Deriving nutrient criteria to support 'good' ecological status in European lakes: An empirically based approach to linking ecology and management

Abstract

European water policy has identified eutrophication as a priority issue for water management. Substantial progress has been made in combating eutrophication but open issues remain, including setting reliable and meaningful nutrient criteria supporting 'good' ecological status of the Water Framework Directive. The paper introduces a novel methodological approach - a set of four different methods - that can be applied to different ecosystems and stressors to derive empirically-based management targets. The methods include Ranged Major Axis (RMA) regression, multivariate Ordinary Least Squares (OLS) regression, logistic regression, and minimising the mismatch of classifications. We apply these approaches to establish nutrient (nitrogen and phosphorus) criteria for the major productive shallow lake types of Europe: high alkalinity shallow (LCB1; mean depth 3-15 m) and very shallow (LCB2; mean depth < 3 m) lakes. Univariate relationships between nutrients and macrophyte assessments explained 29-46% of the variation. Multivariate models with both total phosphorus (TP) and total nitrogen (TN) as predictors had higher R² values (0.50 for LCB1 and 0.49 for LCB2) relative to the use of TN or TP singly. We estimated nutrient concentrations at the boundary where lake vegetation changes from 'good' to 'moderate' ecological status. LCB1 lakes achieved 'good' macrophyte status at concentrations below 48-53 μg/l TP and 1.1-1.2 mg/l TN, compared to LCB2 lakes below 58-78 μg/l TP and 1.0-1.4 mg/l TN. Where strong regression relationships exist, regression approaches offer a reliable basis for deriving nutrient criteria and their uncertainty, while categorical approaches offer advantages for risk assessment and communication, or where analysis is constrained by discontinuous measures of status or short stressor gradients. We link ecological status of macrophyte communities to nutrient criteria in a user-friendly and transparent way. Such analyses underpin the practical actions and policy needed to achieve 'good' ecological status in the lakes of Europe.

Keywords: Eutrophication; Macrophytes; Nitrogen; Nutrients; Phosphorus; Water Framework Directive.

Graphical abstract

Fig. 1

Relationship between common metric for macrophytes and a) total phosphorus and b) total nitrogen for high alkalinity very shallow (L-CB2) lakes showing high/good and good/moderate boundaries. Solid line shows type II RMA regression, dotted lines show upper and lower quartiles of residuals.

Fig. 2

Relationship between mean TP and TN in high alkalinity very shallow lakes (L-CB2). Dotted lines show contours of predicted TN and TP concentration when macrophyte EQR is at a) high/good and b) good/moderate boundary (±25th & 75th residuals of prediction). Horizontal and vertical lines show intersection with RMA regression of observed TP and TN showing good moderate boundary concentrations.

Fig. 3

Binary logistic regression (± 95% confidence limits) between total phosphorus/nitrogen and the probability of macrophytes from high alkalinity very shallow (L-CB2) lakes being classified as a) moderate or worse, b) good or worse. Lines show potential good/moderate and high/good boundary values at p = 0.5 and intersections with fit ±95% confidence limits, and alternative values at p = 0.75 and p = 0.25 (good/moderate only) reflecting differing levels of precaution.

Fig. 4

Percentage of water bodies where macrophyte or nutrient classifications for ecological status differ in comparison to the level used to set the boundary values for good/moderate or worse (top row) and high/good or worse (bottom row) for a) total phosphorus and b) total nitrogen in high alkalinity very shallow (L-CB2) lakes. Lines are loess smooths, vertical lines mark mean and range of intersections which identify the good/moderate boundary.

Fig. 5

Comparison of total phosphorus (a) and total nitrogen (b) criteria for different lake types/different criteria setting methods.