Epibiotic assemblages on the pen shell Pinna rudis (Bivalvia,Pinnidae) at Matiota Beach,São Vicente Island,Cabo Verde

The rough pen shell Pinna rudis Linnaeus, 1758 (family Pinnidae), a mollusc with an Atlantic–Mediterranean distribution, is able to live in coarse sandy substrates. Considering its shell structure and ecological characteristics, P. rudis can enhance biodiversity by providing a substrate for settlement on its shell. For this reason, we compared the diversity of benthic taxa around P. rudis shells with the species diversity on P. rudis shells, at Matiota Beach, São Vicente Island, Cabo Verde. We sampled an area of 900 m2 and recorded data in situ to estimate the population size of P. rudis and the epibiotic and benthic community diversity. The average density of P. rudis estimated in the sampled area was 6.6 ind. 100 m–2 and the highest density was found at between 2 and 3 m depth, mostly in biogenic and sandy substrates. The epibiotic species diversity on P. rudis shells was significantly higher than the species diversity in the microhabitat around the shells. The P. rudis shell seems to play an important role in increasing the biodiversity of the ecosystem, with some species found only as epibionts on P. rudis.     

The Mollusc Thais haemastoma -An Exhibitor of 'Imposex' and Potential Biological Indicator of Tributyltin Pollution

Abstract. In the Azores the gastropod Thais haemastoma shows varying degrees of imposex, the induction of male sex characters in the female. It is inferred that imposex in T. haemastoma is caused by tributyltin (TBT) associated with boats using organotin-based antifouling paints. The two indices - relative penis size and vas deferens sequence - employed previously for Nucella lapillus , were used without alteration. T. haemastoma is only the third species reported as exhibiting 'effective sterilisation' of the female. Unlike Nucella lapillus, T. haemastoma has a planktonic larva, allowing populations to be sustained even when local TBT pollution is high. This makes T. haemastoma potentially a better indicator species, as monitored populations can be maintained by the influx of larvae. T. haemastoma may thus provide a cheap method of assessing the extent of TBT contamination in warm waters, particularly in the Mediterranean and Atlantic Islands. Preliminary examination of material from a marina in Spain showed a high incidence of 'sterile' animals.     

Water-soluble inorganic ions of size-differentiated atmospheric particles from a suburban site of Mexico City

During the MILAGRO campaign, March 2006, eight-stage cut impactors were used to sample atmospheric particles at Tecámac (T1 supersite), towards the northeast edge of the Mexico City Metropolitan Area, collecting fresh local emissions and aged pollutants produced in Mexico City. Particle samples were analyzed to determine total mass concentrations of Ca²⁺, Mg²⁺, NH₄ ⁺, K⁺, Cl^?, SO₄ ^2?, and NO₃ ^?. Average concentrations were 22.1 ± 7.2 μg m^?3 for PM₁₀ and 18.3 ± 6.2 μg m^?3 for PM_1.8. A good correlation between PM₁₀ and PM_1.8, without influence from wind patterns, indicates that local emissions are more important than the city’s pollution transported to the site, despite the fact that Tecámac is just 40 km away from Mexico City. A lack of diurnal patterns in the PM_2.5/PM_1.8 ratio supports this conclusion. The inorganic composition of particles suggests that vehicles, soil resuspension, and industries are the main pollutant sources. Finally, the particles were found to be neutralized, in agreement with observations in the Mexico City Metropolitan Area.     

Toponym matching through deep neural networks

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.