Collecting photos from a fixed location can give us an idea of which animal species are utilizing the same space. Often hours, or even days, elapse between photos of two different species crossing the same area. However, a small percentage of our trail camera photos capture moments of more than one species in the frame together. We recently took inventory of these multi-species instances, and despite our data set growing to over 2.5 million animal triggers, only around 4,300 of them contain multiple species within the same photo.
So far, we have confirmed 128 combinations of species appearing in photos together, 119 of which are combinations of two species and 9 of which are combinations of three species. Deer are most commonly one of the animals present in these multi-species occurrences. 37 of these multi-species occurrences are unique combinations of species that have only been observed once in our data set (orange lines in figure 1). A few examples of these include elk with turkey, red fox with opossum, and other bird with porcupine.
In the future, we hope to perform formal analyses with these data, however there are certain challenges that we must consider. An example of one such consideration for analyzing multi-species trail camera information is detectability. In general, trail cameras have a higher chance of firing if an animal that wanders in its field of view is both large and close to the camera. This might account for the high number of instances of deer and “other birds” occurring together. Birds, especially small birds, may be present at the site many times throughout the day, but may only be captured on camera when a deer, which is generally large enough to reliably trigger the camera, steps into the frame.
One observation that can be made from this preliminary analysis is that species that tend to utilize a particular habitat type may be more likely to be pictured together. For example, we have photos of mink and beaver together as well as muskrat and raccoon. These combinations are intuitive because all four species are commonly associated with water.
Another observation is that many of the combinations are of two species that do not have a strong predator-prey relationship. For example, deer and turkey are the two species most commonly pictured together, and neither is a predator of the other. Conversely, both bobcats and turkeys are relatively well-represented in the data set, yet we might not expect to see the two species together considering one is a predator and the other might be prey. Indeed, we have only observed one trigger of the two species together.
We hope that Snapshot Wisconsin can continue to shed light on interactions between species such as deer and predators of deer, as this was an early goal of the project. In the meantime, we ponder the many ways in which these space- and time- dependent occurrences are unique.
If you find an interesting interaction between two species on Snapshot Wisconsin, send it to us at DNRSnapshotWisconsin@Wisconsin.gov or share it on our Zooniverse page!
The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
The opportunity to classify photos of wildlife from across Wisconsin draws a diverse array of individuals to our Zooniverse page. Some volunteers are trail camera hosts themselves and enjoy classifying photos from other camera sites. Zooniverse also offers this opportunity to those who are unable to host a camera but still wish to participate in the project.
The maps here were created using Google Analytics data, which can anonymously record information about users who access a webpage, such as their nearest city. This data shows us that Snapshot Wisconsin reaches an audience far beyond Wisconsin, and even beyond the United States! In total, volunteers from 696 cities across 41 countries have interacted with the Snapshot Wisconsin Zooniverse page since 2016. 190 of those cities are in Wisconsin.
Each dot represents just one city, regardless of the number of individuals who accessed the site in that location. For example, the dot for the city of Madison could represent thousands of users. Zooming in on Wisconsin, we see that many dots are centered around the most populous areas, such as Madison, Milwaukee, Minneapolis and Chicago. This pattern can be attributed to the fact that these areas also host the highest concentration of suburbs.
Regardless of the volunteer’s location, each classification we receive is important to the success of Snapshot Wisconsin.
The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
One of the easiest ways to participate in Snapshot Wisconsin is by classifying photos through a website called Zooniverse. Zooniverse is a crowdsourcing service that is accessible to anyone, anywhere, and the site has hosted Snapshot Wisconsin since 2016. Snapshot Wisconsin’s most prolific Zooniverse volunteer has contributed over 65,000 classifications to the project’s dataset. To date, 1.9 million trail camera photos have been processed through Zooniverse, and more than 7,500 different individuals have registered to participate.
Zooniverse volunteers play a pivotal role in Snapshot Wisconsin. Analyzing volunteer participation gives staff a better idea of how to effectively engage volunteers and can also offer researchers a look at how patterns in participation relate to the overall quality of the data acquired from the platform.
In the interest of exploring a quantitative assessment of volunteer participation in Snapshot Wisconsin through Zooniverse, researchers conducted a Latent Profile Analysis (LPA) of our volunteers. LPA can be used to organize a given sample size of people into groups based on observable variables, such as user activity over time. Through this, researchers were able to ascertain how many different groups of people exist in the sample, which individuals belong to which group, and what characteristics are unique to each group. This allowed researchers to hone in on specific patterns in user engagement.
Researchers identified measurable variables unique to each volunteer and their activity on Zooniverse between November 2017 and February 2019. These included the number of days each volunteer was active, time elapsed between active days, and the amount of time volunteers spent on the site on active days. From this, researchers parsed volunteers into three profiles: temporary, intermittent and persistent.
Temporary volunteers are those who exhibited rigorous participation, but only for a short period of time. Intermittent are those characterized by the significant amount of time elapsed between a relatively small number of active days. Persistent are those who demonstrated high levels of activity across the entire period examined.
Measures of accuracy specific to each group revealed that temporary volunteers demonstrate lower accuracy in their classifications compared to intermittent volunteers. Though intermittent volunteers tended to allow more time to go by between active days, the consistent practice ultimately made their classifications more accurate.
In this instance, we may turn to an old adage: practice makes perfect. It comes as no surprise that practice and accuracy are correlated, and that volunteers become better at classifying photos with more time spent doing so. In the graphic on the right, all four photos are of porcupines, though they are of varying degrees of difficulty when it comes to classification. Though classifying photos like these may be tricky at first, over time certain characteristics begin to stand out more readily – a porcupine may be identified by their lumbering gait, or the way that their quills appear from different angles and in different light. The more frequently one sees these traits, the easier they become to identify. Volunteers who participate at any level, whether temporary, intermittent, or persistent, are of great value to the project, and the more time spent on Zooniverse, the more likely that the classifications assigned to each photo are accurate.
Citizen science is an integral part of the Snapshot Wisconsin project, and is in fact core to its mission, which is to rally the knowledge and resources of citizens across Wisconsin and throughout the world to build a comprehensive and highly accurate portrait of Wisconsin wildlife. No two Zooniverse volunteers are quite the same, and each individual informs our understanding of how citizen science can be utilized effectively in research. No matter how one chooses to participate, participation alone brings us closer to our goal.
The following piece was written by OAS Communications Coordinator AnnaKathryn Kruger for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
Weasels are the most commonly misidentified animal in the entire Snapshot Wisconsin dataset. What might make them so tricky to identify, and just how do they differ from the other members of their family for whom they are often mistaken, like marten or mink?
As far as their phylogenic standing, weasels belong to the superfamily Musteloidea. Contained within Musteloidea are the families Mephitidae, which includes skunks; Mustelidae, including weasels, otters, ferrets and kin; and Procyonidae, with raccoons and their neotropical brethren. In examining the dataset of Snapshot Wisconsin photos that have received an expert classification, researchers have determined that weasels and mink are the two most difficult species for volunteers to classify.
The two avenues for classification available to volunteers through Snapshot Wisconsin are MySnapshot and Zooniverse. MySnapshot is the outlet available to those who monitor trail cameras, where they can classify the animals in the photos captured on their own camera. Zooniverse is a public forum where volunteers classify photos that are served up at random from Snapshot Wisconsin cameras across the state. Photos are captured in sets of three, called “triggers”, and volunteers classify the entire set at once.
When evaluating accuracy in classification, researchers focus on two variables: recall and precision. Both variables provide measures of accuracy for a group of volunteer classifiers, either from MySnapshot or Zooniverse, compared to expert classification. Recall addresses the question: out of all the weasel triggers in our dataset, how many did volunteers classify as weasels? Whereas precision addresses the question: how many triggers classified as weasels by volunteers were actually weasels?
Between both MySnapshot and Zooniverse, volunteers generally demonstrate high recall and precision when classifying animals that belong to the whole superfamily Musteloidea. When it comes to classifying individual species, we can see that animals like skunks, otters and raccoons are easier to classify correctly on account of their distinctive traits, but weasels are quite similar in physical appearance to the species with whom they share a family, namely mink. This makes weasels particularly easy to misidentify.
Triggers containing weasels and mink are most often missed completely on Zooniverse, with a recall value for these species of 41%. Out of an expertly classified sample size of 15 weasels, only 10 of the 15 were identified correctly by volunteers and 4 additional triggers classified as weasels on Zooniverse were not weasels. This puts recall and precision for weasel classification at a measly 67% and 71%, respectively.
For mink, Zooniverse and MySnapshot share low recall, with approximately 65% of mink photos missed completely on Zooniverse, and 39% missed on MySnapshot. However, the triggers that were classified were largely classified correctly, with perfect precision on Zooniverse and 87% precision on MySnapshot.
So how can we successfully identify a weasel versus some other mustelid, and vice versa? There are three types of weasels in Wisconsin. The long-tailed weasel is the largest of the three. They are typically 13-18 inches in length with a 4-6 inch black-tipped tail. Their coats are brown and their bellies and throats are cream-colored, though they transition completely to white in the winter. The short-tailed weasel is Wisconsin’s most common weasel. Smaller than the long-tailed weasel, the two share their coloring, which makes them more difficult to differentiate. The only discernible difference is the tail length. The third type of weasel is the least weasel, aptly named as it is the smallest of the three at roughly 6 inches. Though this weasel has coloring similar to the others, the least weasel has a short tail without the black tip.
Weasels look rather similar to mink, though mink are dark-colored and larger than weasels with long tails and glossy coats. They weigh between 1.5-2 lbs. Another mustelid closely resembling the weasel is the American pine marten, an endangered furbearer with a penchant for climbing. They have large rounded ears and a bushy tail, and their fur varies in shades of brown from almost yellow to almost black. Snapshot Wisconsin has only one confirmed photo of a marten, as the species is incredibly rare in Wisconsin.
Badgers seem like a no-brainer, with their characteristic striped heads and wide bodies. They are significantly larger than weasels and have long claws well-suited to digging. Despite their distinctive appearance, badgers are subject to misidentification as well. 22% of the triggers classified as badgers on MySnapshot were not badgers. The same goes for the fisher, another sizeable mustelid weighing in at an average of 15 pounds, with dark brown fur and a bushy tail.
Classification is a tricky business, especially when it comes down to mustelids. Snapshot Wisconsin relies on thousands of volunteers to classify the nearly 34 million photos in our dataset, which they generally do with tremendous success. Though the weasel is a trickster, their phylogenic camouflage can be discerned with a trained eye – the same can be said for their Mustelidae cousins. Each accurately classified photo, mustelid or no, brings Snapshot Wisconsin closer to a complete representation of Wisconsin wildlife, and better informs our management of these species.
The following piece was written by OAS Communications Assistant Claire VanValkenburg for the Snapshot Wisconsin newsletter. To subscribe to the newsletter, visit this link.
Although white-tailed deer are common in Wisconsin woods, we never seem to grow tired of pointing them out along the highway, watching them traverse our backyards and observing them on hikes. But as temperatures drop and snow falls, certain members of our herd will become harder to spot.
Al, a Vilas County trail camera host, recounts seeing one of these alabaster animals in 1994: “As we were sitting on the deck, a white deer appeared in the bright sunshine on the far side of the lake. It appeared to not only be white but glowing with a light from within, surrounded by the light green spring vegetation,” Al says.
Although seeing one can be striking, white deer are just like normal deer except for their fair coats. DNR researchers sampled the entire Snapshot Wisconsin dataset to piece together this map of where white deer were flagged using comments on MySnapshot and tagged photos on Zooniverse. They found that sightings occurred in 12 counties across the state, but were most frequent in central and northern Wisconsin.
A white deer’s coloring is caused by a lack of melanin in the deer’s skin, but they aren’t necessarily always albino. True albinism is extremely rare, and an albino deer would have pink eyes, ears, hooves, all-white fur and poor eyesight. It’s more likely that Wisconsin’s white deer population is mostly leucistic, which is caused by a recessive genetic trait found in about 1% of all white-tails.
Leucistic deer (commonly referred to as “piebald deer”) can have a variety of white, brown or black markings. Some leucistic deer are akin to pinto horses, while others may have a completely bleached coat with black hooves and noses. The trick to identifying a leucistic deer from an albino is in the eyes. Deer with pink eyes are most likely true albinos, whereas piebalds have gray, blue or black eyes because albinism affects eyes whereas leucism does not.
While differentiating the two may be tricky, the law is clear. According to page 21 of the 2019 Wisconsin DNR Deer Hunting Regulations, it is illegal to “possess albino or all-white deer which are entirely white except for the hooves, tarsal glands, head and parts of the head unless special written authorization is obtained from the department.” Currently, this is true in all areas of Wisconsin.
Nature photographer Jeff Richter came eye-to-eye with a white deer nearly two decades ago, and he’s been dedicated to capturing their beauty on camera ever since. Richter is the photographer of the book “White Deer: Ghosts of the Forests” by John Bates and told In Wisconsin Reporter Jo Garrett that despite the photographs, some people still believe they’re made up.
“We’ve actually had a couple of stores where clerks overheard people that’ve picked up the book and were looking at it and said, ‘Boy this is really neat, if only they were real,’” Richter said.
They’re real, alright. The one Al spotted all those years ago left a palpable impression on Al and his wife who, at the time, were looking to buy a house. They spotted the white deer across the lake when they were considering the purchase of what is now their current home.
“It actually may have had a small influence on the purchase of our home!” Al says.
Whether they’re common in your neck of the woods or not, white deer are striking animals. The stark contrast of their coats against Wisconsin woodlands makes them a pearly find among the herd. Keep your eyes peeled and your camera ready now, before they blend into a blanket of snow, perfectly hidden throughout the winter months.
Wisconsin is renowned for being home to a well-established population of white-tailed deer. They are an undeniably important part of Wisconsin’s forests and farmland and are the animal that appears most frequently on Snapshot Wisconsin trail cameras. Since its inception in 2016 the project has accrued a massive supply of deer photos. This vital cache of information offers researchers the opportunity to make population-level observations about things like movement and activity patterns, and how these change with the seasons.
Out of a sample of 1.4 million Snapshot Wisconsin trail camera photos of deer, antlerless deer take the lead at 63%. The remaining 37% comprises antlered (13%), adult unknown (15%) and fawns (8%).
In their ongoing analyses of these photos, scientists at the Wisconsin DNR have noted that deer show strong crepuscular patterns near both the summer and winter solstices. The word crepuscular refers to the interim between night and day, or both dawn and dusk. Deer are more active closer to sunrise and sunset than they are at any other time of day across any season. In winter, there is an observable preference toward sunset – most likely because afternoon is the warmest time of the day, and therefore the best for foraging. The opposite goes for the longer days in summer, when deer seem to prefer sunrise, as it is cooler and foraging at that time is less energetically expensive.
During the summer, antlered deer are the most likely to stick to this crepuscular pattern. On the other hand, antlerless deer and fawns are a little more unpredictable. Fawns are generally more active throughout the day, as are antlerless deer, though to a lesser extent. Antlerless deer are also more active through the night. Assuming that most antlerless deer are does, their deviation from the crepuscular pattern can be attributed to their need to move to and from spots where they drop their fawns in the time following birth. Despite their penchant for daytime activity, after the first 12 weeks fawns begin to mirror the activity of their mothers, gradually falling into the recognizable crepuscular pattern.
As for winter, both antlered and antlerless deer are seen to be most active at sunset. At this point, fawns are indistinguishable from does and are therefore not differentiated from the rest of the population in the analysis. During the winter, antlered deer are more active during the night and less active during the day than antlerless. Predictably, the annual rut drives a significant uptick in activity for male deer in late October, a pattern familiar to Wisconsin drivers who may be liable to encounter deer more frequently around the same time.
Snapshot Wisconsin’s growing cache of deer photos sheds light on deer activity as it varies through the seasons and even day-to-day. Recognizing these patterns bolsters our knowledge of how deer interact with and move through the landscape. These photos are also used to investigate population dynamics and determine fawn-to-doe ratios to better track the growth of the herd. Such a plentiful supply of information on an important species like deer is of great value to our researchers, whose analyses are a critical component of wildlife management decision-making at the Wisconsin DNR.
Whooping crane 1-17 is, according to his personal biography, a natural-born leader. He is confident, vigilant, quick to take a jab at a potential threat and allegedly able to spot a worm at 50 yards.
This two-year-old crane, born and raised from captive breeding stock at the Patuxent Wildlife Research Center in Laurel, Maryland, is one of a rare species that has been newly restored to North America after overexploitation in the mid-20th century nearly drove them to extinction.
Whooping crane 1-17 appeared this spring on a Snapshot Wisconsin camera in Jackson County, much to the excitement of the Snapshot Wisconsin crew and the researchers stewarding 1-17’s journey across the landscape. View an interactive story map outlining 1-17’s journey here.
“The conservation story behind [whooping cranes] is a marvelous story, involving a lot of effort and a lot of money,” said Davin Lopez, conservation biologist with the National Heritage Conservation Bureau in the Wisconsin Department of Natural Resources. “Although they’re recovering, they’re an incredibly rare species – I mean, people come from far and wide to see them – they’re a big, five-foot-tall, charismatic, pure white bird, so they’re pretty striking out there, very visible on the landscape. People find them very beautiful.”
Efforts toward the reintroduction of migratory whooping cranes to eastern North America began in 1999 with the formation of the Whooping Crane Eastern Partnership (WCEP). WCEP was founded as a collaborative project between the U.S. Fish and Wildlife Service, the International Crane Foundation, Operation Migration, the Wisconsin Department of Natural Resources, the United States Geological Survey, Patuxent Wildlife Research Center in Maryland, the USGS National Wildlife Health Center, the International Whooping Crane Recovery Team, the National Fish and Wildlife Foundation and the Natural Resources Foundation of Wisconsin.
The whooping crane is critically endangered in North America and has only one major migratory population, the Aransas-Wood Buffalo population (AWBP). This flock breeds in Canada, winters in Texas and comprises 505 birds as of December 2018.
Per the stipulations of the International Whooping Crane Recovery Plan, which outlined the need to establish one or more migratory crane populations in addition to the AWBP, WCEP has overseen the successful establishment of an eastern migratory population (EMP) of whooping cranes. In 2001, 7 individual cranes were guided in their migration from Wisconsin to Florida by aircraft, and 6 were guided back in the spring. As of July 2019, the estimated population size of the EMP is 87 individuals.
One significant barrier to the growth of wild whooping crane populations is the high mortality rate amongst wild chicks. Since 2002, WCEP has supplemented the wild crane population with chicks raised in captivity. These chicks were originally raised through costume-rearing, wherein the chick is raised by a human in costume. In recent years, researchers have transitioned to parent-rearing, where birds are raised in captivity by adult cranes, with minimal human intervention. 1-17 himself was a supplemental, costume-reared chick.
“Depending on the year, we get a certain number of chicks to release to supplement our wild population, and the bird in question, 1-17, is one of those birds. We also rely on natural reproduction, though historically we haven’t had a lot of it. That’s been one of our major struggles,” said Lopez. “Ultimately, we want to get to the point where we have a self-sustaining population out there that is above 100 birds at least, where we wouldn’t have to supplement any more birds.”
Crane 1-17 began his journey in the fall of 2017 in White River Marsh in Wisconsin with his sisters 2-17 and 8-17. When the birds failed to migrate on their own in a timely manner, they were relocated to Goose Pond in Indiana, and from there the trio flew to Talladega County in Alabama, where they spent the winter.
Come spring, the three cranes aimed north, but it swiftly became clear to researchers that they did not know the way back to Wisconsin. They spent some time in Illinois, and then 1-17 and 2-17 split from their sister and moved on to summer in Iowa.
At the end of November 2018, 1-17 and 2-17 took off from a stint in Northern Illinois and headed for the Wheeler National Wildlife Refuge in Alabama. Though they were met with a snowstorm en route, the pair persevered and were briefly reunited with 8-17 before she migrated to Tennessee in December.
In spring of 2019, 1-17 and 2-17 were observed wandering north and south through Indiana and Illinois, and eventually they found their way back to Wisconsin. The pair went their separate ways in April of 2019, and 1-17 was soon after captured on a Snapshot Wisconsin camera in Jackson County.
There is something of a learning curve when it comes to migratory behavior, and, as demonstrated by crane 1-17, there is sometimes a significant amount of wandering before whooping cranes grow to a reproductive age and settle.
The privilege of seeing one of these magnificent birds may be reserved for the select few who happen upon them out of sheer luck, but the population has been stable for several years and researchers look to the future of this species with optimism. Rare species like the whooping crane also become more visible as the state’s capacity for monitoring wildlife expands, and Snapshot Wisconsin spearheads this mission with a growing network of trail cameras posted throughout the landscape. As the project progresses, it will become easier to track the species that typically go undetected in wildlife surveys, better informing conservation efforts as well as broadening the public’s experience of Wisconsin wildlife.
“If you want to see a crane, we hope that you’re able to go out and find one,” said Lopez. “They may be rare in Jackson County, but we hope they’re a permanent fixture on the landscape in Wisconsin during the summer.”
Snapshot Wisconsin relies on different sources to help classify our growing dataset of more than 27 million photos, including our trail camera hosts, Zooniverse volunteers and experts at Wisconsin DNR. With all these different sources, we need ways to assess the quality and accuracy of the data before it’s put into the hands of decision makers.
A recent publication in Ecological Applications by Clare et. al (2019) looked at the issue of maintaining quality in “big data” by examining Snapshot Wisconsin images. The information from the study was used to develop a model that will help us predict which photos are most likely to contain classification errors. Because Snapshot-specific data were used in this study, we can now use these findings to decide which data to accept as final and which images would be best to go through expert review.
Perhaps most importantly, this framework allows us to be transparent with data users by providing specific metrics on the accuracy of our dataset. These confidence measures can be considered when using the data as input for models, when choosing research questions, and when interpreting the data for use in management decision making.
The study examined nearly 20,000 images classified on the crowdsourcing platform, Zooniverse. Classifications for each specie were analyzed to identify the false-negative error probability (the likelihood that a species is indicated as not present when it is) and the false-positive error probability (the likelihood that a species is indicated as present when it is not).
The authors found that classifications were 93% correct overall, but the rate of accuracy varied widely by species. This has major implications for wildlife management, where data are analyzed and decisions are made on a species-by-species basis. The graphs below show how variable the false-positive and false-negative probabilities were for each species, with the whiskers representing 95% confidence intervals.
Errors by species
We can conclude from these graphs that each species has a different set of considerations regarding these two errors. For example, deer and turkeys both have low false-negative and false-positive error rates, meaning that classifiers are good at correctly identifying these species and few are missed. Elk photos do not exhibit the same trends.
When a classifier identifies an elk in a photo, it is almost always an elk, but there are a fair number of photos of elk that are classified as some other species. For blank photos, the errors go in the opposite direction: if a photo is classified as blank, there is a ~25% probability that there is an animal in the photo, but there are very few blank photos that are incorrectly classified as having an animal in them.
Assessing species classifications with these two types of errors in mind helps us understand what we need to consider when determining final classifications of the data and its use for wildlife decision support.
When tested, the model was successful in identifying 97% of misclassified images. Factors considered in the development of the model included: differences in camera placement between sites; the way in which Zooniverse users interacted with the images; and more.
In general, the higher the proportion of users that agreed on the identity of the animal in the image, the greater the likelihood it was correct. Even seasonality was useful in evaluating accuracy for some species – snowshoe hares were found to be easily confused with cottontail rabbits in the summertime, when they both sport brown pelage.
Not only does the information derived from this study have major implications for Snapshot Wisconsin, the framework for determining and remediating data quality presented in this article can benefit a broad range of big-data projects.
In late February this year, a Snapshot Wisconsin trail camera deployed in Vilas County captured an American marten (Martes americana). This is the first time an American marten has been captured on a Snapshot Wisconsin camera! The below American marten was identified by the trail camera host, Ashley, and the identification was then confirmed by several species experts in the Wisconsin DNR. While American marten can vary in color, they are best identified by their pale buff to orange throats, dark legs and tails, vertical black lines running above the inner corners of their eyes, and bushy tails that account for one-third of their total length.
Extirpated from Wisconsin in the 1940’s, these small members of the weasel family were later reintroduced to the state and placed on the Wisconsin Endangered Species List in 1972 due to loss of suitable habitat. Marten are restricted to the northern portion of the state where they reside in dense, mature forests with preference for areas that are a mix of coniferous and deciduous trees.
Did you know that marten are excellent climbers? They use this skill not only to hunt down prey, but also to avoid potential danger. These solitary animals are very territorial, with territories spanning an average of two square miles for males and one square mile for females. Although the breeding season lasts from July to August, fertilized eggs do not fasten to the uterine wall until January or February. Females birth two to four kits in March or April, and raise their young in tree dens without any male assistance.
There is still much to be learned about American marten, as their nocturnal lifestyle and often shy demeanor make them a difficult species to study. Follow this link for more information about American marten in Wisconsin, and stay tuned to discover what rare species will be captured next on Snapshot Wisconsin trail cameras!
The Snapshot Wisconsin team is often asked why we accept data only from our Snapshot-specific cameras. While there are several reasons, the reason that was highlighted in the April 2019 newsletter was because Snapshot Wisconsin cameras are programmed to take a single photo at 10:40 a.m. each day. Although 10:40 may seem like an arbitrary time, this corresponds to the approximate time that a NASA satellite flies over Wisconsin and collects aerial imagery. (More information on how NASA data and Snapshot data are complementary can be found in this blog post.)
It may be difficult to recognize the value of a blank photo in wildlife research, but a year-long series of these photos allows us to examine something very important to wildlife: habitat condition. For each camera site, the time-lapse photos are loaded into the statistical software, “R,” where each pixel in the image is analyzed and an overall measure of greenness is summarized for the entire photo. That measure, called the Green Chromatic Coordinate, can be used to identify different “phenophases,” or significant stages in the yearly cycle of a location’s plants and animals. These stages can be delineated on a graph, called a phenoplot, where a fitted curve reveals the transition day-by-day. The 2018 phenoplot for one Snapshot Wisconsin camera site is seen below.
In 2018, 45 camera sites had a complete set of 365 time-lapse photos, but we expect many more sites to be included in the 2019 analyses. The relatively small sample size for 2018 is due in part to many counties not being opened for applications until partway through the year, but also because time-lapse data are rendered unusable if the date and time are not set properly on the camera. This may happen when the operator accidentally sets the time on the 12-hour clock instead of the 24-hour clock, or if the hardware malfunctions and resets the date and time to manufacturer settings—this is why we ask our volunteers to verify the camera’s date and time settings before leaving the site each time they perform a camera check.
The information derived from these analyses will be integrated into wildlife models. For example, the objective of one ongoing DNR research project is to understand linkages between deer body condition and habitat, which includes what’s available to deer as forest cover and food resources, as well as weather-related factors, such as winter severity or timing of spring greenup. The project currently uses weather data collected across the state to estimate snow depth, temperature, and winter severity, and creates maps based off this information.
Snapshot’s time-lapse cameras offer a wealth of seasonal information regarding type of forest cover and food sources, as well as weather-related information. In the future, phenological data obtained from Snapshot cameras could be used to create “greenup maps” that provide estimates of where and when greenup is occurring, and potentially test that information as a means of better understanding how environmental factors affect deer health, such as whether an early spring greenup improved deer body condition the next fall.