Session 2a Working with more difficult data sets: short gradients Case study: phytobenthos in very large rivers in Romania (Danube and tributaries) Source: www.wwf.pada.org

Phytobenthos in very large rivers in Romania Adopted intercalibration metric (pICM) as national metric Adopted mean position of H/G and G/M boundaries as national boundaries But: Shallow slope Low r2 (0.060) Few sites below (intercalibrated) G/M boundary Extrapolation leads to very high boundaries Consider using data from other Member States X-GIG intercalibration dataset, in this case …

Phytobenthos in very large rivers in Romania Understand the reasons for a weak regression e.g. look at levels of other potential stressors Here, phosphorus data are plotted against other water chemistry variables Dotted lines show present Danube Commission thresholds No obvious candidates here for an alternative stressor Kelly, Chiriac, Soare-Minea, Hamchevici & Birk (2018) Hydrobiologia (in press)

Phytobentos in very large rivers in Romania Adding X-GIG data extends the gradient and confirms that Romanian stretches of the Danube generally have relatively low concentrations of dissolved phosphorous. R2 increases to 0.29 Several options now for using toolkit Similar options may be possible in other cases Need to allow for ”country” effects (see IC documentation) May need to be normalized (see TK_Normalise.xlsx) (Easy for Romania as they use pICM as national metric)

Phytobentos in very large rivers in Romania Check linearity Blue line: linear regression Red line: loess regression (via stat_smooth() command in ggplot2) See also 01_TKit_check_data.R in toolkit Apply toolkit approaches …

Potential boundary values for Romanian very large rivers (summarised as g L-1 P) Method Lower Centre Upper OLS1 139 229 RMA 109 162 OLS2 70 97 Average medians 90 Average quartiles 79 47 75th quartile 66 mismatch 107 Logistic regression 91 140 Decision tree 94 Range of national boundary values (Table 4-4 from CIS Guidance Manual) 25th percentile 70 (as TP) Median 140 (as TP) 75th percentile 200 (as TP) Current Romanian P good/moderate standards for large rivers: 160- 270 g L-1 soluble P 280 – 500 g L-1 total P

Dealing with short gradients How to increase gradient length Data from similar types in neighbouring MS? Intercalibration datasets? Data from other types in your own country? Boundaries in this case were consistent: may need to normalise these in other cases (normalisation template included) Need to account for differences between datasets See IC documentation for guidance on calculating offsets Linear mixed effect models with MS (or type) as fixed effect