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Record W4206331214 · doi:10.1101/2022.01.11.475793

Global monitoring of soil animal communities using a common methodology

2022· preprint· en· W4206331214 on OpenAlexaff
Anton Potapov, Xin Sun, María J.I. Briones, George Gardner Brown, Erin K. Cameron, Jérôme Cortet, Nico Eisenhauer, Saori Fujii, Stefan Geisen, Konstantin B. Gongalsky, Carlos A. Guerra, I. Tanya Handa, Charlene Janion‐Scheepers, Zoë Lindo, Jérôme Mathieu, Maka Murvanidze, Uffe N. Nielsen, Stefan Scheu, Olaf Schmidt, Clément Schneider, Julia Seeber, Jiří Tůma, Alexei V. Tiunov, Andrey S. Zaitsev, Diana H. Wall

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicStudy of Mite Species
Canadian institutionsWestern UniversityUniversité du Québec à MontréalSaint Mary's University
FundersDeutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-LeipzigConselho Nacional de Desenvolvimento Científico e TecnológicoNational Natural Science Foundation of ChinaDeutsche Forschungsgemeinschaft
KeywordsSoil mesofaunaSoil biologyMicrofaunaSoil biodiversitySoil functionsEnvironmental scienceSoil organic matterSoil ecologyEnvironmental resource managementBiodiversityEcologySoil waterSoil scienceBiology

Abstract

fetched live from OpenAlex

Summary Here we introduce the Soil BON Foodweb Team, a cross-continental collaborative network that aims to monitor soil animal communities and food webs using consistent methodology at a global scale. Soil animals support vital soil processes via soil structure modification, direct consumption of dead organic matter, and interactions with microbial and plant communities. Soil animal effects on ecosystem functions have been demonstrated by correlative analyses as well as in laboratory and field experiments, but these studies typically focus on selected animal groups or species at one or few sites with limited variation in environmental conditions. The lack of comprehensive harmonised large-scale soil animal community data including microfauna, mesofauna, and macrofauna, in conjunction with related soil functions, limits our understanding of biological interactions in soil communities and how these interactions affect ecosystem functioning. To provide such data, the Soil BON Foodweb Team invites researchers worldwide to use a common methodology to address six long-term goals: (1) to collect globally representative harmonised data on soil micro-, meso-, and macrofauna communities; (2) to describe key environmental drivers of soil animal communities and food webs; (3) to assess the efficiency of conservation approaches for the protection of soil animal communities; (4) to describe soil food webs and their association with soil functioning globally; (5) to establish a global research network for soil biodiversity monitoring and collaborative projects in related topics; (6) to reinforce local collaboration networks and expertise and support capacity building for soil animal research around the world. In this paper, we describe the vision of the global research network and the common sampling protocol to assess soil animal communities and advocate for the use of standard methodologies across observational and experimental soil animal studies. We will use this protocol to conduct soil animal assessments and reconstruct soil food webs on the sites included in the global soil biodiversity monitoring network, Soil BON, allowing us to assess linkages among soil biodiversity, vegetation, soil physico-chemical properties, and ecosystem functions. In the present paper, we call for researchers especially from countries and ecoregions that remain underrepresented in the majority of soil biodiversity assessments to join us. Together we will be able to provide science-based evidence to support soil biodiversity conservation and functioning of terrestrial ecosystems.

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)0.009
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.104
GPT teacher head0.290
Teacher spread0.186 · 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 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

Citations21
Published2022
Admission routes1
Has abstractyes

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