Metazoan/microbial biostalactites from present‐day submarine caves in the Mediterranean Sea

Biostalactites formed by metazoan–microbialite associations from three submerged marine caves in the Plemmirio Peninsula (south of Syracuse, Ionian Sea) are randomly distributed and show different sizes and morphologies, as well as variations in surface roughness/smoothness. The biostalactites consist of crusts a few centimeters thick of small serpulids and other metazoans, associated with fine‐grained carbonate; the larger ones often include a nucleus of serpulid tubes (Protula). The metazoans include mainly serpuloideans, sponges, bryozoans and foraminifers but microbial carbonates are also significant components. The composition of both the living communities and thanatocoenoses on the outer surfaces, as well as the composition and fabric of the internal framework, were analysed and used to reconstruct the history of the caves. All of the identified sessile faunas mainly consist of cryptic and sciaphilic dwellers that reflect cave conditions and their variations through time. The distribution pattern, composition and abundance of the present‐day dwellers largely depend on the degree of roughness of the biostalactite surfaces and their positions within the caves. It has been suggested that the Protula specimens in the nuclei represent pioneer populations that formed aggregates during the early cave colonization phase, in response to relatively high food supply from seawater inflow and intruding continental waters. By contrast, the outer metazoan–microbialite carbonates reflect more confined conditions in the caves caused by Holocene sea‐level rises. Hypotheses are proposed for biostalactite growth, taking into account information about the growth rates of some constituents, and evidence of dissolution effects. Similarities and differences between these biostalactites and other Holocene deposits previously described from submarine caves in the Mediterranean Sea and in tropical reefs are discussed.     

An approximate nearest neighbors search algorithm for low-dimensional grid locations

We propose a new algorithm for the problem of approximate nearest neighbors (ANN) search in a regularly spaced low-dimensional grid for interpolation applications. It associates every sampled point to its nearest interpolation location, and then expands its influence to neighborhood locations in the grid, until the desired number of sampled points is achieved on every grid location. Our approach makes use of knowledge on the regular grid spacing to avoid measuring the distance between sampled points and grid locations. We compared our approach with four different state-of-the-art ANN algorithms in a large set of computational experiments. In general, our approach requires low computational effort, especially for cases with high density of sampled points, while the observed error is not significantly different. At the end, a case study is shown, where the ionosphere dynamics is predicted daily using samples from a mathematical model, which runs in parallel at 56 different longitude coordinates, providing sampled points not well distributed that follow Earth’s magnetic field-lines. Our approach overcomes the comparative algorithms when the ratio between the number of sampled points and grid locations is over 2849:1.