University of the Aegean

About: University of the Aegean is a education organization based out in Mytilene, Greece. It is known for research contribution in the topics: Population & Tourism. The organization has 2818 authors who have published 8100 publications receiving 179275 citations. The organization is also known as: UAEG.

Which Taxa Are Alien? Criteria, Applications, and Uncertainties

Abstract: Human activities such as the transport of species to new regions and modifications of the environment are increasingly reshaping the distribution of biota. Accordingly, developing robust, repeatable, and consistent definitions of alien species that serve scientific and policy purposes has become of prime importance. We provide a set of classification criteria that are widely applicable across taxa and realms and offer guidance on their use in practice. The criteria focus on (a) acknowledging the role of assessment uncertainty, (b) incorporating time since introduction, (c) considering infraspecific taxonomic ranks, and (d) differentiating between alien species whose survival depends on explicit human assistance from those that survive without such assistance. Furthermore, we make recommendations for reducing assessment uncertainty, suggest thresholds for species assessment, and develop an assessment scheme. We illustrate the application of the assessment criteria with case studies. Finally, the implications for alien species management, policy, and research are discussed.

Mass Loading and Fate of Linear and Cyclic Siloxanes in a Wastewater Treatment Plant in Greece

Abstract: The occurrence and fate of 5 cyclic (D3 to D7) and 12 linear (L3 to L14) siloxanes were investigated in raw and treated wastewater (both particulate and dissolved phases) as well as in sludge from a wastewater treatment plant (WWTP) in Athens, Greece. Cyclic and linear siloxanes (except for L3) were detected in all influent wastewater and sludge samples at mean concentrations of (sum of 17 siloxanes) 20 μg L(-1) and 75 mg kg(-1), respectively. The predominant compounds in wastewater were L11 (24% of the total siloxane concentration), L10 (16%), and D5 (13%), and in sludge were D5 (20%) and L10 (15%). The distribution of siloxanes between particulate and dissolved phases in influents differed significantly for linear and cyclic siloxanes. Linear siloxanes showed higher solid-liquid distribution coefficients (log K(d)) than did cyclic compounds. For 10 of the 16 compounds detected in influents, the removal efficiency was higher than 80%. Sorption to sludge and biodegradation and/or volatilization losses are important factors that affect the fate of siloxanes in WWTPs. The mean total mass of siloxanes that enter into the WWTP via influent was 15.1 kg per day(-1), and the mean total mass released into the environment via effluent was 2.67 kg per day(-1).

Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi‐spectral remotely sensed data

Abstract: Automated feature extraction based on prototypes is only partially successful when applied to remotely sensed imagery of natural scenes due to the complexity and unpredictability of the shape and geometry of natural features. Here, a new method is developed for extracting the locations of treetops by applying GIS (Geographical Information System) overlay techniques and morphological functions to high spatial resolution airborne imagery. This method is based on the geometrical and spatial properties of tree crowns. Airborne data of the study site in the New Forest, UK included colour aerial photographs, LIDAR (Light Detection And Ranging) and ATM (Airborne Thematic Mapper) imagery. A DEM (Digital Elevation Model) was generated from LIDAR data and then subtracted from the original LIDAR image to create a Canopy Height Model (CHM). A set of procedures using image contouring and the manipulation of the resulting polygons was implemented to extract treetops from the aerial photographs and the CHM. Criteria were developed and threshold values were set using a supervised approach for the acceptance or rejection of features based on field knowledge. Tree species were mapped by classifying the ATM data and these data were co-registered with the treetop layer. For broadleaved deciduous plantations the success of treetop extraction using aerial photographs was 91%, but was much lower using LIDAR data. For semi-natural forests, the LIDAR produced better results than the aerial photographs with a success of 80%, which was considered high, given the complexity of these uneven aged stands. The methodology presented here is easy to apply as it is implemented within a GIS and the final product is an accurate map with information about the location, height and species of each tree.