MSc Geographical Information Science

This module will explore the theory and principles of landscape ecology and the spatial techniques available to test, demonstrate and put into practice these principles.

Students will explore ecological data (habitat, species and topographic) across a range of spatial contexts and explore the effect of landscape configuration, land-use and land-cover combinations, on biogeographical patterns, ecological functions and species distribution. This will be done through the application of spatial techniques such as density functions, point patern analysis and graph theory, as well as through computational landscape ecology metrics and analyses.

Equip students with knowledge on the theory of spatial ecology, primarily focused on landscape-ecological methods and principles.

Students will gain proficiency in computational ecology acquiring skills that are highly transferable and sought-after by employers in environmental consultancy and data science.

Development of object-based programming skills (R), GIS-based and ecological knowledge and skills will create highly skilled and employable graduates for roles in ecological, environmental, commerce, planning and related fields.

Students will have access to a range of GIS, programmatic and online materials and resources that will see them greatly increase their digital literacy.

Syllabus (indicative curriculum content):

Lecture One: Course orientation and introduction Spatial Ecology and R

Lecture Two: Scale in ecology

Lecture Three: Species Distribution Modelling

Lecture Four: Advanced Species Distribution Modelling

Lecture Five: Introduction to Assessment 2

Lecture Six: Landscape Connectivity

Lecture Seven: Advanced Connectivity Modelling

Lecture Eight: Diversity

Lecture Nine: Course summary and re-cap; assessment tips.

The module will be delivered through nine one-hour lecture sessions and 11 two-hour practica classes.

All materials will be delivered synchronously but with the addition of asynchronous access and materials. For example, practical classes are made available through web pages and examples are facilitated trough interactive apps developed by the convenor and made available to students.

Links to all resources are made available through the course VLE page.

• Key concepts in spatial ecology, knowledge of landscape ecological processes and reflecting on their importance across a range of contexts.
• Use of spatial, ecological and geo-statistical techniques such as networks, density functions, species distribution models, buffers and landscape ecology metrics.
• Accessing, interpreting and analysing large spatial datasets such as multi-band imagery, land-use datasets and species record data.

• Identifying key spatial components of ecological systems.
• The ability to critically evaluate the opportunities and limitations of applying spatial data and techniques to the analysis of complex systems.
• Employ critical analytical factors such as scale and spatial context.
• Model ecological processes.

• Use of R as a GIS.
• Statistical analysis.
• Programmatic geo-computation skills.

• Ability to express complex ideas related to spatial ecological.
• Experience of project design and report-writing.
• Ability to communicate knowledge and reasoned arguments on complex topics and debates.
• The ability to source, manage and operationalize spatial data and techniques to explore real-world problems.

Assessment 1 - Submission of practical workbook. 
1000 words.
Verbal and written feedback. 
40% weighting.

Assessment 2 - Individual student project on a chosen species ecology application (e.g. species, habitat). 
Examples will be provided or students can propose their own with guidance.
2000 words.
Verbal and written feedback. 
60% weighting.

Fletcher, R. and Fortin, M., 2018. Spatial ecology and conservation modeling (p. 523). Cham: Springer International Publishing.

Miguet, P., Jackson, H.B., Jackson, N.D., Martin, A.E. and Fahrig, L., 2016. What determines the spatial extent of landscape effects on species?. Landscape ecology, 31, pp.1177-1194.

Guisan, A. and Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological modelling, 135(2-3), pp.147-186.

Renner, I.W., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, S.J., Popovic, G. and Warton, D.I., 2015. Point process models for presence‐only analysis. Methods in Ecology and Evolution, 6(4), pp.366-379.

Dennis M, Huck J, Holt C, McHenry E. 2024. A mechanistic approach to weighting edge-effects in landscape connectivity assessments. Landsc Ecol 39, 68.

Teaching and learning will be designed to be inclusive through providing materials online in advance of sessions in accessible formats (visual media, lecture recordings). Students can engage with discussion in class and online via the VLE discussion forum.

Assessment instructions and criteria are clearly communicated in advance of the deadline via the course webpages and all lectures are recorded.

Individualised feedback is provided for all students for both assessments, and formative feedback is provided in class and via the discussion board. Assessments are spaced to give students time to act on feedback.

Essential software used as part of this unit is freely available, open source and meets accessibility requirements (e.g., R Studio).