Anne McIntosh, BSc (Honours), MSc, PhD

About

You can learn more about me and my teaching and research interests by visiting my personal academic website HERE. Learn more about my current teaching offerings by clicking on the courses link below.

Research

My research is focused on ecological recovery of forested and grassland ecosystems after both natural and anthropogenic disturbance agents including:

• Recovery of biodiversity after varying level of harvest in boreal and boreo-nemoral forests
• Recovery of soil and vegetation on grasslands and forested lands on certified reclaimed wellsites
• Recovery of lodgepole pine plant and microbial communities after mountain pine beetle attack
• Responses of lodgepole pine plant communities in areas around the world where lodgepole pine has been introduced
• My first MSc student, Raiany Silva, (2017-19) explored restoration of wetland areas degraded by off-highway-vehicle use in Eagle Point Blue Rapids near Drayton Valley, AB
• Ecological sustainability in rural landscapes - examining the role of forest, grassland, and wetland habitats in biodiversity conservation and carbon sequestration/valuation within Beaver County. (PI: Glynnis Hood, Co-PIs myself and Glen Hvenegaard (2016-2018)

Teaching

My teaching interests include anything to do with plants, statistics, conservation biology, and all levels of ecology. 

A brief introduction to my teaching philosophy:

One professor can make the difference in transforming the university experience for a student; I focus on being that professor who will make the difference for my students. I aim to inspire my students to move forward excited about taking what they learn in-class beyond the classroom and eager to learn more . As a teacher my overarching goal is to create an engaging and supportive learning environment where my students will gain knowledge, as well as critical thinking, and communication skills, while also discovering an appreciation (or even better a passion) for what they are learning about and how they are learning. Here are my six personal tenets about teaching and learning, associated learner-centered goals for my students: i) passion can ignite students; ii) learning begins with a student-centered ‘circle of niceness’ supportive learning environment; iii) students must be engaged in their learning; iv) students are individuals first (and so am I); v) risk is worth the reward; and vi) learning is an iterative process. My long-term ongoing goal is that I engage students to discover ways to experience multiple ‘wow’ factors during their time in my classes, and that my caring nature, passionate energy, and enthusiasm for what they are learning helps get them there.

I recently received a NSERC Discovery Grant. If you think you would be a good fit in my lab group, email me explaining why - be sure to attach a copy of your resume and transcript.

AUBIO 315 - Advanced Biological Analysis

Application of statistical and informatics methods for analysis of biological datasets, with an emphasis on ecological datasets (e.g., plant, bird and bat communities) using both univariate and multivariate techniques. Students will also develop critical skills in research design, organization and interpretation of data, and written and oral communication skills. Prerequisites AUBIO 253 and AUSTA 215.

AUBIO 323 - Plant Biology

Comparative survey of the anatomy, morphology, function, life cycles, and evolutionary features of algae and non-vascular and vascular plants. Taxonomic and ecological considerations are also presented. Prerequisite: AUBIO 112 or AUBIO 212 (2022) or AUBIO 214.

AUBIO 418 - Directed Reading II

Supervised library research project. Prerequisites: Third-year standing, *6 in Biology at the 200 level. Notes: Admission to the course normally requires a minimum GPA of 3.0 in Biology. An Application for Individual Study must be completed and approved before registration in the course.

AUSTA 215 - Statistical Methods for the Natural Sciences

Experimental design, data presentation and analysis; descriptive statistics, probability distributions and statistical hypothesis testing; parametric and nonparametric tests, correlation and regression; use of statistical software. Prerequisites: Mathematics 30-1 or 30-2; one of AUBIO 111, AUCHE 110, AUCSC 111 (2021), 113, AUENV 120, AUGEO 120, AUMAT 110, 116, 120, AUPHY 102 (2021), 104 (2021), 110 (2021), 120. Notes: The course does not count toward the major in Mathematics and Physics or the minor in Mathematics. Credit may be obtained for only one of AUSTA 153, 213, 215, AUPSY 213 (2020).

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