February #SuperSnap
February #SuperSnap goes to these white-tailed deer fawns spotted in Columbia County! A female deer, also known as a doe, will give birth to her young in May or June and will typically only have 1 fawn the first year of mating. In subsequent years, she will likely give birth to 2 fawns, and in rare occasions up to 3 or 4! White-tailed deer have special adaptations and behaviors to help the young survive from predators. For instance, the young have a reddish-brown coat color with small white spots, which helps them blend in with forested areas. Additionally, the doe will only visit their young 3-4 times a day to avoid leading predators to their location!
A huge thanks to Zooniverse participant @charleysangel 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 the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.
Sources:
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.
June #SuperSnap
It is time to bring back the monthly #SuperSnap ! Check out this series of a bobcat from Trempealeau County. This individual is wonderfully camouflaged with its environment, blending in with last year’s decaying plant matter in this spring photo series. Bobcats (Lynx rufus) have a distinctive mottled fur coat that allows them to disappear from sight in a great variety of landscapes. This characteristic contributes to their impressive adaptability; they are the most widespread wild cat in North America!
There were lots of amazing submissions this month. A huge thanks to Zooniverse participant @AUK 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.
Sources:
https://sciencing.com/adaptations-bobcat-8153982.html
https://www.britannica.com/animal/bobcat
Amidst the Pandemic, Citizens Create a Boost in Classifications
The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
We would like to thank everyone who has helped classify Snapshot photos during the pandemic. We are happy to see so many of our volunteers connecting with nature while at home.
We want to share how the pandemic has affected the Snapshot Wisconsin team and offer a look at the surge in classifications. Jennifer Stenglein, Research Scientist at the Wisconsin DNR, shares some encouraging findings about the rise in daily classifications and Zooniverse classifiers during the pandemic.
The Snapshot Team During the Pandemic
“I think Snapshot has been a success story,” said Stenglein. “The effects of COVID-19 haven’t been very detrimental to the team. Change happened, and we adjusted. Some of our workload shifted, but we continued to be Snapshot – just in a slightly different way.”
The Snapshot team is very collaborative. Each week, team members must heavily coordinate with each other to keep photos moving to the next steps and interacting with a volunteer base of over 2,000 people. However, since Wisconsin’s Safer at Home order went into effect in late March, the Snapshot team has been teleworking from home. Stenglein said, “To me, that’s been the biggest change: not having face-time with the team. But the team has transitioned really well to having online meetings.”
There is now a bigger focus on getting photos to Zooniverse for our volunteers to classify. With more people at home, Snapshot photos are being classified at a faster rate than before. This increase in daily classifications is what first alerted the team to the boost.
Investigating the Surge
Stenglein has been a member of the Snapshot team since its beginning in 2013. She leads the scientific program at Snapshot and plays a vital role in turning quantitative data into usable metrics (for example, calculating county fawn-to-doe ratios). Stenglein was the first to offer to investigate the boost in detail, as she was excited to learn more about our volunteers and their behavior.
The first step to investigating this boost was to determine what time frames should be compared. Two time frames were needed: One from before the pandemic and one during the pandemic. “Our Zooniverse volunteers come from all over the world, and the pandemic has affected different places differently. But a lot of our Zooniverse traffic comes from Wisconsin, so we thought selecting a timeframe that was relevant to Wisconsin was a good approach.” Additionally, because Snapshot Wisconsin is a Wisconsin DNR program, a Wisconsin-centric time frame seemed natural.
To narrow down the time frames even more, the team needed to choose a specific date as the delineator between the two time frames. “March 15th correlates to about the time when the Department of Health Services started collecting their stats. It was less than two weeks later that the Safer at Home order went into effect. We felt like this date was a good mix of a time frame important to Wisconsin and a line in the sand. We needed that [break] point between pre-pandemic and mid-pandemic.” At least two months of data was needed to ensure reliability, so the two months prior (pre-pandemic: Jan. 15–March 14, 2020) and the two months after (mid-pandemic: March 15-May 15, 2020) were chosen.
With the time frames set, Stenglein began compiling the data for the analysis. However, before we get to Stenglein’s analysis, it helps to understand the difference between a photo, a trigger and a classification. Those who have classified Snapshot photos before will recall that you were given three photos at a time to classify as a set. Volunteers are asked to classify the set as one whole, meaning they would tag the set as having a red fox if a red fox was present in at least one of the three photos. Each set of photos is called a trigger and gets classified by multiple volunteers before it is retired on Zooniverse to ensure accuracy. Each time a trigger is classified by a volunteer, that is called a classification. Simply said, three photos make a trigger, and a classification is each time a volunteer looks at that trigger before the trigger retires.
The Boost: Analyzed
In total, our volunteers made 460,604 classifications during the pandemic – a huge increase from the 255,208 classifications prior to the pandemic. While each classification doesn’t perfectly translate to the number of triggers retired, this boost is a huge increase in Snapshot’s turnover rate for triggers. Additionally, a total of 1,168 unique classifiers logged in during the pandemic, almost double the number from pre-pandemic.
Classifiers were also, on average, classifying 11.6% more triggers each time they logged on. Once we saw that people were classifying for longer, we wondered what other behavioral shifts have occurred since the pandemic started. One big shift is seen in our established classifiers, or volunteers who were already active on Zooniverse before the pandemic. Stenglein saw that established classifiers were logging in and classifying on significantly more days than before, and they were classifying more triggers each time. However, Stenglein noticed that there were fewer sessions per day. A session is similar to a sitting in that one volunteer could classify triggers over multiple sittings in a single day. Altogether, our established classifiers were classifying more triggers, on more days, but fewer times per day than pre-pandemic.
The most classifications in a single day occured on March 29, less than a week after the Safer at Home order went into effect in Wisconsin. A total of 17,155 triggers were classified on that single day.
Graphic created by Ryan Bower.
On intuition, Stenglein had an idea to check if there was a difference among the days of the week. Do volunteers classify differently on the weekends than on weekdays? “The weekend/weekday idea was something that I thought of because it was coming up on the weekend. I was thinking, ‘Man, these days all feel the same.’ I got intrigued by this question and wanted to know,” said Stenglein. “But I didn’t even know what the baseline was.”
Stenglein continued, “I was very interested to find out that [normally, pre-pandemic] weekends have much fewer classifications compared to weekdays. I could have hypothesized the opposite and convinced myself.” In fact, the total number of triggers classified rose for both weekends and weekdays, although unequally. We saw an increase of 34% during weekdays (1,507 more triggers classified each weekday) and 86.7% during weekends (2,452 more triggers classified each day of the weekend).
“I was really fascinated that both weekdays and weekend classifications went up [during the pandemic], but when you compare them now, there no longer is a distinction between them. I think that was my favorite finding,” said Stenglein. “With fewer options of things to do on the weekend, perhaps [volunteers] are willing to sit down and spend more time on Zooniverse.”
Stenglein also compared the mid-pandemic period (March 15–May 15, 2020) to a similar time frame from the year prior (March 15–May 15, 2019). Stenglein said, “It helped me to really believe in this boost idea when I looked at the same time period from the previous year and found almost identical results. There is really something unique about this [mid-pandemic] time period.”
Continued Support
Stenglein said, “There is value in communicating this data back to volunteers who are working extra hard right now by classifying photos. You are increasing our capacity to turn over photos and helping us get through our backlog.” In fact, classifiers helped process over 10% of Snapshot’s backlog since the pandemic started.
The Snapshot staff were excited to see this rise in classifications because a quicker turnaround time for photos means more up-to-date data for wildlife management decisions. Continuing to reduce the backlog of photos is an important way that the public (and Snapshot volunteers specifically) can contribute to the project and wildlife monitoring.
“Back in January, we had a meeting about how to deal with unclassified photos. We came up with all these great new ideas, but a pandemic was not among them. The pandemic is never good news, but it provided an opportunity to get through so many unclassified photos in a way that wouldn’t have been possible,” said Stenglein.
But in order to provide key measures to decision makers about animal distribution and abundance, Snapshot needs continued support from the public to keep classifying photos and monitoring wildlife in Wisconsin. Stenglein said, “Snapshot is a volunteer program. There is no Snapshot without its volunteers, and they are what makes Snapshot successful. Citizen science [projects] are a wonderful thing to participate in, and it’s a way to work with others towards research, science and wildlife decision support. People care about helping monitor wildlife and helping the DNR understand the wildlife in their own backyards.”
Stenglein and the Snapshot team want to thank the thousands of volunteers who have helped classify photos during the pandemic. “We are super grateful for the increased traffic and time that people have been putting into Zooniverse.”
Stenglein added, “On a personal note, I’ve been classifying photos with my son, which has been really fun. I’m glad that Snapshot provides an opportunity to see wildlife [while we are stuck indoors].” The Snapshot team encourages classifying photos together with loved ones. Check out the next article in this newsletter to see more ways to stay connected with nature!
June #SuperSnap
This month’s #SuperSnap features a bird that we don’t see too often on our Snapshot Wisconsin cameras: the Ring-necked Pheasant. Brightly colored plumage, such as on this bird, indicates a male, while females are mostly brown and spotted with black. Pheasants can often be found looking for food in open fields and at the edges of woodlands.
A huge thanks to Zooniverse participant JoyKidd 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.
Birds of Snapshot Wisconsin
With migration in full swing and breeding season upon us, you may be noticing more feathered friends passing by your Snapshot Wisconsin trail camera.
While volunteers are not required to identify most birds down to the species level, we know that many volunteers are curious of what exactly is showing up in front of their trail cameras. According to the Wisconsin Society for Ornithology around 250 bird species can be regularly found in Wisconsin, though more than 400 have been recorded in the state. Of this diverse variety of birds, there are a few that make frequent appearances on Snapshot Wisconsin trail cameras.
Check out the below slideshows to learn the ID’s of some of the common species found. More information about the species can be found in their linked names below. Please note the birds are not accurate size ratios.
Species that volunteers are required to ID:
Learn more about Sandhill Crane, Whooping Crane, Wild Turkey, Ring-necked Pheasant and Ruffed Grouse.
Common woodpeckers:
Learn more about Pileated Woodpecker, Northern Flicker, Hairy Woodpecker and Downy Woodpecker.
Common water birds:
Learn more about Wood Duck, Mallard, Canada Goose and Great Blue Heron.
Common raptors:
Learn more about Bald Eagle, Barred Owl, Red-tailed Hawk and Cooper’s Hawk.
Other common birds:
Learn more about American Robin, Northern Cardinal, Blue Jay, American Woodcock, American Crow, Hermit Thrush, Common Grackle and Red-winged Blackbird.
For those interested in exploring more Snapshot Wisconsin birds, this year the Snapshot Wisconsin team embarked on a new project with all the “other bird” photos showing up in front of the trail cameras. Snapshot Wisconsin Bird Edition is a collaboration between Snapshot Wisconsin and the Wisconsin DNR Natural Heritage Conservation. The goal is to identify all of Snapshot Wisconsin’s bird images to a species level and to look for evidence of breeding. Breeding observations will be reported to the Wisconsin Breeding Bird Atlas II and observations of uncommon, rare, or endangered species will be reported to the Natural Heritage Conservation. Learn more and get started at birds.snapshotwisconsin.org.
April #SuperSnap
This month’s #SuperSnap features a juvenile bald eagle from Dodge County. This camera location had so many great bird photos and #SuperSnap nominations that it was difficult to pick one!
Once this immature eagle is fully grown, it will have a wingspan of up to 7ft! Females usually lay their eggs within the first couple weeks of April, so in the next month there should be plenty of eagle chicks hatching. Eagles can often be seen soaring above bodies of open water, searching for fish to eat.
A huge thanks to Zooniverse participant eaglecon 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.
March #SuperSnap
This month’s #SuperSnap features a porcupine (Erethizon dorsatum) from Vilas county making its way down a path of prairie flowers. It may seem surprising, but these stout, ambling creatures can often be found at the tip-top of trees snacking away on bark, stems, leaves, fruits, and other springtime buds.
A huge thanks to Zooniverse participant LynnGrace 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.
Maps of the Zooniverse
The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
The opportunity to classify photos of wildlife from across Wisconsin draws a diverse array of individuals to our Zooniverse page. Some volunteers are trail camera hosts themselves and enjoy classifying photos from other camera sites. Zooniverse also offers this opportunity to those who are unable to host a camera but still wish to participate in the project.
The maps here were created using Google Analytics data, which can anonymously record information about users who access a webpage, such as their nearest city. This data shows us that Snapshot Wisconsin reaches an audience far beyond Wisconsin, and even beyond the United States! In total, volunteers from 696 cities across 41 countries have interacted with the Snapshot Wisconsin Zooniverse page since 2016. 190 of those cities are in Wisconsin.
Each dot represents just one city, regardless of the number of individuals who accessed the site in that location. For example, the dot for the city of Madison could represent thousands of users. Zooming in on Wisconsin, we see that many dots are centered around the most populous areas, such as Madison, Milwaukee, Minneapolis and Chicago. This pattern can be attributed to the fact that these areas also host the highest concentration of suburbs.
Regardless of the volunteer’s location, each classification we receive is important to the success of Snapshot Wisconsin.
Snapshot Wisconsin 