A Legume Ethylene-Insensitive Mutant Hyperinfected by Its Rhizobial Symbiont

Development of the Rhizobium-legume symbiosis is controlled by the host plant, although the underlying mechanisms have remained obscure. A mutant in the annual legume Medicago truncatula exhibits an increase of more than an order of magnitude in the number of persistent rhizobial infections. Physiological and genetic analyses indicate that this same mutation confers insensitivity to the plant hormone ethylene for multiple aspects of plant development, including nodulation. These data support the hypothesis that ethylene is a component of the signaling pathway controlling rhizobial infection of legumes.     

Gravity Waves in Jupiter's Thermosphere

The Atmosphere Structure Instrument on the Galileo probe detected wavelike temperature fluctuations superimposed on a 700-kelvin temperature increase in Jupiter's thermosphere. These fluctuations are consistent with gravity waves that are viscously damped in the thermosphere. Moreover, heating by these waves can explain the temperature increase measured by the probe. This heating mechanism should be applicable to the thermospheres of the other giant planets and may help solve the long-standing question of the source of their high thermospheric temperatures.     

Bayesian spatial modeling of data from avian point count surveys

We present a unified framework for modeling bird survey data collected at spatially replicated survey sites in the form of repeated counts or detection history counts, through which we model spatial dependence in bird density and variation in detection probabilities due to changes in covariates across the landscape. The models have a complex hierarchical structure that makes them suited to Bayesian analysis using Markov chain Monte Carlo (MCMC) algorithms. For computational efficiency, we use a form of conditional autogressive model for modeling spatial dependence. We apply the models to survey data for two bird species in the Great Smoky Mountains National Park. The algorithms converge well for the more abundant and easily detected of the two species, but some simplification of the spatial model is required for convergence for the second species. We show how these methods lead to maps of estimated relative density which are an improvement over those that would follow from past approaches that ignored spatial dependence. This work also highlights the importance of good survey design for bird species mapping studies.