Tag Archive | Zooniverse

Un-deer the Weather

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:

Swollen chest

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.”

Warts

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

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August #SuperSnap

This month’s #SuperSnap features a pair of wood ducks from Richland County! Their colorful head makes them stand out against the early spring growth in this vernal pool. The wood duck (Aix sponsa) does not have any close relatives in North America (Audubon). This makes it a unique bird that prefers the shaded waters in woodland areas. 

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Thank you Zooniverse volunteers Kjreynolds1957 and Nsykora for nominating these birds. Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.

July #SuperSnap

This month’s #SuperSnap features a coyote (Canis latrins) as it approaches a Snapshot Wisconsin camera deployed in Racine County. Snapshot Wisconsin recently surpassed 30 million trail camera images – staff members and volunteers alike are consistently amazed by some of the images coming out of the project. Thank you to Zooniverse volunteers WINature and Swamp-eye for nominating this series!

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Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.

June #SuperSnap

This month’s #SuperSnap features a mink from Waupaca county, stepping into ice cold water. Mink are amazing swimmers and divers. Even in the winter, you ask? Yes, thanks to insulation from a thick underfur & oily hair, minks maintain their aquatic lifestyle year round, although less so when it’s cold.

Thanks @Tjper for nominating this sequence!

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Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.

May #SuperSnap

This month’s #SuperSnap features one of the best quality wolf photos ever captured on a Snapshot camera. Thanks to @crazylikeafox and @smuerett for bringing attention to this one from Waupaca County! The Wisconsin DNR, along with other organizations, have monitored wolf populations in numerous ways including with a network of volunteers who conduct winter tracking surveys. If you want to learn more about wolves in general, visit our wolf fact sheet.

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Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.

May Science Update: Maintaining Quality in “Big Data”

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.

False-positive, false-negative

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).

false_negative_graph

Figure 2 from Clare et al. 2019 – false-negative and false-positive probabilities by species, estimated from expert classification of the dataset. Whiskers represent 95% confidence intervals and the gray shading in the right panel represents the approximate probability required to produce a dataset with less than 5% error.

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.

Model success

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.

bear_photo

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.

April #SuperSnap

This month’s supersnap goes to an inquisitive red fox (Vulpes vulpes) chasing prey, nominated by @AUK. Red fox are known for their intelligence and cunning. These abilities help them to survive all over the world in a diverse set of habitats including mountains, deserts, grasslands, urban environments and here in Wisconsin!

The University of Wisconsin Madison has launched a project, the Urban Canid Project, to investigate red fox and coyote use of urban landscapes. Similar to Snapshot Wisconsin, the Urban Canid Project uses the power of citizen science to collect data on space use, behavior and population demographics of city dwelling canids. To learn more about the project, check out this link.

 

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Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.

March #SuperSnap

This month’s #SuperSnap features an Oneida County pair of North American porcupine (Erethizon dorsatum) nominated by Zooniverse volunteer cjpope!

Did you know that a baby porcupine is referred to as a “porcupette”? Porcupine give birth to a single porcupette. Porcupettes enter the world with soft quills, which harden within an hour. Contrary to popular belief, porcupine cannot shoot their quills – although they still come in handy for defense!

 

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Continue classifying photos on Zooniverse and hashtagging your favorites for a chance to be featured in the next #SuperSnap blog post. Check out all of the nominations by searching “#SuperSnap” on the Snapshot Wisconsin Talk boards.

Thank you for Season 10!

thank youThanks all for another terrific season on Snapshot Wisconsin!  I can’t believe Season 10 of the project has come and gone.  As some of you may have noticed, this season was special, not just because it was our 10th, but it also looked a little different than past seasons.

This season a random selection of our volunteers had the option to work through a series of levels where they were asked not only about the wildlife in the photo, but also about the habitat seen in the photo (e.g. how much snow or green vegetation there was in the photo).  The data contributed by these volunteers produced valuable information that will help us to better understand the relationship between Wisconsin animals and the habitat where they live.  Several recent blog posts have highlighted why this relationship is so important (see here, here, and here if you missed the posts!)

Why did only some volunteers see the levels?

The addition of levels was a big departure from how our Snapshot Wisconsin website has been formatted.  We wanted to carefully examine how this modified experience affects volunteer behavior, learning, and connection to the community. Only a portion of users got to see the experimental site, so we can accurately assess it.  This test is actually part of my research as a PhD student on the Snapshot Wisconsin project.

As team member on Snapshot Wisconsin, my role is to understand the people side of citizen science.  I ask questions like: Why do volunteers get involved in citizen science?  What do volunteers take away from participating?  My goal is to provide feedback that can improve volunteer experience and the science that our project produces.  This season is just one part of that effort.

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White-tailed deer in snow

What are the next steps?

Right now, I’m busy looking at the results of this season. In the near future, Snapshot Wisconsin will return to its normal look.  Whether or not people responded positively to the levels will affect whether the Snapshot Wisconsin Team decides to use the levels during some future seasons.  When I have results to share, we’ll be sure to link to them on the Talk boards and this blog.

How can you help?

One way we’ll assess how volunteers responded to the levels is by looking at how many classifications they completed.  We also want to hear from you directly–regardless of whether or not you had access to the levels.  Snapshot Wisconsin volunteers will receive an email from Zooniverse asking them to complete a survey about their experience this past season.  Your responses are essential in helping us to evaluate Season 10.

What will happen with the photos that have not yet been retired from Season 10?

A handful of photos were not retired before Season 10 ended.  While Season 11 is running, we’ll be busy doing some analysis of the photos to see which need more classifications. We’ll then re-post these photos in Season 12 and beyond.

If you have questions don’t hesitate to reach out to me via private message on Zoonvierse (@anhaltcm) or on the comments here!  On behalf of the whole team, thank you again for Season 10!

Snap-a-thons

What is a Snap-a-thon you may ask? Take a guess from one of three options below.

  1. A wildlife photography marathon.

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Source: Bored Panda

  1. A classification party with the Snapshot Wisconsin project.

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  1. A marathon for snapping turtles.

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Source: A.B. Sheldon, WDNR

 

If you selected option 2, you are right!

If you read our newsletter or visit our website often, you will notice that the Snapshot Wisconsin project generates a lot of data. We have collected nearly 21 million photos so far. These photos become useful to support wildlife management decisions only when they have a classification tag attached to them and their accuracy is reliable. We have help on hand – more than a thousand trail camera hosts and nearly six thousand Zooniverse volunteers helping us classify these pictures. The idea behind a Snap-a-thon is to spread the word about the project even farther while running a fun competition using the Zooniverse website.

How a Snap-a-thon works is very simple: participants team up or play alone to classify pictures on Snapshot Wisconsin’s Zooniverse page for a set amount of time, typically 20 minutes. Each team is given a checklist of species. During the competition, participants tick off any of the listed species that they see and classify correctly. For uncommon or difficult-to-classify species, participants must raise their hands to get verification from the project team before their classifications are counted. Uncommon species or uncommon occurrences (like multiple species seen together in a photo sequence) also earn participants a higher score. In the end, we tally up the scores and declare a winner. So far, we’ve had 4 such contests and our contestants want to keep classifying even after the time is up. So, it’s pretty addictive!

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Snap-a-thon checklist

 

Pictures from previous Snap-a-thons:

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Snap-a-thon at UW-Madison

 

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Snap-a-thon at the International Crane Foundation in Baraboo, Wisconsin

 

If you’d like to host your own Snap-a-thon, drop us an email at DNRSnapshotWisconsin@wisconsin.gov and we’ll provide you with resources!