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Toward a functional trait approach to bee ecology

2024· preprint· en· W4403886097 on OpenAlexaboutno aff
Madeleine M. Ostwald, Víctor H. González, C. P. Chang, Nydia Vitale, Mariano Lucía, Katja C. Seltmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
Fundersnot available
KeywordsTraitEcologyBiologyComputer science

Abstract

fetched live from OpenAlex

IntroductionInferring generalizable patterns in species dynamics, distributions, and functional variation are central aims of ecology and evolutionary biology (MacArthur, 1972). Trait-based approaches, which quantify phenotypic characteristics that impact organisms’ fitness and/or functional role, provide a tractable comparative framework for understanding communities, ecosystems, and evolutionary processes (Mcgill et al., 2006; Violle et al., 2007). Functional trait studies have proliferated over the past two decades, addressing foundational questions in community ecology (Cadotte et al., 2015; Mcgill et al., 2006; Violle and Jiang, 2009), biogeography (Violle et al., 2014), and conservation biology (Cadotte et al., 2011; Wellnitz and Poff, 2001) across taxonomic groups. These works emphasize the promise of trait-based research for generating novel insights into central ecological concepts and theories.Increasingly, bee researchers are recognizing the utility of trait-based approaches for a wide variety of applications in ecological research. Bees (Hymenoptera: Apoidea: Anthophila) represent more than 20,000 species worldwide and display dramatic interspecific variation in morphology and behavior (Figure 1), including traits that mediate pollination services and responses to global environmental change (Supplementary Table 1). Exploration of functional traits has long been a cornerstone of bee research, yet only recently have these traits been systematically applied in bee ecological studies as a comparative framework for understanding community-level processes. Given their major functional role as the primary animal pollinators of terrestrial ecosystems (Ollerton et al., 2011), the bees represent a group ripe for exploration through a functional ecological lens.Here, we review an emerging body of literature that quantifies functional traits across bee communities to address questions in bee ecology. In doing so, we address the following questions: How have functional traits been used to study bee ecology? What have been the major outcomes and limitations in bee functional trait research? How might this framework be leveraged to address urgent questions in the study of global bee declines? We review the variety of methods used to quantify bee trait variation, highlight common methodological problems and inconsistencies, and recommend best practices. Additionally, we describe geographic, taxonomic, and trait biases across the body of bee functional trait work, and highlight research areas that merit particular attention in future studies. Finally, we emphasize the value of open trait data sharing, and propose a roadmap toward a global bee functional trait database, including an initial aggregated dataset of 3369 morphological measurements from 1209 bee species.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.120
GPT teacher head0.283
Teacher spread0.164 · 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 designTheoretical or conceptual
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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