3790 research outputs found
Sort by
Peromyscus responses to alternative forest management systems in the Missouri Ozarks, USA
Get PDFOperational-scale forest management experiments are long-term investments because harvest treatment effects may be dynamic throughout one or more rotation lengths. We examined deermouse (Peromyscus spp.) abundance over the first 20 years of the Missouri Ozark Forest Ecosystem Project (MOFEP), which assesses ecological responses to even-aged, uneven-aged, and no-harvest forest management systems applied at landscape scales in the Missouri Ozarks. In the spring of each of 11 sampling years, we sampled Peromyscus populations on two permanent trap grids on each of nine study sites (n = 3 sites per management system). Management entries occurred in 1996 and 2011, with small mammal sampling conducted during two pre-treatment years (1994–1995), and Years 2–5 (1998–2001), Years 13–14 (2009–2010), and Years 16–18 (2012–2014) after the first entry. We estimated abundance for each grid in each sampling year with Bayesian closed-population mark-recapture models, and modeled variation in abundance with negative binomial log-linear mixed effects models. Uneven- and even-aged management systems caused similar increases in Peromyscus abundance that were detectable shortly after the first management entry [proportional effect of even-aged management on Peromyscus abundance vs. no-harvest management: posterior median = 1.8, 95% credible interval 1.0–3.2; proportional effect of uneven-aged vs. no harvest management = 1.7 (1.0, 2.8)]. These effects were not surprising given positive effects of harvest treatments on understory cover and food resources. However, the consistency of this increase was less expected, as we observed no conclusive dissipation of harvest effects even in Years 13–14 after the first entry or amplification of harvest effects after the second entry. Observing extremely high system-wide yearly variation in Peromyscus abundance, we did not detect evidence of increasing divergence in effects of these three management systems or of any area-wide trends in abundance during 1994–2014. However, over subsequent decades, we expect higher potential for divergence in Peromyscus abundance as the three management systems differentially shape forest structure and tree species composition. Thus, the MOFEP study offers a unique framework for building and testing hypotheses about patterns and mechanisms of long-term changes in Ozark forests and effects on vertebrate communities.
3
1994–2014. However, over subsequent decades, we expect higher potential for divergence in 44 Peromyscus abundance as the three management systems differentially shape forest structure and 45 tree species composition. Thus, the MOFEP study offers a unique framework for building and 46 testing hypotheses about patterns and mechanisms of long-term changes in Ozark forests and 47 effects on vertebrate communities.PublishedYe
Improving Representation of Crop Growth and Yield in the Dynamic Land Ecosystem Model and Its Application to China
Get PDFTo accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process-based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM-AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O-3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM-AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980-2012. Results show that the DLEM-AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model-based yield estimation for the three major crops at the national scale for the period 1980-2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM-AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site-specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change.PublishedYe
Research Data Management for Environmental Science
Get PDFEnvironmental science has been a fertile testing ground for research data management tools and techniques. Disciplines within environmental science were some of the first to openly discuss and grapple with issues in research data management, in part because of the nature of the studies and the data involved. This seminar will present a summary of current best practices with regards to research data management in environmental science and a discussion of areas where data habits can be improved. There will also be an opportunity to ask specific questions about research data management that apply to given lab groups or projects.n
A Bayesian Approach to Detect the Firms with Material Weakness in Internal Control
Get PDFCapturing of relevant patterns in company’s financial data and the implications on the reporting are important for
various financial statement users to identify the triggers of the significant deficiencies and material weaknesses. The
objective of this study is to construct a company-specific risk score for the companies’ internal weaknesses, as well
as to uncover the conditional relations between the independent predictors of firms’ material weaknesses. To do so,
Tree Augmented Naive Bayes (TAN) and Logistic Regression (LR) algorithms are employed to analyze the data
obtained from COMPUSTAT (Research Insight) for one year before the Material Weakness in Internal Control
(MWIC) disclosure on several operating and financial ratios such as total asset turnover, profitability, capital
intensity, size, current ratio, and operating performance. The proposed TAN method provides novel information on
the interactions among the predictors and the conditional probability of MWIC for a given set of relevant firm
characteristics.PublishedYe
Imagining the Essence of Christianity: Religion, Heart, and Mind in George Eliot
No full textArticle from the George Eliot Review, digitized and hosted by the George Eliot Review Online.Publishe
From a Freudian Point of View: The Development of Rosamond Vincy’s Personality
No full textArticle from the George Eliot Review, digitized and hosted by the George Eliot Review Online.Publishe
Data on morphology, hatch, survival, and deformities used for SAS modelling and final VC results table
No full textThe dataset includes all the combined data for larval morphology, embryonic survival, hatch success, and rate of deformities. One value is given for each trait and for each basket, in which the three replicate baskets per family were averaged for a total of 120 rows per dataset. All data was analyzed using SAS statistical analysis software (v.9.1; SAS Institute Inc., Cary, NC, USA). Residuals were evaluated for normality (Shapiro–Wilk test) and homoscedasticity (plot of residuals) to ensure they met model assumptions. Data were log(10) or arcsine square root transformed to meet these assumptions when necessary. Alpha was set at 0.05 for testing main effects and interactions. Traits were analyzed independently at each sampling point. Least squared means (LSMs) and VCs (% of overall variation due to random effects) were constructed using the restricted maximum likelihood (REML) method with the SAS PROC MIXED statement. The factorial mating design measures the variance of the male (sire) and female (dam) effects and the sire x dam interaction effects, allowing us to infer the VCs in terms of combining ability as an additional interpretation of maternal/paternal random effects.
For variation across temperature:
Each trait was analyzed using a multi-factorial ANOVA (with fixed effect of temperature and random effects including maternal/paternal effects and all interactions terms) based on the means for each basket. Denominator degrees of freedom for all F-tests were approximated using the Kenward Roger procedure. A posteriori analyses performed on fixed effects were constructed using Tukey’s multiple comparisons method. To test for significant variability among VCs greater than zero in the PROC MIXED model, likelihood ratio statistics were generated from the -2[Res]tricted log-likelihood estimate of the full model and then with each VC held to 0 using the PARMS statement. The probabilities were halved to account for the one-tailed probability and obtain the significance level for each VC.
For variation within temperatures:
Separate PROC GLM models were used to analyze the following random effects at each temperature: maternal, paternal, and maternal × paternal interactions. VCs were generated directly for the model using the PROC VARCOMP statement.
The following document shows the results of the SAS procedures and the variance components. VCs were calculated as percents and determined to be significant by the p-values. All significant VCs are highlighted in green for each trait.In reviewN
Discovery and broad relevance may be insignificant components of course-based 1 undergraduate research experiences (CUREs) for non-biology majors
Get PDFCourse-based undergraduate research experiences (CUREs) are a type of laboratory learning environment associated with a science course in which undergraduates participate in novel research. According to Auchinchloss et al. (2104), CUREs are distinct from other laboratory learning environments because they possess five core design components, and while national calls to improve STEM education have led to an increase in CURE programs nationally, less work has specifically focused on which core components are critical to achieving desired student outcomes. Here we use a backward elimination experimental design in order to test the importance of two CURE components for a population of non-biology majors: the experience of discovery and the production of data 30 broadly relevant to the scientific or local community. We found nonsignificant impacts of either laboratory component on students’ academic performance, science self-efficacy, sense of project ownership, and perceived value of the laboratory experience. Our results challenge the assumption that all core components of CUREs are essential to achieve positive student outcomes when applied at scale.PublishedYe