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.
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:
Hi everyone! I’m Jessica Knackert, one of the newest additions to the Snapshot Wisconsin volunteer management team. Before coming to the DNR, I graduated from the University of Wisconsin-Madison, where I studied zoology, science communication, and environmental studies. I engaged in a lot of great opportunities to share science with the public during my undergraduate career. I wrote numerous articles on research related to climate change, urban canids, and biotechnology. I also provided hands-on demonstrations at community science events focused on culturing stem cells and caring for non-human primates.
Outside of science outreach, I was a research assistant for the Carnivore Coexistence Lab at UW-Madison. I supported a graduate student examining the impact of an African lion reintroduction in Akagera National Park, Rwanda. This project fell in the same realm of wildlife research as Snapshot Wisconsin by using trail cameras to monitor animal populations and behavior. I also worked at the Milwaukee County Zoo. Being a part of the visitor services department gave me the chance to interact with thousands of guests from all over the nation each day. This role also allowed me to broaden the Zoo’s guided tour program by incorporating topics like conservation, wildlife research, and animal enrichment.
Working for a project like Snapshot Wisconsin provides the perfect opportunity to combine my experience in both the research and outreach sides of science. While I loved classifying photos of iconic African wildlife halfway across the world, I’m eager to refamiliarize myself with the diversity of species that live closer to home. I’m also excited to apply my training in science communication to expand upon and diversify educator outreach for the project. Snapshot Wisconsin is a great way for people of all ages to gain first-hand experience in learning the scientific process. Greater educator participation would allow students across the state to explore Wisconsin’s great outdoors while engaging with DNR professionals and other community members when inside the classroom.
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.
In wildlife conservation and management, population estimates are highly desired information and tracking them gives important insights about the health and resilience of a population through time. For example, Wisconsin Department of Natural Resources (WI DNR) annually estimates the size of the deer population in more than 80 Deer Management Units (roughly the size of counties). Fun fact – Snapshot Wisconsin contributes data on deer fawn-to-doe ratios to make these population estimates possible.
Annual population growth can be estimated by dividing the population estimate in the current year by the population estimate in the previous year (we call this growth rate lambda). A lambda = 1 is a stable population, a lambda < 1 is a declining population, and a lambda > 1 is a growing population.
What leads to the stability, growth, or decline of a population is the foundation of population dynamics. Population dynamics are a way to understand and describe the changes in wildlife population numbers and structure through time. The processes for growth are births and immigration into the population, and the processes for decline are deaths and emigration away from the population, which leads to the following formula at the heart of population dynamics:
Population size this year = Population size last year + births – deaths + immigrants – emigrants
In established wildlife populations we often focus solely on the births (called recruitment) and deaths within a wildlife population and assume immigration and emigration are equal and therefore cancel each other out.
For deer, the birth part of the equation is captured by those fawn-to-doe ratios mentioned earlier, and the death portion is estimated as a combination of mortality sources. One source is deer harvest, and because Wisconsin requires registration of harvested deer, we have a pretty good understanding of this mortality source. Other mortality sources are from natural causes and are best assessed through radio-collaring and tracking deer through their lifetimes.
Bobcat and fisher are two other Wisconsin species whose births and deaths are estimated annually. For these species, the recruitment into each population is estimated from our understanding of how many kittens (bobcat young) and kits (fisher young) are born into the population. The data come from the reproductive tracts of harvested females. The reproductive tracts contain scars for each placenta that was attached, thereby providing information on pregnancy rates and litter sizes. Similar to deer, information on mortality in these population comes from registered harvest and estimates of other non-harvest sources of mortality collected from radio-collaring research studies.
We are developing ways for Snapshot Wisconsin to contribute to our understanding of wildlife population dynamics. A real value of Snapshot Wisconsin is that it tracks all types of wildlife. For each species, we can develop metrics that will help us better track its population dynamics, and therefore gain a better understanding of the current status and trajectories of our wildlife populations.
One of these metrics is the proportion of cameras that capture a photo of a species within a time and spatial area. We can treat this metric as an index to population size, which is very useful for tracking populations across space and time. If we see a trend in the proportion of cameras in some part of the state showing an increase or decrease in this metric, that gives us information about the distribution and movement of species. For example, the southern border of fisher distribution in Wisconsin (currently around the center of the state) has been thought to be shifting further south. This metric can help us document when and where this shift may be occurring. This metric is now tracked for 19 Wisconsin species on the Snapshot Wisconsin data dashboard.
In the following graphics, you can see the proportion of trail cameras detecting bobcat in each ecological landscape of Wisconsin in 2017, 2018 and 2019. The patterns are consistent across these three years and show the distribution of bobcats is across two-thirds of the state. We will be tracking this metric and others for bobcats, as well as for other Wisconsin species.
The power of Snapshot Wisconsin is just beginning to emerge as we are collecting consistent, year-round, and multi-year data in this effort. Thanks to all of our volunteers who help make this possible!
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 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.
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.
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.
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.”
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
Everyone has a certain seasonal change that tells them spring is around the corner. For me, it’s seeing the crocuses pop up in the yards around Madison, along with hearing the red-winged blackbirds trill in the tall grass. Below are a few examples of Wisconsin wildlife and plants to look for as the snow melts and the temperature and daylight increases.
You can explore the seasonal patterns of different species on the Snapshot Wisconsin Data Dashboard. The Data Dashboard is updated with data from our trail cameras over time. To check out current data as of spring 2021, select a species from the list on the left side. Then, scroll over to the Animal Activity graph on the right-hand side of the page. Select the “by Month” option beneath the graph in order to see what changes typically occur in March.
You’ll find some common springtime patterns captured on our Snapshot cameras, like cottontails as they are increasingly out and about. In fact, the appearance of cottontails is twice as likely in March as it is February.
Americans give a lot of power in predicting spring to the groundhog, or as we call it in the classification interface, a woodchuck. We don’t see woodchucks out and about until March on the Snapshot cameras. This is an increase from zero detections in January and February while they are hibernating.
Fishers appear on Snapshot cameras more in March than during any other time of the year! This might be because they usually give birth in February and mate in March and April, so there is a lot of activity in the fisher lifecycle during this part of the year.
One of the most recognizable signs of spring is the return of bird species. You can see that Snapshot cameras capture a huge jump in detection of Sandhill cranes starting in March as they return north.
Although Snapshot Wisconsin is a project focused on the fauna in our communities, there are also a bunch of neat flora to look out for as spring comes around. Keep your eyes out for pussy willows, daffodils, Siberian squill, and other trees, shrubs and ground cover that will begin to blossom in the background of our trail camera photos.
And if you are curious about firsts elsewhere, the USA National Phenology Network posts the status of spring across the country. You can watch as spring comes to different regions and track trends, temperatures, and species as you await the arrival of spring in your own backyard.
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.”
To better understand the significance of Stauffer’s work, it helps to know how elk have historically been counted in Wisconsin.
“Long before I came onto the scene, the primary way of counting elk was to go out and count them all,” said Stauffer. This method requires extensive time in the field and considerable local knowledge about where elk groups often hang out. Researchers could count some elk by their numbered collars, but they also needed to know how many uncollared elk were in each group. The elk herd grew over the years, and more and more elk did not have identifiable collars. This added another challenge for researchers who were trying to count all the unmarked elk (and make sure they weren’t double counting any of them).
Since the estimate of the elk population size still needed to include an unknown number of these unmarked individuals, the DNR started experimenting with models that didn’t require individual identifications. These new models were also a boon because the herd was reaching too large a size to efficiently collar. It was becoming too much of a time investment and was expensive.
Instead, these models are based on images from the Snapshot camera grid, as discussed in the previous article, but even these camera-based models had room for improvement. Thus, Stauffer began researching a model that incorporated the best of both approaches: a model that was based on the camera data but still incorporated limited individual identification back into the model.
Stauffer looked into a variety of models but zeroed in on one type of model in particular. Stauffer explained that this model belongs to a class of models called spatial mark resight models. Spatial mark resight models combine the best of both marked and unmarked models. Stauffer’s model identifies individuals by their collars but also makes inferences from the photos of unmarked elk at the same time.
Spatial mark resight models also relax a major assumption made by the previous camera model, the closure assumption. “This assumption states that the number of elk at a particular camera location doesn’t change from one encounter occasion to the next, and it is clearly violated. Elk are wandering from camera to camera,” said Stauffer. Stauffer’s hybrid model relaxes the closure assumption and attempts to figure out the minimum number of distinct elk it can identify from the pictures.
Collared elk are often easy to identify in the photos. These collared elk are given the ID assigned to their respective collar number so that all photos of a particular elk share the same ID. The model also attempts to assign IDs to uncollared elk in the photos. The model uses probabilistics to assigns IDs to all remaining elk – either uncollared elk or unknown elk (because the collar or the collar number isn’t visible in the photo) – based on characteristics visible in each photo.
Fortunately, Stauffer’s model uses as much information as it can get from the photos when assigning IDs. For example, if one photo is of a calf and another photo is of a cow, then the model won’t assign the same ID to these animals. After all, we know those are two distinct elk, not one. Similarly, a marked but unidentified elk with one collar type can’t be the same as another unidentified elk with a different collar type. The model even uses spatial data to differentiate unmarked elk from two different photos. For example, photos at two locations close together might be from the same elk, but photos from two distant locations probably represent two different elk.
Capitalizing on all the information available in the Snapshot photos, the model makes an estimate of how many elk are likely in Wisconsin’s elk herds. As the elk herds continue to grow, this modeling approach helps estimate the elk population and hopefully saves the DNR time and money.
How well does the model work?
“[Technically,] the spatial count model doesn’t require any information about individual IDs, but it performs pretty poorly without them,” said Stauffer. “There is a series of papers from about 2013 on that shows if you add information about individuals to spatial counts, you can really improve the accuracy and precision of the spatial model.”
“Theoretically, this makes the model estimates more precise,” said Stauffer. To check, Stauffer collaborated with a colleague to run a bunch of simulations with known, perfect data, and the model worked reasonably well. These simulation results are encouraging because the model wasn’t massively overpredicting or underpredicting the number of elk in the herds, both of which could have management implications for elk.
When asked if identifying individuals from photographs is worth the extra effort, Stauffer said, “Working with models that don’t require individual IDs still requires considerable time to classify photos. Identifying individuals is only a little bit more work on top of that. In general, when you can’t meet the assumptions of a model, then it is worth getting individual identifications, if you can.”
Just how much additional effort should be put into individual IDs? Stauffer believes part of the answer comes from asking what other information can be obtained from the collars. “If we are already putting the collars on those we capture or release, then we might as well get as much out of them as possible, such as through using photographs [like Snapshot does],” said Stauffer.
Incorporating Another Year
After the Snapshot team finishes assembling the 2020 elk dataset, a large dataset comprised of the data from all the Snapshot photos of elk in 2020, Stauffer will run his model using this new dataset and generate an estimate of last year’s final elk population. Stauffer’s estimate will be closely compared to other estimates generated by the previous camera-based models and through collaring efforts alone to see how well each approach performs.
Stauffer took a minute to reflect on his work so far with the elk population estimate. Stauffer said, “The modeling process has been really rewarding, diving into this topic in a depth that I would not have done if I did not have this Snapshot photo dataset to work with. The simulation also went well. It illustrated that the model works the way we claim it works, which is good. Fitting the model to the elk data is mostly encouraging, but it shows that there are situations where it doesn’t do as good of a job as we hoped. Specifically, for calves, it still needs to be fine-tuned.”
From physically counting elk to modeling counts of only unknown individuals to modeling counts of both unknown and known individuals, Wisconsin’s approach to estimating elk abundance has evolved through time. Chances are, as the composition and distribution of the herd changes in the coming years, the approach will evolve even more. But for the next few years, Stauffer’s work will help direct how we count elk now.