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Keywords = circalittoral rocky shelf

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18 pages, 4782 KiB  
Article
Cemented on the Rock. A Pleistocene Outer Shelf Lithobiont Community from Sicily, Italy
by Antonietta Rosso, Agatino Reitano and Rossana Sanfilippo
Geosciences 2020, 10(9), 343; https://doi.org/10.3390/geosciences10090343 - 29 Aug 2020
Cited by 2 | Viewed by 3092
Abstract
The lithobiont community encrusting an early Pleistocene palaeocliff cropping out north of Augusta (SE Sicily, Italy) was investigated based on field observations and laboratory inspection of two rocky samples. Bryozoans, serpulids, brachiopods and bivalves encrusted part of the exposed surfaces that were bored [...] Read more.
The lithobiont community encrusting an early Pleistocene palaeocliff cropping out north of Augusta (SE Sicily, Italy) was investigated based on field observations and laboratory inspection of two rocky samples. Bryozoans, serpulids, brachiopods and bivalves encrusted part of the exposed surfaces that were bored mostly by clionaid sponges. Bryozoans, with at least 25 species detected on the rocky samples, are the most diversified skeletonized lithobionts also accounting for the highest number of colonies/specimens and highest coverage. Brachiopods, with the only species Novocrania anomala and a few but large cemented valves, cover wide surfaces. Serpulids, with two species identified on the sampled rocks and further two on the outcrop, were intermediate. A multiphase colonization is present, including a final epilithobiont community locally formed on eroded surfaces exposing a network of pervasive borings. The co-occurrence of very sciaphilic species having circalittoral to bathyal distributions suggests that the studied community thrived on a rocky substratum located near or at the shelf break, probably belonging to the shelf break (or RL) biocoenosis, also in agreement with observations on the fossil content of neighboring marly sediments. The observed relationships among colonizers largely represent mere superimpositions, and real interactions are not enough to state species competitiveness. Full article
(This article belongs to the Special Issue Quaternary Sedimentary Successions)
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28 pages, 11009 KiB  
Article
3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf
by Elena Prado, Augusto Rodríguez-Basalo, Adolfo Cobo, Pilar Ríos and Francisco Sánchez
Remote Sens. 2020, 12(15), 2466; https://doi.org/10.3390/rs12152466 - 31 Jul 2020
Cited by 17 | Viewed by 5775
Abstract
The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from [...] Read more.
The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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