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Movement ecology (MOVE03)
17 February 2020 - 21 February 2020£275.00 – £580
The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS to novel biologging data) in ecology and evolution, covering analysis techniques applicable broadly to most taxa, from micro-organisms to plants and animals. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining hands-on sessions with new biologging technology with computer-based practicals in R and specialized software for biologging data developed by the SLAM lab at Swansea University. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts.
Research postgraduates, practicing academics and primary investigators in ecology and management and environmental professionals in government and industry. The course will also be of interest to researchers in geography, mathematics and computer science working on movement analyses.
Venue – Margam Discovery Centre, Magram Park, Port Talbot SA13 2UA – Google Map
Availability – 24 places
Duration – 5 days
Contact hours – Approx. 37 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner and refreshments. Accommodation is multiple occupancy (max 3- 4 people) single sex en-suite rooms.
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Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PR~statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PR~statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of statistical, mathematical and physical concepts. Specifically, generalised linear regression models, including mixed models; basic knowledge of trigonometry, basic knowledge of calculus; basic knowledge of physics as relevant for biological systems (e.g forces and work, magnetism).
Assumed computer background
Good familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models (up to GLM) & generate simple exploratory and diagnostic plots. Knowledge of more advanced models, such as mixed models, will be helpful, as will a basic recollection of mathematical analysis.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed and sufficient RAM to to load and analyse moderately large datasets. The DDMT software for biologging data visualization and analysis will be provided during the course. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links
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Meet at Margam Discovery Centre 18:00 (Download directions PDF).
Monday 25th – Classes from 09:00 to 17:00
Module 1: Introduction to Movement Ecology – Theory, followed by hands on experience of deploying and collecting GPS & Biologging data (Daily Diary tags), incl. attachment methods & data quality
Module 2: Accelerometers & pressure/depth sensors– quantifying speed, behaviour, individual state & energetics
Tuesday 26th – Classes from 09:00 to 17:00
Module 3: Magnetometers – identifying orientation and animal behaviour
Module 4: Dead reckoning – reconstructing sub-second animal trajectories
Wednesday 27th – Classes from 09:00 to 17:00
Module 5: Movement path analysis I – from steps and turns to movement path segmentation
Module 6: Movement path analysis II – the squared displacement method and movement modes (foraging trips, home range, dispersal, migration, nomadism)
Thursday 28th – Classes from 09:00 to 17:00
Module 7: Space use analysis – Home range estimation (2D & 3D) and quantification of individual interactions (static and dynamic approaches)
Module 8: Modelling animal movements: statistical and mathematical approaches
Friday 1st – Classes from 09:00 to 16:00
Module 9: Habitat use and resource selection: from classical models to integrated step selection models
Modules 10: Joint modelling of animal movement and behaviour
“This course was very good and useful for me while doing a PhD! The three instructors showed each different aspects of movement ecology from collecting data ourselves to analysing this data with different methods related to behaviours, movements paths and home ranges. I really liked the hands-on experience of collecting both accelerometer and magnetometer data ourselves and analysing this data in specialised software developed by one of the instructors. The individual support we got during the practicals was very helpful and I feel like I have a better understanding of all the concepts and methods relating to movement ecology after this course. Additionally, we learned more about the maths aspect behind specific movement models, which was challenging as I do not have a maths background, but it was well explained, and I am more comfortable with applying these statistic models now. I did have the change to apply them to my own data which was very useful! Lastly the group was great, and I have learned a lot from meeting other people which work in similar fields. Together with the instructors and the course director – who were all very friendly and professional – they made the week into a productive and fun week.”
Marine Biologist, Bio-Inspired Flight Lab, University of Bristol, UK (Attended MOVE course)