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 many volunteers may be aware, Snapshot Wisconsin operates continuously and year-round. This is distinct from many other studies and monitoring efforts focused upon wildlife that typically evaluate population status during a particular time of year and look at changes between years. One belief that underpins Snapshot Wisconsin is that because the environment consistently changes across the year, how animals move, behave, live, die also changes. Consistent and continuous data collection provides the project much richer insights into animal habitat associations, and it also gives the project the ability to evaluate which time of year deserves most focus–when should we be monitoring things?
As discussed in previous posts, we heavily rely on spatial data produced by processing satellite imagery in order to quantify species’ habitats and estimate or predict where animals are. Recall that two important sensors or satellites are Landsat, which has fine spatial resolution but captures imagery of Wisconsin less frequently, and Modis (on the Aqua and Terra Satellites) that has coarser spatial resolution, but captures an image of Wisconsin daily. Data produced from Modis imagery is incredibly useful for capturing, say, the timing of larger scale phenomena like big snow events, or the onset of either long-term snow coverage in the winter or green up in the spring. The behaviors and activity of animal species are often connected to the timing of these environmental cues (or others, like temperature, or the length of daylight).
One animal species that is particularly sensitive to seasonality is the black bear: bears spend the winter in dens in a state of torpor. In brown bears, previous research has suggested that the timing of bear den entry is sensitive to environmental factors, while the timing of ending torpor is more related to individual physiology. One thing we are interested in is whether bear behavior (out and about, or in torpor) exhibits any correlation with the variation in the timing of plant green-up and senescence across space, and whether “mismatch” between when bears exit dens and when plants green up (plants like sedges are an important food source for bears early in the year) seems to have any population consequences.
Snapshot Wisconsin cameras capture bears growing and moving across seasons.
While Wisconsin vegetation greens up from late winter to mid-summer, bears also become increasingly present across the landscape.
BUT…..while satellite imagery provides an excellent overhead synopsis of plant activity, it is not always clear which plants are making the image green. Bears–and many other animal species–primarily eat green matter at ground level rather than leaves at the tops of trees. Snapshot Wisconsin’s cameras provide a ground view that we can relate to satellite images to get a sense of what airborne imagery is responding to. In the long term, this will allow improved estimates of where animals are and at what time.
As graduate student on the Snapshot Wisconsin project, part of my role is to help the team better understand their volunteers and conduct research that will assist with program improvement. One way I do this is by surveying trail camera hosts when they enter the program and after they have been participating in Snapshot Wisconsin for one year.
Developing a survey takes more work than you might expect! Some things, like age or occupation, are relatively easy to measure. However, abstract concepts like satisfaction or attitudes are much more difficult to capture in a survey. These abstract concepts must be measured in more indirect ways, and typically social scientists develop a number of survey questions or items to measure a concept.
For example, let’s say I wanted to measure someone’s job satisfaction through a survey. You could ask, “How happy are you overall with your job?” (Rate 1-5).
In order to capture more aspects of job satisfaction, it would be better to ask: “How happy are you with each of the following parts of your job? Autonomy, work load, salary, coworker relations, etc.” (Rate each 1-5).
Bear with me while I get theoretical for a moment…
Imagine you have a whole universe of survey items you could ask someone about job satisfaction. If you choose just one question to ask them, that question is not likely to be a good representation of their job satisfaction as a whole. However, if you ask them multiple questions, you get a much better representation of their job satisfaction.
Let me use an analogy. If I want to know all the different species of mammals found in a particular county and I put out just one trail camera in that county, it isn’t likely to be sufficient. I put out a whole bunch of cameras across the county, I’d get a much more accurate count.
Often, I get this question from people who take surveys: Why do some of these survey questions seem so similar to one another? Can’t you ask this with just one question?
The answer is: if we are asking about an abstract concept in a survey, assessing it indirectly though multiple questions is the best way to go if we want valid scientific results.
Through email and the internet it is so easy to deliver surveys and if you are like me, you get a survey in your inbox from some business or organization just about every week. Hopefully this sheds a little light on what goes on behind the scenes before you get that “new mail” notification.
For those of you who have completed a Snapshot Wisconsin survey, your responses are truly valued. We are learning a lot; see here for some early results and keep your eyes on the blog for more. If you are interested in learning more about the science behind surveys, let me know in the comments!
One of the objectives of Snapshot Wisconsin is to record the occurrence of rare species including: moose, cougar, Canada lynx, marten, jackrabbit, Whooping crane, Spotted skunk, and wolverine. With a statewide network of nearly 1,300 trail cameras, sooner or later we were bound to capture one of these rare Wisconsin species. Two years into the project, Snapshot Wisconsin captured its first – moose (Alces alces)!
Earlier this month, we received an email from a volunteer in Oneida county with the subject ‘Picture of Moose.’ We nearly jumped out of our seats exclaiming “Moose! Moose! Moose!”
From the size and proportions of the animal, it was easy to tell that it was indeed moose. Moose can reach upwards of 1,500 pounds and stand up to 7 feet tall, dwarfing our commonly seen White-tailed deer. When we shared the picture around, our Wildlife Research team leader remarked, “That part of the state is definitely moose-y.” The bogs of Oneida, Vilas, and Iron counties have had the most moose sightings in the recent years, making “moose-y” an apt description.
Upon querying our Snapshot Wisconsin database, we found another moose identified on a camera in Vilas county – this one hosted by an educator. Both of these sightings were from spring this year, and both were correctly identified by the volunteer – hurray, no ‘moose-takes’ there!
Moose are categorized as a species of special concern in Wisconsin due to their relatively low numbers, in 2016 there were only 32 possible or probable observations reported.
Whether you are a Zooniverse volunteer or a trail camera host, please let us know if you see a rare species in a Snapshot Wisconsin photo. If you spot them in the wild or on a personal trail camera, report the observation using the Wisconsin large mammal observation form. In the meantime, we hope you finding these pictures as ‘a-moos-ing’ as we do!
What looks like a chicken, lives in the prairie and has one of the most phenomenal displays of courtship in the animal kingdom? You guessed it, the greater prairie chicken (Tympanuchus cupido)! Earlier this spring, Snapshot Wisconsin teamed up with WDNR biologists in the Buena Vista Grasslands area to implement a prairie chicken trail camera monitoring project in Wisconsin.
In the 1950’s, the greater prairie chicken was close to extinction in the state of Wisconsin. The WDNR, in partnership with conservation groups, established a prairie chicken management program. Every year in early spring, WDNR biologists begin thoroughly surveying known greater prairie chicken lekking grounds to track population size and locations of leks. The protection and monitoring of the species has helped the comeback of the prairie chicken in Wisconsin. Currently, a few thousand chickens can be found in the central part of the state.
This year, Snapshot Wisconsin deployed 15 cameras to help supplement monitoring efforts. Trail cameras can efficiently and continuously survey known lekking grounds to count peak numbers of males at each lek.
The team had a blast deploying trail cameras at lekking grounds. For those of you who own trail cameras, you may be familiar with the walk test. In the chicken camera test, we ended up crawling to test if a chicken would be detected on camera. This resulted in a lot grass in our clothes and an equal amount of laughter.
For more information on Wisconsin’s prairie chickens check out this link.
Have you ever wondered about the scientific applications of your deer behavior classifications? Check out this recent article from NASA featuring Snapshot Wisconsin researcher John Clare! The work compares the “vigilant” and “foraging” deer behavior classifications from Zooniverse across space. In some areas, deer tended to be vigilant more often than they foraged, in other areas it was the other way around, and in still other areas deer tended to exhibit each behavior equally. The research can’t yet determine the “why” behind these patterns (likely a combination of vegetation, predator and weather patterns), but it’s great to see the Zooniverse behavior classifications used this way! Traditionally, behavior studies like this would require researchers to go out in the field and directly observe animals. You can imagine that to undertake a statewide study would require lots of eyes and travel hours! Thanks to Snapshot Wisconsin trail camera hosts and the people powered Zooniverse platform, we have a way to collect these data across larger swaths of space and time than was possible before.
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!
As many volunteers know, one of the primary purposes of Snapshot Wisconsin is to get a better handle on where species are located throughout the state. We now have enough pictures classified (and a reasonable handle on the effects of error within the classification process) to be able to start the process.
The essence of this exercise is to model associations between 1) locations where animals are and are not found on trail cameras, and 2) environmental or spatial characteristics at those camera locations. As noted in a previous blog post, in order to effectively map predictions about animal distribution, we need to have spatially explicit environmental variables, many of which are derived from satellite imagery. Examples of environmental variables include land cover, seasonal patterns in plant productivity/greenness, snow cover, and the intensity of night-lighting, which is a good index for human activity.
Below are a collection of maps resulting from our first attempt at mapping statewide species distributions based on Snapshot Wisconsin data.
A couple things to note:
- How we think about commonness or rarity depends on the species being considered. For example, “less common” for turkey may mean an area is visited by only a few turkeys over a couple weeks, while “less common” for bear may mean that the area has not had a bear visit for a year or more.
- There are some imperfections. In particular, the tip of the Bayfield peninsula tends to exhibit some patterns that are probably wrong. Mapping will continue to be an iterative process based on our best metrics.
The below maps are accurate enough to be useful, but there’s always room for improvement. We hope that volunteers find this first round of analysis interesting, and maybe even useful for classifying.
Our July #SuperSnap was all about fishers, and we’re just going to keep on rolling on the fisher train! This science update was inspired by recent comments on a photo of a fisher in central Wisconsin. The location of the photo might cause confusion if you base where fishers *should* be on the range map we have posted. The map shows fisher range extending to only the very northern part of the state:
Whereas we’ve seen fishers on Snapshot Wisconsin cameras in counties pretty far south:
In the case of a species like fisher, which was reintroduced to Wisconsin in the 1950s and expanded its range quickly, static distribution maps go out of date quickly. This brings up a larger point about range maps being inaccurate because they are based on old, incomplete or faulty data. We provide range maps to give volunteers an indication of where they are more likely to find a certain species, but these maps are by no means perfect. The fact that we do not have very good statewide data on the distribution of most species is indeed a major reason for starting a project like Snapshot Wisconsin!
Note that the above map shows counties where we’ve seen Snapshot Wisconsin photos correctly classified as fisher. Many of the gray counties do not have any Snapshot Wisconsin cameras and so we do not have any photos there yet. This is not to say there are no fishers in the gray counties!