Snapshot Wisconsin cameras capture tons of deer throughout the year. In fact, deer account for nearly two-thirds of the wildlife captured on Snapshot Wisconsin trail cameras. Since there are so many photos of deer taken, we see some deer that look like they might be hurt or have a disease. Here are a few examples of deer who are looking a little under the weather and what might be ailing them:
Nancy (or known by her Zooniverse handle @NBus) is a wildlife health expert here at the Wisconsin DNR who let us know that a swollen chest like this is not unusual in deer. Nancy shared the following response to this image, “It is likely either an abscess (pus-filled) from a penetrating wound that carried bacteria under the skin or a seroma (serum-filled; serum is the non-cellular portion of the blood, not the red and white cells) from a blunt trauma to the chest. The chest is a common part of the body for deer to injure as they run and impact something. And gravity then allows the accumulated pus or serum to gather in a bulge on the lower chest. In either case, the body will likely be able to resolve it and the deer will be fine.”
Another example that shows up semi-frequently is warts. Like many mammals, deer are susceptible to warts caused by a virus. These growths, called cutaneous fibromas, are caused by the papilloma virus. Usually the deer’s immune system can keep the warts in check or get rid of them. Sometimes if the warts appear in areas that obstruct the deer’s ability to eat, they could become a larger issue (source).
Thin and scraggly
Finally, we will touch on thin or scraggly looking deer. Especially in the spring, deer can start looking very skinny and ragged. This one above is shedding its winter coat and is probably a little thin since this was taken in the middle of May in Wisconsin, when food can be hard to come by. However, this is not outside the norm for deer this time of the year. We are often used to thinking of an image of plump deer, but in reality, the appearance can vary greatly based on time of year and food availability.
If you want to see more examples of common deer health issues please visit our previous deer health blog titled, “Is this deer sick?” from February 2018. Learn more about Wisconsin health by visiting this DNR link.
Recently, I was running, lost in my thoughts, and—WHOOPS—almost tripped over a shivering opossum crossing the bike path! After we both recovered our wits, I jogged in place and watched it waddle away, naked tail dragging through the snow. I rubbed my gloved—and still cold—hands together and wondered, why the heck do opossums live in Wisconsin?
When I got home, some Googling revealed an interesting fact: Wisconsin is at the limit of the opossum’s geographic range. In turn, this got me wondering—what governs the limits of a species’ range?
Ecologists typically classify range-limiting factors as either abiotic or biotic. Abiotic factors do not involve living organisms; climate is the quintessential example. Biotic factors are interactions with other organisms. A classic example is competition between organisms, which is a direct biotic interaction. However, biotic interactions can also be indirect, such as when one species improves or degrades habitat for another. Abiotic and biotic factors usually work in concert to limit an organism’s range.
The opossum I saw behind Olbrich Gardens bespeaks both. Opossums, with their naked tails and ears, have a difficult time surviving cold environments. And yet, opossums live in snowy Wisconsin! However, this is a relatively new phenomenon—opossums did not occur in Wisconsin until the 1850’s, when their range expanded northward. The opossum’s conquest of Wisconsin has been aided and abetted by another organism, namely Homo sapiens. Humans provide extra resources (like trash), which help opossums survive Wisconsin’s cold winters. A biotic interaction has helped opossums overcome an abiotic limitation.
Regardless of the exact cause, opossums reach the northern limit of their range in Wisconsin. Several other species reach range limits in the state, a fact that can come in handy while classifying Snapshot Wisconsin photos. Look a photo’s metadata—what county was it taken in? In some cases, this can narrow down identification possibilities. For example, any rabbit-looking creature in Waueksha County is likely an eastern cottontail, since snowshoe hares do not occur in southern Wisconsin. A good source for species range maps is NatureServe Explorer.
For more information about opossums, see this recent Snapshot Wisconsin blog post by Emily Buege.
For more information about the opossum’s range expansion northward, I recommend reading Walsh and Tucker (2017).
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
From time to time, we find photos of animals with white coats that don’t typically have white coats. Are these animals albino?
Albinism is caused by a genetic mutation whereby cells called melanocytes are “switched off” and fail to produce the pigment melanin. Melanin colors hair, skin, feathers, scales and eyes. Albinism can occur in any animal that has melanocytes, including mammals, reptiles, amphibians, birds and fish. Albino individuals may appear pure white, or may just be lighter overall than non-albino individuals.
Albinism is a rare condition, and there are several more common explanations for light coloration in animals. One is leucism, a condition caused by recessive genes that restrict the creation and/or distribution of pigment-making cells throughout the body during early development. Leucistic individuals may have light coloration overall, or they may have a light patch or patches scattered across the body.
Since either condition can result in individuals that appear white or very light in color, how can the casual observer tell the difference? It’s helpful to look at the eyes. Eye pupils normally look black because pigments at the back of the eye absorb light. In albino individuals unable to produce melanin, however, eyes appear red because light is reflected off blood vessels in the retina rather than being absorbed by pigment. This is why albino individuals experience a heightened sensitivity to light, and often some degree of blindness. Since the melanocytes that produce eye color are not affected by leucism, leucistic individuals typically have normal eye color.
So, is the deer in the photo above albino or leucistic? Based on the dark eyes, it’s more likely leucistic.
White deer (albino or not) are protected from hunting in certain places, including Wisconsin. Why? White deer are not a special “breed” or subspecies, but they do hold social significance. People love seeing them, and some believe that they are good luck or magical. Certain areas of the state are now known for their white deer, and we are likely to keep spotting them on Snapshot Wisconsin cameras from time to time.
More info on color variation in animals:
- Albinism and Leucism: Origins and Differences
- Causes of Color: Biological Pigments
- What colour is that bird?
- Piebald mystery solved: Scientists discover how animals develop patches
Our Snapshot Wisconsin trail cameras capture images from all over the state and throughout the year. Sometimes folks spot a critter that looks a bit different from the others and ask, “Is this deer sick?” In most cases, the answer is “not likely.” In this post we are sharing some of our most frequently asked questions about deer appearance and wildlife diseases.
This deer looks skinny! Is it sick? Could it have CWD?
Winter in Wisconsin can be quite rough for a deer! In summer food sources are abundant, but come wintertime deer have to rely on less nutritious forage like twigs, lichens, or leftovers in harvested crop fields. Because resources vary significantly with the season, a deer’s weight will also vary. After particularly long winters, deer may look very skinny the following spring and even in to early summer. But not to worry; they will put the weight back on in no time.
CWD (chronic wasting disease) is a fatal nervous system disease that affects deer, elk, and moose. CWD has been found in wild deer in 23 Wisconsin counties, with highest prevalence in the southern part of the state. Clinical signs of CWD include diminished muscle tone and emaciation, but outward symptoms often do not appear for months or years after infection. The disease is best confirmed through a lab test for the disease; physical appearance based on trail camera images is not a reliable indicator. More likely a deer is skinny because of poor food resources in winter and not because of CWD or some other disease.
What is wrong with this deer’s coat?
Each spring deer molt or lose their winter coats. The thick grey hairs that make up the winter coat are replaced with a new reddish-brown summer coat. This molting process can happen quite quickly and during the transition deer can look a little ratty and rough. This is a normal process and nature’s way of making sure deer are “dressed” for the temperature.
This deer has an injury. Can you notify someone or help this deer?
Sometimes deer with physical injuries show up in our photos. This is common for wild animals. These injuries can be caused by any number of reasons, such as scraping against a fence or perhaps from a predator. In many cases, the small injuries will heal quickly, leaving a scar or patch bare of hair. In cases where the injury is major (say from a car collision) and the deer cannot recover, the animal will become an important food source for scavengers.
All of the photos appear on Zooniverse many months after they have been taken, and the animal may no longer be in the area. Although reporting the observation via Zooniverse will not be helpful, Wisconsinites who personally observe sick or dead animals can make a timely report to their local DNR office or contact a licensed wildlife rehabilitator.
Here in the northern hemisphere, the autumn days are getting shorter and shorter. It’s getting darker earlier in the day, and our eyes have to adjust to dim conditions.
Without the help of fire or electric lights, we humans are pretty bad at night vision. Unlike many other animals, our eyes lack a specialized reflective surface that aids sight at night and in low light environments (caves, under water, etc.). This surface, called a tapetum lucidum, located behind the retina, acts as a mirror to reflect light photons.
Light enters the eye and hits photo receptors in the retina. Some light, however, will miss the photo receptors and pass past the retina. The tapetum lucidum reflects that light and gives it a second chance to hit the photo receptors and illuminate the scene.
Some of this light is reflected back out of the eye, which is why some animals’ eyes appear to glow in nighttime trail camera photos. All types of camera flash, even the low-glow infrared flash of the Snapshot Wisconsin cameras, can reflect off the tapetum lucidum and cause an animal’s eyes to light up. (This is not the same as the red-eye effect seen in photos of human eyes which is caused by light reflecting off the blood vessel-rich choroid behind the retina.)
There is variation in mineral content and structure of the tapetum lucidum, which causes eyeshine in different species – and even different breeds of dog – to look different. Eyeshine may appear white, blue, green, yellow, pink or red. It’s too bad nighttime trail cam photos are in black and white and we can’t see these color differences!
Animals having a tapetum lucidum (not extensive):
- carnivores: canids and felids
- grazing animals: sheep, goats, cattle, horses
- fruit bats
- ray-finned fishes and cartilaginous fishes including sharks
- owls and a few other nocturnal birds
- crocodilians including alligators (bright red eyeshine – spooky!)
Animals lacking a tapetum lucidum (not extensive):
- higher apes including humans
*I’ve read that squirrels don’t have a well developed tapetum, but flying squirrel eyes certainly glow in our nighttime trail cam photos. Anyone who can shed some light on this mystery, please leave a note in the comments!
- What causes the red eye effect? Yale Scientific Magazine.
- Candid Creatures: How Camera Traps Reveal the Mysteries of Nature. Johns Hopkins University Press.
- Why do animals’ eyes glow in the dark? NPR All Things Considered.
- Comparative morphology of the tapetum lucidum (among selected species). Veterinary Ophthalmology 7(1):11-22.
- Crystals of riboflavin making up the tapetum lucidum in the eye of a lemur. Letters to Nature.
- Ocular comparative anatomy of the family Rodentia. Veterinary Ophthalmology.
You may have heard that Snapshot Wisconsin researchers ask volunteers to place cameras at least 100 yards (~100 m) away from bait and feed. Bait and feed are materials placed outdoors to intentionally attract wild animals, and may include food, scent materials, salt, minerals, grains, birdfeeders, and carcasses. Bait and feed attract really interesting animals that we love to see on camera, so why do we ask our trail camera hosts to avoid them?
One of the most common (but generally unfounded!) critiques of citizen science is that data collected by citizens may not be as reliable as data collected by professional scientists. We are currently wrapping up an analysis of image classification accuracy, and the evidence suggests that the crowd-based species consensus is generally very accurate. Keep up the great work! Read More…
Welcome to Snapshot Wisconsin! We are only 24 hours in and are already nearly 20% complete. Thanks for all you do!
As you’ve probably already noticed, white-tailed deer and elk are captured frequently on our trail cameras. This is because both species are abundant in the region of Wisconsin where the trail cameras are located. In addition, deer and elk are large, mobile animals that travel the visible wildlife trails along which the cameras are placed. By classifying deer and elk in terms of adults and young, you are helping us to understand the population dynamics of the species.
But there’s more to learn from the trail camera photos, including an understanding of deer and elk behavior. Having additional information on behavior will allow us to investigate the impact predators have on the behavior of deer and elk. Trail cameras are unique in that they allow us to look at both the behavioral response to predators, in addition to the population response.
This Season we’re asking for your help to identify 5 behaviors for deer and elk: foraging, vigilance, interaction between individuals, resting, moving, and staring at the camera. How we are defining each of these behaviors is listed below, as well as in the field guide (tab on the right hand side of the page).
Foraging: Head down or below shoulder height and eating something or, rarely, a deer/elk obviously eating foliage above the shoulder
Vigilant: Head is up, ears erect, and alert posture
Interaction: Any direct physical interaction with another deer/elk; can be aggressive (fighting), play, or grooming
Camera Stare: Looking directly at the camera
Resting: Deer/elk has bedded down in front of the camera
Moving: Traveling by either walking or running