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This course builds on foundational statistics and covers a range of methods widely used in modern-day ecology and conservation research, starting from simple linear models and generalised linear models, to mixed effects models with grouped random effects, and linear models with covariance structures for random effects that cannot be grouped. Each topic of the curriculum builds upon the previous one, guiding participants from basic to advanced concepts. By the end of the course, participants will gain practical skills to confidently apply these methods to your own research.

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Venue: Menglun Town, Mengla County, Xishuangbanna Dai Autonomous Prefecture, Yunnan Province, 666303, China

Dates: June 22nd – June 26th, 2026 (UTC+8:00)

Registration: Registration is open until 15:00, 30 April, 2026 (UTC+8:00). Once registration closes, you will be notified of your acceptance status around 16 May.

Target enrollment: ~25 participants

Pre-requisites for applications:

(1) Participants should have seen at least one semester of statistics at university or college.

(2) Participants should be familiar with R. Students familiar with S and SAS may also apply. Please provide evidence in line with each of these requirements when applying for the course.

(3) The course is divided into a series of modules that build on each other towards more complex linear models. The underlying linear models are explained so experience with linear algebra will be very helpful though not essential to complete the course.

 

Fee:

(1) No registration fee for the course

(2) Costs to be covered by participants include:

• Visa application fees

• Transportation to and from the venue

• Accommodation

• Meals during the workshop

• Personal medication and accident insurance

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Instructors:

Prof. Kyle Tomlinson (Xishuangbanna Tropical Botanical Garden, Chinese Academy of Science)

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PI of Community Ecology and Conservation Group of Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences (XTBG), works on landscape conservation, forest ecology, savanna ecology, and functional trait diversity. Kyle is an experienced statistics instructor and has been invited to run statistic workshops during ATBC-Asia chapter annual meetings, and during Advanced Fieldcourse in Ecology and Conservation (XTBG's annual international training program) since 2013. Since 2020 he has been the associate editor of the Journal of Ecology.

 

Course outline:

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Module 1: Classical methods (~ 1 day)

The first day revises basic statistical concepts and classical methods, and links them via the linear model and the generalised linear model.

Lecture 1: Linear models 

Lecture 2: General linear models

[END OF DAY 1]

 

Module 2: Modelling with grouped non-independent data (~ 2.5 days)

The bulk of this course deals with the problem of non-independence of data and how to fix it appropriately using linear models. The main issue is recognizing how individual data points may be related to one another, which means that they share information. Our statistical tests assume that there is no relatedness between residuals generated by fitting models to the data.  In this first section we deal with situations where that relatedness can be reasonably described by grouping related data points. This has the dual benefits of improving residual independence and also accounting for additional information in the data, potentially improving our test sensitivity. In doing this, we also recognise that accounting for this relatedness may not be essential to our questions; so we treat these additional model terms as random effects, and the predictors which are core to our questions we treat as fixed effects. Models with both fixed and random effects are called mixed effects models.

Lecture 3: Intro to linear mixed models

Lecture 4: LMM types

[END OF DAY 2]

Lecture 5: Inference with LMMs

Lecture 6: Predictions with LMMs

[END OF DAY 3]

Lecture 7: GLMMs

[END OF DAY 4]

 

Module 3: Modelling with non-independent data that cannot be grouped (~1.5 days)

Certain types of data are non-independent, but the non-independence cannot be corrected by grouping, e.g. autocorrelation in spatial and temporal data, which depend on individual pairwise distances between points. In this module we introduce methods for dealing with non-independence between data points using correlation matrices.

Lecture 8: Generalised least squares

Lecture 9: Phylogenetic regression

Lecture 10: Combining random effects and autocorrelation

Recognising that all data could contain both residual relatedness due to groups and due to pairwise relatedness means we need a model that can handle both situations.

The r code version we use here is based on Bayesian methods.

[END OF DAY 5]

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If you have any questions about the course, please email:

XIA Xue (Course Coordinator): xiaxue@xtbg.org.cn, and/or

Kyle Tomlinson (Core Instructor): kyle.tomlinson@xtbg.org.cn

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