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Download ##HOT## Data 1826 Rar

In the Mediterranean Sea, the symbiosis between the gorgonian Paramuricea clavata (Risso, 1826) and the polychaete Haplosyllis chamaeleon Laubier, 1960 (Annelida, Syllidae, Syllinae) has only been documented from the western basin. Our findings extend its geographic distribution to the north-central basin and represent the first record of H. chamaeleon in Italy and Croatia. Periodic observations from the Ligurian Sea allowed establishing that the symbiont occurs on P. clavata almost throughout the year, showing a reproductive period longer than previously reported. Morphometric comparisons of three Mediterranean populations, from Portofino Promontory (Ligurian Sea), Cape of Creus (Catalan Sea) and Chafarinas Archipelago (Alboran Sea) proved that there were no significant differences in body measurements, whilst the observed differences in dorsal cirri length pattern could be consider intra-specific. Our behavioural observations confirm that the species had (i) a kleptoparasitic behaviour, (ii) did not cause injuries to the host and (iii) did not induce the host to generate any malformation.

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As much as 26% of the polychaetes living in association with cnidarians are Syllidae (Molodtsova et al. 2016). Syllids have complex taxonomy (Aguado et al. 2012) and their life strategies often include associations with sponges, cnidarians, molluscs, crustaceans, echinoderms and other polychaetes (Martin and Britayev 1998, 2018). However, only a few have octocoral hosts; amongst them Imajimaea draculai (San Martín and López, 2002) is a parasite of the pennatulacean Funiculina quadrangularis (Pallas, 1766) (Nygren and Pleijel 2010), whilst Haplosyllis villogorgicola Martin et al., 2002, associated to the gorgonian Villogorgia bebrycoides (Koch, 1887) from the Canary Islands, and Haplosyllis anthogorgicola Utinomi, 1956, a symbiont of the gorgonian Acanthogorgia bocki Aurivillius, 1931, from Japan, are known to be kleptocommensals (Martin et al. 2002). However, probably the best-known species is Haplosyllis chamaeleon Laubier, 1960, which lives in association with the gorgonian Paramuricea clavata (Risso, 1826).

To analyse the infestation characteristics, we collected apical branches of both of yellow and red chromotypes of P. clavata (about 15 cm long) by scuba diving (Table 1). For the temporal monitoring, we collected 192 apical branches of P. clavata at about 40 m depth along the Portofino Promontory (Ligurian Sea, Italy) in September and November 2016, from May to October 2017, in January and February 2018 and in May and June 2018 (Table 2). In all sites, we randomly cut the branches with sharp scissors and enclosed them in individual plastic zip-bags to prevent faunal loss. Detailed collection information for the specimens collected in the Cap of Creus and the Chafarinas Archipelago can be found in Martin et al. (2002). The Italian polychaetes were identified according to Laubier (1960), López et al. (1996), Martin et al. (2002), and Lattig and Martin (2009). All records of H. chamaeleon and the related information were included in a datasheet following Di Camillo et al. (2018a) (Electronic Supplementary Material 1) to be analysed.

According to Lattig and Martin (2009), the main morphological traits were measured on well-preserved, complete polychaete specimens (from Portofino, Cap of Creus and the Chafarinas Archipelago); 36 specimens for body measurements and six specimens for dorsal cirri length pattern (as number of antennae and cirri articles) were placed on slides with glycerine and measured using a micrometric scale. The inter-population morphometric differences were analysed for size-independent data (by dividing all individual measurements by their respective body width) according to Martin et al. (2017) and Meca et al. (2019). All data were normalised prior to the analyses. Data ordinations were performed by principal component analysis (PCA) and the significance of the obtained clusters was assessed by one-way analyses of similarity (ANOSIM) based on Euclidean distance resemblance matrices, both for the main body measurements and for the dorsal cirri length pattern. The morphometric characters most contributing to the average inter-population differences were estimated and shown as percentages according to Martin et al. (2017) and Meca et al. (2019). PCA and ANOSIM were executed with PRIMER, version 6.1.11, copyright by PRIMER-E Ltd. 2008 (Clarke and Warwick 2001; Clarke and Gorley 2006).

The association of H. chamaeleon with P. clavata was chiefly recorded along the Mediterranean coast of Spain (Martin et al. 2002; Musco and Giangrande 2005), as well as in the Alboran Sea (Baratech and San Martín 1987; López et al. 1996; Martin et al. 2002). Two additional reports, from the coasts of Egypt (Abdelnaby 2014) and from the Arafura Sea (Australia) (Wilson 2006), have to be considered doubtful. Particularly, the latter, likely corresponds to Haplosyllis depressa Augener, 1913, currently accepted as Trypanobia depressa (Augener, 1913) (Aguado et al. 2015). Therefore, our findings represent the first record for Italian and Croatian waters and extend the geographic range of H. chamaeleon to the Tyrrhenian and Adriatic Seas (Castelli et al. 2008; Micac and Musco 2010; Micac 2015). Except for the finding of the polychaete in association with P. grayi in the Iberian Atlantic coasts, our results contribute to confirm the probable overlapping distribution of H. chamaeleon with those of the purple and yellow host chromotypes (Di Camillo et al. 2018b; Pica et al. 2018), which are widely but exclusively distributed in the Mediterranean Sea (Ponti et al. 2019). In Portofino, however, only part of the studied colonies of P. clavata seemed to host H. chamaeleon (although we cannot discard that, in a given individual, the worms could be present in unsampled branches) and the polychaetes were present all year round, with a particularly high infestation in summer. The infestation rates were overall very low, which agrees with previously published data on other known populations (Martin et al. 2002; Lattig and Martin 2009).

Haplosyllis chamaeleon is chiefly found on the apical portions of the gorgonian branches, where the high density of polyps probably maximises feeding opportunities (Laubier 1960; Martin et al. 2002). As previously reported in Martin et al. (2002), the syllids were observed to directly enter the coelenteron through the polyp pharynx (Fig. 5), likely to steal food, without inducing modifications of the host morphology; moreover, juveniles of our population from Portofino, were also observed completely to disappear inside the polyp, suggesting that they may enter also the stem canals (Fig. 5). This appeared to be a typical kleptoparasitic behaviour (i.e. stealing food captured by the host polyp) (Martin et al. 2002). However, the coincidence in colour morphs between host and symbiont observed in their different populations, including those collected in this study in Italian and Croatian waters, does not allow to discard the possibility of the symbiont feeding on host tissues, a behaviour that has also been reported in this species and in other symbiotic syllids (Pawlik 1983; Magnino and Gaino 1998; Martin et al. 2002; Lattig and Martin 2011). Kleptoparasitism has also been reported for different cnidarian associates. Amongst them, the caprellids Pseudoprotella phasma (Montagu, 1804), living on the hydroid Eudendrium glomeratum Picard, 1952 (Bavestrello et al. 1996), and Paracaprella pusilla Mayer, 1890, living on Eudendrium racemosum (Cavolini, 1785) (Ros and Guerra-García 2012) In turn, the nudibranch Cratena peregrina (Gmelin, 1791), associated to E. racemosum, feeds preferentially on hydroids that have recently captured preys, thus combining kleptoparasitism with predation (Willis et al. 2017). In light of our data, we cannot discard such a combination for H. chamaeleon. It has also been suggested that the presence of the symbiont could generate some benefit for the host, particularly by cleaning its surface (Martin et al. 2002), a possibility that would move the association towards mutualism and that we cannot discard in light of our data. This possibility certainly merits further behavioural observations.

CC and DM conceived and designed research. LP, BC, CGDC conducted experiments. CC, DP and DM performed the underwater sampling. LP, BC, CGCD and DM analyzed data. LP, BC and CGDC wrote the original draft. All authors read and approved the manuscript. CC covered the costs of field activities and lab analyses.

Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/or predicting future courses of action with high precision and advanced decision-making strategies. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the resulting analytics process and decisions made thereof. In comparison to traditional data techniques and platforms, artificial intelligence techniques (including machine learning, natural language processing, and computational intelligence) provide more accurate, faster, and scalable results in big data analytics. Previous research and surveys conducted on big data analytics tend to focus on one or two techniques or specific application domains. However, little work has been done in the field of uncertainty when applied to big data analytics as well as in the artificial intelligence techniques applied to the datasets. This article reviews previous work in big data analytics and presents a discussion of open challenges and future directions for recognizing and mitigating uncertainty in this domain. 041b061a72


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