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Record W4401668133 · doi:10.1111/geb.13899

Predation on Live and Artificial Insect Prey Shows Different Global Latitudinal Patterns

2024· article· en· W4401668133 on OpenAlexafffund
Elena L. Zvereva, Benjamin Adroit, Tommi Andersson, Craig R. A. Barnett, Sofia Branco, Bastien Castagneyrol, Giancarlo M. Chiarenza, Wesley Dáttilo, Ek del‐Val, Jan Filip, Jory Griffith, Anna L. Hargreaves, Juan Antonio Hernández‐Agüero, Isabelle Leite de Holanda Silva, Yixuan Hong, Gabriella J. Kietzka, Petr Klimeš, M. Juhani Koistinen, Oksana Y. Kruglova, Satu Kumpula, Paula Lopezosa, Martí March‐Salas, Robert J. Marquis, Yuri M. Marusik, Angela T. Moles, Anne Muola, Mercy Murkwe, Akihiro Nakamura, Cameron Olson, Emilio Pagani‐Núñez, Anna Popova, Olivia Rahn, Alexey Reshchikov, Antonio Rodriguez‐Campbell, Seppo Rytkönen, Kateřina Sam, Antigoni Sounapoglou, Robert Tropek, Cheng Wenda, Guorui Xu, Yu Zeng, Maxim Zolotarev, Natalia A. Zubrii, Vitali Zverev, Mikhail V. Kozlov

Bibliographic record

VenueGlobal Ecology and Biogeography · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsMcGill University
FundersMinistry of Science and Higher Education of the Russian FederationNatural Sciences and Engineering Research Council of CanadaMinistério da Ciência, Tecnologia e Ensino SuperiorMitacsChinese Academy of SciencesCarl Tryggers Stiftelse för Vetenskaplig ForskningGrantová Agentura České RepublikyNational Natural Science Foundation of ChinaBiotieteiden ja Ympäristön Tutkimuksen ToimikuntaFundação para a Ciência e a TecnologiaMcGill University
KeywordsPlasticinePredationEcologyBiologyPredatorBorealInsect

Abstract

fetched live from OpenAlex

ABSTRACT Aim Long‐standing theory predicts that the intensity of biotic interactions increases from high to low latitudes. Studies addressing geographic variation in predation on insect prey have often relied on prey models, which lack many characteristics of live prey. Our goals were to explore global latitudinal patterns of predator attack rates on standardised live insect prey and to compare the patterns in predation on live insects with those on plasticine prey models. Location Global forested areas. Time Period 2021–2023. Major Taxa Arthropods, birds. Methods We measured predation rates in 43 forested locations distributed across five continents from 34.1° S to 69.5° N latitude. At each location, we exposed 20 sets of three bait types, one set per tree. Each set included three live fly larvae (maggots), three live fly puparia and three plasticine models of the puparia. We used glue rings to isolate half of the sets from non‐flying predators. Results Arthropod attack rates on plasticine prey decreased linearly from low to high latitudes, whereas attack rates on maggots had a U shaped distribution, with the lowest predation rates at temperate latitudes and the highest rates at tropical and boreal latitudes. This difference emerged from intensive predator attacks on live maggots, but not on plasticine models, in boreal sites. Site‐specific attack rates of arthropod predators on live and plasticine prey were not correlated. In contrast, bird attack rates on live maggots and plasticine models were positively correlated, but did not show significant latitudinal changes. Main Conclusions Latitudinal patterns in predation differ between major groups of predators and between types of prey. Poleward decreases in both arthropod and combined arthropod and bird predation on plasticine models do not mirror patterns of predation on our live prey, the latter likely reflecting real patterns of predation risk better than do patterns of attack on artificial prey.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations13
Published2024
Admission routes2
Has abstractyes

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