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Article

Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds

Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Viale delle Scienze 11/A, I-43124 Parma, Italy
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Academic Editor: Kay Van Damme
Water 2021, 13(9), 1217; https://doi.org/10.3390/w13091217
Received: 17 March 2021 / Revised: 20 April 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
(This article belongs to the Special Issue Species Richness and Diversity of Aquatic Ecosystems)
Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences. View Full-Text
Keywords: fuzzy clustering; nutrients; chlorophyll; association rules fuzzy clustering; nutrients; chlorophyll; association rules
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MDPI and ACS Style

Bellin, N.; Racchetti, E.; Maurone, C.; Bartoli, M.; Rossi, V. Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds. Water 2021, 13, 1217. https://doi.org/10.3390/w13091217

AMA Style

Bellin N, Racchetti E, Maurone C, Bartoli M, Rossi V. Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds. Water. 2021; 13(9):1217. https://doi.org/10.3390/w13091217

Chicago/Turabian Style

Bellin, Nicolò, Erica Racchetti, Catia Maurone, Marco Bartoli, and Valeria Rossi. 2021. "Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds" Water 13, no. 9: 1217. https://doi.org/10.3390/w13091217

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