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Aim: The identification of the mechanisms determining spatial variation in biological diversity along elevational gradients is a central objective in ecology and biogeography. Here, we disentangle the direct and indirect effects of abiotic drivers (climatic conditions, and land use) and biotic drivers (vegetation structure and food resources) on functional diversity and composition of bird and bat assemblages along a tropical elevational gradient. Location: Southern slopes of Mt. Kilimanjaro, Tanzania, East Africa. Methods: We counted birds and recorded bat sonotypes on 58 plots distributed in near-natural and anthropogenically modified habitats from 700 to 4,600 m above sea level. For the recorded taxa, we compiled functional traits related to movement, foraging and body size from museum specimens and databases. Further, we recorded mean annual temperature, precipitation, vegetation complexity as well as the number of fruits, flowers, and insect biomass as measures of resource availability on each study site. Results: Using path analyses, we found similar responses of bird and bat functional diversity to the variation in abiotic and biotic drivers along the elevational gradient. In contrast, the functional composition of both taxa showed distinct responses to abiotic and biotic drivers. For both groups, direct temperature effects were most important, followed by resource availability, precipitation and vegetation complexity. Main Conclusions: Our findings indicate that physiological and metabolic constraints imposed by temperature and resource availability determine the functional diversity of bird and bat assemblages, whereas the composition of individual functional traits is driven by taxon-specific processes. Our study illustrates that distinct filtering mechanisms can result in similar patterns of functional diversity along broad environmental gradients. Such differences need to be taken into account when it comes to conserving the functional diversity of flying vertebrates on tropical mountains.
Background: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. Methods: One hundred patients (median age, 69 years; range, 19–94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). Results: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). Conclusions: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment.