Two students leaning down in the forest looking at the ground with a vibrant green fern in front of them

Conserve Wildlife Habitats. Preserve the Future.

Wildlife Ecology and Conservation—MS

Explore the relationship between wildlife, habitats, and human impacts. Focus on wildlife management, conservation, and policy topics. At Michigan Tech, our master's in wildlife ecology and conservation addresses challenges to biodiversity and other issues that you care about. Be part of the solutions that tomorrow needs.

Our unique program makes the most of our location. Study wildlife ecology and conservation in the Upper Peninsula, a region facing rapid environmental shifts due to climate change and evolving forestry practices. Michigan Tech, located in a rural setting where diverse woodland transitions from northern hardwoods to boreal forests, offers an unparalleled opportunity to study a wide range of wildlife. In addition to 5,000 acres of research forest on the mainland, we're proud to host the Isle Royale Wolf-Moose Project, the world’s longest continuous study of a predator-prey relationship. Explore and learn in our living outdoor laboratory of vast forested areas and rich biodiversity.

Tech's program is flexible, allowing you to develop a research path aligned with your career goals. Choose from a wide range of interest areas, including applied or avian ecology, conservation biology or genetics, nutrient cycling, and beyond. Pursue the answers to societal challenges through action-based education, interdisciplinary research, and innovation aimed at improving quality of life and promoting equity.

Master’s Program

In the CFRES Wildlife Ecology and Conservation MS program, tailor your study plans to align with personal academic and research goals. Whether you’re into conservation genetics and wildlife management, conservation policy, or avian and mammal ecology, here you can delve into specialized topics while gaining a broad base of knowledge. Michigan Tech’s specialized curriculum and research opportunities prepares you to tackle the contemporary conservation challenges you care about most.

Work with collaborative, interdisciplinary faculty monitoring wolf populations, banding birds or studying conservation genetics.
Assess wildlife diversity, zoonotic disease impacts on human health, indigenous cultural receptiveness and education reform.
Monitor species health, study habitat dynamics, and develop conservation strategies.
Study wildlife techniques, ecosystem resilience and conservation strategies to protect global biodiversity.
Gain a global perspective with faculty conducting active research in forest systems around the world, including neotropical and afrotropical regions.
Whatever your research focus, you’ll become an expert who can tackle environmental challenges and improve quality of life for all species on a broader, ecological scale.

Whatever your research focus, you’ll become an expert who can tackle environmental challenges and improve quality of life for all species on a broader, ecological scale.

High Demand Professions

Graduate ready to apply your knowledge and skills to a career in wildlife ecology and conservation. Maintain wildlife populations and habitats as a wildlife manager. Educate the public about wildlife species and conservation issues while working with landowners, industries, and citizen groups. Survey wildlife populations, implement population controls, and enforce wildlife laws and regulations as a wildlife law enforcement officer. Or become a wildlife consultant and evaluate ecosystems to determine environmental impacts from proposed actions. Whatever path you choose, you’ll have the hands-on field and lab experience and professional networking opportunities to seek advancement in a growing field.

MS Course Schedule

If you are working toward your master of science degree, you'll select your study program in consultation with your advisor and committee.

Course Work Requirements Specific to CFRES

Note: One upper-division statistics class from the list of suggested courses

FW 5800 - Master's Graduate Seminar

Presentation by students of current natural resource- related problems and research. Some instruction on presentation skills.

Credits: 1.0
Lec-Rec-Lab: (0-1-0)
Semesters Offered: Fall, Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate

FW 5810 - Research Methods in Natural Resources

In this course students will discuss and gain experience of; developing frameworks for writing scientific manuscripts and short research proposals, conducting literature reviews, critically reviewing papers, creating effective scientific posters and presentations, and sharing research findings with media.

Credits: 2.0
Lec-Rec-Lab: (2-0-0)
Semesters Offered: Fall, Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate

Note: FW 5810 is not required for Peace Corps and Masters of Forestry students, except 4+1

Upper Division Statistics Class Options

MA 4710 - Regression Analysis

Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course. Some prior experience with R is expected.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Summer
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

MA 4720 - Design and Analysis of Experiments

Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The R programming language is an integral part of the course.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring, Summer
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

MA 4730 - Nonparametric Statistics

Introduces nonparametric techniques that require less restrictive assumptions on the data. Topics include statistical inference concerning location and dispersion parameters as well as the general distributions. Goodness-of-fit tests for count and ordinal data are also discussed.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, in odd years
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

MA 4760 - Mathematical Statistics I

Covers joint probability distributions, functions of random variables, sampling and limiting distributions, introduction to parameter estimation.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): (MA 3720 or EE 3180) and MA 3160

MA 4770 - Mathematical Statistics II

Continuation of MA4760. Theory of point and interval estimation; properties of estimators, theory of hypothesis testing, analysis of variance, analysis of categorical data and other topics as time allows

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Pre-Requisite(s): MA 4760

MA 4790 - Predictive Modeling

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data visualization and exploratory methods, the normal theory regression model, logistic and Poisson regression, linear and quadratic discriminant analysis, and classification with logit models. Some prior experience with R is expected.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780

MA 5701 - Statistical Methods

Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring, Summer
Restrictions: Must be enrolled in one of the following Level(s): Graduate

MA 5711 - Mathematical Statistics I

Review of distribution theory and transformation theory of random variables. Topics include sufficiency; exponential and Bayesian models; estimation methods, including optimality theory; basics of confidence procedures and hypothesis testing, including the Neyman-Pearson framework.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): MA 4450 and (MA 4770 or MA 4705)

MA 5730 - Nonparametric Statistics

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, in odd years
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

MA 5731 - Linear Models

A unified development of linear statistical models that includes the following topics: matrices and quadratic forms, normal and chi-square distribution theory, ordinary and generalized least squares modeling, estimability, estimation and tests of hypothesis.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): MA 4710 and MA 4720 and MA 4760 and MA 4330

MA 5732 - Generalized Linear Models

The focus of this course is on generalized linear models (GLM), including the structure of GLM, statistical theory for GLM (maximum-likelihood estimation of GLM and hypothesis tests), and their applications. Also covers generalized linear mixed and random effects models.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring, in odd years
Pre-Requisite(s): (MA 4710 or MA 5731) and (MA 4770 or MA 5712)

MA 5741 - Multivariate Statistical Methods

Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Pre-Requisite(s): (MA 4710 or MA 4720) and MA 2320

MA 5750 - Statistical Genetics

Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring, in odd years

MA 5761 - Computational Statistics

Introduction to computationally intensive statistical methods. Topics include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): MA 4770(C) or (MA 4700 and MA 5701)

MA 5770 - Bayesian Statistics

The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, in even years
Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760

MA 5781 - Time Series Analysis and Forecasting

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)

MA 5790 - Predictive Modeling

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring
Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)

MA 5791 - Categorical Data Analysis

Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring, in odd years

FW 5411 - Applied Data Analysis

Using statistical tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data using R.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s): FW 5412

FW 5412 - Data Analysis in R

Use of R for basic data manipulation, statistical summary and statistical analysis. Topics include installing R, data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed-effects models.

Credits: 1.0
Lec-Rec-Lab: (0-1-0)
Semesters Offered: Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s): FW 5411

FW 5557 - Applied Spatial Statistics

Focus on spatial statistical methods such as spatial regression, geographically weighted regression and cluster analysis. ArcMap is utilized for analysis.

Credits: 3.0
Lec-Rec-Lab: (2-0-3)
Semesters Offered: Spring
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s): FW 5550

Approval of equivalent courses can be given by the College of Forest Resources and Environmental Science graduate program director in consultation with the student's advisory committee. The advisory committee may specify certain courses in which a student must achieve a grade of B or better.

Delivery Options

MS, Wildlife Ecology and Conservation: Thesis Option

This option requires a research thesis prepared under the supervision of the advisor. The thesis describes a research investigation and its results. The scope of the research topic for the thesis should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 12 months or three semesters following the completion of coursework by regularly scheduling graduate research credits.

The minimum requirements are as follows:

Total Credit Requirements
Option Parts	Credits
Coursework (minimum)	20 Credits
Thesis research	6-10 Credits
Total (minimum)	30 Credits

Distribution of Coursework Credit
Distribution	Credits
5000-6000 series (minimum)	12 Credits
3000-4000 (maximum)	12 Credits

Programs may have stricter requirements and may require more than the minimum number of credits listed here.

MS, Wildlife Ecology and Conservation: Report Option

This option requires a report describing the results of an independent study project. The scope of the research topic should be defined in such a way that a full-time student could complete the requirements for a master’s degree in twelve months or three semesters following the completion of coursework by regularly scheduling graduate research credits.

Of the minimum total of 30 credits, at least 24 must be earned in coursework other than the project:

Total Credit Requirements
Option Parts	Credits
Coursework (minimum)	24 Credits
Report	2-6 Credits
Total (minimum)	30 Credits

Distribution of Coursework Credit
Distribution	Credits
5000-6000 series (minimum)	12 Credits
3000-4000 (maximum)	12 Credits

Programs may have stricter requirements and may require more than the minimum number of credits listed here.

MS, Wildlife Ecology and Conservation: Coursework Option

This option requires a minimum of 30 credits be earned through coursework. A limited number of research credits may be used with the approval of the advisor, department, and Graduate School. See degree requirements for more information.

A graduate program may require an oral or written examination before conferring the degree and may require more than the minimum credits listed here:

Distribution of Coursework Credit
Distribution	Credits
5000-6000 series (minimum)	18 Credits
3000-4000 (maximum)	12 Credits

Application Process and Admissions Requirements

Applications are reviewed on an individual basis using a holistic approach. Fill out our free graduate application online to apply to any of our programs. Official transcripts and scores are not required for the initial application, although you will need to upload them later.

Graduate School Admissions Process

Applying to Graduate School is free (no application fees) and fast (no official transcripts or test scores are needed to start). The application process involves three easy steps. International applicants are required to pay a non-refundable $10 processing fee per application.

See Admissions Steps

Graduate School Admissions Options

Michigan Tech offers several admissions options in order to meet the educational needs of students from a variety of backgrounds. Students should review the options available to them and apply for the program that will best help them achieve their personal educational goals.

See Admissions Options

Graduate School Requirements

To be considered for admission to the Graduate School as a degree- or certificate-seeking student, you need to:

have a bachelor's degree or its equivalent from an accredited institution, and
be prepared for advanced study in your chosen field, as demonstrated by your previous degree and your scholastic record.

See additional application requirements, including required materials:

Student Statements

Official Transcripts

Program Specific Requirements

Program Specific

2 Letters of Recommendation
Résumé / Curriculum vitae
GPA - All grade points are considered and applicants will be admitted at the discretion of the program
GRE not required
No Additional Documents Accepted

International Students

TOEFL: Recommended Score of 79 iBT
IELTS: Recommended Score of 6.5 overall

Michigan Tech requires a minimum 79 overall TOEFL or 6.5 overall IELTS score.

Admissions Decisions

Made on a rolling basis

International Student Requirements

International Students must apply and be accepted into a degree-granting program in order to earn a graduate certificate. A non-refundable $10 processing fee per application is required.

See International Applicants

Accelerated Requirements

Program Specific

2 Letters of Recommendation
Minimum bachelor's overall GPA of 3.0 at graduation
Résumé/curriculum vitae
No GRE required

Eligible Undergraduate Majors

All majors

Graduate Learning Goals

Our flexible coursework requirements let you explore the vast scope of wildlife ecology and conservation. Tailor your degree to your research interests, whether it’s conservation genetics and wildlife management, conservation policy, avian ecology, mammal ecology and more.

Contribute to wildlife ecology and conservation through coursework, report, or research project.
Apply existing research methodologies and techniques or critically analyze and evaluate your own findings and the findings of others in the field.
Practice responsible research and communicate results both in writing and orally.
Gain a comprehensive and nuanced understanding of the field and delve into specialized topics while gaining a broad base of knowledge.

Degree Requirements

CFRES Degree Requirements

Check with your advisor about requirements for your program.

CFRES Graduate Student Handbook

Graduate School Resources

Dive Deeper and Untangle the Web of Connections Between Humans, Wildlife, and Habitats

Whether you’re passionate about protecting natural resources, fascinated by ecological data, or invested in balancing the demands of human needs with sustainability, this degree will help you broaden your knowledge base while honing in on your specialty.

Consider a MS in Wildlife Ecology and Conservation and join our diverse ecosystem of environmental stewardship if you have a background in the following (or related fields):

Ready to take the next step?

Learn more about the wildlife ecology and conservation at Michigan's flagship technological university.

Funding Opportunities

Biofuels, urban land development, grazing patterns, hydrodynamics, government policies, climate change, forest restoration—there are multiple opportunities to be involved in meaningful work. The abundance of private and public funding, and our friendly atmosphere, is a powerful formula to help decide what kind of research you want to do and who you want to do it with.

We work diligently to continuously acquire research funding, and we are successful.
Research the researchers. Match your area of interest with a faculty member, and contact them directly—your best chance to be matched with an assistantship and research project.
Check our open research positions often, where you will also find a listing of fellowships, jobs, and other funding opportunities.
Acceptance to the Graduate School and a graduate program in CFRES does not guarantee financial support.

Fellowships and Scholarships

*Financial support in the form of an assistantship is based on teaching or research needs and available resources, as well as the qualifications of individuals applying. Other funding opportunities may be available. Please contact the Graduate School for more information.