A Bayesian network model linking nutrient management actions in the Tully catchment (northern Queensland) with Great Barrier Reef condition
Shenton, Will, Hart, Barry T., and Brodie, Jon (2010) A Bayesian network model linking nutrient management actions in the Tully catchment (northern Queensland) with Great Barrier Reef condition. Marine and Freshwater Research, 61 (5). pp. 587-595.
|PDF (Published Version) - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
View at Publisher Website: http://dx.doi.org/10.1071/MF09093
Correlating catchment management actions with improvements in the ecological condition of downstream coastal regions is challenging. We describe a Bayesian network (BN) model that predicts the effects of nitrogen-fertiliser management strategies in the Tully River catchment (northern Queensland) on the condition of inshore reefs of the Great Barrier Reef (GBR). The model consists of three linked submodels that relate sugarcane nitrogen management with runoff into the Tully River and nitrate concentration in the GBR lagoon, predicts phytoplankton biomass in the GBR lagoon from the nitrate inputs, and links the phytoplankton biomass with three marine influences to predict the probability of the reefs being dominated by coral (good) or macro-algae (bad). Four scenarios were modelled – current and the ‘six easy steps’ nitrogen management, and active and depleted algal grazing (herbivory) of the reef. The model predicts an increased probability of the reef being coral-dominated with current fertiliser practice and with active reef herbivory, with increased algal-dominance if reef herbivory is decreased. Introduction of a better nitrogen-fertiliser management with active herbivory resulted in an increased probability of coral dominance. This comparative-scenario analysis highlights the importance of both agricultural nutrient management practices and marine processes in predicting reef condition.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||decision support, fertiliser, nitrogen.|
|FoR Codes:||05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050205 Environmental Management @ 100%|
|SEO Codes:||96 ENVIRONMENT > 9606 Environmental and Natural Resource Evaluation > 960604 Environmental Management Systems @ 100%|
|Deposited On:||01 Mar 2011 14:41|
|Last Modified:||18 Oct 2013 01:12|
Last 12 Months: 0
|Citation Counts with External Providers:|
Repository Staff Only: item control page