In this tutorial, I cover:

1. The difference between single season (static) and multi-season (dynamic) occupancy models,
2. Fitting dynamic occupancy models with the R package `unmarked`, and
3. Making inferences, predictions, and plotting results from dynamic occupancy models.

The code and sample data from this tutorial are available on GitHub.

In this tutorial, I cover:

1. Fitting and comparing multiple occupancy models with the R package unmarked,
2. Model-averaging predictions* of occupancy
3. Model-averaging predicted* relationships between occupancy and covariates

*some conditions may apply ;)
The code (and sample data) from this tutorial are available on GitHub.

Let's get started!

In this tutorial, I cover:
1. What occupancy models are useful for
2. Fitting single-season occupancy models with the R package `unmarked`
3. Testing goodness-of-fit for single-season occupancy models

The code (and sample data) from this tutorial are available on GitHub.

Let's get started!

Mark-recapture models are a useful framework to test hypotheses about what drives differences in wildlife survival and detection probability. However, it is important to assess the goodness-of-fit (GOF) for these models before we make inferences.

What causes lack of fit?

• assumption violations
• model misspecification (e.g. missing important variables), or
• extra-binomial noise or overdispersion

In this tutorial, I deal with overdispersion by:

1. Calculating the variance inflation factor ("c-hat").
2. Adjusting model selection when there is evidence of overdispersion.

The code from this tutorial is available on my GitHub page for mark-recapture workshops.

Let's get started!

Mark-recapture models are a useful framework to test hypotheses about what drives differences in wildlife survival and detection probability. However, it is important to assess the goodness-of-fit (GOF) for these models before we make inferences.

​What causes lack of fit?

• assumption violations (more on that below!),
• model misspecification (e.g. missing important variables), or
• unmodelled heterogeneity (e.g. some ‘trap happy’ or ‘trap-shy’ animals)

In this tutorial, I cover testing assumptions of the Cormack-Jolly-Seber model using `R2ucare`.

​Mark-recapture studies are useful for estimating population size and survival in wildlife populations, but can be overwhelming because of the massive amount of literature on their development and application.

Let’s dip our toes into some capture-recapture models using the famous `dipper` data set and the marked package in R.

The syntax (and approach) is almost identical using RMark to access the widely used MARK program. The code from this tutorial is available on my GitHub page for mark-recapture workshops.

Let’s get started!

Kernel density estimators are commonly used for estimating the home range of animals (see my previous post on estimating home ranges with kernels).

​But, a common issue is that the kernel contour for an animal extends beyond a boundary the animal cannot cross.

Can we adjust the density estimator and limit kernel contours to not cross boundaries?
The code and data I use in this tutorial are available on my on my GitHub repo.

One of my favourite ways to visualize wildlife tracking data is to animate paths. Using `ggmap` and `gganimate`, it has never been easier to show-off your hard-earned tracking data with animated maps you can use in presentations, meetings, and at dinner parties.

The code and data I use in this tutorial are available on my on my GitHub repo.