My brother Ian was a picky eater. Breakfast was always a bowl of Crispex. For lunch, he ate a PB&J and refused to eat the crusts. I was the opposite. Even as a young child, I loved proverbially “gross” foods like mushrooms and started drinking coffee when I was twelve.
Turns out that some animals are like Ian and some are like me. For example, monarch caterpillars only eat milkweed. We call animals like the monarch specialists. Conversely, some animals will eat, well, just about anything. Raccoons, for example, are equally happy eating crayfish from the creek or scraps from your garbage can. We call such species generalists.
Diet isn’t the only thing to be picky about! Some species exhibit preferences for precise habitat types. For example, the Kirtland’s Warbler breeds only in young jack pine barrens, primarily in Michigan, but also occasionally in Wisconsin. On the other hand, some species are ubiquitous. The coyote is an exemplar habitat generalist—you might spot one in the wilds of the Chequamegon-Nicolet National Forest or in a suburb of Milwaukee.
Taken together, diet and habitat comprise what we call the ecological niche of a species. You can think of a niche as the “cubbyhole” that a species occupies within the broader tapestry of its environment. The breadth of a niche is a continuum from extreme specialists (like Kirtland’s Warblers) to extreme generalists (like raccoons). Some species fall between those extremes; deer are a great example. Deer are strict herbivores, but they can be found in many different habits, from forests to farmlands. So, not every species can be neatly classified as a generalist or a specialist.
Scientists are interested in generalists and specialists because they exhibit different responses to change. Like a trained craftsman whose job is replaced by a machine, the specialist has nowhere to go when the environment changes. Generalists, on the other hand, can capitalize on the vacant niche space and colonize altered landscapes. Given the widespread changes humans are exerting on the earth, we are seeing global proliferation of generalists while many specialists are disappearing, a process known as biotic homogenization.
This may seem dire, but the more we learn about generalists and specialists, the more we’ll be able to do to maintain biodiversity and lose fewer specialists. In the meantime, I encourage you to think about the animals you see on a regular basis. Is that squirrel outside your window an ecological jack-of-all-trades? Are there any habitat specialists that live on your property? And maybe even think about your own niche—are you a generalist, a specialist, or somewhere in between?
Stop for a second and try to visualize 23 million of something. The number of species on the planet? That’s roughly 8.7 million. The number of residents in Wisconsin? Nah, that’s not even 8 million! How about photos collected by the Snapshot Wisconsin project since 2016? Ding ding ding! (well – 23,706,425 photos to be exact, not like we are counting or anything…)
Up until recently, Snapshot Wisconsin volunteers were uploading roughly one million photos total per month – but this number is bound to increase after nearly doubling our volunteer base! When we share this statistic during trainings and presentations, we always know to expect the question, “How do you keep up with all of those photos!?”
“Well, it’s a little complicated”, is how we generally start the answer. In this blog post we will dive into how the project has been maintaining this vast amount of data so far, and exciting prospects for the future of Snapshot Wisconsin.
Filtering Photos for Zooniverse:
Zooniverse is a crowdsourcing platform hosting sites for a large variety of projects, including Snapshot Wisconsin. Here anyone with internet access can go online and classify images collected by trail cameras in the project. Before photos are sent to Zooniverse, Snapshot Wisconsin trail camera hosts have the opportunity to view and classify their own photos.
While the volunteers are required to identify and remove human photos, classifying blanks and animals is extremely helpful! Why is this? We conducted an analysis to determine the accuracy of single classifications made by volunteers in their MySnapshot accounts, versus consensus classifications made by volunteers on Zooniverse. We were able to identify species that volunteers are really great at classifying (e.g. deer, squirrels, raccoons, turkeys, etc.)! When these photos are classified by volunteers in their MySnapshot accounts, we do not send them to Zooniverse. Instead we take the volunteer’s classification as the “final classification” for the image, which helps cut back on the number of photos for which we rely on Zooniverse classifications.
Crowd Sourced Classifications on Zooniverse:
The remainder of photos are uploaded to Zooniverse in “seasons”, with each season containing anywhere from 30,000 to 50,000 images. Once a season wraps up, staff members can be caught scrambling around the office getting a new season ready to go. To prepare for each season we need to upload photos and their information, as well as manually review photos to ensure no humans or excessive number of blanks get uploaded. On average, it has taken roughly 3 months for a season to be fully classified and a new season to be uploaded, which generally includes a break that lasts a few weeks.
On Zooniverse, multiple volunteers will view and classify each photo to produce a consensus classification. Photos are viewed by upwards of 11 volunteers if a consensus isn’t reached early on; if a consensus is never reached the photos will go on to expert review by staff members. Since launching in 2016, Zooniverse volunteers have helped the project move through 9 full seasons of photos. Season 10 just launched last week! Across these seasons, the project has had over 6,000 registered volunteers participate and classify 2,253,244 images, or an average of 2,596 photos per day.
Moving Forward – Machine Learning to Classify Animals:
Recently a team of researchers created a computer model using machine learning that classifies images captured by trail cameras. Machine learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion buy feeding them data and information in the form of observations and real-world interactions¹. In this case, the computer model was provided over 3 million images of animals, each that had already been classified by a human, to aid the computer in “learning” to determine which species is which in trail camera images.
The trained model was able to classify approximately 2,000 images per minute at 98% accuracy on images of species collected in the United States. The Snapshot Wisconsin team recently prepared a large set of classified images to further improve the model, and to potentially be incorporated into the project to help keep up with the booming number of images collected. This isn’t to say that volunteer classifications (MySnapshot or Zooniverse) would be replaced, but using automated classifications could help ramp up the speed at which the project produces viable data.
To view the scientific paper, Machine learning to classify animal species in camera trap images: application in ecology, visit this link: https://www.biorxiv.org/content/biorxiv/early/2018/06/13/346809.full.pdf
- Faggella, Daniel (2018, October 29). What is Machine Learning? Retried from https://www.techemergence.com
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!
The Snapshot Wisconsin team (mainly our awesome summer intern, Ally) spent a lot of time over the summer prepping equipment for our statewide launch. We had over 200 kits made and thought that was a good amount. None of us could have predicted the phenomenal response from new volunteers! Since August 9th we have had more than 1100 people signup to host Snapshot Wisconsin cameras across the state. Additionally, more than 300 people had signed up in non-open counties over the last 2 years. So, things at Snapshot Wisconsin have been super busy, to put it mildly. We started our fall training schedule last week with in person training in Platteville and Darlington. This week we are off to Merrill and Crandon (the remainder of our training schedule can be seen here). We also launched a new online training system, including brand new videos, last week. More than 200 people have completed the new online training system and we are working on getting them setup with MySnapshot accounts and getting equipment out the door. Thanks to all the volunteers for their patience and enthusiasm for getting started with our project. We have been working on some automation to better manage the multitudes of new volunteers, in time that should help us to be more efficient.
We are really excited to spend our fall traveling, meeting new volunteers and seeing new photos come in from all over Wisconsin. Stay tuned for more behind the scenes blog posts to come!
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!
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!
We are excited to announce that Snapshot Wisconsin is entering
Phase 2, meaning the project is now open in all 72 counties on both private and public land!
Snapshot Wisconsin launched in 2016, starting off in only two counties. The project has since grown reaching 26 counties on privately owned lands, while accepted applications from educators and tribal affiliates statewide. Phase 2 of the project will provide an even more accurate “snapshot” of Wisconsin’s unique and diverse wildlife, while expanding the opportunity to all corners of the state for volunteers to experience firsthand the fauna occupying their wild lands.
The Snapshot Wisconsin team is also debuting a collection of lesson plans, all incorporating photos, data, and concepts related to the project.
From the Snapshot Wisconsin program, you may be familiar with wildlife monitoring using trail cameras. Trail cameras are one wildlife monitoring tool classified into a group of monitoring techniques that are considered non-invasive, meaning that the technique causes little or no impact on the animal’s normal activity, ecology or physiology. By contrast, invasive monitoring techniques include any type of wildlife monitoring that has a direct, human caused impact on an animal (GPS collaring, tagging, close observation are a few examples). In this blog post series, we are going to highlight other non-invasive monitoring methods and include ways you can get involved in these types of non-invasive monitoring! Our first post on non-invasive monitoring is focused on.. tracking!
Tracking involves locating animal footprints and identifying the species. This monitoring technique can be done during all times of year in snow, mud, dirt or sand. You can learn a lot about an animal by its tracks. For example, you can tell what gait the animal was in (walk, trot, lope, spring), where it was heading to and from and if the animal was travelling in a group or alone.
Researchers can use tracks to estimate abundance, home ranges and behavior patterns. This can be especially helpful for monitoring more elusive animals that are sensitive to human disturbance.
One research project that uses tracks to estimate abundance is the Wisconsin winter wolf count. Using tracks in the snow, the DNR can estimate a minimum wolf count. For more information about that project, check out this link.
Stay tuned for more non-invasive survey method blog posts! Upcoming will be a post featuring how scat, hair and even eDNA play a role in wildlife research.
While Snapshot Wisconsin’s volunteers are busy deploying trail cameras, uploading photos, and classifying wildlife on Zooniverse – what on Earth is there left for the staff to do? Well, with over one thousand project volunteers, there’s quite a bit that must go on behind the scenes as well! From prepping equipment, to answering volunteer questions, to getting lost in the woods (only sometimes) – no two days look the same. This blog post will give you a “snapshot” into life behind the scenes for staff members.
A lot of time is spent preparing equipment. Every camera needs specialized software and labels, SD cards, batteries, a charger, a camera mount and snazzy bag to hold it all together. Equipment needs to be recorded in a database, and frequently stocked up on for incoming volunteers! Some weeks staff members are sending upwards of 30 equipment kits to newly enrolled volunteers who have completed training.
For technical difficulties, malfunctioning equipment or general questions, staff members are only a phone call or email away! Problem solving skills are a must for the Snapshot Wisconsin team. Rock star staff member Vivek can frequently be spotted answering volunteer calls while simultaneously working to maintain the Snapshot Wisconsin database, with over 22 million photos this can be a full time job in itself.
One of our favorite “behind the scenes” task involves exploring pieces of the state where the staff can witness some Wisconsin wildlife first hand, and interact with the project volunteers. Staff members are constantly kept on their toes with a wide variety of assignments; other daily tasks range from creating lesson plans, to manipulating data, to writing outreach content (example here!) As busy as we can be kept, we all greatly enjoy the work and have such an immense appreciation for our volunteers that keep the project running! Thank you!
As we proceed through Wisconsin’s four seasons each year, you may appreciate the sight of colorful songbirds in springtime and notice the distinctive V-shape formation of Canada Geese as they fly south in the fall. These species are referred to as “migratory birds”, or populations of birds that travel from one place to another at regular times during the year.
Why do birds migrate?
Birds migrate in search of resources needed for their survival. Migratory birds primarily pursue sources of food or nesting locations to raise their young. In Wisconsin, we see an influx of bird species in springtime as warm weather returns and insect populations increase. As temperatures begin to drop in the fall, food supply dwindles and the birds fly south.
How do birds migrate?
Scientists believe there are many factors that trigger the migration of bird populations. Birds respond to changes in their environment such as day length, temperature, and availability of food resources. Additionally many birds go through hormonal changes with the arrival of new seasons. These hormonal shifts may affect your caged birds at home, you may recognize restless behavior in spring and fall. This restlessness around migratory periods is referred to as zugunruhe.
It isn’t fully understood how birds have developed such impressive navigation skills, but there are several factors that guide them. Birds can use directional information using the sun, stars, and even earth’s magnetic field. Landmarks, position of the setting sun, and even smell plays a role for various species.
How do scientists study migratory birds?
Several methods have been developed to track and study migratory birds including banding, satellite tracking, and by attaching geolocators to individuals. At Snapshot Wisconsin, trail cameras are now being added to the list of tools! Using preliminary data gathered from Zooniverse, the below slideshow shows the detections of Sandhill Cranes on Snapshot Wisconsin cameras throughout the year. The study of migration can be immensely beneficial for conservation efforts by pinpointing wintering and nesting locations to monitor potentially threatened or endangered populations.