By Viorel D. Popescu, Lecturer and Director of the M.S. in Biodiversity Data Analytics Program, School of Professional Studies
From the Kunming-Montreal Global Biodiversity Framework to voluntary and mandatory corporate nature-related disclosure and the emerging biodiversity credits market, one thing is becoming painfully clear: Our ambitions for nature are now outpacing our ability to track and measure it. Governments, companies, and financial institutions are asked to set targets, disclose impacts, demonstrate additionality, and track outcomes for biodiversity. Yet, in most places, we still lack timely, spatially explicit, and trustworthy data on where species actually occur.
This gap is not just a technical inconvenience; it is a matter of governance. Despite the emergence of market-based mechanisms that encourage the protection and restoration of natural ecosystems and their services, nature is broadly considered a public good and must be managed and governed as such. This is a familiar problem in the governance of public goods: you cannot manage what you cannot credibly measure. Dietz’s classic review of elements quintessential for sustainable governance of public goods—information generation, infrastructure provision, political bargaining, enforcement, and adaptive institutional design—highlights the importance of credible data generation as a critical first step. As such, to achieve progress on meeting biodiversity conservation targets and disclosure standards, we need to generate information on the state of nature that is of high quality, granular, timely, trustworthy and understandable. The infrastructure element of Dietz’s framework refers to the ability to generate data efficiently and at scale (large sample sizes, across large geographic extents, repeated with high enough frequency and statistical power to evaluate changes in biodiversity). Inability to meet these elements is becoming the main bottleneck for credible conservation action, nature finance, and biodiversity-inclusive decision-making.
A New Way Forward: Scaling Biodiversity Measurement
A new paper on environmental DNA (eDNA) proposing a new “eDNA-aware” statistical model, which I recently co-authored with colleagues from China and the UK, illustrates both the scale of the problem and a realistic path forward. Environmental DNA captures genetic material shed by organisms into water, soil, or air, allowing species detection without direct observation. In just 56 calendar days, stream aquatic eDNA collected from 101 locations detected nearly 400 vertebrate species (mammals, birds, reptiles, amphibians, and fishes), including dozens of IUCN-listed species, across more than 30,000 km² of rugged, biodiverse terrain in western China spanning from the foothills of the Himalayas to tropical forests bordering Myanmar. This is the type of terrain where conventional biodiversity monitoring is most costly and least frequent worldwide. Such a level of coverage for low field effort would be unimaginable with traditional surveys alone (for example, via camera traps or bioacoustics).
While biodiversity surveys of this extent and breadth would have been unimaginable a few years ago, one of the main lessons of this research is pairing new data types with the right analytical frameworks. eDNA metabarcoding generates a fundamentally different kind of biodiversity data: automated, high-throughput, taxonomically broad, and collected at broad spatial scales. Yet these strengths come with complexity and assumptions; we can have false negatives (we don’t know if the species not being detected means that it is truly absent), false positives (species are detected where they are not supposed to be due to sample contamination), and uneven detectability across species or taxonomic groups. Applying conventional ecological models without accounting for these realities can lead to overconfident, and sometimes misleading, conclusions.
The paper introduces a new class of statistical models designed specifically for eDNA data. These models explicitly account for observation error at multiple stages (field sampling and laboratory) and consider the changes in composition of vertebrate communities (for example, from cold Himalaya mountains in the north to tropical forests in the south) when predicting patterns of occurrence for all species. The result is more conservative, more credible maps of species distributions, which is exactly what is needed when decisions carry regulatory, financial, or reputational consequences. These methods were also able to detect a “protected area effect,” with more occurrences of sensitive and threatened species inside protected areas and more occurrences of domestic and invasive species outside protected areas. This provides independent, data-driven validation that eDNA monitoring can detect real conservation outcomes.
Why Does This Matter Beyond Academia?
Because biodiversity policy, corporate biodiversity impacts disclosure, and biodiversity credit markets all rely on the same foundation: defensible, auditable evidence of biodiversity state and change. Many metrics and datasets exist for assessing the state of nature, and they are largely based on an incredibly rich set of high-resolution remote sensing products. These are great for assessing the state of, and changes in, forest cover, ecosystems from space, but they are blind to the state of animal species and communities inhabiting these systems. This is where eDNA helps fill a critical blind spot.
Rapid eDNA surveys, combined with appropriate analytics, can:
- Support national reporting under global biodiversity targets
- Enable baseline and counterfactual assessments for restoration and conservation projects
- Provide scalable, repeatable monitoring for impact disclosure
- Reduce uncertainty in biodiversity additionality claims for emerging credit markets
Nature can now be measured at scale. Advances in sampling methods, bioinformatics, and biostatistics make routine biodiversity measurement feasible; they allow us to move from “proof of concept” studies to repeatable monitoring that can inform adaptive management and conservation. This is not an argument to replace traditional surveys. Camera traps, expert fieldwork, and long-term ecological studies remain indispensable, but they are no longer sufficient on their own. If we want biodiversity to meaningfully enter decision making at the pace and scale now demanded, we need complementary tools that are faster, cheaper per unit area, and analytically robust. The convergence of new biodiversity data streams and modern statistical methods is creating the opportunity to match the pace of biodiversity data collection and analysis with real-time decision making in policy, finance, and conservation. The challenge now is institutional, not technological: investing in the analytics capacity, standards, and governance needed to turn these data into decisions.
Biodiversity Analytics Training Matters
The next phase of conservation, corporate disclosure, and biodiversity markets will not be limited by ambition or data; it will be limited by analytical capacity. Translating eDNA, remote sensing, and other rich biodiversity data streams into credible decisions requires professionals who understand ecology, statistics, data science, and decision making under uncertainty.
That gap is precisely what the new M.S. in Biodiversity Data Analytics at Columbia University is designed to address. The program trains scientists, practitioners, and decision makers to work with emerging biodiversity data streams and remote sensing products, apply rigorous statistical methods, and produce evidence that stands up in policy, finance, and corporate settings. If we want global biodiversity commitments, impact disclosure, and nature markets to be credible — not just aspirational — we need to invest in the people who can turn data into decisions.
Views and opinions expressed here are those of the authors, and do not necessarily reflect the official position of Columbia School of Professional Studies or Columbia University.
About the Program
The Master of Science in Biodiversity Data Analytics program at Columbia University equips a new generation of leaders with the data literacy, analytical tools, and interdisciplinary expertise to design evidence-based solutions that benefit both people and nature.
Designed for both working professionals and early-career changemakers, this online program allows students to learn from anywhere while gaining the skills to collect, analyze, and translate biodiversity data into meaningful action. Coursework prepares students to take on nature data applications across industries, from ESG finance to urban planning to environmental consulting, and culminates in a hands-on capstone with industry partners that provides practical experience, valuable networks, and the tools to make an immediate impact.
The priority application deadline for the M.S. in Biodiversity Data Analytics program is February 15, with a final deadline of June 1. Learn more about the program here.
