Defining nutrient thresholds that protect and support the ecological integrity of aquatic ecosystems is a fundamental step in maintaining their natural biodiversity and preserving their resilience. With increasing catchment pressures and climate change, it is more important than ever to develop clear methods to establish thresholds for the status classification and management of waters. This must often be achieved using complex data and should be robust to interference from additional pressures as well as ameliorating or confounding conditions. We use both artificial and real data to examine challenges in setting nutrient thresholds in unbalanced and skewed data. We found significant advantages to using binary logistic regression over other techniques. However, one of the key challenges is objectively selecting a probability to derive the nutrient threshold. For this purpose, the examination of the proportions of matching and mismatching status classifications of nutrients and a biological quality element using a confusion matrix is a key step that should be more widely adopted in threshold selection.
PHILLIPS Geoff;
TEIXEIRA Heliana;
KELLY Martyn;
SALAS HERRERO Maria Fuensanta;
VARBIRO Gabor;
LYCHE SOLHEIM A;
KOLADA Agniezska;
FREE Gary;
POIKANE Sandra;
2023-12-08
ELSEVIER
JRC134552
0048-9697 (online),
https://www.sciencedirect.com/science/article/pii/S0048969723075010?via%3Dihub,
https://publications.jrc.ec.europa.eu/repository/handle/JRC134552,
10.1016/j.scitotenv.2023.168872 (online),
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