Supplementary Information
Fig. S1. Partial dependency plots from the random forest analysis showing the relationships between ecological quality (EQ) status and catchment field percentage (field catchment %) for the three river size classes (<100 km2, 100-1000 km2, and >1000 km2) and the three agricultural pressure classes (<10%, 10-20%, and >20% agriculture in catchment area). The plots characterize the average individual effect of catchment field percentage on the EQ after the effects of the other predictor variables (Fig. 3) were accounted for. The y-axis values thus do not represent the raw data. Ecological status (bad = 0.1, poor = 0.3, moderate = 0.5, good = 0.7, and high = 0.9).
Fig. S2. Partial dependency plots from the random forest analysis showing the relationships between ecological quality (EQ) status and hydromorphological alteration (HYMO alteration) for three river size classes (<100 km2, 100-1000 km2, and >1000 km2) and three agricultural pressure classes (<10%, 10-20%, and >20% agriculture in catchment area). The plots characterize the average individual effect of HYMO alteration on EQ after the effects of the other predictor variables (Fig. 3) were accounted for. The y-axis values thus do not represent the raw data.
Ecological status (bad = 0.1, poor = 0.3, moderate = 0.5, good = 0.7, and high = 0.9).
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Fig. S3. Partial dependency plots from the random forest analysis showing the relationships between ecological quality (EQ) status and catchment forest percentage (forest catchment %) for three river size classes (<100 km2, 100-1000 km2, and >1000 km2) and three agricultural pressure classes (<10%, 10-20%, and >20% agriculture in catchment area). The plots characterize the average individual effect of catchment forest percentage on the EQ after the effects of the other predictor variables (Fig. 3) were accounted for. The y-axis values thus do not represent the raw data. Ecological status (bad = 0.1, poor = 0.3, moderate = 0.5, good = 0.7, and high = 0.9).
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