Man with short dark hair, dark rimmed glasses, grey sweater & whie collared shirt
Office
McCardell Bicen Hall 372
Tel
(802) 443-5218
Email
dallen@middlebury.edu
Office Hours
Spring 2024: Tuesdays 10:30-12 and Fridays 9-10:30

Courses Taught

Course Description

Ecology and Evolution
In this introduction to ecology and evolutionary biology we will cover the topics of interspecific interactions (competition, predation, mutualism), demography and life-history patterns, succession and disturbance in natural communities, species diversity, stability and complexity, causes of evolutionary change, speciation, phylogenetic reconstruction, and population genetics. The laboratory component will examine lecture topics in detail (such as measuring the evolutionary response of bacteria, adaptations of stream invertebrates to life in moving water, invasive species and their patterns of spread). We will emphasize experimental design, data collection in the field and in the laboratory, data analysis, and writing skills. This course is not open to seniors and second semester juniors in the Fall. 3 hrs. lect./disc./3 hrs. lab

Terms Taught

Spring 2021, Fall 2021, Fall 2022

Requirements

DED, SCI

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Course Description

Experimental Design and Statistical Analysis
Experimental design is one of the most important parts of doing science, but it is difficult to do well. How do you randomize mice? How many replicate petri plates should be inoculated? If I am measuring temperature in a forest, where do I put the thermometer? In this course students will design experiments across the sub-areas of biology. We will run student designed experiments, and then learn ways to analyze the data, and communicate the results. Students planning to do independent research are encouraged to take this course. (BIOL 0140 or BIOL 0145).

Terms Taught

Fall 2020, Fall 2023

Requirements

DED

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Course Description

Plant Community Ecology
This course will explore the structure and dynamics of plant communities, with a particular emphasis on temperate forest communities. We will investigate patterns in community diversity and structure, explore how plant populations and plant communities respond to environmental disturbances, and investigate the effects of anthropogenic influences (climate change, introduced species, habitat conversion) on plant communities. Labs will emphasize fieldwork at local research sites, and will provide exposure to techniques of experimental design in plant ecology and basic approaches to describing plant community structure and dynamics. (BIOL 0140) 3 hrs. lect./3 hrs. lab.

Terms Taught

Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024

Requirements

SCI

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Course Description

Seminar in Plant Ecology
Global climate change has led to a huge effort to collect data on the state of the planet, including measurements of temperature, atmospheric and oceanographic conditions, and species distributions and phenologies. Ecologists have never had access to such quantities of data, and thus need new methods for their description and analysis. In this course we will explore how to use statistical models to make sense of these data: how to develop, choose, and fit the best model for a particular data set. The course will be project-based, culminate in an independent project, and use the statistical software, R. (BIOL 0140 and one statistics course required, no R experience required.) 3 hr. sem./3 hr. lab

Terms Taught

Spring 2020

Requirements

DED, SCI

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Course Description

Independent Study
In this course students complete individual projects involving laboratory and/or field research or extensive library study on a topic chosen by the student and a faculty advisor. Prior to registering for BIOL 0500, a student must have discussed and agreed upon a project topic with a member of the Biology Department faculty. Additional requirements include attendance at all Biology Department seminars and participation in any scheduled meetings with disciplinary sub-groups and lab groups. This course is not open to seniors; seniors should enroll in BIOL 0700, Senior Independent Study. (BIOL 0211. Approval required) 3 hrs. disc.

Terms Taught

Spring 2020, Fall 2020, Winter 2021, Spring 2021, Fall 2021, Winter 2022, Spring 2022, Fall 2022, Winter 2023, Spring 2023, Fall 2023, Winter 2024, Spring 2024, Fall 2024, Winter 2025, Spring 2025

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Course Description

Senior Independent Study
In this course students complete individual projects involving laboratory and/or field research or extensive library study on a topic chosen by the student and a faculty advisor. Prior to registering for BIOL 0700, a student must have discussed and agreed upon a project topic with a member of the Biology Department faculty. Additional requirements include attendance at all Biology Department seminars and participation in any scheduled meetings with disciplinary sub-groups and lab groups. (BIOL 0211. Approval required; open only to seniors) 3 hrs. disc.

Terms Taught

Spring 2020, Fall 2020, Winter 2021, Spring 2021, Fall 2021, Winter 2022, Spring 2022, Fall 2022, Winter 2023, Spring 2023, Fall 2023, Winter 2024, Spring 2024, Fall 2024, Winter 2025, Spring 2025

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Course Description

Senior Thesis
Seniors majoring in Biology who have completed one or more semesters of BIOL 0500 or BIOL 0700 and who plan to complete a thesis should register for BIOL 0701. In this course students will produce a written thesis, deliver a public presentation of the research on which it is based, and present an oral defense of the thesis before a committee of at least three faculty members. Additional requirements include attendance at all Biology Department seminars and participation in any scheduled meetings with disciplinary sub-groups and lab groups. Open to Biology and joint Biology/Environmental Studies majors. (BIOL 0211 and BIOL 0500 or BIOL 0700 or waiver; instructor approval required for all students) 3 hrs. disc

Terms Taught

Spring 2020, Fall 2020, Winter 2021, Spring 2021, Fall 2021, Winter 2022, Spring 2022, Fall 2022, Winter 2023, Spring 2023, Fall 2023, Winter 2024, Spring 2024, Fall 2024, Winter 2025, Spring 2025

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Course Description

Data Science Across Disciplines
In this course we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating meaningful visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, students will attend a combined lecture where they will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will break out into smaller groups to apply these tools to domain-specific research projects in Art History, Biology, Economics, or Japanese and Linguistics.
Students enrolled in Professor Abe’s (Japanese) afternoon section will use the tools of data science to create visualizations of social and emotive meanings that surface through Japanese language/culture materials. Participants will use these visualizations to engage in various theoretical and pedagogical topics pertaining to (educational) linguistics.
Students enrolled in Professor Allen’s (Biology) afternoon section will use the tools of data science to investigate the drivers of tick abundance and tick-borne disease risk. To do this students will draw from a nation-wide ecological database.
Students enrolled in Professor Anderson’s (History of Art and Architecture) afternoon section will use the tools of data science to create interactive visualizations of the Dutch textile trade in the early eighteenth century. These visualizations will enable users to make connections between global trade patterns and representations of textiles in paintings, prints, and drawings.
Students enrolled in Professor Myers’ (Economics) afternoon section will use the tools of data science to create an interactive visualization of the landscape of abortion policy and access in the United States. This visualization will allow users to explore how abortion access varies across the country and how this variation in turn correlated with demographic, health, and economic outcomes.
This course will utilize the R programming language. No prior experience in statistics, data science, programming, art history, biology, economics, or Japanese is necessary.

Terms Taught

Winter 2021

Requirements

DED, SCI, WTR

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Course Description

Senior Independent Study
In this course, seniors complete an independent research or creative project on a topic pertinent to the relationship between humans and the environment. During the term prior to enrolling in ENVS 0700, a student must discuss and agree upon a project topic with a faculty advisor who is appointed in or affiliated with the Environmental Studies Program and submit a brief project proposal to the Director of Environmental Studies for Approval. The expectations and any associated final products will be defined in consultation with the faculty advisor. Students may enroll in ENVS 0700 as a one-term independent study OR up to twice as part of a multi-term project, including as a lead-up to ENVS 0701 (ES Senior Thesis) or ENVS 0703 (ES Senior Integrated Thesis). (Senior standing; Approval only)

Terms Taught

Fall 2020, Winter 2021, Winter 2022, Winter 2023, Winter 2024, Winter 2025

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Course Description

Senior Integrated Thesis
This course is the culminating term of a multi-term independent project, resulting in a senior thesis on a topic pertinent to the relationship between humans and the environment and that meaningfully integrates perspectives, methodologies, and/or approaches from multiple academic divisions (e.g., humanities/arts, natural sciences, social sciences). Approval to enroll is contingent on successful completion of at least one term (and up to two) of ENVS 0700 and approval of the Environmental Studies Program. The project, carried out under the co-supervision of two faculty advisors from different academic divisions of whom at least one is appointed in or affiliated with the Environmental Studies Program, will result in a substantial piece of scholarly work that will be presented to other ENVS faculty and students in a public forum and defended before the thesis committee. (Open to Senior ENVS majors) (Approval Only)

Terms Taught

Spring 2021

View in Course Catalog

Course Description

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Geography, Political Science, Restorative Justice, or Healthcare. This course will use the R programming language. No prior experience with R is necessary.

GEOG 1230: In this section, students will use data science tools to explore the ways migration systems in the United States changed during the COVID-19 pandemic. We will draw on data collected from mobile phones recording each phone’s monthly place of residence at the census tract level. The dataset includes monthly observations from January 2019 through December 2021 allowing the analysis to compare migration systems pre-pandemic with those during the pandemic.

INTD 1230A: Data is a powerful tool for improving health outcomes by making programmatic choices to support justice. In this afternoon section of Data Across the Disciplines, students will be working with Addison County Restorative Justice (ACRJ) on understanding patterns in the occurrence of driving under the influence. ACRJ has over 1,000 cases and would like to better understand their data and come up with ways to access information. We will explore how identity, geography, and support impact outcomes from DUI cases. Using statistical analysis and data visualizations, along with learning about ethical data practices, we will report our findings.

INTD 1230B: Let’s dive into the minutes and reports of local towns to develop an accessible news and history resource. Could this be a tool for small newspapers to track local news more easily? Can we map this fresh data for a new look across geographies? Do you want to help volunteer town officials make decisions and better wrangle with their town’s history and data?
In this course we will develop a focused database of documents produced by several municipal boards and commissions. We will engage in conversation with local officials, researchers, and journalists. This course aims to introduce students to making data from real world documents and the people that make them to generate useful information that is often open but frequently difficult to sift through.

MATH/STAT 1230: Students will explore pediatric healthcare data to better understand the risks correlated with various childhood illnesses through an emphasis on the intuition behind statistical and machine learning techniques. We will practice making informed decisions from noisy data and the steps to go from messy data to a final report. Students will become proficient in R and gain an understanding of various statistical techniques.

PSCI 1230: How do candidates for U.S. national office raise money? From whom do they raise it? In this section we will explore these questions using Federal Election Commission data on individual campaign contributions to federal candidates. Our analysis using R will help us identify geographic patterns in the data, as well as variations in funds raised across types of candidates. We will discuss what implications these patterns may have for the health and functioning of democracy in the U.S.

Terms Taught

Winter 2023

Requirements

DED, SOC, WTR

View in Course Catalog

Course Description

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Geography, Linguistics, Political Science, or Writing & Rhetoric. This course will use the R programming language. No prior experience with R is necessary.

GEOG: Students will apply data science tools to explore the geography human-environment relationships around protected areas. We will use household survey and land cover data from locations across the humid tropics where the Wildlife Conservation Society has been tracking human wellbeing and forest resource use in high-priority conservation landscapes. Projects and visualizations will be presented back to WCS to inform their ongoing monitoring and management in these sites.

LNGT: In this section, we will learn how to collect and analyze Twitter data in R. We will focus on social metrics and geographical locations to examine language variation in online communities across the United States. While the emphasis will be placed on linguistics, the statistical and analytical tools will help you work with other types of Twitter corpora in the future.

PSCI: Students will use cross-national data to explore relationships between conflict events and political, social, and economic factors in each nation. What factors contribute to conflict and violence? Our focus will be to find patterns in the data using the tools in R and discuss what those patterns suggest for addressing rising conflict and resolving ones that have already experienced violence.

WRPR: Students will learn to conduct writing studies research through working with "big data” from a multiyear survey of first-year college students about their academic confidences, attitudes, and perceptions. We will explore how educational access, identity, and language background impacts survey responses. Using statistical analysis and data visualizations, as well as writing, we will report our findings.

Terms Taught

Winter 2023

Requirements

DED, SOC, WTR

View in Course Catalog

Course Description

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Geography, Political Science, Restorative Justice, or Healthcare. This course will use the R programming language. No prior experience with R is necessary.

PSCI 1230: How do candidates for U.S. national office raise money? From whom do they raise it? In this section we will explore these questions using Federal Election Commission data on individual campaign contributions to federal candidates. Our analysis using R will help us identify geographic patterns in the data, as well as variations in funds raised across types of candidates. We will discuss what implications these patterns may have for the health and functioning of democracy in the U.S.

INTD 1230A: Data is a powerful tool for improving health outcomes by making programmatic choices to support justice. In this afternoon section of Data Across the Disciplines, students will be working with Addison County Restorative Justice (ACRJ) on understanding patterns in the occurrence of driving under the influence. ACRJ has over 1,000 cases and would like to better understand their data and come up with ways to access information. We will explore how identity, geography, and support impact outcomes from DUI cases. Using statistical analysis and data visualizations, along with learning about ethical data practices, we will report our findings.

INTD 1230B: Let’s dive into the minutes and reports of local towns to develop an accessible news and history resource. Could this be a tool for small newspapers to track local news more easily? Can we map this fresh data for a new look across geographies? Do you want to help volunteer town officials make decisions and better wrangle with their town’s history and data?
In this course we will develop a focused database of documents produced by several municipal boards and commissions. We will engage in conversation with local officials, researchers, and journalists. This course aims to introduce students to making data from real world documents and the people that make them to generate useful information that is often open but frequently difficult to sift through.

GEOG 1230: In this section, students will use data science tools to explore the ways migration systems in the United States changed during the COVID-19 pandemic. We will draw on data collected from mobile phones recording each phone’s monthly place of residence at the census tract level. The dataset includes monthly observations from January 2019 through December 2021 allowing the analysis to compare migration systems pre-pandemic with those during the pandemic.

MATH/STAT 1230: Students will explore pediatric healthcare data to better understand the risks correlated with various childhood illnesses through an emphasis on the intuition behind statistical and machine learning techniques. We will practice making informed decisions from noisy data and the steps to go from messy data to a final report. Students will become proficient in R and gain an understanding of various statistical techniques.

Terms Taught

Winter 2023

Requirements

DED, SOC, WTR

View in Course Catalog

Course Description

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Geography, Linguistics, Political Science, or Writing & Rhetoric. This course will use the R programming language. No prior experience with R is necessary.

GEOG: Students will apply data science tools to explore the geography human-environment relationships around protected areas. We will use household survey and land cover data from locations across the humid tropics where the Wildlife Conservation Society has been tracking human wellbeing and forest resource use in high-priority conservation landscapes. Projects and visualizations will be presented back to WCS to inform their ongoing monitoring and management in these sites.

LNGT: In this section, we will learn how to collect and analyze Twitter data in R. We will focus on social metrics and geographical locations to examine language variation in online communities across the United States. While the emphasis will be placed on linguistics, the statistical and analytical tools will help you work with other types of Twitter corpora in the future.

PSCI: Students will use cross-national data to explore relationships between conflict events and political, social, and economic factors in each nation. What factors contribute to conflict and violence? Our focus will be to find patterns in the data using the tools in R and discuss what those patterns suggest for addressing rising conflict and resolving ones that have already experienced violence.

WRPR: Students will learn to conduct writing studies research through working with "big data” from a multiyear survey of first-year college students about their academic confidences, attitudes, and perceptions. We will explore how educational access, identity, and language background impacts survey responses. Using statistical analysis and data visualizations, as well as writing, we will report our findings.

Terms Taught

Winter 2023

Requirements

DED, SOC, WTR

View in Course Catalog

Areas of Interest

Forest ecology

I am interested in successional dynamics in oak-hickory forests. Many such forests in eastern North America are under rapid successional change as the oak canopy is replaced by individuals of more mesophytic species. There are multiple hypotheses for the driver of this change: fire suppression, decreasing severity and prevalence of drought, or changes to herbivore communities. Most of this work takes place in a 23-ha forest dynamics plot in Pinckney, MI. This plot is part of the Smithsonian Institution’s ForestGEO global network of forest research plots. As such data from this plot are part of large collaborative studies on other aspects of forest ecology. 

Ecology of tick-borne diseases

I am also interested in the abiotic and biotic drivers of tick-borne disease prevalence. For this project I largely focus on Borrelia burgdorferi, the Lyme disease agent, in Ixodes scapularis, the blacklegged tick. I take a joint empirical-theoretical approach for this project. I am developing a model which predicts enzootic persistence of a tick-borne disease in a given abiotic and biotic context. Then I will parameterize the model with field-collected and literature values. If the model is successfully validated, it will be interrogated to better understand the drivers of disease persistence.This ongoing work takes place in Addison County, Vermont.

Publications