Options for managing human threats to high seas biodiversity

Areas beyond national jurisdiction (ABNJ) constitute 61% of the world's oceans and are collectively managed by countries under the United Nations Convention on the Law of the Sea (UNCLOS). Growing concern regarding the deteriorating state of the oceans and ineffective management of ABNJ has resulted in negotiations to develop an international legally binding instrument (ILBI) for the conservation and sustainable use of biodiversity beyond national jurisdiction under UNCLOS. To inform these negotiations, we identified existing and emerging human activities and influences that affect ABNJ and evaluated management options available to mitigate the most pervasive, with highest potential for impact and probability of emergence. The highest-ranking activities and influences that affect ABNJ were fishing/hunting, maritime shipping, climate change and its associated effects, land-based pollution and mineral exploitation. Management options are diverse and available through a variety of actors, although their actions are not always effective. Area-based management tools (ABMTs), including marine protected areas (MPAs), were the only consistently effective option to mitigate impacts across high-ranked activities and influences. However, addressing land-based pollution will require national action to prevent this at its source, and MPAs offer only a partial solution for climate change. A new ABNJ ILBI could help unify management options and actors to conserve marine biodiversity and ensure sustainable use. Incorporating a mechanism to establish effective ABMTs into the ILBI will help deliver multiple objectives based on the ecosystem approach

Biomass estimations of invasives Yaupon, Chinese Privet and Chinese Tallow in east Texas Hardwood and Pine Ecosystems

Forest understory fuels can have profound effects on fire behavior and crown fire initiation. Accurate fire behavior prediction in understory fuels is an essential component for estimating fire intensity and severity during wildfire and prescribed fire events. This study focused on estimating temporal and seasonal changes in fuel loading parameters associated with the expansion of invasive yaupon (Ilex vomitoria), Chinese privet (Ligustrum sinense), and Chinese tallow (Triadica sebifera) in East Texas pine and hardwood ecosystems. Fuel loading data of invasive species infested sites indicated significant increases in understory biomass when compared to 1988 estimates, suggesting a clear need to revise regional fuel models. Multiple and simple regression biomass prediction equations were developed for all three-invasive species to facilitate fuel load estimates. These improved prediction equations will enhance fire management efforts as well as invasive species mitigation efforts in east Texas

Behavior and Impact of Zirconium in the Soil–Plant System: Plant Uptake and Phytotoxicity

Because of the large number of sites they pollute, toxic metals that contaminate terrestrial ecosystems are increasingly of environmental and sanitary concern (Uzu et al. 2010, 2011; Shahid et al. 2011a, b, 2012a). Among such metals is zirconium (Zr), which has the atomic number 40 and is a transition metal that resembles titanium in physical and chemical properties (Zaccone et al. 2008). Zr is widely used in many chemical industry processes and in nuclear reactors (Sandoval et al. 2011; Kamal et al. 2011), owing to its useful properties like hardness, corrosion-resistance and permeable to neutrons (Mushtaq 2012). Hence, the recent increased use of Zr by industry, and the occurrence of the Chernobyl and Fukashima catastrophe have enhanced environmental levels in soil and waters (Yirchenko and Agapkina 1993; Mosulishvili et al. 1994 ; Kruglov et al. 1996)

Spatial distribution of cultural ecosystem services demand and supply in urban and suburban areas: a case study from Shanghai, China

In the urban ecosystem, the demand for cultural ecosystem services (CES) has greatly increased, and the imbalance of CES supply and demand has been prominent. This paper integrated multi-source data to analyze and visualize the spatial differences in CES demand and supply capacity between Shanghai urban center and suburbs. Based on the geo-tagged photo data, the spatial distribution differences of the four types of CES demand, Recreation & tourism services (RTS) demand, Aesthetic services (AS) demand, Heritage & cultural services (HCS) demand, and Spiritual & religious services (SRS) demand, were analyzed. Residents and tourists had a strong demand for recreation and tourism, and the spatial agglomeration effect was the most obvious. Overall, CES demand was more concentrated in urban center, while the spatial distribution of suburbs was relatively discrete. At the same time, there were under supply areas of CES near the Huangpu River in urban center and suburbs. Results from bivariate Moran's I method showed: 1) there was a significant positive spatial correlation between CES demand and CES supply capacity in urban center; 2) CES supply had a positive external impact on CES demand; and 3) the increase in CES supply capacity can promote the growth of CES demand

Seagrass Posidonia oceanica (L.) Delile as a marine biomarker: A metabolomic and toxicological analysis

A human-made environmental disaster due to the shipwrecked of Costa Concordia cruise vessel on the Tuscan Island of Giglio (Italy) coast and the possible pollutants release has been feared, so requiring the activation of removal operations and the monitoring of the marine environment. In the present study, the seagrass Posidonia oceanica (L.) Delile was used as a bioindicator for the impact of the Costa Concordia accident on the marine and coastal habitat. Different P. oceanica samples were collected in the shipwrecked site under different light conditions. Using high-performance thin-layer chromatography, metabolic analysis of the samples was carried out in order to highlight possible changes in the secondary metabolism due to the permanent shading and the presence of pollutant traces. Moreover, sample mutagenicity, as a consequence of the possible absorption of environmental toxicants leaked by the wreck, was assessed by the Ames test. The results highlighted the permanence of the Concordia-induced alteration in the plant secondary metabolites. However, absorption of chemical pollutants and carcinogens was not reported; this point was confirmed by the lack of mutagenic effects found for the samples tested. Our results clearly evidence that the environmental impact of Costa Concordia wreck and removal operations on P. oceanica was mainly due to the lack of light in the marine habitat. Present methodological approach, which combines metabolomic and genetic ecotoxicological analysis, could represent a suitable strategy to evaluate the impact of human disasters on the ecosystem and to monitor the environmental changes

SANTO: Social Aerial NavigaTion in Outdoors

In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones