Archive by Author | Claire Viellieux

From Testing Cameras in Her Backyard to a Statewide Monitoring Program

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

Jen Stenglein, Quantitative Research Scientist at the Wisconsin DNR and one of the longest-serving Snapshot staff members, walks us through the early years of the program and how Snapshot Wisconsin expanded into the massive project that it is today.

If you are a newer Snapshot volunteer, then here is your chance to learn more about the program’s early history. For those who lived much of the history firsthand (especially the early adopters), this article might be a trip down memory lane. Either way, we hope you get something from this recounting of the past and connect more strongly with the program.

A Grant and a Collaboration

Snapshot Wisconsin’s origin stems from a NASA grant that the University of Wisconsin-Madison received in 2013. The grant aimed to lay the groundwork for a citizen science program for monitoring wildlife that would be launched by the Wisconsin Department of Natural Resources (DNR). Soon after, the DNR created Snapshot Wisconsin and started what would become a massive project.

Stenglein got involved while the project was still in the planning phase. “I was finishing my PhD at the time in Madison, WI and heard about the project. Thanks to my connection with the university, I already knew many of the major players involved,” said Stenglein. “Some of the initial project planning happened before I came in, so the project was basically waiting for someone to figure out the logistics.”

What cameras should volunteers use? How should the cameras be set up to capture the best photos? How would they get equipment to volunteers and train them? There were many questions and fewer answers.

A doe and two fawns

2014: Figuring out the Logistics

In 2014, Stenglein began to answer these questions by running tests in her backyard. “I had a whole line of cameras set up in my backyard, each a different model. We also had cameras out behind the DNR building [to test a second location]. There were so many questions we needed to answer,” recalled Stenglein.

At the time, the Snapshot team was comprised of only two people: Stenglein and Christine Anhalt-Depies, the current project coordinator for Snapshot Wisconsin. Stenglein was working on the program full-time, while Anhalt-Depies was devoting half her time to support Snapshot Wisconsin. Piece by piece, they ran tests and figured out what cameras and setup the first volunteers would use.

Stenglein recalled figuring out other logistics too like where the cameras would go. “I remember looking at a map of Wisconsin and making the decision to divide townships into quarters. That would be our grid setup,” said Stenglein. “Those grid blocks were about the right size [roughly nine square miles] for what we wanted and left space for over 6,000 cameras around the state. That sounded like a doable maximum.”

By the end of the year, Stenglein and Anhalt-Depies had finished enough of the equipment testing to put their plan to the test, starting with Wisconsin’s elk herd.

2015: The First True Test

Elk at the time were just being reintroduced in Wisconsin. There was one small, existing elk population (reintroduced from Michigan), but that population hadn’t taken off how people hoped. A second effort was being set up to bring Kentucky elk to Wisconsin, and those elk were coming in just as Snapshot became ready to test out their program.

“We thought it would be a really great opportunity to test Snapshot Wisconsin on a known population. All of the elk were radio-collared, [so we knew how many were being added to the area.] It was a perfect test to see how well our equipment and methods would hold up,” said Stenglein.

But of course, things didn’t go perfectly as planned. One near miss stood out to Stenglein and captured some of the hecticness of getting the program up and running.

“We almost didn’t have the cameras in time,” explained Stenglein. The camera delivery came in late on the same day that we were scheduled to set up the cameras. “We already had folks waiting in the field, and I had to plead with the delivery driver [to prioritize delivering our cameras].” There were some near misses like that, but Stenglein said they worked through them all in the end.

By the end of the year, a few hundred cameras had been deployed across the elk zones, and the program was officially running. Volunteers now ran the cameras, and images were starting to stream in.

Two bull elk clashing antlers

2016: Expanding the Program

Once the team felt they were in a solid routine, they started thinking about expanding Snapshot to more of the state. “It was nice to have the elk grid up and running already, because we knew how the logistics would function,” said Stenglein.

The Snapshot team focused on recruiting educators, even seeking out a couple grants to build collaborations with different educator groups. “Educators seemed like a good place to start, because they affect so many people in their daily life,” said Stenglein. “They could help us reach more people faster.”

To start, the team mainly accepted volunteers from only two Wisconsin counties: Sawyer and Iowa Counties. “We heard from lots of people [around the state] who were excitedly awaiting enrollment, but we wanted to roll things out slowly [to work out any new kinks in the process]. For example, we didn’t want to have a bunch of people getting equipment, only to be frustrated by the IT system not working properly yet,” said Stenglein.

Stenglein and the team were enrolling volunteers at a steady pace, but volunteers had to attend an in-person training session before they received their equipment. Since the team was still only three to four people, there were a limited number of trainings offered. That bottleneck kept the expansion to a manageable pace.

The project was going well though. By the end of 2016, Snapshot had expanded to nine counties (adding Iron, Jackson, Manitowoc, Waupaca, Dodge, Racine and Vernon Counties). The IT infrastructure was working properly, supporting the in-flow of data. All of the planning that Stenglein and the team did was starting to pay off.

The team even launched their first first season of photos on Zooniverse, the crowd-sourcing platform. “Zooniverse was just an itty-bitty platform back then,” joked Stenglein, “but it helped us process photos much faster than we could have without it.”

2017: Growth and Rare Species Detections

Just as 2016 saw a growth to nine counties accepting volunteers, 2017 saw a similar growth. By the end of the year, one quarter of the state’s counties, or 18 in total, were accepting volunteers. St. Croix, Oneida, Marinette, Clark, Dane, Grant, Marathon, Rusk and Taylor counties were all added to the list in 2017. Additionally, over 1,000 volunteers had joined the program by this point, and the program was accepting volunteers even faster than before.

Coverage of the state was starting to fill in enough to be useful from a data perspective. For example, the Snapshot program saw its first rare species detection in 2017. It was a moose from Price County. “I remember it was really exciting because we were waiting for a rare species,” said Stenglein. The team quickly saw more rare species detections in rapid succession too, including a marten and whooping crane. “That whooping crane was extra exciting because we could ID the individual [from the colored bands on its legs] and learn more about it,” added Stenglein.

A whooping crane with colored bands on its legs

2018: Gearing up for a Statewide Launch

Up until early-2018, the Snapshot team was adding counties to spread out coverage across the state. However, by March 2018, there were 26 counties involved. “At that point, adding counties was getting arbitrary,” said Stenglein. “Most areas of the state had at least one county involved already.” It was time to start accepting volunteers from all 72 counties: a true statewide launch.

Many improvements to the team and infrastructure had smoothed out most of the kinks in the system. The team had grown in size, and that additional capacity helped speed up onboarding of new volunteers. A new version of the cameras was also being used, which took fewer blank photos, and training had moved online to cut down on staff travel times. Everything was giving a green light for launch.

On August 9th, Snapshot Wisconsin officially launched statewide. Stenglein said the statewide launch was when it felt like Snapshot truly hit its stride. “I really felt like that point in time was pivotal for the project.”

Immediately after the statewide launch, the size of the program exploded. The team was able to accept much of the backlog of volunteers that had previously been unable to join the program. In 2018 alone, over 1,200 volunteers and 1,174 new trail cameras were added to the project, almost doubling Snapshot’s size.

2019: More Staff and a Slew of Publications

To compensate for the doubling of the volunteer base, four new Snapshot positions were added to the team, and Anhalt-Depies took over as the project coordinator. The added support was very timely because the program continued to expand as more and more volunteers joined.

Additionally, enough data had come in by this point that the team (especially Stenglein) could start publishing their findings.

The program had already been generating data for the management of certain species, including generating fawn-to-doe ratios for deer and population estimates for each elk herd. However, until 2019, the project hadn’t published any peer-reviewed publications.

In a flurry, five scholarly publications were released in 2019 by the Snapshot Wisconsin team or one of the graduate students working with the program. Five publications in a single year is substantial, but it meant something extra to the Snapshot team.

“It was great to [finally] show the work we’d done on the data side of Snapshot,” said Stenglein. “In some ways, it took longer than we expected, because we thought that we’d have stuff to share right away. However, Snapshot’s value is the accumulation of data and the time series we’ve built up over the years, so it was appropriate that it took some time to get to the first publication.”

A raccoon mom and several young

2020: An Important Year for Snapshot

2020 was a weird but important year for Snapshot. According to Stenglein, the team didn’t slow down much in 2020. In fact, many important milestones happened this year. The first of which was a huge boom of activity on Zooniverse.

People suddenly had more free time than usual, and many people used that time to classify photos on Zooniverse. Snapshot Wisconsin’s page saw substantially more users (and specifically new users) than normal. No surprise that photos were being classified faster as well. In fact, the team even had to adjust staff responsibilities to make sure there were photos on the platform. What a great problem to have, right?

Another exciting change during 2020 was the release of the Snapshot Wisconsin Data Dashboard, an interactive tool that lets the public play with Snapshot data. Anyone could explore the data of 19 Wisconsin species and see where (and when) each species was detected.

Stenglein said that releasing a product like the Data Dashboard had been the plan from the beginning, but the team didn’t originally know what form it would take. “Open data has been an important goal of the project, especially because of our collaboration with NASA and the University of Wisconsin.” It just took time to figure out what form the product would take and to make sure the data were accurate enough.

Most of our volunteers will know that Snapshot Wisconsin also reached a total of 50 million photos near the end of 2020. That is an impressive amount of data to receive and process. According to Stenglein, this milestone meant that Snapshot was finally a “big project.”

“It meant that we had the data that we wanted, and everything was working. There was a big sense of accomplishment, and for me, it meant that all that planning had paid off,” said Stenglein.

The fact that so many milestones happened in 2020 speaks to the sustained efforts of our volunteer base. Stenglein said, “The volunteers totally rallied and continued to bring the data in. That kept the project going. The fact that volunteers kept checking their cameras and classifying photos was big for us. Thank you.”

Reflecting on the Past

As the end of 2021 inches closer, the team reflected on where they’ve come as a program since Stenglein’s backyard experiments in 2014. They remember the near-miss with the elk cameras and the statewide launch in 2018. They remember the first rare species detection and the release of the first public-facing data visualization product, the Data Dashboard.

It has taken a lot of work to get to this point, both from our staff and our volunteers. The team wants to thanks its volunteers for their contributions over the years, whether you just joined or have been with us since the beginning. Every classification matters, just as all of our volunteers matter to us. Thank you for seven years of excitement and support!

Highlighting Sandhill Cranes on the Data Dashboard

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

Continuing with the bird theme, the Snapshot Team wanted to highlight one of the five specific species that can be chosen while classifying photos: the sandhill crane. At the same time, the team wanted to use the new 2020 data on the Data Dashboard, so they decided to do both!

The team invited fellow DNR researcher, Jess Jaworski, Assistant Waterfowl Research Scientist within the Office of Applied Science, to look through the sandhill crane data on the Data Dashboard. Jaworski is currently working on waterfowl research, but she previously worked with cranes.

Jaworski’s graduate research involved studying the nesting behaviors of cranes in Wisconsin. “My graduate research was focused on the nest success of the reintroduced whooping crane population at the Necedah National Wildlife Refuge. The majority of my work was monitoring incubation behaviors of both whooping cranes and sandhill cranes under duress of an avian-specific black fly. This fly caused a wide-spread and synchronous abandonment of nests.” Jaworski put up several trail cameras at nests and went through thousands of photos to monitor behaviors at those nests; Not that different from what Snapshot Wisconsin does.

A Bit Of Background On Sandhill Cranes

Before we dive in, let’s make sure everyone knows a bit about sandhill cranes. Jaworski was happy to share her knowledge of sandhill crane behavior.

Wisconsin’s sandhill cranes are part of the Eastern population of migratory sandhill cranes, and there are over 70,000 individuals in this population. As implied by the term “migratory,” they don’t spend the entire year in Wisconsin. Jaworski explained that these birds spend the winter down South. Around mid-March, they come back north to their breeding grounds and establish pair bonds.

Sandhill cranes are typically a monogamous species, so they will find a mate and pair off if they don’t already have one. “They usually try to find a pair bond within up to two years of birth, and they start nesting at three to six years in open marsh wetlands, although sandhill cranes can nest in a wide variety of habitats. They hopefully will hatch within a 28-day incubation period and fledge their young within two to three months. Once that is done [usually in September/October], they migrate back to their wintering grounds.” Come the next March, they start the cycle over again.

Diving In To The Data Dashboard

Jaworski was curious how well the trail camera data would match the description she gave above. The team sat down with her to see. At first glance, Jaworski said the data seemed pretty consistent with what she knows about their behaviors and where cameras were located around the state.

Take the map of detections by county, for example. Jaworski pointed out a higher percentage of crane detections in the southeast quadrant of the state. “That is consistent with their habitat [preferences]. They typically nest in open marshes, and the map matches where I know wetlands exist in the state,” said Jaworski. “Dodge County has cranes in the Horicon Wetland Area, for example. To the northwest, there are more cameras picking up these birds, potentially from the Crex Meadow Area. There is a large amount of birds in Adams County nearby to Juneau County where birds nest at the Necedah National Wildlife Refuge, which is where I did my graduate work.”

Jaworski also looked at detections by the ecological landscape, a clickable option to the left of the map. Instead of counties, the map is blocked out into 16 regions with unique ecological attributes and management opportunities. “Generally, the southern and eastern sections of the state have more open, wetland areas, so I’m not surprised there are more detections in those areas. There are also a lot of agricultural fields here too,” said Jaworski.

“Sandhill cranes can adapt easily to human-made landscapes like agricultural fields, and it isn’t uncommon to see them nesting in smaller wetlands near agricultural fields, for example. If there are a lot of cameras in these areas, then there will be more sightings of sandhill crane.” In contrast, the northern part of the state tends to be more forested land, so the southeast is the ideal habitat for a crane looking to build a nest.

Activity by Month_Sandhill Crane_0

Activity By Month And Hour Of The Day

So far, the detection locations matched what Jaworski expected to see, but one of the more interesting features of the dashboard is the breakdown of detections by month and by hour of the day. How well would the data hold up?

Jaworski started with the month data and immediately zeroed in on the lull in detections during the winter months. “This is exactly what I’d expect to see,” said Jaworski. “These migratory cranes are down south in their winter grounds [during these months]. When you get to March and April, I see a heightened activity pattern from cranes migrating back and nesting. Then, there is a lull again later in the year, as they start migrating back south.”

Jaworski also noticed that the migration south occurs over a much longer period of time than the migration back, as seen by a more gradual decline in detections in September and October. “That could be a product of different nest initiation times or different successes/failures throughout the nesting period. If birds nested earlier, then they will have fledged their young earlier than others and potentially leave the state sooner.” Alternatively, pairs who failed to successfully rear a fledgling may start over again if there is time. These pairs wouldn’t be able to migrate as early as pairs who succeeded on their first try, and that may lead to more detections later in the year.

The Snapshot team discussed how the placement of cameras also can influence the detection of species like the sandhill crane. Not all species spend their time in areas that are easy for trail cameras to watch. Not many Snapshot cameras overlook the center of a lake or marsh, which can lead to biases in detections for certain species.

However, Jaworski did confirm that cameras set up near-ideal nesting habitats will be much more likely to detect cranes. Cranes can be seen while they are up and about from their nests, looking for food, or when adults swap who is incubating their nest.

Jaworski also looked at sandhill crane activity throughout the day. “In the morning hours, they will leave their roosting areas. When pairs are forming pair bonds, they will do dawn unison calls. You can often hear them in the early morning hours, [and the calls are quite distinct]. Throughout the day, they are probably feeding and moving about the wetland, so detections are more common then. In the evening, they return to their roosting site for the night.”

All in all, there were pretty clear patterns in the activity graph, and those patterns match what Jaworski expected to see. There is a small amount of variation between the hours of the daytime, but Jaworski didn’t think those peaks and valleys represented any meaningful behaviors for sandhill cranes. Jaworski said, “It is hard to determine fine-tuned patterns throughout the day. It could simply be from a bias in where the cameras are placed.”

Activity by Hour_Sandhill Crane

The 2020 Data Are Accurate And Consistent

Jaworski and the Snapshot team adjusted the date slider in the left-most column of the dashboard to look at only the 2020 data. The 2020 data showed all of the same patterns that we’ve already mentioned and is consistent with what we know about where cranes are distributed across the state. “It shows that there is nothing unusual about this past year that indicated sandhill cranes are moving from their range or aren’t where you would normally see them occur,” said Jaworski.

Jaworski played around with the date slider some more and looked at each of the other years’ data individually. She noticed that the number of detections increased each year, starting from 2017. “It is really cool that detections are increasing. It says that interest in the program is also increasing,” said Jaworski. “Snapshot’s expansion each year provides more information about where these birds are located. Each year, you will find more detections, which helps inform research for this species. I also really like that there is a record of that data so that we can go back and analyze it if any questions arise in future studies.”

Jaworski’s Parting Thoughts

Before everyone parted ways, Jaworski shared some final thoughts with the team about the program and its impact.

“It’s wonderful that a program like Snapshot exists. If somebody is interested in knowing what is going on with a particular species, it is awesome that Snapshot allows people to find that information through the Data Dashboard. It is a great opportunity for people to get involved.

Additionally, that type of cooperation between researchers and those who aren’t in research is invaluable and helps inform [our] research. Its great from a research perspective and a curiosity perspective when we collaborate.

Plus, getting involved [in citizen science] can spark an interest in a science career! A lot of us in research didn’t initially start out that way. Many of us started out as citizens who observed something interesting or maybe as kids who tagged along with our parents while they were doing outdoor activities. Looking at species or finding out what a scientist did inspired us.

My family comes from a natural resource background. My dad started out as a forester, and my mom worked as a park ranger and a boating officer in New Mexico. I tagged along with my mom quite often when she was giving presentations at the nature center. We were outside recreating a lot, camping and fishing. It had a big influence on my life and my career choice.”

Jaworski encouraged more people to check out the Data Dashboard and learn something new about one of the species available. The Snapshot team suggests looking at the data in a similar way to how Jaworski did, piece by piece and thinking about what a species might be up to in different areas and at different times. It is a great way to think about the lives of these species. Plus, with the addition of the 2020 data, there is more data than ever to look at.

 

Using Snapshot’s Bird Photos in New Ways

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

A male American woodcock stretches his wings skyward in a courtship display, a great-horned owl strikes an unknown target on the forest floor and a male northern cardinal duteously feeds his newly fledged young.

These are moments in the lives of birds captured by Snapshot Wisconsin trail camera photos. Until recently, however, many of these avian images were hidden within the Snapshot Wisconsin dataset, waiting to be uncovered by a team of bird enthusiasts. Unlike how they normally watch birds, from behind a pair of binoculars, this time they were behind a keyboard.

When Snapshot volunteers classify an image, they normally can choose from a list of around 40 wildlife species. Only five of these species are among Wisconsin’s 250 regular bird species: wild turkey, ruffed grouse, ring-necked pheasant, sandhill crane, and the endangered whooping crane. These five species are options on the list because they either are of special management interest within the Wisconsin DNR or are easier to detect by Snapshot Wisconsin cameras.

The rest of the bird photos are classified into a catchall group, called “Other Bird.” Until recently, the “Other Bird” images were considered incidental images, but the increasing size of this category caught the attention of the Snapshot Wisconsin team. In fact, “Other Bird” is the second most common classification of the six bird categories, only second to Wild Turkey (Figure 1, Panel A), which comprises over a quarter of all bird photos.

The team reached out to the Wisconsin DNR’s Bureau of Natural Heritage Conservation (NHC) to brainstorm ideas on how to leverage the “Other Bird” dataset, which had amassed 150,000 images at the time and was still growing.

Great horned owl on a log

Planting A Seed Of Collaboration

During their discussion with the NHC, the idea was brought up that these “Other Bird” images could contribute to the Wisconsin Breeding Bird Atlas II (WBBA II). The WBBA II is an enormous, multi-year field survey to document breeding birds and their distribution across the state. Information like the frequency of breeding and which areas birds are breeding in help the DNR see changes in breeding status for many bird species. This information can also be compared to data from the previous survey (from 1995 to 2000) and sets a benchmark for future comparisons as well.

The current survey uses data collected from between 2015 and 2019. Coincidentally, the earliest Snapshot images are from 2015 as well, so the dates of the survey aligned quite well. This collaboration seemed like a good fit.

However, there are some important differences between data collected from birding in the field and from images captured by Snapshot trail cameras. For example, many birds spend much of their time in the canopy, outside the camera’s field of view. Additionally, birders often use sound cues to identify signs of breeding in the field. Trail camera images do not contain these types of breeding cues. Lastly, certain breeding behaviors can be too fleeting to observe from a set of three images.

The team wasn’t sure yet if the trail camera photos would truly contribute much to the WBBA II.

A western kingbird flying across a prairie

A Collaboration Was Born

Members of the Snapshot Wisconsin and NHC teams ran a test of the “Other Bird” photos. They reviewed a small, random subset of images and learned that many of the birds could be identified down to the species level. The teams also found enough evidence of breeding, such as sightings in a suitable habitat (for breeding) or the presence of recently fledged young. Both teams decided to go ahead with the collaboration and see what they could find.

The full dataset was sent to a special iteration of Zooniverse, called the Snapshot Wisconsin Bird Edition, and birders began classifying. All of the “Other Bird” images were classified down to the species level, as well as assigning a breeding code to each image. In just over a year, the large collection of bird photos was classified, thanks to some dedicated volunteers.

The NHC’s Breeding Bird Atlas Coordinator, Nicholas Anich, extracted these new records and added them to the WBBA II. The atlas utilizes a statewide survey block system that is based on a preexisting grid from the United States Geological Survey. The survey block system requires that certain blocks be thoroughly surveyed in order for the atlas to have adequate statewide coverage, and many of the new Snapshot data points contributed to these priority survey blocks. Anich said, “[The Snapshot data] will be valuable information for the WBBA II, and we even discovered a few big surprise species, [such as] Spruce Grouse, Western Kingbird, and Whooping Cranes.”

In addition to these rare species, many of the high-value classifications were what Anich described as breeding code “upgrades.” The observed species already had been recorded in a given block, but the photos showed stronger evidence of breeding than had previously been reported. For example, an adult of a given species may have already been spotted in the area during the breeding season, but a photo showed a courtship display. The courtship display is stronger proof of breeding in the area than a single adult sighting.

A spruce grouse in a field

How Useful Were the Snapshot Photos?

Both the (in-person) birding efforts and the trail camera photos picked up species that the other did not, so both approaches brought different strengths to the table.

One of the strengths of the trail cameras was that they are round-the-clock observers, able to pick up certain species that the in-person birding efforts missed. Anich said he noticed that nocturnal species (American Woodcock and Barred Owl) and galliforms (Wild Turkey, Ruffed Grouse) were more common in the Snapshot dataset than reported by the birders in the field, in certain areas at least. “Running into gamebirds was a bit the luck of the draw,” Anich said.

Both Anich and the Snapshot team agreed that the trail cameras were best used in conjunction with in-person surveys, rather than a substitute for each other because they each observed a different collection of species.

OtherBird_infographic

Insights Into The “Other Bird” Category

As a bonus for anyone who is interested in this project, the Snapshot team analyzed the photos classified for the WBBA II and created an infographic of the orders and families included. The photos included were captured between 2015 and 2019.

An immediate trend the team saw was that many of the birds were from species with larger body sizes, ground-dwelling species and species that spend time near or on the ground. For example, Anseriformes (ducks and geese) and Pelecaniformes (herons and pelicans) are the second and third most common order in the “Other Bird” category. The next most observed groups include woodpeckers, hawks, eagles, owls and shorebirds. While these birds may not spend all of their time near the ground, food sources for these species are often found in the stratum, an area where most trail cameras are oriented.

It was interesting that the most common order (comprising over half of the “Other Bird” classifications) was from the bird order Passeriformes (perching birds or songbirds). This order does not initially appear to fit the trend of ground-dwelling or larger-bodied birds. However, closer inspection revealed that the most common families in this order did fit the trend. For example, Turdidae (thrushes, especially American Robins), Corvidae (crows, ravens and jays) and Icteridae (blackbirds and grackles) comprised much higher percentage of the photos than any other families.

Thanks To Everyone Who Helped Classify Bird Photos On Zooniverse!

Overall, the Snapshot Wisconsin Bird Edition project was a huge success. In total, 154 distinct bird species were identified by nearly 200 volunteers, and over 194,000 classifications were made. The Snapshot Wisconsin and WBBA II teams extend a huge thank you to the Zooniverse volunteers who contributed their time and expertise to this project. The team was happy to see such strong support from the Wisconsin birding community, as well as from around the globe.

If you weren’t able to help with this special project, stay tuned for other unique opportunities to get involved as Snapshot continues to grow and use its data in new ways. If you contributed to the project, reach out to the Snapshot team and let them know what your favorite species to classify was.

The 2020 Data Are Now Available on the Data Dashboard

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

The Snapshot Team is happy to announce that the data from 2020 are now available on the Data Dashboard. Explore the 2020 dataset yourself today!

Snapshot’s Data Dashboard is a data visualization tool that lets the public interact with the data collected from over 2,000 trail cameras spread across the state. The Data Dashboard first was made available to the public in October 2020 and showcased the data of 18 species. Since then, an additional species have been added to the list, and the Snapshot Team plans to add more over time.

One of the unique features of the dashboard is that it lets people choose which data they want to visualize. You can look at data from individual years by selecting the desired date range on the slider along the left side of the dashboard. Four distinct years (2017-2020) are available to peruse. When a new date range is selected, the map of Wisconsin will update and show only the data for the selected dates, allowing anyone to see trends over time.

Check out the 2020 data on Snapshot Wisconsin’s Data Dashboard:
https://widnr-snapshotwisconsin.shinyapps.io/DataDashboard

Volunteer Highlight- River Bend Nature Center in Racine County

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 volunteers have been asking to hear about unique ways others engage with the program. Today, the Snapshot Wisconsin team highlights, not an individual, but a group that manages one of the longer-running Snapshot trail cameras – River Bend Nature Center (RBNC) in Racine County.

RBNC is an outdoor environmental education center that leases and manages about 80 acres of upland and lowland forest, as well as a six-acre prairie, from the county. River Bend’s primary mission is environmental education, conservation and sustainability with a variety of programs for all ages, ranging from little tikes to seniors.

Christa Trushinsky, Naturalist and Director of Education at RBNC, has worked at the nature center since 2016 and oversees their Snapshot trail camera. “I went to grad school for Environmental Conservation, so I’m very interested in exactly what Snapshot Wisconsin does, looking at the dynamics of land and the species that use it,” said Trushinsky. Trushinsky first heard about the program from a Snapshot Wisconsin team member she went to graduate school with and got in touch with her to learn more.

Little did Trushinsky know, that connection would later play a role in developing many of the nature center’s programs.

A building

River Bend Nature Center, Racine County, Spring 2021

A Good Fit for River Bend Nature Center

River Bend has been hosting a trail camera for four years now and has found some intriguing ways to incorporate Snapshot photos into their teaching. “Snapshot Wisconsin is such a crucial tool for what we are trying to do here, especially for species that are elusive or nocturnal,” said Trushinsky.

Trushinsky said they often use Snapshot photos in their Skulls, Skins, and Scat program to help kids identify species that they wouldn’t normally be able to see. “Since some animals are nocturnal or very elusive, we can use the images to prove that these animals are out there [in the forest] and using the landscape,” explained Trushinsky. “Seeing proof of these animals in the neighboring forest makes them real to the kids in a special way. The animals are more than
something they see on TV – they are real and nearby.”

The trail camera pictures also act as a segue to the hands-on portion of the program, where participants look for animals and signs of animals (e.g. nests, burrows and tracks). “If we find an animal that can be handled, we talk to the kids about how to do so gently and appropriately,” said Trushinsky. Sometimes the children interact with an animal for the first time, such as feeling a slug’s sliminess or a snake’s scaliness. “That’s all part of it, showing them how to handle wildlife appropriately, as well as which to respect and stay away from.”

RBNC incorporates Snapshot pictures in other ways as well. Staff have introduced the concept of predator-prey relationships to children by showing time-lapse photos of predators tracking their prey. Trushinsky recalled an example of a doe walking by the camera, and a minute later, a coyote followed closely behind. Trushinsky uses Snapshot photos to start discussions about different relationship dynamics between the species seen on the camera.

Trushinsky has also taken Snapshot images off-site and given presentations at schools and colleges. To highlight examples of camouflage, she shows participants sets of pictures from the trail camera and asks them if they can figure out where the animal is and identify it. “Basically, I introduce it as, ‘Hey, this is Snapshot Wisconsin. You guys could be doing this on your property!’ I talk about what [species] we see at River Bend and take them through the process of classifying photos. Kids especially seem to get a kick out of it,” said Trushinsky.

A fawn and doe

Learning Lessons Themselves

While most of what RBNC does is focused on educating others, they have also learned more about he land they manage by hosting a Snapshot trail camera. Their trail camera has confirmed which species inhabit their land, as well as how the species use the land at different times of the year.

The RBNC trail camera is in a unique location, tucked away in a floodplain area of the lowland forest. During the spring season, the Root River surges, spilling over into a nearby pond, flooding the lowland forest. The flooding dramatically changes the landscape around the camera. Herons, wood ducks, mallards, and other birds can be found wading and swimming in the forest around the camera. Since RBNC’s camera looks out over the flooded area, they capture some great images that have excited birders who visit the nature center. “These are species you typically don’t see using a forest habitat. You might also see swimming muskrats or mink [while the area is still covered in water],” added Trushinsky. “It’s offered a great place to raise early season ducklings — with lots of cover.”

As the season shifts towards summer, the water drains, and a new batch of animals begins to use the area. Tall grass soon fills everything the camera sees, and species like deer move in. Does raise their fawns in the tall grass, and other little land creatures start to emerge.

Trushinsky said the trail camera pictures tell such a different story every season, with different animals showing up and using the land in their own unique ways. “The Snapshot camera helps us see what species are out there and if there are any novel or threatened species we need to be aware of. The presence of these species may even impact our land use plans,” said Trushinsky.

To date, RBNC’s camera has seen deer, opossums, raccoons, mink, muskrats, coyotes, mallards and great blue herons at this single camera location, just to name a few. They have also been able to identify certain butterfly and bird species (like the golden warbler) from the images, even though Snapshot doesn’t currently classify these species. The RBNC staff are hoping to see a river otter this year, but they haven’t seen one at this location yet.

Trushinsky shared her thoughts on joining Snapshot Wisconsin and the center’s unique camera location. Check out the video to hear her describe the camera in her own words.

Boardwalk

Advice for Others

Trushinsky had some parting advice for other nature centers and groups who are considering hosting a Snapshot trail camera. “Snapshot is something very easy to get into and do. There isn’t that much of a time commitment needed. You can leave the camera out there and check it every three months. The biggest time commitment is just getting to the camera and classifying the photos.”

Trushinsky also shared some of the little tricks that she has discovered over the years.

  • Make sure the camera is in a place where you already see signs of wildlife. You won’t capture many photos of animals if wildlife aren’t using that area.
  • Put the camera in a location that is harder for people to get to, especially if you have people who visit your land. Whenever you go out there, you leave a scent, which can impact how animals use the area. It’s good to use the same route to the camera with each visit.
  • Be ready to thaw a frozen lock in the winter. Trushinsky learned that one the hard way.
  • Be prepared—wear mosquito repellent or longer layers in the summer and burr-resistant clothing in the fall. If you go through tall grass to get to your camera, always check for ticks in the spring!
  • Be aware that there may be a lot of little bugs that like to make their home inside of the camera case. Bring a tool or rag to remove them if you don’t like insects.
  • If you are using a tree to mount your camera, don’t forget to loosen the cable lock or strap on it – that allows the tree to continue to grow.

If you are thinking about hosting your own Snapshot trail camera, check out the Snapshot Wisconsin website or visit the Apply to Host a Trail Camera page! You never know what you might find in your area.

Snapshot and Satellites: A Creative Pairing

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

A few months ago, the Snapshot team said farewell to someone who has worked with the Snapshot Wisconsin team for several years. John Clare, a former graduate student at the University of Wisconsin-Madison, completed his PhD in December, 2020 and has moved on to a post-doctoral position at the University of California-Berkley.

While completing his doctoral degree, Clare worked in Drs. Ben Zuckerberg and Phil Townsend’s labs, and his research has helped push Snapshot Wisconsin to the next level, expanding the capabilities and reach of Snapshot Wisconsin. Although he played a behind-the-scenes role, one of a few students studying how to use Snapshot data in new and useful ways, his contributions to the team are appreciated, so the Snapshot team decided to share a piece of his research with those of you who follow this newsletter.

Clare first connected with Snapshot Wisconsin when current Snapshot team leader, Jen Stenglein, got in touch. Stenglein, Quantitative Research Scientist at the DNR and a leading member on the data analysis side of Snapshot Wisconsin, was interested in the sampling parameters of Clare’s Masters research, which also dealt with trail cameras. Stenglein hoped to learn from that project and apply lessons to Snapshot Wisconsin.

“I didn’t know about Snapshot Wisconsin until after talking with [Stenglein],” said Clare. “I later saw a posting for a PhD assistantship related to the program, and I applied. That is how I first got involved.”

Two beavers interacting in a stream

Two beavers captured on a Snapshot WI trail camera in Ashland County.

Clare was one of the first graduate students working with the project. He initially helped get the program up, and later he started to sort through the data and deliver some useful results.

While we don’t have the space to cover everything he worked on for his dissertation, Clare and the Snapshot team wanted to share a small piece of Clare’s research with the volunteer community and showcase part of how Clare contributed to the project.

Leveraging Snapshot Data

A central goal of Clare’s dissertation was to develop strategies to better leverage the spatial and temporal capabilities of the Snapshot database. Clare mentioned that two unique features of Snapshot Wisconsin are that Snapshot operates both statewide and year-round. Many other monitoring programs can’t operate at such wide and long scales because it would be too resource intensive for them. Fortunately, Snapshot has the help of thousands of people across Wisconsin (and the globe) to overcome that resource barrier and operate statewide and year-round.

“[Using data from all over the state and from all times of the year], we can explore questions in ways we didn’t have the ability to before,” said Clare, and Clare investigated a few of these questions in his dissertation. Two of Clare’s research questions were what broad factors drive where species are distributed and how species are active across the year.

To answer both questions, Clare needed to build a special type of model that leveraged both spatial and temporal data at the same time. Not an easy feat.

Setting Up Clare’s Model

Clare needed a unique model that could account for how species are spatially distributed around the state and temporally distributed throughout the year. “I think it’s important to take advantage of both the spatial and temporal components at the same time,” said Clare. “The question isn’t just where are species located, but also how species are distributed at time x, time y and time z.”

Both the spatial and temporal scales were needed because there are components of the environment that vary strongly across space and time. Snow depth, for example, is not fixed over the course of the year. One week, there may be six inches, and the next week there may be twelve. Snow depth also varies spatially. A few miles could be the difference between seeing snow on the ground or not. Many environmental factors are highly dynamic and variable like this, so Clare needed to think of these factors within a model that accounts for both.

It is common for models to use one type of data but incorporating both is a challenge. The main challenge is having enough data (and data of the right types) to run this kind of analysis. Fortunately, Snapshot images have both location and time data attached to each image.

A doe and two turkeys in a field

Another unique aspect of Clare’s model is that it considers multiple species at once. “We were pretty sure that individual species are distributed dynamically throughout space and time, but entire communities have not been heavily studied in the same way,” Clare said. “The appeal of using a spatial-temporal structure across the entire community is that we can explore which species are interacting with others at different parts of the state and at different times of the year.”

This concept isn’t new to the realm of modeling, but it is hard to accomplish. Researchers would need separate data for each additional species they added in the model. It can be hard enough getting data for one species, let alone multiple. However, that is where Snapshot shines best. Volunteers can tag up to 50 unique species in their Snapshot photos, so an equal number of species-specific datasets can be pulled and created from the larger Snapshot dataset.

“The advantage of a multispecies approach is that you can take into account the responses of each of those species, as opposed to modeling one species and assuming the results apply uniformly for other species,” said Clare.

Driving Distribution and Activity

Knowing he had the ability to answer his questions, Clare thought about which factors might be most influential across the entire community, in terms of predicting where species were located and how active they are. “We had a couple ideas about what these factors might be,” said Clare. “Some were related to seasonal variation like the amount of snow and the greenness of the vegetation.”

Snow depth can change substantially from day to day, even during the winter. Snow depth could dramatically impact how species move around and where food is available. Snow can even correlate to which species are even seen during parts of the year. For example, black bear behavior is often related to the winter, and thus with snow.

Wisconsin’s black bears sleep through the winter. Since winter is also associated with snow, black bear activity inversely correlates well (in Clare’s model) with snow. When there’s more snow on the ground, we are (most likely) deep into winter and see the least activity from black bears.

Bear_Clark_SSWI000000009609092C

Another environmental variable that Clare was interested in was vegetative greenness. Vegetative greenness is, from space, how green the landscape looks. In the spring, trees will start to bud burst, and the grass will grow. The landscape itself will just be greener than the previous months, and more nutritional energy will flood into the food web. Vegetative greenness varies throughout the year and can impact how animals use the land, depending on when and where food is available.

For example, a black bear’s seasonal activity could reflect the cycle of vegetative greenness. Black bears maximize their activity at times of the year when there are more food resources around, either plants or prey. These times of the year may strongly correlate to peak greenness of the landscape, or so Clare theorized.

But you might be thinking, “Wait, can the Snapshot cameras measure vegetative greenness and snow depth from the trail camera photos?” The answer is possible, but more research is needed before we can use the cameras that way. Instead, Clare used daily satellite images of the state to calculate vegetative greenness and snow depth.

Linking Satellites with Snapshot Wisconsin

Clare used satellite images from NASA to measure snow depth and vegetative greenness. Part of Clare’s assistantship position was funded by a NASA grant whose purpose was to figure out ways to integrate a continuous stream of animal observations with a continuous stream of Earth observations coming from space. Between the trail camera data and the satellite data, Clare aimed to find connections that were meaningful to wildlife management.

Consider winter severity in deer population modeling, for example. Winter severity is already used by the DNR to predict the impact of winter on deer populations and plays a partial role in making harvest decisions for the subsequent fall. One hope of the NASA collaboration was to develop more integrated measures like winter severity for deer overwintering, especially ones that impact multiple species in similar ways.

Using the images from satellites passing over the state, Clare derived data on land use, surface temperature, vegetative greenness and snow depth. All of these variables were tested across spatial and temporal scales for all classifiable species.

Fox_Red_Waukesha_SSWI000000007462874

Confirmation and Surprise

Clare wanted to share two results from his dissertation with the Snapshot community. One of these results was a confirmation of what he expected, but the other was surprising and took longer for him to wrap his head around.

“I wasn’t surprised that snow depth was a major negative driver of species activity,” Clare said. “We expected that because snow provides a refuge for some species [and a signal for other behavioral changes like hibernation].” These behavior changes cause sightings of these species to drop off during the winter and strengthens the negative correlation between snow depth and species activity. Snow is also associated with winter, when species tend to be less active to conserve energy resources.

What was more surprising was that the peak period of species activity was not associated with the peak of the growing season, or when the land was at its greenest. Clare expected these two peaks to match because there would be a maximum amount of food on the landscape. However, after some rethinking, Clare came up with a new theory about why peak activity wasn’t at peak greenness. “What we [now] think is happening is that animals don’t have to move around as much during the peak of the growing season. They don’t have to go as far to find food. It is all in one
aisle,” Clare said.

As for linking satellite data with wildlife data, snow depth and vegetative greenness both were the best predictors of species distribution and activity out of the environmental variables Clare tested. Even though vegetative greenness didn’t function how he predicted it would, it still was a good predictor of community activity and distribution. Both of these variables showed promise as potential satellite-based metrics that NASA and the DNR can use to better predict how the environment is impacting the greater wildlife community.

What’s Next?

Now that Snapshot Wisconsin has a few years of data across most of the state, Snapshot will start looking into broader trends like year-to-year weather variation and how species habitat associations may vary from year-to-year.

“As we anticipate global changes, including more extreme events like polar vortexes, heavy rain and droughts, there is a need to understand how species react to different weather phenomenon. By looking at how species are distributed at finer time scales, we can start to address those types of questions. That wasn’t the exact focus of my research, but my research can help us start to quantify what [counts] as an extreme event for different species,” explained Clare. However, that work will be done by someone else, since Clare has graduated and moved to California.

Clare took a moment to reflect on his years working with Snapshot Wisconsin. Clare said, “My favorite part has been seeing the broader project move from a concept to an operating system. It has been really exciting to see that dream come to fruition. Most of that credit is due to the folks on the Snapshot team like Jen Stenglein and Christine Anhalt-Depies.”

Clare was also appreciative of the community of volunteers that sustain Snapshot Wisconsin. “It has been rewarding to see so many Wisconsin residents get involved,” said Clare. “I’ve been blown away with how smoothly and effectively it all has worked.”

With Clare moving on to the next step of his career, the Snapshot team wishes him the best and thanks him for helping the program get set up and running, as well as his contributions on the research side of Snapshot.

Thanks John Clare, and good luck!

What Makes Data “Good”?

The following piece was written by Snapshot Wisconsin’s Data Scientist, Ryan Bemowski. 

Have you ever heard the term “Data doesn’t lie”? It’s often used when suggesting a conclusion based on the way scientific data tells a story. The statement is true, raw data is incapable of lying. However, data collection, data processing, data presentation and even the interpretation can be skewed or biased. Data is made “good” by understanding its collection, processing, and presentation methods while accounting for their pitfalls. Some might be surprised to learn it is also the responsibility of the consumer or observer of the data to be vigilant while making conclusions based on what they are seeing.

A graphic showing how data moves from collection to processing and presentation.

Data Collection

Thanks to the data collection efforts of more than 3,000 camera host volunteers over 5 years, Snapshot Wisconsin has amassed over 54,000,000 photos. Is all this data used for analysis and presentations? The short answer is, not quite. Snapshot Wisconsin uses a scientific approach and therefore any photos which do not follow the collection specifications are unusable for analysis or presentation. Under these circumstances, a certain amount of data loss is expected during the collection process. Let’s dive more into why some photos are not usable in our data analysis and presentations.

Data Processing

When data is considered unusable for analysis and presentation, corrections are made during the data processing phase. There are numerous steps in processing Snapshot Wisconsin data, and each step may temporarily or permanently mark data as unusable for presentation. For example, a camera which is baited with food, checked too frequently (such as on a weekly basis), checked too infrequently (such as once a year), or in an improper orientation may lead to permanently unusable photos. This is why it is very important that camera hosts follow the setup instructions when deploying a camera. The two photo series below show a proper camera orientation (top) and an improper camera orientation (bottom). The properly oriented camera is pointed along a flooded trail while the improperly oriented camera is pointed at the ground. This usually happens at no fault of the camera host due to weather or animal interaction but must be corrected for the photos to be usable for analysis and presentation.

Good Data Graphic2

A properly oriented camera (top) compared to an improperly oriented camera (bottom).

In another case, a group of hard to identify photos may be temporarily marked as unusable. Once the identity of the species in the photo is expertly verified by DNR staff, they are used for analysis and presentation.

Data Presentation

Usable data from the data processing phase can be analyzed and presented. The presentation phase often filters down the data to a specific species, timeframe, and region. With every new filter, the data gets smaller. At a certain point the size of the data can become too small and introduces an unacceptably high potential of being misleading or misinterpreted. In the Snapshot Wisconsin Data Dashboard, once the size of the data becomes too small to visualize effectively it is marked as “Insufficient Data.” Instead, this data is being used for other calculations where enough data is present but cannot reliably be presented on its own.

Good Data Graphic 3

Snapshot Wisconsin Data Dashboard presence plot with over 5,800,000 detections (left) and a similar plot with only 72 detections sampled (right).

Let’s use the Data Dashboard presence map with deer selected as an example. The photo on the left contains 5,800,000 detections. A detection is a photo event taken when an animal walks in front of a trail camera. What if we were to narrow down the size of the data that we are looking at by randomly selecting only 72 detections, one per county? After taking that sample of one detection per county, only 12 of the detections had deer in them, as shown by the photo on the right. The second plot is quite misleading since it appears that only 12 counties have detected a deer. When data samples are too small, the data can easily be misinterpreted. This is precisely why data samples that are very small are omitted from data presentations.

There are a lot of choices to make as presentations of data are being made. We make it a priority to display as much information and with as much detail as possible while still creating reliable and easily interperatable visualizations.

Interpretation

In the end, interpretation is everything. It is the responsibility of the observer of the data presentation to be open and willing to accept the data as truth, yet cautious of various bias and potential misinterpretations. It is important to refrain from making too many assumptions as a consumer of the presentation. For example, in the Snapshot Wisconsin Data Dashboard detection rates plot (shown below), cottontails have only a fraction of the detections that deer have across the state. It is quite easy to think “The deer population in Wisconsin is much larger than the cottontail population,” but that would be a misinterpretation regardless of how true or false the statement may be.

A bar graph showing detections per year of the five most common species.

Remember, the Snapshot Wisconsin Data Dashboard presents data about detections from our trail cameras, not overall population. There is no data in the Snapshot Wisconsin Data Dashboard which implies that one species is more populous than any other. Detectability, or how likely an animal is to be detected by a camera, plays a major role in the data used on the Snapshot Wisconsin Data Dashboard. Deer are one of the largest, most detectable species while the smaller, brush dwelling cottontail is one of the more difficult to detect.

So, is the data “good”?

Yes, Snapshot Wisconsin is full of good data. If we continue to practice proper data collection, rigorous data processing, and mindful data presentations Snapshot Wisconsin data will continue to get even better. Interpretation is also a skill which needs practice. While viewing any data presentation, be willing to accept presented data as truth but also be vigilant in your interpretation so you are not misled or misinterpret the data presentations.

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.”

Elk Herd

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.

An antlered bull elk with a tracking collar

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.

Bull Elk

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.

An elk cow, bull, and two calves.

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.”

Elk herd walking through the snow

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.

Elk calf

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.

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!