Our Thesis
According to the UN Environment Programme, for every dollar invested in protecting and conserving nature, $30 is spent destroying. With the inertia of an extractive economy working against us, how can we turn the ship around and start building a nature-positive future today? And for funders seeking to make a difference, what is the best and highest use of their philanthropic capital in this urgent time of crisis? These are the questions that have driven the thesis of the Nature Data Lab.
Tropical forest in Borneo by J. Nais, 2007.
Working in environmental philanthropy over the past 15 years, we’ve seen first hand the outsized role that even relatively small investments in science and technology can play in shifting global policy and finance flows…
- Early investments made in convolutional neural networks at Global Fishing Watch enabled for the first time the remote detection of illegal activity on the high seas, leading to the interception of over 400 fishing vessels carrying hundreds of millions of dollars in illicit cargo (including victims of human trafficking).
- A grant to fund supercomputer capabilities at Arizona State University for the Global Airborne Observatory enabled the processing of high resolution hyperspectral data to map the world’s most biodiverse tropical forests in unprecedented detail, leading to the protection of millions of hectares of land in Borneo, Hawaii, and Peru.
- A grant to the Mother Tree Program helped to unravel the symbiotic relationship between mycorrhizal fungi and old-growth trees in the Pacific Northwest, leading to discoveries in underground nutrient and information flows that could be critical to the survival of forests in the future.
- And as described in Our Story, a series of grants supporting a coalition of scientists to identify the lands most in need of conservation, led to the development of the Global Deal for Nature, which introduced the 30x30 target in scientific literature, later adopted with the support of hundreds of Indigenous and community-led organizations as part of the UN Global Biodiversity Framework.
These are just a few examples of many, but they act as a testament to the power of philanthropy for targeted scientific research, data production, and technological innovation. Over the past decade, science philanthropy has grown in stature as its significant societal impact has become evident across many disciplines, leading to initiatives like the Science Philanthropy Alliance, Renaissance Philanthropy, Coefficient Giving, and the new Fund for Science & Technology. But out of an estimated $30 billion per year in U.S. science philanthropy, the vast majority (~70%) goes to medical research, and more than 60% of grant dollars are given within the donor’s home state. This leaves a relatively small amount of funds for other scientific disciplines, in particular those with an international focus. And now U.S. scientists working in fields like climate change, hydrology, and ecosystem health are suffering from greatly reduced budgets due to recent cuts in federal science funding, affecting important NDF programs like the Long Term Ecological Network (LTER) and the National Ecological Observatory (NEON).
This is unfortunate. Investments in science and data infrastructure have proven to deliver a high ROI, providing the enabling conditions for increased public and private finance. One great example of this is the network of research organizations and NGOs that grew to form a robust and vibrant ‘climate data ecosystem’ that not only built a consensus around the urgency of tackling the climate crisis, but also provided the data and scientific products needed to onboard an array of institutional stakeholders. Over time, this built confidence in pathways to achieve net zero carbon emissions globally, and ultimately resulted in a massive scale-up of financial resources – from just $200B per year in 2008 to $2 trillion in 2025 – a 10x increase in just 17 years with more than 10,000 companies now setting net-zero targets (via SBTi).
The Climate Data Ecosystem, a systems map produced by Climate Arc documenting 45 leading contributors to climate data that drive increased financial flows to support the global energy transition. (Climate Arc, 2022).
What made this scale-up in finance possible? Certainly, gradually declining capex costs for renewable energy infrastructure played a major role. Foundations, impact investors, and new products like Renewable Energy Credits (RECs) helped build the portfolio of successful case studies, generating more confidence in the sector. But shifting $2 trillion dollars required a lot more “behind the scenes” work. Recently 100+ scientists, policy experts, and finance leaders were convened by Climate Arc to map out the network of data providers helping to onboard major financial institutions in the net zero transition (banks, pension funds, sovereigns, large asset managers, and corporates). Back in 2008, some of these data providers existed but weren’t linked together, and most weren’t on the gameboard at all. The exercise revealed 8 distinct categories of data providers – from scientific benchmarking and asset-level data aggregators, to disclosure platforms and third-party verifiers – most of which were informally linked together. The conclusion was clear..
Without high quality, comprehensive data, financial decision makers cannot move money into solutions at the speed we need. – Meryam Omi, Climate Arc
Who funded this climate data ecosystem? Governments provided satellite monitoring, and initiatives like the International Panel of Climate Change (IPCC) and the International Renewable Energy Agency (IRENA) were given sustained funding through the UN and other multilateral facilities (co-funded by governments). Private sector funding also played a role, particularly in supplying commercial ratings for publicly traded equities. But so many of the players that built this climate data ecosystem were launched and/or funded by philanthropy – SBTi, CDP, TPI, Climate TRACE, Carbon Tracker, CPI, RMI – just to name a few. It’s hard to imagine a 10x increase in climate finance without the strategic role played by philanthropy in building this ecosystem of data providers.
A data ecosystem for nature
After reading about Our Challenge, it will come as no surprise that a comparable data ecosystem for nature does not yet exist. We just hit the $200B/yr mark in nature finance (an increase of roughly $60B/yr from 2020 levels), but according to the Financing Nature 2025 report from the Paulson Institute, we need to increase funding for nature five-fold, reaching at least $1 trillion by 2030. If we don’t, we are unlikely to reverse biodiversity extinctions and get the world on track for a nature-positive future.
It took approximately 17 years to 10x climate finance to $2 trillion in 2025. The same year we hit $200B in nature finance, but now need to 5x those funding levels in 5 years. Note: the Paulson Institute report calls for $1.15 trillion in total nature funding by 2030, but we discount this by 10% to account for overlaps with climate finance flows.
The big problem is that the current nature data pipeline isn’t yet built out to facilitate a rapid scale-up in finance. A recent survey of leading asset owners and managers by Responsible Investor (RI) found that nearly two-thirds feel they have insufficient data to transition their portfolios into nature-positive investments. While facilities like IBAT allow the private sector to access some data from NGO providers, these resources are far from complete (less than half of countries have performed biodiversity inventories). We also don’t have anything comparable to an ‘IPCC for nature’, which would synthesize a myriad of models to establish common scientific baselines. The closest thing, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), does exceptional work but is inadequately funded and lacks a political mandate to deliver model intercomparisons or global benchmarks for use across sectors, hence the recent call for a BMIP process.
The International Union for Conservation of Nature (IUCN) facilitates convenings and white papers through a wide array of working groups, but contributions are largely unfunded and performed on a volunteer basis. Similarly, GEO BON, which coordinates biodiversity observations and monitoring to support policy and environmental legislation, largely operates on a volunteer basis. New entrants like the Taskforce on Nature-related Financial Disclosures (TNFD) has just proposed a roadmap to increase market access to global nature data, and the Science Based Targets Network (SBTN) has launched a global platform to help companies and municipalities set voluntary nature-positive targets. So great things are happening, but we’re still in the early days of forming a robust data ecosystem for nature.
A major concerted philanthropy effort is needed to help this emergent data ecosystem to coalesce into something that can help to move trillions into the nature-positive economy. Unfortunately there is only a small handful of environmentally-focused philanthropies that understand why critical investments in science are needed. These include Moore Foundation, Laudes Foundation, MacArthur Foundation, Arcadia Foundation, Walton Foundation, and the Bezos Earth Fund (BEF). There are also no major pooled philanthropic vehicles in the biodiversity space. For example, there is no biodiversity equivalent to a ‘Climateworks’ (which mobilized $2B for climate) or a ‘ClimateLead’ (which mobilized $5B for climate). CLUA is one collaborative loosely connected to biodiversity (which mobilized $1B through its FPC collaborative), but its grantmaking is restricted to climate and land use.
All of these philanthropic efforts are wonderful and much-needed, as the climate and environment space remains critically underfunded (its share of global philanthropy is stuck at about 2% of total giving). But as has been made clear by the 2025 Planetary Health Check, the current collapse in biodiversity and ecosystems dramatically exceeds the Planetary Boundaries, posing an even more immediate threat than climate change. So what’s holding everyone back? People across the political spectrum love nature, as shown in a recent study finding 91% of Americans (including 86% of conservatives) feel we should be investing more in nature conservation and restoration. Given both the urgency and popularity of nature, how do we mobilize philanthropic resources to accelerate the nature-positive transition before it’s too late? NDL believes nature data is the missing key, with the potential to unlock the floodgates of new funding for nature.
What is nature data?
The phrase “nature data” gets thrown around quite a bit these days, and there is some general confusion about what it means. In the following chart, we simplify the life cycle of data production in all its phases, beginning with “raw data” – a direct observation made by a scientific instrument…
The nature data lifecycle, starting with a direct “observation” made by a scientific instrument. Raw data is cleaned up, aggregated, and taxonomically harmonized, resulting in a series of “measurements” which can then be transformed, through one or more modeling processes, into “information” in the form of metrics or indicators. Multiple streams of information can be incorporated into a Decision Support System (DSS), leading to a series of actions performed on the ground. Finally a Monitoring, Reporting and Verification (MRV) system records these actions and their positive impact, resulting in a new set of data points that can be cycled through a second clean-up, measurement, and modeling process. The upper half of the cycle is focused on instrumentation, and the lower half on Informatics. Nature Data Lab, 2026.
In the nature space, we classify 5 major instrumentation categories for collecting raw data:
“ground data” requires a human presence on-the-ground (soil sampling, collecting scat, catching insects and eDNA, tree measurement, photographic or audio recordings via iNaturalist or other applications with GIS location data)
“in situ data” requires the installation of audio, visual, or chemical sensors, which capture data over a long period of time, for example camera traps
“drone data” is provided by lightweight remotely operated devices mounted with cameras and flown at lower elevations (below or above the canopy) producing extremely high resolution data
“airborne data” can be provided by a manned aircraft or unmanned balloons, mounted with heavier audio, radio, or LiDAR instruments
“remote data” is provided by Earth Observation (EO) satellites, which collect either optical data across a wide spectrum of bandwidths, or scan physical structure using Synthetic Aperture Radar (SAR) or LiDAR
Satellite imagery of a forest canopy at 6 scales. It’s only at < 1m resolution that natural forest structure can be clearly perceived. Courtesy of Asner Lab, 2020.
Once raw data is collected it begins a journey of transformation, aggregated and harmonized into a set of measurements (also referred to as “data”), which then are transformed into information (again often called “data”). For example, a satellite will collect petabytes of optical data detecting electromagnetic radiation, ranging from visible red-green-blue (RGB) for standard imaging to multispectral and hyperspectral bands that can detect near-infrared (NIR). These pixels are assembled into patterns across large areas (aka measurements), which can then be modeled into useful products like biomass estimates of a forest (aka information). These outputs are also considered “data” which can be used in a Decision Support System (DSS), for example, to prioritize the allocation of resources for a forest restoration project.
But what is the most useful nature data? This is the question that drives the Nature Data Lab (NDL) thesis, which focuses on the lower half of the data cycle – Ecological Informatics (aka ecoinformatics) – defined as the application of computation, data analytics, and modeling to ecological data to solve environmental challenges. To answer the question about data usefulness, we have to work backwards from our ultimate challenge – to halt and reverse biodiversity loss. And what causes biodiversity loss? A meta analysis of 87 papers found that land use change, and consequent habitat loss, is by far the dominant driver of biodiversity declines in the terrestrial domain. And agricultural commodities are primarily to blame, threatening 86% of all species at risk of extinction. A recent study examines the link between biodiversity impacts from land-use change and shifts in commodity production (1995-2022), finding that conversions in forest biomes account for approximately 57% of biodiversity loss with another 30% occurring in non-forest biomes, largely concentrated in tropical regions:
Biodiversity impacts of global land-use change from 1995 to 2022, driven by increases in agri-food imports. Adapted from Fig. 3 (Cabernard et al. 2024).
Three Responses
There are hundreds of different actions that could be taken to halt and reverse biodiversity loss, all of which are important. But given the information above, NDL has narrowed its focus to three main responses to the crisis:
- Protecting intact species assemblages and the habitats that harbor them
- Restoring degraded and under-utilized lands to create biodiversity uplift
- Supporting the transition away from business practices that cause harm
All three responses require specific data to drive strategic action…
The first response, in line with Targets 3 & 4 of the GBF, aims to protect intact species assemblages and habitats from future land conversions, requiring specific data inputs. While an institutional investor may want to know the location of high-biodiversity areas simply in order to avoid them, conservation planners working to designate new protected areas need a much greater level of insight into the dynamics underlying ecosystems. Perhaps it’s no surprise that one year after the GBF deadline for governments to submit their National Biodiversity Strategy and Action Plans (NBSAPs), only 28% have managed to do so. Prioritization is really difficult with numerous trade-offs to balance. For example, high priority areas for biodiversity often do not align with areas delivering ecosystem services. Initiatives like SPACES and the UN Biodiversity Lab are providing support for governments to develop their 30x30 targets, but it’s clear in many regions that actionable data is not always available to help steer these analyses.
SPACES and the UN Biodiversity Lab are two initiatives working to turn data into action, by assisting developing countries with their 30x30 targets under the UN Kunming-Montreal Global Biodiversity Framework (GBF).
Beyond setting area-based targets at the jurisdictional level, there is a parallel need for nature data to support the ‘computational allocation’ of capital – determining the best and highest use of private or philanthropic dollars for conservation using global models or regional metrics. This process requires not just an understanding of biodiversity on the ground, but also the social and cultural dynamics present in the region. Philanthropic donors, for example, may have a special interest in preserving rare species by supporting Indigenous or local community organizations. This particular question led to a series of papers supported by NDL in partnership with Conserve, including “Conservation Imperatives…” (Dinerstein et al. 2024) and a forthcoming prioritization study that factors in human pressure on rare species sites (currently in review). Such modeled data can also be useful for project developers seeking to issue nature-based REDD+ carbon credits with strong biodiversity co-benefits.
The second response, restoring degraded lands in line with Target 2 of the GBF, requires both higher resolution (spatial) and higher cadence (temporal) data to drive decision making. While the same underlying data sets used in prioritizing conservation areas can be leveraged to select optimal restoration sites – for example highly degraded ecoregions could be prioritized for restoration – more detail is required to select restoration sites that could measurably uplift biodiversity or enhance ecosystem services like water provisioning. This might include data on ecosystem fragmentation, which can be used to strategically position restoration sites working to reconnect critical habitat areas, enhancing the movement of species and breeding opportunities between populations of animals. Or it might require high resolution hydrological data (e.g. the location of springsheds or water towers). High-quality MRV data is needed for these projects to demonstrate improvements over time, especially if they are financed through commercial mechanisms such as biodiversity net gain (BNG) credits or payment for ecosystem services (PES) schemes.
The third response, in line with Target 15 of the GBF, is being led largely by institutional investors through a risk mitigation lens. In our Dummies’ Guide to the “nature action alphabet soup” we unpack the major push in the past few years by investors and policymakers to understand the nature-related dependencies of publicly traded companies, the environmental risks posed by harmful activities in their supply chains (e.g. deforestation or water pollution), and the financial implications (aka double materiality) of both. In this context, “nature data” has a specific job, helping investors to have visibility for the first time into the biophysical impacts associated with their AUM, as is well described in this Rabobank explainer series.
Investors want to transition away from harmful companies (and to coax companies into better practices), but they have specific data needs that are not currently met by off-the-shelf ESG rating products. A new study shows that company rankings exhibit low correlation between biodiversity impact tools, which “...rely on non-standardized methodologies and workflows that lack clear theoretical foundation, peer-review and scrutiny.” Another study comparing 45 leading biodiversity assessment tools finds that their biodiversity-related indicators “...differ greatly by scope, data and evaluation method” making them difficult to reconcile. These discrepancies are currently impeding action. There are many reasons why the outputs of these tools are so divergent, but we can identify one key factor – a lack of clarity on the measurement of ecosystem condition and agreed “reference conditions” (RCs), a critically important concept to track the degradation or improvement of an ecosystem over time.
State of Nature (SON)
All three of the responses discussed above have one thing in common – they all require high quality data that has been well-formatted for use in decision making, particularly in capital allocation, meeting the RACER criteria (Relevant, Accepted, Credible, Easy-to-use, and Robust). This is no small task. A recent meta-analysis of over 3000 studies landed on 130 indicators applied to soils, insects, birds, and habitats that could be utilized in support of the EU’s new Nature Restoration Law. Nature, it turns out, is really quite hard to measure.
Fortunately the Nature Positive Initiative (NPI) over the past two years has built a consensus around 4 universal indicators plus 5 case-specific indicators to measure SON, each of which have 2-3 metrics with varying degrees of difficulty – Entry-level (E), Standard (S), and Advanced (A). Commentary has recently closed on a draft version of the indicators, and it’s expected that by Fall 2026, the world will finally have a comprehensive, and well-agreed approach to measuring change in an ecosystem as a result of human activity.
4 universal and 5 case-specific indicators to measure State of Nature (SON) from “Building Consensus on State of Nature Metrics to Drive Nature Positive Outcomes” consultation by NPI. The small boxes within each square refer to the Entry-level, Standard, and Advanced approaches to measuring progress within the indicator. Empty boxes are metrics in development. Nature Data Lab, 2026.
This is a big deal. Finally we can take the work of defining “nature” out of the realm of ontological debate and into the 21st century with a set of clear, well-agreed indicators and metrics. But we’ve got a lot of work to do. Significant investment is needed to build out the data pipeline so that we can provide high quality spatial and temporal information to inform decision makers in the nature-positive space. A group of 44 biologists and ecologists recently published a list of “Nine changes needed to deliver a radical transformation in biodiversity measurement”. The first – using novel technology to integrate data sources – lies at the heart of the Nature Data Lab thesis. There are hundreds of new and exciting developments in the field of ecosystem measurement, but significant funding will be needed to bring these new sources of information together into modeled products that are both credible and useful.
NDL’s mission is to help galvanize philanthropic resources for the build-out of the new nature data ecosystem, supporting leading scientists and technologists working to advance our understanding of the natural world around us. At NDL we’re specifically focused on Informatics – the lower half of the nature data lifecycle – the process of transforming formatted data through modeling to produce actionable insights and information, which can then be incorporated into decision support systems (DSS). We believe that investing in these data products now will provide a critical unlock to achieving the ultimate goal – increasing global finance for nature conservation and restoration from $200B today to $1 trillion by 2030.
There are four critical gaps we’ve identified that require focus in the 2026-2030 time frame, and these form the basis of Our Programs.