Tag Archives: Deep Learning

Issue 10.10: Conservation, Molecular Techniques, Stats and More

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The October issue of Methods is now online!

We’re a little lat on this post, but there’s another great issue of Methods in Ecology and Evolution online now.

This month, we cover movement ecology, plant cover class data, acoustic indices, local adaptations an much more.

There’s more information below on the Featured Articles selected by the Senior Editor and all of our freely available papers (Practical Tools and Applications articles are always free to access for everyone upon publication, whether you have a subscription or not). Continue reading →

Assessing Sea Turtle Populations: Can We Get a Hand From Drones and Deep Learning?

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Post provided by PATRICK GRAY

An olive ridley sea turtle in Ostional, Costa Rica. ©Vanessa Bézy.

Understanding animal movement and population size is a challenge for researchers studying any megafauna species. Sea turtles though, add a whole additional level of complexity. These wide-ranging, swift, charismatic animals spend much of their time underwater and in remote places. When trying to track down and count turtles, this obstacle to understanding population size becomes a full-on barricade.

Censusing these animals doesn’t just satisfy our scientific curiosity. It’s critical for understanding the consequences of unsound fishing practices, the benefits of conservation policy, and overall trends in population health for sea turtles, of which, six out of seven species range from vulnerable to critically endangered. Continue reading →

Citizen Science Projects Have a Surprising New Partner – The Computer

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Below is a press release about the Methods in Ecology and Evolution article ‘Identifying animal species in camera trap images using deep learning and citizen science‘ taken from the University of Minnesota-Twin Cities.

The computer’s accuracy rates for identifying specific species, like this warthog, are between 88.7 percent and 92.7 percent. Image credit: ©Panthera

The computer’s accuracy rates for identifying specific species, like this warthog, are between 88.7 percent and 92.7 percent. ©Panthera

For more than a decade, citizen science projects have helped researchers use the power of thousands of volunteers who help sort through datasets that are too large for a small research team. Previously, this data generally couldn’t be processed by computers because the work required skills that only humans could accomplish.

Now, computer machine learning techniques that teach the computer specific image recognition skills can be used in crowdsourcing projects to deal with massively increasing amounts of data—making computers a surprising new partner in citizen science projects.

Continue reading →