Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations.

ABSTRACT: Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile reg

SUBMITTER: Leontidou K 

PROVIDER: S-EPMC10274583 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature