Phenology of Daphnia in a Northern Italy pond during the weather anomalous 2014

Rossi, Valeria; Maurone, Catia; Benassi, Giorgio; Marková, Silvia; Kotlı́k, Petr; Bellin, Nicolò; Ferrari, I.

doi:10.4081/jlimnol.2015.1216

Cited by 2 publications

(4 citation statements)

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“…The presence of D. pulex in this area is important in the framework of conservation ecology. In fact, we have shown that it is the native European species instead of the invasive North American asexual clone (Rossi et al, 2015). Here we extend our research to 24 surrounding water bodies located in the Po river basin in the Cre-mona province that is one of the most productive areas of the Northern hemisphere (Bassanino et al, 2011).…”

Section: Introductionmentioning

confidence: 64%

“…In the Po river Basin, we found a high genetic diversity within the D. longispina complex (five haplotypes with Pi 0.02077). Between 2011 and 2014, D. pulex dominated in the pond Pastore III while in 2015 it was partly replaced by D. longispina (Rossi et al, 2015;Rossi et al, in preparation). Due to high population fluctuation and spatial/temporal dispersal by resting eggs, D. longispina could have colonized the pond Pastore III from surrounding ponds.…”

Section: Discussionmentioning

confidence: 99%

“…To verify morphological identification of Daphnia species, one to 11 collected specimens per each locality were used in molecular analyses according to the number of species per site. In the locality where species replacement was recorded, Pastore III (Rossi et al, 2015), up to 5 individuals per species were analysed. To verify Daphnia species identity, 12S rRNA diagnostic marker was used.…”

Section: Methodsmentioning

confidence: 99%

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Daphnia diversity in water bodies of the Po River Basin

et al. 2016

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Shallow water bodies dominate the areal extent of continental waters and host a proportion of biodiversity higher than the percentage of Earth’s surface they cover. Daphnia is a key component of small aquatic ecosystems food webs. Here we present the result of a survey in 24 ponds located in the core of Po river Basin, to assess the actual spreading of Daphnia species in one of the most productive areas of the Northern hemisphere. By using diagnostic genetic markers (12S rRNA and ND5 genes) we identified five Daphnia species: D. ambigua, D. curvirostris, D. longispina, D. obtusa and D. pulex in fourteen ponds. Additional analyses of two nuclear genes (LdhA and Rab4) revealed that D. pulex in the study area is native European strain. In opposite, D. ambigua shared haplotype with the North-Eastern American lineage that was introduced to Europe by long-distance dispersal. In the Po river Basin we identified a highly divergent lineage of D. longispina group that formed a clade with individuals from northern European Russia and might represent a new Daphnia species. Daphnia species in the Cremona province have European origin, except for D. ambigua which is a North American species spreading across Europe. Future attention will require monitoring of invasive species, particularly D. ambigua and the North American invasive clone of D. pulex that is already present in Northern Italy.

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Section: Introductionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Daphnia diversity in water bodies of the Po River Basin

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“…Data recorded in 2014 and in 2015 were compared as, in the study area, mean temperatures in this 2-year period were very different. In 2014, the winter was much warmer while late spring and summer were much colder than the average recorded in the past and in 2015 [49]. In most of 2015, mean monthly temperatures were much warmer than the average recorded during the past.…”

Section: Introductionmentioning

confidence: 79%

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

Bellin

Racchetti

Maurone

et al. 2021

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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.

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Phenology of Daphnia in a Northern Italy pond during the weather anomalous 2014

Abstract: This note reports a comparison between Daphnia phenology in the weather anomalous 2014 and a previous three years period (2011)(2012)(2013)

Cited by 2 publications

References 60 publications

Daphnia diversity in water bodies of the Po River Basin

Daphnia diversity in water bodies of the Po River Basin

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

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