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Record W4384661702 · doi:10.1038/s41598-023-36795-w

Climate, host and geography shape insect and fungal communities of trees

2023· article· en· W4384661702 on OpenAlexfundno aff
Iva Franić, Eric Allan, Simone Prospero, Kalev Adamson, Fabio Attorre, Marie‐Anne Auger‐Rozenberg, Sylvie Augustin, Dimitrios Ν. Avtzis, Wim Baert, Marek Barta, Kenneth Bauters, Amani Bellahirech, P Boroń, Helena Bragança, Tereza Brestovanská, May Bente Brurberg, Treena I. Burgess, Daiva Burokienė, Michelle Cleary, Juan C. Corley, David R. Coyle, György Csóka, Karel Černý, Kateryna Davydenko, Maarten de Groot, Julio Javier Díez, H. Tuğba Doğmuş Lehtijärvi, Rein Drenkhan, Jacqueline Edwards, Mohammed Elsafy, Csaba Béla Eötvös, Roman Falko, Jianting Fan, Nina Feddern, Ágnes Fürjes‐Mikó, Martin M. Goßner, Bartłomiej Grad, Martin Hartmann, Ludmila Havrdová, Miriam Kádasi Horáková, Markéta Hrabětová, Mathias Just Justesen, Magdalena Kacprzyk, Marc Kenis, Natalia Kirichenko, Marta Kovač, Volodymyr Kramarets, Nikola Lacković, Victoria Lantschner, Jelena Lazarević, Marianna Leskiv, Hongmei Li, Corrie Lynne Madsen, Chris Malumphy, Dinka Matošević, Iryna Matsiakh, Tom W. May, Johan Meffert, Duccio Migliorini, Christo Nikolov, Richard O’Hanlon, Funda Oskay, Trudy Paap, Taras Parpan, Barbara Piškur, Hans Peter Ravn, J Richard, Anne Ronse, Alain Roques, Beat Ruffner, Alberto Santini, Karolis Sivickis, Carolina Soliani, Venche Talgø, М. А. Томошевич, Anne Uimari, Michael D. Ulyshen, Anna Maria Vettraino, Caterina Villari, Yongjun Wang, Johanna Witzell, Milica Zlatković, René Eschen

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersNational Research, Development and Innovation OfficeBundesamt für UmweltForeign, Commonwealth and Development OfficeRussian Science FoundationAustralian Centre for International Agricultural ResearchAgentúra na Podporu Výskumu a VývojaMinistry of Agriculture of the People's Republic of ChinaJavna Agencija za Raziskovalno Dejavnost RSUniwersytet Rolniczy im. Hugona Kołłątaja w KrakowieHungarian Scientific Research FundDepartment of Forestry, Fisheries and the EnvironmentEesti TeadusagentuurEidgenössische Technische Hochschule ZürichSiberian Branch, Russian Academy of SciencesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMcGill UniversityGénome QuébecDepartment of Agriculture, Environment and Rural Affairs, UK GovernmentInstituto Nacional de investigação Agrária e VeterináriaAgriculture and Agri-Food CanadaNational Science Foundation
KeywordsHost (biology)InsectEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Non-native pests, climate change, and their interactions are likely to alter relationships between trees and tree-associated organisms with consequences for forest health. To understand and predict such changes, factors structuring tree-associated communities need to be determined. Here, we analysed the data consisting of records of insects and fungi collected from dormant twigs from 155 tree species at 51 botanical gardens or arboreta in 32 countries. Generalized dissimilarity models revealed similar relative importance of studied climatic, host-related and geographic factors on differences in tree-associated communities. Mean annual temperature, phylogenetic distance between hosts and geographic distance between locations were the major drivers of dissimilarities. The increasing importance of high temperatures on differences in studied communities indicate that climate change could affect tree-associated organisms directly and indirectly through host range shifts. Insect and fungal communities were more similar between closely related vs. distant hosts suggesting that host range shifts may facilitate the emergence of new pests. Moreover, dissimilarities among tree-associated communities increased with geographic distance indicating that human-mediated transport may serve as a pathway of the introductions of new pests. The results of this study highlight the need to limit the establishment of tree pests and increase the resilience of forest ecosystems to changes in climate.

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)0.001
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 score0.005
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.216
Teacher spread0.203 · 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

Citations27
Published2023
Admission routes1
Has abstractyes

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