Disentangling the environmental‐heterogeneity–species‐diversity relationship along a gradient of human footprint
Bibliographic record
Abstract
Decades of study have attempted to define a generalized environmental-heterogeneity-biodiversity (EH-BD) relationship, with the traditional MacArthurian niche-based hypothesis remaining as the dominant reference point; i.e., increasing heterogeneity promotes biodiversity by increasing resource opportunities. However, studies have frequently reported negative or nonsignificant relationships. In a vast majority of them, environmental heterogeneity was defined along a gradient of increasing randomness, toward complete disorder. A new conceptual framework could help to reconcile the array of observed relationships. Using an extensive literature review, we test a conceptual framework proposing that the direction of environmental-heterogeneity-biodiversity relationships is contingent on the level of human footprint to which an ecosystem is subjected (the anthropocline). The results reveal that highly modified and seminatural ecosystems are characterized by a dominance of positive and negative EH-BD relationships, respectively, whereas natural ecosystems show mixed responses. Out of this novel framework arises the revised perspective that natural ecosystems are typified, not by maximal or minimal, but by intermediate levels of environmental heterogeneity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".