A first-of-its-kind report assessing the current field of conservation technology and various tools’ ability to diagnose, understand and address the most critical environmental challenges of our time finds three emerging technologies have particularly promising trajectories to advance conservation over the next ten years.
Artificial intelligence, specifically machine learning and computer vision, environmental DNA (eDNA) and genomics, and networked sensors are named the top three emerging conservation technologies in A Global Community-Sourced Assessment of the State of Conservation Technology, published today in Conservation Biology.
The report, led by WILDLABS and Colorado State University and supported by their NGO partners and tech sector leaders Microsoft and Arm, surveyed 248 conservationists and technologists across 37 countries using the WILDLABS.NET platform, asking them to rate 11 widely used tools for their capacity to advance conservation. More than 90 percent of respondents rated each of the top three emerging technologies as ‘very helpful’ or ‘game changers.’ Although these three technologies ranked among the lowest when it came to current overall performance, their promising trajectories show their substantial room for and likelihood of further development, potentially making them areas ripe for investment and exploration.
The three technologies represent new frontiers in wildlife conservation, at a time when protecting and restoring the natural world has never been more important or urgent.
“This timely research, for the first time, puts data behind what many working in conservation technology know all too well to be true. While the promise of technology to support conservation and sustainability efforts has never been greater, lack of funding, capacity and coordination continue to hamper progress. The COVID-19 pandemic and recent UN IPCC climate change report demonstrate in stark terms that humanity’s relationship with nature is broken. Conservation technology has a critical role to play to help avoid future pandemics and the worst consequences of climate change. To maximize impact, there needs to be an order (or two) magnitude increase in sustainable funding to help develop, deploy and scale conservation technology solutions such as machine learning, eDNA, and networked sensors across a wide swath of conservation and development challenges.”
— Colby Loucks, Vice President for Wildlife Conservation, WWF-US
Artificial intelligence is increasingly being used in the field to analyse information collected by wildlife conservationists, from camera trap and satellite images to audio recordings. AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings – hugely reducing the manual labour required to collect vital conservation data.
Environmental DNA (eDNA), meanwhile, is being used by pioneering conservationists to collect a wealth of biodiversity data quickly and easily, simply by scanning samples of water or soil. Traces of animal DNA can reveal the presence of previously unobserved species in a local area. A few small samples can contain the DNA of dozens of species and give a detailed snapshot of an ecosystem quickly and efficiently, data that can be used to make the case for greater protections for an area.
Finally, networked sensors allow camera traps, acoustic recorders, tracking devices and other conservation hardware to connect online, forming a comprehensive picture of animal movements and behaviour, becoming the ‘eyes and ears’ of conservationists and local communities, enabling monitoring, tracking and instant alerts about imminent threats.
Respondents also identified which tools are already meeting expectations in the field. The tools with the highest overall performance ratings were GIS and remote sensing, drones, and mobile apps, with more than 70 percent of respondents rating them ‘good’ or ‘very good.’
Despite technology’s progress, systemic challenges across the conservation sector inhibit the development and adoption of promising conservation technologies. Respondents identified unsustainable financing, lack of coordination across efforts and inadequate capacity building as the top three challenges encountered when developing and adopting conservation technology.
Importantly, a range of financial and technical barriers were found to disproportionately affect women and individuals in developing countries, highlighting the need to evaluate and address the potential exclusion of critical conservation stakeholders in this rapidly developing field.
- Respondents from countries with developing economies were more than four times as likely to report being constrained by technology costs and access to financial support for technology development
- Female respondents were also nearly four times as likely to report challenges securing financial support for technology development, and more than twice as likely to report being constrained by insufficient technical skills for technology adoption
Despite these challenges, the majority of respondents said conservation technology is becoming increasingly accessible, tools are evolving quickly and the culture is becoming more collaborative. More than half of respondents said they feel more optimistic about the future of conservation technology than they did 12 months ago.
With artificial intelligence, genetics, and sensors already revolutionizing many of the world’s largest business sectors, this study makes clear the tremendous opportunity to invest in harnessing their potential for conservation. However, it is also clear that advancing the field of conservation technology will require more than investing in promising tools.
According to input from the study’s focus groups with nearly 50 leading experts, overcoming these systemic challenges to achieve scalable impact calls for a dramatic shift in approach, from a patchwork landscape of one-off projects competing for limited resources to an internationally coordinated organizational ecosystem with innovative funding mechanisms to back it.