Are We Capturing Faunal Intactness? A Comparison of Intact Forest Landscapes and the "Last of the Wild in Each Ecoregion"
Bibliographic record
Abstract
Ecologically intact ecosystems are becoming increasingly limited on the planet, making their identification and conservation an important priority. Intact forest landscapes (IFLs) are defined as forests that are mainly free of significant anthropogenic degradation and at least 500 km2 in size. Here we define a new metric, the Last of the Wild in each Ecoregion (LWE), as a preliminary scoping of the most intact parts of each ecoregion. IFL and LWE are approaches among a broad family of techniques to mapping ecological integrity at the global scale. Although both implicitly include species integrity as a dimension of intactness, this is inferred rather than directly measured. We assessed whether LWE areas or IFL were better at capturing species where they are most abundant using species distribution data for a set of forest species for which range-wide data were available and human activity limits the range. We found that IFL and LWE methods identified areas where species we assessed are either absent or at too low an abundance to be ecologically functional. As such many IFL/LWE polygons did not have intact fauna. We also show that 54.7% of the terrestrial realm (excluding Antarctica) has at least one species recorded as extinct and that two thirds of IFL/LWE areas overlap with areas where species have gone extinct in the past 500 years. The results show that neither IFL or LWE identifies areas of ecologically intact fauna adequately, underscoring a strong need to obtain additional site-level survey data to confirm faunal intactness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".