The Species Black List
Vertebrates the Red List cannot yet speak for · IUCN v2026‑1
Vertebrates the Red List cannot yet speak for · IUCN v2026‑1
What this is
The IUCN Red List is the world's authority on extinction risk, and it has two blind spots. Some species are assessed and come back Data Deficient — IUCN looked, and there was not enough information to say. Others are Not Evaluated — nobody has looked at all. In practice both are treated as though the species were safe.
What the record actually says
Neither category is covered by the commitment that counts. Target 4 of the Kunming-Montreal Global Biodiversity Framework binds governments to halting the extinction of known threatened species — wording that excludes both of these groups by definition. The money follows the same line: under 3.5% of Mohamed bin Zayed Species Conservation Fund awards have gone to Data Deficient taxa, and under 1% at the People's Trust for Endangered Species.
IUCN's own guidance says Data Deficient species should receive the same attention as threatened ones. It is not followed. So the gap is not only a gap in knowledge — it is an exclusion written into policy and funding, and it is the reason for compiling this register at all.
Every species in the register, one mark each
Hover a mark to name the species; select one to open its record.
What the tiers mean
Every species is placed in one of five restriction tiers according to how narrow its published record is. That is the only thing the tier measures, and it is the axis the whole register is ranked on — a smaller known range is what would make a species qualify as threatened under IUCN Criterion B. Tiers 3, 4 and 5 are the priority stratum, the group whose ranking has been tested against IUCN's own reassessments. The tested break in risk falls between tier 2 and tier 3, not higher up.
Data Deficient status on its own predicts very little. Of the species here that IUCN has since reassessed, the group as a whole came out threatened at roughly the ordinary all-species rate. The narrowly restricted subset is different, and that distinction — not the Data Deficient label — is what this register is for.
This is a ranked subset, not a census. IUCN v2026‑1 holds 8,659 Data Deficient species in vertebrate classes; this register covers 3,031 of them — 35%. The method needs a published sentence describing each species' range, and the corpus behind it is one reference work. Of the 2,964 Data Deficient vertebrates that corpus names, every one is in the register; the other 5,628 are never named in it at all. So the right reading is "of the Data Deficient vertebrates with a usable published range statement, these are the most narrowly restricted" — not "these are the ones that matter most".
How long is a blank allowed to stand? The Red List export says only that a species is Data Deficient now. Its assessment history, pulled from IUCN's API, says since when — and the answer reframes the register. The median species here has been Data Deficient for 16 years; 475 have held the label for twenty years or more. And 1,210 of them — 40% — have been assessed more than once, meaning IUCN returned, looked again, and wrote Data Deficient a second or third time. Those are not species awaiting a first look. For scale, the median time IUCN has actually taken to resolve a Data Deficient listing is 16 years, so 1,011 species in this register are already past it.
The species outside were screened out, not overlooked. Richardson takes an explicit position on the Data Deficient category: most of it, in his judgement, is junior synonyms or valid species not in immediate danger, while genuinely threatened species get lost there for want of follow-up research. He admits a Data Deficient species when its record is narrow enough to imply risk — and that screen is measurable, since he kept 28% of IUCN's Data Deficient ray-finned fishes against 72% of the birds, dropping the deep-sea groups almost entirely because naturally rare is not the same as threatened. So the omitted species are neither unexamined nor shown to be safe: one author's judgement, made without the research he says the category lacks, is not an assessment. It also means this corpus is pre-selected for the trait the register ranks on, which is why 92% of records state some restriction — the register tests an expert's screen against IUCN's later verdicts rather than discovering the pattern from scratch.
Held-out test
Between the previous data and v2026‑1, IUCN independently reassessed 108 of these species and gave each a real category. The ranking rules never saw those outcomes, so they work as an answer key. Two caveats belong with the result: it rests on 28 species in the priority stratum, so the interval matters more than the point estimate; and both the ranking and the assessment draw on the same primary literature, so this is agreement between two readings of one evidence base rather than a blind prediction. Share reassessed as threatened or extinct:
Does that depend on trusting IUCN's “not threatened” calls? It is a fair worry: the Red List Authorities have been working the Data Deficient backlog down through specialist-group consensus rather than new fieldwork, so a species can be moved to Least Concern because nobody reported a threat. Tested by discarding the thinnest-evidenced calls, the result gets stronger, not weaker. Two measures agree. By specimen record, 53.6% becomes 62.5% when calls resting on five or fewer records worldwide are set aside. By citation — pulling every assessment's own reference list from IUCN's API — not one of the 140 reassessments cites nothing newer than the Data Deficient listing it replaced; and restricting the test to the ones citing new research about that particular species gives 83.3%. What the citation check also shows, and this is the real concession: most of these reassessments cite no new species-specific research at all — 74 of 101 non-threatened outcomes. That is how sparse-data assessment works, and it applies about equally to the threatened outcomes (26 of 39), so it does not tilt the comparison. Methods §4a has both tables and the limit of the argument.
Composition
By class
By restriction tier
Every row carries the sentence it was judged on. Select a species to read it.
| Species | Class | Tier | Basis | Locality | Realm | DD since |
|---|
Density of georeferenced specimen and observation records for the species in this register, drawn from GBIF. Switch layers to isolate a single restriction tier or view them together. Select any shaded cell or locality ring to open its species in the register. Scroll or pinch to zoom, drag to pan; cells subdivide as you zoom in, so finer clusters resolve rather than the same blocks getting bigger.
A dense patch means records exist there, which is not the same as species being concentrated there. Georeferenced records cluster around institutions, expeditions and coastlines that somebody surveyed. Read the map as a picture of where the evidence sits, not of where the risk is.
These are point records, not range maps, and they come from a different source than the localities on the Sites page — so the two will not correspond exactly. Individual records can also be plain wrong; GBIF's own flagged coordinate errors are excluded, but some survive. That is why this shows density rather than authoritative dots.
The source is organised geographically, so species cluster by place. Where a single locality carries many of them, one protected area could cover the lot — the cheapest form of conservation available.
Caveat: these localities are extracted at inconsistent grain. Some are a single named river, some an entire ocean basin. Treat the small, specific ones as actionable and the broad ones as an artefact of the source.
Where a locality has an account of its own in the book, that account's opening sentence is shown — which is what disambiguates a name like Espiritu Santo or Cordillera Central, both of which occur more than once in the world and one of which occurs twice in this book. The rest were recovered from the species' own records, where the place is named in the entry itself; those lines say so. Nothing here is geocoded or inferred. Select a locality to open its species in the register.
These are not places. They are habitat categories the source defines and then groups species under — terra firma forest, cerrado, miombo woodland. They are kept because the groupings are real, but no protected area can be drawn around them.
Nobody with a specialist's authority. The ranking is produced by a text-classification rule that reads the source's own range statements and sorts them by severity. No taxonomist or conservation biologist has reviewed the individual species. What has been tested is the rule's average behaviour against IUCN's independent reassessments — a statement about the group, not about any one row.
These are not Red List assessments and must not be cited as such. They are a prioritised research agenda: an argument about where to look.
Target 4 of the Kunming-Montreal Global Biodiversity Framework commits governments to halting the extinction of known threatened species — a phrase that excludes both categories in this register by definition. Funding follows the same line: under 3.5% of Mohamed bin Zayed Species Conservation Fund awards have gone to Data Deficient taxa, and under 1% at the People's Trust for Endangered Species. IUCN's own guidance says Data Deficient species should get the same attention as threatened ones, and it is not followed.
So the consequence of having no category is not merely that a species is poorly known. It is excluded from the target that mobilises national action and from the funds that pay for fieldwork — which is what makes deciding which of these species to look at first worth doing carefully.
| Evidence of risk | Matthew Richardson, Threatened and Recently Extinct Vertebrates of the World: A Biogeographic Approach, Cambridge University Press, 2023. Vertebrates only — the scope follows the book's. |
|---|---|
| Extinction risk | IUCN Red List v2026‑1, 178,011 taxa, via GBIF's Darwin Core mirror (accessed 2026‑07‑28). |
| Taxonomy | GBIF Backbone, for synonym resolution and for independent verification of assessment status. |
| Map points | GBIF georeferenced occurrence records, queried by strict taxon key. Basemap is Natural Earth 110m land (public domain). |
Every species is assigned one of five restriction tiers. The tier records one thing only: how narrow the species' published record is, read from the range statement in Richardson's own text. Tier 5 means the species is known from a single specimen or nothing beyond its type locality; tier 1 means the source makes no restriction claim at all.
What a tier is not: it is not an IUCN category, not a threat level, not a measure of how endangered the species is, and not a count of individuals. Two species in the same tier may face completely different pressures. The tier is a statement about the evidence, not about the animal.
Restriction is the axis because IUCN Criterion B allows a species to qualify as threatened on a small range plus evidence of decline: area of occupancy under 2,000 km² for Vulnerable, 500 for Endangered, 10 for Critically Endangered. A species known only from its type locality sits, on its face, inside the Critically Endangered envelope. That is why a narrow record is worth ranking on. Tiers 3, 4 and 5 together are the priority stratum, and they are the only part of the ranking that has been tested. The boundary sits there because that is where the tested risk actually jumps — tier 2 came out threatened at 20%, tier 3 at 67% — and not because the top of the scale is the most severe: it is not, see Methods.
| Tier | Basis | Species |
|---|
No area of occupancy has been computed. A priority flag argues that Criterion B should be evaluated; it is not a finding that it is met.
The priority rate lands close to the 56% that Borgelt et al. (2022, Communications Biology) predicted for Data Deficient species generally — which suggests that figure belongs to the narrowly restricted subset, and that an unranked list dilutes a real signal with vague-range species.
Two caveats belong with that number whenever it is quoted. The top stratum rests on 19 species, which is thin. And IUCN reassesses where data has appeared, which probably favours species that turned out to be more widespread — a bias running against the finding rather than creating it. Separately, the five tiers were defined before any outcome was seen, but the decision to split them at 4 was made after, which inflates the significance somewhat.
A locality is recorded two ways, and the Sites page distinguishes them.
From a locality account. Richardson organises the book geographically, so most species sit inside a paragraph introduced by a place. That place, and its opening sentence, are attached to every species in the paragraph. This is the stronger provenance and it covers 519 species.
From the species entry itself. Where a species is not inside a locality account, the place is very often named in its own sentence anyway — "collected from the upper Pungwe River", "collected off Mauritius". Pattern extraction over those sentences recovers a locality for a further 1,213 species, taking coverage from 19% to 60% of the register and the site list from 42 to 102. Clusters that were entirely invisible before appear this way — the Gulf of California holds 19 species, none of which had a locality until now.
Named physical features (Salween River, Gulf of Panama, Mount Kinabalu) are preferred over a bare capitalised word after a preposition, and a capture is rejected if it collides with the species' own common name or genus. The place is only ever taken from the text, never guessed — if the sentence names no place, the species keeps no locality, which is why 1,330 still have none.
These names are then looked up in a gazetteer to place them on the map, which is a separate step with its own failure modes and its own verification — see Geocoding the locality names below. Nothing in the register or on the Sites page depends on that lookup: those show the name the source gave, and the coordinates exist only for the map.
The register stores localities as text taken from Richardson's prose — "The Mano River", "Arrowsmith Bank", "Espiritu Santo". Those were not run through a gazetteer to make the map: place-name geocoding is ambiguous exactly where this corpus is ambiguous, and a map that confidently places a species in the wrong ocean is worse than no map. The points are real georeferenced specimen and observation records from GBIF, queried per species.
One trap governed that query, and it is the same species of error that wrecked the first never-assessed list. GBIF's free-text name search must not be used, because many of these names are synonyms and it silently returns records of the accepted species instead:
| Anampses viridis, free-text search | 3,071 records | centroid inland in New South Wales |
|---|---|---|
| Anampses viridis, strict taxon key | 3 records | all at Réunion |
The book describes that species as known from a single specimen; the 3,071 belong to Anampses caeruleopunctatus, its widespread senior synonym. Every name is therefore resolved to a GBIF usage key by strict match first, and records are pulled by taxon key. Records GBIF itself flags as having bad coordinates are excluded, and points are capped at 30 per species so well-collected species cannot dominate the surface.
What the map is not. Not range maps — these are point records. Not a picture of where risk is concentrated — it maps collecting effort at least as much as biology, because georeferenced records cluster where institutions and expeditions have worked. And not the same basis as the Sites page, which comes from the book rather than from GBIF, so the two will not correspond exactly. Some bad coordinates survive the filtering, which is why the page shows density rather than authoritative dots.
The register's place names are also plotted, as rings, because refusing to look up "Salween River" while happily printing it was inconsistent. But a gazetteer answers any string, and two checks are needed, not one.
Type agreement catches the obvious failures: asked for "The Cordillera Central", Nominatim returns a university in Baguio. A name ending "River" must come back as a waterway, "Island" as an island; buildings, roads and campuses are rejected outright. That removed — of — candidates.
But type agreement cannot see a same-type homonym, and those are common. "Congo River" resolved to a real river of that name in Sierra Leone, 4,000 km from the Congo, passing cleanly as river/river. "Dunk Island" resolved near Sydney rather than Queensland. So each geocode is then measured against the GBIF records of the species that sit at that locality — an independent witness already held:
| Agree within 500 km — plotted | — |
|---|---|
| Disagree by more — discarded | — |
| No species with records to check against — off by default | — |
Around one in five checkable geocodes was wrong by more than 500 km, and none of those errors was detectable from the name or the feature type alone. That rate is the reason the unverified — are not plotted by default: there is no reason to think they are cleaner, and they cannot be tested. The validated rings add — species that have no georeferenced record of their own — modest, but every one is corroborated.
A locality naming two places is kept but never plotted. — strings in the register are compounds — "Athi and Tana River", "Caroline and Marshall Islands". There is no single coordinate for them, and every way of reducing one to a single place either drops the noun that identified it ("Caroline") or manufactures somewhere that does not exist ("Oman and Masirah Island" → "Oman Island"). They are left whole, excluded from the gazetteer, and the evidence sentence shows both places.
The most telling number is the one that cannot be plotted. Of the 3,062 species here, 805 have no georeferenced record anywhere in GBIF, and 727 cannot be placed even by a cross-checked locality. They are the least visible members of an already invisible group, and they are absent from the map.
An earlier version of this page said 943 rather than 629. That figure was wrong: the occurrence fetch had been running eight concurrent requests, GBIF answered 399 of them with HTTP 429, and the script cached those failures as though they were results — so re-running never retried them and 16.6% of the register was silently recorded as having no records. Because the register is sorted by tier, the failures landed disproportionately on tier 1, which appeared to be 3.6% mappable when it is in fact 80%. The fetcher now backs off exponentially, uses four threads, and never caches a failure.
An earlier composite score added points for a species not having been recorded recently and for threats named at its locality. Testing destroyed it: the composite was non-monotonic, and naming a site-level threat turned out inversely associated with being threatened. The reason is legible in hindsight — broad, well-documented regions attract threat prose but hold wide-ranging species, while a genuine type-locality endemic gets one terse line. Those fields survive on each record as description, and are not ranked on.
Proving that IUCN has never assessed a species is much harder than it looks, because the two sources disagree about naming constantly. Deciding it by failing to find a name produced a false-positive rate near 90%. Four distinct failure modes had to be closed:
| Failure | Example |
|---|---|
| Genus reassigned | Amblyopsis rosae is IUCN's Troglichthys rosae (Near Threatened) |
| Genus and family reassigned | Aphanius iberus is IUCN's Apricaphanius iberus |
| Homonym within an order | Sinosuthora przewalskii, an Asian parrotbill, matching Grallaria przewalskii, a South American antpitta |
| Rank change | Cyanoramphus cookii, in fact Least Concern |
Species known only from pre-1500 remains were also removed. The Red List covers extinctions from 1500 AD onward, so those fall outside its remit by policy rather than by neglect — counting them as coverage gaps would be a category error.