Data for "Effects of urbanization on local adaptation and eco-evolutionary feedbacks in white clover"
Bibliographic record
Abstract
Data required for all analyses described in the paper “Effects of urbanization on selection, local adaptation, and eco-evolutionary feedbacks” (2025), testing how urban and rural environments affect the fitness and ecological interactions of white clover (Trifolium repens, L.; Fabaceae). Data is from a reciprocal transplant common garden experiment conducted in 2023 in the Greater Toronto Area, Canada using white clover from urban and rural populations and that either did or did not produce hydrogen cyanide (HCN). All analyses were conducted in R version 4.4.2 DATA FILES: 1. biomass_2023.csv contains dry end-of-season aboveground biomass data. Data Explanation: Source_Hab: Source population habitat of the white clover plant. U = urban (plant comes from an urban population), R = rural (plant comes from a rural population). Population_Num: Source population of the white clover plant, from Santangelo et al. 2020. Populations 1-5 are urban, populations 22-27 are rural. Plant_Num: Parent plant within each population. ID_Num: Individual number for plants from the same parent. Stolon: A or B, two stolon cuttings were taken from each plant to produce the final sample size. Tag_ID: Unique identifier for each plant, in the format Population Number - Plant Number - ID Number - Stolon HCN_Status: 0 or 1, 0 = does not produce HCN, 1 = does produce HCN. Site_Hab: Urban or Rural. Habitat of the common garden site where the plant was located. Site_ID: Common garden site name Dry_Biomass_g: biomass data in grams 2. leafarea.csv contains output from Easy Leaf Area analysis of leaf area data from May and July 2023, and May 2024, used to calculate growth rate. Data Explanation: filename: Unique identifier for each plant May_green: Number of green pixels identified by Easy Leaf Area in the May 2023 area photos May_red: Number of red pixels (coin for scale) identified by Easy Leaf Area in the May 2023 area photos May_leafarea: Leaf area (cm) calculated by Easy Leaf Area in the May 2023 area photos May_photodate: Date the area photo was taken in May 2023 Jul_green: Number of green pixels identified by Easy Leaf Area in the July 2023 area photos Jul_red: Number of red pixels (coin for scale) identified by Easy Leaf Area in the July 2023 area photos Jul_leafarea: Leaf area (cm) calculated by Easy Leaf Area in the July 2023 area photos Jul_photodate: Date the area photo was taken in July 2023 May24_green: Number of green pixels identified by Easy Leaf Area in the May 2024 area photos May24_red: Number of red pixels (coin for scale) identified by Easy Leaf Area in the May 2024 area photos May24_leafarea: Leaf area (cm) calculated by Easy Leaf Area in the May 2024 area photos May24_photodate: Date the area photo was taken in May 2024 3. survival_2023.csv contains overwinter survival data. Data Explanation: Source_Hab: Source population habitat of the white clover plant. U = urban (plant comes from an urban population), R = rural (plant comes from a rural population). Population_Num: Source population of the white clover plant, from Santangelo et al. 2020. Populations 1-5 are urban, populations 22-27 are rural. Plant_Num: Parent plant within each population. ID_Num: Individual number for plants from the same parent. Stolon: A or B, two stolon cuttings were taken from each plant to produce the final sample size. Tag_ID: Unique identifier for each plant, in the format Population Number - Plant Number - ID Number - Stolon HCN_Status: 0 or 1, 0 = does not produce HCN, 1 = does produce HCN. Site_Hab: Urban or Rural. Habitat of the common garden site where the plant was located. Site_ID: Common garden site name Survival_Sept2023: 0 or 1, 0 = not alive in September 2023 (end of field season), 1 = alive in September 2023. Survival_May2024: 0 or 1, 0 = not alive in May 2024, 1 = alive in May 2024 4. Flowers_2023.csv contains data on inflorescence production. Data Explanation: Source_Hab: Source population habitat of the white clover plant. U = urban (plant comes from an urban population), R = rural (plant comes from a rural population). Population_Num: Source population of the white clover plant, from Santangelo et al. 2020. Populations 1-5 are urban, populations 22-27 are rural. Plant_Num: Parent plant within each population. ID_Num: Individual number for plants from the same parent. Stolon: A or B, two stolon cuttings were taken from each plant to produce the final sample size. Tag_ID: Unique identifier for each plant, in the format Population Number - Plant Number - ID Number - Stolon HCN_Status: 0 or 1, 0 = does not produce HCN, 1 = does produce HCN. Site_Hab: Urban or Rural. Habitat of the common garden site where the plant was located. Site_ID: Common garden site name N_flowers_collected: Number of flower heads collected during seed collections N_Flowers_notcollected: Number of flower heads not collected (unripe at time of final harvest) Total_flowers: Total number of flower heads (sum of previous two columns) 5. seedmass_2023.csv contains seed mass data. Data Explanation: Source_Hab: Source population habitat of the white clover plant. U = urban (plant comes from an urban population), R = rural (plant comes from a rural population). Population_Num: Source population of the white clover plant, from Santangelo et al. 2020. Populations 1-5 are urban, populations 22-27 are rural. Plant_Num: Parent plant within each population. ID_Num: Individual number for plants from the same parent. Stolon: A or B, two stolon cuttings were taken from each plant to produce the final sample size. Tag_ID: Unique identifier for each plant, in the format Population Number - Plant Number - ID Number - Stolon HCN_Status: 0 or 1, 0 = does not produce HCN, 1 = does produce HCN. Site_Hab: Urban or Rural. Habitat of the common garden site where the plant was located. Site_ID: Common garden site name Seed_mass_g: mass of seeds produced in grams 6. herbivory.csv contains herbivory data. Herbivory was evaluated as the percentage of leaf area missing from each leaflet on three leaves per plant at two time points: July and August. Source_Hab: Source population habitat of the white clover plant. U = urban (plant comes from an urban population), R = rural (plant comes from a rural population). Population_Num: Source population of the white clover plant, from Santangelo et al. 2020. Populations 1-5 are urban, populations 22-27 are rural. Plant_Num: Parent plant within each population. ID_Num: Individual number for plants from the same parent. Stolon: A or B, two stolon cuttings were taken from each plant to produce the final sample size. Tag_ID: Unique identifier for each plant, in the format Population Number - Plant Number - ID Number - Stolon HCN_Status: 0 or 1, 0 = does not produce HCN, 1 = does produce HCN. Site_Hab: Urban or Rural. Habitat of the common garden site where the plant was located. Site_ID: Common garden site name Jul_Leaf1_leaflet1: Leaf area missing in July on Leaf 1, leaflet 1 Jul_Leaf1_leaflet2: Leaf area missing in July on Leaf 1, leaflet 2 Jul_Leaf1_leaflet3: Leaf area missing in July on Leaf 1, leaflet 3 Jul_Leaf2_leaflet1: Leaf area missing in July on Leaf 2, leaflet 1 Jul_Leaf2_leaflet2: Leaf area missing in July on Leaf 2, leaflet 2 Jul_Leaf2_leaflet3: Leaf area missing in July on Leaf 2, leaflet 3 Jul_Leaf3_leaflet1: Leaf area missing in July on Leaf 3, leaflet 1 Jul_Leaf3_leaflet2: Leaf area missing in July on Leaf 3, leaflet 2 Jul_Leaf3_leaflet3: Leaf area missing in July on Leaf 3, leaflet 3 Aug_Leaf1_leaflet1: Leaf area missing in August on Leaf 1, leaflet 1 Aug_Leaf1_leaflet2: Leaf area missing in August on Leaf 1, leaflet 2 Aug_Leaf1_leaflet3: Leaf area missing in August on Leaf 1, leaflet 3 Aug_Leaf2_leaflet1: Leaf area missing in August on Leaf 2, leaflet 1 Aug_Leaf2_leaflet2: Leaf area missing in August on Leaf 2, leaflet 2 Aug_Leaf2_leaflet3: Leaf area missing in August on Leaf 2, leaflet 3 Aug_Leaf3_leaflet1: Leaf area missing in August on Leaf 3, leaflet 1 Aug_Leaf3_leaflet2: Leaf area missing in August on Leaf 3, leaflet 2 Aug_Leaf3_leaflet3: Leaf area missing in August on Leaf 3, leaflet 3 Jul_Leaf1_Total: Mean leaf area missing in July on Leaf 1 Jul_Leaf2_Total: Mean leaf area missing in July on Leaf 2 Jul_Leaf3_Total: Mean leaf area missing in July on Leaf 3 Aug_Leaf1_Total: Mean leaf area missing in August on Leaf 1 Aug_Leaf2_Total: Mean leaf area missing in August on Leaf 2 Aug_Leaf3_Total: Mean leaf area missing in August on Leaf 3 Jul_Average: Mean percent leaf area missing across all leaves in July Aug_Average: Mean percent leaf area missing across all leaves in August Jul_VertHerb: Binary vertebrate herbivory in July (0 = no vertebrate herbivory; 1 = vertebrate herbivory). Vertebrate herbivory was assigned when at least 1 leaf was completely missing. Aug_VertHerb: Binary vertebrate herbivory in August (0 = no vertebrate herbivory; 1 = vertebrate herbivory). Vertebrate herbivory was assigned when at least 1 leaf was completely missing. Jul_InvertHerb: Binary invertebrate herbivory in July (0 = no invertebrate herbivory; 1 = invertebrate herbivory). Invertebrate herbivory was assigned when there was >1 and < 100% leaf area missing on at least 1 leaf. Aug_InvertHerb: Binary invertebrate herbivory in August (0 = no invertebrate herbivory; 1 = invertebrate herbivory). Invertebrate herbivory was assigned when there was >1 and < 100% leaf area missing on at least 1 leaf. Jul_Leaf1_Vert: Binary vertebrate herbivory in July (0 = no vertebrate herbivory; 1 = vertebrate herbivory) on leaf 1. Vertebrate herbivory was assigned when the leaf was completely missing. Jul_Leaf2_Vert: Binary vertebrate herbivory in July (0 = no vertebrate herbivory; 1 = vertebrate herbivory) on leaf 2. Vertebrate herbivory was assigned when the leaf was completely missing. Jul_Leaf3_Vert: Binary vertebrate herbivory in July (0 = no vertebrate herbivory; 1 = vertebrate herbivory)
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".