MétaCan
Menu
Back to cohort
Record W6944841561 · doi:10.21966/cz48-d388

100 Islands Research Program Terrestrial Vegetation Data - BC Central Coast - 2015, 2016, 2017

2020· dataset· en· W6944841561 on OpenAlexaboutno aff

Bibliographic record

VenueHakai Institute · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSpecies richnessBiogeographyMetadataVegetation (pathology)Environmental dataInsular biogeographyRaw dataBiodiversitySubmarine pipeline

Abstract

fetched live from OpenAlex

These data were collected as part of the Hakai Institute’s 100 Islands Research Program. The purpose of 100 Islands was to study how marine subsidies, island characteristics, and other drivers affect vegetation communities on islands of the Central Coast. This dataset includes two folders of data – one which includes the raw forms of the data and was used for Owen Fitzpatrick’s MSc thesis analyses (Folder 1), and one that was used by Debora Obrist, Owen Fitzpatrick, and co-authors to evaluate the scale-dependence of marine inputs on island plant communities (Folder 2). Folder 1: All files in Folder 1 described in Hakai Institute Advanced Metadata Form – 100 Islands Fitzpatrick. Folder 2: plant-sib-island-analysis-data.csv - Island-level rarefied plant species richness and environmental data for 92 islands. plant-sib-plot-analysis-data.csv - Sampling plot-level plant species richness and environmental data for 1381 plots across 90 islands with complete plot-level environmental data. hmsc-percent-cover-long.csv - Plot-level percent cover by species. hmsc-species-names.csv - Conversion from 4-letter codes into Latin names for species. hmsc-xycoords.csv - File containing key to link wrack data to plot-level species data and XY coordinates to make joint species distribution model spatially explicit. plant-sib-metadata.docx - Complete metadata for all Folder 2 files. Uses: Islands in a sea of nutrients: testing subsidized island biogeography - Owen Fitzpatrick, MSc thesis: https://dspace.library.uvic.ca/handle/1828/9312 In this thesis, Owen uses the theory of island biogeography as a basis for exploring patterns of diversity on islands. He incorporates the growing recognition that marine and terrestrial food webs are linked by the flow of subsidies, which may alter the productivity of recipient ecosystems. He established a large-scale observational study to test whether marine subsidies alter the relationship between island area and plant species richness, and if the impact of those subsidies is mediated by landscape-scale habitat characteristics such as island area and shoreline slope. Scale-dependent effects of marine subsidies on island biogeographic patterns of plants - Obrist et al. 2022, Ecology and Evolution, doi: 10.1002/ece3.9270, code for analyses: https://github.com/debobrist/plant-sib We know that species richness is determined by different mechanisms at different spatial scales, but the role of scale in effects of marine inputs on island biogeography are still unknown. We used this dataset to evaluate the influence of island characteristics and marine inputs (seaweed wrack biomass and marine-derived nitrogen in the soil) on plant species richness at both a local (plot) and regional (island) scale on 92 islands in British Columbia, Canada. We found that the effects of subsidies on species richness depend on the spatial scale of investigation. Although we found no effects of marine subsidies on island levels, we found that plots with more marine-derived nitrogen (δ15N) in the soil hosted fewer species. We found no effect of wrack at either scale. To look at possible mechanisms driving this decrease in diversity, we used a joint species distribution model identify species-level responses to marine subsidies and effects of biotic interactions among 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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0060.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.076

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 head0.235
GPT teacher head0.444
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueHakai InstituteFrench-language works237,207