As outlined in our Biodiversity 101, we are facing a biodiversity crisis that threatens our livelihoods and economy. Species are going extinct at 10–1000x the natural extinction rate and we may be headed toward the sixth mass extinction. Biodiversity is essential to the supply and stability of ecosystem services, the benefits humans derive from ecosystems, such as climate regulation, biomass provisioning, and pollination. With roughly 60% of global GDP at least moderately dependent on ecosystem services, there is no future for the status quo.
Global action on biodiversity is rapidly gaining momentum. At COP15 in December 2022, 196 countries agreed to halt and reverse biodiversity loss through the Kunming-Montreal Global Biodiversity Framework (GBF), committing to protect and restore 30% of land and oceans and close the $700 billion funding gap. In the private sector, over 50,000 companies will soon be required report on biodiversity through the Corporate Sustainability Reporting Directive (CSRD) and voluntary frameworks such as the Taskforce for Nature-related Financial Disclosures (TNFD) and Science Based Targets Network (SBTN) have emerged to make biodiversity reporting mainstream.
Halting and reversing biodiversity loss is a huge opportunity and will require a portfolio of actions across all sectors, including reimagining how we build and design cities and infrastructure, rethinking how we use land, freshwater, and the ocean, and transforming how we extract and use energy and resources. Transparent, standardised, and high-quality biodiversity data and analysis are key to both informing and evidencing these transitions.
The data problem
However, the world currently lacks the measurement infrastructure to manage biodiversity at a global level. Vast amounts of data are needed to inform the status of biodiversity, understand drivers of biodiversity loss, determine companies’ impacts and dependencies, and ultimately, to guide target-setting, monitor progress, and direct nature-positive decisions. Quality data is essential to provide trust and transparency to biodiversity markets and close the $700 billion funding gap.
However, the challenges behind building a biodiversity monitoring infrastructure are multifaceted:
(1) Biodiversity is significantly more complex than carbon, making measurement and metrics challenging. Unlike the “simple” tCO2e metric for greenhouse gases, biodiversity is inherently complex, context and location-dependent, and non-fungible. It encompasses genetic, species, and ecosystem diversity, and cannot easily be distilled into a simple metric. Currently, over 3,000 metrics are used to assess corporate biodiversity impact, making metric selection and comparison challenging.
(2) Traditional biodiversity monitoring methods are labour and time-intensive. Ecologists have historically collected data through on-the-ground surveys using basic equipment and counting methods. This is inherently unscalable and limits the spatial, temporal, and taxonomic coverage of biodiversity monitoring. Data is also not typically gathered in a way that can be easily consolidated and manipulated for further analysis.
(3) Existing biodiversity data has significant gaps, being geographically biased towards accessible, well-studied areas and taxonomically biased toward visible or easier-to-sample species and habitats. Only 6.74% of Earth’s surface has been adequately surveyed, while over half of record focus on <2% of known species. This lack of robust data hinders rigorous analysis and forecasting, especially when using data-heavy tools like AI or satellite-based models.
(4) Historically, biodiversity has been systemically undervalued and risks not widely reported on. Only 1.1% of financial institutions report on nature risk, while only 10% of Fortune 100 companies have robust biodiversity targets. With thousands of organisations facing biodiversity reporting for the first time, many may not have the internal expertise to adequately manage and report on biodiversity. Meanwhile, tools that exist today for corporate assessments of biodiversity are largely generic datasets that are based on industry averages and mixes of old and current data. While these are useful screening tools, they lack company-specific, up-to-date data, undermining the accuracy of biodiversity assessments and alignment with upcoming frameworks.
The data solution
Biodiversity monitoring, reporting, and verification (MRV) technologies are emerging to address these challenges. Novel MRV tools, from eDNA and bioacoustics to LiDAR and AI, are improving the speed, accuracy, and scalability of biodiversity assessment. Some technologies directly monitor species, while others focus on mapping ecosystems to indirectly infer other components of biodiversity. Below we outline some promising tools to monitor and manage biodiversity and ecosystems at a greater scale than previously possible.
- Environmental DNA: Samples of water, soil, or air are collected onsite and the small fragments of DNA shed by organisms into the environment, called eDNA, are amplified, sequenced, and compared against a genomic database to identify the species present. eDNA is an accurate, scalable, and cost-effective approach that can identify multiple species from a single sample, providing a comprehensive snapshot of genetic and species diversity. While data processing requires expertise and dedicated lab space, eDNA sampling is simple and has the potential to unlock granular, specific, and accurate biodiversity data at scale. Combined with remote sensing and AI, as being done by NatureMetrics, eDNA provides essential ground-truthing to understand ecosystem condition and species diversity at scale.
- Bioacoustics: Acoustic monitoring devices coupled with AI can identify species based on natural soundscapes. This provides a low-cost, scalable, and non-invasive tool for detecting species presence, abundance, and even behaviour based on vocalisations. While limited to vocal species and affected by background noise, bioacoustics is being rapidly advanced by AI and improved acoustic sensors.
- Cameras: Camera traps have been used for decades to monitor wildlife. Now, visual data from cameras are being interpreted, analysed, and understood using AI to derive biodiversity insights. Stationary cameras are being used to track birds on wind farms, automate pollinator monitoring, and monitor aquatic environments. For the broader public, various mobile apps combine cameras with AI to identify species through citizen science.
- Earth observation — drones: Drones equipped with sensors (e.g., LiDAR, cameras, thermal) can efficiently map habitats, monitor ecosystems, assess disturbances, and analyse communities over large areas. This enables more frequent and detailed ecosystem monitoring than traditional methods, especially in inaccessible regions. However, drones don’t provide the granular species diversity metrics of ground-based methods and may require specialised training and permits to operate legally.
- Earth observation — satellite data & other aerial vehicles: Various data layers, including multi & hyperspectral imaging, radar, and LiDAR, can be layered and analysed to map ecosystems over large areas. High-resolution satellite data combined with ground observations and AI can identify plant species and ecosystems, understand threats, assess ecosystem services, and extrapolate biodiversity insights over large areas. Recent advancements include using LiDAR to map habitats from space and the establishment of national biodiversity observation networks. While the scalability of satellite technology is hugely promising, it cannot yet substitute for site-level sampling and is most powerful when calibrated with accurate ground-truth data like eDNA.
- Data aggregators, reporting platforms, and other end-uses: Software and platform services that aggregate existing biodiversity data across different sources, derive helpful insights, and help report these findings in a standardised, automated manner are emerging to help make biodiversity monitoring mainstream. Companies operating in this space often serve multiple use-cases, including regulatory compliance, risk assessment and management, and monetisation of biodiversity uplifts. Access to accurate, complete data is a key differentiator and enabler for these companies.
- Artificial Intelligence: AI is being leveraged across all of these tools to analyse data and inform biodiversity insights. It can be used to automate species identification based on bioacoustics or other imagery, predict species presence based on habitat conditions, identify threats or anomalies, and forecast alternative future states based on conservation actions. AI is an exceptionally powerful tool for biodiversity management and it’s essential that models are trained on large volumes of high-quality data.
2150 take
The market for biodiversity data is exploding with demand driven by multiple sectors. With $58 trillion of GDP dependent on ecosystem services, biodiversity loss is a fundamental business risk that is rapidly becoming the next sustainability frontier after climate change. Under CSRD, thousands of companies are required to report on biodiversity for the first time from 2025, while reporting frameworks like TNFD and SBTN are rapidly following their climate counterparts, TCFD and SBTi. Asset managers with over €22 trillion AUM have committed to biodiversity-related targets and reporting, 40% of financial institutions are considering treating nature risk as financial risk, and progressive corporations are increasingly complementing Net Zero with Nature Positive. Biodiversity financing mechanisms are also on the rise, including biodiversity-related bonds and loans, offsets, debt-for-nature swaps, and credits. Access to relevant, up-to-date, decision-useful biodiversity data underpins all of these movements.
As a fast-growing sub-category of Nature Tech, biodiversity MRV is an essential tool that can catalyse action against biodiversity loss. We believe companies that go on to be the most successful in capturing significant market share will be those that (1) have an edge on access to data through scalable data collection and analysis, strong corporate connections to scale across sites and levels, and the buildout of large proprietary datasets; (2) are able to stack different data layers to offer different levels of insight subject to customer requirements; and (3) maintain high levels of credibility through accurate data sourcing, strong internal expertise, and the use of ground-truthing to develop robust satellite and AI models. Global declines in biodiversity are a wake-up call for businesses, financial institutions, and governments to act. At 2150, we believe investing in biodiversity MRV now can create the data infrastructure needed to halt and reverse biodiversity loss in the future.
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2150 is a venture capital firm investing in technology companies that seek to sustainably reimagine and reshape the urban environment. 2150’s investment thesis focuses on major unsolved problems across what it calls the ‘Urban Stack’, which comprises every element of the built environment, from the way our cities are designed, constructed and powered, to the way people live, work and are cared for. Find out more at www.2150.vc. 2150 is a part of Urban Partners.
Check out our other UNSUSTAINBLE series here: Industrial heat & steam; Water Scarcity; Steel; The Paradox of Cities; Concrete and Cement; Windows; Cooling.
