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Determination of the source rocks for the diatexites from the Edough Massif, Annaba, NE Algeria
(2012)
The crystalline Edough Massif is located in the oriental part of the Algerian coastline. It consists of two tectonically superposed units of gneisses, augen-gneisses and migmatitic gneisses in the lower unit and micaschists in the upper unit. The crystalline rocks underwent a low to moderate degree of metamorphism; the gneisses suffered partial melting. They display migmatitic features such as nebulitic structures with contorted leucosome layers and K-feldspar porphyroblasts and thus can be classified as diatexites. The mineralogical composition of these rocks is very homogenous and consists of K-feldspar, micas and quartz. The feldspar-rich, arkosic nature of the outcrop implies a granitic source rock. High K2O/Na2O ratios and high A/CNK > 1.1 indicate an S-type granite source and a peraluminous composition of the protolith respectively. Chondrite normalized REE distribution patterns of the Edough diatexites show gently inclined patterns with a minor negative Eu anomaly (Eu/Eu* = 0.36-0.49), which points to a very slightly differentiated granitic source. The REE pattern and trace element data of the diatexites are similar to those of average Proterozoic upper continental crust, which suggests that they are derived mainly from upper continental crust and were deposited in continental margins.
Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change.