(Table 1) Compressional wave velocities for rocks from DSDP Hole 31-293 in the Philippine Sea

Compressional wave velocities measured in gabbroic rocks and metabasites recovered from Site 293 of Leg 31 in the Philippine Sea (on the Central Basin Fault) are correlative with seismic velocities determined for Layer 3. The lower crustal origin for these rocks suggested by this data is further supported by the similarity between these samples, dredge haul samples from fracture zones in the main ocean basins and rocks found in ophiolite complexes. These plutonic rocks were possibly introduced to the sea floor by movements along the Central Basin Fault, a major tectonic feature in the Philippine Sea, or formed as part of new ocean crust within a leaky transform fault.

Geochemistry of picrite basalts from the Liohi Volcano

Research of the ocean floor using the Mir submersibles carried out south of the Hawaiian Archipelago allowed to recover flows of recent picrite basalts. Lava vents are confined to a field of development of open fractures of a gjar type. Basalts represent initial lava flows in the structure of the Hawaiian volcanic archipelago. Considering contents of alkali and rare-earth elements in them, the picrite basalts of the bottom could be assigned to a series of island tholeiites. They are products of high level melting of asthenospheric matter at depth about 75-80 km as a result of decompression near a deep fracture that occurred in the lithosphere and asthenosphere. Similar picrite basalts were found in the base of the youngest volcano of the Hawaiian chain the Loihi Volcano. With respect to contents of alkali metals, these rocks are assigned to the subalkaline series of rocks formed during melting of garnet lherzolites. This could probably be explained by supply of melts from deeper levels of the asthenosphere after partial packing of an initial magma effluent fracture.

Sediments and tephra layers from the southern Tyrrhenian abyssal plain near Marsili Seamount

From the south-eastern Tyrrhenian deep-sea floor, four sediment cores of "Meteor" cruise 22 (1971) are described. These cores were taken in the basin between the Aeolian Islands and the Marsili Seamount, an elevation of more tha 3000 m above the sea floor. The sedimentation of the deep-sea basin is distinguished by a sequence of turbidites with a high sedimentation rate. The composition of the clastic material and the position of the cores in the mouth area of the morphologically very pronounced Stromboli Canyon suggest an interpretation of the turbidite sequence as fan of this canyon onto the deep-sea floor. A white rhyolitic pumice-tephra at the base of the 4 m thick sequence of turbidites in core M22-102 has been correlated with the Pelato eruption of the island of Liparo in the 6th century A.D. At the foot of the Marsili Seamount - apparently in morphologically elevated positions - the influence of the turbidite sedimentation increases, the rate of sedimentation is lower and stratigraphic omissions are probable. Here, rather compacted globigerina marls have been found in only 15 -25 cm depth. In addition, volcanic material in the form of lapilli layers, palagonitized ashes and detrital volcanic sands of the Marsili Seamount have been encountered in this area. An up to 3 cm thick layer of completely palagonitized basaltic ash intercalates with the marls at the base of two cores. Layers of very fresh olivine basaltic lapilli in core 103 and palagonitized lapilli of latitic composition in core 104 testify to an explosive submarine volcanism of the Marsili Seamount. According to the stratigraphy of core 103, the latest manifestations of this basaltic volcanism belong to the late Pleistocene (Emiliana huxleyi-zone of Nannoplankton stratigraphy) The basaltic lapilli are glassy to perhyaline with phenocrysts or microphenocrysts predominantely of olivine. The petrological character of the basaltic volcanites with high MgO, Ni, Cr and high MgO/FeO- and Ni/Co-ratios exhibits primitive basaltic features. These basalts clearly differ from basalts of the ocean floors, mid-ocean ridges and marginal basins. Prominent features are a missing iron-enrichment trend and low TiO2. Al2O3 tends to be high, as well as K2O and related trace elements (Ba, Sr). In spite of silica undrsaturation and high color index, the Marsili basalt exhibit some analogies with the calcalkaline basalts of the Aeolian arc, as well as the undersaturated basalts of some other circumoceanic areas.

Distribution models for mountain plant species: The value of elevation

The climatic conditions of mountain habitats are greatly influenced by topography. Large differences in microclimate occur with small changes in elevation, and this complex interaction is an important determinant of mountain plant distributions. In spite of this, elevation is not often considered as a relevant predictor in species distribution models (SDMs) for mountain plants. Here, we evaluated the importance of including elevation as a predictor in SDMs for mountain plant species. We generated two sets of SDMs for each of 73 plant species that occur in the Pacific Northwest of North America; one set of models included elevation as a predictor variable and the other set did not. AUC scores indicated that omitting elevation as a predictor resulted in a negligible reduction of model performance. However, further analysis revealed that the omission of elevation resulted in large over-predictions of species' niche breadths-this effect was most pronounced for species that occupy the highest elevations. In addition, the inclusion of elevation as a predictor constrained the effects of other predictors that superficially affected the outcome of the models generated without elevation. Our results demonstrate that the inclusion of elevation as a predictor variable improves the quality of SDMs for high-elevation plant species. Because of the negligible AUC score penalty for over-predicting niche breadth, our results support the notion that AUC scores alone should not be used as a measure of model quality. More generally, our results illustrate the importance of selecting biologically relevant predictor variables when constructing SDMs.