Tag Archive | Data Dashboard

Population Dynamics for Tracking Wildlife Populations Through Time

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.

A doe and a fawn

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.

A g

Examples of graphs showing stable, growing, and declining populations based on their lambda value.

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

A turkey hen with five poults

These young turkey poults would contribute to the number of “births” in a population estimate for turkeys.

A red fox with a rabbit in its mouth

This cottontail fell prey to a red fox. Animals that have died would contribute to the number of “deaths” in the population estimate for their species.

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.

A deer with a radio collar around its neck.

This deer is part of the WI DNR’s radio-collar tracking program.

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.

A bobcat with a radio collar around its neck.

A bobcat with a WI DNR radio-collar.

Bobcat kittens.

Bobcat kittens captured on a Snapshot Wisconsin trail camera. 

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.

Three maps of Wisconsin showing bobcat detections on Snapshot Wisconsin cameras.

Maps showing proportion of bobcat detections on Snapshot Wisconsin cameras in different ecological landscapes in 2017 (far left), 2018 (middle), and 2019 (far right).

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!

What Makes Data “Good”?

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.

A graphic showing how data moves from collection to processing and presentation.

Data Collection

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.

Data Processing

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.

Good Data Graphic2

A properly oriented camera (top) compared to an improperly oriented camera (bottom).

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.

Data 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.

Good Data Graphic 3

Snapshot Wisconsin Data Dashboard presence plot with over 5,800,000 detections (left) and a similar plot with only 72 detections sampled (right).

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.

Interpretation

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.

A bar graph showing detections per year of the five most common species.

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.

Exploring Signs of Spring with the Snapshot Wisconsin Data Dashboard

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.

A graph showing increased cottontail rabbit sightings in MarchA cottontail rabbit sitting on a log

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.

A graph showing increased woodchuck sightings in March through MayA woodchuck

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.

Graph showing increased fisher sightings in MarchA fisher walking through the snowy woods

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.

A graph showing increased Sandhill Crane sightings in MarchA sandhill crane in springtime

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.

Building The Data Dashboard – From Inception to Release

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

This edition of the Snapshot newsletter is focused on a new Snapshot Wisconsin product, the Data Dashboard. The Data Dashboard is an interactive tool that let’s the public explore the data that Snapshot has collected in a new and exciting way. This dashboard marks an important step towards Snapshot’s goal of making its data more accessible to the volunteers who helped collect it, as well as the public more broadly.

You can learn more about the development of the dashboard, as well as what features are currently available on it, in this edition of the Snapshot newsletter, or you can check out the dashboard for yourself at https://widnr-snapshotwisconsin.shinyapps.io/DataDashboard/.

The idea of the Data Dashboard originated about two years ago, stemming from a desire to bring what Snapshot has learned full circle back to the volunteers and public. However, no one knew at the time what would eventually manifest from that desire. No one knew that the Data Dashboard was on the horizon.

The core purpose of Snapshot Wisconsin is to provide data for wildlife decision support and bringing citizens into that process. Citizen science projects like Snapshot Wisconsin rely upon the public’s aid to accomplish tasks that would be unfeasible or impossible otherwise, and making sure volunteers feel fulfilled and satisfied with their time investment is important to the Snapshot team.

As part of her dissertation research, Christine Anhalt-Depies, the project coordinator of Snapshot Wisconsin, investigated why volunteers joined the program, hoping to learn their motivations and reasons for staying. Her surveys taught the team much about what they needed to focus on to keep volunteers feeling fulfilled.

Two of the survey’s findings stood out to the team. First, many volunteers cared deeply about contributing to wildlife monitoring and wanted to see what wildlife was in their area and the state. Volunteers also wanted to see the fruit of their work, how the project was contributing to wildlife management decisions across Wisconsin. The Snapshot team knew from these findings that they needed something that would close the loop, bringing the data full circle.

To help close the loop, the Snapshot team decided to focus on making Snapshot findings more available and accessible to the public. They needed a tool that everyone could use and learn from. With that daunting task in front of them, the team started by investigating what kinds of platforms were even available for this type of visualization tool.

The Snapshot team explored many options but ultimately settled on R Shiny, an extension of the statistical software R, and soon a prototype was being built. The initial prototype of the dashboard started as a map of Wisconsin, but the team wanted to show more. The team needed a specialist with a vision of what the tool could become.

A Fresh Face Joins The Team

In November of this last year (2019), a new member joined the Snapshot Wisconsin team, and he would play a leading role in developing the Data Dashboard.

Ryan Bemowski, Data Scientist and Engineer at the Wisconsin DNR and developer of the Data Dashboard, recalled what it was like joining the project at this time. “There were many discussions still going on about what the tool was going to look like. The team was trying to figure out how they could best display the data in a way that was meaningful to volunteers and the public,” said Bemowski. “My primary task was to get the visualizations made by deciding what data to display and come up with ideas for the visualizations.”

Bemowski spent the next eight months reimagining the look and feel of the Data Dashboard. Bemowski kept one central piece of the prototype, the map of Wisconsin, but simplified it to show the percentage of cameras in each county or management unit that have detected the selected species at least once.

During these months, he also dived into what other data would be shown on the dashboard. Snapshot Wisconsin cameras have taken over 47 million photos to date, but many of these photos don’t contain animals. The majority of the photos that do contain animals will be classified correctly. However, the team knows that not all the photo classifications are accurate, complicating their use. Some species are particularly difficult to classify, often being mistaken for similar species, such as wolves (below left) and coyotes (below right).

Bemowski and the team wanted to show as much data as possible. However, they didn’t want to add data to the dashboard that was inaccurate, so they needed a way to determine how accurate the community was at classifying each species.

Bemowski explained the team’s approach to solving the inaccuracy issue. “We ran a round of accuracy analyses to see how accurate [classifications] are for each species, but we ended up only able to do the analysis for certain subsets of species,” said Bemowski. An accuracy analysis is a test to determine how many of the photos of a species are correctly classified using a sample of photos. The team’s accuracy analysis yielded an accuracy rate for most species. However, it didn’t work for certain species. “When you run an accuracy analysis, you must have a big enough sample for each species. In our case, some species like beaver just didn’t have enough photos to be analyzed properly.”

Additionally, a few species were analyzed but still excluded because of their low accuracy rate. Volunteers are overall very good at identifying most Wisconsin species, but some species are difficult and had a low accuracy. Bemowski mentioned that the team will continue to run more accuracy analyses and work towards getting all species on the dashboard eventually.

The Soft Launch, Feedback And Improvements

After much work, Bemowski and the team were ready mid-July to test out their tool with a soft launch to a handful of volunteers. They chose the 1,700 volunteers who host Snapshot trail cameras and asked for feedback on the dashboard. Around 90 responses came back, and three main themes emerged that the team began to think about.

First, the volunteers wanted clearer explanations about why species were included or excluded. Second, there was a desire that certain species be added to the dashboard, namely wolf and elk. Lastly, volunteers wanted to see data at a more granular scale, such as being able to see the data for specific cameras instead of countywide.

After reading the survey responses, Bemowski and the team set about incorporating this feedback and getting the dashboard ready for the full launch to the public. The first theme was easy to address. Bemowski added a clickable pop-up (below the species list) to the dashboard that shows the accuracy rate for each species. Simultaneously, Bemowski and the team were able to finish the accuracy analyses of three more species and added them to the dashboard: Snowshoe hair, pheasant and fisher.

The second theme that emerged was a desire to see certain, high-interest species on the dashboard. Bemowski explained, “The reason we excluded some species [for the soft launch] is because, below a certain accuracy, we know some of the data is incorrect. If a species has a 50% accuracy rate, for example, we know that 50% of the data being shown is correct and 50% is incorrect.” The data for species with an accuracy rate below 95% is still stored and used in other ways, but the team wanted all the data on the dashboard to meet a minimum quality standard.

The team thought of a clever way to balance the public’s desire to see high-interest species like wolf and elk with their data accuracy standards. The photos of species with low accuracy and species of special interest go through an extra round of classification by species experts to confirm which photos are correct, so the accuracy of the expert-classified subset of photos is 100% and meets the data quality standards. The Snapshot team collectively decided that the best way to include the data for elk and wolves was to only use the expert-confirmed photos on the dashboard. Bemowski said, “We want to show correct data, as accurate as possible, without holding anything back.” This decision was the best compromise the team came up with.

The last theme focuses on granularity, or the scale to which you can “zoom in.” Some volunteers wanted to see beyond the county scale and look at data from individual cameras. Bemowski was conflicted about the topic of granularity though. “We don’t want to give the exact latitude and longitude of the cameras because of privacy concerns, but we still wanted to give as much granularity as possible.” Showing data at the county level is as granular (or “zoomed in”) as Snapshot can currently go, but the team hopes to eventually have individual dashboards for each camera’s host so that they can see the data for their cameras. However, that is still in the planning stage.A map of wisconsin showing bobcat detection by counties

What’s Next For The Dashboard?

Now that the dashboard is released, Bemowski reflected upon what the dashboard means to the project. “I’ve realized recently how big of a step this dashboard is for Snapshot Wisconsin: To go from collecting millions of photos to showing people what the data is saying and really analyzing that in depth. That’s a big step. Looking back, the intention has always been to transform Snapshot data into actionable outcomes,” but the Data Dashboard marks a major step towards Snapshot sharing its findings with the public.

The Data Dashboard’s current version is focused on making Snapshot’s findings available to the public, but more updates and additions to the dashboard are already on the planning board. “The Data Dashboard is an evolving product,” said Bemowski. “We are going to go through many iterations of it to improve the quality and abundance of data and species available. The next step is to build upon the dashboard so it can be more meaningful for decision makers, such as the county deer adivory councils (CDACs) and other wildlife management organizations around Wisconsin.”

Bemowski offered some advice for anyone going outside to interact with nature. “Improve your chances of seeing your favorite species by using the Dashboard to observe wildlife during their times of peak activity or use the dashboard to see if your favorite species have been detected in your county. Even if you don’t see your favorite species, you may be surprised at other wildlife you do see.”

Bemowski and the Snapshot team are momentarily celebrating reaching this important milestone before they continue to improve the dashboard. Bemowski said, “I wish I had something like this as a kid. I grew up in Wisconsin. I grew up a hunter and fisher; someone who enjoys the outdoors. I always heard that there isn’t enough information about wildlife, that people just don’t know what is there, and that all the ‘guesses’ from wildlife organizations [about what is around] aren’t true.”

“But the Data Dashboard is as close as you can get to really knowing what is out there,” continued Bemowski. “Drawing from a growing collection of over 2.3 million animal sightings from over 2,200 cameras locations across Wisconsin, the dashboard is a really awesome way to discover and experience these animal sightings.”

If you want to explore the Data Dashboard yourself, you can access it at https://widnr-snapshotwisconsin.shinyapps.io/DataDashboard/.

A Volunteer Explores The Data Dashboard

The following piece was written by OAS Communications Coordinator Ryan Bower for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.

This edition of the Snapshot newsletter is focused on a new Snapshot Wisconsin product, the Data Dashboard. The Data Dashboard is an interactive tool that let’s the public explore the data that Snapshot has collected in a new and exciting way. This dashboard marks an important step towards Snapshot’s goal of making its data more accessible to the volunteers who helped collect it, as well as the public more broadly.

You can learn more about the development of the dashboard, as well as what features are currently available on it, in this edition of the Snapshot newsletter, or you can check out the dashboard for yourself at https://widnr-snapshotwisconsin.shinyapps.io/DataDashboard/.

Snapshot Wisconsin released its new data visualization tool to the public today. The tool is called the Data Dashboard and is a major step towards bringing the project full circle. The Data Dashboard offers volunteers and the public a new way to engage directly with this data, letting people choose which data they want to visualize.

At launch, the data for 18 animal species is available to explore, including how active species are during different times of the day and year and how the species are spatially distributed across the state. Data can be viewed for specific counties or statewide, and the data from maps and graphs can even be downloaded to share with others.

Christine Anhalt-Depies, project coordinator for Snapshot Wisconsin, described the intention and purpose of the dashboard. “The purpose of the dashboard is to close the loop – to make sure that people who are involved in collecting data have an opportunity to see the outputs of their work,” said Anhalt-Depies. “This first iteration of the dashboard is focused on giving the public a chance to explore data they’ve helped to generate.”

Exploring The Data Dashboard With A Volunteer 

One of the best features of the Data Dashboard is that it lets people explore the wildlife in our state at their pace. The dashboard is open ended and can be explored in whatever order you want, but sometimes a guide is nice to follow along with. A member of the Snapshot team virtually sat down with a long-term Snapshot volunteer on Skype and let them explore the Data Dashboard.

Tim Sprain has been a volunteer with Snapshot Wisconsin for nearly 3 years. He hosts three Snapshot cameras in three separate counties. Sprain is a middle school teacher and uses Snapshot in his classroom to describe biology concepts in an interactive way. Sprain shared his screen with Sarah Cameron, a member of the Snapshot team and head of the educator engagement side of Snapshot. Here are some of the highlights from the discussion between Sprain and Cameron.

The Map Of Wisconsin – Counties and Ecological Landscapes

While Sprain was getting set up to share his screen, Cameron said, “One thing I like to remember when talking about the dashboard is the fact that we were literally sitting in a room at the DNR office about two years back, brainstorming how we can share the data that the volunteers were collecting back with them. We had this grand idea of a dashboard but had no idea how we were going to accomplish it. It’s been really exciting to see the progress over the years and have a product now that we can give back to our volunteers – something they can connect to.”

Sprain agreed and added, “A lot of times, people don’t have the knowledge or resources to understand what animals are in their backyard. I see this dashboard as a huge resource to educate them. My goal [as an educator] is to be a catalyst for experiential learning, and Snapshot Wisconsin is all about making sure people get a chance to be involved.”

“Years ago,” continued Sprain, “[DNR staff] pointed me towards what the DNR offers to families and students other than hunting, fishing and trapping, and I’ve kept up on the email updates that the DNR sends out. When Snapshot Wisconsin came onboard, it was a natural fit.” Sprain finished loading the Data Dashboard on his computer and was ready to explore the dashboard. Sprain started with the map of Wisconsin (on the left side of the dashboard). The map shows how many of the trail cameras in a given area have captured the selected species. The map can be broken into counties or ecological landscapes, such as the Northern highlands or Southwest savanna.

Map of bear detections by county

Map of bear detections by ecological landscape

Sprain said, “I’m excited to share the map of Wisconsin with my students, because it has ecological landscapes. It will help them understand how humans and animals use different landscapes and that animals have adaptations to suit the habitat in their landscape. Habitats are the most important piece towards maintaining our rich diversity of animals and flora in the state, which is an indication of our health as humans. This awareness can help build the understanding that we are all in this together. It’s also cool that you have the ecological zones because ecological borders are more real [to wildlife] than county lines.”

Sprain hovered over the three counties he has cameras in and started clicking on different species. Sprain mentioned how he has had to use a different approach this year to incorporating Snapshot into his teaching, since Sprain’s school district is starting virtually. Sprain explained, “In a normal year, [my class and I] would take a trip in the first week of school. I take them to a forested park and ask them what they notice. The kids ask me questions [about the plant and animals they see], and I introduce Snapshot Wisconsin by showing the trail camera pictures. I ask again, ‘What is this,’ or ‘What do you see now?’”

“Often times students will recognize a deer, but they get confused when they see a coyote, fox or wolf,” said Sprain. Sprain recalled a special moment of discovery he had with his students. “When they saw a fisher, it was just the most wonderful moment of learning. They didn’t know what a fisher is, but [to them] it looked sort of like a wolverine. They couldn’t quite figure it out.” Sprain used this moment to teach his students something new about fishers. “Now, they feel this connection to knowing something that they didn’t know before about this mysterious creature living in their bluffs.”

A fisher.

A fisher captured on a Snapshot Wisconsin trail camera.

Sprain hovered over La Crosse county specifically, since that is where the camera that saw the fisher was located. Sprain was excited to discover that there was only one camera out of 13 in the county that had seen a fisher. It was his camera.

Cameron added, “It will be really cool to share with your students that the data they helped collect got that one point out of 13 in La Crosse county. I’m also glad you mentioned fisher, because that wasn’t there [until recently.]” Fisher, as well as four other species, was added to the dashboard in response to the feedback volunteers gave during a soft launch to trail camera hosts.

Sprain joked, “You should have seen the look on the landowner when I shared the [fisher discovery] with the family. They were dumbfounded. They didn’t even know what a fisher was!”

“Oh, that’s so funny,” said Cameron. “That was one of our early and exciting findings of the project, realizing that fishers were a lot farther south in the state than we expected. We’ve had a lot of people come to us and say, ‘Wow I caught a fisher on my property! I didn’t know they were down here.’”

Why Species Were Included Or Excluded

Sprain moved on from the map and started looking more closely at the Species list next to the map. There are 18 species currently available on the dashboard – some common species and some rare like wolf and elk.

“Students always ask after all the carnivores, as well as ones that are unique like porcupine,” said Sprain. “I love that porcupine is on the list! The kids love porcupine.” Between his three cameras and a few annual field trips, Sprain’s class gets to see quite a few of our Wisconsin species. “We don’t have any wolves on our cameras yet, but I see [on the dashboard] that other cameras have picked them up in my counties… so they are there.”

Sprain and Cameron continued to discuss different animals that his students like seeing, and the conversation turned towards species that weren’t included on the species list, namely beaver.

Cameron directed Sprain to look under the species list at the blue text about missing species. Cameron said, “We thought that would be a question that would come up quite a bit, so this popup provides some extra information [on why some species were included and other excluded.]”

This popup explains the main criteria to be included on the dashboard, having a 95% accuracy rate. An accuracy rate is a measure of how often volunteers correctly classify a given species. If photos of a species are classified correctly at least 95% of the time, then that species was added to the dashboard. There is also a table with every species that Snapshot volunteers can classify, such as the partial table below. Each species on the list has an accuracy rate and explanation if it wasn’t included.

Anhalt-Depies jumped back into the conversation and said, “For species with low accuracy, we will eventually be able to provide an expert review of that species’ photos, such as badger with an accuracy of .624 [which is below the .95 or 95% cutoff]. We will be reviewing those photos and including them in a later version of the dashboard.”

The Snapshot team knows there are some classification errors in the data, but anything with an accuracy above .95 is acceptable for this purpose. Sprain ask Anhalt-Depies about the two cameras in Milwaukee county with snowshoe hare detections, and Anhalt-Depies explained that those are likely misclassified and within the 5% of the time the data was incorrect.

Sprain replied, “I like that there is a discussion of accuracy and human error. Science is not perfection, but to see these [accuracy rate] numbers… now I understand why a species wasn’t included.”

Sprain and Cameron each commented on how impressed they were overall with the accuracy of Snapshot volunteers. Cameron said, “One thing I encourage for volunteers who want to help increase their accuracy is to involve others. You [Sprain] mentioned that your students work together to classify photos. My dad calls me basically every time he checks his camera to verify that a photo is an opossum… or a racoon. Just having a couple eyes on the photos increases accuracy but also makes it a more enjoyable experience working with others.”

Sprain jumped in and added, “That is what it is all about, a social network of citizens contributing to science! Working together for a much bigger purpose but also feeling great about seeing the wilderness and ecology in the state of Wisconsin – that is the goal.”

Visualizing Animal Activity and Detection Rates

Soon, Sprain had moved on to the next section of the dashboard, the graph of animal activity. The graph, located on the right side of the dashboard, plots how many times a species has been seen at each hour of the day or each month of the year, depending on which option you choose.

“I like that you can see activity by hour or month,” said Sprain. Sprain started making connections between activity patterns he observed and known behavior of those species. Bears, for example, become more active during the spring and summer months yet are rarely seen during the winter. “I’ll ask my students the question, why are they so active during certain months? Some students think it is a difference in population size, instead of a behavioral difference like preparing for hibernation or reproduction. I get a lot of giggles in the seventh-grade classroom, but the question is very relevant to teaching animal ecology and biology.”

The graph can also be adjusted to show which hours of the day a species is more active. The dashboard shows that sandhill crane, for example, are mainly active during the day but are most active between 10:00am-1:00pm. Bird watchers could increase their chance of spotting one by using the dashboard to find its peak activity times. The same could apply to any of the 18 species shown on the dashboard.

The second tab of the activity chart shows detection rates. Initially set to show the top five species detected state-wide, the detection rate chart can show much more than you may expect. If the selected species isn’t among the top five species, it will appear as a sixth bar on the chart. Additionally, individual counties can be selected to show the data for both the state and selected county. Using these features together, Sprain was able to discover that bobcat, elk, porcupine, and wolf were detected slightly more often in Jackson county, where one of his cameras is located.

Overcoming Insufficient Data

Three counties don’t show species data and are labelled as insufficient data. These counties have less than five cameras. If enough data isn’t being pulled from cameras in a county, then the dashboard doesn’t make a lot of sense. For example, let’s say you live in a county with only one camera. If a rare species like the whooping crane walks across that camera, then that species now has a 100% detection rate (one camera out of one camera) in that county. The dashboard would give off the impression that whooping cranes were extremely common in that county, when the species is actually quite rare. This is why Snapshot requires a minimum of five cameras per county before the dashboard will show data for it. If you live in a county that is labeled as Insufficient Data, then consider hosting a camera or encouraging others to host one. There are a lot of opportunities, not only on private lands, but also on public lands to host Snapshot cameras.

Cameron said, “There are a lot of ways to get involved, no matter where you are or whether you have access to land. Snapshot is a really unique project in that there are so many ways to get involved. You could host a trail camera, classify photos online or just participate in some of the educator resources. There is a lot to explore and to offer!”

Even if you are just interested in learning about wildlife in the state, you can help out Snapshot by giving them feedback on the dashboard. There is a survey at the bottom of the dashboard for anyone, not just volunteers, to offer ideas for future versions of the dashboard. After all, the Data Dashboard is designed to help Snapshot come full circle, so they want to hear from you. While not every idea can be implemented, the major themes in the survey responses impact what the team focuses on moving forward.

Cameron reiterated, “The data dashboard isn’t just for Snapshot volunteers. It is for anyone interested in Wisconsin wildlife. Snapshot and the Data Dashboard are special because they wouldn’t be possible without our trail camera hosts and folks classifying online.”

Let’s Discover Our Wildlife Together

Snapshot’s slogan, “Let’s Discover Our Wildlife Together,” isn’t a mistake. Snapshot is a project about people (from Wisconsin and across the globe) working together to monitor our wildlife. “While we know so much about Wisconsin wildlife, there is still so much we don’t know,” said Cameron. “Having tools like this dashboard help us fill in those gaps and get a more complete picture of what is really happening in Wisconsin.”

Sprain added, “[The dashboard lets] each citizen take away a piece of Wisconsin culture and have it become a part of their life. There is no denying the presence of these animals on the cameras. That is the power of this data.”

Whatever your motivation for wanting to see Wisconsin wildlife, check out Snapshot Wisconsin’s Data Dashboard. Version 1.0 is now available to the public.

Lastly, Anhalt-Depies offered Snapshot’s hope for the future of the dashboard. “We are now at a point where the project has really good coverage across the state. Our hope is that, as the dashboard evolves, it becomes a powerful tool for decision makers,” so keep an eye out for what is available in future versions of the dashboard!

You can visit the Data Dashboard at https://widnr-snapshotwisconsin.shinyapps.io/DataDashboard/.