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Record W4284990279 · doi:10.1111/gcb.16335

A millennium of increasing diversity of ecosystems until the mid‐20th century

2022· article· en· W4284990279 on OpenAlexafffund
Inês S. Martins, María Dornelas, Mark Vellend, Chris D. Thomas

Bibliographic record

VenueGlobal Change Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversité de Sherbrooke
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsNatural Sciences and Engineering Research Council of CanadaEuropean CommissionLeverhulme Trust
KeywordsEcosystemSpecies evennessLand useEcologyGeographyBiodiversityEcosystem diversityTerrestrial ecosystemDiversity (politics)Global changeSpatial ecologyHomogenization (climate)Environmental sciencePhysical geographySpecies richnessClimate changeBiology

Abstract

fetched live from OpenAlex

Land-use change is widely regarded as a simplifying and homogenising force in nature. In contrast, analysing global land-use reconstructions from the 10th to 20th centuries, we found progressive increases in the number, evenness, and diversity of ecosystems (including human-modified land-use types) present across most of the Earth's land surface. Ecosystem diversity increased more rapidly after ~1700 CE, then slowed or slightly declined (depending on the metric) following the mid-20th century acceleration of human impacts. The results also reveal increasing spatial differentiation, rather than homogenisation, in both the presence-absence and area-coverage of different ecosystem types at sub-global scales-at least, prior to the mid-20th century. Nonetheless, geographic homogenization was revealed for a subset of analyses at a global scale, reflecting the now-global presence of certain human-modified ecosystem types. Our results suggest that, while human land-use changes have caused declines in relatively undisturbed or "primary" ecosystem types, they have also driven increases in ecosystem diversity over the last millennium.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.951

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.222
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2022
Admission routes2
Has abstractyes

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