DOI: 10.5061/dryad.pzgmsbd1m
title: Data from: Effects of a heat wave event on the chemical ecology of species interactions in the potato agroecosystem

description: Heat waves, brief periods of unusually high temperatures, are increasing
 in frequency and intensity globally. Such extreme weather events can alter
 plant chemistry, disrupting species interactions that contribute to pest
 suppression or increase their performance. Yet, most heat wave studies
 focus on pairwise interactions, leaving us with a poor understanding of
 how complex agroecosystems respond to temperature extremes. We addressed
 this knowledge gap by simulating an experimental heat wave in the field on
 potato plants (Solanum tuberosum) and the Colorado potato beetle (CPB),
 Leptinotarsa decemlineata (Coleoptera: Chrysomelidae), in the presence or
 absence of their mutualistic microbial symbionts and another pest, the
 potato aphid (Macrosiphum euphorbiae). Here we report changes in CPB
 performance and leaf chemistry, specifically, volatile organic compounds
 (VOCs) and glycoalkaloids content in host plants.
description: <strong>Experimental design</strong>
 To assess the effect of a heat wave event on pest performance and
 plant chemistry in different ecological contexts, we conducted a field
 experiment during the 2022 growing season at the Michigan State University
 Kellogg Biological Station (KBS) (Hickory Corners, MI). We generated a
 4-day heat wave event on potato plants exposed to damage by CPB larvae
 with or without microbial symbionts and/or simultaneous damage by potato
 aphids. <em>CPB colony</em>
 All larvae were obtained from a colony maintained at the KBS
 greenhouse facilities with adults sourced from the Montcalm Research
 Station, MI. We obtained CPB without symbiotic bacteria by splitting the
 colony into separate cages with antibiotic-free and antibiotic-treated
 potato plants. Antibiotic-treated plants were sprayed thoroughly with an
 antibiotic solution consisting of 5 % Tetracycline, 5 % Neomicine, and 2.5
 % Streptomycine in DI water. <em>Field
 experiment</em> We planted seed potatoes
 individually within 1 m<sup>2</sup> plots in mid-May. When
 plants began sprouting and before naturally occurring CPB populations
 arrived, we allowed a subset of plants to be colonized by natural
 populations of potato aphids and covered the rest with 1
 m<sup>2</sup> by 80 cm tall mesh cages (Lumite, Inc., GA).
 When plants were about six weeks old, they were assigned to one of the
 following herbivory treatment combinations: 1) “control” plants with no
 herbivores; 2) “CPB” alone; 3) CPB treated with antibiotics “CPB(ab)”; 4)
 CPB with aphids “CPB+aphid”; and 5) antibiotic-treated CPB with aphids
 “CPB(ab)+aphid”. Plants in the “CPB” and “CPB(ab)” groups received 10 CPB
 neonate larvae from the antibiotic-free and the antibiotic-treated colony,
 respectively. For the “CPB+aphid” and “CPB(ab)+aphid” treatment groups, we
 added CPB larvae from their corresponding colonies as described above to
 the aphid colonized plants and covered with the protective
 cages. <em>Heat wave
 treatment</em> After one week of initiating
 bioassays, half of these plants were assigned randomly to either an
 ambient or heat wave group. Each herbivore x heat wave treatment
 combination had 15 replicates, totaling 120 plants. We simulated a single
 4-day heat wave event in mid-July using an electric 300-Watt ceramic
 heater (Tempco, Inc., IL) hung in the opening of a pyramidal open-top
 chamber with a wood frame and anti-condensate greenhouse plastic sides (6
 mil, 91 % light transmittance, Poly-Ag Corp., CA). This system increased
 daily temperatures by an average of 10 °C and 4 °C at night, achieving
 average temperatures of 41.84 °C ± 9.86 (diurnal) and 25.16 °C ± 4.2
 (nocturnal) in the heat wave plots compared to 31.8 °C ± 9.26 (diurnal)
 and 21.91 °C ± 3.43 in ambient plots.
 <strong>Data collection</strong>
 <em>Insect performance</em> We
 censused CPB larvae twice, once before the heat wave and once after to
 quantify performance (searching for 5 minutes per plant). Adult beetles
 were collected in mid-August prior to removing experimental cages, frozen
 immediately after collection, oven-dried and sexed. We then weighted them
 and measured pronotum length to assess adult performance.
 <em>Plant chemistry</em> We
 analyzed potato’s four main glycoalkaloids (solanine, chaconine,
 dehydrosolanine, and dehydrochaconine), and leaf volatiles to assess
 whether heat stress changes plant secondary chemistry under different
 types of biotic stresses. Glycoalkaloids were extracted from plant tissue
 and analyzed using LC/MS/MS at the Michigan State University’s Mass
 Spectrometry and Metabolomics Core. Approximately 100
 mg of plant tissue from each plant were collected in pre-weighted 2 mL
 tubes. Frozen tissue was ground in a pre-frozen bead beater (Retsch,
 MM400) at 30/s until fully ground. Samples were extracted with 1 mL
 extraction buffer (80:20 v/v methanol:water, 0.1% formic acid, and 100 nM
 of digitoxin as internal standards). After incubating at 4°C on a rocking
 platform for 16 hours, samples were centrifuged at 4 °C for 10 minutes at
 14,000 rpm (Eppendorf, 5810 R). Supernatants (10 µL) were diluted into a
 990 µL cold extraction buffer and stored at -20 °C. Samples were analyzed
 using a Waters Xevo G2-XS Quadrupole-Time-of-flight LC/MS/MS system with a
 Waters Acquity BEH-C18 UPLC column (2.1 x 100 mm) in positive ion mode.
 Compounds were eluted using a binary gradient of solvent A (0.1% formic
 acid in water) and solvent B (acetonitrile) at a flow rate of 0.3 mL/
 minute at 40°C following a stepwise gradient: 98.0 % A, 2.0 % B; 0.50 min,
 85.0 % A, 15.0 % B; 5.00 min, 40.0 % A, 60.0 % B; 7.00 min, 1.0 % A, 99.0
 % B; 8.00 min, 1.0 % A, 99.0 % B; 8.01 min, 98.0 % A, 2.0 % B; 10.00 min,
 98.0 % A, 2.0 % B. We identified compounds in the Waters MassLynx software
 based on mass spectrometry, confirmed with digitoxin as internal standard,
 and quantified using the Waters Quanlynx MS software. Prior to statistical
 analysis data was normalized to internal standards and tissue sample
 mass. Leaf volatiles were collected immediately after
 the heat wave event over a period of three days from 118 plants including
 at least 10 replicates per each treatment combination. Sampling occurred
 between 11 AM and 2 PM during which time temperatures ranged from 23-43 °C
 following dynamic headspace sampling procedure. Briefly, we selected one
 vegetative stem per plant of similar biomass and enclosed them in a 35 x
 35 cm nylon oven bags (Reynolds, USA) unsealed on one side. The bags were
 tied with twisters and volatiles were allowed to equilibrate for 30
 minutes, after which, samples were pumped for 30 minutes through a scent
 trap using an air sampling pump (Ointik; Push-Pull Active Air Sampling
 Vacuum Pump) set to a pre-trap flow rate of 350 mL/min. Stems were saved,
 dried, and weighted. Ambient controls (n = 10) were taken from the area
 adjacent to the experimental plots where air was collected using an empty
 oven bag and sampled following the same methodology as the experimental
 samples. These ambient samples were used to identify contaminants or
 background compounds from surrounding vegetation. Leaf
 and ambient samples were analyzed using thermal desorption Gas
 Chromatography-Mass Spectrometry (GC-MS), together with one blank/unused
 scent traps to detect potential contaminants in the trap system. Peak
 deconvolution, integration, and tentative compound identification were
 performed in the Automated Mass Spectral Deconvolution and Identification
 System using the 2020 NIST mass spectral library. Data filtering was
 performed in the bouquet package. Peaks were included if they had mass
 spectral match scores greater than 80 %, a maximum peak area of at least
 20,000 counts, and if they were present in more than 20 % of the samples.
 Additionally, we only included compounds with peak areas four times higher
 than the mean area of the ambient and blank controls. Caprolactam
 (compounds present in oven bags) and compounds with high retention times
 (above 15 minutes) were excluded as contaminants. Stems used for volatile
 collection were saved, dried, and weighted. Volatile emissions were
 quantified based on peak values and were standardized by dry weight of the
 sampled stem.
description: --- title: "Heat wave impact on the chemical ecology of multi-species
 interactions in an agroecosystem" author: "Nayeli Carvajal"
 date: "2025-10-6" output: html_document editor_options:
 markdown: wrap: 72 -------- ```{r knitr::opts_chunk$set(echo = FALSE) ```
 # Heat wave impact on the chemical ecology of multi-species interactions
 in an agroecosystem
 [https://doi.org/10.5061/dryad.pzgmsbd1m](https://doi.org/10.5061/dryad.pzgmsbd1m) # GENERAL INFORMATION 1. Title of Dataset: “Heat wave impact on the chemical ecology of multi-species interactions in an agroecosystem" 2. Author Information A. Person(s) responsible for collecting the data Name: Nayeli Carvajal, Institution: Michigan State University Address: 288 Farm Ln, East Lansing, MI 48824 Email: [carvaj14@msu.edu](mailto:carvaj14@msu.edu){.email} B. Principal Investigator Name: William Wetzel Institution: Montana State University Address: Land Resources & Environmental Sciences, Bozeman, MT USA. Email: [william.wetzel@montana.edu](mailto:william.wetzel@montana.edu){.email} 3. Date of data collection (single date, range, approximate date): June-October 2022 4. Geographic location of data collection: MSU Kellogg Biological Station, MI, United States 5. Keywords used to describe the data topic: chemical ecology, agroecology, Colorado potato beetle, potato aphid, heat wave, symbiotic microbes, *Solanum tuberosum*, thermal stress 6. Language information: English 7. Information about funding sources that supported the collection of the data: This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2020-67013-31919 from the USDA National Institute of Food and Agriculture. # DATA & FILE OVERVIEW 1. For each filename, a short description of what data it contains "CPB_censuses.csv" contains data for larval count before and after the heat wave and adult count. “CPB_adult_fitness.csv” reports data for CPB adult sex and size as determined by dry weight and pronotum length for each adult CPB collected in the field. "leaf_glycoalkaloids.csv" contains abundance data for glycoalkaloids from host plants. "leaf_volatiles.csv" contains leaf volatile abundance data from host plants. 2. Format of the file if not obvious from the file name: comma separated values, file extension “.csv” 3. Additional related data collected that was not included in the current data package: No 4. Are there multiple versions of the dataset? No # SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: N/A 2. Links to publications that cite or use the data: Carvajal-Acosta, et al. 2025. "Effects of a heat wave event on the chemical ecology of species interactions in the potato agroecosystem". Environmental Entomology. [http:/DOI.10.1093/ee/nvaf104](http:/DOI.10.1093/ee/nvaf104) 3. Was data derived from another source? No 4. Recommended citation for this dataset: Carvajal-Acosta et al. (2025). Heat wave impact on the chemical ecology of multi-species interactions in an agroecosystem. Dryad, Dataset # METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: **CPB survival and adult performance**: We censused CPB larvae twice, once before the heat wave and once after to quantify performance (searching for 5 min. per plant).Adult beetles were collected in mid-August prior to removing experimental cages, frozen immediately after collection, oven-dried and sexed. We then weighted them and measured pronotum length to assess adult performance.  **Glycoalkaloids**: We analyzed potato’s four main glycoalkaloids (solanine, chaconine, dehydrosolanine, and dehydrochaconine). Glycoalkaloids were extracted from plant tissue and analyzed using LC/MS/MS at the Michigan State University’s Mass Spectrometry and Metabolomics Core. Approximately 100 mg of plant tissue from each plant were collected in pre-weighted 2 mL tubes. Frozen tissue was ground in a pre-frozen bead beater (Retsch, MM400) at 30/s until fully ground. Samples were extracted with 1 mL extraction buffer (80:20 v/v methanol:water, 0.1% formic acid, and 100 nM of digitoxin as internal standards). After incubating at 4°C on a rocking platform for 16 hours, samples were centrifuged at 4°C for 10 minutes at 14,000 rpm (Eppendorf, 5810 R). Supernatants (10 µl) were diluted into a 990 µL cold extraction buffer and stored at -20°C. Samples were analyzed using a Waters Xevo G2-XS Quadrupole- Time-of-flight LC/MS/MS system with a Waters Acquity BEH-C18 UPLC column (2.1 x 100mm) in positive ion mode. Compounds were eluted using a binary gradient of solvent A (0.1% formic acid in water) and solvent B (acetonitrile) at a flow rate of 0.3 mL/ minute at 40°C following a stepwise gradient: 98.0% A, 2.0% B; 0.50 min, 85.0% A, 15.0% B; 5.00 min, 40.0% A, 60.0% B; 7.00 min, 1.0% A, 99.0% B; 8.00 min, 1.0% A, 99.0% B; 8.01 min, 98.0% A, 2.0% B; 10.00 min, 98.0% A, 2.0% B.  **Leaf volatiles:** Leaf volatiles were collected immediately after the heat wave event over a period of three days from 118 plants. Sampling occurred between 11 AM and 2 PM during following dynamic headspace sampling procedure. We selected one vegetative stem per plant of similar biomass and enclosed them in a 35 x 35 cm nylon oven bags unsealed on one side. The bags were tied with twisters and volatiles were allowed to equilibrate for 30 minutes, after which, samples were pumped for 30 minutes through a scent trap using an air sampling pump set to a pre-trap flow rate of 350 mL/min. Stems were saved, dried, and weighted. Ambient controls (n=10) were taken from the area adjacent to the experimental plots where air was collected using an empty oven bag and sampled following the same methodology as the experimental samples.These ambient samples were used to identify contaminants or background compounds from surrounding vegetation.Leaf and ambient samples were analyzed using thermal desorption Gas Chromatography-Mass Spectrometry (GC-MS), together with one blank/unused scent traps to detect potential contaminants in the trap system.  2. Methods for processing the data: "CPB_survival.csv" No processing required. The dataset reports the raw count data from the pre- and post-heat wave surveys and adult count. This data was later processed for statistical analysis.  “leaf_glycoalkaloids.csv” We identified compounds in the Waters MassLynx software based on mass spectrometry, confirmed with digitoxin as internal standard, and quantified using the Waters Quanlynx MS software. Prior to statistical analysis data was normalized to internal standards and tissue sample mass.  “leaf_volatiles.csv" Peak deconvolution, integration, and tentative compound identification were performed in the Automated Mass Spectral Deconvolution and Identification System (AMDIS) using the 2020 NIST mass spectrallibrary. Data filtering was performed in the bouquet package. Peaks were included if they had mass spectral match scores greater than 80%, a maximum peak area of at least 20,000 counts, and if they were present in more than 20% of the samples. Additionally, we only included compounds with peak areas four times higher than the mean area of the ambient and blank controls. Caprolactam (compounds present in oven bags) and compounds with retention times above 15 minutes were excluded as contaminants. Stems used for volatile collection were saved, dried, and weighted. Volatile emissions were quantified based on peak values and were standardized by dry weight of the sampled stem.  ```{=html} ``` 1. Instrument- or software-specific information needed to interpret the data: Microsoft Excel or any other data processing software. 2. Describe any quality-assurance procedures performed on the data:\ For leaf volatiles, we ensured that all identified compounds had about the same retention times. Compounds with different retention times at the "second" digit, were identified as "unknowns". 3. People involved with sample collection, processing, analysis and/or submission: Michael Kalwajtys, Gabriel Veltri, Sarah Momimee, and Sophia Knightly were involved in the field data collection. Averie Hannah, Rachel Rantz, and Darla Knuth processed field data in the lab (i.e., measured and weighted adult CPB). Alma N. Carvajal Acosta analyzed the data and submitted the data sets. # I.- DATA-SPECIFIC INFORMATION FOR: “CPB_censuses.csv” 1. Number of variables: 7 2. Number of cases/rows: 89 3. Variable List: plant_ID (unique plant number) herb_tmt (indicates the herbivore treatment combination assigned to the plant) heatwave_tmt (indicates the heat wave treatment consisting of heat wave or ambient conditions assigned to the plant) larval_count1 (indicates the number of CPB found in the plant in the pre-heat wave census) larval_count2 (indicates the number of CPB found in the plant in the post-heat wave census) adult_count (indicates the number of adult CPB collected from the plant) aphid_count (number of aphids found initially in the plant, before the hebivore and the heat wave treatments were applied) 4. Missing data codes: No. 5. Specialized formats or other abbreviations used: None. # II. DATA-SPECIFIC INFORMATION FOR: "CPB_adult_fitness.csv" 1. Number of variables: 7 2. Number of cases/rows: 119 3. Variable List: plant_ID (unique plant number) herb_tmt (indicates the herbivore treatment combination assigned to the plant) heatwave_tmt (indicates the heat wave treatment consisting of heat wave or ambient conditions assigned to the plant) pronotum_length (measured length of pronotum in mm) mass (dry weight of adult CPB in mg) sex (sex of specimen either male or female) notes (any observation on the state of specimen) 4. Missing data codes: N/A 5. Specialized formats or other abbreviations used: N/A for "not available" unidentifiable data. # III. DATA-SPECIFIC INFORMATION FOR: “leaf_glycoalkaloids.csv" 1. Number of variables: 8 2. Number of cases/rows: 149 3. Variable List: plant_ID (unique plant number) herb_tmt (indicates the herbivore treatment combination assigned to the plant) heatwave_tmt (indicates the heat wave treatment consisting of heat wave or ambient conditions assigned to the plant) leaf_mass (weight of the leaf material in mg used to extract glycoalkaloids) solanine (concentrations of solanine detected in leaf material) through.... d_chaconine (concentrations of dehydrochanonine detected in leaf material) 4. Missing data codes: No 5. Specialized formats or other abbreviations used: dehydro prefix was abreviated as "d_" for dehydrochaconine and dehydrosolanine. # IV. DATA-SPECIFIC INFORMATION FOR: “leaf_volatiles.csv" 1. Number of variables: 61 2. Number of cases/rows: 118 3. Variable List: plant_ID (unique plant number) herb_tmt (indicates the herbivore treatment combination assigned to the plant) heatwave_tmt (indicates the heat wave treatment consisting of heat wave or ambient conditions assigned to the plant) collection_date (the date the sample was collected) alpha-Copaene (indicates emission rates of alpha-Copaene emitted from leaf samples in 1 hr, representing abundances) through.... Tricyclo[2.2.1.0(2,6)]heptane, 1,7-dimethyl-7-(4-methyl-3-pentenyl)-, (-)- (indicates emission rates of this compound emitted from leaf samples in 1 hr, representing abundances) 4. Missing data codes: No 5. Specialized formats or other abbreviations used: No