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Record W4405830073 · doi:10.1111/brv.13177

Key concepts and a world‐wide look at plant recruitment networks

2024· review· en· W4405830073 on OpenAlexaff
Julio M. Alcántara, Miguel Verdú, José Luis Hernando Garrido, Alicia Montesinos‐Navarro, Marcelo A. Aizen, Mohamed Alifriqui, David Allen, Ali A. Al‐Namazi, Cristina Armas, Jesús M. Bastida, Tono Bellido, Gustavo B. Paterno, Herbert Briceño, Ricardo A. C. de Oliveira, Josefina G. Campoy, Ghassen Chaieb, Chengjin Chu, Elena Constantinou, Léo Delalandre, Milén Duarte, Michel Faife‐Cabrera, Fatih Fazlioglu, Edwino S. Fernando, Joel Flores, Hilda Flores‐Olvera, Ecaterina Fodor, Gislene Ganade, Marı́a B. Garcı́a, P. García‐Fayos, Sabrina S. Gavini, Marta Goberna, Lorena Gómez‐Aparicio, Enrique González‐Pendás, Ana González‐Robles, Kahraman İpekdal, Zaal Kikvidze, Alicia Ledo, Sandra Lendínez, Hanlun Liu, Francisco Lloret, Ramiro Pablo López, Álvaro López‐García, Christopher J. Lortie, Gianalberto Losapio, James A. Lutz, Frantíšek Máliš, Antonio J. Manzaneda, Vinícius Marcilio‐Silva, Richard Michalet, Rafael Molina‐Venegas, José A. Navarro‐Cano, Vojtêch Novotný, Jens M. Olesen, Juan Pablo Ortíz-Brunel, Mariona Pajares‐Murgó, Antonio J. Perea, Vidal Pérez‐Hernández, María Ángeles Pérez‐Navarro, Nuria Pistón, Iván Prieto, Jorge Prieto‐Rubio, Francisco I. Pugnaire, Nelson Ramírez, Rubén Retuerto, Pedro J. Rey, Daniel A. Rodriguez‐Ginart, Ricardo Sánchez‐Martín, Çağatay Tavşanoğlu, Giorgi Tedoradze, Amanda Tercero‐Araque, Katja Tielbörger, Blaise Touzard, İrem Tüfekcioğlu, Sevda Türkiş, Francisco M. Usero, Nurbahar Usta‐Baykal, Alfonso Valiente‐Banuet, Alexa Vargas‐Colin, Ioannis Ν. Vogiatzakis, Regino Zamora

Bibliographic record

VenueBiological reviews/Biological reviews of the Cambridge Philosophical Society · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
FundersEuropean Regional Development FundAgencia Estatal de InvestigaciónGeneralitat ValencianaEuropean Commission
KeywordsEcologyCommunityCanopyAbiotic componentPlant communityToolboxGeographyEcosystemBiologyComputer scienceSpecies richness

Abstract

fetched live from OpenAlex

Plant-plant interactions are major determinants of the dynamics of terrestrial ecosystems. There is a long tradition in the study of these interactions, their mechanisms and their consequences using experimental, observational and theoretical approaches. Empirical studies overwhelmingly focus at the level of species pairs or small sets of species. Although empirical data on these interactions at the community level are scarce, such studies have gained pace in the last decade. Studying plant-plant interactions at the community level requires knowledge of which species interact with which others, so an ecological networks approach must be incorporated into the basic toolbox of plant community ecology. The concept of recruitment networks (RNs) provides an integrative framework and new insights for many topics in the field of plant community ecology. RNs synthesise the set of canopy-recruit interactions in a local plant assemblage. Canopy-recruit interactions describe which ("canopy") species allow the recruitment of other species in their vicinity and how. Here we critically review basic concepts of ecological network theory as they apply to RNs. We use RecruitNet, a recently published worldwide data set of canopy-recruit interactions, to describe RN patterns emerging at the interaction, species, and community levels, and relate them to different abiotic gradients. Our results show that RNs can be sampled with high accuracy. The studies included in RecruitNet show a very high mean network completeness (95%), indicating that undetected canopy-recruit pairs must be few and occur very infrequently. Across 351,064 canopy-recruit pairs analysed, the effect of the interaction on recruitment was neutral in an average of 69% of the interactions per community, but the remaining interactions were positive (i.e. facilitative) five times more often than negative (i.e. competitive), and positive interactions had twice the strength of negative ones. Moreover, the frequency and strength of facilitation increases along a climatic aridity gradient worldwide, so the demography of plant communities is increasingly strongly dependent on facilitation as aridity increases. At network level, species can be ascribed to four functional types depending on their position in the network: core, satellite, strict transients and disturbance-dependent transients. This functional structure can allow a rough estimation of which species are more likely to persist. In RecruitNet communities, this functional structure most often departs from random null model expectation and could allow on average the persistence of 77% of the species in a local community. The functional structure of RNs also varies along the aridity gradient, but differently in shrubland than in forest communities. This variation suggests an increase in the probability of species persistence with aridity in forests, while such probability remains roughly constant along the gradient in shrublands. The different functional structure of RNs between forests and shrublands could contribute to explaining their co-occurrence as alternative stable states of the vegetation under the same climatic conditions. This review is not exhaustive of all the topics that can be addressed using the framework of RNs, but instead aims to present some of the interesting insights that it can bring to the field of plant community ecology.

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)0.002
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0010.003
Scholarly communication0.0040.012
Open science0.0010.001
Research integrity0.0010.002
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 head0.373
GPT teacher head0.352
Teacher spread0.021 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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