Elk Camera Updates
Co-authored by Ally Magnin and Emily Donovan
You may have noticed recently on the blog that the Snapshot team has spent some time in the Northwoods conducting fieldwork in the elk grids. But what was our motive as researchers?
Wisconsin’s elk herds are dynamic and do not necessarily occupy the same area all the time. Young individuals may temporarily disperse, cows split off from larger herds to give birth, and the herd as a whole may shift their range as they seek out suitable habitat. While this is to be expected, it creates an interesting problem for camera trap research.
A bull elk from Black River Falls, WI with a GPS collar.
Since 2015, Snapshot Wisconsin has had a portion of our project dedicated to monitoring reintroduced elk herds. Cameras were deployed in grids much smaller than the usual Snapshot Wisconsin grid, increasing the density of cameras in the herd reintroduction area and making it more likely to capture photos of elk. As the herds shifted their range, however, some cameras no longer detected elk. To begin to address this mismatch between our elk grids and the herds’ ranges, Data and Spatial Analyst Emily Buege Donovan conducted an analysis.
Donovan began by combining several elk-related data sources to assess the quality of each camera site. Among these data sources were the most recent GPS locations of collared elk. A portion of the state’s elk are fitted with GPS collars, which transmit a location every 13 hours. GPS collar data is commonly used in wildlife research and management to better understand the movement patterns and resource selection of animal populations. In the present study, Donovan used these data to predict the likelihood that a camera will regularly detect elk. See Figure 1 for an example of the camera locations in relationship to the collar data. Camera locations in the northwest portion of the map have low probability of capturing elk, whereas cameras in the southeast have a high probability of capturing elk photos.
Fig 1. Example map of Snapshot Wisconsin cameras within the elk grids and the 2019 elk collar data.
However, because not all elk in Wisconsin are collared, the collar data could not be used exclusively to determine whether a camera site should remain active in elk monitoring efforts. Donovan also needed to bring in the historical elk detections for each camera site. How long had it been since an elk was detected at this site? How many elk photos were taken by each camera? By combining the collar data, photo data, and several other factors, such as ease of access by the volunteer and habitat type, Donovan created a scoring system to determine the best camera locations. Low scoring cameras were marked for removal, and high scoring cameras were marked to stay on the landscape.
Once we determined which elk blocks should be removed, we reached out to the volunteer who was assigned to each of those blocks and requested their assistance in removing the camera. For the blocks that didn’t have a volunteer assigned, our team planned fieldwork for the summer of 2020 to remove the cameras.
Snapshot team member Ally Magnin during elk camera fieldwork.
Many of the cameras marked for removal were deployed over three years ago, so navigating to them proved difficult in some cases. We traversed tamarack swamps, bushwhacked through thick understory, hopped across streams, and puzzled over satellite imagery to reach each destination. Our team enjoyed the challenge!
In addition to removing old cameras, we also conducted camera checks on the blocks that didn’t currently have a volunteer assigned in order to get them ready for a new volunteer to monitor, and replaced cameras that had shown signs of malfunction. We made it a priority to take diligent notes about how to navigate to each camera site to make navigation easier for future volunteers.
Overall, it was a very productive field season that provided the team with the opportunity to step away from our computer screens and into the outdoors. It also gave us an even greater appreciation for the work our volunteers do to monitor their cameras.
Are you interested in monitoring a camera as a part of our elk project? Sign up today at elk.snapshotwisconsin.org. Applications are reviewed when blocks open up, and we will contact you with more information once you’re accepted!
Check out our other elk-related blog posts below:
Elk Snapshots Mean Better Elk Modeling
Individuals Matter Too! – When You Can ID Them
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.”
Identifying Individuals
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’s 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.
Elk Snapshots Mean Better Elk Modeling
The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
At the start of every year, DNR staff begin compiling a large dataset of elk sightings from the previous calendar year, and the data, once compiled, is used to calculate the total number of elk that live in the state. This method has been a standard practice since the second reintroduction of elk to Wisconsin.
What some of you may not know is that Snapshot plays an important role in counting elk by providing sightings, particularly of bulls. In fact, Snapshot has more than 250 of its cameras (over 10% of all Snapshot cameras) dedicated to monitoring elk alone. These cameras are clustered in the three areas of the state with part of the elk herd – Clam Lake, Flambeau River and Black River Falls. These elk cameras are arranged into a grid-like pattern in each area, just like the rest of the Snapshot grid, except that the density of cameras in the elk grid is a lot higher.
A few members of the Snapshot team are among those working on the 2020 elk dataset, so the team decided to focus this newsletter on elk and how they use photos to learn about the elk herd.
The Snapshot team invited Dr. Jennifer Price Tack, Large Carnivore and Elk Research Scientist and fellow scientist within the Office of Applied Science, to add her perspective to this newsletter on why Snapshot’s photos matter for elk.
The task of integrating Snapshot data into the elk model was originally the work of Joe Dittrich, who laid a solid foundation for Price Tack. Since Price Tack joined the Office of Applied Science at the end of 2019, she has been using Snapshot data to model how various quota alternatives will affect the elk herd size in the years to come.
“My research focuses on [elk] populations because populations are the scale at which we manage wildlife,” said Price Tack. Population is the starting point for all decisions that are made about managing wildlife in Wisconsin. The status of a population determines how decisions are made, policy is framed, quotas are set, permits are allocated, and so on… Population is the unit of concern for the DNR.
Price Tack continued, “While individual animals are important and make up a population, our ability to manage them breaks down some at the individual level, [simply] due to the infeasibility of monitoring individuals.”
For species like elk, which normally lack easy-to-identify markings, individual identification is often difficult. Possible, as discussed in the next article, but difficult. Thus, populations tend to be the scale of most species work at the DNR, including Price Tack’s work on elk.
As Price Tack walks us through her research on the elk population, check out the unique way that Snapshot photo data are used to monitor this large herbivore population.
Feeding Photo Data Into The Model
Photos of elk can have multiple forms of data in them, beyond just what animals are present in the picture. There is camera location data, for example, which provides information about which areas of land the elk are using and not using.
There is also movement data. The Snapshot team learned that elk calves, cows and bulls have different movement patterns and are seen at different rates throughout the year. When bulls are the most active, for example, cows tend to be less active.
The camera data also helps Snapshot determine a calf-to-cow ratio for elk. Although, it isn’t as simple as dividing all the calf photos by the number of cow photos. Cows move around more than calves do and are more detectable in photos, given their larger size. Using knowledge about calf/cow visibility, calves and cows are modeled separately, and those numbers are then used to calculate the calf-cow ratio for elk.
“I remember first learning about Snapshot and thinking it is such a cool resource! There is so much you can do with camera data.” said Price Tack. “I have experience working in other systems that use camera data, so I know [firsthand] that using camera data has a lot of benefits” – benefits like providing many forms of data at once and being more cost-effective than extensive collaring. “I wanted to tap in and work with these folks.”
Price Tack mentioned that she even had the Snapshot logo in her interview presentation. She was already thinking about how to get the most out of Snapshot’s camera data.
“Now that I’m here, my focus is on filling research needs to inform decisions,” Price Tack continued. “[Our research] is going to be critical to helping wildlife management and species committees make informed decisions for elk, such as deciding elk harvest quotas in the upcoming years. Snapshot data is one tool we can use to fill those research needs. It is available, and I’d like to use it as much as feasible.”
Besides estimating the population of the elk classes (e.g. calves, cows and bulls), Snapshot data is currently being used to help estimate population parameters and help us understand what is happening with the population. Population parameters are estimates of important characteristics of the population, such as recruitment (birth rate), mortality (death rate) and survival rates of different elk classes within the population.
Price Tack’s model uses matrix algebra to take an initial elk population size and projects the population into the future, using what we know about elk population parameters. In other words, the model can predict how large the elk population is likely to grow in the years to come. There is natural variation however, that can cause some years to be unpredictably good or bad for elk, so the model needs to be updated each year to keep its accuracy as high as possible.
Thanks to Snapshot’s camera data, we have a system in place to calculate each year’s population parameters and continue updating the model each year. This should help us catch if anything of concern happens to the population and (hopefully) fix it before it becomes a threat.
Improving the Elk Model
Another of Price Tack’s tasks related to Snapshot is improving the elk model. Many of the improvements Price Tack is researching aim to address data collection for a larger population.
The elk population was very small when the DNR first reintroduced elk to the state in 1995 and again in 2015. The DNR used intensive monitoring methods back then to collar (and track) every elk in the herd, since intensive methods are best suited for small populations. However,with the elk herd doing so well, it won’t be long before a different approach is needed. The DNR wants to transition to a method more appropriate for a larger elk population.
Currently, the DNR is early in the process of ramping up non-invasive, cost-effective methods like Snapshot monitoring and toning down the collaring effort. Although, this transition will take time, happening over the next few years.
Price Tack also mentioned another modification under consideration. Price Tack and the Snapshot team are looking into repositioning some of the cameras within the elk grid. Currently, the elk grid doesn’t perfectly align with where the elk are congregating. There are a few cameras outside of the elk range that don’t see any elk, and there are edges of the elk’s range that extend beyond where the cameras are deployed. Repositioning the cameras should mean more elk pictures, which means more elk data.
The Frontier of Camera Monitoring
The role of Snapshot in monitoring elk is evolving, and Price Tack and the Snapshot team believe it is for the better. While they can’t guarantee that Snapshot will always play a central role in collecting data on elk, Snapshot will fill this role for the next few years at least.
Price Tack said, “This is the frontier of using camera trap data for elk. Every year, new approaches to using camera trap data are being developed. That has me excited that, even though we don’t have all the answers now, more opportunities may be on the horizon.”
You can also get more elk-related news by signing up for the Elk in Wisconsin topic on GovDelivery. Joining this email list (or others like it, including a GovDelivery topic for Snapshot Wisconsin) is the best way to make sure you don’t miss out on news you are interested in.
Bugling Elk in Wisconsin
Snapshot Saturdays are a weekly feature on the Wisconsin Department of Natural Resource’s Facebook page. Give them a Like to keep up with recent DNR news and to view the weekly Snapshot Saturdays.
Wisconsin elk may be a marvel to see, but witnessing the sound of their bugling is an unforgettable experience. Elk begin bugling in late August, and you can hear their bellows through the end of September.
This Snapshot Saturday features a bull elk captured on camera in the Flambeau River State Forest. If you’re near one of the elk reintroduction areas this month, be sure to keep an ear out!
Interested in hosting your own Snapshot Wisconsin camera? Visit our webpage to find out how to get involved: https://dnr.wi.gov/topic/research/projects/snapshot/.
Elk Monitoring Opportunity
Snapshot Saturdays are a weekly feature on the Wisconsin Department of Natural Resource’s Facebook page. Give them a Like to keep up with recent DNR news and to view the weekly Snapshot Saturdays.
Are you curious to see what Wisconsin elk are up to? Get an up-close look at the elk herds in the Flambeau River State Forest, Clam Lake or Black River Falls areas by monitoring a Snapshot Wisconsin trail camera. Trail cameras provide valuable data for herd management and give volunteers a unique window into Wisconsin’s woods.
No experience necessary, all training and equipment are provided. Volunteers must be able to participate for at least one year and check the camera at least once every three months. Submit a volunteer application today at www.SnapshotWIElkSignup.org.
Bugle Days Rendezvous 2018
The Rocky Mountain Elk Foundation hosted their annual Bugle Days Rendezvous this past weekend to celebrate the RMEF volunteers and elk in Wisconsin! This year the event was hosted in the Flambeau River State Forest, one of the sites where elk have been reintroduced in the state. Bugle Days Rendezvous offers RMEF volunteers a unique opportunity to partake in a weekend of “elk camp” including exciting field trips, herd updates, comradery, and importantly the sights and sounds of bugling Wisconsin elk.
Snapshot Wisconsin team members Sarah Cameron and Taylor Peltier were granted the opportunity to partake in the festivities this year, and give a presentation about elk monitoring with Snapshot Wisconsin. Although the two missed out on spotting any early morning elk with the rest of the RMEF, they still were able to witness the sounds of howling wolves, discovered several elk tracks along back roads, and even found a sneaky tree frog hiding behind one of the Snapshot Wisconsin trail cameras they visited. It was a weekend well spent!
Find out more about the Rocky Mountain Elk Foundation in Wisconsin, including upcoming events and how you can get involved!
Elk Calf Searching
After two days of meticulous searching in the rain, a crew of about ten people (including two Snapshot team members) dejectedly walked out of the forest. We were searching for elk (Cervus canadensis) calves in the Clam Lake and Flambeau River State Forest regions of Wisconsin, and had not had any luck thus far. Just as we were leaving, a biologist on the crew softly yelled “elk!”. Nestled into the side of a tree was a small brown creature perfectly camouflaged with the surrounding dead leaves. We estimated that we had walked by the little calf three times without noticing her!
The female elk calf that Snapshot Wisconsin team members helped to find. She was a little soggy from the rain.
The elk biologists put a blindfold over the elk calf to keep her calm. With hushed voices, they took measurements, applied ear tags, fitted her with a VHF (very high frequency) collar for location tracking and then moved away. Collars provide information on mortality, movement and herd interactions throughout the calves’ lifetimes. Collectively, this data can be used to help inform management decisions for Wisconsin’s elk herds.
Elk calves are fitted with VHF collars and ear tags for identification and location tracking. Photograph credit: Wisconsin Department of Natural Resources
For more information about Wisconsin’s elk herds, check out this link.
Out and About with Snapshot Wisconsin
The Snapshot Wisconsin team was in Milwaukee the last few days attending the Annual Meeting of the Wisconsin Chapter of The Wildlife Society.
Susan Frett, Christina Locke, Christine Anhalt-Depies and John Clare pose for a group selfie!
This event was a great opportunity to learn about research being done throughout Wisconsin as well as other parts of the world. We attended talks about new methods to estimate deer recruitment in Wisconsin; carnivore detection and abundance in the Apostle Islands National Lakeshore; climbing behavior of Gray Fox; and the Wisconsin Citizen-based Monitoring Network just to name a few. We are hoping to be able to have some guest posts on the blog about other camera trap research projects in the future.
John gave a presentation entitled “Validation of crowd-sourced trail camera image classifications” which had some great information about classification accuracy of Zooniverse volunteers as compared to expert classifications. Christina’s presentation was “Snapshot Wisconsin: Updates from our first year of volunteer-based wildlife monitoring with trail cameras”. Susan focused on the elk monitoring project with a presentation called “Using Cameras and Volunteers to Monitor Elk Reintroduction in Wisconsin”.
Susan’s Elk Monitoring Presentation
The conference was also a great opportunity to socialize with colleagues from other parts of Wisconsin and see a bit of downtown Milwaukee.
Snapshot Wisconsin 