Collecting photos from a fixed location can give us an idea of which animal species are utilizing the same space. Often hours, or even days, elapse between photos of two different species crossing the same area. However, a small percentage of our trail camera photos capture moments of more than one species in the frame together. We recently took inventory of these multi-species instances, and despite our data set growing to over 2.5 million animal triggers, only around 4,300 of them contain multiple species within the same photo.
So far, we have confirmed 128 combinations of species appearing in photos together, 119 of which are combinations of two species and 9 of which are combinations of three species. Deer are most commonly one of the animals present in these multi-species occurrences. 37 of these multi-species occurrences are unique combinations of species that have only been observed once in our data set (orange lines in figure 1). A few examples of these include elk with turkey, red fox with opossum, and other bird with porcupine.
In the future, we hope to perform formal analyses with these data, however there are certain challenges that we must consider. An example of one such consideration for analyzing multi-species trail camera information is detectability. In general, trail cameras have a higher chance of firing if an animal that wanders in its field of view is both large and close to the camera. This might account for the high number of instances of deer and “other birds” occurring together. Birds, especially small birds, may be present at the site many times throughout the day, but may only be captured on camera when a deer, which is generally large enough to reliably trigger the camera, steps into the frame.
One observation that can be made from this preliminary analysis is that species that tend to utilize a particular habitat type may be more likely to be pictured together. For example, we have photos of mink and beaver together as well as muskrat and raccoon. These combinations are intuitive because all four species are commonly associated with water.
Another observation is that many of the combinations are of two species that do not have a strong predator-prey relationship. For example, deer and turkey are the two species most commonly pictured together, and neither is a predator of the other. Conversely, both bobcats and turkeys are relatively well-represented in the data set, yet we might not expect to see the two species together considering one is a predator and the other might be prey. Indeed, we have only observed one trigger of the two species together.
We hope that Snapshot Wisconsin can continue to shed light on interactions between species such as deer and predators of deer, as this was an early goal of the project. In the meantime, we ponder the many ways in which these space- and time- dependent occurrences are unique.
If you find an interesting interaction between two species on Snapshot Wisconsin, send it to us at DNRSnapshotWisconsin@Wisconsin.gov or share it on our Zooniverse page!
The destiny of a Snapshot Wisconsin photograph is to contribute to a one-of-a-kind data set, ultimately supporting management decisions related to Wisconsin’s wildlife. However, the development of this data set is not the only goal of the project. The photos, through their collection and distribution, also serve to pursue the goal of public engagement. As the project has grown, as has our collection of incredible wildlife photos, and the photos have begun to speak for themselves. For me, personally, the fact that we can’t entirely control what the photos will look like (i.e. which animals will present themselves for a photo, and what they will be doing) adds to the appeal. Trail camera photos show life uninhibited – especially with the increasing quality of the photos as technology improves. Many of our crowd-sourcing volunteers and trail camera hosts can attest that not every trigger captures a moment in time with crisp detail, however. That is the nature of trail cameras. Recently achieving the milestone of over 2,000 cameras on the landscape, Snapshot Wisconsin has learned some simple camera deployment tricks to increase the chances of catching a spectacular photo.
A trail camera operator who wants to maximize the visual appeal of their photos can benefit from considering some properties of traditional photography. First, let’s talk about light. The camera will read the amount of available light and automatically adjust its settings, but the camera operator does have some control over the direction of light in the photographs. Some Wisconsin animals have crepuscular activity patterns, meaning they are most active at dawn and dusk, when the sun is low on the horizon. We often recommend positioning cameras to face north so that animals will never be back-lit and overpowered by the bright sun, as they may be if the camera is facing east or west. We recommend north over south because Wisconsin’s relatively high-latitude position in the Northern Hemisphere means that the sun will always be hanging in the southern sky, especially in the wintertime. The best compass bearing for the camera will undoubtedly vary from site-to-site, however. Take the above turkey photo, for example. This photo was taken at 10:42 AM, and the shadows cast by the trees indicate that the sun is behind the subject. This camera is likely facing east. In this case, the forest is dense enough to block out the light directly, and the trail appears to be coming up a slope. The key here is to consider where in the camera’s field of view the light will be coming from.
The next consideration, image sharpness, is a little more difficult to control. There are a lot of factors that can play into how crisp or soft an image will be, including how quickly the animal is moving, where it decides to enter the frame, and when it visits the camera site (sharp nighttime photos are notoriously rare). For locations along a clearly defined game or maintained trail, a few helpful considerations can be made. Our cameras tend to take the sharpest images when the subject is around 10-15 feet away. Placing the camera at approximately this distance from where animals are expected to cross in front of the camera can increase the chances that the animal will be in focus.
The final element that I’d like to touch upon is composition. What makes the composition of a photo “good” is difficult to discuss, not only because it’s a matter of opinion, but also because the composition within the frame can change based on the time of year. Not to mention, we can’t always predict where in the frame the animal is going to be captured and in what position! This is tied to one of my sentiments at the beginning of the post – the composition is often special because we can’t control the details. This concept known as “natural design”. It’s familiar to most of us who have a fondness for nature; a glen of naturally-scattered ferns has a special quality that just wouldn’t be the same if they were hand-placed. Our trail camera hosts often have a relationship with the natural design of their unique camera site, which has resulted in Snapshot staff curating individual collections of our own favorite snaps. Of course, being aesthetically pleasing does not give a photo any more weight in our data set, but the potential for such photographs is another reason that Snapshot Wisconsin is such a special project.
Feeling artsy? Check out the past blog, “Getting artsy with Snapshot Wisconsin” to hear more about the creative side of the project.
Within the scientific field of animal behavior, research topics such as parental care, natural selection, and feeding tendencies seem to arise far more frequently than animal play. After all, a life in the wild tends to revolve less around play and more around survival. For some animals, however, play is an integral part of their lifestyles and ultimately their perseverance. River otters, for example, are social animals with a playful and charismatic reputation. As their name suggests, river otters do not typically stray far from waterways, and some Snapshot Wisconsin cameras are perfectly positioned to capture interesting otter behavior. We have observed otters grooming together, wrestling with one another, and – perhaps most amusingly for our staff and volunteers – sliding across the snow. At the bottom of this post there is a compilation of otter slide photos.
Undeniably, sliding across snow or mud is an effective method for locomotion when you compare it an otter’s normal gate – a cylindrical body bounding on short legs. It’s the kind of body shape that glides effortlessly through the water but doesn’t demonstrate the same sort of grace on land. Those proportions make it especially tough to traverse snow, just take it from the otter pictured on the right.
Is sliding truly just an efficient way to travel, or does the otter’s seemingly spirited nature play a role in this behavior as well? A 2005 paper published in the Northeastern Naturalist suggests that it could be both. The study analyzed 5 minutes and 49 seconds of video of wild otters in Pennsylvania. The otters were observed sliding 16 times, an excessive number for the sake of conserving energy.
The term “otter slide” doesn’t just refer to a mode of transportation, however. It can also refer to the marks near riverbanks that are left when otters slide in and out of the water. Often repeated otter sliding will occur near latrine sites, where the animals will go to deposit and read scent-coded messages from other otters in the area. The slides are such a great indicator of otter presence, that the Wisconsin DNR conducts aerial surveys in the winter to help determine population trends. Whatever the motivation is behind the sliding behavior, we certainly enjoy watching it on our trail cameras.
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.
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!