Do Frogs Get Their Kicks on Route 66? Continental U.S. Transect Reveals Spatial and Temporal Patterns of Batrachochytrium dendrobatidis Infection

The chytrid fungus Batrachochytrium dendrobatidis (Bd) has been devastating amphibians globally. Two general scenarios have been proposed for the nature and spread of this pathogen: Bd is an epidemic, spreading as a wave and wiping out individuals, populations, and species in its path; and Bd is endemic, widespread throughout many geographic regions on every continent except Antarctica. To explore these hypotheses, we conducted a transcontinental transect of United States Department of Defense (DoD) installations along U.S. Highway 66 from California to central Illinois, and continuing eastward to the Atlantic Seaboard along U.S. Interstate 64 (in sum from Marine Corps Base Camp Pendleton in California to Naval Air Station Oceana in Virginia). We addressed the following questions: 1) Does Bd occur in amphibian populations on protected DoD environments? 2) Is there a temporal pattern to the presence of Bd? 3) Is there a spatial pattern to the presence of Bd? and 4) In these limited human-traffic areas, is Bd acting as an epidemic (i.e., with evidence of recent introduction and/or die-offs due to chytridiomycosis), or as an endemic (present without clinical signs of disease)? Bd was detected on 13 of the 15 bases sampled. Samples from 30 amphibian species were collected (10% of known United States' species); half (15) tested Bd positive. There was a strong temporal (seasonal) component; in total, 78.5% of all positive samples came in the first (spring/early-summer) sampling period. There was also a strong spatial component—the eleven temperate DoD installations had higher prevalences of Bd infection (20.8%) than the four arid (<60 mm annual precipitation) bases (8.5%). These data support the conclusion that Bd is now widespread, and promote the idea that Bd can today be considered endemic across much of North America, extending from coast-to-coast, with the exception of remote pockets of naïve populations

Stratigraphic and petrological data on the Late Cretaceous Durkan Complex (North Makran domain, SE Iran): an example of plume-type ophiolite.

Ophiolitic basaltic and metabasaltic rocks are widespread within accretionary and collisional belts and their tectono-stratigraphic setting and petrological features are sensitive to different geodynamic settings of formation. Among these different basaltic rocks, those with oceanic island basalt (OIB) chemical affinity are of special interest as they may represent remnants of deformed oceanic seamounts or, in other words, plume-type ophiolites (sensu Dilek and Furnes, 2011). It follows that multidisciplinary studies including stratigraphic and petrological data are fundamental to constraint the tectono-magmatic setting of formation and the geodynamic significance of the basaltic rocks within accretionary and collisional belts. In the Makran Accretionary Prism (SE Iran), the North Makran domain consists of distinct tectonic units representing remnants of the Cretaceous-Paleocene accretionary-subduction complex formed in response to the northward subduction of the Neo-Tethys oceanic lithosphere. Among these units, the Durkan Complex shows abundant basaltic and meta-basaltic rocks as well as volcaniclastic rocks. We present here a summary of the results of geological and stratigraphic studies, as well as petrological investigations of the volcanic rocks forming the Durkan Complex. The latter is composed by distinct tectonic slices showing either non-metamorphic or slightly metamorphosed successions, which record volcanic activity and sedimentation during the Late Cretaceous in a seamount cap, seamount slope, and nascent seamount. Basaltic and metabasaltic rocks display transitional chemical affinity with compositions resembling those of plume-type mid-oceanic ridge basalts and within-plate OIB compositions with a clear alkaline affinity. Trace element and REE petrogenetic models show that the Durkan basaltic rocks were generated from the partial melting of depleted sub-oceanic mantle source that was metasomatized by OIB-type chemical components in a within-plate oceanic setting. Collectively, these multidisciplinary data indicate that the Durkan Complex include fragments of oceanic seamount and can be regarded as a plume-type ophiolite, possibly formed in association to a Late Cretaceous mantle plume activity in the Neo-Tethys Ocean

Algorithms for Solving Satisfiability Problems with Qualitative Preferences

Abstract. In this work we present a complete picture of our work on computing optimal solutions in satisfiability problems with qualitative preferences. With this task in mind, we first review our work on computing optimal solutions by imposing an ordering on the way the search space is explored, e.g., on the splitting heuristic in case the DPLL algorithm is used. The main feature of this approach is that it guarantees to compute all and only the optimal solutions, i.e., models which are not optimal are not even computed: For this result, it is essential that the splitting heuristic of the solver follows the partial order on the expressed preferences. However, for each optimal solution, a formula that prunes non-optimal solutions needs to be retained, thus this procedure does not work in polynomial space when computing all optimal solutions. We then extend our previous work and show how it is possible to compute optimal solutions using a generate-and-test approach: Such a procedure is based on the idea to first compute a model and then check for its optimality. As a consequence, no ordering on the splitting heuristic is needed, but it may compute also nonoptimal models. This approach does not need to retain formulas indefinitely, thus it does work in polynomial space. We start from a simple setting in which a preference is a partial order on a set of literals. We then show how other forms of preferences, i.e., quantitative, qualitative on formulas and mixed qualitative/quantitative can be captured by our framework, and present alternatives for computing “complete ” sets of optimal solutions. We finally comment on the implementation of the two procedures on top of state-of-the-art satisfiability solvers, and discuss related work.