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

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

Unsupervised machine learning and geometric morphometrics as tools for the identification of inter and intraspecific variations in the Anopheles Maculipennis complex

Geometric morphometric analysis was combined with two different unsupervised machine learning algorithms, UMAP and HDBSCAN, to visualize morphological differences in wing shape among and within four Anopheles sibling species (An. atroparvus, An. melanoon, An. maculipennis s.s. and An. daciae sp. inq.) of the Maculipennis complex in Northern Italy. Specifically, we evaluated: (1) wing shape variation among and within species; (2) the consistencies between groups of An. maculipennis s.s. and An. daciae sp. inq. identified based on COI sequences and wing shape variability; and (3) the spatial and temporal distribution of different morphotypes. UMAP detected at least 13 main patterns of variation in wing shape among the four analyzed species and mapped intraspecific morphological variations. The relationship between the most abundant COI haplotypes of An. daciae sp. inq. and shape ordination/variation was not significant. However, morphological variation within haplotypes was reported. HDBSCAN also recognized different clusters of morphotypes within An. daciae sp. inq. (12) and An. maculipennis s.s. (4). All morphotypes shared a similar pattern of variation in the subcostal vein, in the anal vein and in the radio-medial cross-vein of the wing. On the contrary, the marginal part of the wings remained unchanged in all clusters of both species. Any spatial-temporal significant difference was observed in the frequency of the identified morphotypes. Our study demonstrated that machine learning algorithms are a useful tool combined with geometric morphometrics and suggest to deepen the analysis of inter and intra specific shape variability to evaluate evolutionary constrains related to wing functionality

phenology of daphnia in a northern italy pond during the weather anomalous 2014

This note reports a comparison between Daphnia phenology in the weather anomalous 2014 and a previous three years period (2011-2013), in a shallow water body of Northern Italy (Bodrio del pastore III) where we recorded D. pulex. In 2011-2013, Daphnia population showed 1-2 density peaks from mid spring to early summer, it declined in July-August and did not recover, from ephippia, until the following spring. The seasonal dynamics was probably related to the species thermal tolerance. Males and ephippial females appeared at the beginning of growth season according to a typical feature of Daphnia populations from temporary habitats. The presence of the Chaoborus larvae resulted in juvenile adaptive predator-avoidance cyclomorphosis. In 2014, in the study area, mean winter air temperature was much warmer than average recorded during the past three years while it was much colder than average in July and August. This reflected the relatively rainy and cloudy summer months: the winter and summer precipitations total was above the previous three years average. In 2014, Daphnia was found all over the year and showed a maximum peak of density in November. The general increase of Daphnia was related to a shift in D. pulex population phenology, seasonal growth started earlier and lasted longer, and to the occurrence of D. longispina. Both species were identified by genetic markers and phylogenetic analyses of ND5 sequences placed isolates from the Bodrio del pastore III into the European D. pulex group. Both populations reproduced by cyclical parthenogenesis and showed cyclomorphosis. However, D. pulex produced more males and ephippial females than D. longispina. Their seasonal dynamics were quite different: D. longispina dominated in late summer while D. pulex showed the highest density in November. The presence of D. pulex in the Bodrio is important in the framework of conservation ecology especially because we have showed that it is native European strain instead of the invasive North American clone that replaced native D. pulex throughout Africa and was already recorded in Italy. We provide some indications and discuss how Daphnia phenology of shallow lakes of temperate areas may be susceptible to inter-annual variability in weather conditions.</p

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

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

Assessing the Extinction Risk of Heterocypris incongruens (Crustacea: Ostracoda) in Climate Change with Sensitivity and Uncertainty Analysis

Organisms respond to climate change in many different ways and their local extinction risk may vary widely among taxa. Crustaceans from freshwater temporary ponds produce resting eggs to cope with environmental uncertainty and, as a consequence, egg banks have a fundamental role for population persistence. The egg bank dynamics of six clonal lineages of Heterocypris incongruens (Ostracoda) from Northern Italy were simulated. Clonal lineages W1 and W2 are the most common “winter ecotypes”, clonal lineages S1 and S2 are allochthonous “summer ecotypes” and clonal lineages I1 and I2 are relatively rare and generalist in terms of seasonality. Fecundity and proportion of resting eggs vary by clonal lineage, temperature and photoperiod. The clonal extinction risk was estimated in present climate conditions and under climate change. For comparison, and to assess the potential colonization of northern ponds, clonal lineages from Lampedusa Island (Southern Italy), L, were considered. Cohen’s general model was used for simulating egg bank dynamics and the extinction rate of each clonal lineage was estimated with uncertainty analysis. A 30 year simulation in present and climate change conditions was carried out. Extinction rates were lower in climate change conditions than in present conditions. Hydroperiod, hatching rate and egg deterioration rate were the critical factors that affected extinction rates. Extinction rates varied among clonal lineages. This suggests that H. incongruens might be able to have multiple responses to climate change due to its genetic diversity. In climate change conditions, W clonal lineages underwent a niche expansion, while a mismatch between photoperiod and hydroperiod might generate a detrimental effect on the phenology of summer S clonal lineages that might cause their extinction. Southern clonal lineages L, showing an intermediate extinction rate, might colonize northern temporary ponds

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

This note reports a comparison between Daphnia phenology in the weather anomalous 2014 and a previous three years period (2011-2013), in a shallow water body of Northern Italy (Bodrio del pastore III) where we recorded D. pulex. In 2011-2013, Daphnia population showed 1-2 density peaks from mid spring to early summer, it declined in July-August and did not recover, from ephippia, until the following spring. The seasonal dynamics was probably related to the species thermal tolerance. Males and ephippial females appeared at the beginning of growth season according to a typical feature of Daphnia populations from temporary habitats. The presence of the Chaoborus larvae resulted in juvenile adaptive predator-avoidance cyclomorphosis. In 2014, in the study area, mean winter air temperature was much warmer than average recorded during the past three years while it was much colder than average in July and August. This reflected the relatively rainy and cloudy summer months: the winter and summer precipitations total was above the previous three years average. In 2014, Daphnia was found all over the year and showed a maximum peak of density in November. The general increase of Daphnia was related to a shift in D. pulex population phenology, seasonal growth started earlier and lasted longer, and to the occurrence of D. longispina. Both species were identified by genetic markers and phylogenetic analyses of ND5 sequences placed isolates from the Bodrio del pastore III into the European D. pulex group. Both populations reproduced by cyclical parthenogenesis and showed cyclomorphosis. However, D. pulex produced more males and ephippial females than D. longispina. Their seasonal dynamics were quite different: D. longispina dominated in late summer while D. pulex showed the highest density in November. The presence of D. pulex in the Bodrio is important in the framework of conservation ecology especially because we have showed that it is native European strain instead of the invasive North American clone that replaced native D. pulex throughout Africa and was already recorded in Italy. We provide some indications and discuss how Daphnia phenology of shallow lakes of temperate areas may be susceptible to inter-annual variability in weather conditions.</p

Molecular Barcoding: A Tool to Guarantee Correct Seafood Labelling and Quality and Preserve the Conservation of Endangered Species

The recent increase in international fish trade leads to the need for improving the traceability of fishery products. In relation to this, consistent monitoring of the production chain focusing on technological developments, handling, processing and distribution via global networks is necessary. Molecular barcoding has therefore been suggested as the gold standard in seafood species traceability and labelling. This review describes the DNA barcoding methodology for preventing food fraud and adulteration in fish. In particular, attention has been focused on the application of molecular techniques to determine the identity and authenticity of fish products, to discriminate the presence of different species in processed seafood and to characterize raw materials undergoing food industry processes. In this regard, we herein present a large number of studies performed in different countries, showing the most reliable DNA barcodes for species identification based on both mitochondrial (COI, cytb, 16S rDNA and 12S rDNA) and nuclear genes. Results are discussed considering the advantages and disadvantages of the different techniques in relation to different scientific issues. Special regard has been dedicated to a dual approach referring to both the consumer’s health and the conservation of threatened species, with a special focus on the feasibility of the different genetic and genomic approaches in relation to both scientific objectives and permissible costs to obtain reliable traceability