One of the remarkable elements of the Snapshot Wisconsin program is our ability to work closely with University collaborators across the state. When trail camera hosts upload and classify their photos, they provide valuable data for our program. Research collaborators can then use this data to answer critical questions about our state’s wildlife.
Hannah Butkiewicz is one of Snapshot Wisconsin’s current collaborators. Growing up in rural Wisconsin, Hannah became interested in wildlife during a high school internship. From monitoring Karner Blue butterflies, to tracking wolves, planting prairies, and sampling fresh-water mussels, Hannah describes that summer as “life-changing.” Thanks to the mentorship of one of her teachers, she went on to pursue her interests in wildlife research. Hannah is currently working towards her M.S. at UW-Stevens Point’s Natural Resources program under the supervision of Professor Jason Riddle.
Hannah is investigating three main questions that will provide important information for wildlife management decision support:
- What are the estimated ratios of poults (young turkeys) to hens (adult female turkeys), and what is the average brood (group of offspring) size?
- Is there a difference in wild turkey reproduction and population growth between habitats that are more than 50% forested or less than 50% forested?
- What is the effectiveness of using Snapshot Wisconsin trail camera images to assess wild turkey reproduction and population growth?
In order to answer these questions, Hannah’s research will use two different data sources. First she will analyze wild turkey data that our volunteers have collected from all over the state. These turkey photos are from April through August of 2015-2020. So far, Hannah and her research assistant have reviewed nearly 50,000 of these photo triggers. When they look at each photo, they document the number of hens, poults, toms (adult males), and jakes (juvenile males). This information will help her answer her first research question relating to hen-to-poult ratios and average brood size. It will also help her determine if there is a difference in reproduction and population growth between habitats that are more than 50% forested or less than 50% forested.
The other side of Hannah’s research involves working with a select number of Snapshot Wisconsin volunteers to place additional cameras and sound recording equipment near existing Snapshot trail cameras. A single trail camera is limited in how many animals it can capture because it only detects what passes directly in front of its view. In order to better compare the Snapshot Wisconsin trail camera triggers to the turkeys present outside of the camera’s range, three additional cameras were placed around several deployed Snapshot cameras to form a 360-degree view of the surrounding area (see Figure 1). The automated recording unit will be used to record any turkey calls from individuals that are not within view of the trail cameras, either due to foliage or distance. Hannah plans to check these extra cameras and recording units once a month for the rest of this summer. Having additional trail camera photos and sound recordings of turkeys will help her determine the efficiency of using Snapshot Wisconsin trail camera images for monitoring wild turkey reproduction and population growth. It will also allow her to adjust her hen-to-poult ratio estimates.
When describing her experience as a graduate student working on this project, Hannah said, “The overall experience so far has been great! I am enjoying all my classes and have developed professional relationships with my advisers, graduate students, campus professors and other professionals. Graduate school requires a lot of hard work and dedication, but it sure helps to have a great team!”
Hannah plans to finish her research in August of 2021 in order to have time to write her thesis and graduate by December of next year. We wish Hannah the best of luck in continuing with her graduate studies and we look forward to providing updates on final research findings in the future!
The following piece was written by project coordinator Christine Anhalt-Depies, Ph.D. for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
The sight of deer fawns and their mothers along roadsides and in fields may be a sign to some that summer has arrived in Wisconsin. For an ecologist, fawns represent the new “recruits”, or the number of individuals that are added the deer population each year. Understanding the number of fawns on the landscape is an essential part of estimating the size of the deer herd in Wisconsin. Since launch of Snapshot Wisconsin, trail camera photos have played an increasingly important role in this process.
Fawn-to-doe ratios, along with information collected from harvested animals, are the primary way the Wisconsin DNR determines the size of the deer population prior to harvest. Simply put, a fawn-to-doe ratio is the average number of fawns produced per adult doe. This important metric varies across the state and year to year. The number of fawns produced per doe can depend on food availability, winter severity and resource competition, among other factors. For example, cold temperatures and deep snow in a given year can be difficult on the health of does, resulting in fewer fawns come spring. Southern Wisconsin farmland, on the other hand, provides good food sources for deer, and fawn-to-doe ratios are typically higher in these regions compared to northern forested areas.
Traditional surveys used to gather information about fawns and does, such as roadside observations, can have limitations due to factors like weather, topography, or time. Snapshot Wisconsin helps fill critical gaps by contributing additional data and providing improved spatial coverage. Eric Canania, Southern District Deer Biologist with the Wisconsin DNR, explained, “Snapshot’s camera coverage differs from traditional [fawn-to-doe ratio] collection methods by allowing access to observations within the heart of private lands… Although the state of Wisconsin boasts a fair amount of public land, the primary land type is still in private ownership. This means that it’s very important for us to provide [fawn-to-doe ratio] values that come from private and public lands alike and can be collected in various habitat [and] cover types.” In 2019, Snapshot Wisconsin data contributed fawn-to-doe ratios in every single county — the first time this has happened since Snapshot Wisconsin’s launch. In fact, 2019 marks a 50% increase in data collection by Snapshot trail camera volunteers compared to the previous year.
To calculate fawn-to-doe ratios, researchers look across all photos at a given camera site during the months of July and August. Having already survived the first few weeks of life in early summer, fawns seen in these months have made it through the riskiest time in their lives. July and August are also ideal for detecting fawns. They are no longer hiding from predators but instead moving around at the heel of their mother. Their characteristic spots also make them easily distinguishable from yearling or adult deer. With the critical help of volunteers, researchers identify and count all photos with does and/or fawns in them from a given camera. They then divide the average number of fawns in these photos by the average number of does. This accounts for the fact that the same doe and fawn(s) may pass in front of the same camera many times throughout the summer months. Averaging all the data from across a county, researchers can report a Snapshot Wisconsin fawn-to-doe ratio.
Fawn-to-doe ratios and population estimates are key metrics provided to Wisconsin’s County Deer Advisory Councils (CDACs). “CDACs are responsible for making deer management recommendations [to the Department] within their individual county,” explained Canania. In this way, “Snapshot provides an awesome opportunity for Wisconsin’s public to become involved and help us produce the most accurate deer management data possible.”
The Snapshot Wisconsin team is often asked why we accept data only from our Snapshot-specific cameras. While there are several reasons, the reason that was highlighted in the April 2019 newsletter was because Snapshot Wisconsin cameras are programmed to take a single photo at 10:40 a.m. each day. Although 10:40 may seem like an arbitrary time, this corresponds to the approximate time that a NASA satellite flies over Wisconsin and collects aerial imagery. (More information on how NASA data and Snapshot data are complementary can be found in this blog post.)
It may be difficult to recognize the value of a blank photo in wildlife research, but a year-long series of these photos allows us to examine something very important to wildlife: habitat condition. For each camera site, the time-lapse photos are loaded into the statistical software, “R,” where each pixel in the image is analyzed and an overall measure of greenness is summarized for the entire photo. That measure, called the Green Chromatic Coordinate, can be used to identify different “phenophases,” or significant stages in the yearly cycle of a location’s plants and animals. These stages can be delineated on a graph, called a phenoplot, where a fitted curve reveals the transition day-by-day. The 2018 phenoplot for one Snapshot Wisconsin camera site is seen below.
In 2018, 45 camera sites had a complete set of 365 time-lapse photos, but we expect many more sites to be included in the 2019 analyses. The relatively small sample size for 2018 is due in part to many counties not being opened for applications until partway through the year, but also because time-lapse data are rendered unusable if the date and time are not set properly on the camera. This may happen when the operator accidentally sets the time on the 12-hour clock instead of the 24-hour clock, or if the hardware malfunctions and resets the date and time to manufacturer settings—this is why we ask our volunteers to verify the camera’s date and time settings before leaving the site each time they perform a camera check.
The information derived from these analyses will be integrated into wildlife models. For example, the objective of one ongoing DNR research project is to understand linkages between deer body condition and habitat, which includes what’s available to deer as forest cover and food resources, as well as weather-related factors, such as winter severity or timing of spring greenup. The project currently uses weather data collected across the state to estimate snow depth, temperature, and winter severity, and creates maps based off this information.
Snapshot’s time-lapse cameras offer a wealth of seasonal information regarding type of forest cover and food sources, as well as weather-related information. In the future, phenological data obtained from Snapshot cameras could be used to create “greenup maps” that provide estimates of where and when greenup is occurring, and potentially test that information as a means of better understanding how environmental factors affect deer health, such as whether an early spring greenup improved deer body condition the next fall.
Are you ready to celebrate Citizen Science Day?
Before we dive into the details, let’s start with what is citizen science? There are many definitions for citizen science, which may also be referred to as community science, crowd-sourced science or volunteer monitoring. The Oxford English Dictionary defines citizen science as,
“Scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions.”
Citizen scientists partaking in Snapshot Wisconsin monitor trail cameras across to state to gather year-round data about wildlife. Data collected from the project help inform wildlife management decisions at the WDNR, and also engage the public in learning about the state’s natural resources. Snapshot Wisconsin has over one thousand volunteers hosting trail cameras across the state, and hundreds more from around the globe helping to identify the wildlife caught on camera on Zooniverse.
Citizen Science Day is hosted annually to celebrate and recognize the projects, researchers, and dedicated volunteers that contribute to citizen science all over the world. Mark your calendars for April 13th, this year’s Citizen Science Day kick-off! The Citizen Science Association and SciStarter have teamed up to promote events in celebration of citizen science. Are you interested in celebrating Citizen Science Day this year? Check out SciStarter’s project finder to find Citizen Science Day events near you!
You can celebrate citizen science any day of the year by participating in Snapshot Wisconsin, whether you are interested in hosting a trail camera or identifying the exciting critters captured on camera (which can be done from anywhere!)
One of Snapshot Wisconsin’s major goals is to alleviate some of the burden associated with time-consuming in-person survey techniques. This is possible because trail cameras can serve as round-the-clock observers in all weather conditions. Annual Greater Prairie-Chicken lekking (breeding) surveys were identified as having good potential to be supplemented by Snapshot Wisconsin cameras, and a pilot study was conducted in spring 2018.
The Greater Prairie-Chicken (GPC) is a large grouse species native to grassland regions of central Wisconsin. During the breeding season each spring, males compete for female attention by creating a booming noise and displaying their specialized feathers and air sacks. This ritual occurs on patches of land known as leks, as seen in the photo above. Wisconsin DNR Wildlife Management staff identify leks in the early spring and return to each site twice in the season to count the number of booming males. The number of males present on the leks is used as an index to population size. Three Snapshot Wisconsin cameras were deployed on each of five leks – one camera facing each direction except for east to reduce the number of photos triggered by the rising sun. The cameras were deployed from late March through mid-May, and all in-person surveying was conducted within the same period.
As seen in the graph above, Snapshot Wisconsin trail cameras recorded male GPC at all five of the study sites. This is significant because GPC were only detected on three of the five leks according to the in-person surveys. On leks A, B, and D, where both in-person and camera surveying detected GPC, the in-person maximum of male GPC was higher. However, when the trail camera maximum is averaged across all survey days, the maximum is nearly the same for both survey methods (8.5 in-person, 8.3 trail camera).
In-person surveying requires the observers to arrive before dawn and remain in the blind until after the early morning booming has finished. Snapshot Wisconsin cameras record the hourly activity on the lek while minimizing the risk of disturbance due to human presence. The graph above displays the total number of male GPC photos captured by hour and shows a small uptick in photos around 7 p.m. Because the in-person surveys do not include evening observations, Snapshot Wisconsin data offer a way to examine the lek activity at all hours.
Additionally, continuous data collection is not only useful in capturing the activity of GPC, but offers insight into the dynamics of Wisconsin’s grassland ecosystems. In total, Snapshot Wisconsin cameras collected over 3,000 animal images including badger, coyote, deer, other bird species, and more. Some photos were even a little surprising. Pictured above is a coyote just feet away from prairie chicken. We might expect the GPC to flee in the presence of a predator, but this one appears to be standing its ground. In the upcoming pilot year two, we hope to gather even more information about the interactions within and among species found on these leks.
One of the major Wildlife Management implications for Snapshot Wisconsin is the project’s contributions toward a system the DNR uses to calculate the size of the white-tail deer population in Wisconsin. Fawn-to-doe ratios, or FDRs, are found by dividing the number of does by the number of fawns seen during the summer months and are summarized by the (82) management units across the state.
In total, three programs contribute to FDR estimates: Snapshot Wisconsin, Operation Deer Watch, and the Summer Deer Observation Survey. An advantage of incorporating Snapshot Wisconsin data in these estimates is that Snapshot cameras tend to be placed in secluded, natural areas, whereas the other two collection methods are opportunistic, meaning they’re biased toward counting deer seen near roadways.
One challenge associated with trail camera data is that the same individual animals may walk by the camera multiple times throughout the data collection period. To account for this, we average the total number of does seen in photos with at least one doe, and then average the total number of fawns in each photo containing at least one fawn. We then take the average number of fawns and divide it by the average number of does.
Fawns and does may or may not be in the same photo to contribute to their respective averages. Defining a single camera-level average for each site drastically reduces the amount of data involved but ensures that the FDR is not skewed toward does, which tend to appear much more frequently on Snapshot cameras.
The above maps show the camera sites that contributed to FDR estimates in 2017 and in 2018. Photos from exclusively July and August were analyzed. A site only contributes to the estimate if there were at least 10 doe observations in one of the two months, but can be counted twice if it had at least 10 doe observations in both months. Statewide, 897 cameras contributed to 2018 FDR estimates, a 44% increase from the 622 sites that contributed in 2017. Some deer management units decreased in sample size from 2017, but
Above are the results of the 2017 and 2018 FDR estimates using Snapshot Wisconsin data. Only deer management units with a minimum of 5 camera sites were included in the analysis. In 2018, the range of FDR was 0.75 – 1.2, which is an overall increase from the range of 0.62 – 1.13 in 2017. Snapshot Wisconsin was launched statewide in August 2018, meaning most cameras in the newly open counties were not deployed until after the data collection period. We expect that the number of cameras in the 2019 analysis will increase again, which would give us even more accurate estimates.
For January’s Science Update, also featured in The Snapshot monthly e-newsletter, we explored the accumulation of Snapshot Wisconsin photos over time and how the number of photos taken fluctuates with the seasons. To date, our data set contains more than 24 million photos, and their content is a vital component of the Snapshot Wisconsin project.
The bar chart above indicates that over half of the photos are blank. This can be attributed to the fact that our cameras contain a motion trigger function, which is designed to capture wildlife as it moves through the frame. However, this mechanism only detects movement and cannot differentiate between animals and vegetation. This means that on windy days during the spring green up period, thousands of blank photos can be captured. Occasionally cameras will malfunction and continuously take blank photos without being triggered by motion. This issue was more prevalent with earlier versions of our cameras; the model we currently use does not take as many blank photos. Additionally, over time volunteers have learned that trimming vegetation in front of their camera helps prevent blank photos.
Every day at 10:40AM, the cameras are programmed to record a time lapse photo. This is not only to document the “spring green up” period and the “fall brown down” period, but also to sync ground-level measures of greenness with satellite data. These photos are primarily used by our partners at UW-Madison and compose 7% of our data set.
It is not uncommon for our trail camera hosts to trigger the camera themselves during check events, which is the cause of most of the 3% of photos that are tagged as human. Although these photos are removed from the data set prior to analysis, they can be helpful in instances where the camera has been recording photos with the wrong date and time. A photo of a hand in front of the camera combined with the date and time reported by the volunteer at each check event are enough for us to adjust the date and time for the whole set of photos.
Twenty percent of the Snapshot Wisconsin photos are untagged, meaning they have yet to be classified as blank, human or animal. Many of these photos will be sent to the crowd sourcing website, Zooniverse, for classification. We hope to implement a program to automatically classify photos to work through this backlog as well.
Finally, about 14% of Snapshot Wisconsin photos are of confirmed animals. In the graph above, we have broken down which species appear in these photos. Deer are by far the most common species, appearing in about two-thirds of photos, followed by squirrels, raccoons, turkey, cottontail rabbits, coyotes, and elk. The remaining 8 percent of animal tags are divided up across 34 categories including other bird, opossum, snowshoe hare, bear, crane, and fox. Elk may have a higher proportion of triggers than expected because Snapshot Wisconsin cameras are placed more densely in the elk reintroduction areas than in other areas of the state.
As a graduate student, conferences are an opportunity for me to share my research and connect with others doing similar work. Recently I had the opportunity to travel to beautiful Utah for the International Symposium on Society and Resource Management. This conference brings together scientists and practitioners to share research on the interaction between society and natural resources.
I learned about many innovative research efforts – a study aiming at making camping more sustainable by decreasing the impact on the natural environment (Marion et al.) and another that used survey data to understand what sources of scientific information were most trusted by the public (Schuster et al.).
Some of my favorite sessions were workshops I attended on how to be a better collaborator and communicate with journalists. I also had the opportunity to present some of my research on volunteer’s experiences participating in Snapshot Wisconsin (stay tuned to find out more!)
Of course the views weren’t shabby either. Now back to data analysis and writing!
After two days of meticulous searching in the rain, a crew of about ten people (including two Snapshot team members) dejectedly walked out of the forest. We were searching for elk (Cervus canadensis) calves in the Clam Lake and Flambeau River State Forest regions of Wisconsin, and had not had any luck thus far. Just as we were leaving, a biologist on the crew softly yelled “elk!”. Nestled into the side of a tree was a small brown creature perfectly camouflaged with the surrounding dead leaves. We estimated that we had walked by the little calf three times without noticing her!
The elk biologists put a blindfold over the elk calf to keep her calm. With hushed voices, they took measurements, applied ear tags, fitted her with a VHF (very high frequency) collar for location tracking and then moved away. Collars provide information on mortality, movement and herd interactions throughout the calves’ lifetimes. Collectively, this data can be used to help inform management decisions for Wisconsin’s elk herds.
For more information about Wisconsin’s elk herds, check out this link.
We’ve gotten some great questions from volunteers on species distributions. One from early in the project was, “Do the ranges of gray fox and red fox overlap?” We couldn’t answer that at the time since there is no comprehensive tracking effort for gray fox in Wisconsin. Great news: we now have enough data from Snapshot Wisconsin photos that we can start shedding light on questions like this!
So far, we’ve had 6099 photo subjects classified as canids on Zooniverse from photos taken at 484 cameras. Of these, 5832 classifications from 465 cameras had enough agreement among users that we feel confident in these classifications, while 267 classifications from 19 cameras need review by experts before a final classification is determined.
Do we find different species of canid at the same camera site? Yes we do, but some combinations are more commonly found than others. The below graph shows that coyotes are the most commonly seen canid in Snapshot Wisconsin photos, and most cameras capturing canids have so far only captured coyotes. The most commonly seen multi-species mixes are coyote and fox. We’ve captured relatively few photos of wolves so far, but most cameras that have captured photos of wolves have also captured coyotes and/or fox. (Note that cameras in the elk areas are not included in this graph, since those cameras are more clustered than our other cameras and are not representative of the state.) Click on the graph to view a larger version.
The below map shows the canid data summarized by county. Data from the elk areas are included here and seen in the three small, square polygons. Note that since we do not have cameras in all parts of the state, and since different cameras have been active for different amounts of time, a lack of sightings in an area does not mean that a species is absent there – just that we haven’t seen it on our cameras (yet)! For example, we know from other data sources that wolves occur in more northern counties than what we’ve found on Snapshot Wisconsin cameras so far.
What we can say about these data so far:
- Coyote, gray fox, and red fox are found across the state.
- Photos of gray fox and red fox are sometimes captured on the same camera, and their ranges appear to have considerable overlap.
- Wolves are very infrequently detected compared to the other canid species.
As always, as we continue to expand the Snapshot Wisconsin program, we’ll be able to fill in more of the spaces in the map!