STATS 776 : Estimating Animal Abundance


2021 Semester One (1213) (15 POINTS)

Course Prescription

Fundamentals of the statistical methods that underly capture-recapture, distance sampling and occupancy analysis, focusing on the critical role that p, the probability of detection, plays in estimating n, the number of animals, or psi, the probability of species presence. Extensions to these fundamental tools including spatially explicit, genetic, and hierarchical methods will be covered.

Course Overview

This course is accessible to both statisticians and biologists, taught by lecturers who work in both fields. The course is delivered as ten 2-hour seminars where students will be taught the statistics that underly capture-recapture, distance sampling, occupancy analysis and model selection. Students will learn the critical role that p, the probability of detection, plays in estimating n, the number of animals, or phi, the probability of species presence. Extensions to these fundamental tools including spatially explicit methods, genetic methods, and hierarchical methods, will all be introduced. Students will be assessed throughout the semester by assignments using real-world data from published ecological studies. This course will be useful for any student that expects to undertake ecological or conservation studies of animals in their career.

Course Requirements

Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and describe the role of probability of detection in obtaining unbiased estimates (Capability 1 and 2)
  2. Analyse animal abundance and species occupancy datasets (Capability 1 and 3)
  3. Accurately develop models of animal abundance and species occupancy (Capability 1 and 5)
  4. Be able to undertake model selection (Capability 1 and 3)
  5. Communicate results from analyses of animal abundance datasets (Capability 4 and 6)


Assessment Type Percentage Classification
Assignments 100% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5

Key Topics

Spatial capture-recapture
Distance sampling
Occupancy analysis
Model selection

Special Requirements


Workload Expectations

This course is a standard 15 point course and students are expected to spend 150 hours per semester involved in each 15 point course that they are enrolled in.

For this course, you can expect 20 hours of lectures, 50 hours of reading and thinking about the content, 40 hours working with R software and 40 hours of work on assignments.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including seminars of the course.
Lectures will not be available as recordings.
The course will not include live online events.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Royle & Dorazio 2008 Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities.

Buckland et al. 2015 Distance Sampling: Methods and Applications.

Mackenzie et al. 2017 Occupancy Estimation and Modeling 2nd Edition: Inferring Patterns and Dynamics of Species Occurrence.

Burnham & Anderson 2002 Model Selection and Multimodel Inference.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Level 1:  Delivered normally as specified in delivery mode.
Level 2 / 3 / 4: All teaching activities and assessments are delivered remotely.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 28/01/2021 11:58 a.m.