ABSTRACT
The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for Hyla squirella, a widespread frog species that occurs in a broad range of habitat types. We collected microsatellite data from 675 samples representing 20 localities from the southeastern USA and used machine-learning methodologies to identify significant habitat features associated with genetic structure. In simulation, we confirm that our machine-learning algorithm can successfully identify landscape features responsible for generating between-population genetic differentiation, suggesting that it can be a useful hypothesis-generating tool for landscape genetics. In our study system, we found that H. squirella were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median pR(2) = 47.78) than spatial distance alone (median pR(2) = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of H. squirella. We discuss how these habitat types may mechanistically facilitate dispersal in H. squirella. This study provides empirical support for the hypothesis that habitat-use can be an informative correlate of genetic differentiation, even for species that occur in a wide range of habitats.
Subject(s)
Anura/genetics , Artificial Intelligence , Ecosystem , Genetic Variation , Genetics, Population/methods , Algorithms , Animals , Anura/physiology , Cluster Analysis , Computer Simulation , Microsatellite Repeats , Models, Genetic , Population Density , Southeastern United States , Species SpecificityABSTRACT
The authors reviewed the reports of all emergent EEG (EmEEG) performed in our hospital within 1 hour of the test being ordered over a period of 52 months. Two hundred sixty-one EmEEG (12.8% of all EEG) were performed. The most common reason to order the test was a change in mental status or coma (17.6%). Although EmEEG was ordered to rule out status epilepticus (SE) in 60.2% of cases, this diagnosis was made in only 10.7% of patients. The only independent predictor for SE was a history of cardiac or respiratory arrest (odds [95% CI], 6.8 [2.7 to 16.9]).
