Fast analysis of decabrominated diphenyl ether using low-pressure gas chromatography electron-capture negative ionization mass spectrometry

Peer reviewe

Functional traits predict species co-occurrence patterns in a North American Odonata metacommunity

The probability of occurrence of a given species in a target locality and assemblage is conditioned not only by environmental/climatic variables but also by the presence of other species (i.e., species co-occurrence). This framework, already complex in nature, becomes even more complicated if one considers the functional traits of species that, in turn, might influence the structure of metacommunities in various ways. Depending on the ecological and environmental setting, functional similarity (i.e., convergence in morphological and ecological traits) between species might either reduce their co-occurrence due to high niche overlap driving negative interactions or promote it if the similar traits are associated with local habitat suitability. Similarly, functional divergence might either promote species co-occurrence by limiting negative interactions through niche separation or reduce it through trait mediated environmental filtering. Therefore, discriminating between these alternative scenarios—predicting whether two species will tend to co-occur or not based on their traits—is extremely challenging. Here, we develop a novel protocol to tackle the challenge, and we demonstrate its effectiveness by showing that ecological species traits can predict species co-occurrence in a large dataset of North American Odonata. To this end, we first used the Hierarchical Modeling of Species Communities framework to quantify the pairwise species co-occurrence after controlling for environmental and climatic factors. Then, we used machine learning to generate models which proved capable of predict accurately the observed co-occurrence patterns from species functional traits. Our approach offers a generalizable analytical framework with the potential to clarify long-standing ecological questions

Differences in metal sequestration between zebra mussels from clean and polluted field locations

Organisms are able to detoxify accumulated metals by, e.g. binding them to metallothionein (MT) and/or sequestering them in metal-rich granules (MRG). The different factors involved in determining the capacity or efficiency with which metals are detoxified are not yet known.In this work we studied how the sub-cellular distribution pattern of cadmium, copper and zinc in whole tissue of zebra mussels from clean and polluted surface waters is influenced by the total accumulated metal concentration and by its physiological condition. Additionally we measured the metallothionein concentration in the mussel tissue. Metal concentration increased gradually in the metal-sensitive and detoxified sub-cellular fractions with increasing whole tissue concentrations. However, metal concentrations in the sensitive fractions did not increase to the same extent as metal concentrations in whole tissues. In more polluted mussels the contribution of MRG and MT became more important. Nevertheless, metal detoxification was not sufficient to prevent metal binding to heat-sensitive low molecular weight proteins (HDP fraction). Finally we found an indication that metal detoxification was influenced by the condition of the zebra mussels. MT content could be explained for up to 83% by variations in Zn concentration and physiological condition of the mussels