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Technology makes studying wildlife easier, but access isn’t equal

  • Studying primates and other wildlife in nature has long been a challenge owing to their diverse habitats and limitations on established research patterns.
  • But a pair of recent studies highlights how the emergence of new technology, ranging from camera traps to drones, has made the work easier in recent years.
  • Still, exorbitant costs and lack of technical know-how mean the technology isn’t easily accessible to researchers across the world.

Studying primates in the wild has historically been a tough task.

Alexander Piel knows the challenges too well. Since 2005, the biological anthropologist has been studying primates in countries like Tanzania, Madagascar, Kenya and Senegal. For one, the vastly diverse habitats made it difficult to track them: while some live in dense tropical forests, many others are found in savannas; some are entirely terrestrial, while others live in trees. Apart from the constraints imposed by location, there are significant gaps in the ability to monitor animals when researchers just aren’t around.

“The data followed researcher patterns,” Piel, a lecturer in anthropology at University College London, tells Mongabay in a video interview, referring to how the data are usually collected during the day, with most of it being what field researchers can detect themselves through binoculars and cameras. “We didn’t know a lot about what happened when we were not present, and so we had large spatial and temporal gaps.”

The emergence of newer technology in recent years, however, might be changing how data about primates are collected and analyzed by researchers. A study co-authored by Piel and published earlier this year in the International Journal of Primatology summarizes some of the tools — ranging from camera traps to drones — that researchers have adopted to ensure more efficient, continuous and non-invasive monitoring of primates.

Researchers have adopted to ensure more efficient, continuous and non-invasive monitoring of primates using tools like camera traps and drones. Image by Mokhamad Edliadi/CIFOR via Flickr (CC BY-NC-ND 2.0).

“The conservation of primates, and vertebrate species in general, is undergoing a revolution in the way of obtaining data with the advancement of emerging technologies such as the use of drones, AI and the IoT,” Geison Mesquita, a biologist and environment consultant who co-authored another study about the rise of technology in tracking wild animals, tells Mongabay in an email interview.

Eyes and ears in the forest

Camera traps and passive acoustic monitoring — cameras and microphones — have helped provide “eyes and ears” in the forest, both being tools that enable long-term monitoring because they can be deployed over extended periods of time. Drones incorporate both functions, and give researchers valuable new perspectives. Armed with infrared-trigger cameras, they’ve also made it easier for researchers to spot primates or other wildlife that live in trees.

Fecal samples have also long been used to study the genetics and diets of primates. But with labs to analyze samples often being located far from where the fieldwork is conducted, a lot of time and money is spent on storing, processing and transporting the samples. The emergence of portable genomics labs has helped solve many of those issues, facilitating new ways to store and analyze samples.

But despite these and many other advances, the researchers interviewed by Mongabay all say they approach new technology with caution. The limitations, Piel says, are multifold.

First, not everyone has the training and wherewithal to use newer technology. Piel says that while acoustic sensors are very straightforward to set up and use, camera traps aren’t. “You need to set them up a certain way and the menus are often only in one language and so you start to see these barriers on who can use the technology,” he says. The energy-hungry nature of camera traps also makes it imperative to constantly check on their functioning.

Drone image of the rainforest in Gola Rainforest National Park in Grebo-Krahn National Park. Image by Rhett A. Butler/Mongabay.

Other financial, technological and often political constraints set in when it comes to the use of drones. “You get what you pay for, and the less you pay, the more likely that if you bump into a tree, you will lose your drone,” Piel says. “Plus, they are carrying technology that is not cheap, like a lidar sensor or some camera that is taking high-resolution footage of a forest. So if you lose the vehicle, you also lose your sensor and, with it, your data.”

Too much data to process

The high cost and technical hurdles aside, there are also barriers that exist in efficiently analyzing and using the enormous amounts of data collected with the use of new technology. “The amount of data we are now generating completely outpaces our ability to analyze it, as biologists at least,” Tara Stoinski, president and CEO of the Dian Fossey Gorilla Fund, tells Mongabay in a video interview. “And so you are just sitting on troves and troves of data.”

While tools like artificial intelligence and machine learning have made data analysis much easier, Stoinski says it’s sometimes challenging to form collaborations with the right people who have the technical know-how to analyze the data. “If you don’t have those synergies already worked out, or you can’t build them because of a number of reasons, then the technology suddenly is not so useful,” she says. “You might as well have spent that time walking in the forest because then at least you have your data.”

Stoinski emphasizes the need to establish more collaborations between researchers in the field and those who have the technical knowledge. Often, the skill set or technology to perform the analysis is lacking in the countries where the fieldwork is done. That means data and samples have to be exported, often to labs and facilities in Europe or the U.S. “It’s a lot of processing and permissions and it just slows down getting your results,” Stoinski says, reiterating the need to establish more such collaborations in the Global South.

Mesquita, with the Institute Baguaçu of Biodiversity Research in Brazil, agrees. He cites cases he’s seen where the prohibitive cost and technical difficulties have prevented technology from being adopted for wildlife conservation. In Brazil, he says, very few conservation initiatives involve the use of GPS tracking, let alone drones, because of financial constraints. Increasing the availability of these technologies to researchers across the world through cost reduction and ease of use is a challenge, he says, one that needs to be urgently addressed.

A researcher installs an acoustic sensor to record bats. Image by Alison McCartney via Flickr (CC BY 2.0).

“Many developing countries, where large areas of the planet’s biodiversity are usually found, are still unable to use these technologies,” Mesquita says. Given the unfolding biodiversity crisis, researchers can’t afford to lose more time attempting to find ways to collect and analyze data, he says. “The speed with which the loss of biodiversity has been occurring means we have to be more practical and fast in obtaining biodiversity data.”

Citations:

Piel, A. K., Crunchant, A., Knot, I. E., Chalmers, C., Fergus, P., Mulero-Pázmány, M., & Wich, S. A. (2022). Noninvasive technologies for primate conservation in the 21st century. International Journal of Primatology, 43(1), 133-167. doi:10.1007/s10764-021-00245-z

Mesquita, G. P., Mulero-Pázmány, M., Wich, S. A., & Rodríguez-Teijeiro, J. D. (2022). A practical approach with drones, smartphones, and tracking tags for potential real-time animal tracking. Current Zoology. doi:10.1093/cz/zoac029

Banner image: A red tailed sportive lemur in Madagascar. Image by Rhett A. Butler/Mongabay.

Abhishyant Kidangoor is the 2022 Sue Palminteri WildTech Reporting Fellow, which honors the memory of Mongabay Wildtech editor Sue Palminteri by providing opportunities for students to gain experience in conservation technology and writing. You can support this program here.

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