The Nature Data Gap: How Echo Labs Is Building an "Ecosystem Fingerprint" to Make Ecological Condition Predictable
For this edition of Member Spotlight: Ask the Nature Tech Experts, we spoke with Echo Labs, a Focused Research Organization (FRO) built to fix a specific problem in conservation: most ecological data only tells you what already happened, not what's coming. That leaves decision-makers reacting after the damage is done instead of intervening while there's still time. Echo Labs' answer is something they call an Ecosystem Fingerprint, a standardized way to represent ecosystem health that pulls together field data, sensors, and remote sensing into one comparable unit. Backed by a grant from the UK's Advanced Research and Invention Agency (ARIA), it's one of the first FROs to launch in the UK.
Photo Credits: Unsplash
About Echo Labs
Q. Echo Labs is a Focused Research Organization, a structure most people haven't come across before. Can you explain what an FRO is and what makes it different from a startup or an academic lab?
A Focused Research Organization, or FRO, is a time-boxed, mission-specific research effort built to answer one well-defined scientific question that's too big, too infrastructure-heavy, or too commercially unproven for existing institutions to take on. The model was pioneered by Convergent Research in the US, and Echo Labs is one of the first FROs to launch in the UK, backed by a grant from the UK's Advanced Research and Invention Agency (ARIA).
The structure sits deliberately between a startup and an academic lab. Unlike a startup, we're not chasing product-market fit or investor returns, we're a non-profit with a fixed, multi-year budget and a defined technical milestone to hit. Unlike an academic lab, we're not organised around grant cycles or publication incentives. We operate more like a startup internally: small, fast, cross-functional teams with hard go/no-go checkpoints.
That combination lets FRO’s do something neither model is well-suited for: build shared infrastructure- datasets, hardware, models, or standards, that's too early-stage for commercial investment and too applied for typical academic funding, but that the wider field needs to exist before anyone else can build on top of it.
Q. What is the core problem Echo Labs was built to solve, and why does it matter for the nature tech sector?
Echo Labs exists to solve a representation problem: ecosystems are complex, dynamic, and multidimensional, but the data we collect about them is fragmented, static, and mostly retrospective. Ecological science today is very good at describing what's already happened, a forest that's degraded, a reef that's collapsed, and far worse at anticipating change while there's still time to act. Without predictive models of ecological resilience, environmental decisions stay reactive, made only after damage is already irreversible.
That gap matters for the whole nature tech sector because it's the layer everything else sits on top of. Conservation, natural capital finance, and land-use policy all depend on being able to measure ecosystem condition consistently and compare it over time and space. Right now that foundation doesn't really exist: nature is largely absent from the systems that govern markets, policy, and planning, in large part because what isn't measured well can't be managed.
We are tackling three self-reinforcing challenges:
What should we measure? We are building a unified, continuously updating dataset that stitches together field observations, sensors, remote sensing, and event-driven sampling. The goal is to learn from ecological change over time and determine what data are actually needed to represent an ecosystem's health.
What new signals can we extract? We blend data sources to amplify distinct signals of how ecosystems look, sound, and behave. This uncovers a common set of variables for optimized sampling and treats ecological data identically to geospatial data: a set of overlapping layers, each one capturing a dimension of the living world the others miss. We are turning ecological observations from bespoke point clouds to standardized vectors that capture the underlying signal. We call the result an Ecosystem Fingerprint: a single representation per site that compresses raw field and sensor data, fuses ground and remote-sensing signals, and plugs into models as a shared, standardized unit.
How can monitoring systems efficiently scale? In the long term we want to test whether models of ecosystem condition can generalize across geographies, while identifying uncertainty and guiding new data collection. The aim is to prove that ecosystem monitoring can scale into a continuously updating, large-scale map of ecological change.
Photo Credits: Unsplash
Q. Ecological data today is often described as fragmented and hard to act on. What would it take to change that?
In workshops we've run with data holders: academic labs, corporates, natural capital investors, long-term monitoring schemes, the same frictions come up again and again: market sensitivity, land-access relationships, sensitive-species risk, GDPR, institutional IP and competitive advantage, publication priority, and reputational risk if data quality gets scrutinised. Very few organisations currently have a reason to share that outweighs those risks. Changing that starts with real incentives to contribute - payment for data, credit and co-authorship, contribution to a recognised public good, and reciprocal access to a pooled dataset that's more valuable than what any one organisation holds alone.
The second piece is standardisation. Right now there's no consensus on what data is actually useful, at what spatial and temporal granularity, in what format, or with what metadata and provenance so even when organisations do want to share, their data usually can't be meaningfully combined with anyone else's.
The third piece is shared infrastructure that makes sharing itself lower-risk and lower-cost. Storing and hosting raw,multimodal, ecological data is expensive enough today that it discourages sharing outright, and most contributors won't hand over data without fine-grained control over how it's used. We need real “data commons” with configurable access controls that let a contributor set exactly how much to expose: spatial and temporal granularity, taxonomic sensitivity for protected species, automatic GDPR flagging, tiered licensing, and embargo periods.
The Work
Q. Who needs what you're building, and how do you see them using it?
Three groups need this most directly: researchers, policymakers, and capital allocators, though we think of the underlying data and model layer as something all three can build on, rather than three separate “products”. Ecologists could get a shared, structured way to ask new questions using overlapping multimodal data and other researchers could test linking ecological change to fields like climate and human health. Land-use policymakers and regulators could get more granular, defensible measurement that can support enforcement and stronger regulation, rather than static snapshots. And in capital markets, investors and insurers could get a foundation for pricing ecological risk that today is hard to price because it is not being measured consistently.
In practice, we're already working with early partners across each of these lines, to make sure what we build is usable inside their existing workflows, not just technically interesting to us.
Photo Credits: Unsplash
Q. How is ecosystem monitoring typically done today, and what does that mean for anyone trying to make decisions based on it?
On-the-ground fieldwork (species surveys, soil sampling, acoustic and camera-trap deployments) is genuinely rigorous and diligent, and it captures the fine-grained detail that actually indicates condition. It's also expensive and bespoke: teams design their own surveys for different sites, so results are hard to generalise or compare from one landscape to the next, and the costs make it not scalable. Satellite-based Earth observation is the opposite. It's continuous and genuinely global (the only realistic way to get planetary-scale coverage), which is why we think of it as the scaling backbone for ecosystem monitoring. But on its own it still isn't good enough at detecting condition: it has real biotic blind spots, and misses much of the ground-level detail that on-the-ground data captures.
For anyone trying to make a decision it often means choosing between two imperfect options: expensive, high-fidelity data for a handful of well-studied sites, or cheap, global, but comparatively shallow coverage everywhere else. The recent working assumption is that Earth observation and ground-level data are better together, at least for the foreseeable future: EO for scale, ground data for nuance and calibration.
Q. You're building a public good. How do you think about making your outputs useful for both scientific and commercial applications?
We think about this less as a tension and more as a sequencing question. Echo Labs is a non-profit, and everything we produce in this phase: the datasets, the modelling frameworks, the ecosystem fingerprint is designed as open scientific infrastructure: peer-reviewable, reproducible, and useful to researchers regardless of who they work for (academia or companies) and that's deliberate. This kind of foundational, unproven infrastructure doesn't have an obvious profit motive yet, so it needs philanthropic and public funding. Our job is to make sure the underlying representation of nature is trustworthy and allows others (and us if we decide to) to build commercial applications on top.
Impact
Q. If what you're building works, what becomes possible for conservation, finance, and policy that isn't possible today?
If this works, the biggest shift is that ecological decisions stop being purely reactive. Right now we can describe decline after it happens; we're rarely able to see it coming clearly enough to intervene before an ecosystem crosses an irreversible threshold.
For capital markets, it means natural capital investment and lending decisions which today are badly hindered by the inability to price ecological risk can be grounded in something measurable and comparable across sites. For policy, more granular, standardised measurement gives land-use regulators an evidentiary foundation for stronger regulation and enforcement, rather than static, occasional assessments. Longer term, we think this kind of infrastructure could do for ecology what standardised atmospheric measurement did for weather: turn it from a descriptive discipline into a predictive one, and help coordinate new markets and governance frameworks around nature's services.
Photo Credits: Unsplash
Community and Collective
Q. What drew you to the Nature Tech Collective, and what are you hoping to contribute to and take from this community?
We were drawn to the Nature Tech Collective because it's one of the few communities explicitly trying to close the nature data gap sector-wide, rather than everyone rebuilding the same fragmented infrastructure in isolation. As an early-stage organization we're not always in a position to build extensive relationships to test our ideas, so a community built around matchmaking, shared learning, and honest case studies, is exactly what we need at this stage.
Personal
Q. What made you want to build a new kind of scientific institution rather than take a more conventional path?
Kaja Wasik, CEO: I’ve never taken conventional paths. I have built two companies straight from my PhD and pioneered a benefit-sharing model that returned equity and revenue to the communities that participated in our research, supported by truly visionary and ethical VC funds. I then moved to Kenya to live on a conservancy, and that's where the idea behind Echo Labs took shape - together with my now co-founders Molly Blank and Tosca Tindall, who were doing hands-on restoration and nature finance (quite literally - we gave gorillas digital wallets) work on the ground there with Natural State (Kenya) and Tehanu (Rwanda).
What became clear living there is that the barrier to better environmental decisions usually wasn't a lack of will, it was a lack of measurement and that requires investment in infrastructure and technology. There is no nature market - so a startup was never a fit and it's also not something academia is structured to finance and build at the pace or scale needed.
Q. What gives you confidence this is the right moment for this work?
Remote sensing is genuinely global and continuous rather than patchy, so there's planetary coverage to build on. Machine learning has matured to the point where models can learn shared representations across very different data types and scales: model capability has caught up to ecological complexity faster than our data infrastructure has kept pace. And IoT sensing technologies have become affordable and scalable enough to deploy widely, rather than staying confined to a handful of well-funded research sites. That changes the economics of the whole problem, which is really why the timing matters.
There's also a political and regulatory window that didn't exist even three or four years ago. Nature-related financial disclosure has moved from a niche ESG topic to something regulators are pushing for: the EU's CSRD now mandates biodiversity reporting, TNFD-aligned disclosures are gaining traction and central banks (like the ECB) are creating first applications of a Nature Value at Risk frameworks.
