressures acts through different processes and mechanisms on different ecosystem properties, it is increasingly well understood that assessment of seagrass population status cannot rely on any single metric ( Unsworth et al, 2015 ), and several studies have conducted multivariate assessments of resilience in seagrass ( Jones and Unsworth, 2016 ; Jahnke et al, 2020 ; Bertelli et al, 2021 ; Krumhansl et al, 2021 ) and other coastal vegetative ecosystems ( Battisti et al, 2020 ). However, there is now a need to advance this multivariate bioindicator approach, to understand the connections between components of seagrass ecosystems, linking environmental variables, seagrass genotype, seagrass phenotype, and associated biodiversity.…”

Fine-scale ensemble species distribution modeling of eelgrass (Zostera marina) to inform nearshore conservation planning and habitat management

“…To assemble a dataset of eelgrass occurrence records across the model domain sufficiently large for model calibration and validation, we gathered direct observations of eelgrass presence −absence from prior field studies (Wong et al, 2013; Vandermeulen, 2017;Wong, 2018;Wilson et al, 2019a;Krumhansl et al, 2021), public inventories (Wilson and Lotze, 2019;Environment and Climate Change Canada, 2020), and unpublished sources (Wong unpubl.). Sampling methods and spatial precision varied among sources.…”

“…We selected environmental variables for model predictors based on relevance from knowledge of eelgrass biology (Koch, 2001;Krause-Jensen et al, 2011;Krumhansl et al, 2021;Murphy et al, 2021) and for those with coverage over the study area (Figure S1, Table S2) at a spatial resolution approaching the precision of the camera survey species observations (~30 m). We used bathymetric data from a digital elevation model (Greenlaw unpubl.)…”