Appendix C. Model validation comparing the status (presence or absence) of Zostera muelleri at each of the case study locations predicted by the model with past seagrass distribution maps for Moreton Bay.

Model validation comparing the status (presence or absence) of Zostera muelleri at each of the case study locations predicted by the model with past seagrass distribution maps for Moreton Bay

Appendix A. Combinations of probabilities used for each of the input nodes to predict the likelihood of starting seagrass biomass at different impact levels (Step 4) and to determine the effect of starting seagrass biomass on the likelihood of more seagrass biomass (Step 5).

Combinations of probabilities used for each of the input nodes to predict the likelihood of starting seagrass biomass at different impact levels (Step 4) and to determine the effect of starting seagrass biomass on the likelihood of more seagrass biomass (Step 5)

Data_Sheet_1_Highly Disturbed Populations of Seagrass Show Increased Resilience but Lower Genotypic Diversity.DOCX

<p>The response of seagrass systems to a severe disturbance provides an opportunity to quantify the degree of resilience in different meadows, and subsequently to test whether there is a genetic basis to resilience. We used existing data on levels of long-standing disturbance from poor water quality, and the responses of seagrass (Zostera muelleri) after an extreme flood event in Moreton Bay, Queensland, Australia. Sites were grouped into high and low disturbance categories, in which seagrass showed high and low resilience, respectively, as determined by measuring rates of key feedback processes (nutrient removal, suppression of sediment resuspension, and algal grazing), and physiological and morphological traits. Theoretically, meadows with higher genotypic diversity would be expected to have greater resilience. However, because the more resilient meadows occur in areas historically exposed to high disturbance, the alternative is also possible, that selection will have resulted in a narrower, less diverse subset of genotypes than in less disturbed meadows. Levels of genotypic and genetic diversity (allelic richness) based on 11 microsatellite loci, were positively related (R² = 0.58). Genotypic diversity was significantly lower at highly disturbed sites (R = 0.49) than at less disturbed sites (R = 0.61). Genotypic diversity also showed a negative trend with two morphological characteristics known to confer resilience on seagrass in Moreton Bay, leaf chlorophyll concentrations and seagrass biomass. Genetic diversity did not differ between disturbed and undisturbed sites. We postulate that the explanation for these results is historical selection for genotypes that confer protection against disturbance, reducing diversity in meadows that contemporarily show greater resilience.</p

Sensitivity analysis for output node (coral reef condition) to values at all other nodes in the Bayesian belief network.

<p>Nodes with a greater entropy reduction value have a greater influence on the outcome of the output node.</p

Map of study locations, ecosystem types, marine reserves, and major estuaries entering central Moreton Bay, Australia.

<p>Habitat layers courtesy Queensland Herbarium.</p

Optimising Land-Sea Management for Inshore Coral Reefs - Fig 3

<p>Performance of management actions: (A) likelihood of a change in state when only a single action is implemented); (B) the proportion of outcomes that resulted in positive effects on coral reef condition; (C) the proportion of outcomes that resulted in above median effects on coral reef condition; and (D) the proportion of outcomes that resulted in the highest 5% of positive effects on coral reef condition. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164934#pone.0164934.t001" target="_blank">Table 1</a> for full details of each management option.</p

Managers often aim for synergistic effects between multiple interventions to increase 'bang for their buck' (i.e. outcome per dollar spent).

<p>Figure represents all combinations of management actions that resulted in both positive outcomes for coral reef condition, and that resulted in synergistic additions to coral reef condition (i.e. above and beyond the sum of the values of each action). Outcomes are ordered from left to right from lowest to highest modelled state of good reef condition for each number of interventions implemented. Here, the coloured ‘base' effects (green = two interventions, light blue = three interventions, dark blue = four interventions, grey = five interventions) indicates the sum of the individual interventions in isolation, and 'Synergistic Effect' indicates the additional effect of these interventions when combined.</p

Summary of levels and justifications for management node inputs.

<p>Here, Intervention 1 relates to management levels that are likely to be politically and socially agreeable, and Intervention 2 relates to the full levels of management scope and intensity of management interventions that, based on current scientific evidence, are likely to have the greatest benefits. Detailed information on the selection of these levels and the data underlying the chosen percentiles is provided <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164934#pone.0164934.s001" target="_blank">S1 Appendix</a>.</p