PREDICTING COMMON CRUPINA HABITAT WITH GEOGRAPHIC AND REMOTE SENSING DATA

Field surveys for common crupina, as part of an eradication program, are time intensive and could be made more efficient if common crupina habitat could be predicted. Slope, aspect, and vegetation data were used as generalized plant community variables to predict common crupina habitat using a transformed logistic regression. Models were constructed using either aspect or slope as an explanatory variable such that one model predicted the overall effect of either slope or aspect and a set of models predicted the effect of slope or aspect at each of three vegetation classes. A second data set was used to validate the prediction equations for slope and aspect. The proposed models fit the data well and validations were successful as indicated by analysis of residual plots. The probability of finding common crupina was highest for southeast to southwest aspects. In addition, common crupina was most likely to occur, overall, at 25 to 30% slope with decreasing probability at gentler and steeper slopes. Slope models fitted at each vegetation class indicated maximums at 25 to 30% slope for forest and mesic grassland areas but the maximum for arid grasslands was 50% slope. A field detection survey of common crupina that was directed according to probability of occurrence differences along aspect and slope gradients could reduce the area surveyed to 34 to 42%, respectively, of the study area (using a probability cutoff of 30% of the model\u27s maximum). Detection surveys directed according to slope models would find 14% more common crupina than aspect models but would survey 8 to 11 % more area. Models that considered vegetation class, when contrasted with models that did not consider vegetation class, did not decrease the total area surveyed while maintaining the same percentage of common crupina found

Perceptions and Management of Ventenata by Producers in the Inland Pacific Northwest

Ventenata is an annual grass that has invaded agricultural and wildland settings in the Inland Pacific Northwest, causing economic and ecological losses. We know little about producers’ perceived risks and management of ventenata. We present results of surveys in 2011 and 2014 targeting producers across affected counties in Idaho and Washington. Awareness of ventenata and costs to producers increased across that time interval. Respondents attending ventenata Extension events adopted recommended management strategies more than those who did not attend. Our study documents the importance of continued integrated pest management research in concert with stakeholder engagement and education

MODELING DISPERSAL OF YELLOW STARTHISTLE IN THE CANYON GRASSLANDS OF NORTH CENTRAL IDAHO

Yellow starthistle is an invasive plant species that reduces productivity and plant diversity within the canyon grasslands of Idaho. Early detection of yellow starthistle and predicting its spread have important managerial implications that could greatly reduce the economic/environmental losses due to this weed. The spread of an invasive plant species depends on its ability to reproduce and disperse seed into new areas. Typically, information on the factors that directly affect a plant’s ability to reproduce and subsequently disperse seed is not available or difficult to obtain. Alternatively, topographic factors, such as slope and aspect as well as competitive correlates such as vegetation indices related to plant community biomass could be used to model plant survival and seed movement. In this research, several spatial network models incorporating these variables were considered for the prediction of yellow starthistle dispersal. Models will differed in their assessment of plant movement costs, which can be separated into two processes, survival to reproduction and seed dispersal. The candidate models were evaluated based on their predictive ability and biological relevance. Topographical variables, slope and aspect, were found to be significant contributors to yellow starthistle dispersal models, whereas vegetation indices did not improve the prediction process. The optimal model was applied to an area in central Idaho for predicting the dispersal of yellow starthistle in 1987 given a known 1981 infestation

A New Lecture-Tutorial for Teaching about Molecular Excitations and Synchrotron Radiation

Light and spectroscopy are among the most important and frequently taught topics in introductory, college-level, general education astronomy courses. This is due to the fact that the vast majority of observational data studied by astronomers arrives at Earth in the form of light. While there are many processes by which matter can emit and absorb light, Astro 101 courses typically limit their instruction to the Bohr model of the atom and electron energy level transitions. In this paper, we report on the development of a new Lecture-Tutorial to help students learn about other processes that are responsible for the emission and absorption of light, namely molecular rotations, molecular vibrations, and the acceleration of charged particles by magnetic fields.Comment: 13 pages, 7 figures Accepted for publication in The Physics Teache

Using graphical and pictorial representations to teach introductory astronomy students about the detection of extrasolar planets via gravitational microlensing

The detection and study of extrasolar planets is an exciting and thriving field in modern astrophysics, and an increasingly popular topic in introductory astronomy courses. One detection method relies on searching for stars whose light has been gravitationally microlensed by an extrasolar planet. In order to facilitate instructors' abilities to bring this interesting mix of general relativity and extrasolar planet detection into the introductory astronomy classroom, we have developed a new Lecture-Tutorial, "Detecting Exoplanets with Gravitational Microlensing." In this paper, we describe how this new Lecture-Tutorial's representations of astrophysical phenomena, which we selected and created based on theoretically motivated considerations of their pedagogical affordances, are used to help introductory astronomy students develop more expert-like reasoning abilities.Comment: 10 pages, 10 figures, accepted for publication in the American Journal of Physic

PREDICTION OF YELLOW STARTHISTLE SURVIVAL AND MOVEMENT OVER TIME AND SPACE

Yellow starthistle is a noxious weed that has become a serious plant pest with devastating impact on ranching operation and natural resources in western states. Early detection of yellow starthistle and predicting its spread has important managerial implications and greatly reduce the economic losses due to this weed. The dispersal of yellow starthistle consists of two main components, plant survival and seed movement. Resources and direct factors relating to these components are not typically available or are difficult to obtain. Alternatively, topographic factors, such as slope, aspect and elevation, are readily available and can be related to plant survival and seed movement. In this study, several GIS network models incorporating these topographic factors are considered for the prediction of yellow starthistle spread. The models differed in their assessment of the costs of movement derived from these factors. Models were evaluated based on their predictive ability and residual analysis. The optimal model gave an accurate estimate of the dispersal boundary for the study area. Further validation of the estimated model using an independent data set from a larger area also verified its predictive capability

Hyperspectral data processing for repeat detection of small infestations of leafy spurge.

Abstract Leafy spurge (Euphorbia esula L.) is an invasive plant species in the north central and western U.S. and southern Canada. Idaho has established populations in the north and southeastern regions which are spreading into new sites. This study demonstrates the ability of high resolution hyperspectral imagery to provide high quality data and consistent methods to locate small and low percent canopy cover occurrences of leafy spurge. Locating leafy spurge in its early stages of invasion is critical for land managers in order to prioritize treatment, conservation, and restoration activities. Hyperspectral data were collected in 2002 and 2003 for the study area in southeastern Idaho. The imagery was classified with the Mixture Tuned Matched Filtering (MTMF) algorithm. Although classifications from single date images provided discrimination of leafy spurge at approximately 10% cover in one 3.5 m pixel, for repeatability and consistency purposes, the threshold for leafy spurge discrimination is approximately 40% cover. We hypothesize that georegistration errors, small differences in leafy spurge reflectance, training endmember selection, and image processing and field validation biases between years influence multi-date detection limits. Although hyperspectral imagery is costly, in some situations, the advantages of having reliable and repeatable mapping abilities for discrimination of economically damaging invasive species such as leafy spurge outweigh the image and processing costs.

Genome editing to the rescue: sustainably feeding 10 billion global human population

Modern animal breeding strategies based on population genetics, molecular tools, artificial insemination, embryo transfer and related technologies have contributed to significant increases in the performance of domestic animals, and are the basis for a regular supply of high quality animal derived food at acceptable prices. However, the current strategy of marker- assisted selection and breeding of animals to introduce novel traits over multiple generations is too pedestrian in responding to unprecedented challenges such as climate change, global pandemics, and feeding an anticipated 33% increase in global population in the next three decades. Here, we propose site-specific genome editing technologies as a basis for “directed” or “rational selection” of agricultural traits. The animal science community envisions genome editing as an essential tool in addressing critical priorities for global food security and environmental sustainability, and seeks additional funding support for development and implementation of these technologies for maximum societal benefit

Multi-model Ensemble Simulations of Tropospheric NO2 Compared with GOME Retrievals for the Year 2000

Abstract. We present a systematic comparison of tropospheric NO2 from 17 global atmospheric chemistry models with three state-of-the-art retrievals from the Global Ozone Monitoring Experiment (GOME) for the year 2000. The models used constant anthropogenic emissions fromIIASA/EDGAR3.2 and monthly emissions from biomass burning based on the 1997–2002 average carbon emissions from the Global Fire Emissions Database (GFED). Model output is analyzed at 10:30 local time, close to the overpasstime of the ERS-2 satellite, and collocated with the measurements to account for sampling biases due to incomplete spatiotemporal coverage of the instrument. We assessed the importanceof different contributions to the sampling bias: correlations on seasonal time scale give rise to a positive bias of 30–50% in the retrieved annual means over regions dominated by emissions from biomass burning. Over the industrial regions of the eastern United States, Europe and eastern China the retrieved annual means have a negative bias with significant contributions (between –25% and +10% of the NO2 column) resulting from correlations on time scales from a day to a month. We present global maps of modeled and retrieved annual mean NO2 column densities, together with the corresponding ensemble means and standard deviations for models and retrievals. The spatial correlation between the individual models and retrievals are high, typically in the range 0.81–0.93 after smoothing the data to a common resolution. On average the models underestimate the retrievals in industrial regions, especially over eastern China and over the Highveld region of South Africa, and overestimate the retrievals in regions dominated by biomass burning during the dry season. The discrepancy over South America south of the Amazon disappears when we use the GFED emissions specific to the year 2000. The seasonal cycle is analyzed in detail for eight different continental regions. Over regions dominated by biomass burning, the timing of the seasonal cycle is generally well reproduced by the models. However, over Central Africa south of the Equator the models peak one to two months earlier than the retrievals. We further evaluate a recent proposal to reduce the NOx emission factors for savanna fires by 40% and find that this leads to an improvement of the amplitude of the seasonal cycle over the biomass burning regions of Northern and Central Africa. In these regions the models tend to underestimate the retrievals during the wet season, suggesting that the soil emissions are higher than assumed in the models. In general, the discrepancies between models and retrievals cannot be explained by a priori profile assumptions made in the retrievals, neither by diurnal variations in anthropogenic emissions, which lead to a marginal reduction of the NO2 abundance at 10:30 local time (by 2.5– 4.1% over Europe). Overall, there are significant differences among the various models and, in particular, among the three retrievals. The discrepancies among the retrievals (10–50% in the annual mean over polluted regions) indicate that the previously estimated retrieval uncertainties have a large systematic component. Our findings imply that top-down estimations of NOx emissions from satellite retrievals of tropospheric NO2 are strongly dependent on the choice of model and retrieval.JRC.H.2-Climate chang