Bats aren’t just for Halloween: Insectivorous Bats in North America

Post provided by CHLOE ROBINSON (@CVROBINSON92)

The Hoary bat (Lasiurus cinereus) is the most widespread bat in the US. ©Veronica Zamora-Gutierrez

Hello! This is my first post as Blog Editor for Methods in Ecology and Evolution and I’m thrilled to be starting with an exciting, thought-provoking topic in the wake of Halloween. But first, let me introduce myself. I currently work as a Postdoctoral Fellow and Project Manager in the Hajibabaei Lab at the Centre for Biodiversity Genomics (University of Guelph, ON, Canada) and my undergraduate and postgraduate degrees are both from Swansea University (UK). My research background is largely focused around the application of environmental DNA (i.e. free DNA found in natural environments) to detect and monitor aquatic species and answer ecological questions through both single-species detection and DNA metabarcoding.

At the moment, I’m working on the STREAM project, which combines community-based monitoring with DNA metabarcoding to gain a better understanding of freshwater health across Canada. One of my favourite parts about being in this position is the opportunity to get involved with other research being conducted in the Hajibabaei Lab. This is how I branched out into the wonderful world of bat ecology. Continue reading

Remote Camera Network Tracks Antarctic Species at Low Cost

Below is a press release about the Methods in Ecology and Evolution article ‘Estimating nest‐level phenology and reproductive success of colonial seabirds using time‐lapse cameras‘ taken from NOAA Fisheries.

Camera system in place in an Adélie and gentoo penguin colony ©Jefferson Hinke, NOAA Fisheries

Camera system in place in an Adélie and gentoo penguin colony ©Jefferson Hinke, NOAA Fisheries

An international research team has developed a simple method for using a network of autonomous time-lapse cameras to track the breeding and population dynamics of Antarctic penguins, providing a new, low-cost window into the health and productivity of the Antarctic ecosystem.

The team of scientists from NOAA Fisheries and several other nations published in the journal Methods in Ecology and Evolution, descriptions of the camera system and a new method for turning static images into useful data on the timing and success of penguin reproduction. They say that the system monitors penguins as effectively as scientists could in person, for a fraction of the cost. Continue reading

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

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