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Record W4399331500 · doi:10.1111/ddi.13869

Spatiotemporal patterns of forest pollinator diversity across the southeastern United States

2024· article· en· W4399331500 on OpenAlexaff
Michael D. Ulyshen, Cory K. Adams, J. Rodger Adams, Susan B. Adams, Mickey Bland, Don C. Bragg, Chuck Burdine, Mac A. Callaham, Richard Chaney, Gregg Chapman, Patsy Clinton, Cinnamon Dixon, Jacob Floyd, Phillip Jordan, Tara L. Keyser, Stephanie H. Laseter, Rachel E. Mallinger, Virginia L. McDaniel, Bryan T. Mudder, C. Dana Nelson, Katherine Odanaka, A. Christopher Oishi, Shawna Reid, Samm Reynolds, Kevin M. Robertson, Daniel Saenz, Mary Anne Sword Sayer, Nathan Schiff, Brian Scholtens, Joel Scott, Martín A. Spetich, Melanie K. Taylor, John Willis, Andrew D. Young

Bibliographic record

VenueDiversity and Distributions · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of GuelphYork University
FundersU.S. Forest Service
KeywordsEcologyGeographyPollinatorSpecies richnessContext (archaeology)EcoregionPollinationBiologyPollen

Abstract

fetched live from OpenAlex

Abstract Aim Efforts to understand how pollinating insect diversity is distributed across large geographic areas are rare despite the importance of such work for conserving regional diversity. We sought to relate the diversity of bees (Hymenoptera: Apoidea), hover flies (Diptera: Syrphidae), and butterflies (Lepidoptera) to ecoregion, landscape context, canopy openness, and forest composition across southeastern U.S. forests. Location Nineteen experimental forests across nine states in the southeastern U.S. Methods We established 5–7 plots on each experimental forest. In each, we sampled pollinators monthly (March–September) using coloured pan traps, and collected data on local forest characteristics. We used the National Land Cover Database (NLCD) to quantify surrounding landcover at different spatial scales. Results Bee richness was negatively correlated with both the amount of conifer (pine) forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hover flies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked 2 months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions. Main Conclusions Our findings reveal ecoregional differences in pollinator communities across the southeastern U.S. and highlight the importance of landscape context and local forest conditions to this diverse fauna. The closed broadleaf forests of Appalachia and the open conifer‐dominated forests of the Coastal Plain support particularly distinct pollinator communities with contrasting seasonality. Our results suggest pine forests may reduce pollinator diversity in regions historically dominated by broadleaf forests. However, efforts to create more open canopies can help improve conditions for pollinators in planted pine forests. Research exploring associations between forest pollinators and different broadleaf tree taxa is needed to better anticipate the impacts of various management activities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.048
GPT teacher head0.222
Teacher spread0.174 · 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.

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

Citations8
Published2024
Admission routes1
Has abstractyes

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