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Record W4206760292 · doi:10.1016/j.tree.2021.12.009

Pathways for cross-boundary effects of biodiversity on ecosystem functioning

2022· review· en· W4206760292 on OpenAlexafffund
Michael Scherer‐Lorenzen, Mark O. Gessner, Beatrix E. Beisner, Christian Messier, Alain Paquette, Jana S. Petermann, Janne Soininen, Charles A. Nock

Bibliographic record

VenueTrends in Ecology & Evolution · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité du Québec en OutaouaisUniversity of AlbertaUniversité de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftBundesministerium für Bildung und ForschungUniversité du Québec à Montréal
KeywordsBiodiversityEcosystemTotal human ecosystemEcosystem servicesMeasurement of biodiversityEnvironmental resource managementEnvironmental scienceSpecies richnessEcologyEcosystem healthGeographyBiodiversity conservationBiology

Abstract

fetched live from OpenAlex

The biodiversity-ecosystem functioning concept asserts that processes in ecosystems are markedly influenced by species richness and other facets of biodiversity. However, biodiversity-ecosystem functioning studies have been largely restricted to single ecosystems, ignoring the importance of functional links - such as the exchange of matter, energy, and organisms - between coupled ecosystems. Here we present a basic concept and outline three pathways of cross-boundary biodiversity effects on ecosystem processes and propose an agenda to assess such effects, focusing on terrestrial-aquatic linkages to illustrate the case. This cross-boundary perspective of biodiversity-ecosystem functioning relationships presents a promising frontier for biodiversity and ecosystem science with repercussions for the conservation, restoration, and management of biodiversity and ecosystems from local to landscape scales.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.031
GPT teacher head0.297
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations102
Published2022
Admission routes2
Has abstractno

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