1. Multiple Pressures nutrient boundary setting
Geoff Phillips
&
(Sebastian Birk FP7 MARS)
Slides 2 – 3 were kindly provided by Sebastian Birk from the EU MARS project

3. Multiple stressors: management implications
What is the (combined) effect of stressors?
Dominance (1 + 0 = 1) or Additive (1 + 1 = 2)
Interactions  “Ecological surprises“
Synergistic (1 + 1 = 3) (e.g. Nutrients & Temperature)
 Requires, for instance, more protective nutrient standards.
Antagonistic (1 + 1 = 1) (e.g. Nutrients & Hydropeaking)
 Requires combined stressor mitigation to avoid worsening.

4. Paired-stressor effects: interactions
LAKES
(n= 58 cases)
RIVERS
(n = 122 cases) *
Birk et al., in prep.
Share of interactions across lakes and rivers

5. Dealing with multiple pressures remains a challenge
Consider what variables might influence biological status other than nutrients
Turbidity suspended sediment
Humic substances
Shade (rivers)
Grazing
Toxic substances
Sediment deposition
Hydromorphological change
Boosted regression trees (BRT)
Allow us to identify important variables and rank them

6. Boosted regression to predict Macrophyte EQR
Sediment accumulation < -0.19
High
Low
Combination of 3500 individual regression trees, using boosting to minimise the loss of model performance by adding new tree models that best reduces overall model deviance.
Final model is a linear combination of all trees
Alkalinity >63 mgCaCO3/l
Nitrate <1.7 mg/l
Alkalinity >75 mgCaCO3/l
Nitrate <0.8 mg/l
Optimum model
P EQR <0.69
0.77
0.60
0.77
0.78
0.47
0.57
Increase plant deviation from reference
Overall model explains 63% of total deviance
Sol P EQR is the most important variable (22%)
Alkalinity (19)%
Sediment accumulation (13%)
Nitrate(10%)
Indicators of a degraded lowland stream

7. Partial dependence plots showing predicted Macrophyte EQR for each environmental variable
Data England & Wales
GAM added to indicate trajectory of change

8. How does this influence boundary setting?
Simplify by looking at 2 pressure combinations
For river phytobenthos nutrients (Soluble P & nitrate or total oxidised N) explain most of the variation, but BRT analysis shows fine sediment load is also important
How does fine sediment load interact with nutrients?
Investigate this using multiple regression models
We can specify additive models
EQR = c + ax1 + bx2 + error
or a model that includes an interaction
EQR = c + ax1 + bx2 + d(x1x2) + error

9. Plot the effect of one stressor while keeping the other stressor constant
Here the effects of sediment deposition and nitrate on status of phytobenthos is
Antagonistic
The response of EQR to nitrate is lower when sediment deposition is high
The response of EQR to sediment deposition is lower when nitrate is high
Without interaction the slope of the lines would be identical, but the level of the lines would be different
Red – 2nd stressor high,
green – 2nd stressor medium,
blue – 2nd stressor low

10. River phytobenthos EQR
The effect can be visualised by plotting the 2 pressure with the EQR as a response surface (identified by contours (blue line high/good boundary, green line good/moderate, yellow line moderate poor)
Solid lines – interaction model
Dotted line – no interaction
River phytobenthos EQR
For some sites reducing either pressure in isolation could reach the good/mod boundary if there was no interaction, but with interaction a much bigger reduction would be required
Reducing both provides the most likely chance of reaching the boundary.

11. Summary
Toolkit provides relatively simple approaches to setting boundaries
In some situations simple models may not work
Combining and dividing data sets may help (e.g. harmonise other pressure levels)
Value judgements are needed to make use of other approaches – quantile modelling
More use may need to be made of advanced techniques
BRTs, multiple regression models
more results needed from 2 pressure combinations
Should be a general comparability of boundary values across similar EU water bodies
Uncertainty and limitations of modelling allow for relatively broad range of boundaries
Compare results with other MS
Greater emphasis on ecological understanding, general rules, less emphasis on analysis of specific (small) data sets