The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
Elk are similar to deer in that they lack identifiable markings most of the time. This makes it hard to know whether an elk in one photo is the same elk that appears in another photo. However, some elk in Wisconsin have uniquely numbered collars, making it possible to identify one individual elk from another.
Using these collars, researchers can piece together all the Snapshot photos of that elk and follow its movement through time. Knowing that the elk in two different photos is the same individual holds a special type of power for researchers and tells them extra information about the size of the elk herd. That is, if the researchers can leverage that additional information.
Glenn Stauffer, Natural Resources Research Scientist within the Office of Applied Science, is leading the initiative to identify individual elk and use these data to improve the annual elk population estimate. Stauffer said, “I was approached because of my quantitative modeling experience to evaluate different ways of using the elk photographs as data to fit an elk model. [Collectively,] the various models I and others have worked on provide a range of options to estimate the [elk] population size and to evaluate how reliable the models are.”
To better understand the significance of Stauffer’s work, it helps to know how elk have historically been counted in Wisconsin.
“Long before I came onto the scene, the primary way of counting elk was to go out and count them all,” said Stauffer. This method requires extensive time in the field and considerable local knowledge about where elk groups often hang out. Researchers could count some elk by their numbered collars, but they also needed to know how many uncollared elk were in each group. The elk herd grew over the years, and more and more elk did not have identifiable collars. This added another challenge for researchers who were trying to count all the unmarked elk (and make sure they weren’t double counting any of them).
Since the estimate of the elk population size still needed to include an unknown number of these unmarked individuals, the DNR started experimenting with models that didn’t require individual identifications. These new models were also a boon because the herd was reaching too large a size to efficiently collar. It was becoming too much of a time investment and was expensive.
Instead, these models are based on images from the Snapshot camera grid, as discussed in the previous article, but even these camera-based models had room for improvement. Thus, Stauffer began researching a model that incorporated the best of both approaches: a model that was based on the camera data but still incorporated limited individual identification back into the model.
Stauffer looked into a variety of models but zeroed in on one type of model in particular. Stauffer explained that this model belongs to a class of models called spatial mark resight models. Spatial mark resight models combine the best of both marked and unmarked models. Stauffer’s model identifies individuals by their collars but also makes inferences from the photos of unmarked elk at the same time.
Spatial mark resight models also relax a major assumption made by the previous camera model, the closure assumption. “This assumption states that the number of elk at a particular camera location doesn’t change from one encounter occasion to the next, and it is clearly violated. Elk are wandering from camera to camera,” said Stauffer. Stauffer’s hybrid model relaxes the closure assumption and attempts to figure out the minimum number of distinct elk it can identify from the pictures.
Collared elk are often easy to identify in the photos. These collared elk are given the ID assigned to their respective collar number so that all photos of a particular elk share the same ID. The model also attempts to assign IDs to uncollared elk in the photos. The model uses probabilistics to assigns IDs to all remaining elk – either uncollared elk or unknown elk (because the collar or the collar number isn’t visible in the photo) – based on characteristics visible in each photo.
Fortunately, Stauffer’s model uses as much information as it can get from the photos when assigning IDs. For example, if one photo is of a calf and another photo is of a cow, then the model won’t assign the same ID to these animals. After all, we know those are two distinct elk, not one. Similarly, a marked but unidentified elk with one collar type can’t be the same as another unidentified elk with a different collar type. The model even uses spatial data to differentiate unmarked elk from two different photos. For example, photos at two locations close together might be from the same elk, but photos from two distant locations probably represent two different elk.
Capitalizing on all the information available in the Snapshot photos, the model makes an estimate of how many elk are likely in Wisconsin’s elk herds. As the elk herds continue to grow, this modeling approach helps estimate the elk population and hopefully saves the DNR time and money.
How well does the model work?
“[Technically,] the spatial count model doesn’t require any information about individual IDs, but it performs pretty poorly without them,” said Stauffer. “There is a series of papers from about 2013 on that shows if you add information about individuals to spatial counts, you can really improve the accuracy and precision of the spatial model.”
“Theoretically, this makes the model estimates more precise,” said Stauffer. To check, Stauffer collaborated with a colleague to run a bunch of simulations with known, perfect data, and the model worked reasonably well. These simulation results are encouraging because the model wasn’t massively overpredicting or underpredicting the number of elk in the herds, both of which could have management implications for elk.
When asked if identifying individuals from photographs is worth the extra effort, Stauffer said, “Working with models that don’t require individual IDs still requires considerable time to classify photos. Identifying individuals is only a little bit more work on top of that. In general, when you can’t meet the assumptions of a model, then it is worth getting individual identifications, if you can.”
Just how much additional effort should be put into individual IDs? Stauffer believes part of the answer comes from asking what other information can be obtained from the collars. “If we are already putting the collars on those we capture or release, then we might as well get as much out of them as possible, such as through using photographs [like Snapshot does],” said Stauffer.
Incorporating Another Year
After the Snapshot team finishes assembling the 2020 elk dataset, a large dataset comprised of the data from all the Snapshot photos of elk in 2020, Stauffer will run his model using this new dataset and generate an estimate of last year’s final elk population. Stauffer’s estimate will be closely compared to other estimates generated by the previous camera-based models and through collaring efforts alone to see how well each approach performs.
Stauffer took a minute to reflect on his work so far with the elk population estimate. Stauffer said, “The modeling process has been really rewarding, diving into this topic in a depth that I would not have done if I did not have this Snapshot photo dataset to work with. The simulation also went well. It illustrated that the model works the way we claim it works, which is good. Fitting the model to the elk data is mostly encouraging, but it shows that there are situations where it doesn’t do as good of a job as we hoped. Specifically, for calves, it still needs to be fine-tuned.”
From physically counting elk to modeling counts of only unknown individuals to modeling counts of both unknown and known individuals, Wisconsin’s approach to estimating elk abundance has evolved through time. Chances are, as the composition and distribution of the herd changes in the coming years, the approach will evolve even more. But for the next few years, Stauffer’s work will help direct how we count elk now.