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 journalArticle

22 Citations (Scopus)

Abstract

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
Pages (from-to)300-315
Number of pages16
JournalEcological Applications
Volume7
Issue number1
StatePublished - Feb 1 1997

Fingerprint

seagrass
sensitivity analysis
nutrient
epiphyte
experiment
prediction
productivity
ecosystem
biomass
shading
water management
management practice
porewater
water column
watershed
salinity
sediment
temperature

Keywords

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

ASJC Scopus subject areas

  • Ecology

Cite this

Investigating the management potential of a seagrass model through sensitivity analysis and experiments. / Fong, Peggy; Jacobson, Myrna E.; Mescher, Mark C.; Lirman, Diego; Harwell, Matthew C.

In: Ecological Applications, Vol. 7, No. 1, 01.02.1997, p. 300-315.

Research output: Contribution to journalArticle

Fong, Peggy ; Jacobson, Myrna E. ; Mescher, Mark C. ; Lirman, Diego ; Harwell, Matthew C. / Investigating the management potential of a seagrass model through sensitivity analysis and experiments. In: Ecological Applications. 1997 ; Vol. 7, No. 1. pp. 300-315.
@article{ec8939ce31614af68cbf26adae004990,
title = "Investigating the management potential of a seagrass model through sensitivity analysis and experiments",
abstract = "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.",
keywords = "Ecosystem model, Environmental and seasonal, Florida, Model validation, Predictive capability, Seagrasses, Sensitivity analysis, Uncertainty, Variability",
author = "Peggy Fong and Jacobson, {Myrna E.} and Mescher, {Mark C.} and Diego Lirman and Harwell, {Matthew C.}",
year = "1997",
month = "2",
day = "1",
language = "English",
volume = "7",
pages = "300--315",
journal = "Ecological Appplications",
issn = "1051-0761",
publisher = "Ecological Society of America",
number = "1",

}

TY - JOUR

T1 - Investigating the management potential of a seagrass model through sensitivity analysis and experiments

AU - Fong, Peggy

AU - Jacobson, Myrna E.

AU - Mescher, Mark C.

AU - Lirman, Diego

AU - Harwell, Matthew C.

PY - 1997/2/1

Y1 - 1997/2/1

N2 - 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.

AB - 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.

KW - Ecosystem model

KW - Environmental and seasonal

KW - Florida

KW - Model validation

KW - Predictive capability

KW - Seagrasses

KW - Sensitivity analysis

KW - Uncertainty

KW - Variability

UR - http://www.scopus.com/inward/record.url?scp=0030616826&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030616826&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0030616826

VL - 7

SP - 300

EP - 315

JO - Ecological Appplications

JF - Ecological Appplications

SN - 1051-0761

IS - 1

ER -