The correlation value (R2) between observed and simulated yields ranged from 0.87 to 0.94 with very high modeling efficiencies.
The mean Root Mean Squared Error (RMSE) between observed and simulated values at the end of winter was 3.3% water per horizon and 56 kg N/ha, which is relatively good owing to the high spatial and temporal variability of soil water and nitrate content.
Visual predictive check (VPC) simulations indicated good agreement between observed and simulated data for all studies (Supplementary Figures S2 and S3) and covariates (Supplementary Figure S4).
Interestingly, we found that for all eight viruses, the optimization process resulted in a significant improvement in the similarity between observed and simulated data.
Using data from other streams, it was shown that between-streams natural variability was a source of discrepancy between observed and simulated data.
We suggest that the poor fit between observed and simulated data for some data sets may be attributable to low-level infection on farms making regression difficult due to insensitivity of the egg count method at low values, or a pattern for immunity in ewes that contradicted the model assumptions.
However, the results obtained from other statistical indicators, including RMSE, MBE, and SDBE, show that the difference between observed and simulated DO values is low, indicating satisfactory results.
Increasing intensity of disturbance (regimes of disturbance corresponding to two turnovers of assemblages in Figure S1) emphasized the difference between observed and simulated data.
Statistically significant differences between observed and simulated expected distributions were evaluated with the sum of the square deviations (SSD) and Harpending's raggedness index (hg) [ 66, 67].
Additionally, using the least-squares approach, there was no significant difference between observed and simulated data under an expansion model for either form (blunts: SSd, P = 0.108; Harpending's raggedness index, P = 0.116; sharps: SSd, P = 0.1680; Harpending's raggedness index, P = 0.1000).
