L. Gopal results 40
· Newest (Page 1/2, per page 25)
Author: L. Gopal ×Clear All Filters
Search Results
Application of a light detection and ranging digital elevation model for defining and mapping lakeshore riparian areasNARA Subscribed
In this study, a Light Detection and Ranging-based Digital Elevation Model was developed to delineate and assess the extent of lakeshore riparian environments across the Southwest regions of British Columbia, Canada. The analysis revealed substantial variability in riparian zone area and extent across diverse physiographic settings, with elevation emerging as the primary driver of riparian extent. Low-elevation lake basins wer...
A global analysis of field body temperatures of active squamates in relation to climate and behaviourNARA Subscribed
Aim Squamate fitness is affected by body temperature, which in turn is influenced by environmental temperatures and, in many species, by exposure to solar radiation. The biophysical drivers of body temperature have been widely studied, but we lack an integrative synthesis of actual body temperatures experienced in the field, and their relationships to environmental temperatures, across phylogeny, behaviour and climate. Locatio...
Temperature and the pace of lifeNARA Subscribed
A global analysis of viviparity in squamates highlights its prevalence in cold climatesNARA Subscribed
Aim Viviparity has evolved more times in squamates than in any other vertebrate group; therefore, squamates offer an excellent model system in which to study the patterns, drivers and implications of reproductive mode evolution. Based on current species distributions, we examined three selective forces hypothesized to drive the evolution of squamate viviparity (cold climate, variable climate and hypoxic conditions) and tested...
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severityNARA Subscribed
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the pr...