Unshifting the baseline: a framework for documenting historical population changes and assessing long-term anthropogenic impacts
Bibliographic record
Abstract
Ecological baselines—reference states of species' distributions and abundances—are key to the scientific arguments underpinning many conservation and management interventions, as well as to the public support to such interventions. Yet societal as well as scientific perceptions of these baselines are often based on ecosystems that have been deeply transformed by human actions. Despite increased awareness about the pervasiveness and implications of this shifting baseline syndrome, ongoing global assessments of the state of biodiversity do not take into account the long-term, cumulative, anthropogenic impacts on biodiversity. Here, we propose a new framework for documenting such impacts, by classifying populations according to the extent to which they deviate from a baseline in the absence of human actions. We apply this framework to the bowhead whale ( Balaena mysticetus ) to illustrate how it can be used to assess populations with different geographies and timelines of known or suspected impacts. Through other examples, we discuss how the framework can be applied to populations for which there is a wide diversity of existing knowledge, by making the best use of the available ecological, historical and archaeological data. Combined across multiple populations, this framework provides a standard for assessing cumulative anthropogenic impacts on biodiversity. This article is part of a discussion meeting issue ‘The past is a foreign country: how much can the fossil record actually inform conservation?’
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".