The new workflow, developed by Elena Berhocoïrigoin and colleagues at CNRS, Université de Lorraine, Sorbonne Université, and partner institutions, addresses a deceptively simple bottleneck. Data-aggregation initiatives have made hundreds of millions of biodiversity records accessible online, but accessibility is not the same as usability. Before occurrence records can be fed into ecological models, they must be reconciled across three dimensions at once: space, time, and taxonomy. Records must be checked for coordinates that fall on land, for missing or implausible depths, for dates that are vague or absent, and for species names that are misspelled, outdated, or refer to organisms that do not live in the sea at all.

Marinedb is designed as a modular toolkit rather than a rigid pipeline. Users can freely combine its components into workflows tailored to their own study objectives and the quirks of their data. That flexibility matters because marine biodiversity data arrive from an extraordinary variety of sources: research vessels, museum collections, diver surveys, acoustic monitoring, and citizen-science programs, each with its own conventions and quality standards. The package is openly available on GitHub, and the curated datasets it produced have been deposited with persistent identifiers on Zenodo, allowing other researchers to reproduce or extend the work without starting from scratch.

To demonstrate the tool’s power, the team applied it to three of the largest marine biodiversity resources in existence: the Global Biodiversity Information Facility (GBIF), the Ocean Biogeographic Information System (OBIS), and the Jellyfish Database Initiative (JeDI). Together these archives span hundreds of millions of records located at sea and more than one hundred thousand species. The researchers first used marinedb to assess data quality across all three sources non-destructively, meaning the original records were left intact while copies were scrutinized. This diagnostic step alone produced one of the most comprehensive quality audits of marine occurrence data ever attempted.

The audit revealed substantial, and strikingly source-dependent, problems. Some records were located on land, an obvious red flag for marine species. Many lacked depth information entirely, even though depth is a fundamental environmental variable for ocean-dwelling organisms. Temporal information was heterogeneous, incomplete, or of low precision, making it difficult to align sightings with environmental conditions. Other records lacked species-level identification, referred to non-marine taxa, or carried weak or unspecified evidential support for the observation. The team also found absence records mixed in with presences, and a strong taxonomic imbalance, with some groups of organisms heavily documented while others remain nearly invisible in the global record.

These issues are not mere technical annoyances; they can distort the conclusions of large-scale ecological analyses. Species distribution models, which predict where species occur by relating occurrence records to environmental predictors such as temperature, salinity, and depth, are only as good as the data behind them. A single mislabeled coordinate can place a coral reef species on a mountain range, and a poorly dated record can blur the climate signal that researchers are trying to detect. By quantifying these problems systematically, marinedb gives the research community both a clearer picture of the data’s weaknesses and a practical means of correcting them.

After the diagnostic phase, the team applied a tailored dataset-production workflow to generate a ready-to-use global marine occurrence dataset designed specifically for large-scale modeling of present-day species distributions. The result comprises nearly 159 million records representing almost 140,000 species, each cleaned and standardized so that modelers can link them directly to environmental predictors without repeating the painstaking curation process themselves. The dataset is split into source-specific curated versions for GBIF, OBIS, and JeDI, as well as an integrated global product, giving users the option of working with a single resource or the combined whole.

The timing of this release is significant. Machine learning, and deep learning in particular, is transforming how ecologists study biodiversity, but neural networks are notoriously data-hungry. Multi-species and deep-learning approaches require extensive and diverse occurrence records to perform well, and until now the technical burden of assembling such datasets has kept many research groups out of the game. By lowering that barrier, marinedb could accelerate a wave of large-scale studies of marine species distributions, from forecasting how fish stocks will shift as oceans warm to identifying priority areas for marine protected areas.

The open-source ethos of the project is central to its design. Because the code is freely available and the curation steps are explicit and reproducible, other researchers can inspect exactly how the data were treated, adapt the workflow to their own needs, or challenge particular decisions. This transparency stands in contrast to older practices in which data cleaning happened behind closed doors, buried in undocumented spreadsheets and personal scripts. The modular architecture also means the framework can accommodate future data sources and new quality checks as the biodiversity informatics landscape evolves, preserving flexibility for use cases the original authors did not anticipate.

For a field that has long aspired to a complete picture of life in the ocean, the arrival of a curated, model-ready dataset of this size marks a genuine milestone. Nearly 159 million cleaned records spanning almost 140,000 species will not answer every question about marine biodiversity, and the audit makes clear that gaps and biases remain embedded in the underlying archives. But it transforms the starting point for anyone modeling where ocean species live today and how those distributions may change tomorrow. As climate change reshapes marine ecosystems at unprecedented speed, tools that turn scattered, messy observations into reliable, reproducible science have never been more urgently needed.

Subject of Research: Open-source curation and integration of global marine biodiversity occurrence data for species distribution modeling

Article Title: marinedb: An open-source, flexible and scalable Python workflow for curating, standardizing, and integrating marine biodiversity data

Article References: Berhocoïrigoin, E., Le Noac'h, P., Laviale, M., Ayata, S.-D., Fix, J., & Pradalier, C. (2026). marinedb: An open-source, flexible and scalable Python workflow for curating, standardizing, and integrating marine biodiversity data. https://doi.org/10.5194/essd-2026-771

Image Credits: AI Generated

DOI: 10.5194/essd-2026-771

Keywords: marine biodiversity, marinedb, Python, GBIF, OBIS, JeDI, species distribution modeling, data curation, open-source software, occurrence records, data quality, ocean ecology

The research, led by Ariel Levi Simons along with Christopher Timlin-Broussard, Avicka Willis, and Jerelyn Lee, tackles one of the most persistent blind spots in urban ecology. Fungi underpin nearly every terrestrial ecosystem, decomposing organic matter, cycling nutrients, forming mycorrhizal partnerships with plants, and even acting as pathogens that shape community structure. Yet despite this ecological centrality, fungi remain chronically understudied in biodiversity monitoring, and their responses to urban stressors have been documented far less thoroughly than those of birds, mammals, or flowering plants. The new study demonstrates that publicly archived community science data can begin to close that gap at a spatial scale no single research project could achieve.

To measure urbanization, the team turned to the National Land Cover Database, which classifies every 30-meter pixel of the continental United States by the fraction of its area covered by surfaces impervious to water flow. This metric serves as a proxy for the hallmark of urban development: sealed ground. When rain cannot infiltrate soil, nutrient flows are disrupted, runoff accelerates, and groundwater recharge plummets. Previous research has shown that surface runoff can double when just 10 to 20 percent of a landscape is paved, and that groundwater recharge in urbanized areas can drop by as much as half. For soil-dwelling organisms, these hydrological upheavals fundamentally alter the resource base on which communities depend.

The fungal observations came from the Global Biodiversity Information Facility, or GBIF, an open archive where community scientists and researchers deposit species records. The researchers extracted every fungal occurrence recorded in California between 2015 and 2024, an initial haul of more than 342,000 records. After filtering to retain only species-level identifications, the dataset still contained nearly 300,000 occurrences, overwhelmingly contributed by human observers. Each record was then assigned to an ecological guild, or trophic mode, using the FUNGuild tool, sorting the fungi into pathotrophs, saprotrophs, and symbiotrophs. Ambiguous assignments and low-confidence classifications were discarded, leaving roughly 180,000 occurrences with clear ecological roles.

Working with opportunistically collected data requires statistical care, because sampling effort varies wildly across space and time. The team addressed this by estimating sample coverage for each putative assemblage, defined as all occurrences within a single imperviousness pixel in a given year and trophic mode, using the iNEXT framework. Only assemblages meeting coverage thresholds of 70, 80, or 90 percent were retained for analysis, a stringent filter that reduced the dataset to between roughly 1,700 and 499 assemblages and about 10,000 occurrences at the strictest threshold. Species richness, the study’s measure of alpha diversity, was then estimated by tracking the asymptotic behavior of richness against sampling effort, with rare species weighted equally to guard against undercounting.

The results paint a nuanced picture. For saprotrophic fungi, the decomposers that break down dead organic matter, estimated richness declined significantly with increasing impervious coverage, though the signal emerged clearly only at the strictest 90 percent coverage threshold. Notably, most of this decline appears to occur as soon as any impervious surface is recorded in the immediate vicinity, suggesting that even modest sealing of the ground may push fungal communities past an ecological threshold. However, when the researchers compared assemblages in areas with and without any impervious cover using nonparametric rank-sum tests, the difference in richness did not reach statistical significance, a reminder of how sensitive such analyses are to sample size and distributional skew. The data were heavily skewed toward unpaved areas, with skewness values rising from 1.64 before filtering to 1.87 after, reflecting a well-documented tendency of community scientists to record species in natural areas rather than city centers.

The more striking and consistent finding concerned beta diversity, the variation in community composition from place to place. For both saprotrophic and symbiotrophic fungi, the dispersion of beta diversity, measured as the average distance of assemblages from a group centroid in Jaccard dissimilarity space, declined significantly with increasing impervious coverage across all coverage thresholds. When assemblages from paved and unpaved areas were compared directly, the declines in dispersion were highly significant, with p-values as low as 10 to the power of negative 15 for saprotrophs and 10 to the power of negative 7 for symbiotrophs. In plain terms, fungal communities in paved landscapes are significantly more similar to one another than those in unsealed habitats, a signature of biotic homogenization. Pathotrophic fungi showed no such pattern, though the authors caution that the small number of recorded occurrences makes it impossible to distinguish a true ecological signal from a statistical artifact.

Why would urbanization flatten the differences between fungal communities? The authors propose ecological filtering as a central mechanism. The mechanical and chemical disturbances associated with development, from soil compaction to heavy metal runoff from roads, impose a narrow set of stresses that only a subset of species can tolerate. Land conversion disrupts the intricate architecture of mycorrhizal networks, while the urban heat-island effect alters temperature and rainfall regimes known to drive fungal richness. Compounding this, urbanization transforms plant communities, replacing native vegetation with a cosmopolitan suite of non-native ornamentals, and each plant community carries its own fungal symbionts. The massive transport of soil that accompanies construction may further homogenize underground communities by mixing propagules across regions. The result is a distinctly urban fungal flora, filtered from a once-diverse regional pool.

The study also underscores both the promise and the pitfalls of community science as a biodiversity monitoring tool. The fungal records were dominated by two phyla, Ascomycota and Basidiomycota, and by saprotrophic and symbiotrophic species, biases that mirror the tendency of amateur mycologists to document mushrooms with conspicuous, easily identified fruiting bodies. Microscopic fungi, aquatic chytrids, and arbuscular mycorrhizal Glomeromycota barely registered. Seasonal fruiting patterns were also obscured by the annual aggregation of records. Yet the sheer volume of data, growing year over year, proved sufficient to test the study’s central hypotheses at the scale of an entire US state, something previously feasible only with intensive DNA-based surveys.

Looking ahead, the authors argue that the era of molecular monitoring is poised to transform fungal conservation. Environmental DNA sampling can resolve species that never produce visible mushrooms, detecting microscopic inhabitants of soil that escape human observation entirely, and fungal datasets are expected to expand rapidly in both volume and taxonomic resolution. As urban land cover continues its global expansion, the California findings offer a warning and a template: the invisible kingdom beneath our feet responds to pavement in measurable ways, and with the right tools, we can now track it. Whether that tracking translates into protection for urban soil communities remains one of the defining ecological challenges of the century ahead.

Subject of Research: Effects of impervious urban surfaces on fungal richness and beta diversity in California

Article Title: Evidence for Reductions in Fungal Richness and Beta Diversity Dispersion Associated With Impervious Surfaces in California

Article References: Simons, A. L., Timlin‐Broussard, C., Willis, A., & Lee, J. (2026). Evidence for Reductions in Fungal Richness and Beta Diversity Dispersion Associated With Impervious Surfaces in California. Ecology and Evolution, 16(10), Article e74448. https://doi.org/10.1002/ece3.74448

Image Credits: AI Generated

DOI: 10.1002/ece3.74448

Keywords: urbanization, fungi, biodiversity, impervious surfaces, community science, GBIF, beta diversity, mycology, California, ecological homogenization, soil ecology, citizen science

The origins of GBIF lie in a diagnosis made by the international scientific community in the years following the 1992 Rio Earth Summit. The newly signed Convention on Biological Diversity created an urgent demand for reliable information about the distribution and status of the world’s species, but the knowledge needed to meet that demand was scattered across a small number of institutions, concentrated overwhelmingly in wealthy countries, with no shared mechanism to bring it together. The Organisation for Economic Cooperation and Development’s Megascience Forum concluded that an international mechanism was needed to make biodiversity data and information accessible worldwide. That conclusion led directly to the establishment of GBIF in 2001, an infrastructure designed from the outset around the principles that would later be formalised as FAIR: data that are findable, accessible, interoperable and reusable.

What began as an effort to digitise the contents of natural history museums has evolved into something far broader. Over 25 years, GBIF has continually expanded to accommodate new kinds of biodiversity information, from digitised natural history specimens and citizen science observations to DNA-based records derived from molecular work. Most recently, the infrastructure has grown to handle structured survey and monitoring data designed to support large-scale biodiversity tracking, allowing researchers to move beyond simple presence records toward understanding population trends over time. In 2026, GBIF adopted the Catalogue of Life as its taxonomic backbone, the result of a multi-year collaboration to build shared infrastructure for reconciling species names across datasets. This taxonomic backbone is technically critical, because the same species can carry many names and the same name can refer to different species; reconciling these labels is what makes billions of heterogeneous records searchable and comparable.

The architecture underpinning this achievement is deliberately distributed. GBIF’s work is carried out through a network of voting country participants and organisational participants, each represented by a national or thematic node that mobilises data, builds local capacity and connects regional biodiversity communities to the global infrastructure. Rather than collecting data into a single central repository, GBIF indexes data that publishers maintain at source. More than 2,700 institutions, including museums, universities, government agencies, citizen science platforms and a growing number of private-sector organisations, have published data through the network, with new publishers joining at a rate of more than two every three days, bringing the total number of organisations involved to nearly 3,500. This model allows a comparatively small coordination office to steward an exponentially growing body of information contributed by thousands of independent institutions.

The scientific return on this investment is measurable and striking. GBIF-mediated data now underpin an average of eight new peer-reviewed research papers per day, and have contributed to more than 15,000 publications to date. The applications span an extraordinary range of fields: climate change research uses occurrence records to model how species ranges shift as temperatures rise; food security studies draw on crop wild relatives data; public health researchers map disease vectors; and invasive species managers track the spread of organisms beyond their native ranges. An independent 2023 assessment estimated that GBIF generates roughly 12 euros in societal benefit for every euro invested, with researcher time savings alone valued at 35 million euros annually. Few scientific infrastructures can document such a direct and quantifiable return on public investment.

The influence of GBIF data extends well beyond academic journals into the machinery of international environmental policy. GBIF-mediated data support multiple indicators under the Kunming-Montreal Global Biodiversity Framework, the landmark agreement through which the world’s nations have committed to halting and reversing biodiversity loss. The same records inform assessments by the International Union for Conservation of Nature and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, two of the most influential bodies in global conservation science. Increasingly, the private sector is also turning to GBIF-mediated data to meet emerging nature-related disclosure requirements, a development that signals how thoroughly open biodiversity data has penetrated from the museum basement into corporate boardrooms and regulatory frameworks.

Dr Joe Miller, GBIF’s Executive Secretary, reflected on the anniversary and the anniversary publication that documents it. He described pride in serving as Executive Secretary at this milestone and in contributing to the publication, which details the history of GBIF both as an infrastructure and a global network. In his view, the article pays tribute to everyone involved in the network across a quarter century, including staff, participants, nodes, volunteers, data publishers and data users, representing the past, present and future of the organisation. He expressed the hope that the paper will help strengthen the network and its capacity to respond to biodiversity data needs both now and in the future.

Yet the anniversary is also an occasion for candour about persistent shortcomings. The global loss of biodiversity continues to be described as a crisis in major international assessments, even as the data infrastructure has improved dramatically. The data themselves reflect long-standing imbalances in the practice of science. Regions with high biodiversity, particularly in the Global South, remain underrepresented, while well-studied, charismatic groups such as birds are heavily overrepresented compared with less visible taxa. Many marine species remain inadequately documented, as do hard-to-identify cryptic habitats and organisms. These are not merely technical problems; they mirror inequalities in scientific funding, institutional capacity and research priorities that have shaped the field for generations.

Closing these gaps was, in fact, part of the original motivation for creating GBIF a quarter-century ago, and the organisation’s own assessment is that the network cannot resolve these asymmetries alone. Doing so will depend on broader shifts in global scientific funding, data culture and capacity, alongside GBIF’s continued efforts to expand its participant network and to diversify the types of data it can mobilise. There is also an ongoing technical challenge of data heterogeneity and quality. Because GBIF indexes data rather than directly vetting every record, it relies on its network of data publishers to maintain quality at source and on users to report issues. This distributed model is what keeps the network scalable, allowing billions of records to flow into a single index, but it is not without friction, and users must remain alert to errors, misidentifications and gaps in even the most comprehensive datasets.

Looking toward its next 25 years, GBIF is guided by its 2023 to 2027 Strategic Framework, which prioritises continued growth of the participant network, expanded support for survey and monitoring data, and deeper integration of DNA-derived biodiversity records, all aimed at closing the longstanding geographic and taxonomic gaps in digital biodiversity data worldwide. The stakes of this work could hardly be higher. As nations race toward the targets of the Kunming-Montreal Global Biodiversity Framework, and as environmental change accelerates faster than traditional monitoring can follow, the ability to see life on Earth in near real time has become indispensable. From a proposal born of a post-Rio data crisis to an index of 3.8 billion records consulted dozens of times each day by working scientists, GBIF’s first quarter-century demonstrates what sustained, open, international cooperation can build. Its next chapter will determine whether the world can finally fill in the blanks on the map of life before they disappear.

Subject of Research: The history, impact and future challenges of the Global Biodiversity Information Facility as an open biodiversity data infrastructure

Article Title: GBIF marks 25 years of FAIR biodiversity data, now spanning 70 countries and 3.8 billion records

Article References: GBIF marks 25 years of FAIR biodiversity data, now spanning 70 countries and 3.8 billion records. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: GBIF, biodiversity data, open science, FAIR principles, natural history collections, Kunming-Montreal Global Biodiversity Framework, data infrastructure, citizen science, taxonomic backbone, Catalogue of Life, biodiversity policy, data gaps