50 years of invertebrate conservation under the United States Endangered Species Act—history and threats to species
Bibliographic record
Abstract
Introduction The United States Endangered Species Act celebrated its 50th anniversary in 2023. As a hallmark piece of environmental legislation, the Act has successfully prevented the extinction of hundreds of species. During these last 50 years, we have observed the decline of many species of invertebrates, resulting in the listing of 356 species. Methods Here, we summarize the state of endangered invertebrates using text mining to review all listing documents, including listing decisions, species status assessments, critical habitat designations, and status reviews. In our review, we evaluate the most prevalent threats for aquatic and terrestrial invertebrates. Results We found that invertebrates have been assessed and listed consistently in the past 50 years, and the last eight years have seen an uptick in status reviews. Further, we find that pollution, natural system modifications (such as dams), and intrinsic factors (such as small population sizes or number of populations) are the major contributing threats to aquatic invertebrates. On the other hand, problematic biotic factors (such as invasive species), climate change, residential and commercial development, and pollution are the major threats to terrestrial invertebrates. Discussion Overall, our study reviews the current threats to invertebrates and provides a baseline for the next 50 years in the face of a shifting threat and conservation arena.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".