Summary 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’.

Anthropogenic perturbations including habitat loss and emerging disease are changing pollinator communities and generating novel selection pressures on plant populations. Disruption of plant–pollinator relationships is predicted to cause plant mating system evolution, although this process has not been directly observed. This study demonstrates the immediate evolutionary effects of pollinator loss within experimental populations of a predominately outcrossing wildflower. Initially equivalent populations evolved for five generations within two pollination treatments: abundant bumblebee pollinators versus no pollinators. The populations without pollinators suffered greatly reduced fitness in early generations but rebounded as they evolved an improved ability to self-fertilize. All populations diverged in floral, developmental, and life-history traits, but only a subset of characters showed clear association with pollination treatment. Pronounced treatment effects were noted for anther–stigma separation and autogamous seed set. Dramatic allele frequency changes at two chromosomal polymorphisms occurred in the no pollinator populations, explaining a large fraction of divergence in pollen viability. The pattern of phenotypic and genetic changes in this experiment favors a sequential model for the evolution of the multitrait “selfing syndrome” observed throughout angiosperms.

This article reports on a design-based implementation research (DBIR) project that addresses the question: How can classrooms be supported at scale to achieve the threedimensional learning goals of the Next Generation Science Standards? Inherent in this question are three key design challenges: (i) three-dimensional learning-the multidimensional changes in curriculum, assessment, and instruction required for three-dimensional learning; (ii) scale-the necessity of change at multiple scales in educational systems; and (iii) diversity-achieving rigor in our expectations with responsiveness to the enduring diversity of our students, classrooms, and schools. We discuss findings from the Carbon TIME project, which focuses on teaching carbon cycling and energy transformations at multiple scales. Findings focus on design and knowledge building in three interconnected contexts. (i) Assessment-understanding and assessing students' three-dimensional learning. Learning progression frameworks provide insight into students' reasoning and the basis for efficient and reliable classroom and large-scale assessments that have used automated scoring of constructed responses for over 80,000 tests. (ii) Classrooms-classroom discourse and learning communities. Six Carbon TIME units are based on an instructional model that scaffolds students' engagement with phenomena as questioners, investigators, and explainers. The units support substantial learning and reduce the achievement gap between high-pretest and lowpretest students, but with substantial differences among

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.

BackgroundEcological 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.MethodsWe 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.ResultsWe 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).ConclusionOur 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.