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.
January #SuperSnap
Check out these fawn triplets from an Oneida County Snapshot Wisconsin trail camera. Whitetail deer can have one to three fawns each spring, but twins are most common. Finding a set of triplets while classifying photos is certainly a treat!
A huge thanks to Zooniverse participant @pito for the #SuperSnap nomination!
Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.
Virtual Bald Eagle Watching Days 2021
Bald Eagle Watching Days has been an established community event in Sauk Prairie, Wisconsin since 1987. Bald eagles can often be found near rivers that provide ample fish, and the Wisconsin River that runs through Sauk Prairie has made this a perfect location for eagle watching.
With public health and safety a main concern, the annual Bald Eagle Watching Days have been moved online this year. The events will be live-streamed for everyone to watch from the comfort of their own homes and can be accessed by clicking here.
Events will take place on Jan. 16th and 23rd as well as Feb. 6th and 20th. As is custom, Bald Eagle Watching Days is kicking off with a live release of rehabilitated bald eagles!
Other exciting events include presentations on eagles in Native American culture, the wintering ecology of eagles in the lower Wisconsin riverway, bald eagle behavior, a bird of prey show, and many more!
In 2019, I was able to attend Bald Eagle Watching Days in person. Hundreds of people crowded together in a park along the Wisconsin River to witness the release of a few rehabilitated bald eagles. It was a frigid January day, and I remember questioning whether standing out there was worth it. However, as the wildlife rehabilitators began to prepare the eagles for release, I decided it was definitely worth it. As far away as I was, I remember being awe-struck by how large they were. The rehabilitators told us the story of how the eagles had come into their care, and then with a huge woosh, one by one they soared into the air. A hush fell across everyone at the park as we were all overcome by strong emotions. Viewing these magnificent raptors online may not be exactly the same experience as seeing them in person, but I have no doubt that their majesty and power will be conveyed just the same. Help send them off with your support and well-wishes by tuning in on January 16th!
Partnering with the Natural Resources Foundation and a New Snapshot Store!
The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
Some of our volunteers may already know that Snapshot Wisconsin recently partnered with the Natural Resources Foundation of Wisconsin (NRF), but we at Snapshot haven’t shared many of the details about this partnership yet, including new opportunities available to volunteers and a Snapshot merchandise store!
Christine Anhalt-Depies, Project Coordinator for Snapshot Wisconsin, virtually sat down with Cait Williamson, Director of Conservation Programs within the NRF, to chat about what this partnership means to their programs, as well as highlight how their volunteers benefit from this partnership.
What is the Natural Resources Foundation of Wisconsin?
The NRF is a non-profit foundation that is all about funding conservation work. The NRF has a long history of supporting the DNR’s priority projects, especially those that involve species with the greatest conservation need. The NRF also connects people to conservation opportunities throughout the state through their Field Trip program, the Great Wisconsin Birdathon and many other funded programs.
The NRF was established in 1986 after significant cuts to the DNR budget. The DNR leadership at the time recognized a need to have an independent foundation to bridge private sector support for natural resources and conservation work being done, so the NRF grew to fill that need.
Williamson added, “We support over 200 different projects each year from small-scale school projects to large-scale conservation projects, even [projects] at the federal level. Our niche is conservation funding, leveraging resources from corporations, foundations and individuals. Putting those financial resources to the highest priority conservation needs for Wisconsin.”
Snapshot Wisconsin fits well into the types of projects that the NRF funds, so this partnership was a natural fit. Plus, forming this partnership had a special twist in store for Anhalt-Depies and Williamson.
Forming this Partnership
Anhalt-Depies and Williamson first sat down over coffee over a year ago to discuss forming this partnership. However, the meeting was more than just a brainstorming session about what the partnership would look like. It was a reunion for Anhalt-Depies and Williamson.
The pair first met while Anhalt-Depies was working on her project for her master’s degree nearly a decade ago. Part of the project involved radio-collaring wolves, so Anhalt-Depies collaborated with the DNR. Meanwhile, Williamson was an intern with the DNR, assigned to work with the wolf program. Anhalt-Depies and Williamson worked together very closely for months until the field work was done, then they went their separate ways.
Anhalt-Depies later went on to work with Snapshot Wisconsin and eventually became the project coordinator. On the other hand, Williamson started working at the NRF and became the one who builds partnerships with the DNR (and other conservation or education groups), as well as determines what the NRF’s priorities are. Williamson said, “I have the fun job of giving away the funds we help raise and knowing those are going to have the most impact they can.”
Anhalt-Depies explained what motivated her to reach out to Williamson again. “As Snapshot grew to a state-wide program, we identified the need to partner with groups that could help us expand our reach, especially in the area of supporting education. NRF seems like a natural fit, based on their goals and mission. I reached out to [Williamson]. We sat down and had a great brainstorming session about what a partnership between the two would look like and how we could help each other reach our goals.”
Williamson added, “It was fun to reconnect with [Anhalt-Depies]. We [at the NRF] have heard about and been informally in-the-loop on Snapshot Wisconsin, so it seemed like a natural fit because of what Snapshot is – engaging people and providing critical data. It was a no brainer for us.”
“It’s awesome to be able to offer yet another opportunity for our members and our partners to engage with Wisconsin’s natural resources,” said Williamson, and engage, they can. Not only do Snapshot volunteers benefit, but so do the NRF’s members.
How do Snapshot Volunteers and NRF’s Members Benefit?
Anhalt-Depies shared how this partnership benefits Snapshot volunteers. “This partnership helps expand our reach,” said Anhalt-Depies. “It brings the Snapshot program to a new audience, helping us to continue to grow.”
Anhalt-Depies continued, “The fundraising support NRF brings to the partnership will also help to increase our educational impact. Roughly 15% of Snapshot volunteers are educators. This partnership will expand the resources available to these educators, helping them to better integrate conservation education into their classroom.”
At the same time, the NRF and its members benefit from connecting to Snapshot Wisconsin. Williamson said, “From a conservation side, we [the NRF] are all about supporting priority needs for our state. Science-based conservation and the data that informs it is super critical, so we are happy to be able to support Snapshot and growing the program.”
“For our members, Snapshot Wisconsin is a really good way to connect them with something they can do,” continued Williamson. “Whether they are hosting cameras or classifying images on Zooniverse, it gives them one more way that they can give back to Wisconsin’s natural resources.”
A Message From Our NRF Partners
Another important way that the NRF is helping Snapshot Wisconsin is through providing funding opportunities, either through donations or merchandise sales. Our partners shared with us a special message for Snapshot volunteers, announcing these new ways to help out the Snapshot program.
At the Natural Resources Foundation of Wisconsin, we believe that nature has inherent value and that people have the ability to make a difference. We are the bridge connecting people who want to help with meaningful opportunities to make a lasting impact on Wisconsin’s lands, waters, wildlife and future stewards. We are very excited about our new partnership with Snapshot Wisconsin, which will connect our NRF members with meaningful volunteer opportunities, directly fund efforts that inform conservation decisions and help us learn more about Wisconsin’s amazing wildlife.
Want to show off your love for Snapshot Wisconsin and help support this incredible program? Check out our Snapshot Wisconsin storefront, featuring t-shirts, sweatshirts, hats and mugs – every purchase directly supports Snapshot! You can also donate directly by visiting
WisConservation.org/donate and designating your gift to Snapshot Wisconsin.
Between connecting our volunteers to the resources of the NRF network and new funding to expand what Snapshot is able to do, the Snapshot team is excited for this partnership. There is so much synergy between Snapshot’s goals and NRF’s mission that this partnership is a natural fit.
The partnership has also brought back together Anhalt-Depies and Williamson, and each shared some parting words for readers.
Williamson said, “Five years back, we had a visioning session about what we were really accomplishing for conservation in the state. We are not the boots on the ground ourselves, but what we do is connect people to that. There are so many ways people can make a difference. Whether it is through philanthropy, volunteering their time or just the personal choices they make to support our state’s natural resources… We are all about how people can make a difference, and Snapshot is one of those ways.”
Anhalt-Depies added, “Snapshot is people-powered research. We have thousands of volunteers who are donating their time and making a huge impact on the number of photos collected and total information gathered on our state’s natural resources. This partnership with the NRF helps to make their efforts go just a bit further, and at the end of the day, that is what matters.”
December #SuperSnap
There were lots of great photos tagged as #SuperSnap this month, but our top pick has to be this series of a coyote scampering up a tree! This is very interesting behavior for coyotes, as they lack claws that can adequately grip tree bark. This individual was likely chasing after lunch when its prey escaped up the tree.
Tree climbing is far more common in gray foxes. Check out this past blog post!
A huge thanks to Zooniverse participant @firehorse66 for the #SuperSnap nomination!
Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.
What Happens to Photos Once Uploaded?
The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
Since Snapshot reached 50 million photos, the Snapshot team felt it was a good time to address one of the most asked questions about photos: what happens to photos once they are uploaded by volunteers? At first, the process seems complicated, but member of the Snapshot team, Jamie Bugel, is here to walk us through the process, one step at a time.
Bugel is a Natural Resources Educator and Research Technician at the DNR, but she works on the volunteer side of Snapshot. Bugel said, “I mainly help volunteers troubleshoot issues with their equipment or with their interactions with the MySnapshot interface. I am one of the people who answer the Snapshot phone, and I help update the user interface by testing functionality. There is also lots of data management coordination on the volunteer side of the program that I help with.”
Bugel listed off a few of the more common questions she and the rest of the Snapshot team get asked, including who reviews photos after the initial classification, what happens to the photos that camera hosts can’t identify and how do mistakes get rectified. “We get asked those [questions] on a weekly to daily basis,” said Bugel.
It Starts With a Three-Month Check and an Upload
Every three months, trail camera hosts are supposed to swap out the SD card and batteries in their trail camera. At the same time, volunteers fill out a camera check sheet, including what time of day they checked the camera, how many photos were on the SD card and if there was any equipment damage.
“You should wait at least three months to check their camera, because you won’t disturb the wildlife by checking more often. We want to view the wildlife with as minimal human interference as possible,” said Bugel. “At the same time, volunteers should check [their camera] at least every three months, because batteries don’t last much longer than three months. Checking this often is important to avoid missing photos.”
After the volunteer does their three-month check, they bring their camera’s SD card back to their home and enter the information on their camera check sheet into their MySnapshot account and upload their photos.
Bugel said it can take anywhere from 4 to 48 hours for the photos to appear in the volunteer’s MySnapshot account. Fortunately, the server will send an email when the photos are ready, so volunteers don’t have to keep checking. Volunteers can start classifying their photos after receiving the email.
Initial Classification By Camera Hosts
The first round of classification is done by the trail camera hosts. The returned photos will sit in the Review Photos section of their MySnapshot account while the host classifies the photos as Human, Blank or Wildlife. The wildlife photos are also further classified by which species are present in the photo, such as beaver, deer or coyote.
This initial classification step is very important for protecting the privacy of our camera hosts, as well as helps on the back end of data processing. Over 90% of all photos are classified at this step by the camera hosts. When they are done classifying photos, they click “review complete,” and the set of photos is sent to the Snapshot team for the second round of classification.
Staff Review
The second round of classification is the staff review. Members of the Snapshot team review sets of photos to verify that all human or blank photos have been properly flagged. “For example, a deer photo may include a deer stand in the background. That type of photo will not go to Zooniverse because there is a human object in the photo,” said Bugel. Fortunately, nearly all human photos are taken during the initial camera setup or while swapping batteries and SD card, so they are usually clumped and easy to spot.
The second reason that staff review photos after the initial classification is for quality assurance. Since some animal species are tricky to correctly classify, someone from the Snapshot team reviews sets to verify that the photos were tagged with the correct species. This quality assurance step helps rectify mistakes. “Sometimes there are photos classified as blank or a fawn that are actually of an adult deer,” said Bugel. “We want to catch that mistake before it goes into our final database.”
In cases where the set of photos wasn’t classified by the camera host, the team will also perform the initial classification to remove human and blank photos. The Snapshot team wants to make sure any photos that reveal the volunteer’s identity or the location of the camera are removed before those photos continue down the pipeline.
Branching Paths
At this point in the process, photos branch off and go to different locations, depending on what classification they have. Blank (43%) and human (2%) photos are removed from the pipeline at this point. Meanwhile, the wildlife photos (20%) move on to either Zooniverse for consensus classification or move directly to the final dataset. The remaining photos don’t fall into one of our categories, such as the unclassified photos still awaiting initial review.
Photos of difficult-to-classify species, such as wolves and coyotes, are sent to Zooniverse for consensus classification. Bugel explained, “The photos [of challenging species] will always go to Zooniverse, even after volunteer classification and staff member verification, because we’ve learned we need more eyes on those to get the most accurate classification possible,” another layer of quality assurance.
Alternatively, photos with easy-to-classify species, such as deer or squirrel, go directly to the final dataset. Bugel said, “If a photo is classified as a deer or fawn, we trust that the volunteer correctly identified the species.” These photos do not go to Zooniverse.
Zooniverse
Photos of difficult-to-classify species or unclassified photos move on to Zooniverse, the crowdsourcing platform, for consensus classification. “Wolf and coyote photos, for example, always go to Zooniverse, because it is so hard to tell the difference, especially in blurry or nighttime photos,” said Bugel.
The Snapshot team has run accuracy analyses for most Wisconsin species to determine which species’ photos need consensus classification. All photos of species with low accuracies go to Zooniverse.
On Zooniverse, volunteers from around the globe classify the wildlife in these photos until a consensus is reached, a process called consensus classification. Individual photos may be classified by up to eleven different volunteers before it is retired, but it could be as few as five if a uniform consensus is reached early. “It all depends on how quickly people agree,” said Bugel.
Team members upload photos to Zooniverse in sets of ten to twenty thousand, and each set is called a season. Bugel explained, “Once all of the photos in that season are retired, we take a few days break to download all of the classifications and add them to our final dataset. Then, a Snapshot team member uploads another set of photos to Zooniverse.” Each set takes roughly two to four weeks to get fully classified on Zooniverse.
To date, over 10,400 people have registered to classify photos on Zooniverse, and around 10% of the total photos have been classified by these volunteers on Zooniverse.
Expert Review
It is also possible for no consensus to be reached, even after eleven classifications. This means that no species received five or more votes out of the eleven possible classifications. These photos are set aside for later expert review.
Expert review was recently implemented by the Snapshot team and is the last step before difficult photos go into the final dataset. The team has to make sure all photos have a concrete classification before they can go into the final dataset, yet some photos never reached a consensus. Team members review these photos again, while looking at the records of how each photo was classified during initial review and on Zooniverse. While there will always be photos that are unidentifiable, expert review by staff helps ensure that every photo is as classified as possible, even the hard ones.
The Final Dataset and Informing Wildlife Management
Our final dataset is the last stop for all photos. This dataset is used by DNR staff to inform wildlife management decisions around the state.
Bugel said, “The biggest management decision support that Snapshot provides right now is fawn-to-doe ratios. Jen [Stenglein] uses Snapshot photo data, along with data from other initiatives, to calculate a ratio of fawns to does each year and that ratio feeds into the deer population model for the state.”
Snapshot has also spotted rare species too, such as a marten in Vilas county and a whooping crane in Jackson county. Snapshot cameras even caught sight of a cougar in Waupaca county, one of only a handful of confirmed sightings in the state.
The final dataset feeds into other Snapshot Wisconsin products, including the Data Dashboard, and helps inform management decisions for certain species like elk. Now that the final dataset has reached a sufficient size, the Snapshot team is expanding its impact by feeding into other decision-making processes at the DNR and developing new products.
The Snapshot team hopes that this explanation helps clarify some of the questions our volunteers have about what happens to their photos. We know the process can seem complicated at first, and the Snapshot team is happy to answer additional questions about the process. Reach out to them through their email or give them a call at +1 (608) 572 6103.
The Snapshot Team’s Favorite Photos from the First 50 Million!
The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
Snapshot Wisconsin recently reached an important milestone: its 50 millionth photo! We’ve been watching the tally of photos get closer and closer to 50 million for the last few months, and we are thrilled that the moment is finally here.
Snapshot Wisconsin started as a pilot program in only two counties in 2016 but expanded statewide in 2018. Today, we have over 1,800 volunteers, monitoring over 2,100 trail cameras across the state. Furthermore, the Snapshot program receives approximately 45,000 photos per day from all these cameras. Just stop and think about how incredible that is!
As a thank you to everyone who has helped the program out or followed its success (and to celebrate the 50 millionth photo milestone), the Snapshot Wisconsin team selected some of their favorite photos from the first 50 million and used them to build an interactive map of Wisconsin. This tool highlights each photo and tells a short story about the photo itself or the species shown. It serves as a “snapshot” of how the program has grown over the years.
Rare species sightings, unusual animal behaviors, species facts, and even a few multi-species encounters can all be seen in the interactive map. Check it out!
November #SuperSnap
This month’s #SuperSnap features a curious gray fox from Dunn County checking out our trail camera.
A huge thanks to Zooniverse participant bzeise for the #SuperSnap nomination!
Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.
Trail Cameras and Rutting Behavior
Trail cameras not only offer a window to view wildlife in their natural habitat, but they can also provide interesting data on animal behavior! One great example of this is the deer rut. As deer begin moving around at an increased rate during the rut, the animals trigger motion activated trail cameras and cause a spike in photos.
This is one of the many interesting observations that users have found with Snapshot Wisconsin’s new Data Dashboard. The Data Dashboard allows users to visualize trail camera data collected from across Wisconsin. Explore on your own at http://datadashboard.snapshotwisconsin.org.
