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Course outline

This course consists of 6 chapters or modules, each made up of 2-4 short videos. These are a mix of theory and practice - see the descriptions below.




Pre-design planning

The 3 videos in this module introduce you

to important concepts behind survey design decisions

1.1: Design-based inference (8 min)

Learn about the design-based paradigm of inferring animal abundance, requiring a random sample of the survey region.

1.2: Stratified sampling (5 min)

Learn how dividing your survey region up into subregions can deliver better design-based inferences.

1.3: Model-based inference (4 min)

Learn about an alternative paradigm for inferring abundance, which relies on relating animal density and detectability to covariates.

Micro-level design


In the 4 videos in this module we look at how to place camera traps

for a single survey

2.1: Foundations of SCR survey design (10 min)

We cover some of the key concepts behind SCR survey design, including precision and bias, and the importance of sample size.

2.2: Examples of good and bad designs (9 min) 

Concepts from the previous video are put into practice by illustrating a few "hero" (good) and "villain" (bad) designs.

2.3: Software for designing SCR surveys (5 min)

We look at software for creating and testing potential survey designs.

2.4: New developments in SCR survey design (10 min)

SCR survey design is an evolving field and in this last video of the module we look at some newer, more advanced approaches to survey design.

Macro-level survey design

The 2 videos in this module look at an extra design stage useful when surveying very large regions

3.1: Foundations (13 min)

We learn what macro-level design is, why spatial balance is important, and one way to achieve this: using Halton points.

3.2 Software (8 min)

We look at a Shiny application that selects Halton points for macro-level survey regions

Modelling animal encounter rates

SCR models jointly model animal density and detectability. The 2 videos in this model look at detectability

4.1: Encounter functions 

We build a function to answer the key question "how often do we expect a camera to detect an animal?"

4.2: Encounter function design issues

We learn about the link between encounter rate modelling and camera trap placement.

Modelling animal density

Having covered detectability, the 2 videos in this module look at the other side of an SCR model: a model for animal density

5.1: Kinds of density models 

We learn about spatial models for where animals have their activity centres, including the case where density is the same everywhere. 

5.2: Density models design issues

We learn about the link between density modelling and camera trap placement.

Fitting models using secr

These 2 videos show you the basics of fitting SCR models using the R package secr

6.1: Data setup (6 min)

We describe some of the basic options for setting up data for analysis with secr.

6.2: Fitting models

We learn about using secr to estimate the parameters of the SCR model and draw inferences about animal density and detectability.

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