Elk Camera Updates
Co-authored by Ally Magnin and Emily Donovan
You may have noticed recently on the blog that the Snapshot team has spent some time in the Northwoods conducting fieldwork in the elk grids. But what was our motive as researchers?
Wisconsin’s elk herds are dynamic and do not necessarily occupy the same area all the time. Young individuals may temporarily disperse, cows split off from larger herds to give birth, and the herd as a whole may shift their range as they seek out suitable habitat. While this is to be expected, it creates an interesting problem for camera trap research.
A bull elk from Black River Falls, WI with a GPS collar.
Since 2015, Snapshot Wisconsin has had a portion of our project dedicated to monitoring reintroduced elk herds. Cameras were deployed in grids much smaller than the usual Snapshot Wisconsin grid, increasing the density of cameras in the herd reintroduction area and making it more likely to capture photos of elk. As the herds shifted their range, however, some cameras no longer detected elk. To begin to address this mismatch between our elk grids and the herds’ ranges, Data and Spatial Analyst Emily Buege Donovan conducted an analysis.
Donovan began by combining several elk-related data sources to assess the quality of each camera site. Among these data sources were the most recent GPS locations of collared elk. A portion of the state’s elk are fitted with GPS collars, which transmit a location every 13 hours. GPS collar data is commonly used in wildlife research and management to better understand the movement patterns and resource selection of animal populations. In the present study, Donovan used these data to predict the likelihood that a camera will regularly detect elk. See Figure 1 for an example of the camera locations in relationship to the collar data. Camera locations in the northwest portion of the map have low probability of capturing elk, whereas cameras in the southeast have a high probability of capturing elk photos.
Fig 1. Example map of Snapshot Wisconsin cameras within the elk grids and the 2019 elk collar data.
However, because not all elk in Wisconsin are collared, the collar data could not be used exclusively to determine whether a camera site should remain active in elk monitoring efforts. Donovan also needed to bring in the historical elk detections for each camera site. How long had it been since an elk was detected at this site? How many elk photos were taken by each camera? By combining the collar data, photo data, and several other factors, such as ease of access by the volunteer and habitat type, Donovan created a scoring system to determine the best camera locations. Low scoring cameras were marked for removal, and high scoring cameras were marked to stay on the landscape.
Once we determined which elk blocks should be removed, we reached out to the volunteer who was assigned to each of those blocks and requested their assistance in removing the camera. For the blocks that didn’t have a volunteer assigned, our team planned fieldwork for the summer of 2020 to remove the cameras.
Snapshot team member Ally Magnin during elk camera fieldwork.
Many of the cameras marked for removal were deployed over three years ago, so navigating to them proved difficult in some cases. We traversed tamarack swamps, bushwhacked through thick understory, hopped across streams, and puzzled over satellite imagery to reach each destination. Our team enjoyed the challenge!
In addition to removing old cameras, we also conducted camera checks on the blocks that didn’t currently have a volunteer assigned in order to get them ready for a new volunteer to monitor, and replaced cameras that had shown signs of malfunction. We made it a priority to take diligent notes about how to navigate to each camera site to make navigation easier for future volunteers.
Overall, it was a very productive field season that provided the team with the opportunity to step away from our computer screens and into the outdoors. It also gave us an even greater appreciation for the work our volunteers do to monitor their cameras.
Are you interested in monitoring a camera as a part of our elk project? Sign up today at elk.snapshotwisconsin.org. Applications are reviewed when blocks open up, and we will contact you with more information once you’re accepted!
Check out our other elk-related blog posts below:
Elk Snapshots Mean Better Elk Modeling
December #SuperSnap
Pictures of these two black bears from Lincoln County quickly captured the attention of our Zooniverse community. That’s why we deemed a photo of this pair our December #SuperSnap. Black bear cubs are born during January and February each year. The cubs remain with their mother for about 18 months. During this time, mother bears will teach their cubs how to climb trees, find shelter, and forage for food before sending them off into the world on their own.
A huge thanks to Zooniverse participant @momsabina 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.
November #SuperSnap
This playful coyote family from Rusk County has been deemed our #SuperSnap for November. Coyote parents are resourceful when preparing a den for their pups. Some coyotes will repurpose abandoned burrows from skunks, woodchucks, foxes, badgers, and even other coyotes to create a den. Female coyotes will also prepare several den sites that include multiple entrances for a quick escape if threatened by predators.
A huge thanks to Zooniverse participant @oregano 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.
Taking a Bite Out of Deer Aging
The age composition of a population can tell us a lot of useful information. In whitetail deer (Odocoileus virginianus), age data provides information about deer herd characteristics, harvest or mortality pressure on a specific age group, and general progress of a wildlife management program overall. A common way to age deer is through tooth wear and replacement. Let’s chew into this technique.
As a determined undergraduate, I voluntarily participated in a few of the DNR’s attempts to collect age and sex data of whitetail deer through in-person registration. Those data were collected from hunters pulling into the local gas station to show off (and ultimately register) their deer. Polite small talk was usually cut off by the sight of my clipboard, knife, flashlight, and jaw spreader. With a cheery smile I’d ask, “May we collect some information about the age and sex of your deer for management purposes?”. Most hunters gladly gave us the chance to examine their deer, but every now and then a trophy buck would pull in– and we knew better than to ask. Why? Because aging deer by tooth wear and replacement requires spreading (and sometimes cutting) the jaw and cheek to get a better look at the back teeth. In-person registration is no longer done, nowadays the DNR gets aging data from deer processors and CWD sampling.
Illustration of the sideview of a whitetail deer’s jaw and teeth. Credit: WIDNR
Although aging deer from tooth wear and replacement has its limitations, it is the quickest and cheapest way to determine the age of a deer. It requires determining which teeth are present in the jawbone and how worn those teeth are. The data determine which of the following age classes a deer falls into: fawn (younger than 1 year), yearling (1-1.5 years), or adult (categorized as 2, 3, 4-5, 6-8, 9-11, or 12+ years).
Fawns
Fawns usually have only three or four fully erupted teeth along each side of their jaw. The first three are temporary premolars and are often called “milk teeth”. Deer are born with these teeth fully erupted in place (unlike humans). It is important to note that the third premolar has three cusps. A deer with only three or four fully erupted teeth along the jaw is a fawn (Image A).
Image A. Example of a fawn’s teeth. Credit: Indiana DNR.
Yearlings
Yearlings are described as approximately 1.5 years old in the fall and generally have six fully erupted teeth on each side of the jaw. The third premolar is worn down by now but should still only have three cusps as it has not yet been replaced by a permanent tooth (Image B).
Image B. Example of a yearling’s teeth around 18 months. Credit: Indiana DNR.
At 18-19 months old the temporary premolars (first and second premolars) have been replaced by permanent premolars and the third premolar has now become permanent with only two cusps. A deer with six fully erupted teeth along the jaw is a yearling (Image C).
Image C. Example of a yearling’s teeth after 18-19 months. Credit: Indiana DNR.
Adults
Adult deer are 2.5 years and older. They will have six fully erupted teeth along each side of the jaw: three permanent premolars and three permanent molars. At this point, it is no longer as simple as counting the teeth and cusps. It is going to take a sharp eye to observe the amount of tooth wear on the teeth. Over time, teeth wear down increasing the width of the dentin exposed along each cusp. Deer older than yearlings are aged through wear of the cusps closest to the tongue on the cheek teeth. For 2.5 years and older, the third premolar is stained. The fourth tooth shows little wear, having a distinct point, and the dentine is thinner than the white enamel. As the deer ages, the cusp points will be worn down and the teeth will become relatively flat (Image D).
Image D. Example of an adult deer’s teeth. Credit: Indiana DNR.
By no means am I an expert in aging deer. As you may now understand, learning how to age deer using tooth wear and replacement is not a one-day deal. Like everything, it takes practice. While we only took a bite out of deer aging, years of training and practice can allow researchers (and undergraduate volunteers) to age a deer down to the exact year. As mentioned, using tooth wear and replacement isn’t the most accurate technique for aging deer, but it is the most hands-on (and fun) approach.
Sources:
October #SuperSnap
This month’s #SuperSnap goes to the slick pair of North American River Otters featured below from Vilas County. Otters are known to produce slide marks as they move their bodies along ice, snow, and mud on the edge of riverbanks. As the only species in the state to produce these distinct tracks, the Wisconsin DNR performs a series of aerial surveys in the winter to search for the presence of otter slide marks. This data is then incorporated into population estimates for the species.
A huge thanks to Zooniverse participant @Megeth 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.
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.
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.
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.
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.”
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!
September #SuperSnap
Check out this #SuperSnap of a woodchuck caught by one of our cameras in La Crosse County! Also known as groundhogs, these furry rodents are true hibernators during the cold winter months in Wisconsin. During this time, they can drop their body temperature down to 37 °F and lower their heart rate from 80 to 5 beats per minute. They typically emerge just in time to provide a spring weather forecast in early February. 😉
A huge thanks to Zooniverse participant @oregano 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.
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 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.”
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.
July #SuperSnap
This Wisconsin icon from Iowa County is crowned our July #SuperSnap! Badgers don’t show up often on Snapshot Wisconsin cameras, and it is even more rare to capture one in the daytime. Have you ever seen a badger on your trail camera? Or even better, in person?
A badger captured on a Snapshot Wisconsin trail camera.
A huge thanks to Zooniverse participant @WINature for this #SuperSnap nomination.
Continue classifying photos on Zooniverse and sharing your favorites with #SuperSnap – your submission might just be next month’s featured photo! Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.
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.
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 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.
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.
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.
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