Individual-based model highlights the importance of trade-offs for virus-host population dynamics and long-term co-existence

Viruses play diverse and important roles in ecosystems. In recent years, trade-offs between host and virus traits have gained increasing attention in viral ecology and evolution. However, microbial organism traits, and viral population parameters in particular, are challenging to monitor. Mathematical and individual-based models are useful tools for predicting virus-host dynamics. We have developed an individual-based evolutionary model to study ecological interactions and evolution between bacteria and viruses, with emphasis on the impacts of trade-offs between competitive and defensive host traits on bacteria-phage population dynamics and trait diversification. Host dynamics are validated with lab results for different initial virus to host ratios (VHR). We show that trade-off based, as opposed to random bacteria-virus interactions, result in biologically plausible evolutionary outcomes, thus highlighting the importance of trade-offs in shaping biodiversity. The effects of nutrient concentration and other environmental and organismal parameters on the virus-host dynamics are also investigated. Despite its simplicity, our model serves as a powerful tool to study bacteria-phage interactions and mechanisms for evolutionary diversification under various environmental conditions.     

Measuring phenological variability from satellite imagery

Abstract. Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.