Integrating interspecific traits into biophysical models of seagrass dispersal
Journal Publication ResearchOnline@JCUAbstract
The resilience of seagrass meadows strongly depends on the dispersal of their propagules, which fosters recovery and replenishment after disturbances. However, predicting dispersal patterns across dynamic coastal environments and large spatial and temporal scales remains challenging due to the lack of empirical observations. Biophysical models, integrating oceanic and atmospheric drivers with species-specific traits such as buoyancy and lifespan, are commonly used to simulate propagule transport. Yet, few studies account for the interspecific and interannual variability inherent in tropical seagrass ecosystems. Here we present a high-resolution seagrass biophysical dispersal model applied to 11 tropical seagrass species across the entire Great Barrier Reef World Heritage Area (GBRWHA), Australia, and run this model over a 6-year period (2011–2016). We use this model to assess how the interspecific variability in the buoyancy and windage of seagrass propagules affect their dispersal patterns and how these patterns further vary both seasonally and interannually. Our results reveal that species-specific factors such as their windage and buoyancy, as well as the season and region in which they disperse had the largest influence on dispersal distance. H. spinulosa and S. isoetifolium showed the greatest dispersal in the Whitsunday region, while the wet season promoted higher local retention due to lower wind speeds. From a management perspective, this highlights the need to account for species-specific information when devising seagrass management strategies. The outcomes of this research reveal the inherent complexities of predicting multi-species dispersal over large spatial and temporal scales, with broader implications for predicting dispersal in complex coastal ecosystems.
Journal
Ecological Modelling
Publication Name
Ecological Modelling
Volume
510
ISBN/ISSN
1872-7026
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Pages Count
12
Location
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Publisher
Elsevier
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Date
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EISSN
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DOI
10.1016/j.ecolmodel.2025.111329