Investigating the management potential of a seagrass model through sensitivity analysis and experiments

Peggy Fong, Myrna E. Jacobson, Mark C. Mescher, Diego Lirman, Matthew C. Harwell

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Loss of seagrass-dominated ecosystems worldwide has been attributed to anthropogenic modifications of watersheds; in response, proper management of these systems has become a priority. In this paper, sensitivity analysis and comparison of model predictions to field observations identified conditions under which a subtropical to tropical seagrass ecosystem model would be a useful management tool. Sensitivity analysis indicated that under low-nutrient conditions, physical factors such as temperature, light, and salinity controlled model predictions of seagrass and epiphyte biomass, but that when nutrients were abundant (5 μmol/L sediment pore water P; 10 μmol/L water column P) control shifted to biological interactions. This analysis suggests that important areas for future research include formulations for biomass-dependent productivity (e.g., competition for nutrients or light) and the effects of altered nutrients on epiphyte productivity and shading. Model predictions matched the seasonal abundance of seagrasses measured in three distinct seagrass communities in Biscayne Bay, Florida, suggesting that in its present form the model could be useful to managers to run 'what-if' scenarios in order to make long-term decisions about upstream water management practices, including allowable nutrients and freshwater diversion. These management decisions are currently being considered without the benefit of a model.

Original languageEnglish (US)
Pages (from-to)300-315
Number of pages16
JournalEcological Applications
Issue number1
StatePublished - Feb 1997


  • Ecosystem model
  • Environmental and seasonal
  • Florida
  • Model validation
  • Predictive capability
  • Seagrasses
  • Sensitivity analysis
  • Uncertainty
  • Variability

ASJC Scopus subject areas

  • Ecology


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