Data from: A multi-year case study highlighting the influence of hydrological conditions on epidemic dynamics in a natural plant pathosystem
Data files
Nov 25, 2024 version files 6.07 MB
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2023_Epidemiology_Final_Total_Surveys.xlsx
17.94 KB
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cleaned_daily_temp.rds
37.08 KB
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cleaned_daily_weather.rds
32.40 KB
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Cleaned_Epi.rds
3.18 KB
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cleaned_temp.rds
3.15 MB
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cleaned_weather.rds
1.40 MB
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Epidemiology.xlsx
460.34 KB
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Plant_locations.xlsx
844.50 KB
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Population_density.xlsx
108.69 KB
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README.md
9.91 KB
Abstract
The scale of influence of hydrological and thermal conditions on disease remains uncertain for most wild plant pathosystems, thus restricting our ability to predict the impacts of climate change. Analysis of the spatiotemporal spread of a fungal rust pathogen throughout four naturally occurring flax populations over the course of five growing seasons reveals relationships between epidemic magnitude and snow cover, relative humidity and temperature, as well as an unexpectedly significant effect of severe drought on disease progression. These results indicate that climate change will likely disrupt wild plant epidemics, and points to a need for further epidemiological studies characterizing the effects of environmental conditions on population-level disease dynamics in natural pathosystems.
Please reach out to Keenan Duggal (keenand@princeton.edu) with any questions about the data and/or scripts associated with this manuscript:
https://doi.org/10.5061/dryad.98sf7m0tc
Description of the data and file structure
This ecological study was purely observational, and as such no experimental efforts were taken during the collection of data.
Overview:
There are four field sites where observations of flax rust epidemiology were made. They are first described in
Miller, I.F., Jiranek, J., Brownell, M. et al. Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen. Sci Rep 12, 14823 (2022). https://doi.org/10.1038/s41598-022-18851-z
as follows:
- “The lowest site, ‘cement creek’ (CC; approximately 38.82156° N, 106.86893° W), falls within a sage brush meadow on the boundary between the mountain shrub and montane vegetation zones at 2440 m elevation. The second lowest site, ‘bus turnaround’ (BT; approximately 38.97130° N, 106.99595° W), is situated in an open subalpine meadow at the base of Gothic Mountain at ~ 2940 m. The second highest site, ‘gothic mountain’ (GM; approximately 38.97969° N, 107.01937° W), is also in the subalpine zone on a steep hillside within a clearing in an evergreen forest on the lower slopes of Gothic Mountain at 3220 m. The highest site, ‘high meadow’ (HM; approximately 38.96779° N, 107.02184° W), is on an exposed meadow at the upper fringes of the subalpine zone on the shoulder of Gothic Mountain at 3,410 m.”
Key of Common Variables in this Dataset:
Site (abbreviations):
- BT: Bus Turnaround
- CC: Cement Creek
- GM: Gothic Mountain
- HM: High Meadow
Each site was gridded into a cartesian coordinate system:
- X: X coordinate of plant (between 0 - 9 meters) in gridded survey plot
- Y: Y coordinate of plant (between 0 - 19 meters) in gridded survey plot
- x: Add to X coordinate for precise location within gridded survey plot (between 0 - 1 meters)
- y: Add to Y coordinate for precise location within gridded survey plot (between 0 - 1 meters)
Example: If the location of a plant is described by X=2, x=0.5, Y=4, y=0.9, it can be found 2.5 meters from the left border and 4.9 meters from the bottom border of the site.
Other Common Variables:
- Year: Year of observation
- Disease class (Class(0-5)):
- 0: 0% of plant tissue is visibly infected
- 1: 0-20 % of plant tissue is visibly infected
- 2: 20-40 % of plant tissue is visibly infected
- 3: 40-60 % of plant tissue is visibly infected
- 4: 60-80 % of plant tissue is visibly infected
- 5: 80-100 % of plant tissue is visibly infected
- Tag: Unique numerical identifier for a plant (~10% of plants were tagged in each site)
- Date (or Date Observed): Date that survey was performed
- Notes: Not used for analyses but rather for internal record keeping
- days_since_june_1st: # days of since June 1st of the current year
*** Important note: Blank cells in any of the following files do not imply missing data. Some of the reasons for empty cells:*
- * In different weeks and on different days, different disease measurements were taken (e.g., within-host infection quantification vs between-host epidemiological dynamics tracking), and these data were aggregated into files like “Epidemiology.” *
- *In different years, different data was collected (e.g., 2023 was the first year that used the streamlined “Disease Class” measurement to approximate within-host intensity. *
- *Sometimes, “Notes” were made to describe findings from the field. *
- Not all plants were “tagged”, but those that were have additional measurements and data associated with them
Please contact the corresponding author if there is any confusion of how to use this data.
Files and variables
File: 2023_Epidemiology_Final_Total_Surveys.xlsx
Description: End-of-the-year disease surveys for each site.
Unique Variables
- New?: 1 indicates that the plant was found newly diseased on the date of observation. Blank indicates it is not newly diseased.
File: cleaned_daily_temp.rds
Description: Temperature (degrees Celsius), relative humidity (%) and dew point (degrees Celsius) measurements were collected continuously every five minutes for the duration of the observational period. For this file, the daily average of each variable in each site and year was calculated and the mean, maximum and minimum of each of these is reported below. Temp: temperature, rh: relative humidity, dp: dew point.
Unique Variables
- mean_temp:
- min_temp
- max_temp
- mean_rh
- min_rh
- max_rh
- mean_dp
- min_dp
- max_dp
File: cleaned_daily_weather.rds
Description: Leaf moisture (leaf_wetness), rainfall (rain), sunlight intensity (solar_radiation), wind speed (ws), wind direction (wd), gust speed (gust_speed), soil moisture (water_content) measurements were collected continuously every five minutes for the duration of the observational period. For this file, the daily average of each variable in each site and year was calculated and the mean, maximum and minimum of each of these is reported below. For instrumentation details and descriptions of units, please refer to
Miller, I.F., Jiranek, J., Brownell, M. et al. Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen. Sci Rep 12, 14823 (2022). https://doi.org/10.1038/s41598-022-18851-z
Unique Variables
- “mean_leaf_wetness” “min_leaf_wetness” “max_leaf_wetness” “mean_rain” “min_rain” “max_rain” “mean_solar_radiation” “min_solar_radiation” “max_solar_radiation” “mean_ws” “min_ws” “max_ws” “mean_wd” “min_wd” “max_wd” “mean_gust_speed” “min_gust_speed” “max_gust_speed” “mean_water_content” “min_water_content” “max_water_content”
File: Cleaned_Epi.rds
Description: Summarized epidemiological data used for analyses
Unique Variables
- Disease_date: Date of survey
- count: total number of plants in site
- new_infections: # newly infected plants observed
- prevalence_noRecov: prevalence (total infections / count) without factoring in recovery
- prevalence: prevalence (total infections / count) with factoring in recovery
- Date_yearless: Date with common year (2000) – included to have a common axis in analyses
File: Population_density.xlsx
Description: Mapping the total density and approximate locations of all plants in each site
Unique Variables
- #H: Number of healthy plants in each 1mX1m grid
- #D: Number of diseased plants in each 1mX1m grid
- #Seedlings: Number of seedlings in each 1mX1m grid
File: Plant_locations.xlsx
Description: Mapping the total density and approximate locations of all plants in each site
Unique Variables
- Status: Healthy (H), Diseased (D), X (Dead), L (Lost)
- Height.cm: Maximum height of plant in centimeters
File: Epidemiology.xlsx
Description: Raw epidemiological survey data
Unique Variables
- Date First Observed Diseased: Date that infection was first spotted on plant
- Total # Stems: Total number of stems on plant
- Total # Diseased Stems: Total number of diseased stems on plant
- Max Height: Maximum height of plant in cm
- Flowers: Yes or No indicating whether plant was flowering at time of survey
File: cleaned_weather.rds
Description: Leaf moisture (leaf_wetness), rainfall (rain), sunlight intensity (solar_radiation), wind speed (ws), wind direction (wd), gust speed (gust_speed), soil moisture (water_content) measurements were collected continuously every five minutes for the duration of the observational period. For instrumentation details and descriptions of units, please refer to
Miller, I.F., Jiranek, J., Brownell, M. et al. Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen. Sci Rep 12, 14823 (2022). https://doi.org/10.1038/s41598-022-18851-z
Unique Variables
- times: Date and time
File: cleaned_temp.rds
Description: Temperature (degrees Celsius), relative humidity (%) and dew point (degrees Celsius) measurements were collected continuously every five minutes for the duration of the observational period. Temp: temperature, RH: relative humidity, Dew_point: dew point.
Unique Variables
- times: Date and time
Code/software
All code was run using R version 4.2.3. Packages needed are included at the top of each script.
Analyses_and_Figures.R : is a script that includes all of the code used for analyses and figure generation associated with this manuscript.
epi_data_aggregation.R : is a script that was used to clean, process and aggregate all of the epidemiological survey data from each year.
weather_data_aggregation.R : is a script that was used to clean, process and aggregate all of the weather data from each year.
Access information
Data was derived from the following sources:
- The RMBL Spatial Data Platform: https://www.rmbl.org/scientists/resources/data-catalog/
- Specifically raster data describing Elevation, Growing Degree Days (and Snow-free Growing Degree Days, & Days of contiguous snow cover were accessed using the coordinates for each site (described above).
- Please reach out to the corresponding author for more information about how to access this data.
A comprehensive description of the study design and methodologies used are delineated in the following paper:
Miller, I.F., Jiranek, J., Brownell, M. et al. Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen. Sci Rep 12, 14823 (2022). https://doi.org/10.1038/s41598-022-18851-z