Machine learning predicts the growth of cyanobacterial genera in river systems and reveals their different environmental responses.
Sci Total Environ
; 946: 174383, 2024 Oct 10.
Article
en En
| MEDLINE
| ID: mdl-38960197
ABSTRACT
Cyanobacterial blooms are a common and serious problem in global freshwater environments. However, the response mechanisms of various cyanobacterial genera to multiple nutrients and pollutants, as well as the factors driving their competitive dominance, remain unclear or controversial. The relative abundance and cell density of two dominant cyanobacterial genera (i.e., Cyanobium and Microcystis) in river ecosystems along a gradient of anthropogenic disturbance were predicted by random forest with post-interpretability based on physicochemical indices. Results showed that the optimized predictions all reached strong fitting with R2 > 0.75, and conventional water quality indices played a dominant role. One-dimensional and two-dimensional partial dependence plot (PDP) revealed that the responses of Cyanobium and Microcystis to nutrients and temperature were similar, but they showed differences in preferrable nutrient utilization and response to pollutants. Further prediction and PDP for the ratio of Cyanobium and Microcystis unveiled that their distinct responses to PAHs and SPAHs were crucial drivers for their competitive dominance over each other. This study presents a new way for analyzing the response of cyanobacterial genera to multiple environmental factors and their dominance relationships by interpretable machine learning, which is suitable for the identification and interpretation of high-dimensional nonlinear ecosystems with complex interactions.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Monitoreo del Ambiente
/
Cianobacterias
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Ríos
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Aprendizaje Automático
Idioma:
En
Revista:
Sci Total Environ
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Países Bajos