Meteorite impacts have been recognized as an important geological process shaping the surfaces of planetary bodies at various length and time scales (Osinski & Pierazzo, 2013). Currently, 190 impact structures are confirmed on the Earth (Earth Impact Database, http://www.passc.net, 2020). These meteorite impacts, together with extraterrestrial structures, constitute the ground truth of various aspects of the impact processes.The typical hypervelocity impact into a solid planetary surface induces the formation of a crater involving (a) a depression lined by strongly deformed and modified rock/sediment, (b) partially back filled with impact melted rock and impact breccias, and (c) a raised rim at the crater edge. The contrasting physical properties of the pre-and post-impact structure and materials, such as change in porosity, rock types, or fluid content, make geophysical methods applicable at the crater site (Barton et al., 2010;Gilder et al., 2018).The Wabar crater field (Figure 1) is a group of three closely spaced (rim diameters 114 m for Philby-B, 64 m for Philby-A, and 11-m crater from the impact of a tiny meteorite segment) very young impact craters

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Benthic foraminifera are unicellular eukaryotic organisms that play significant roles in marine food webs. Monitoring changes in their distributions and abundances are crucial to understand their roles and functions and to predict the effects of environmental impacts and climatic changes. Numerous biotic and abiotic factors influence foraminiferal distribution and abundance, but the ecology of individual taxa (i.e., autoecology) is difficult to define and this commonly results in a generalization of functions in their environment. In this study, we explore methods for inferring foraminiferal species ecology and niche requirements using Species Distribution Models (SDM). We modelled the distributions of four benthic species (i.e., Peneroplis planatus, P. pertusus, P. arietinus and Coscinospira hemprichii) that host algal endosymbionts against a large collection of predictors (ecologically meaningful environmental variables, EMEV) variables in the Arabian Gulf. To identify combinations of effective predictor variables, we compiled several models and then selected three with different variable set combinations to compare their predictive performance. Mean iron concentration, diffusion attenuation, and dissolved oxygen were identified as the most important variables determining the distribution of these species. Our model successfully predicts their current habitat suitability in the Arabian Gulf (AUROC = 92%). The model also identified areas along the western coastline of the Gulf as highly suitable habitats for the four species (Habitat Suitability Index > 0.8). Then, the model was spatially extended over the world’s oceans and marginal seas, reliably identifying areas with known distributions of the four species (AUROC = 89%). Here we demonstrate how a SDM model can be a useful tool in capturing complex habitat features for benthic organisms. Such models, when correctly applied using several remotely sensed environmental and geological variables, can be a very useful ecological tool for spatial and temporal predictions even in the current context of global climate change.

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<p>In this study, we explore the use of Species Distribution Models (SDM) to infer spatial distribution of four species of benthic foraminifera around the globe. We modelled the distributions of <em>Peneroplis planatus, P. pertusus, P. arietinus </em>and<em> Coscinospira hemprichii </em>against a large collection of ecologically meaningful environmental variables (EMEV) variables in the Arabian Gulf. To identify combinations of effective predictor EMEV, we compiled several models and narrowed down to a subset based on set of predictive performance metrics. Mean iron concentration, diffusion attenuation, and dissolved oxygen were identified as important variables influencing the distribution of these species. The modelling task is essentially composed of two parts (1) Initial modelling of the actual known distributions of species in a well-defined basin and subsequent validation. (2) Spatial extrapolation over a global extent. Our model successfully predicted current habitat suitability for the four species within Arabian Gulf basin (AUROC = 92%).  It also identified areas along the western coastline as highly suitable habitats (Habitat Suitability Index > 0.8). Further, it reliably identified areas with known distributions of the four species (AUROC = 89%) around the world. Here we demonstrate how a SDM model can be a useful tool in capturing complex habitat features for benthic organisms and reduce sampling and accessibility concerns.</p>