Better Representation Is Needed in U.S. Endangered Species Act Implementation
Bibliographic record
Abstract
In the United States, the U.S. Fish and Wildlife Service uses the concepts of resilience, redundancy, and representation—often known as the “3Rs”—to guide implementation of the Endangered Species Act, which requires the U.S. government to designate imperiled species as threatened or endangered, and take action to recover them. The Service has done little, however, to relate the 3Rs to the statutory requirements of the Act. Here we focus on interpreting the concept of representation given core tenets of science and conservation policy. We show that the Service's current interpretation, which focuses on a narrow set of characteristics intrinsic to species that facilitate future adaptation, falls far short of a reasonable interpretation from the scientific literature and other policy, and has significant consequences for the conservation of threatened and endangered species, including those found in other countries. To illustrate the shortcomings in practice, we discuss the cases of the Lower 48 gray wolf (Canis lupus) delisting, the proposed Red-cockadedWoodpecker (Picoides borealis) downlisting, and the possible downlisting of the Canada lynx (Lynx canadensis). We then propose an alternative interpretation of representation that accommodates the Service's narrow interpretation and broadens it to include the importance of intraspecific variation for its own sake as well as extrinsic characteristics such as a species' role in ecological communities. We argue that this interpretation better reflects the intent of the Endangered Species Act, the best available science, and policy needs for conserving imperiled wildlife, all of which recognize the importance not only of preventing global extinction but also of preventing ecological extinction and extirpation across significant portions of a species' range.
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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 | 0.088 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".