VCU Environmental Research Methods

This lesson, which is based around a recently published paper in the Journal of Ecological Applications, introduces students to both parametric and non-parametric approaches to statistical analysis using R. Key concepts covered include Analysis of Variance and Kruskal-Wallis tests, Shapiro-Wilk and Levene's testing, least significant difference post-hoc testing, and the Shannon Diversity Index. In addition to providing a detailed guide to these quantitative approaches, the lesson includes environmental background context and a section on the art of data wrangling to prepare raw data for analysis. The entire lesson is R-based, meaning that all parts of the lesson are intended to be viewed in RStudio, or some other Integrated Development Environment.

In this lesson, students will learn about the exotic European honeybee (Apis mellifera), and why they are considered an important pollinator. They will be prompted to think about the impacts of urbanization and how to may favor one species over another. They will also use R and data from the focal paper to complete a statistical analysis.

In this lesson, students will learn more about the wildlife disease known as white nose syndrome and how it affects certain bat species more than others. Students will learn about the important ecosystem services bats provide and why bat conservation is important to overall ecosystem health. To demonstrate the negative impacts of WNS on Myotis lucifugus, students will learn how to fit a logistic regression model in R and analyze how the probability of catching this species has decreased since the introduction of WNS in West Virginia. Students will evaluate the fit of the model using a diagnostic plot and the summary output, and they will learn how to interpret the coefficients. This lesson contains a short video introduction, student and instructor handouts, the curated data, and annotated student and instructor versions of the R code. The R-markdown and the lesson handout provide a step-by-step tutorial on how to do a logistic regression in R. The handouts are intended to compliment the original paper, and students will be prompted to answer questions about the paper itself and the R code.

In this lesson, students will explore the drivers of the western monarch butterfly decline. Students will compare the effects of climate factors and land use factors on the western monarch butterfly population. Students will also be introduced to partial least squares regression (PLSR), and then apply PLSR to address the monarch population decline question.

In this lesson, students will learn about the various mark-recapture and tagging methods that are used in various scientific disciplines and the information they can gather. Key elements of experimental design will be discussed and then students will learn about American shad and the current problems that they are facing. Students will then be introduced to survival analysis and Cox proportional hazards model before using these to recreate survival analysis graphs in R like those of the focal paper.

In this lesson students will learn about the impacts of urbanization, and the conservation challenges it poses to wildlife, in particular avifauna. Introductory ecology topics such as the theory of island biogeography and habitat fragmentation will be discussed, and the student will learn about the beneficial role of native plants in urban residential landscaping. A focal paper will be used to better explore these topics, and data from this observational study will be utilized to introduce generalized linear models in the R programming environment. The student should have some prior basic knowledge of introductory ecology concepts, introductory statistics and have R studio installed on their computer with a basic understanding of this programming language. Upon completion of this lesson, students will learn how urban residential yards contribute to the overall green space in urbanized areas and be used as a conservation strategy to mitigate habitat loss. In R, the student will learn how to conduct statistical analyses and determine if the species area relationship and distance effects of the theory of island biogeography predict bird richness in this study system.

In this lesson, students will receive an introduction to survival analysis in R utilizing data on bee mortality caused by pesticide exposure. In their code, students will assess survivability, recreate survival plots, and analyze the results from chi-square tests.

Urbanization effects stream ecosystems by introducing pollutants to the streams.. This has detrimental effects on the life of the streams such as the aquatic macroinvertebrates who are in charge of leaf litter decomposition.

In this lesson, students will receive an introduction to Poisson Generalized Linear Mixed Models in R utilizing data on coral diseases. In their code, students will assess if seagrass status has an effect on different types of two coral diseases.

This module examines the relationship between street trees, urban avifauna, and socioeconomic gradients in the highly urbanized county of Los Angeles, California. Using edited data from a published study, students will learn how to run and interpret a generalized linear model with negative binomial distribution in RStudio.

The purpose of this lesson is to explore the effects that three varying degrees of soil disturbance has on the squash bee abundance that is observed. We will learn about the importance of plant-pollinator interactions, and the methods used to analyze pollinator count data. Using R Studio, an open-access software program, we will fit a linear mixed model using the lme4 package, an anova() test. Finally, we will learn about how community-based monitoring efforts can inform future pollinator conservation efforts.

The purpose of this lesson is to help us better understand how we are able to conserve wetland-dependent wildlife. We will be exploring, manipulating, and analyzing data in R using a Poisson distributed generalize linear model (GLM) with interacting effects. The data consists of two pool-breeding amphibians and how their populations respond to the hydrology and surrounding terrestrial habitats.

This lesson discusses ocean acidification, its causes, and the potential dangers that it may pose to aquatic ecosystems by analyzing the relationship between intertidal invertebrates. While focusing on the large issue of ocean acidification, this lesson also provides an introduction to generalized linear models and data visualization using the R programming language while utilizing data from "Ocean acidification alters the response of intertidal snails to a key sea star predator" by Jellison et al. (2016).

This lesson introduces two global change factors, natural habitat loss and exotic plant invasion, and analyzes how these stressors impact the biodiversity and abundance of pollinator species. Pearson's correlation coefficient and general linear models are introduced to analyze these relationships, and the creation of graphics are used to visualize these relationships.

This lesson explores the concept of blue economies and includes a detailing lesson in R that involves basic statistics and building complex graphics.

This publication explores the behavioral influence of pharmaceutical pollution on a species of estuarine crab. Fluoxetine is a commonly prescribed, fairly effective anti-depressant and anti-anxiety medication. Its effects are well-documented in humans, but little research has been conducted to determine how concentrations of fluoxetine within aquatic habitats alter the survivorship and behavioral patterns of the species living there. The researchers that put forth the data at the source of this project expose the Oregon shore crab to varying levels of fluoxetine and record their behaviors. The research itself consists of two different portions: intraspecific interactions and interspecific interactions. As a result of this experiment, the crabs exposed to 30 ng/L of fluoxetine were found to be less risk-averse, leading them to get into more fights amongst their own species and become more likely to be killed by predators.

This lesson uses a t-test and ANOVA to determine if higher temperatures will have a significant impact on change in mangrove height, change in belowground biomass, and change in surface elevation in a transition area between salt marsh and mangrove forest.

This lesson focuses on urbanization and its negative effects on species, specifically amphibians. The lesson will also provide hands-on statistical analyses and critical thinking questions to promote a better understanding of this ecological problem.

This QUBES lesson uses statistical analysis to test whether or not oyster restoration sites in Virginia are successfully increasing the number of oysters and whether or not water quality is improving at the same sites during the same time period.

An urban heat island is an urban or metropolitan area that is on average far warmer than the surrounding rural areas. In this lesson we will study the impact these warmer temperatures have on the carbon sequestration of urban trees.

QUBES lesson on ingestion of microplastics by coral. Lesson will give environmental background and then use R for hands-on statistical analyses, followed by critical thinking questions.

This lesson will provide an explanation of mountaintop removal mining, and potential harms associated with it. Then the lesson will move into a statistical analysis of salamanders living in streams near mountaintop removal sites.

In this lesson, students will explore the relationship between reef cover and human disturbance. Students will manipulate a large dataset and perform normality tests, data transformations, correlations, and a simple linear regression in R Studio.

In this lesson, users will play the role of a fisheries biologist by tackling a contemporary fisheries research problem by performing data normality tests, data transformations, nonparametric analysis, and data visualization in R Studio.

Creating and comparing boosted regression tree models to multiple regression models.

This learning module was designed to introduce students to the aquatic nutrients nitrogen and phosphorus, their roles in ecosystem function and nutrient pollution, and what impacts climate change might have on their presence and effects in aquatic systems

This module examines the Maryland striped bass moratorium (1985-1989) as a fisheries management success story. Maryland DNR striped bass young of the year data is utilized for least squares linear regression analysis.