The scale of the effort reflects a long-standing problem in marine mycology. Fungi have been recorded in marine environments around the world for well over a century, typically growing on inert or living substrates in shallow temperate waters as well as in deep, cold and dark habitats far from the reach of sunlight. Yet the knowledge accumulated about these organisms has remained fragmented across research articles published in many different scientific fields, from botany to microbiology to oceanography. According to the authors, this scattering of data has undermined any comprehensive understanding of fungal biodiversity and function in the sea, slowing the transition from a basic awareness of biodiversity patterns toward a quantitative ecological understanding grounded in what fungi actually do in their ecosystems.

To break through that fragmentation, the research team assembled a curated dataset of fungal records from the Mediterranean Sea and annotated each record with its potential function and trophic behavior, following the criteria of FUNGuild, a widely used framework for assigning ecological roles to fungi. FUNGuild classifies fungi into functional guilds based on how they obtain nutrients, distinguishing for example saprotrophs that decompose dead organic matter, pathogens that attack living hosts, and symbionts that live in mutually beneficial relationships with other organisms. By applying this standardized annotation to thousands of historical records, the team transformed a scattered literature into a machine-readable resource that can be analyzed, mapped and compared across studies.

The numbers tell a story of both richness and imbalance. The 4,623 records gathered from the 125 selected articles span the full environmental range of the Mediterranean, from coastal areas to deep-sea habitats, and resolve into 914 unique taxa. Among these, the Dikarya, the large fungal group that encompasses the ascomycetes and basidiomycetes, the two most familiar divisions of the fungal kingdom, dominate the record. Ascomycetes in particular have long been the workhorses of marine mycology, readily identifiable on submerged wood, algae and animal hosts. In contrast, the zoosporic lineages, fungi that produce motile spores propelled by flagella and that include many water molds and other aquatic specialists, are markedly underrepresented in the compiled data.

That underrepresentation matters more than it might first appear. Zoosporic fungi are known to play significant roles in aquatic food webs, parasitizing algae and other microorganisms and recycling nutrients in planktonic communities. If they are systematically missing from the Mediterranean record, the dataset cannot yet claim to portray the full functional architecture of the sea’s fungal life. The authors are candid about this limitation: the compiled dataset highlights biases in mycological biodiversity knowledge, and those biases are precisely what make the resource valuable. By making the gaps explicit and quantifiable, the study gives future researchers a clear map of where to direct their sampling effort, whether toward underexplored basins, underrepresented lineages, or ecological functions that have rarely been tested.

Geography emerges as another of the study’s central findings. Fungal records in the dataset are concentrated on the western side of the Mediterranean basin, a pattern that almost certainly reflects the distribution of research activity rather than the true distribution of fungi. The western Mediterranean, with its dense network of marine stations in France, Italy and Spain, has historically attracted far more mycological attention than the eastern reaches of the sea. This kind of sampling bias is a familiar headache for biogeographers working in many taxa and many regions, and the new dataset makes it possible to see the skew in the Mediterranean fungal record with unusual clarity. Correcting it will require coordinated surveys in the southern and eastern basins, where the fungal diversity of Posidonia meadows, lagoons and deep hypersaline habitats remains largely uncharted.

Perhaps the most sobering conclusion of the study concerns function. Although the team succeeded in annotating the potential trophic behavior of the recorded fungi using the FUNGuild criteria, they found that fungal functions were rarely thoroughly investigated in the underlying literature. In other words, most of the 125 source articles identified which fungi were present but stopped short of asking what those fungi were doing. This is a critical gap, because ecological function is where fungi earn their place in the marine story. Saprotrophic marine fungi break down tough plant polymers such as cellulose and lignin that wash into the sea, accelerating the return of carbon and nutrients to the food web. Pathogenic fungi can regulate populations of algae and invertebrates, sometimes with dramatic effects during disease outbreaks. Symbiotic fungi associated with algae, seagrasses and marine animals may influence host health and stress tolerance in ways that are only beginning to be explored.

The Mediterranean Sea is an especially compelling arena for this kind of synthesis. As a semi-enclosed basin connected to the Atlantic through the narrow Strait of Gibraltar, it hosts a distinctive biota shaped by millions of years of isolation, dramatic salinity gradients and, more recently, pronounced warming. The sea is considered one of the planet’s biodiversity hotspots, and it is also one of the regions most rapidly transformed by climate change, with rising temperatures, acidification and the influx of invasive species reshaping its ecosystems. Fungi are woven into all of these processes, from the decomposition of organic matter in warming waters to the emerging diseases of marine organisms, yet until now no single resource has brought the Mediterranean’s fungal record together in a form suitable for large-scale ecological analysis.

The new dataset is designed to change that. By pairing every record with functional annotation, the authors have created a foundation for studies aimed at comprehensively mapping fungi and their functions in the Mediterranean Sea. Such mapping could inform models of carbon cycling in the basin, help predict where fungal pathogens might flare up as waters warm, and guide the search for biotechnologically useful marine fungi, an area of growing interest given the enzymes and bioactive compounds that marine fungi are known to produce. The open-access publication of the work means that researchers across the Mediterranean countries, and well beyond them, can build directly on the compiled records rather than repeating the laborious literature trawl that produced them.

The study also carries a broader message about how science handles scattered knowledge. Marine fungi sit at the intersection of mycology, marine ecology and microbiology, and no single field has claimed responsibility for synthesizing what is known about them. The Mediterranean compilation demonstrates that even without new fieldwork, a rigorous re-examination of existing literature can yield a resource of genuine scientific value, complete with standardized functional annotations and an honest accounting of its own biases. As the authors note, the transition from knowing that fungi are widespread in the sea to understanding quantitatively what they do there has been slow, and datasets like this one are the bridge across that gap. For a kingdom of life that may number in the millions of species worldwide, and that remains one of the least explored corners of marine biodiversity, the Mediterranean’s 914 recorded taxa represent not an endpoint but a beginning: a first, carefully annotated sketch of a hidden world that future surveys will fill in, basin by basin and function by function.

Subject of Research: Biodiversity and functional annotation of marine fungi recorded in the Mediterranean Sea

Article Title: Biodiversity and Functional Annotation of Fungi Recorded in the Mediterranean Sea

Article References: Marchese, P., Bovio, E., Chinaglia, S., Tarallo, A., Ferrari, E., Martínez, A., & Garzoli, L. (2026). Biodiversity and Functional Annotation of Fungi Recorded in the Mediterranean Sea. Microbial Ecology. https://doi.org/10.1007/s00248-026-02904-1

Image Credits: AI Generated

DOI: 10.1007/s00248-026-02904-1

Keywords: marine fungi, Mediterranean Sea, fungal biodiversity, FUNGuild, trophic modes, mycobiota, Dikarya, zoosporic fungi, deep-sea ecology, biodiversity dataset, fungal ecology, sampling bias

The research, led by Gaurav Kumar Gugliani of the Jaypee Institute of Information Technology together with Arnab Sarkar of the Indian Institute of Technology (B.H.U.) Varanasi, Najmeh Nakhaei Rad of the University of Pretoria, and Christophe Ley of the University of Luxembourg, focuses on the Weibull distribution, the workhorse of wind speed modeling. The Weibull distribution describes wind climates with just two numbers: a shape parameter, which captures how variable or steady the wind is, and a scale parameter, which reflects the characteristic wind speed of a site. Nearly every wind resource assessment, turbine selection study, and wind loading calculation in the world passes through these two parameters. If they are biased, everything downstream inherits that bias.

The problem begins with the Indian Meteorological Department’s operational protocol. Wind speeds are recorded in decimal knots, converted to kilometers per hour, and then disseminated as integers, with fractional parts of 0.55 or greater rounded up and everything below rounded down. The authors point out that this is not a uniquely Indian quirk. Germany’s national weather service, the Deutscher Wetter Dienst, supplies wind data on a 0.1 meter-per-second grid even though a single anemometer pulse corresponds to 0.12 meters per second, meaning that recorded pulses of 0.12, 0.24, and 0.36 meters per second are logged as 0.1, 0.2, and 0.4, and the value 0.3 meters per second never appears in the record at all. Such discretization breaks the statistical continuity of the underlying physical process, and no amount of careful fitting downstream can fully undo it.

To quantify the damage, the team turned to Monte Carlo simulation, the statistical equivalent of a wind tunnel for data. They generated synthetic wind speed samples of one million values drawn from a known Weibull distribution, deliberately chose parameters inspired by real Indian wind regimes, with a shape parameter of 1.5 and a scale parameter of 10.8 kilometers per hour, and then pushed those samples through a numerical replica of the entire IMD pipeline: discretization to 0.1 knots, conversion to kilometers per hour, and integer rounding. When the Weibull probability density function was fitted to the original high-resolution data, it matched beautifully. When it was fitted to the disseminated integer data, it visibly underfit the histogram. The rounding had left fingerprints all over the distribution.

Having established that the bias is real, the researchers set out to reverse it. They repeated the simulation across an enormous grid of parameter combinations, varying the true shape parameter from 0.50 to 4.00 in steps of 0.05 and the true scale parameter from 2.0 to 25.0 kilometers per hour in steps of 0.10. For each pair, they estimated Weibull parameters from both the pristine and the corrupted samples using maximum likelihood estimation, generating a vast catalogue of matched biased and unbiased estimates. The goal was audacious in its simplicity: learn the mathematical mapping that takes the biased numbers an analyst actually receives and returns the unbiased numbers that would have been obtained from the original measurements.

The mapping they settled on is a second-order bivariate polynomial response surface, one for each Weibull parameter. The choice was deliberate. Linear surfaces proved too crude to capture the nonlinear interplay between the biased shape and scale estimates, while higher-order polynomials offered only marginal gains at the cost of complexity and overfitting risk. The quadratic form struck the right balance between accuracy, stability, and, crucially, practical usability: a practitioner can simply plug the biased estimates into two explicit equations and read off corrected values, no iterative numerical procedures required. The coefficients were determined by ordinary least squares fitting across the full simulation grid.

The validation is where the results become genuinely striking. To test whether the correction surfaces were merely memorizing their training data, the team generated an entirely independent validation grid, offset by half a step from the training nodes so that no validation point coincided with any training point, and drew fresh million-value samples with new random seeds. The reconstructed shape parameters matched the true values with a coefficient of determination of 0.9998, a mean absolute percentage error of just 0.85 percent, and a root mean square error of 0.0147. The scale parameter, which is more sensitive to the absolute magnitude of the wind speeds and therefore suffers compounded distortion through the sequential knot-to-kilometer-per-hour-to-integer conversions, was reconstructed with a coefficient of determination of 0.9723 and a mean absolute percentage error of 4.17 percent. These are, by any statistical standard, excellent reconstruction figures.

Applied to real data from 37 Indian meteorological stations, the correction revealed systematic patterns. The biased shape parameter generally overestimated the corrected value, and the biased scale parameter consistently exceeded its corrected counterpart. The magnitude of the distortion varied with the wind regime: stations with low or moderate shape parameters, such as Amritsar, Jaipur/Sanganer, Cochin, and Calcutta/Dum Dum, showed noticeable corrections in both parameters, while stations with higher shape parameters, such as Tuticorin H.P., Indore, and New Kandla, saw smaller shape corrections but still substantial scale adjustments. This matters because surface-level wind measurements, the kind taken at ten meters near airports, are precisely the ones with low shape parameters, where the flow is heavily modified by buildings, vegetation, and terrain.

The most dramatic consequences emerge when the biased parameters are used to estimate wind power density, the standard measure of the energy available in the wind, which depends on the cube of the wind speed and therefore amplifies any parameter error. Across the full simulation domain, the wind power density computed from disseminated data always underestimated the true value, and in the low-shape-parameter region the ratio dropped to nearly 0.40, meaning wind power potential could be underestimated by almost 60 percent. After applying the correction equations, the ratio of corrected to biased wind power density moved close to unity over most of the parameter domain, generally falling between about 0.90 and 1.18, with residual variation only at the extreme low-shape boundary where the quantity is inherently hypersensitive.

The authors are careful to delineate what their method does and does not fix. The correction removes only the statistical bias introduced at the dissemination stage; it does not compensate for terrain-induced effects from topography, surface roughness, or nearby obstacles, which must be handled by separate, well-established terrain-normalization procedures. The framework was also calibrated specifically for the IMD protocol and the two-parameter Weibull distribution, so applying it to other reporting systems or other distributions would require recalibration. Still, the broader message resonates far beyond Indian meteorology: in an era when climate archives feed directly into renewable energy investment, building codes, and structural safety assessments, the humble act of rounding a number can propagate into decisions worth fortunes. This study provides the first explicit inverse correction framework for recovering unbiased Weibull parameters from rounded wind data, and it suggests that every national weather agency disseminating discretized wind records should be asking the same uncomfortable question about its own archives.

Subject of Research: Correction of quantization-induced sampling bias in Weibull parameter estimation for wind speed modelling

Article Title: Assessing and correcting the effects of biased sampling on Weibull parameter estimation for wind speed data modelling

Article References: Gugliani, G. K., Sarkar, A., Nakhaei Rad, N., & Ley, C. (2026). Assessing and correcting the effects of biased sampling on Weibull parameter estimation for wind speed data modelling. Theoretical and Applied Climatology, 157(10), Article 668. https://doi.org/10.1007/s00704-026-06576-2

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06576-2

Keywords: Weibull distribution, wind speed data, sampling bias, Monte Carlo simulation, wind power density, Indian Meteorological Department, quantization error, maximum likelihood estimation, response surface, wind resource assessment, surface wind observations, statistical correction

The research team set out to answer a deceptively simple question: who is actually represented in mobile location data, and how does that representation vary across space and demographic groups? To do so, the investigators compared the demographic and geographic composition of large-scale mobile location panels against authoritative benchmark populations drawn from the United States Census. By aligning the observed device counts with census figures across multiple spatial resolutions, from the national level down to counties and finer census geographies, the researchers were able to quantify where and for whom mobile location data over-represents or under-represents the underlying population. This benchmarking approach transforms a vague concern about bias into a measurable, mappable quantity.

The multiscale design is central to the study’s contribution. Many previous audits of mobility data focused on a single geographic level, typically the nation as a whole, where biases can average out and appear modest. But the new analysis demonstrates that national aggregates conceal dramatic local variation. A dataset that looks reasonably balanced at the country level may severely distort the picture in individual counties, particularly in rural areas, on tribal lands, and in communities with distinctive age or income profiles. Because many downstream applications, from retail site selection to emergency response planning, operate at precisely these local scales, the discrepancy between national fairness and local distortion carries real consequences.

Technically, the analysis relied on comparing device-level residence estimates with census counts of population by age, sex, race and ethnicity, income, and urbanicity. The researchers computed representation ratios, expressing the share of mobile devices attributed to a group or area relative to that group’s share of the benchmark population. Values above one indicate over-representation and values below one indicate under-representation. By computing these ratios repeatedly across scales and demographic dimensions, the team built a comprehensive portrait of sampling bias that could be decomposed into spatial components, sociodemographic components, and their interactions. This decomposition matters because spatial and demographic biases are intertwined: certain places are home to certain populations, so a geographic gap is often simultaneously a demographic one.

The findings confirm and sharpen long-standing suspicions about who is missing from mobile location data. Older adults, particularly those in the oldest age brackets, appear substantially less often than their share of the population would predict, reflecting lower smartphone adoption, different app ecosystems, and greater privacy sensitivity. Racial and ethnic minorities show uneven representation that varies by region, with some communities under-represented and others appearing at rates closer to parity. Income gradients emerge as well, with lower-income households less likely to contribute devices to commercial location panels, a pattern consistent with disparities in smartphone access, data plan affordability, and the types of applications that collect and monetize location information.

Geographically, the study finds that rural counties are systematically under-represented relative to urban cores, where dense populations, heavy app usage, and the economics of the mobile advertising ecosystem concentrate data collection. The magnitude of the rural gap is not uniform: it widens in regions with older populations and lower broadband penetration, and it narrows in peri-urban counties that blend rural character with metropolitan labor markets. The authors also document variation across states and metropolitan areas, suggesting that regional culture, state-level demographics, and the spatial distribution of technology industries all leave fingerprints on the composition of location panels. For analysts, the practical implication is that bias correction cannot rely on a single national adjustment factor.

Why do these biases arise in the first place? The study situates the problem in the mechanics of how commercial location data are produced. Devices enter these datasets only when users grant location permissions to applications that partner with data aggregators, and only when the resulting location pings pass quality filters designed to remove noise and protect privacy. Each step in this pipeline, from app adoption to permission granting to data brokerage, filters the population in ways that correlate with age, income, race, and place. The result is a dataset that is enormous, in the tens of millions of devices, yet still systematically skewed. Scale, the authors emphasize, is not the same as representativeness; a bigger biased sample is still a biased sample.

The implications ripple across the sciences that depend on mobility data. Epidemiological models that used location data to estimate contact patterns during the COVID-19 pandemic, for example, may have under-counted the movements of older and lower-income individuals, precisely the groups whose vulnerability and exposure dynamics differed most from the smartphone-owning mainstream. Transportation planners who calibrate travel demand models with mobile data risk designing infrastructure around the behavior of a demographically narrow public. Studies of segregation, access to services, and environmental exposure that infer activity spaces from location pings may mischaracterize the daily geographies of communities that are thinly sampled. The new bias maps offer these fields a diagnostic tool: before using location data for a given region or population, researchers can now check how well that region and population are actually represented.

The authors also point toward remedies. Weighting and reweighting schemes, in which devices are assigned statistical weights so that the weighted sample matches census benchmarks, can reduce aggregate bias, though the study cautions that such corrections struggle when entire groups are nearly absent from the data. Hybrid approaches that blend mobile data with traditional surveys, administrative records, or census microdata can anchor estimates to known population structure. Perhaps most importantly, the researchers advocate routine bias auditing as a standard step in any analysis using commercial location data, analogous to the data-quality checks that are customary in survey research. Transparency about representation, they argue, should become a baseline expectation for both data vendors and the scientists who consume their products.

As mobile location data continue to expand into nearly every corner of computational social science, this multiscale audit of the United States serves as both a warning and a roadmap. The warning is that the digital traces left by smartphones reflect the inequalities of digital access and technology adoption, and that these inequalities are written into the data at every geographic scale. The roadmap is a practical methodology for measuring those biases, mapping them across the country, and correcting for them where possible. In an era when decisions about public health, transportation, and resource allocation increasingly rest on algorithmic interpretations of human movement, knowing exactly who is missing from the data is the first step toward ensuring that data-driven decisions serve everyone, not just those who carry a trackable device.

Subject of Research: Spatial and sociodemographic sampling biases in mobile location data across the United States

Article Title: Characterizing the spatial and sociodemographic sampling biases found in mobile location data through a multiscale analysis of the United States

Article References: Embury, J., Dodge, S., & Nara, A. (2026). Characterizing the spatial and sociodemographic sampling biases found in mobile location data through a multiscale analysis of the United States. Scientific Reports. https://doi.org/10.1038/s41598-026-70982-9

Image Credits: AI Generated

DOI: 10.1038/s41598-026-70982-9

Keywords: mobile location data, sampling bias, human mobility, demographics, census benchmarking, spatial analysis, rural under-representation, digital divide, data representativeness, computational social science, Scientific Reports, United States