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.
One of the most incredible things about studying wildlife is that, no matter how much you think you know, something new and surprising will appear. Recently, I had the opportunity to review thousands of photos for an exciting project involving machine learning (which you can read more about in this blog post). A subset of the photos on my plate for review were of Virginia opossums (Didelphis virginiana).
Some might not draw a line between the words “exciting project” and “opossum,” but they truly are an interesting species. For starters, they are North America’s only marsupial, meaning females carry their offspring in a pouch, especially when the young are newly born (see the photo above). Additionally, those of us who live where ticks are a concern can thank opossums for consuming a fair number of these pests.
The first thing I learned about opossums from my time examining the photos is that they can vary widely in color. Above is a small collage of opossums that range in color from almost entirely white (known as leucism) to predominantly dark grey, although the animal pictured in the middle is more representative of Wisconsin’s majority.
Morphology, or the set of physical characteristics an animal displays, is not easily disguised in trail camera photos when compared to something fleeting, like behavior. Often, animals captured in the photos simply appear to be moving across the frame. This expectation is what led me originally to overlook a fascinating opossum behavior. As I flipped through the images, I noticed an infrared trigger in which the animal seemed to have debris stuck to its rear half. I imagined that it had gotten stuck in mud, but when I saw the phenomenon a second time – this time in daylight – I realized that this was no accident. In fact, these opossums were using their prehensile tails intentionally to carry bunches of leaves and twigs.
After doing some research on this behavior, I discovered that this has been documented before, albeit rarely. The consensus on the reason for this behavior is that opossums take their hauls to a temporary den site to use as bedding material. Of the over 3,000 opossum triggers that I was sorting, I only encountered nine in which this behavior was displayed. If I were to randomly choose a photo from the set, I would be more than twice as likely to encounter a raccoon misclassified as an opossum than I would be to have selected a photo of an opossum carrying leaves with its tail. Nine instances do not constitute a large enough sample size to do any major analyses. However, according to this photo set, there does not seem to be any obvious seasonality, with photos spread somewhat evenly from January 2017 through June 2018. Only one trigger was taken during the daytime – likely a product of opossums being primarily nocturnal.
If you stumble upon any interesting Snapshot photos – opossums or otherwise – please reach out to us. You can share them by using the “Talk” function on Zooniverse or by emailing them to DNRSnapshotWisconsin@wisconsin.gov.
My name is Emily Buege – I’m the newest Snapshot Wisconsin team member, and I wanted to do a quick blog post to introduce myself. After obtaining my bachelor’s degree in ecology from Winona State University, I moved to Tuscaloosa, Alabama where I began working toward my master’s degree in environment & natural resources. In the mix, I also spent a summer working at the International Wolf Center in Ely, Minnesota.
My master’s thesis examined the distribution of nesting sites for several native fish species in the Bladen River in Southern Belize. Specifically, I looked at which habitat variables seemed to be most important for each of four species as they chose a site suitable to brood their young. All four species were cichlids, which are well-known for defending their eggs and fry against predators. Not only did that parental behavior make for an easy way to identify and record the nest locations, but it was also fascinating to watch!
Being that my project was through the University of Alabama’s Department of Geography, one can imagine that it was spatial in nature. Combined with my preexisting passion for wildlife conservation, the skills and interests that resulted from my time at UA led me to my new position with Snapshot: Spatial Analyst and Database Manager. I am very excited to dive into these roles, because the project is rich in spatially-explicit data! This is especially true with the launch of Phase 2 – all corners of the state will be reporting wildlife data that has previously been unavailable.
In addition to making more maps with our new data, one of the efforts I’m looking forward to working on is data visualization. Now that Snapshot Wisconsin has collected so much data, there are a lot of opportunities to do visualize that information. Right now, we have no way of allowing the public to interact with the data or to view a select set of photos. We hope that as the project grows, we can develop a tool to do just that. I think that making data interactive and visual allows more people to connect with it on a deeper level.
See you out in the field and on the message boards!