Seagrass trait variation, which results from both local genetic adaptation and phenotypic plasticity, has important effects on ecosystems. Physical drivers underlie these processes and are important determinants of trait variation. Despite this, few studies examine multivariate predictive relationships between sets of physical drivers and sets of seagrass traits. Here, we use redundancy analysis to define this relationship for eelgrass Zostera marina, using traits that represent bed structure, morphology, and physiology and physical drivers that emphasize light conditions and temperature variability on different time scales. We found a relationship between plant size (i.e., leaf length and width, rhizome width, number of leaves) and shoot density that dominated the trait variation. Specifically, as temperatures became warmer, more variable, and light was less limiting, plants became smaller (shorter, narrower, and fewer leaves, thinner rhizomes) but beds became denser. Plant biomass (leaf area index), which increased with decreasing temperature variability and bottom light, further refined this relationship. Overall, temperature variability (i.e., daily temperature range, heat accumulation, time in the optimal temperature range, and tidal and meteorological variability), as well as bottom light, were important predictors of eelgrass traits. We further identified three distinct temperature-light regimes across which traits differed; these included the cool low variability temperature regime with low light, the warm high variability temperature regime with high light, and the intermediate case between these endpoints. Our study identifies specific temperature and light drivers that define certain eelgrass traits and provides a multivariate statistical model that can be used to predict eelgrass trait values from known physical conditions.
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Just Published
Nov 11, 2024
Chen Wang + 5
Open Access