Data from: Managing seagrass resilience under cumulative dredging affecting light: predicting risk using dynamic Bayesian networks

Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We present a risk-based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN).  The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). 

Data supports the following publication:

Wu PP, McMahon K, Rasheed MA, Kendrick GA, York PH, Chartrand K, Caley MJ, Mengersen K. (2017) Managing seagrass resilience under cumulative dredging affecting light: predicting risk using dynamic Bayesian networks. Journal of Applied Ecology, online in advance of print.

Data includes: Validation data used to validate the DBN model for Amphibolis in Jurien Bay, Halophila at Hay Point, and Zostera at Gladstone (supporting information S4 through S6, respectively). 

Lateral Growth from Existing Individuals

Physiological Status of Plants

Rate of Recovery in Shoot Density

Recruitment Rate from Seeds

Seed Density

The datasets were submitted to Dryad Digital Repository 29 November 2017.