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Plants have always represented a key element in landscape delineation. Indeed, plant diversity, whose distribution is influenced by geographic/climatic variability, has affected both environmental and human ecology. The present contribution represents a multi‐proxy study focused on the detection of starch, pollen and non‐pollen palynomorphs in ancient dental calculus collected from pre‐historical individuals buried at La Sassa and Pila archaeological sites (Central Italy). The collected record suggested the potential use of plant taxa by the people living in Central Italy during the Copper‐Middle Bronze Age and expanded the body of evidence reported by previous palynological and palaeoecological studies. The application of a microscopic approach provided information about domesticated crops and/or gathered wild plants and inferred considerations on ancient environments, water sources, and past health and diseases. Moreover, the research supplied data to define the natural resources (e.g., C4‐plant intake) and the social use of the space during that period. Another important aspect was the finding of plant clues referable to woody habitats, characterised by broad‐leaved deciduous taxa and generally indicative of a warm‐temperate climate and grassy vegetation. Other unusual records (e.g., diatoms, brachysclereids) participated in defining the prehistoric ecological framework. Thus, this work provides an overview on the potential of the human dental calculus analysis to delineate some features of the ancient plant ecology and biodiversity. [ABSTRACT FROM AUTHOR]

The advent of metabarcoding (metaB) in aquatic ecology has provided a huge amount of information on plankton biodiversity worldwide. However, the large datasets obtained with that approach are still partially explored, especially for what concerns the study of trophic interactions and food webs. In this study, we analysed a metaB time series from the Long‐Term Ecological Research station MareChiara (LTER‐MC) in the Gulf of Naples, Mediterranean Sea, Italy, to describe the link between plankton diversity and food‐web structure. We derived co‐occurrence networks from metaB time series, identified putative trophic interactions among co‐occurrences based on biological information (body size and trophic habit) available for planktonic organisms detected by metaB, and converted co‐occurrence networks into conceptual models of food webs. The latter showed structural properties resembling ecological processes, because network modularity (the presence of semi‐independent sub‐networks) paralleled trophic hierarchy (the dimensional difference between predator and prey). We also analysed the role of planktonic organisms in maintaining network modularity. The largest predators occupied distinct modules, suggesting niche partitioning, whereas the smallest preys worked as fundamental connectors between larger predators (and different modules). Overall, the presence of trophic hierarchy and modularity shown herein supports the view of the high ecological resilience of plankton, pursued via food‐web rewiring, to environmental shifts. [ABSTRACT FROM AUTHOR]

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments., (Copyright © 2016 Elsevier B.V. All rights reserved.)