Abstract
Ecophysiological stress and physical disturbance are capable of structuring meadows through a combination of direct biomass removal and recruitment limitation; however, predicting these effects at landscape scales has rarely been successful. To model environmental influence on sexual recruitment in perennial Zostera marina, we selected a sub-tidal, light-replete study site with seasonal extremes in temperature and wave energy. During an 8-year observation period, areal coverage increased from 4.8 to 42.7%. Gains were stepwise in pattern, attributable to annual recruitment of patches followed by centrifugal growth and coalescence. Recruitment varied from 13 to 4,894 patches per year. Using a multiple linear regression approach, we examined the association between patch appearance and relative wave energy, atmospheric condition and water temperature. Two models were developed, one appropriate for the dispersal of naked seeds, and another for rafted flowers. Results indicated that both modes of sexual recruitment varied as functions of wind, temperature, rainfall and wave energy, with a regime shift in wind-wave energy corresponding to periods of rapid colonization within our site. Temporal correlations between sexual recruitment and time-lagged climatic summaries highlighted floral induction, seed bank and small patch development as periods of vulnerability. Given global losses in seagrass coverage, regions of recovery and re-colonization will become increasingly important. Lacking landscape-scale process models for seagrass recruitment, temporally explicit statistical approaches presented here could be used to forecast colonization trajectories and to provide managers with real-time estimates of future meadow performance; i.e., when to expect a good year in terms of seagrass expansion. To facilitate use as forecasting tools, we did not use statistical composites or normalized variables as our predictors. This study, therefore, represents a first step toward linking remotely acquired environmental data to sexual recruitment, an important measure of seagrass performance that translates directly into landscape-scale coverage change.
Highlights
Seagrass landscapes are spatiotemporally dynamic [1,2,3]
Initial work focused on the intuitive relationship between meadow contraction and water quality, and the resultant bio-optical and secchi-depth models explain a large proportion of the spatiotemporal variance [10,11,12]
Aimed at more precise forecasting, and fueled by the growing availability of GIS data, contemporary investigators have employed a host of sophisticated numerical and statistical approaches to describe seagrass coverages, viz., habitat suitability [9, 14] and species distribution models [15,16,17,18], ecological process models [19,20,21], and vegetative growth models [22,23,24,25]. These efforts have broadened the range of predictor variables and offer potential advantages over traditional mapping techniques [17, 26, 27]
Summary
Seagrass landscapes are spatiotemporally dynamic [1,2,3]. Metrics of floral composition [4], shoot density [5], areal coverage [6] and landscape configuration [3] have all been shown to vary widely over seasonal to inter-annual times-scales. Aimed at more precise forecasting, and fueled by the growing availability of GIS data, contemporary investigators have employed a host of sophisticated numerical and statistical approaches to describe seagrass coverages, viz., habitat suitability [9, 14] and species distribution models [15,16,17,18], ecological process models [19,20,21], and vegetative growth models [22,23,24,25] Together, these efforts have broadened the range of predictor variables (e.g., salinity, slope, wave energy, current flow, tidal range, sediment characteristics, temperature, etc.) and offer potential advantages over traditional mapping techniques [17, 26, 27]
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