Our Deep Dives are back. In this edition you can expect to learn:

• 🌳 Why AI can support positive Nature outcomes? 

• 🚜 Who are the start-ups leading the way?

• 💰 What is the funding landscape?

• 💪 What are the challenges to be overcome?

• 💡… As well as some hot takes from experts in the field

Readers Beware: We’ve spent the past couple of months huddling up with founders, academics and investors operating at the intersection of Nature and AI to capture a perfect snapshot, but things are changing quickly. We’re sure we won’t have captured the full picture here. Please do call us out to update this picture… In the spirit of speedily but steadily bringing this rapidly evolving AI X Nature space online together!

 🌳 Why AI for Nature?

Climate Change and Biodiversity Loss exacerbate each other. Simply put, rising temperatures will worsen biodiversity loss, releasing more greenhouse gases, creating a negative feedback loop. This will inevitably aggravate a range of societal issues, from civil conflict to resource competition, in turn causing more biodiversity and climate impacts. Conversely, improved ecosystem condition is likely to help store and sequester carbon, support water and food security, and provide greater resilience to climate change impacts.

There is a growing desire to understand and quantify the impact and dependencies that organisations have on Biodiversity. This desire is driven by a growing volume of framework and policies (e.g. the Kunming-Montreal Global Biodiversity Framework or the TNFD); a growing body of Investors seeking to better allocate and engage funds based on material nature-related issues; and a growing number of forward-thinking organisations seeking to better understand their impact and dependencies on biodiversity.

As the saying goes, “what gets measured gets managed”...AI as a tool is uniquely positioned to manage these complex issues. Due to its capacity to gather, complete, and interpret large, complex datasets on biodiversity and ecosystems, it can be used to support stakeholders in taking a more informed approach to combating biodiversity loss. At the same time AI poses its own environmental and ethical challenges.

Innovators are wrangling with two big questions:

• How can we use AI to protect Nature (and who is leading the way)?

• How can we prevent AI from exacerbating harm to humanity, ecosystems, and communities reliant on ecological preservation?

🚜 Question 1) How can we use AI to protect Nature (and who is leading the way)?

Let’s find out how…

1) Locate Interactions with Biodiversity & Ecosystems. The first step after any feel-good corporate biodiversity announcement is to actually understand where their company operates. This may sound simple, but supply chains get pretty complex pretty quickly. Imagine trying to locate the factory of your supplier’s, supplier’s, supplier. Startups are using AI to recognise and classify facilities from large datasets of images to automate mapping of supply chains (i.e. finding a soy processing facility within thousands of km of rainforest).

2) Evaluate Impacts and Drivers of Biodiversity Loss. After organisations have understood the footprint of their organisations they can begin to prioritise locations based on:

1. How important the ecosystems are for biodiversity (e.g. high biodiversity, Areas of rapid decline in ecosystem integrity)

2. How likely the organisation is to have significant dependencies and/or impact on the ecosystem (E.g. Areas of high water stress, Areas of significant resource extraction related to key products)

Historically, corporates have relied on in-house teams or consultancies to do this. Teams would do long field-studies or use pre-canned data sets to investigate factors like ecosystem integrity, species abundance and water stress = Extremely labour intensive . A growing body of AI solutions are increasing the efficiency and transparency of these process and making insights directly available to corporates.

2a) Understanding ecosystems using automated species identification: Biodiversity data is collected from optical, acoustic or thermal sensors deployed in the field or mounted on drone/satellites. AI can then be used to sift through this data to identify species, and estimate various measures of ecosystem health. A major benefit of using AI is the increased efficiency and accuracy of data processing compared to manual analysis.

Examples include: Acoustic & Visuals - Pivotal ; Hyperspectral - Gentian.io.

Two growing bodies of Species Identification Data sources are:

2b) Understanding Impact and Dependencies: Organisations must also understand the impacts their operations are having on ecosystems (See Image below). It is also critical for organisations to understand the dependencies they have on those ecosystems and the risk that their degradation poses on day-to-day business.

3) Mitigate & Manage: Having understood the scope and scale of their impact on biodiversity, organisations are then positioned to do something about it.

3A) Deploying AI to increase system efficiency. We won’t spend much time here but there are thousands of innovations in this space - where AI can generate efficiencies that can reduce pressure on ecosystems IF (and only if) they lead to lower resource use. If the efficiency gains are used to produce more goods, there is likely no net benefit for biodiversity. Examples include …

3B) Support Land Use Management and Restoration: AI can support land managers to understand what opportunities for biodiversity restoration exist on their land, and if active restoration such as tree planting is taking place, what species would be best given local conditions.

3C) Finance: Once corporates have completed the hard work of measurement and mitigation, their is a growing desire to finance biodiversity credits to plug the remaining gap. AI is being used to provide independent and high-resolution data on positive biodiversity impacts. This data can be utilised to monitor and track project implementation and the validity of credits. Stay tuned for an upcoming article…

4) Monitor and Disclose: Pure-play Measure Report Verify (MRV) startups are also leveraging new remote sensing technologies to drive cheaper, more accurate measurement, reporting, and verification of the impact of specific activities taken by corporates to improve Biodiversity. AI technologies are often leveraged here for the synthesis and analysis of the large data-sets generated from these activities.

💰 Funding Landscape

• Resilient: Nature raised $0.35B in 2022 and is on track for similar in 2023. Bumper deals include:

  • BeeWise - Developer of autonomous beehives intended to increase yield, reduce colony loss and eliminate the use of chemical pesticides ($80M Series C)

  • Pano AI - utiliSes a combination of advanced technologies such as AI, cameras, satellites, 5G, and cloud software to swiftly detect and pinpoint wildfires, enabling fire agencies to take necessary actions and prevent their spread. ($17M Series A)

• Early-Stage: Pre-Seed and Seed funding dominate in 2023 representing 90% of all deals

• Small tickets: Total of 73 deals over the last two years with an average deal size of $5.64M

💪 Question 2: How can we prevent AI from exacerbating harm to humanity, ecosystems, and communities reliant on ecological preservation?

AI also has a series of weaknesses it is worth understanding…

• Emissions and Ecosystem Impact: Systems also have a large carbon footprint. Researchers have found the process of training a common large AI model can emit as much as 626,000 pounds of carbon dioxide equivalent, five times the emissions of a car over its lifetime. Ongoing emissions of Hardware (See work of Loic Lannelongue) also must be considered.

• Rubbish In, Rubbish Out: AI models find patterns in the data they are given, so if data is skewed or biased towards certain contexts (for example, biological data can have regional, species, habitat, geographic, threat or language biases) they may fail to extrapolate or find only spurious correlations.

• Tackling a Digital Divide: Powerful nations and companies, many of which are in high-income countries, lead in the development of AI systems, including those applied to nature and biodiversity in low- and lower-middle-income countries. A failure to consult local communities in the deployment of AI systems that will affect their environments and livelihoods risks reinforcing existing social inequalities, neglecting community needs and interests, and worsening the digital divide.Those that benefit least from AI will also generally bare the brunt of the most sever environmental impacts from climate change.

• Training Capacity: Say you’re trying to train an AI model that will process hundreds of thousands of photos from camera traps to identify the species. First you need a team of experts to ‘teach’ the model that that picture of extremely rare species X is actually X. As you can imagine there are a limited folk able to do this, even then three PHD ecologists may come up with different answers for what the species is. There is therefore a challenge both in capacity of AI X Biodiversity models, but also in their accuracy.

 💡Hear it from the experts

A final note…

To this end, there is a great need to merge the digital rights movement with the environmental and climate justice movement. We must remember that AI carries risks as well as opportunities. Nor is it a silver bullet to solve the environmental crisis. But if developed and used responsibly and inclusively, AI can be a valuable aid in protecting our planet's natural wonders and promoting better practices for the well-being of all species

Written by Colin, Ollie, and Patrick - Drop us a message!

Special thanks to Federico - Net Zero Insights, Alice Schmidt, and Cameron Frayling - Pivotal for their insights.