Introduction to Occupancy modelling
Analyse species distributions in R, Nov-Dec 22
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😃 Course summary
A 4-week introductory course in Occupancy modelling for wildlife conservation practitioners and students.
- Suitable for complete beginners or to refresh your skills
- Flexible online, part-time format allows you to choose when you study each week
- You can be based anywhere in the world, and at any stage of your career
- Tutor-led and designed to encourage sociable learning
This course focusses on giving you a strong grasp of the fundamental ideas behind occupancy modelling. Take this course if you want to:
- 🛠️ Fill a skills gap for your research or conservation practice
- 💯 Build your confidence to run occupancy analyses independently
- 😎 Become more effective at field work
- 💪 Increase the value of your team’s outputs
- 👍 Enhance your CV and future employability
Want to know more before signing up? We’ve got you covered!
Scroll down to learn more, or jump straight to price, the learning objectives, or a brief summary of occupancy modelling
✋ Is it for me?
Are you a wildlife conservation professional or student who wants to learn (or re-learn!) the fundamental knowledge and skills of occupancy modelling to assess species distributions? Then yes, it’s for you!
The course is suitable for complete beginners to occupancy modelling, or those wanting to refresh their skills. You do need to have R installed on your computer, and be familiar with basic R functions such as importing .csv data
🤓 What will I learn?
By the end of the course, you will be able to:
- Prepare your field data for analysis
- Run a basic model to estimate occupancy & detectability of your species (single-season single-species model)
- Suggest new ways to use occupancy modelling in your future conservation work
- Explain how to maximise detection of your target species
Weekly topics: 1. Fundamental concepts that underpin occupancy modelling, including detectability, and detection histories 2. Analyse your own data, or a sample dataset that we provide 3. Interpret your results: Assessing model fit and estimating occupancy probability 4. Implement your new knowledge (Optional): Support to practice and apply what you’ve learned in your own work
A future course will cover how to incorporate covariates (environmental/sociological data) into your analysis, to assess which factors affect the distribution of the species
📝 Course details
📅 When?
- 4 weeks
- Monday 21 November to Sunday 18 December 2022
- Part-time: 4-5 hours each week (16-20 hours total) when it suits you
- The deadline to register is 13:00 UTC on Friday 18 November 2022
🌍 Where?
- Wherever you are!
- Online, accessible from anywhere
- Receive joining instructions by email
💷 What does it cost?
We believe that every wildlife conservationist should have access to high-quality, sociable learning opportunities
Sliding scale of course fees:
£7 - £27 - £47 | Individuals in low- or middle-income countries £37 - £67 - £97 | Individuals in high-income countries
See a map1 or list2 of countries by income level
- You decide which price you pay. We suggest that unwaged and students pay the lower fees, and those with higher earnings pay more.
- Everyone gets access to the same learning activities and support. We won’t check up on what you pay - we trust you to make an honest decision about how much you value this course and can afford to pay
- Paying the higher fee (if you can afford it) enables VerdantLearn to keep offering courses at a affordable prices for everyone
If you cannot pay by credit or debit card, please email lucy [dot] tallents [at] verdantlearn [dot] com to arrange an alternative payment method, e.g. Paypal
🤔 Why should I pay?
Learning materials to teach yourself occupancy modeling to exist, but they are hard to find, and it can be even harder to motivate yourself to study alone.
By paying for a course, you gain these benefits:
- Social learning - being part of a cohort, studying side-by-side, learning from each other, and gaining emotional and practical support
- Access to advice and feedback from your tutor in a private area of VerdantLearn’s learning community
- Weekly live calls to ask questions and support each other, held at 2 different times/week to include Australasia and the Americas
- Deadlines and accountability, increasing your motivation to complete the course
- A certificate of completion (open badge) that references your publically-submitted assignments, for those who complete weeks 1-3
- Help to apply your new skills to your own conservation datasets, for those who attend week 4
😅 What do I need to commit?
To participate, you need to:
- Commit 4-5 hours each week to study
- Install R before the course begins
- Be willing to collaborate and learn together
🖥️ Software
We’ll be using R to conduct the analysis, and you can prepare your field data in Excel or similar
📧 Join our mail-list
If you would like to hear about future learning opportunities with VerdantLearn, join our mail-list
❓ What is occupancy modelling, anyway?
Occupancy modelling is a versatile data analysis technique for understanding the spatial distribution of species, and the factors affecting their distribution
It is especially useful for monitoring species which are hard to observe in the field (nocturnal, rare or wary of humans), or cannot be identified individually
Unlike population estimates such as Capture Mark Recapture, occupancy modelling doesn’t require data on recognisable individuals. In fact, you don’t even have to see the species at all! Instead, you can use a wide range of field signs, including tracks, nests, faeces, feeding signs etc. Occupancy modelling can also accept data from camera-traps, hair traps or eDNA. Basically, any type of survey that allows you to detect if the species is present can be used for occupancy modelling! This means you can make efficient use of existing field data 🚀
You need to have field data from repeat surveys 📋 ✔️ ✔️
Repeat surveys allow you to determine whether a failure to record the species is because it is absent from the site, or if it is present but you have simply failed to detect it. You can also combine your species or sign observations with environmental data (known as covariates) to determine what factors affect the species’ distribution, including things like the presence or abundance of food sources, predators, or human activities.