Modules

Module title: Modelling Wildlife Populations
SCQF level: 11:
SCQF credit value: 10.00
ECTS credit value: 5
Module code: ENV11114
Module leader: Paul Ward
School School of Applied Sciences
Subject area group: Animal and Plant Science
Prerequisites

n/a
2020/1, Trimester 1, FACE-TO-FACE, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: SIGHTHILL
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Paul Ward
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The module is taught in weekly lectures (LOs 1-6) and computer-based tutorials (LOs 4-6). The Moodle site will contain lecture material, associated analytical material and links to further reading matter and other information which are available for use by campus and distance students.

Formative Assessment:
Data tuned to the topic-of-the-week will be provided each week for analysis – these exercises are tutor-assisted and accompanied by guidance from the lecture notes. Feedback is subsequently released for each exercise. These exercises feed-forward into the summative assessment.

Summative Assessment:
A selection of datasets is provided from which one is to be analysed – each dataset can be analysed in several ways depending on the questions the student decides to interrogate the data for. The assessment requires determination of appropriate questions, correct analysis and valid interpretation.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 12
Face To Face Tutorial 12
Independent Learning Guided independent study 76
Total Study Hours100
Expected Total Study Hours for Module100


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 100 1-6 15 HOURS= 0, WORDS= 2000
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Description of module content:

A range of Likelihood and Information-Criterion based modelling techniques used in modern population analysis and management. Both open and closed population models based on multinomial probability functions will be covered. Advanced modelling of recovery data using robust-design models with integrated link functions for co-variate inclusion will be used and built upon to create multi-state stochastic models. Variants of these models will be employed in assessing presence-absence data and occupancy estimations. Parameters derived from modelling will then be used in matrix models and population viability analyses.

Learning Outcomes for module:

Upon completion of this module you will be able to:
LO1: derive logarithmic multinomial likelihood statements
LO2: understand the difference between and applications of open and closed models
LO3: design data collection strategies to include robust design assumption relaxations
LO4: understand the operation of link functions and use design matrices to operate them
LO5: competently use advanced modelling techniques including additive and interactive model structures
LO6: understand the operation of matrices and demonstrate their use in population projection and viability analyses.
Indicative References and Reading List - URL:
Contact your module leader
 

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