Molecular phylogenetic evidence for ancient divergence of lizard taxa on either side of Wallace's Line

Wallace's Line, separating the terrestrial faunas of South East Asia from the Australia-New Guinea region, is the most prominent and well-studied biogeographical division in the world. Phylogenetically distinct subgroups of major animal and plant groups have been documented on either side of Wallace's Line since it was first proposed in 1859. Despite its importance, the temporal history of fragmentation across this line is virtually unknown and the geological foundation has rarely been discussed. Using molecular phylogenetics and dating techniques, we show that the split between taxa in the South East Asian and the Australian-New Guinean geological regions occurred during the Late Jurassic to Early Cretaceous in two independent lizard clades. This estimate is compatible with the hypothesis of rifting Gondwanan continental fragments during the Mesozoic and strongly rejects the hypothetical origin of various members of the Australian-New Guinean herpetofauna as relatively recent invasions from South East Asia. Our finding suggests an ancient fragmentation of lizard taxa on either side of Wallace's Line and provides further evidence that the composition of modern global communities has been significantly affected by rifting and accretion of Gondwanan continental plates during the Middle to Late Mesozoic

Hierarchical mission planning with a GA-optimizer for unmanned high altitude pseudo-satellites

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions