Lecturer of Quantitative Ecology

Ph.D., University of New Hampshire
Rm 218, Holdsworth Hall
Ph: 413-545-3114
Email: mgmaclean

CV

Personal page

 

Primary interests

I am passionate about collaborating with forestry, environmental, and community leaders at all levels to better understand the human-environment interactions that impact our forest ecosystems. As a researcher, my work uses innovative quantitative methods to explore how the forested landscape is changing due to human and climate pressures, the impacts of these changes on forest ecosystems and carbon dynamics, as well as how to monitor and model these changes with the goal of informing policy and management decisions.  I also enjoy weaving my research and teaching together to both study and practice inclusive pedagogy within Science, Technology, Engineering, and Mathematics (STEM).

 

Current projects

  • Aboveground carbon consequences of future land use in New England
  • Carbon accounting of timber from current harvest trends in Massachusetts
  • Modeling direct and indirect impacts of forest insects and pathogens
  • Inclusive pedagogy in quantitative undergraduate biology

 

Courses taught

  • Introduction to Quantitative Ecology (NRC 290b/240)

This introductory statistics course aims to provide students interested in ecology with a supportive, encouraging and comfortable environment for developing a sound knowledge of core statistical concepts in ecology. Ecology, the study of the relationships between organisms to one another and their environment, is a discipline concerned with quantifying the relationships we observe in nature. The objective of the course is to demystify statistics and help develop the basic level of understanding that all future ecologists should possess. In this course, you will develop a detailed understanding of why and how to apply the great variety of statistical tools available for answering important ecological questions.

  • Landscape Ecology & Conservation (NRC 597LE)

This course provides students with an introduction to the discipline of landscape ecology, in both theory and practice, with specific applied examples related to the New England landscape. Landscape ecology focuses on the interplay between scale, spatial pattern, and ecological processes; specifically, how to characterize spatial pattern, where it comes from, why it matters, and how it changes through time and/or scale.  Theory and application will weave together throughout the course and students will use their knowledge of landscape ecology theory to better understand practices in land conservation, resource management, and urban planning.  Students will also build skills in ecological spatio-temporal data analysis and modeling to analyze patterns and processes through space and time, and gain an understanding of the power and limitations of these techniques.

  • Applied Ecological Statistics (ECO 636)

Intermediate statistics illustrated using examples from ecology. Topics include ANOVA, linear regression (simple and multiple), correlation, logistic regression, continency tables and nonparametric methods. Techniques discussed in lectures and applied in laboratories.

  • Advanced Statistical Ecology (ECO 697SA)

This course explores statistical problems beyond the classical linear models including mixed effects, non-normal error distributions, autocorrelations, etc.  Much of the course is tailored to the needs of the graduate students in the course.

 

Recent publications

MacLean, M.G., J. Holt, M. Borsuk, M. Markowski-Lindsay, B.J. Butler, D.B. Kittredge, M.J. Duveneck, D. Laflower, D. Orwig, J.R. Thompson, 2020. Potential impacts of insect induced salvage harvests in mixed forests. Forests. https://doi.org/10.3390/f11050498.

Beaury, E.M., A. Barker-Plotkin, C. Brown-Lima, E.J. Fusco, B. Griffin, S. Jourbran, B.B. Laginhas, M.G. MacLean, L. Munro, M. Nelson, S. Talbot, B.A. Bradley, 2020. Regional Invasive Species & Climate Change Management Challenge: Taking Action. Managing invasive species in the context climate change. RISCC outreach publication. https://doi.org/10.7275/k8q5-4f71.

Markowski-Lindsay, M., M. Borsuk, B.J. Butler, M.J. Duveneck, J. Holt, D.B. Kittredge, D. Laflower, M.G. MacLean, D. Orwig, and J.R. Thompson, 2020. Compounding the disturbance: Family forest owner reactions to invasive forest insects. Ecological Economics. 167. DOI: 10.1016/ j.ecolecon.2019.106461.

Holt, J., M.G. MacLean, M. Borsuk, M. Markowski-Lindsay, B.J. Butler, D.B. Kittredge, D. Orwig, D. Laflower, J.R. Thompson, 2019. Classifying landowners into functional types to characterize responses to forest insects. People and Nature. DOI: 10.1002/pan3.10065

Graham, K.K., and M.G. MacLean, 2018. Presence-only modeling is ill-suited for a recent generalist invader, Anthidium manicatum. Ecological Indicators. 89:56-62. DOI: 10.1016/j.ecolind.2018.02.002

MacLean, M.G., 2017. Edge influence detection using aerial LiDAR in Northeastern US deciduous forests. Ecological Indicators. 72:310–314. DOI: 10.1016/j.ecolind.2016.08.034

MacLean, M.G., H. Hertler, and M. Seifert, 2016. Undergraduate research at The School for Field Studies (SFS) Center for Marine Resource Studies in the Turks and Caicos Islands. In: How Undergraduate Research Advances Science Policy, Spring CUR Quarterly. 36(3):21-27. DOI: 10.18833/curq/36/3/3.

MacLean, M.G., and R.G. Congalton, 2015. A comparison of landscape fragmentation analysis programs for identifying possible invasive plant species locations in forest edge. Landscape Ecology. 30(7):1241-1256. DOI: 10.1007/s10980-015-0175-7.

Winrich, C., V.L. Rodgers, M.G. MacLean, D.M. Blodgett, and J. Schaefer, 2015. Science Education as Entrepreneurial Thought and Action® Methodology. In V.L. Crittenden, K. Esper, N. Karst, and R. Slegers (Eds.) Evolving Entrepreneurial Education: Innovation in the Babson Classroom (pp. 159-174). Bingley, UK: Emerald Group Publishing Limited.

MacLean, M.G., and R.G. Congalton, 2013b. PolyFrag: A vector-based program for computing landscape metrics. Journal of GIScience and Remote Sensing. 50(6):591-603.

MacLean, M.G., and R.G. Congalton, 2013a. Applicability of multi-date land cover mapping using Landsat 5TM imagery in the Northeastern US. Photogrammetric Engineering & Remote Sensing. 79(4):359-368.

MacLean, M.G., M.J. Campbell, D.S. Maynard, M.J. Ducey, and R.G. Congalton, 2013. Requirements for prism sampling polygons in an object-based image analysis classification. International Journal of Remote Sensing. 34(7):2531-2547.

MacLean, M.G., and R. Congalton, 2011. Investigating issues in map accuracy when using an object-based approach to map benthic habitats. Journal of GIScience and Remote Sensing. 48(4):457-477.