ZOOL40490 Wildlife Habitat Modelling for Ecology and Conservation

Academic Year 2023/2024

This module provides a unique opportunity for students to acquire skills in habitat modelling and learn how 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 and surveys, visual observations, satellite telemetry data, museum records, preference trials, satellite imagery and drone technology). 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 such as QGIS).

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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 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.

Indicative Module Content:

Indicative topics:
- Habitat use, habitat selection, habitat choice by animals: theory and practical examples.
- Type of data gathered when monitoring animals in the wild: presence/absence spatial data, used/unused spatial data data, presence/available spatial data. Theory and practical examples including analytical approaches.
- Resource selection by animals: sampling protocols and study design.
- Spatial analysis in R (loading, manipulating, and visualising spatial data).
- Revision of the basic statistical concepts needed to analyse animal spatial data: regression models.
- How to build a resource selection function used to explain habitat selection by animals.
- How to produce HTML interactive reports using RMarkdown by RStudio which can be used to display the results of wildlife habitat modelling (used in academic research, conservation, wildlife management, and ecological consultancies).


Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Computer Aided Lab

12
Autonomous Student Learning

101
Total

125
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 expected 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
Continuous Assessment: Multiple Choice Questions, exercises, and/or short questions on the different topics taught in class to make sure students are on track with the skills needed to develop their final project. Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

40
Project: Data analysis of spatial data provided in class (e.g. satellite radiotracking data of deer, wolf, cougar, orangutan) describing habitat selection of the selected species.
 Coursework (End of Trimester) n/a Graded Yes

60

Carry forward of passed components
Yes
 
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
• Group/class feedback, 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).

Lectures and labs will be interactive and based on hands-on activities which will allow students to learn by doing. There is no official textbook and students will be provided with the necessary reading material during the course.
Name Role
Dr Virginia Morera-Pujol Lecturer / Co-Lecturer
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23 Fri 09:00 - 11:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23 Thurs 11:00 - 11:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23 Tues 12:00 - 12:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23 Wed 13:00 - 13:50
Spring