Using Artificial Intelligence to Track Birds’ Dark-of-Night Migrations

Below is a press release about the Methods in Ecology and Evolution article ‘MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks‘ taken from the University of Massachusetts Amherst.

Wood thrush. ©CheepShot

On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services’ network of constantly-scanning weather radars, until recently these data have been mostly out of reach for bird researchers.

“That’s because the sheer magnitude of information and lack of tools to analyse it made only limited studies possible,” says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst. Continue reading

Atlantis: A Model for Biophysical, Economic and Social Elements of Marine Ecosystems

Post provided by ASTA AUDZIJONYTE, Heidi Pethybridge, Javier Porobic, Rebecca Gorton, Isaac Kaplan, and Elizabeth A. Fulton

Increased Demands on a Crowded Ocean

Multiple demands on, and uses of, the ocean. ©Frank Shepherd

The ocean was once a limitless frontier, primed for exploitation of fish and other marine life. Today, a scan of the coastline (in our case off Australia and the US) shows an ocean landscape dotted with aquaculture pens, wind farms, eco-tours, and oil rigs, as well as commercial and recreational fishing boats. This presents marine and maritime managers with the huge challenge of balancing competing social, conservation, and economic objectives. Trade-offs arise even from success stories. For example, seal and sea lion populations are recovering from centuries of hunting, which is great. But now they’re preying heavily on economically valuable species like salmon and cod, creating potential tensions between fisheries and conservation communities. Ecosystem-based management is one way that we can start to address these trade-offs. Continue reading

Thermal Images in R

Post provided by REBECCA SENIOR (@REBECCAASENIOR)

Why use Thermal Images?

Temperature is important in ecology. Rising global temperatures have pushed ecologists and conservationists to better understand how temperature influences species’ risk of extinction under climate change. There’s been an increasing drive to measure temperature at the scale that individual organisms actually experience it. This is made possible by advances in technology.

Enter: the thermal camera. Unlike the tiny dataloggers that revolutionised thermal ecology in the past decade or so, thermal images capture surface temperature, not atmospheric temperature. Surface temperature may be as (if not more) relevant for organisms that are very small or flat, or thermoregulate via direct contact with the surface. Invertebrates and herps are two great examples of these types of organisms – and together make up a huge proportion of terrestrial biodiversity. Also, while dataloggers can achieve impressive temporal extent and resolution, they can’t easily capture temperature variation in space.

Like dataloggers, thermal cameras are becoming increasingly affordable and practical. The FLIR One smartphone attachment, for example, weighs in at 34.5 g and costs around ~US$300. For that, you get 4,800 spatially explicit temperature measurements at the click of a button. But without guidelines and tools, the eager thermal photographer runs the risk of accumulating thousands of images with no idea of what to do with them. So we created the R package ThermStats. This package simplifies the processing of data from FLIR thermal images and facilitates analyses of other gridded temperature data too. Continue reading

New eDNA Programme Makes Conservation Research Faster and More Efficient

Below is a press release about the Methods in Ecology and Evolution article ‘Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets‘ taken from UCLA.

It’s estimated that a person sheds between 30,000 to 40,000 skin cells per day. These cells and their associated DNA leave genetic traces of ourselves in showers, dust — pretty much everywhere we go.

Other organisms shed cells, too, leaving traces throughout their habitats. This leftover genetic material is known as environmental DNA, or eDNA. Research using eDNA began about a decade ago, but was largely limited to a small cadre of biologists who were also experts in computers and big data. However, a new tool from UCLA could be about to make the field accessible and useful to many more scientists.

A team of UCLA researchers recently launched the Anacapa Toolkit — open-source software that makes eDNA research easier, allowing researchers to detect a broad range of species quickly and producing sortable results that are simple to understand. Continue reading

Conservation or Construction? Deciding Waterbird Hotspots

Below is a press release about the Methods in Ecology and Evolution article ‘A comparative analysis of common methods to identify waterbird hotspots‘ taken from Michigan State University.

A mixed flock of waterbirds on the shore of Lake St. Clair. ©Michigan DNR

Imagine your favourite beach filled with thousands of ducks and gulls. Now envision coming back a week later and finding condos being constructed on that spot. This many ducks in one place surely should indicate this spot is exceptionally good for birds and must be protected from development, right?

It depends, say Michigan State University researchers.

In a new paper published in Methods in Ecology and Evolution, scientists show that conservation and construction decisions should rely on multiple approaches to determine waterbird “hotspots,” not just on one analysis method as is often done. Continue reading

A More Reliable Method for Estimating Abundance: Close-Kin Mark-Recapture

Post provided by DANIEL RUZZANTE

Knowing how many individuals there are in a population is a fundamental objective in ecology and conservation biology. But estimating abundance is often extremely difficult. It’s particularly difficult in the management of exploited marine, anadromous and freshwater populations. In marine fisheries, abundance estimation traditionally relies on demographic models, costly and time consuming mark recapture (MR) approaches if they are feasible at all, and the relationship between fishery catches and effort (catch per unit effort or CPUE). CPUEs can be subject to bias and uncertainty. This is why they tend to be considered relatively unreliable and contentious.

Close-Kin Mark-Recapture: Reducing Bias and Uncertainty

There is an alternative method though. It’s known as “Close-Kin Mark-Recapture” (CKMR), and is grounded in genomics and was first proposed by Skaug in 2001. The method is based on the principle that an individual’s genotype can be considered a “recapture” of the genotypes of each of its parents. Assuming the sampling of offspring and parents is independent of each other, the number of Parent-Offspring pairs (POP) genetically identified in a large collection of both groups can be used to estimate abundance. Continue reading

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

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

Scant Amounts of DNA Reveal Conservation Clues

Below is a press release about the Methods in Ecology and Evolution article ‘Empowering conservation practice with efficient and economical genotyping from poor quality samples‘ taken from the Stanford Woods Institute for the Environment.

Wild tiger in India. ©Prasenjeet Yadav

The challenges of collecting DNA samples directly from endangered species makes understanding and protecting them harder. A new approach promises cheap, rapid analysis of genetic clues in degraded and left-behind material, such as hair and commercial food products.

The key to solving a mystery is finding the right clues. Wildlife detectives aiming to protect endangered species have long been hobbled by the near impossibility of collecting DNA samples from rare and elusive animals. Continue reading

How Can Understanding Animal Behaviour Help Support Wildlife Conservation?

Below is a press release about the Methods in Ecology and Evolution article ‘A novel biomechanical approach for animal behaviour recognition using accelerometers‘ taken from the EPFL.

©Arpat Ozgul, University of Zurich

Researchers from EPFL and the University of Zurich have developed a model that uses data from sensors worn by meerkats to gain a more detailed picture of how animals behave in the wild.

Advancement in sensor technologies has meant that field biologists are now collecting a growing mass of ever more precise data on animal behaviour. Yet there is currently no standardised method for determining exactly how to interpret these signals. Take meerkats, for instance. A signal that the animal is active could mean that it is moving; alternatively, it could indicate that it is digging in search of its favourite prey, scorpions. Likewise, an immobile meerkat could be resting – or keeping watch.

In an effort to answer these questions, researchers from EPFL’s School of Engineering Laboratory of Movement Analysis and Measurement (LMAM) teamed up with colleagues from the University of Zurich’s Population Ecology Research Group to develop a behavior recognition model. The research was conducted in affiliation with the Kalahari Research Centre. Continue reading