SAV 
Model Development 
Data Mining 
N Load or proxy, Historical SAV coverage, 
Avg Chi a as proxy for phytoplankton, 
existing models (I V Chi, Kd vZ by species, 
N V SAV Loss by system) _ 
Define Conceptual Model 
Develop Numerical Model 
To Test Conceptual Model 
Develop Empincal Model 
To Test Conceptual Model 
Example cf parameters 
required for an existing 
biomass model 
Totfcl PAR time Ode, 
TS S, Color, Sp ectnlmo del 
Historiceltzulcutxetit 
c ownge, Above/be low 
groundbicm&ss,P ^/P^.P 
vs.I Q]rve,NutrimlT9)iul(c 
( C/HX Temperetur e, Leef 
loss nte ,Poie weter H,D«a 
essimiliticn te dmique 
D oes Mo del Make Sense? 
Yes 
_ i _ 
No 
Field Test Model 
Does Field Test Validate Model? 
Nutrient Criteria For SAV 
7 ^ 
Figure 6. Conceptual diagram of the feedbacks among data mining, model development, field 
monitoring, and experimental hypothesis validation. 
iterative process assures that at each stage in the critical research path, error in data and models 
are sufficiently small that the completed analysis will be accurate enough to make meaningful 
prediction of the SAV response to nutrients. 
Model Development 
Seagrass models are generally a composite of numerical and empirical relationships that provide 
a quantitative prediction of seagrass growth or loss. Each of these components has to be tested 
individually and in concert with other relationships that make up the model. Although all SAV 
models will have components in common, each regional model will be individualized to 
incorporate locally important species, the biogeochemistry of the water-column and sediments. 
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