The Cordycipitaceae Research Atlas

This is the family of the cultivated medicinal Cordyceps militaris, of Beauveria bassiana, the white fungus sprayed on crops to kill pests, and of the depsipeptide toxins that let them do it. COMPASS links each of ~654 accepted species to OpenAlex, tags every study through a domain-expert panel with adversarial cross-model judging (18 percent of tags were kept without a judge verdict because the judge declined to answer on biocontrol content), and geocodes each to its study site. The result is a census of a lineage that sits at the intersection of agriculture, medicine, and one of biology’s strangest life histories.

The methods follow suit: culturing and antibiosis bioassays (2,318 studies) and fermentation and bioprocessing (1,346) dominate. Experimental studies are 43 percent of the corpus. This is an applied science, a field asking, above all, how do we use them?

Beauveria, the biocontrol staple, is the best-studied of the large genera — and even so, 42% of its species are unrecorded. But even Cordyceps is 70% without indexed research; Isaria, 91%; Torrubiella, 80%; Samsoniella, 75%. Fungal synonymy is unresolved and inflates the share with no indexed research in Isaria and Torrubiella, many of whose names have been sunk into Cordyceps, Akanthomyces and Samsoniella. Attention centres on the cultivated medicinal Cordyceps militaris (the zombie-ant fungus and the Himalayan tonic mushroom belong to Ophiocordyceps, in a different family), but most of the family, hundreds of species of insect-specific parasites, has no indexed research.

Conservation framing barely registers: threats appear in just 2% of studies, and where they do, the dominant one is that these fungi are themselves an invasive concern, a reflection of the biocontrol lens, not a worry for the family’s own survival. A lineage we deploy by the tonne against crop pests, and harvest by the tonne for medicine, is one we have made almost no attempt to conserve, or even to count as at risk.

For where research sits against species ranges, coverage cells and access distances, see section 6; for the same sites set against environmental change and split by decade, section 7.

India (83 sites) and China (61) lead. India and Brazil come through tropical entomopathogen diversity and biocontrol research, China through the deep cultural and commercial pull of medicinal Cordyceps (chiefly the cultivated C. militaris). Korea and Colombia punch above their size. But most study sites are national-scale. This is a lab and cultivation science as much as a field one, and much of the family’s tropical range is barely sampled.

1 · Species ranges against study sites

For each of the 654 species we compared two country lists: where it is known to occur, and where its indexed papers were carried out. Known range countries come from GBIF occurrence records of every type, so cultivated and museum records count as presence; in-range is therefore a generous test and outside-only a conservative one. Of the 419 species with range data, 350 (83.5%) have no locatable study site inside any range country. That figure mixes two things that only a paper-level check can separate: species with no research (404 of 654 have no indexed research) and species whose papers name no place a geoparser can locate (151 of the 250 studied species). A further 14 species were studied only outside their known range countries.

No breakdown by IUCN class is possible: the checklist carries no assessed status for any of these species (all 419 with range data are unevaluated).

Range shortfall by country

Each country is shaded by the share of its range species that have no locatable study site inside it: darker means a larger share of the species known to occur there have not been studied in the field there. Unshaded land has no census species in range. The five countries with the most range species lacking a site are labelled (China, Japan, United States, Brazil, Australia); they also head the table below.

2 · Where the study sites are

Each cell is one hexagon of the H3 grid, drawn on its true boundary so that the cells tile without overlap; darker cells hold more distinct study sites. Most cells hold one site; the fieldwork of the whole corpus concentrates in a small number of them.

The densest window, enlarged

The dashed rectangle on the map above marks the window that holds the most study sites (74°E to 128°E, 8°N to 37°N). Here it is at a larger scale and at the grid's own resolution 3 (about 12,400 km² per cell), with its own log ramp.

Many species, few studies

The ten range countries with the most census species and two or fewer locatable study sites from this corpus. Because the range lists include cultivated and museum records, a country can appear here through garden and herbarium holdings rather than wild populations; read the list as a prompt to check, not a verdict.

3 · How far the sites are from a city

Distance from each study site to the nearest populated place of 100,000 people or more is a rough measure of how reachable the fieldwork was. The busiest access points, counted greedily so that no site is counted twice, are Bridgeport (United States of America, 8 sites within 100 km), Girardot (Colombia, 7 sites within 100 km), Coimbatore (India, 6 sites within 100 km); 11.0% of all sites lie within 100 km of one of the ten busiest.

These are straight-line distances, not travel times; roads, terrain, sea ice and protected-area access are not modelled. Study-site coordinates are geoparsed from abstracts, and region-level places sit at their centroid.

4 · Method note

Sources. known range countries = GBIF occurrence-record countries stored per species (every basis of record, top 100 countries, no polygons); access points = Natural Earth ne_10m_populated_places_simple, GitHub master 5.2.0-pre (nvkelso/natural-earth-vector, downloaded 2026-09-29) (populated places with 100,000 people or more), public domain; grid = H3 resolution 3 (h3-js 4.5.0), drawn on the true cell boundaries from h3-js and aggregated one level (h3 cellToParent) on the main maps. A study site is a place a paper's abstract names as the location of the work, resolved to coordinates by a gazetteer and kept only when it came from one of the six trusted geocode sources; 599 of 7,994 studies (7.5%) have one. Density and distance use precise sites only (not a whole country, not a rounded centroid): 482 distinct places.

Caveat. Study-site coordinates come from automated geoparsing; a 40-site manual check found 31 plausible; a 100 to 200 pin human check is planned. Range coverage is a country-level test, so a study in the right country but far from the species' actual distribution still counts as in range.

Provenance. Generated 2026-10-14 from the published database. Figures in this section are recomputed by phase1/spatial_shortfall.js and checked by the figures audit.

In this corpus the rate of environmental change and the density of located research are essentially unrelated: Spearman's rank correlation between the two, unit by unit, is near zero. For forest loss it is −0.024 across 530 ecoregions and −0.015 across 1,827 admin1 regions; for drying it is −0.081 across 233 ecoregions and −0.144 across 603 admin1 regions. These are descriptive figures: they do not show that change causes or deters research, and the units differ in area, accessibility and richness. What they do show is where fast change and little located research coincide, which is the list each block below ranks.

1 · Forest loss 2001 to 2024

Change here is the share of the area that was forest in 2000 (tree cover of 30 percent or more) with a loss event between 2001 and 2024, from Hansen Global Forest Change v1.12 at 30 m. Each ecoregion and each first-level administrative region is placed in one of four quadrants: the top third of the universe by forest loss is high change, the bottom third by located research density (papers of this corpus with a precise study site inside the unit, per 10,000 km²) is low research. The high-change, low-research quadrant is the candidate gap list.

Loss is any stand-replacing event, including fire and the rotational harvest of plantations, and regrowth is not subtracted, so a high value can be a working plantation landscape as easily as a cleared forest; the 30 percent canopy threshold makes open woodland "not forest".

Forest loss 2001 to 2024, cell by cell

2 · Aridity trend 1990 to 2024

Change here is the linear trend of the annual aridity index (precipitation over potential evapotranspiration, P/PET) from 1990 to 2024, as percent change per decade of the 1990 to 1999 mean, from TerraClimate at about 4.6 km; a negative number means drying. Each ecoregion and each first-level administrative region is placed in one of four quadrants: the top third of the universe by drying is high change, the bottom third by located research density (papers of this corpus with a precise study site inside the unit, per 10,000 km²) is low research. The high-change, low-research quadrant is the candidate gap list.

TerraClimate is an interpolated climate surface, so a 35-year trend at 4 km carries station-density and interpolation error, and a relative trend is unstable where the baseline is near zero; the ranking is restricted to drylands (mean P/PET under 0.65) and excludes hyper-arid units (mean under 0.05) for that reason.

Aridity trend 1990 to 2024, cell by cell

3 · When the research happened

The same study sites split by decade of publication. Of the 451 located papers, 65.2% were published in the 2010s or 2020s and 43.5% since 2016. The whole corpus of 7,994 papers, located or not, is 61.0% from those two decades, so located research is more recent than the corpus. Of the 188 ecoregions with a located paper, 33 have none published since 2010; of the 262 admin1 regions, 58.

The decade with the most located papers, cell by cell

4 · Method note

Change layers. Forest loss 2001 to 2024 = UMD/hansen/global_forest_change_2024_v1_12 (v1.12 (2024 update)), 2001-2024, sampled at 1,000 m (native 30 m), CC BY 4.0; Aridity trend 1990 to 2024 = IDAHO_EPSCOR/TERRACLIMATE (TerraClimate monthly, EE catalogue copy (1958-01 to 2024-12 at run time)), 1990-2024, sampled at 4,638 m (native 4,638 m), TerraClimate: CC0 1.0 (public domain). MODIS: NASA, no restrictions; dryland classes after UNEP 1992. Values are area means of a systematic sample over each H3 cell of the grid (the maps above draw them one resolution coarser, as valid-area-weighted means of the child cells) and each unit outline (RESOLVE Ecoregions 2017 and GADM 4.1, simplified for display), computed in Google Earth Engine; boundary pixels are weighted fractionally. Layers are remote-sensing and reanalysis products, not field measurements.

Research. 451 of 7,994 papers in this corpus have a precise trusted study site (the same selection as the density map above); a paper counts once per cell and once per unit it has a site in, and a unit's density is papers per 10,000 km² of its area. Only units of 1,000 km² or more are ranked, and only units in a country where a census species is known to occur, so that a city-state's density is not set against a province's. 0 precise places (open sea, Southern Ocean, atoll centroids) fall in no ecoregion and 0 in no admin1 region; they are in the cell maps but not in the unit tables. Range species without a site is a country-level test (every unit in a country is credited with the country's whole range list), so it is an upper bound.

Reading it. A near-zero correlation says that, across these units, more change does not go with more or with less located research. It does not say that change deters research or that research prevents change, and units differ in area, accessibility, richness and national research capacity. A unit in the high-change, low-research quadrant is a candidate gap, not a proven one: papers whose text names no locatable site are invisible here, and the geoparsing caveat of the section above applies.

Provenance. Generated 2026-10-14 from the published database and the Earth Engine cache. Figures in this section are recomputed by phase1/research_vs_change.js, drawn by phase1/gen_atlas_change_section.js and checked by the figures audit.

Beta This section fits a model of research attention: the number of papers of this corpus with a precise study site inside each ecoregion and each first-level administrative region, predicted from the unit's area, the number of census species whose known range countries include the unit (country level, so it separates countries rather than places within one), its distance to the nearest city of 100,000 people, forest loss and the aridity trend, its biome, realm and latitude band, and how much located research the rest of its country carries. The model predicts where research tends to go. It does not predict ecological importance, threat or need, and a unit with less research than expected is a candidate gap: a place where the record is thinner than in comparable places. It is never proof of neglect. Fit quality is measured out of sample by 5-fold spatial cross-validation (whole realms or whole countries held out): pseudo-R2 0.42 for ecoregions and 0.40 for administrative regions (admin1). Each unit carries a 90 percent prediction interval from the negative-binomial count spread about its expectation. Because located research is strongly clustered (theta under 1), that interval is wide: a unit with no located paper falls below it only when the model expects more than 109.7 papers there (ecoregions) or 298.9 papers there (administrative regions (admin1)), and in this run no unit of either type does. The rankings therefore order units by the tail probability, the chance of a count at or under the observed one given the expectation, and carry the interval label alongside.

1 · Ecoregions: expected against observed Beta

Fit. 763 ecoregions in the universe, 182 with a located paper; negative binomial, dispersion theta 0.57, mean by negative-binomial IRLS, ridge lambda 10, intercept recalibrated by 1.087 so that the expected total equals the observed total. 5-fold spatial cross-validation blocked by realm: pseudo-R2 0.42 (0.36 without the national-effort covariate), Spearman rho 0.43 out of fold. Labels against the 90 percent interval: 0 below, 742 within, 21 above.

The "more than expected" list doubles as a geocoding audit: a small unit with tens of papers against an expectation under one is as likely a place-name collision, a herbarium or a field station as a genuine concentration of fieldwork.

2 · Administrative regions (admin1): expected against observed Beta

Fit. 2,438 administrative regions (admin1) in the universe, 252 with a located paper; negative binomial, dispersion theta 0.46, mean by negative-binomial IRLS, ridge lambda 30, intercept recalibrated by 1.0558 so that the expected total equals the observed total. 5-fold spatial cross-validation blocked by country: pseudo-R2 0.40 (0.36 without the national-effort covariate), Spearman rho 0.35 out of fold. Labels against the 90 percent interval: 0 below, 2,380 within, 58 above.

The "more than expected" list doubles as a geocoding audit: a small unit with tens of papers against an expectation under one is as likely a place-name collision, a herbarium or a field station as a genuine concentration of fieldwork.

3 · Calibration

Out of fold, by decile of expected count (observed / expected): ecoregions: deciles 3 to 8 between 0.81 and 1.43, top decile 1.23; administrative regions (admin1): deciles 3 to 8 between 0.66 and 2.20, top decile 1.35. The middle deciles are the best calibrated; the top decile is under-predicted in every model, because the busiest places (long-term field stations, provinces with a herbarium or a university) exceed what any smooth covariate model gives. That is one more reason the residuals are candidate gaps and candidate artefacts rather than findings.

4 · Method note Beta

Response and exposure. 451 of 7,994 papers in this corpus have a precise trusted study site (5.6 percent); the response per unit is the number of those papers with a site inside it (point in polygon, or the nearest unit within 25 km, as the place explorer counts), so a paper with sites in two units counts once in each: 520 unit-papers over 763 ecoregions and 508 over 2,438 administrative regions (of 847 and 3,686 units in all). The exposure is log(area), with log(area) also entering as a slope so the data set the area elasticity.

Covariates. log_area_km2, log_richness, log_access_km, forest_loss_pct, forest_loss_missing, aridity_trend_pct_per_decade, aridity_trend_missing, log_national_loo_density, national_loo_undefined, biome, realm, lat_band; continuous covariates standardised, categorical levels with fewer than 10 units pooled. Protected-area share is not available (no protected-area table in the database) and is stated as the natural next covariate. Richness is a country-level proxy: every unit of a country is credited with the national list.

Model and validation. Negative binomial (NB2) GLM with a log link and a ridge penalty on the standardised slopes (ecoregions lambda 10, administrative regions (admin1) lambda 30); the mean estimator and lambda are chosen on out-of-fold Poisson deviance over the same spatial folds; the dispersion is estimated by maximum likelihood. 5-fold spatial cross-validation blocked by realm (ecoregions) or country (admin1), never random rows; a held-out realm's own dummy is unseen in training. The 90 percent interval is the 5th and 95th percentile of NB(expected, theta) and carries no parameter uncertainty, so it is if anything too narrow. Zero rule: a unit with no located paper and an expectation under 0.5 is never labelled below and never ranked.

Limits. Located papers only: papers whose text names no locatable site are invisible here, and the geoparsing error rate of the sections above applies. The national-effort covariate carries most of the between-country signal; the fit without it is printed above. Terrestrial units only. This is a Beta layer: the numbers are honest as stated, and they will move when the geocoding, the corpus or the covariates change.

Provenance. Generated 2026-10-14 from the published database (SELECT only) and the change layers' cache. Refresh with node phase1/expected_research.js fungi, redraw with phase1/gen_atlas_expected_section.js; every figure in this section is checked by the figures audit.

This atlas is one view of a larger queryable corpus. A single genus’s evidence profile, the biocontrol-versus-medicine split for any species, or a shortlist of taxa with no indexed research, ready for survey are each a query away, on the same reproducible foundation.