ZOOL40490 Wildlife Habitat Modelling for Ecology and Conservation

Academic Year 2022/2023

This module provides a unique opportunity for students to explore the concepts of habitat modelling and acquire the required skills to analyse species presence data. Such spatial data can be gathered for different species across taxa (from trees to large mammals through invertebrates) and with different techniques and sampling designs (plots, visual observations, satellite telemetry data, museum records, preference trials, etc.). Using recent advances with habitat modelling – namely, resource selection functions and step selection functions – students will learn how to build a habitat suitability model, describe species habitat selection, and forecast hotspots of connectivity or species core areas that need special conservation attention. The techniques taught in this module have clear applications in species ecology, conservation and management. Special focus will be given to mammal movement data, but the skills acquired in this course can be used to model any presence data (including plants). Landscape connectivity describes how the movement of animals relates to landscape structure. The way in which movement among populations is affected by environmental conditions is important for predicting the effects of habitat fragmentation, and for defining conservation corridors, and this class will provide students with the skills needed to solve these challenging tasks. Finally, predictive models taught in this class can be used to predict future scenarios of species occupancy based on forecasted climate change. Students should have some familiarity with R and GIS (ArcMap or similar software).

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students will be able to:
i) significantly improve their R and GIS (ArcMap or similar software) skills;
ii) run a resource selection function using presence-only data and be able to build habitat suitability models;
iii) predict species habitat selection under different scenarios (climate change, habitat change);
iv) become confident with wildlife habitat modelling (different techniques) and acquire the proper skills required to study species ecology and improve their management and conservation.

Student Effort Hours: 
Student Effort Type Hours


Computer Aided Lab


Autonomous Student Learning




Approaches to Teaching and Learning:
This is a truly hands-on experience for the students! The lecturer will introduce and explain key topics and tools used in Wildlife Habitat Modelling and explain their importance in ecology and conservation. Weekly computer-based lab sessions will guide students step-by-step through habitat modelling techniques, receiving continuous support on the projects they are supposed to develop. The class is a perfect balance between theory - when students will have the chance to understand the key concepts - and practice - with several hours in the computer lab to put in practice the wildlife habitat modelling tools. 
Requirements, Exclusions and Recommendations
Learning Recommendations:

Students should have some familiarity (at least the basics) with R and GIS (ArcMap or similar software). Please contact the lecturer if you need further details about your eligibility.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: Submission of the final project developed during computer aided lab and autonomous student learning Coursework (End of Trimester) n/a Graded Yes


Carry forward of passed components
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

The students will continuously receive in-class support and feedback during their projects' development (with special regard to the practical sessions). The students will also receive individual feedback (post-assessment).

Name Role
Dr Julia Jones Lecturer / Co-Lecturer
Dr Adam Kane Lecturer / Co-Lecturer