Tag Archives: Monitoring Wildlife

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 →

Spatially-explicit Power Analysis: A First Step for Occupancy-Based Monitoring

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Post provided by Martha Ellis and Jody Tucker

Where’s Waldo? Trying to find this fisher somewhere in a giant landscape is going to be tricky! ©Mike Schwartz

Where’s Waldo? Trying to find this little guy somewhere in a giant landscape is going to be tricky! © Mike Schwartz

The seemingly basic question of whether a population is increasing, decreasing, or stable can be one of the most difficult to answer. Collecting data on rare and elusive species is hard. Imagine trying to detect a handful of fisher or wolverine across hundreds of thousands of acres – it is physically demanding, time consuming and logistically complicated. And that’s just to do it once! To monitor a population for changes, you have to repeat these surveys regularly over many years. The long-term monitoring that is necessary for conservation requires careful planning and a substantial commitment of resources and funding. So before we spend these valuable resources, it’s critical to know whether the data we are collecting can help us to answer our questions. Continue reading →

Methods in Ecology and Evolution 2015: The Year in Review

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Happy New Year! We hope that you all had a wonderful Winter Break and that you’re ready to start 2016. We’re beginning the year with a look back at some of our highlights of 2015. Here’s how last year looked at Methods in Ecology and Evolution.

The Articles

We published some amazing articles in 2015, too many to mention them all here. However, we would like to say a massive thank you to all of the authors, reviewers and editors who contributed to the journal last year. Without your hard work, knowledge and generosity, the journal would not be where it is today. We really appreciate all of your time and effort. THANK YOU!

mee312268_CoverOpportunities at the Interface between Ecology and Statistics

There was only one Special Feature in the journal this year, but it was a great one. Arising from the 2013 Eco-Stats Symposium at the University of New South Wales and guest edited by Associate Editor David Warton, Opportunities at the Interface between Ecology and Statistics was one of the highlights of 2015 for us. It consists of seven articles written collaboratively by statisticians and ecologists and highlights the benefits of cross-disciplinary partnerships. Continue reading →

Building a Better Indicator

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Post Provided by Charlie Outhwaite & Nick Isaac

Nick and Charlie are giving a presentation on ‘Biodiversity Indicators from Occurrence Records’ at the BES Annual Meeting on Wednesday 16 December at 13:30 in Moorfoot Hall. Charlie will also be presenting a poster on Tuesday 15 December between 17:00 and 18:30 on ‘Monitoring the UK’s less well-studied species using biological records‘ in the Lennox Suite.

Biodiversity Indicators are some of the most important tools linking ecological data with government policy. Indicators need to summarise large amounts of information in a format that is accessible to politicians and the general public. The primary use of indicators is to monitor progress towards environmental targets. For the UK, a suite of indicators are produced annually which are used to monitor progress towards the Aichi targets of the Convention on Biological Diversity as well as for European Union based commitments.  However, this is complicated by the fact that biodiversity policy within the UK is devolved to each of the four nations, so additional indicators have been developed to monitor the commitments of each country.

© Dave Colliers

© Dave Colliers

A range of biodiversity indicators exist within this suite covering the five strategic goals of the Convention; which include addressing the causes of biodiversity loss, reducing pressures on biodiversity and improving status of biodiversity within the UK. Within strategic goal C (improve status of biodiversity by safeguarding ecosystems, species and genetic diversity) there are currently 11 “State” indicators that use species data to monitor progress towards the targets underlying this goal. Most existing species based indicators use abundance data from large scale monitoring schemes with systematic protocols. However, there are other sources of data, such as occurrence records, that can offer an alternative if they are analysed using the appropriate methods. This post will discuss the development of species indicators for occurrence records to complement the current UK species based indicators, specifically relating to the C4b priority species indicator and the D1c pollinators indicator. Continue reading →

Progress and Future Directions for Passive Acoustic Monitoring: Listening Out for New Conservation Opportunities

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Post provided by Ammie Kalan (Post-doctoral researcher at the Max Planck Institute for Evolutionary Anthropology, Department of Primatology)

A Primate Call in a Forest is like a ‘Needle in a Haystack’

An ARU powered by solar energy recording in the Taï national park, Côte d’Ivoire. ©Ammie Kalan

A solar-powered ARU recording in the Taï national park, Côte d’Ivoire.
©Ammie Kalan

Finding a call of a particular primate species within hours and hours of audio recordings of a forest is no easy task; like finding a ‘needle in a haystack’ so to speak. Automated acoustic monitoring relies on the ability of researchers to easily locate and isolate acoustic signals produced by species of interest from all other sources of noise in the forest, i.e. the background noise. This can be much harder than it sounds. Think about whenever you have to use any kind of voice recognition system: seeking out a quiet room will greatly improve the chances you are understood by the robot-like voice on the other end of the phone. If you ever set foot in a rainforest the first thing you’ll notice is that it is anything but quiet. In fact characterizing and quantifying soundscapes has become a marker for the complexity of the biodiversity present in a given environment.

Primate monitoring programmes can learn a great deal from cetacean research where Passive Acoustic Monitoring (PAM) is the norm (since individuals are rarely observable visually). Research on bats and birds can provide excellent examples to follow as well. Automated algorithm approaches including machine learning techniques, spectral cross-correlation, Gaussian mixture models, and random forests have been used in these fields to be able to detect and classify audio recordings using a trained automated system. Such automated approaches are often investigated for a single species but impressive across-taxa efforts have also been initiated within a framework of real-time acoustic monitoring. Implementing these in other research fields could lead to significant advances. Continue reading →