University of St Andrews

Key information

SCOTCAT credits : 15

ECTS credits : 7

Level : SCQF level 11

Semester: 2

Availability restrictions: Not automatically available to General Degree students

Planned timetable: 12.00 noon Mon (odd), Wed and Fri

The module will introduce students to the main types of survey method for wildlife populations. It will cover simple methods in some detail and provide students with a conceptual framework for building understanding of more advanced methods. In the case of multi-species surveys, it will also show how abundance estimates may be combined into biodiversity measures. By the end of the course, students will be able to identify an appropriate assessment method for a given population, design a simple survey to assess the population, perform simple analyses of survey data, and estimate biodiversity trends in a community. Students will get experience in using the methods via computer practical sessions involving design and analyses of surveys.

Relationship to other modules

Pre-requisite(s): Before taking this module you must pass MT3507 or pass MT3508 or pass MT5761

Learning and teaching methods and delivery

Weekly contact: 2.5 lectures (x10 weeks), 1 computer practical or tutorial (x10 weeks)

Scheduled learning hours: 35

Guided independent study hours: 110

Assessment pattern

As used by St Andrews: Coursework = 100%

As defined by QAA
Written examinations : 0%
Practical examinations : 0%
Coursework: 100%

Re-assessment: Coursework = 100%

Personnel

Module coordinator: Dr A Johnston
Module coordinator email alison.johnston@st-andrews.ac.uk

Intended learning outcomes

• Understand the statistical foundations of the main types of wildlife survey methods, and the relationships between them
• Ability to identify an appropriate survey method for a given wildlife population and design a survey to apply the method to the population
• Ability to use wildlife survey data to draw statistically sound inferences about population abundance, distribution and trends, and the uncertainty associated with these inferences
• Ability to combine estimates from mult-species surveys into measures of biodiversity and biodiversity trend in a wildlife community
• Ability to use R software to estimate abundance, distribution and biodiversity from survey data