Data from: The ecological dynamics and consequences of phytoplasma in a spring ephemeral
Data files
Sep 10, 2026 version files 158.18 KB
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2024_Trillium_seed_counts_and_predicted_counts.csv
30.32 KB
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README.md
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Trillium_and_leafhopper_sequencing_results.xlsx
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trillium_code.Rmd
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Trillium_data_collection_-_2024_demography.csv
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Trillium_Field_Data_-_Leaf_and_Height_Phenotypes.csv
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Trillium_Field_Data_-_Multiyear.csv
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Trillium_Field_Data_Frequency.csv
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trillium_seed_pairs.csv
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Abstract
Background and aims: Ecologists have long sought to link within-population dynamics to patterns of species occurrence and persistence across the range. Theoretical models suggest pathogens have important implications for plant populations and perhaps even limit plant distributions, though more empirical evidence is needed, especially in wild plant populations.
Methods: In the field, we characterized the infection dynamics of phytoplasma, a vectored and sterilizing bacterial pathogen, in wild populations of white trillium. We identified the highest molecular matches for the local bacterial strain and putative vectors. We then leveraged iNaturalist data to reveal patterns and potential drivers of phytoplasma occurrence across the range of white trillium.
Key results: Within populations, we found that phytoplasma symptoms were more likely in low host density plots, where insects occupied a higher proportion of trillium, indicative of an encounter-dilution effect. Leafhoppers in the genus Empoasca were positive for phytoplasma, suggesting they are a candidate vector of disease between trilliums at our field site. The closest match to our local phytoplasma was related to ‘Candidatus Phytoplasma pruni’, ribosomal group 16SrIII-F. Phytoplasma symptoms were widely distributed across trillium’s range, even at range edges, and the probability of symptoms was significantly increased by proximity to cropland.
Conclusions: We studied the impacts of a sterilizing, vector-transmitted pathogen on host plant populations and patterns of infection across host range. Our results suggest sap-feeding insects do not occupy all hosts at high host densities, thereby reducing transmission and leading to higher likelihood of finding infection at low host densities, and a higher proportion of host plants infected at low host densities. At a range-wide scale, we found evidence that the pathogen was able to persist across both the core and edge of host ranges.
Dataset DOI: 10.5061/dryad.fqz612k5d
Description of the data and file structure
This repository contains the field data collected from Koffler Scientific Reserve and the files containing R code for analysis. iNaturalist and GBIF data, as well as code for the R function ggField are stored on Github for copyright purposes at https://github.com/JuliaBoyle/trillium_phytoplasma. iNaturalist data with observation fields is also included as a supplemental file with the manuscript.
Files and variables
R coding files
File: trillium_code.Rmd
Description: An R markdown file containing all analysis for the project. Can be opened in R and R studio statistical software.
Data from the field
File: Trillium_and_leafhopper_sequencing_results.xlsx
Description: Sanger sequencing results for forward and reverse amplified barcodes from plant and insect samples. Plants were amplified for phytoplasma 16S region, and insects for COI region.
Variables:
- Sample: Sample name. Phyto pos leaf samples are leaf samples from symptomatic trillium. Samples with COI indicate an insect sample, LH# signifies which leaf hopper morphotype is being tested. F and R at the end of the name indicate Forward or Reverse read, respectively.
- File name: The name of the actual file received from sequencing center.
- Sequence: The nucleotide sequence received from the sequencing center.
- Blast n megablast first result: When we inputted the nucleotide sequence into NCBI's Blast program using megablast algorithm on 18/07/2025, this was the taxonomic match with the highest percent matching identity.
- Percent identity: The amount our sequence matched the best match in the database.
- Relevant matches: The NCBI unique code for the samples with the highest matches.
- Notes: Relevant notes, such as date of search or when the sequencing failed.
File: 2024_Trillium_seed_counts_and_predicted_counts.csv
Description: Data on trillium measured for seed production and the modeled/predicted seed counts. NAs indicate the value was not measured or not relevant.
Variables:
- ID: Unique ID for trillium individual
- Infection status: Whether the trillium individual had any symptoms of phytoplasma presence. Marked as symptomatic or non-symptomatic.
- lf length: Leaf length in cm
- lf width: Leaf width in cm
- stem diameter mm: Stem diameter in mm
- fruit width: Fruit width in mm
- fruit length: Fruit length in mm
- transect number: number to indicate the transect the trillium was measured in
- field notes: Notes on trillium status. Munched= evidence of herbivory. Antho=red pigment in the leaves presumed to be anthocyanins
- Paired data plants: Whether the trillium individual was measured as part of a pair group (value=Yes) or measured for generating the link between trillium size and seed production (value=No).
- seed count: The measured or predicted seed count of that trillium individual
- seed count notes: Notes about seed count, usually to note why a count is 0 (e.g., seed pod deflated or no fruit produced).
- seed count real or predicted: Whether the value in the seed count column was generated by counting the seeds that individual produced (value=Real), or whether it was generated by extrapolating the predicted seed count from the size of the trillium (value=Predicted).
- pair number: The pair number linking the symptomatic and asymptomatic trillium. These paired trillium are close in space to one another. Trillium measured for seed count directly are not paired with other trillium.
File: Trillium_Field_Data_-_Leaf_and_Height_Phenotypes.csv
Description: Data from 2023 measuring the phenotypes of symptomatic and asymptomatic trillium individuals.
Variables:
- Plot: The 1 by 1 m plot where the trillium was measured in. This corresponds with the plots in the infection frequency datasets.
- Population: General location in Koffler Scientific Reserve where the plots were located. Populations were designated by landscape features such as differing hill slopes.
- Infection: Whether the trillium individual was symptomatic with phytoplasma or not. 0=asymptomatic, 1=symptomatic.
- Stem diameter: Stem diameter in mm. NA indicates this value was not measured.
- Leaf length cm: Leaf length in cm
- Sepal length cm: Sepal length in cm
- Height cm: Height in cm of the trillium from the ground to its longest petal.
- Surveyor: Who measured the plant. JB=Julia Boyle. JY= Julia Yang.
- Sepal to leaf ratio: The ratio of the sepal length to the leaf length.
File: Trillium_Field_Data_-_Multiyear.csv
Description: The full infection frequency and trillium density datasets including both 2018 and 2023 years.
Variables:
- Plot: The label of the 1 by 1 m plot sampled. Plots were chosen haphazardly by throwing a flag into trillium.
- Year: Whether the plots were sampled in 2023 or 2018.
- Forest side: Whether the plot was sampled in the forest to the east or west of Dufferin Rd at the Koffler Scientific Reserve.
- Population: General location in Koffler Scientific Reserve where the plots were located. Populations were designated by landscape features such as differing hill slopes.
- Surveyor: Who sampled the plot. JB=Julia Boyle. JY= Julia Yang, AC= Amalia Caballero.
- Latitude: Decimal latitude of the plot surveyed. When not recorded, this value is NA.
- Longitude: Decimal longitude of the plot surveyed. When not recorded, this value is NA.
- Young hemiptera: The number of non-flowering trillium with any evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because non-flowering trillium were not measured.
- Young nohemiptera: The number of non-flowering trillium with NO evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because non-flowering trillium were not measured.
- Health hemiptera: The number of asymptomatic (no phytoplasma) flowering trillium with any evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Health nohemiptera: The number of asymptomatic (no phytoplasma) flowering trillium with NO evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Infec hemiptera: The number of symptomatic ( phytoplasma) flowering trillium with any evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Infec nohemiptera: The number of symptomatic ( phytoplasma) flowering trillium with NO evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Total trilliums: The sum of flowering and non-flowering trillium individuals in the plot.
- Total adult trillium: The sum of flowering trillium individuals in the plot.
- Total infec trilliums: The sum of flowering symptomatic (phytoplasma) trillium individuals in the plot.
- Freq hemiptera total: The number of non-flowering and flowering trillium both with evidence of hemiptera divided by the 'Total trilliums' column. Value is NA in 2018 because these categories were not distinguished.
- Freq_infection_of_total: The 'Total infec trilliums' column divided by the 'Total trilliums' column. Value is NA in 2018 because non-flowering trillium were not measured.
- Young_to_repro_ratio: The ratio of non-flowering trillium number to flowering trillium number in the plot. Value is NA in 2018 because non-flowering trillium were not measured.
- Freq_infection_of_adults: The 'Total infec trilliums' column divided by the 'Total adult trillium' column. Because infection was only assessed in flowering trillium, this is a more accurate assessment of infection rate in the plot.
- Freq_hemiptera_adults: The number of flowering trillium with evidence of hemiptera divided by the 'Total adult trillium' column. Value is NA in 2018 because these categories were not distinguished.
File: Trillium_Field_Data_Frequency.csv
Description: The infection frequency and trillium density dataset in 2023 only.
Variables:
- Plot: The label of the 1 by 1 m plot sampled. Plots were chosen haphazardly by throwing a flag into trillium.
- Population: General location in Koffler Scientific Reserve where the plots were located. Populations were designated by landscape features such as differing hill slopes.
- Surveyor: Who sampled the plot. JB=Julia Boyle. JY= Julia Yang.
- Latitude: Decimal latitude of the plot surveyed. When not recorded, this value is NA.
- Longitude: Decimal longitude of the plot surveyed. When not recorded, this value is NA.
- Young hemiptera: The number of non-flowering trillium with any evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because non-flowering trillium were not measured.
- Young nohemiptera: The number of non-flowering trillium with NO evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because non-flowering trillium were not measured.
- Health hemiptera: The number of asymptomatic (no phytoplasma) flowering trillium with any evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Health nohemiptera: The number of asymptomatic (no phytoplasma) flowering trillium with NO evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Infec hemiptera: The number of symptomatic ( phytoplasma) flowering trillium with any evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Infec nohemiptera: The number of symptomatic ( phytoplasma) flowering trillium with NO evidence of hemiptera or sap sucking insects on the plant. Value is NA in 2018 because these categories were not distinguished.
- Total trilliums: The sum of flowering and non-flowering trillium individuals in the plot.
- Total adult trillium: The sum of flowering trillium individuals in the plot.
- Total infec trilliums: The sum of flowering symptomatic (phytoplasma) trillium individuals in the plot.
- Freq hemiptera total: The number of non-flowering and flowering trillium both with evidence of hemiptera divided by the 'Total trilliums' column. Value is NA in 2018 because these categories were not distinguished.
- Freq_infection_of_total: The 'Total infec trilliums' column divided by the 'Total trilliums' column. Value is NA in 2018 because non-flowering trillium were not measured.
- Young_to_repro_ratio: The ratio of non-flowering trillium number to flowering trillium number in the plot. Value is NA in 2018 because non-flowering trillium were not measured.
- Freq_infection_of_adults: The 'Total infec trilliums' column divided by the 'Total adult trillium' column. Because infection was only assessed in flowering trillium, this is a more accurate assessment of infection rate in the plot.
- Freq_hemiptera_adults: The number of flowering trillium with evidence of hemiptera divided by the 'Total adult trillium' column. Value is NA in 2018 because these categories were not distinguished.
File: trillium_seed_pairs.csv
Description: A file linking trillium identity to a trillium pair. These trillium and their pairs are used for the seed data.
Variables:
- ID: Trillium individual identification. D means diseased, H means Healthy.
- pair number: The pair number linking the healthy and diseased trillium pairs. Diseased trillium were chosen as the focal individual and the nearest asymptomatic trillium was chosen from there to make the pair.
File: Trillium_data_collection_-_2024_demography.csv
Description: Trillium demographic data measured in 2024. In 2024, we sampled 0.5 x 0.5 m plots centered around the focal asymptomatic and symptomatic individuals measured for seed production, and recorded the number of one-leaved, three-leaved vegetative, and three-leaved flowering trillium.
Variables:
- ID: Trillium individual identification. D means diseased, H means Healthy.
- DH_type: Symptom status of the trillium. H=Healthy/asymptomatic, D=Diseased/symptomatic.
- Date: the day and month the plot was assessed in 2024.
- infected adult: Number of symptomatic (phytoplasma) flowering trillium in the plot.
- adult count: Number of asymptomatic (no phytoplasma) flowering trillium in the plot
- juvenile count: Number of trillium with 3 leaves but not flowering or budding in the plot
- one leaf count: Number of trillium with only 1 leaf in the plot
- cot count: Number of cotyledon trillium in the plot
Data from iNaturalist
To characterize spatial patterns of phytoplasma infection of T. grandiflorum at a range-wide scale, we collected data from user observations on iNaturalist. First, we annotated all visually symptomatic T. grandiflorum observations with the observation field “Phytoplasma Infection?=Yes”.
On October 7th, 2025, we downloaded all research-grade iNaturalist observations of T. grandiflorum from the Global Biodiversity Information Facility (iNaturalist contributors, iNaturalist (2025). iNaturalist Research-grade Observations. iNaturalist.org. Occurrence dataset https://doi.org/10.15468/ab3s5x accessed via GBIF.org on 2025-10-07). This data is also included on the Github repository https://anonymous.4open.science/r/trillium_phytoplasma-59FB/.
Then, we merged the observations with their associated metadata and observation fields from iNaturalist (https://www.inaturalist.org/observations), downloaded on the same day. Observation fields are not downloadable from GBIF, requiring this extra step. This data is included with supplementary material and on the Github repository https://anonymous.4open.science/r/trillium_phytoplasma-59FB/.
Code/software
Analysis
Generally, files can be opened using Microsoft Excel or R. Analysis was done in R and RStudio.
Workflow: Download all files from Dryad and the Github/supplementary material. Open R and RStudio and load the trillium_code.rmd file and the ggField SD R file. Run the ggField code to have the function in your environment. Then, follow the code within the trillium code file.
Access information
Other publicly accessible locations of the data:
- All data and code is also be available on Github at https://github.com/JuliaBoyle/trillium_phytoplasma
Some data was derived from the following sources:
- Data from iNaturalist can be downloaded directly from https://www.inaturalist.org/observations. Data on iNaturalist can contain images or information that is copyrighted, and thus is not included here. Data on white trillium (Trillium grandiflorum) should be downloaded for dates observed up to October 7th 2025, including only "research-grade" observations, and including all metadata and observation fields (including “Phytoplasma Infection?=Yes”). Then, subset the data to only CC0, CC-BY, and CC-BY-NC observations.
- The GBIF data is downloadable using the following DOI: iNaturalist contributors, iNaturalist (2025). iNaturalist Research-grade Observations. iNaturalist.org. Occurrence dataset https://doi.org/10.15468/ab3s5x accessed via GBIF.org on 2025-10-07.
Study organisms and site
Trillium grandiflorum (white trillium), is a spring ephemeral native to the deciduous woodlands of eastern North America. There are large populations of wild T. grandiflorum at our study site, the Koffler Scientific Reserve (KSR, www.ksr.utoronto.ca) in Ontario, Canada (44°01'48”N, 79°32'01”W). Symptoms of phytoplasma infection have been observed in trillium populations at KSR since at least the early 2000s (A. Weis, personal communication). Phytoplasma infection is most apparent when the white trillium are flowering, as petals will range from having a streak of green to full virescence or phyllody.
Effects of phytoplasma infection on Trillium grandiflorum growth and reproduction
To investigate whether infection was associated with differences in plant size, we quantified total plant height, leaf length, and sepal length in symptomatic (n = 32) and asymptomatic (n = 30) trillium during peak flowering season (May 20-29, 2023) across 30 locations in an approximately 15-hectare section of mixed deciduous forest at KSR. We determined infection status by visually inspecting petals for virescence; we suspect that some infected individuals have no visible symptoms, so this method likely underestimates the overall infection rate of the population.
To quantify differences in seed production due to infection, we first characterized the relationship between leaf length and seed number in asymptomatic trilliums. We haphazardly sampled 100 individuals on 13 June 2024, aiming to maximize variation in plant size. On these plants, we measured leaf length and dissected fruits to count seed set. We also measured leaf length (May 20-28, 2024) and counted the number of seeds (early to mid June 2024) produced by 81 haphazardly chosen symptomatic trillium. To estimate potential reproductive loss due to infection in a given environment, we identified the nearest asymptomatic trillium of similar size and estimated its predicted seed count from the relationship between leaf length and seed number.
Within-population infection dynamics
We conducted surveys of trillium density and infection frequency in T. grandiflorum populations of KSR in mid-late May 2018 and 2023 (populations indicated in Figure S2). In both survey years, 1 m2 plots were chosen haphazardly by throwing a flag into trillium populations (Figure S1); all plots were >5 m apart and plot locations differed in the two sampling years. To account for spatial proximity of plots, we categorized general sampling locations based on geographic features (e.g., separate hill slopes). In 2018, we sampled 210 plots; we counted the number of flowering T. grandiflorum in the plot and their infection status based on petal virescence. In 2023, we sampled 64 additional plots with the aim of including more high-density populations and surveying potential vectors. We counted the number of non-flowering and flowering T. grandiflorum individuals that had at least three leaves, noted whether phytophagous Hemiptera nymphs were present on the plants, and noted the infection status of flowering trillium. Across both surveys, we estimated the proportion of trillium infected by dividing the number of symptomatic flowering plants by the total number of flowering trilliums (we used only flowering individuals in the denominator because infection was determined by visual petal symptoms). In addition, using data from our 2023 survey, we calculated the proportion of trillium with phytophagous Hemipterans present by dividing the number of trillium with Hemiptera by the number of trillium in the plot; we did this for total trilliums and also for only flowering trillium.
To determine how phytoplasma infection affected the age class distribution of KSR trillium populations, we calculated plot level ratios of vegetative to reproductive trillium and tested for correlations with infection frequency. In 2024, we sampled 0.5 m2 plots centered around the asymptomatic and symptomatic individuals measured for seed production, and recorded the number of one-leaved, three-leaved vegetative, and three-leaved flowering trillium.
Identification of vectors and phytoplasma strain
To identify the phytoplasma present in trillium, we sampled a leaf from a symptomatic flowering plant and extracted DNA using QIAGEN’s DNeasy® PowerSoil® Pro Kit. To assess vector identity and prevalence, we deployed yellow sticky traps (15.24 cm x 25.4 cm) attached to bamboo poles just above trillium height at 0.5 m above ground (Figure S1). We placed 20 traps across the sampled KSR trillium regions on June 6th 2023, collected them after 6 days, and then stored traps at -20℃. We counted and collected potential vectors from each trap, including leafhopper, psyllid, aphid, and planthopper morphotypes. We chose 110 insects across potential vector morphotypes and extracted their DNA using QIAGEN’s DNeasy® Blood & Tissue Kit, following the protocol for insect specimens. We detected and amplified the 16S rRNA region of phytoplasma in insect and plant samples with a nested polymerase chain reaction (PCR) using two universal phytoplasma primer pairs R16mF2/R1 and R16F2n/R2 (Gundersen & Lee 1996), and visualised the presence of phytoplasma using gel electrophoresis. We amplified the mitochondrial cytochrome oxidase subunit 1 region of insect samples positive for phytoplasma using the lib220-1/LepR primers (Hajibabaei et al. 2006). Then, to identify the phytoplasma present in plants and the insects that vector it, we performed Sanger sequencing on PCR products (at the Center for Analysis of Genome Function and Evolution, University of Toronto) and looked for similar sequences using the National Center for Biotechnology Information's nucleotide basic local alignment search tool using the megablast algorithm (Altschul et al. 1990, Chen et al. 2015).
Metapopulation infection dynamics
To characterize spatial patterns of phytoplasma infection of T. grandiflorum at a range-wide scale, we collected data from user observations on iNaturalist. First, we annotated all visually symptomatic T. grandiflorum observations with the observation field “Phytoplasma Infection?=Yes”. On March 14th, 2025, we downloaded all research-grade observations with their associated metadata and observation fields. In total, this dataset included 43,037 observations between 1967-2024 with 846 infected individuals. We removed observations with missing latitudes or longitudes, observations with low or missing positional accuracy (< 10,000), and observations likely to be erroneous with both a latitude above 20°N and a longitude below 65°W, resulting in 32,649 observations. To reduce user bias in the dataset, we limited the maximum number of observations per user to 15, reducing the dataset to 29,412 observations. Then, we used CoordinateCleaner (Zizka et al. 2019) to remove observations at country centroids or known biological institutions, observations with equal latitudes and longitudes, observations with any zeroes as coordinates, and observations with identical coordinates. After cleaning, the dataset had 28,390 total observations and 526 infected observations. Users contributed a mean 1.76 total observations and a median of 1 total observation. The infected observations were observed by 433 users. The vast majority (82.4%) of all observations in the dataset occurred after January 1st, 2020. We reprojected the coordinates of observations using ESRI:102003, an equal area projection that is appropriate for T. grandiflorum’s range.