Pollinator loss causes rapid adaptive evolution of selfing and dramatically reduces genome-wide genetic variability.

Although selfing populations harbor little genetic variation limiting evolutionary potential, the causes are unclear. We experimentally evolved large, replicate populations of Mimulus guttatus for nine generations in greenhouses with or without pollinating bees and studied DNA polymorphism in descendants. Populations without bees adapted to produce more selfed seed yet exhibited striking reductions in DNA polymorphism despite large population sizes. Importantly, the genome-wide pattern of variation cannot be explained by a simple reduction in effective population size, but instead reflects the complicated interaction between selection, linkage, and inbreeding. Simulations demonstrate that the spread of favored alleles at few loci depresses neutral variation genome wide in large populations containing fully selfing lineages. It also generates greater heterogeneity among chromosomes than expected with neutral evolution in small populations. Genome-wide deviations from neutrality were documented in populations with bees, suggesting widespread influences of background selection. After applying outlier tests to detect loci under selection, two genome regions were found in populations with bees, yet no adaptive loci were otherwise mapped. Large amounts of stochastic change in selfing populations compromise evolutionary potential and undermine outlier tests for selection. This occurs because genetic draft in highly selfing populations makes even the largest changes in allele frequency unremarkable.

Detection and plant monitoring programs: lessons from an intensive survey of Asclepias meadii with five observers.

Monitoring programs, where numbers of individuals are followed through time, are central to conservation. Although incomplete detection is expected with wildlife surveys, this topic is rarely considered with plants. However, if plants are missed in surveys, raw count data can lead to biased estimates of population abundance and vital rates. To illustrate, we had five independent observers survey patches of the rare plant Asclepias meadii at two prairie sites. We analyzed data with two mark-recapture approaches. Using the program CAPTURE, the estimated number of patches equaled the detected number for a burned site, but exceeded detected numbers by 28% for an unburned site. Analyses of detected patches using Huggins models revealed important effects of observer, patch state (flowering/nonflowering), and patch size (number of stems) on probabilities of detection. Although some results were expected (i.e. greater detection of flowering than nonflowering patches), the importance of our approach is the ability to quantify the magnitude of detection problems. We also evaluated the degree to which increased observer numbers improved detection: smaller groups (3-4 observers) generally found 90 - 99% of the patches found by all five people, but pairs of observers or single observers had high error and detection depended on which individuals were involved. We conclude that an intensive study at the start of a long-term monitoring study provides essential information about probabilities of detection and what factors cause plants to be missed. This information can guide development of monitoring programs.

Differential regulation of a MYB transcription factor is correlated with transgenerational epigenetic inheritance of trichome density in Mimulus guttatus.

• Epigenetic inheritance, transgenerational transmission of traits not proximally determined by DNA sequence, has been linked to transmission of chromatin modifications and gene regulation, which are known to be sensitive to environmental factors. Mimulus guttatus increases trichome (plant hair) density in response to simulated herbivore damage. Increased density is expressed in progeny even if progeny do not experience damage. To better understand epigenetic inheritance of trichome production, we tested the hypothesis that candidate gene expression states are inherited in response to parental damage. • Using M. guttatus recombinant inbred lines, offspring of leaf-damaged and control plants were raised without damage. Relative expression of candidate trichome development genes was measured in offspring. Line and parental damage effects on trichome density were measured. Associations between gene expression, trichome density, and response to parental damage were determined. • We identified M. guttatus MYB MIXTA-like 8 as a possible negative regulator of trichome development. We found that parental leaf damage induces down-regulation of MYB MIXTA-like 8 in progeny, which is associated with epigenetically inherited increased trichome density. • Our results link epigenetic transmission of an ecologically important trait with differential gene expression states - providing insight into a mechanism underlying environmentally induced 'soft inheritance'.

Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry.

BACKGROUND: Ecological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers. METHODS: We assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions. RESULTS: We found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions). CONCLUSION: Our results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.

A prototype forecasting system for bird-borne disease spread in North America based on migratory bird movements.

The past two decades have seen major outbreaks of influenza viruses and flaviviruses that are spread at least in part by migratory birds. Although much new information has accumulated on the natural history of the viruses, and on the geography of migration by individual bird species, no synthesis has been achieved regarding likely patterns of spread of such pathogens by migratory birds, which constitutes a large-scale challenge in understanding the geography of bird migration. We here present a first step in this direction: a summary of seasonal (breeding, wintering) distributions of all 392 North American bird species that show marked seasonal migratory movements and that meet a series of conditions for inclusion in our analyses. We use species-level interseasonal connectivity among distributional areas to make initial forecasts of patterns of spread of bird-borne diseases via bird migration. We identify key next steps towards improved forecasting of spread patterns of bird-borne pathogens in North America, which will require substantial improvements in knowledge of the geography of bird migration.