I’m often asked how to age tracks. The tracks in the photo below were made by the same foot of the same person wearing the he same boots, however one was made 3 weeks prior to the photo, and the other was made 30 seconds prior.
A simple trick used to help age a track is to compare the subject track to those just made by the observer, or to tracks of a known age.
Tracks erode or decay over time causing sharp edges and steep slopes to soften. Tracks in different substrates will age differently, as will those exposed to different environmental / weather conditions. Find or make a fresh track, then return daily to observe how it changes with age. Tracks made in wet clay followed by a long stretch of dry weather may age/decay/erode very slowly, but will show other signs of aging such as cracking and litter accumulation. Tracks made in snow may age/decay/erode very quickly depending on photo period, ambient temperature, wind conditions, and the type/condition of the snow itself at the time the track was made.
You can also use environmental events evident in the track, or not evident in the track, to help age it. For example if you know it rained yesterday, and the tracks you see have evidence of being rained in, you can deduce that the tracks were made before it rained.
Tracks made by some animals will age differently than others. Hard footed and heavy animals like a moose for example can make tracks that will last for years because they are capable of leaving an impression in hard and erosion resistant substrates due to their weight and the hardness of their hooves. The tracks of a soft-footed animal of similar size, like a bear for example, are likely to age/decay/erode faster than those of a moose, in the majority of substrates. A light and soft-footed animal like a snowshoe hare may make tracks that age very quickly in many substrate types.
As you can see, as with most aspects of tracking, there are a lot of variables to consider when aging tracks, and the answers sought are often not just in what we can see on the ground, but in our awareness of the environment, our knowledge of local species and region, and in the breadth of experience we bring with us.
It takes some practice, and even so, some studies suggest that the best even a very good tracker can do with reliable consistency is to classify tracks into either fresh, or not fresh.
The footprint above (posted to social media on May 25, 2020) is the footprint of a Snowshoe Hare in snow, photographed on March 24th, 2020, in Elk Island National Park, Alberta, Canada.
The track patterns of snowshoe hares are very recognizable, but most people haven’t looked very closely at the details of a single print, so when they encounter a print without a track-pattern, they may not recognize it. A snowshoe hare’s foot is fully furred making details hard to pick out in most conditions, but that in itself is a detail that can lead you to a correct identification. Though the toes may register as distinct appendages, a palm pad (interdigital or metatarsal) will never register clearly, nor a heel pad with snowshoe hare, so the lack of those feature is a good clue.
Another good clue is the toe arrangement in the tracks. Four toes will register in front and hind, and that is different than most rodents which register four toes in the front tracks, and five in the hind. In addition to the number of toes, take account of the symmetry of the arrangement in an individual print. Note how the toes are loaded all to outside of the foot.
The claws of snowshoe hare are thin, fine, and sharp. They don’t always register, but the first time you see them clearly register in a splayed print, you may doubt for a moment that you are looking at the print of a bunny, and think rather that it might be something more dangerous like a lynx, a wolverine, or a dragon.
Earlier I mentioned that snowshoe hare track patterns are very recognizable, so I’d better explain. Snowshoe hares are very consistent in their use of a bounding gait. I the gait the front feet land first, and then the hind feet swing around on either side, and register ahead of the front feet. The hind feet land simultaneously, side by side.
The most likely animal you may have difficulty distinguishing from snowshoe hare in the Edmonton area would be black-tailed jackrabbit. Jackrabbit are similarly sized and shaped animals, and so they leave similar tracks. Habitat is probably your best clue for distinguishing these two species. Jackrabbits prefer open areas where they can see predators coming and use their powerful speed to escape across open terrain. Snowshoe hare on the other hand like to stay in or near the forest where they can put rose bushes, deadfall and thickets between them and any predators. Another indicator is the frequency of off-set hind feet positioning. Where a snowshoe hare only rarely positions its hind feet off-set to one another, a black-tailed jackrabbit will frequently do so.
Team Profile: Shantel KoenigPh.D. Landscape Ecologist & GIS Specialist
Shantel started with Fiera in October of 2016, and brought highly valued capabilities in complex spatial modelling and statistical analysis. She came with a Masters in Geographic Information Systems, and completed her PhD a short while after settling in. Her graduate research focused on using Spatial Interaction Models (SIMS) to model metapopulations and analyze landscape connectivity. Since then, her experience creating Resource Selection Function (RSF) models for species at risk, processing and analyzing wildlife movement (telemetry) data, creating land cover classifications, and conducting habitat connectivity analysis using a variety of spatial modeling techniques has been invaluable. During her time with us she has published two peer reviewed research papers, one in the field of theoretical ecology, and another in the field of remote sensing, and contributed significantly to at least a dozen high profile technical reports. When Shantel isn’t in the office helping to make Fiera awesome, there is a good chance that she is riding a muddy bicycle somewhere really, really fast, playing electric base on a jazz or folk music album, or countering the stigma associated with having advanced statistical skills by posting images of her beloved cat, Ella.
The footprint above (posted to social media on May 12, 2020) is the footprint of a Rock Pigeon in snow, in Old Strathcona, Edmonton Alberta, on April 2nd.
Pigeon footprints are similar to game bird (like grouse and quail) tracks in general size, and in structure except for the length of the backwards toe (called a halux). Pigeons are perching birds, and as such, have a well developed, longer, halux to help them grip branches. The halux of a rock pigeon is roughly 1/2 to 2/3 the length of its lead toe, and it will register in most tracks. Birds that spend a lot of time on the ground like grouse and quail have a reduced halux less than 1/4 the length of the lead toe, and it may not register in the majority of tracks; when it does register, it may only be the tip of the claw that does.
The length and width of bird tracks is helpful for identification. Typical measurements of rock pigeon tracks are 6 cm long (include the halux & claws), and 4.5 cm wide.
Pigeons and grouse tend to leave an alternating (walking or trotting) track pattern. Other birds often hop, leaving a 2 x 2 track pattern.
Watch for bird tracks in snow, sand, and around puddles after a rain. Other birds that frequently leave tracks include ravens, magpies and other corvids, waterfowl, shorebirds, and robins.
UAVs, also known as drones, have become a popular tool in many sectors for collecting detailed, high-resolution imagery at local scales. Most users of UAVs rely on the spectral information (the colours) in a scene to classify features or to generate relative indices of plant vigour or health. When applied to a single scene at a single point in time, important insights can be learned from analyzing the spectral information, such as the percent cover of a class of interest, or locations where plant growth is more or less vigorous. However, when there is a need to compare several different scenes, or to compare the same scene over time, more and more users are finding that the spectral information can be somewhat inconsistent or unstable, which severely constrains the type of analysis that can be performed and the inferences that can be made. We highlight some of these issues in our recently published paper in the ISPRS Journal of Photogrammetry and Remote Sensing (Cao et al. 2019). In particular, changes in the natural lighting and atmospheric conditions between different flights, or in some cases even during the same flight, introduce uncertainty into the spectral data that is collected, which prevents meaningful and reliable information from being generated.