Assessment of marine eutrophication: Challenges and solutions ahead

Currently, no singular indicator for effectively assessing marine
eutrophication is available due to the inherent complexity of marine
ecosystems; instead, multiple indicators are utilized (Primpas and Karydis, 2011; Vollenweider et al., 1998; Uddin et al., 2024). It necessitates
performing statistical analyses on a wide range of indicators utilizing
extensive data sets to elucidate the underlying relationships for assessing eutrophication, including one-dimensional and multi-dimensional
statistical analysis (Kitsiou and Karydis, 2011). Without robust tools in
previous years, this task has become particularly challenging when
confronted with intricate datasets that display inconsistencies in their
patterns. This inconsistency may lead to the potential loss of critical
information associated with the occurrence of eutrophication, despite
some findings being observed as well. Consequently, given the rapid
advancements in artificial intelligence in recent years, there is an urgent
need to develop innovative assessment technologies and methodologies
for understanding the occurrence of marine eutrophication. For
instance, a model named as the Assessment Trophic Status Index,
developed through machine learning techniques, has been successfully
established and utilized to evaluate trophic status in marine ecosystems,
thereby enhancing the accuracy of trophic status assessments (Uddin
et al., 2024). Most importantly, large-scale models that integrate hydrodynamic, biogeochemical, socio-economic factors, and even climate
change must be developed using advanced technologies. This will
enable accurate assessment and prediction of marine eutrophication in
the future. Certainly, all these strategies are closely contingent upon a
thorough understanding of both the interactions among multiple linked
stressors and their impacts on marine ecosystem restoration or rehabilitation (Cloern, 2001).