The Apocynaceae Research Atlas
Of 6,759 accepted Apocynaceae species, 4,292 (63.5 percent) have no indexed paper, and 386 of 5,505 in-vitro pharmacology studies reached a clinical study.
We tagged 32,565 of the 33,900 indexed studies of the Apocynaceae, the family of periwinkle, oleander and milkweed, and the source of the cancer drug vinblastine, against 46 dimensions shared across domains plus domain-specific extensions. The research concentrates on the family's chemistry, does not reach two-thirds of its species, and covers the most threatened species least.
Every species in the family
We load every accepted Apocynaceae species from a taxonomic authority and link each to the indexed literature. A species with no linked papers is recorded as having no indexed research.
The Apocynaceae hold 6,759 accepted species in Version 16 of the World Checklist of Vascular Plants: climbing periwinkles, wax plants, oleanders, the milkweeds that monarch butterflies depend on. We link each accepted name, and every name the checklist treats as its synonym, to OpenAlex, tag every study with a panel of domain-expert models whose tags a second model checks, and place each study at its field site rather than its authors' address. The result is a map of what has and has not been studied in this family, species by species.
Research focus: phytochemistry and pharmacology
Two-thirds of Apocynaceae research is pharmacology and natural-products chemistry. Ecology, taxonomy and agriculture together account for a smaller share.
The family is a major source of monoterpene indole alkaloids, the class that includes vinblastine and vincristine, along with cardiac glycosides and flavonoids. Most studies use laboratory bioassays and spectroscopy; fieldwork is less common.
Pharmacology by evidence stage
Most of the evidence is early-stage. There are about fourteen cell- or enzyme-assay studies for every study with human clinical evidence (5,501 against 386).
30% of studies report a bioactivity: antimicrobial, antioxidant, anticancer, cardiovascular. Grouped by how far each study got toward proof in humans, the counts fall sharply at every stage. Each number counts distinct studies at that stage, so a single molecule may appear at more than one stage. 5,505 studies are at the cell- or enzyme-assay stage; 386 report human clinical evidence (trials, case series and poisoning reports).
Species with no indexed paper
Behind the chemistry, 4,292 of 6,759 species, roughly two of every three, have no indexed research at all. Almost all the research goes to a handful of genera.
The family's largest genera are also its least studied. Hoya (the wax plants) has 568 species, 68% of them with no indexed research; Ceropegia (the lantern flowers), 473 species, 59%; Cynanchum, 73%; Matelea, 83%. In each of the largest genera, most species have no research.
Research effort by IUCN category
Species assessed as threatened have no indexed research at about the same rate as unassessed species and about twice the rate of Least Concern species; Data Deficient species are the least studied.
Among least-concern species, a third have no indexed research. Among vulnerable species the share is 72%, and among data-deficient species 83%. Part of this may be structural: species are often listed as threatened because they are rare, narrowly ranged, or recently described, traits that also reduce how often they are studied, so the relationship is a correlation and may partly reflect those traits. Threats are addressed in 4% of studies.
Most of the family has no IUCN assessment: 5,884 of 6,759 accepted species have not been evaluated.
Study-site countries
Study sites cluster where the family is diverse and there is pharmacological interest, led by Brazil and India.
For where research sits against species ranges, coverage cells and access distances, see section 7; for the same sites set against environmental change and split by decade, section 8.
The map above is fixed. The interactive version plots the same study sites and lets you filter them by this atlas’s own tags, country, year and citations, then read the papers behind any cluster.
Open the interactive map →Brazil (721 located studies at 505 sites) and India (667 studies at 387 sites) lead: India through its long ethnopharmacological tradition, Brazil through the family's Neotropical diversity. Indonesia, China, and the United States follow, where the native milkweeds (Asclepias) carry a large literature of their own. Research concentrates where the family is richest and where there is a reason to look for drugs.
The species with no indexed research are elsewhere
Map instead where the species with no indexed research live, by native range country on the WCVP distribution table, and the picture changes. Brazil (488 such species) and Indonesia (479) rank high on both maps. India, second in located research, has 109 species with no indexed research in its native range; its research concentrates on a familiar, well-worked subset. Beyond the two largest countries, the species with no indexed research cluster across regions the literature has passed over: South Africa (414 species), Venezuela (283), Mexico (272), Madagascar (254), the Philippines (249) and Papua New Guinea (249).
Beyond Brazil and Indonesia, the two distributions differ. Located research is concentrated in a few countries, while the species with no indexed research are spread across the Neotropics, southern Africa and Madagascar, Southeast Asia and Melanesia.
Geographic distribution of study sites
Of the 6,753 species with known range countries, 5,568 (82.5%) have no locatable study site inside any of them; the sites that exist cluster in a few cells and close to cities.
1 · Species ranges against study sites
For each of the 6,759 accepted 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 the Kew World Checklist of Vascular Plants distribution table (TDWG level-3 areas mapped to countries, native and introduced), so a country counts as range wherever the checklist records the species; in-range is a country-level test, generous within a large country and conservative for a species studied only just across a border. Of the 6,753 species with range data, 5,568 (82.5%) 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 (4,292 of 6,759 have no indexed research) and species whose papers name no place a geoparser can locate (1,198 of the 2,467 studied species). A further 84 species were studied only outside their known range countries.
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 (Indonesia, Brazil, South Africa, Venezuela, Mexico); they also head the table below.
| Country | Range species | With an in-country study site | Shortfall | Study sites in country |
|---|---|---|---|---|
| Indonesia | 737 | 93 | 87% | 322 |
| Brazil | 842 | 198 | 77% | 505 |
| South Africa | 697 | 69 | 90% | 64 |
| Venezuela | 435 | 8 | 98% | 7 |
| Mexico | 489 | 100 | 80% | 92 |
| Colombia | 409 | 25 | 94% | 33 |
| Philippines | 374 | 25 | 93% | 36 |
| Malaysia | 393 | 45 | 89% | 38 |
| Madagascar | 355 | 21 | 94% | 26 |
| China | 428 | 94 | 78% | 209 |
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 (72°E to 126°E, 5°N to 33°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.
| Country | Species in range | Study sites in country |
|---|---|---|
| Brunei | 265 | 1 |
| Singapore | 254 | 1 |
| Hong Kong | 243 | 0 |
| Macao | 243 | 0 |
| Malawi | 156 | 1 |
| Guyana | 148 | 0 |
| Somalia | 146 | 0 |
| Central African Republic | 140 | 2 |
| New Caledonia | 132 | 1 |
| Cambodia | 129 | 2 |
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 Sukabumi (Indonesia, 30 sites within 100 km), Surakarta (Indonesia, 28 sites within 100 km), Campina Grande (Brazil, 27 sites within 100 km); 7.4% 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 = Kew WCVP distribution table (TDWG level-3 areas mapped to countries, native and introduced; entity_places role=range, source wcvp / wcvp_introduced; 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; 4,625 of 33,900 studies (13.6%) have one. Density and distance use precise sites only (not a whole country, not a rounded centroid): 3,208 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.
5 · Explore these layers
The coverage cells are a layer on the interactive map, switched on when you open it from here, with the study sites and this atlas's tag filters underneath. A second layer, range shortfall by country, shades each country as the map above does. Any country, ecoregion, administrative region or drawn area can be queried against the whole database in the place explorer, which lists the papers inside it and the topics that are thin there.
Research against change
Three questions of the same study sites: where the environment is changing fastest with the least located research, whether research effort tracks change at all (it does not: the correlation is near zero), and when the located research was published. 68 of 532 ecoregions sit in the high-change, low-research quadrant for forest loss.
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 −0.013 across 532 ecoregions and 0.000 across 1,846 admin1 regions; for drying it is −0.142 across 237 ecoregions and −0.134 across 757 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".
| Ecoregion | Country | Forest lost | Papers with a site | Per 10,000 km2 | Range species without a site |
|---|---|---|---|---|---|
| Piney WoodsTemperate Conifer Forests | United States | 75.4%90% forested in 2000 | 0 | 0.00 | 198 |
| Esperance malleeMediterranean Forests, Woodlands & Scrub | Australia | 69.3%21% forested in 2000 | 0 | 0.00 | 189 |
| California montane chaparral and woodlandsMediterranean Forests, Woodlands & Scrub | United States, México | 65.7%30% forested in 2000 | 0 | 0.00 | 596 |
| Fraser Plateau and Basin conifer forestsTemperate Conifer Forests | Canada | 60.5%90% forested in 2000 | 0 | 0.00 | 23 |
| Naracoorte woodlandsMediterranean Forests, Woodlands & Scrub | Australia | 59.3%18% forested in 2000 | 0 | 0.00 | 189 |
| Peninsular Malaysian peat swamp forestsTropical & Subtropical Moist Broadleaf Forests | Malaysia | 58.0%95% forested in 2000 | 0 | 0.00 | 393 |
| Central American Atlantic moist forestsTropical & Subtropical Moist Broadleaf Forests | Nicaragua, Honduras, Guatemala | 56.4%95% forested in 2000 | 0 | 0.00 | 221 |
| Khangai Mountains conifer forestsTemperate Conifer Forests | Mongolia | 55.5%9% forested in 2000 | 0 | 0.00 | 9 |
| Okanogan dry forestsTemperate Conifer Forests | Canada | 54.3%84% forested in 2000 | 0 | 0.00 | 23 |
| Santa Lucia Montane Chaparral & WoodlandsMediterranean Forests, Woodlands & Scrub | United States | 53.9%52% forested in 2000 | 0 | 0.00 | 198 |
| Region | Country | Forest lost | Papers with a site | Per 10,000 km2 | Range species without a site |
|---|---|---|---|---|---|
| Bình Dương | Vietnam | 96.3%37% forested in 2000 | 0 | 0.00 | 293 |
| Bong | Liberia | 95.5%100% forested in 2000 | 0 | 0.00 | 94 |
| Aveiro | Portugal | 93.7%62% forested in 2000 | 0 | 0.00 | 12 |
| Castelo Branco | Portugal | 91.0%35% forested in 2000 | 0 | 0.00 | 11 |
| Porto | Portugal | 90.9%33% forested in 2000 | 0 | 0.00 | 12 |
| Montserrado | Liberia | 90.4%88% forested in 2000 | 0 | 0.00 | 94 |
| Coimbra | Portugal | 90.2%68% forested in 2000 | 0 | 0.00 | 12 |
| Bomi | Liberia | 89.3%99% forested in 2000 | 0 | 0.00 | 94 |
| Melaka | Malaysia | 88.7%83% forested in 2000 | 0 | 0.00 | 393 |
| Margibi | Liberia | 87.3%95% forested in 2000 | 0 | 0.00 | 94 |
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.
| Ecoregion | Country | Aridity trend | Papers with a site | Per 10,000 km2 | Range species without a site |
|---|---|---|---|---|---|
| Southern Andean steppeMontane Grasslands & Shrublands | Argentina, Chile | −13.7%dry sub-humid | 0 | 0.00 | 233 |
| Khangai Mountains alpine meadowMontane Grasslands & Shrublands | Mongolia | −9.8%dry sub-humid | 0 | 0.00 | 9 |
| Gobi Lakes Valley desert steppeDeserts & Xeric Shrublands | Mongolia | −9.6%arid | 0 | 0.00 | 9 |
| Western Australian Mulga shrublandsDeserts & Xeric Shrublands | Australia | −9.5%arid | 0 | 0.00 | 189 |
| Knysna-Amatole montane forestsTropical & Subtropical Moist Broadleaf Forests | South Africa | −9.2%dry sub-humid | 0 | 0.00 | 697 |
| Khangai Mountains conifer forestsTemperate Conifer Forests | Mongolia | −8.5%semi-arid | 0 | 0.00 | 9 |
| North Arabian highland shrublandsDeserts & Xeric Shrublands | Saudi Arabia | −8.1%arid | 0 | 0.00 | 69 |
| Great Lakes Basin desert steppeDeserts & Xeric Shrublands | Mongolia, Russia | −7.9%semi-arid | 0 | 0.00 | 38 |
| Carnarvon xeric shrublandsDeserts & Xeric Shrublands | Australia | −7.1%arid | 0 | 0.00 | 189 |
| Nullarbor Plains xeric shrublandsDeserts & Xeric Shrublands | Australia | −6.9%arid | 0 | 0.00 | 189 |
| Region | Country | Aridity trend | Papers with a site | Per 10,000 km2 | Range species without a site |
|---|---|---|---|---|---|
| Santiago Metropolitan | Chile | −15.6%dry sub-humid | 0 | 0.00 | 22 |
| Valparaíso | Chile | −13.9%semi-arid | 0 | 0.00 | 22 |
| Coquimbo | Chile | −13.7%arid | 0 | 0.00 | 22 |
| San Juan | Argentina | −12.7%semi-arid | 0 | 0.00 | 222 |
| Govi-Altay | Mongolia | −12.1%arid | 0 | 0.00 | 9 |
| Misratah | Libya | −12.1%arid | 0 | 0.00 | 10 |
| Hovd | Mongolia | −11.1%semi-arid | 0 | 0.00 | 9 |
| Sidi Bou Zid | Tunisia | −11.1%arid | 0 | 0.00 | 12 |
| Libertador General Bernardo O'Hi | Chile | −10.9%dry sub-humid | 0 | 0.00 | 22 |
| Hilmand | Afghanistan | −10.8%arid | 0 | 0.00 | 16 |
Aridity trend 1990 to 2024, cell by cell
3 · When the research happened
The same study sites split by decade of publication. Of the 3,811 located papers, 77.9% were published in the 2010s or 2020s and 54.1% since 2016. The whole corpus of 33,900 papers, located or not, is 67.5% from those two decades, so located research is more recent than the corpus. Of the 471 ecoregions with a located paper, 47 have none published since 2010; of the 897 admin1 regions, 102.
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. 3,811 of 33,900 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. 16 precise places (open sea, Southern Ocean, atoll centroids) fall in no ecoregion and 10 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.
5 · Explore these layers
These layers are on the interactive map as forest loss 2001 to 2024 and aridity trend 1990 to 2024, cell by cell with the value and the located papers on hover, and a decade selector that limits the study-site dots to one decade of publication.
Expected research Beta
How much located research would each place be expected to carry, given its size, species richness, access, environmental change, biome and national research effort, and where the record falls short of that expectation. Cross-validated pseudo-R2 0.63 for ecoregions and 0.48 for administrative regions. Beta: a first model, stated with its interval and its limits.
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 0.63 for ecoregions and 0.48 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 27.2 papers there (ecoregions) or 88.2 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. 771 ecoregions in the universe, 463 with a located paper; negative binomial, dispersion theta 0.86, mean by negative-binomial IRLS, ridge lambda 30, intercept recalibrated by 1.1037 so that the expected total equals the observed total. 5-fold spatial cross-validation blocked by realm: pseudo-R2 0.63 (0.52 without the national-effort covariate), Spearman rho 0.68 out of fold. Labels against the 90 percent interval: 0 below, 740 within, 31 above.
| Ecoregion | Country | Observed | Expected, 90% interval | Tail probability | Label |
|---|---|---|---|---|---|
| Manchurian mixed forestsTemperate Broadleaf & Mixed Forests | China, Russia, North Korea | 0 | 18.5790%: 0 to 60 | 0.069 | within |
| Purus-Madeira moist forestsTropical & Subtropical Moist Broadleaf Forests | Brazil | 0 | 15.9990%: 0 to 51 | 0.077 | within |
| Arabian sand desertDeserts & Xeric Shrublands | Saudi Arabia, United Arab Emirates, Oman | 0 | 14.9690%: 0 to 48 | 0.082 | within |
| Zambezian-Limpopo mixed woodlandsTropical & Subtropical Grasslands, Savannas & Shrublands | Zimbabwe, Mozambique, Zambia | 0 | 11.6090%: 0 to 38 | 0.101 | within |
| Southwest Arabian highland xeric scrubDeserts & Xeric Shrublands | Yemen, Saudi Arabia | 0 | 10.5490%: 0 to 34 | 0.108 | within |
| Northeast Congolian lowland forestsTropical & Subtropical Moist Broadleaf Forests | Congo - Kinshasa, Central African Republic | 0 | 8.5290%: 0 to 28 | 0.128 | within |
| Patagonian steppeTemperate Grasslands, Savannas & Shrublands | Argentina, Chile, Falkland Islands | 0 | 8.4990%: 0 to 28 | 0.129 | within |
| Tenasserim-South Thailand semi-evergreen rain forestsTropical & Subtropical Moist Broadleaf Forests | Thailand, Myanmar (Burma), Malaysia | 0 | 7.5590%: 0 to 25 | 0.141 | within |
| Orissa semi-evergreen forestsTropical & Subtropical Moist Broadleaf Forests | India | 2 | 24.3890%: 0 to 78 | 0.141 | within |
| Borneo montane rain forestsTropical & Subtropical Moist Broadleaf Forests | Indonesia, Malaysia | 1 | 14.6690%: 0 to 47 | 0.151 | within |
| Ecoregion | Country | Observed | Expected, 90% interval | Tail probability | Label |
|---|---|---|---|---|---|
| Caribbean shrublandsDeserts & Xeric Shrublands | Guadeloupe, Curaçao, Martinique | 4 | 0.0590%: 0 to 1 | under 0.001 | above |
| Southern Atlantic Brazilian mangrovesMangroves | Brazil | 24 | 2.4590%: 0 to 9 | 0.001 | above |
| Hawai'i tropical dry forestsTropical & Subtropical Dry Broadleaf Forests | United States | 8 | 0.7690%: 0 to 3 | 0.002 | above |
| Tyrrhenian-Adriatic sclerophyllous and mixed forestsMediterranean Forests, Woodlands & Scrub | Italy, France, Croatia | 28 | 3.7590%: 0 to 13 | 0.002 | above |
| Central Canadian Shield forestsBoreal Forests/Taiga | Canada | 8 | 0.8690%: 0 to 3 | 0.003 | 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. 2,626 administrative regions (admin1) in the universe, 837 with a located paper; negative binomial, dispersion theta 0.60, mean by Poisson quasi-likelihood, ridge lambda 300. 5-fold spatial cross-validation blocked by country: pseudo-R2 0.48 (0.33 without the national-effort covariate), Spearman rho 0.53 out of fold. Labels against the 90 percent interval: 0 below, 2,538 within, 88 above.
| Region | Country | Observed | Expected, 90% interval | Tail probability | Label |
|---|---|---|---|---|---|
| Coahuila | México | 0 | 10.6990%: 0 to 39 | 0.172 | within |
| Alaska | United States | 1 | 22.3190%: 0 to 81 | 0.178 | within |
| Colorado | United States | 0 | 6.6690%: 0 to 25 | 0.224 | within |
| Niger | Nigeria | 0 | 5.9390%: 0 to 22 | 0.239 | within |
| Santa Fe | Argentina | 0 | 5.7690%: 0 to 22 | 0.243 | within |
| Río Negro | Argentina | 0 | 5.5090%: 0 to 21 | 0.249 | within |
| Borno | Nigeria | 1 | 11.6390%: 0 to 43 | 0.258 | within |
| Mahajanga | Madagascar | 0 | 4.9790%: 0 to 19 | 0.263 | within |
| San Luis Potosí | México | 0 | 4.9490%: 0 to 19 | 0.264 | within |
| Chubut | Argentina | 0 | 4.8890%: 0 to 18 | 0.266 | within |
| Region | Country | Observed | Expected, 90% interval | Tail probability | Label |
|---|---|---|---|---|---|
| Ontario | Canada | 38 | 1.7190%: 0 to 7 | under 0.001 | above |
| Lagos | Nigeria | 18 | 1.1390%: 0 to 5 | under 0.001 | above |
| Goriška | Slovenia | 2 | 0.0190%: 0 to 0 | under 0.001 | above |
| Janub Sina' | Egypt | 9 | 0.5090%: 0 to 2 | under 0.001 | above |
| Sicily | Italy | 16 | 1.1490%: 0 to 5 | under 0.001 | 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 1.38, top decile 1.37; administrative regions (admin1): deciles 3 to 8 between 0.69 and 1.16, top decile 1.14. 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. 3,811 of 33,900 papers in this corpus have a precise trusted study site (11.2 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: 4,360 unit-papers over 771 ecoregions and 4,193 over 2,626 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 30, administrative regions (admin1) lambda 300); 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 apocynaceae, redraw with phase1/gen_atlas_expected_section.js; every figure in this section is checked by the figures audit.
5 · Explore this layer
The same units are on the interactive map as the beta layer "Expected vs observed", ecoregions or administrative regions, with observed, expected, interval and label on hover, and a link from each unit to the place explorer. The place explorer shows the same figures for a selected unit under "Beta: expected research".
Summary of the record
Three imbalances in where research has and has not gone.
This atlas is one view of a complete, queryable corpus. A single genus's evidence profile, a country's Apocynaceae research, or a shortlist of threatened species with no indexed research to survey is each a query away, on the same reproducible foundation. A next step turns these gaps into a ranked survey list, scoring each species with no indexed research by the chemistry of its studied relatives, so a search can start where the chemistry is richest. The ranking orders where to look; laboratory work determines what each species holds.
Where to look next
The sections above measure what has and has not been studied. This section ranks the species with no indexed research by where the chemistry of their relatives is concentrated, to indicate where to look first.
Chemistry runs in families: closely related species tend to make the same kinds of compounds. So we score each species with no indexed research by how chemically productive its genus already is: the share of its studied relatives that have yielded a characterised bioactivity or compound. A species with no indexed research whose relatives are chemically productive ranks higher.
| # | Genus | Signature chemistry | Studied species with chemistry recorded | Species with no indexed research | Examples to start with |
|---|---|---|---|---|---|
| 1 | Strophanthus | Cardiac glycosides | 96% (25/26) | 13 | S. barteri, S. bequaertii, S. bullenianus |
| 2 | Kopsia | Monoterpene indole alkaloids | 89% (16/18) | 7 | K. angustipetala, K. harmandiana, K. rajangensis |
| 3 | Melodinus | Monoterpene indole alkaloids | 86% (12/14) | 11 | M. angustifolius, M. baccellianus, M. baueri |
| 4 | Alstonia | Monoterpene indole alkaloids | 84% (32/38) | 6 | A. annamensis, A. curtisii, A. guanxiensis |
| 5 | Tabernaemontana | Monoterpene indole alkaloids | 79% (63/80) | 46 | T. alfaroi, T. allenii, T. amplifolia |
| 6 | Aspidosperma | Monoterpene indole alkaloids | 70% (39/56) | 24 | A. castroanum, A. crypticum, A. darienense |
Definition of the share. For each genus, the studied species are its WCVP-accepted species with at least one indexed paper. A studied species counts as having chemistry recorded when at least one of its papers carries a judge-confirmed tag on the compound_class or bioactive_potential dimension of the flora codebook, that is, a named class of compound or a tested biological activity. The share is the second count over the first; the fraction in brackets gives both. Computed on the published database on 4 October 2026. Tag recall is lower than tag precision (see the tagging note in Methods), so a species whose chemistry was reported but not tagged is counted as without chemistry recorded, and the shares are more likely under- than over-stated. The six genera are the July 2026 selection; the ranking order follows the recomputed shares.
The top genus, Strophanthus, is the source of the cardiac glycoside ouabain, and has 13 species with no indexed research; Rauvolfia, the genus that gave medicine reserpine, sits just below this list at 64% (25 of 39 studied species), with 40 species with no indexed research. The ranking draws only on the chemistry recorded for each genus's studied relatives, and surfaces both from that alone.
A starter survey list
Species with no indexed research drawn from the top genera, a concrete place a screening lab or a taxonomist could begin. Grouped by genus.
Each name is a species with no indexed research, sitting in a genus whose studied relatives are chemically productive. Whether any of them holds a useful compound is determined by laboratory testing.