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Record W2093656707 · doi:10.1890/13-1344.1

Disentangling the environmental‐heterogeneity–species‐diversity relationship along a gradient of human footprint

2014· article· en· W2093656707 on OpenAlexaff
Ian Seiferling, Raphaël Proulx, Christian Wirth

Bibliographic record

VenueEcology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsEcologyEnvironmental gradientEcological footprintGeographyDiversity (politics)FootprintBiologyHabitatSustainability

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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.

Opus teacher head0.039
GPT teacher head0.237
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations79
Published2014
Admission routes1
Has abstractyes

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