Our July #SuperSnap was all about fishers, and we’re just going to keep on rolling on the fisher train! This science update was inspired by recent comments on a photo of a fisher in central Wisconsin. The location of the photo might cause confusion if you base where fishers *should* be on the range map we have posted. The map shows fisher range extending to only the very northern part of the state:
Whereas we’ve seen fishers on Snapshot Wisconsin cameras in counties pretty far south:
In the case of a species like fisher, which was reintroduced to Wisconsin in the 1950s and expanded its range quickly, static distribution maps go out of date quickly. This brings up a larger point about range maps being inaccurate because they are based on old, incomplete or faulty data. We provide range maps to give volunteers an indication of where they are more likely to find a certain species, but these maps are by no means perfect. The fact that we do not have very good statewide data on the distribution of most species is indeed a major reason for starting a project like Snapshot Wisconsin!
Note that the above map shows counties where we’ve seen Snapshot Wisconsin photos correctly classified as fisher. Many of the gray counties do not have any Snapshot Wisconsin cameras and so we do not have any photos there yet. This is not to say there are no fishers in the gray counties!
Astute contributors to Snapshot Wisconsin may have noted that one of the primary partners on the project is NASA. Yes, that NASA, known for space-flights and Neil Armstrong. For many people, the involvement of an air and space agency with a wildlife monitoring project may not be intuitive. Here’s how NASA and Snapshot Wisconsin work together:
NASA doesn’t just focus on our sun, solar system, and broader universe. It also has a dedicated Earth Science branch that houses research related to our atmosphere, weather, energy cycling, and ecosystems. This branch aims to predict change over time in, for example, energy cycling or biodiversity resources.
Some things, like animal population processes, are incredibly difficult to track across large spatial areas. Even with all the Snapshot Wisconsin cameras out on the ground, the total physical area in which we are observing animals is fairly limited – a point where each camera is, with a lot of space in between points. What we need to do is fill in the data gaps between camera locations. In other words, we need to be able to make predictions about areas where we don’t have observations. And this is where NASA comes into play.
NASA maintains a number of satellites that orbit Earth. These satellites carry on-board sensors that record light reflectance off of the Earth’s surface at different wavelengths. The images these satellites take of the Earth’s surface can be used to determine, for example, the locations of different landcover types (forest, wetland, prairie, etc.), or where leaves are growing or senescent, or where and when there is snow-cover. What’s great about these sensors is that they take photos regularly, and over large continuous spaces, so we can collect these data from our trail camera locations AND the spaces in between them.
Two of the more important sensors for our research are the Landsat and MODIS sensors. Landsat images have a spatial resolution of 30 meters (think of this as a pixel size – each pixel is 30 m by 30 m) and a temporal resolution (i.e., gap between flyovers) of 16 days. MODIS images have a spatial resolution of 250 – 500 m, and a temporal resolution of 1-2 days. These sensors are complementary—MODIS’s greater temporal resolution makes it more useful for detecting temporal environmental changes like plant green-up, while Landsat’s greater spatial resolution makes it more useful for detailed mapping of relatively static environmental attributes, like the location of forests, wetlands, and prairies.
How do we use satellite data and trail camera data together? We determine the association between the number of animals we count in trail camera photos and a series of environmental variables taken from satellite data. Understanding these associations gives us an idea of why animals might be more or less abundant in some places than in others, and allows us to suggest actions managers might take. For example, we might find that prairie chickens are highly associated with prairies but not with forests, and so we might suggest removing trees that are encroaching upon prairie land in order to increase prairie chicken numbers.
Without images collected from space, it would be incredibly difficult to reliably predict and map the distribution and abundance of species.
Thanks to a dedicated effort by our volunteers, Wisconsin DNR staff and University of Wisconsin students, we were able to classify all of the elk photos from 2016!
This Science Update features data from the Clam Lake elk area only, due to a lack of elk photos from the Black River Falls area. From GPS collar information, we know that many of the Black River Falls elk prefer to hang out outside of our camera area (perhaps they are bashful?). When we have more Snapshot Wisconsin cameras in the counties surrounding Black River Falls, we hope to have enough data for a Science Update on those elk as well.
There were 120 cameras active in the Clam Lake area in 2016, capturing 3,996 triggers containing elk. After grouping consecutive triggers showing the same elk, we ended up with 305 unique elk events.
We graphed daily activity patterns of antlerless elk and bulls from the 305 unique elk events. Overall, elk were most active between 6 and 9 AM and 5 and 6 PM. Antlerless elk were most active around dawn and dusk, while bull activity peaked later in the morning and evening.
We also graphed monthly elk activity throughout 2016. Because not all of our cameras were active during the entire year, we corrected photo hit rate based on the percentage of cameras active each week. The image below shows this corrected photo count for antlerless elk and bull elk throughout 2016.
The marked spike in bull activity at weeks 36 through 40 indicates the annual rut period. That period corresponds to a sharp drop off in activity level for antlerless elk; cows tend to stay put during that period while bulls move around more. (Curious about why this might be? Click here for more information on elk life history and mating behavior.) The trail cameras give us the ability to pinpoint the time frame of the rut period more precisely than we were previously able.
Because Snapshot Wisconsin trail cameras put a time and date stamp on each photo, we are able to capture the diurnal (daytime), nocturnal (nighttime), and crepuscular (active early and late in the day) behavioral patterns of different species. The graphs below show daily activity patterns using the 24-hour clock for three categories of animals captured on Snapshot Wisconsin trail cameras in Iowa and Sawyer Counties from June 1 – September 7, 2016.
Bears were most active during the day and used the midday hours more than any of the other large mammals, while coyotes and deer showed the strongest crepuscular behaviors:
Porcupines were most active in the early morning hours before sunrise. Mustelids were uniquely active during a short portion of the early daytime hours:
Grouse activity was fairly steady through the day while turkey activity increased as the day progressed:
Food for thought: why might it be beneficial for animals to be more active during certain times of the day and not others?
Each year, the WDNR uses fawn and doe counts from August and September to calculate a fawn-to-doe ratio and estimate the size of the deer herd in Wisconsin. We get the fawn-to-doe ratio by dividing the number of fawns by the number of does. In 2015, the statewide fawn-to-doe ratio was 0.89, meaning there were about 9 fawns for every 10 does. Of course, this number varied a lot across Wisconsin.
Counts submitted by the public via Operation Deer Watch and by WDNR biologists are the primary data we use to calculate fawn-to-doe ratios. This information is very useful but somewhat biased, since observations are made during daylight hours and mainly along roadsides. Snapshot Wisconsin trail cameras give us a new way to count deer because the cameras operate all the time and are placed in more natural spaces.
In our first attempt to use Snapshot Wisconsin trail cameras to calculate fawn-to-doe ratios, we used Snapshot Wisconsin photos from August 2016 that were classified as deer by trail camera volunteers in Iowa and Sawyer Counties. There were 211 deer pictures from 13 cameras in Iowa County and 331 deer pictures from 13 cameras in Sawyer County. This is a very limited sample but it let us look for early patterns.
One thing was immediately obvious: we see the same does and fawns over and over again at each camera site. Before we could come up with any accurate estimates, we would have to account for repeated counts of the same deer. One method we tried was to use the maximum number of fawns and does seen in any single photo from each camera site. This leaves us with a much smaller number of deer observations at each site, but ensures that we do not over-count. When using this method, we end up with preliminary fawn-to-doe ratios between 0.7 and 1.0 that are close to what we would expect.
Stay tuned for more on fawn-to-doe ratios and other results as we continue to add photos and classifications!
How seasonality influences where animals are located is a major scientific focus of Snapshot Wisconsin. Animals move around, so their distribution in space changes through time. Animals also tend to have different phenophases – characteristics associated with specific seasonal or annual environmental phenomena (think spring or fall migration) that can influence how often they are detected by cameras.
A classic phenophase found in mammal species is torpor or hibernation, and bears provide an excellent local Wisconsin example. Whether bears are ‘true hibernators’ or not is often debated. Compared to many smaller mammals that drop their body temperature to near freezing, bears scarcely depress their body temperature at all. Furthermore, small mammals tend to sporadically emerge from hibernation throughout the winter to pass metabolic waste and eat a little…bears do not. Most amazingly, bears can go all winter without eating or moving without substantial muscle atrophy: unlike humans or most other mammals, bears can synthesize new proteins out of the nitrogen contained in their urea. While other animals wake up to pass waste, bears turn this waste into new muscle tissue.
One way in which Snapshot Wisconsin can contribute to our understanding of bear ecology beyond measures of bear distribution or cub production is by providing information regarding the timing of when bears seem to be active. Bears are clearly more regularly photographed during summer and early autumn than during winter:
The pictures provide an indirect cue as to how bears are behaving and the timing of their hibernation. This information can be used to evaluate hypotheses regarding variation in bear hibernation behavior–for example, there is some evidence that bears living in close proximity to humans and human food sources enter hibernation later than bears primarily consuming wild foods.
Seasonal patterns in wildlife images can also provide useful population-level information. Let’s visualize the photographic rates of a species that does not practice hibernation, deer:
There is a less pronounced seasonal pattern (a large drop in June reflects a slightly unbalanced effort across the year–many cameras were being set up at this time in 2015). Still, there is a drop in late winter and early spring that is consistent with what we know about the annual population dynamics of deer in the state–most adult deer die during late winter, and late-winter abundance should always be lower than pre-winter abundance (for comparison, one can find estimates of deer abundance based upon harvest metrics here and here). The number of pictures taken during any one time period is subject to a lot of variation beyond changes in population size, and we do not imply that a 50% increase or decrease in the number of pictures corresponds to a 50% increase or decrease in the number of animals. However, the count and timing of pictures is an input for statistical models that can correct for other sources of variation and be used to formally estimate things like changes in population size.
One of our research goals is to understand where wildlife can be found across the state and how that distribution changes seasonally. Seasonal changes (i.e. phenology) are associated with the availability of cover and food for wildlife and are important in understanding the variability in species distributions. In this project, we are utilizing two ways to understand phenology: remote sensing data and trail camera images.
Remote sensing data: Snapshot Wisconsin is partnering with the National Aeronautics and Space Administration (NASA) to use earth-observing satellites to capture broad scale changes in forest phenology across the state. NASA employs several satellites such as MODIS and Landsat that orbit the earth and take ‘pictures’ of the surface at regular intervals. These ‘pictures’ are composed of measures of light reflected off the surface of the earth. Different land cover types reflect light in distinct ways, and change across seasons as plants begin leaf growth, reach peak green-up, and begin to shed leaves in autumn. Using images from space, we can capture changes in forest productivity, and the timing of forest green-up and brown-down across large regions.
Trail camera images: In addition to the satellite data, we’ve leveraged the trail cameras to capture site specific phenological data. You already know that the trail cameras take motion and heat triggered images. In addition, each camera is programmed to take one photo at approximately 11:00 a.m. each day, generating a set of time series photos. The below video shows an example of green-up at one of our camera sites.
Another exciting application of these trail camera images is the opportunity to validate phenological models. For a number of our cameras, we have fit phenological curves to the camera data (see below graph). Our analysis shows that the daily photo sets closely match MODIS satellite data. Future work will link this type of phenological data to our understanding of wildlife populations.