A new species of Eimeria Schneider, 1885 (Apicomplexa: Eimeriidae) from the eastern gray squirrel,Sciurus carolinensis (Rodentia: Sciuridae: Sciurinae: Sciurini) from Oklahoma,USA

Systematic Parasitology - A new species of Eimeria Schneider, 1885 is described from an eastern gray squirrel (Sciurus carolinensis) from McCurtain County, Oklahoma, USA. Oöcysts of Eimeria...     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.     

Effect of detection heterogeneity in occupancy‐detection models: an experimental test of time‐to‐first‐detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time‐to‐first‐detection (TTD) occupancy models have been proposed as a cost–effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy‐detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala Phascolarctos cinereus populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness‐of‐fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy‐detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness‐of‐fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy‐detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.     

Effects of Rhododendron removal and prescribed fire on bees and plants in the southern Appalachians

Rhododendron maximum is an evergreen shrub native to the Appalachian Mountains of North America that has expanded in recent decades due to past disturbances and land management. The purpose of this study was to explore how bees and plants were affected by the experimental removal of R. maximum followed by a prescribed fire in one watershed compared to a neighboring reference watershed. Bees and plants were sampled for three years in both watersheds. Comparisons were based on the rarefaction and extrapolation sampling curves of Hill numbers as well as multivariate methods to assess effects on community composition. Bee richness, Shannon''s diversity, and Simpson''s diversity did not differ between watersheds in the year after removal but were all significantly higher in the removal watershed in year two, following the prescribed fire. Bee Shannon''s diversity and Simpson''s diversity, but not richness, remained significantly higher in the removal watershed in the third year. Similar but weaker patterns were observed for plants. Comparisons of community composition found significant differences for bees in the second and third year and significant differences for plants in all three years. For both groups, significant indicator taxa were mostly associated with the removal watershed. Because bees appeared to respond more strongly to the prescribed fire than to the removal of R. maximum and these benefits weakened considerably one year after the fire, clearing R. maximum does not appear to dramatically improve pollinator habitat in the southern Appalachians. This conclusion is underscored by the fact that about one quarter of the bee species in our study area were observed visiting R. maximum flowers. The creation of open areas with wildflowers may be a better way to benefit bees in this region judging from the high diversity of bees captured in the small roadside clearings in this study.