DOI: 10.5061/dryad.z8w9ghxsf
title: Data from: Intraspecific plant chemodiversity at plot level has contrasting effects on arthropod functional groups

description: Plant chemistry mediates interactions between plants and their
 environment. While intraspecific chemodiversity at the individual plant
 level is well-studied, the effects of chemodiversity at the plant
 community level on arthropod interactions need more attention. We
 conducted a field experiment to test how intraspecific chemodiversity
 affects plant-arthropod interactions. We manipulated plots of Tanacetum
 vulgare L., varying in chemotype richness and composition, and monitored
 four arthropod functional groups (herbivores, flower visitors, predators,
 and ants) over three seasons. We hypothesized that higher plot-level
 chemotype richness would enhance occurrence across all studied arthropod
 groups but have functional group-specific effects on abundance, resulting
 in reduced herbivore and ant abundance and increased flower visitor and
 predator abundance with increased chemotype richness. Using mixed models,
 we found that increasing plot-level chemotype richness had a limited
 effect on most arthropod group occurrences but led to significant changes
 in abundance. Increasing chemotype richness decreased herbivore abundance,
 while it increased flower visitor abundance. Predatory arthropods and ants
 remained largely unaffected. Furthermore, we found that the presence of
 specific plant chemotypes within a plot had variable effects for
 herbivores across years, as some chemotypes initially promoted higher
 abundances but were associated with lower abundances in the later years.
 We also found that the presence of specific chemotypes had a positive
 effect on the abundances of various flower visitor taxa. Predators and
 ants, on the other hand, showed weaker and more variable responses to
 specific chemotypes. Specifically, higher chemotype richness reduced the
 abundance of herbivores such as Uroleucon tanaceti (2022 and 2023) and
 Brachycaudus cardui (2023), while increasing visitation by flower
 visitors. These results highlight the role of plant chemodiversity in
 shaping insect communities and influencing ecosystem dynamics.
description: <strong>Data collection</strong>
 From 2021 to 2023, we assessed herbivores, flower visitors,
 predators, and ants. Each year, herbivore communities were monitored by
 counting individual aphids on the plants from their first appearance until
 nearly none were found. Although the focus was on aphids, other herbivores
 were scarce. Therefore, the generalization to herbivores here reflects
 predominantly aphids. Counts were conducted 13 times from June to August
 2021, eight times from May to August 2022, and three times from June to
 August 2023. Each count, executed within a day between 7 h and 19 h,
 involved a meticulous scan of each plant, which started from the tip of
 each stem and proceeded to the end, examining both sides of each leaf.
 When multiple aphid species were present on a plant, we recorded the total
 number of each. In cases of high aphid densities (&gt;100), we
 estimated the total by counting aphids on 1 cm of stem or 1
 cm<sup>2</sup> of leaf three times in different stems and
 leaves for each plant, then multiplying the average by the estimated
 lengths of stems (in cm) or the estimated areas of leaves (in
 cm<sup>2</sup>) colonized by the aphids. We used plot-level
 occupancy and cumulative plot-level number of aphids to analyze occurrence
 and abundance. Flower visitors were assessed by
 counting the flower heads of individual plants during the flower-visiting
 recording. Flower visitors were counted three times in July 2021 and once
 in August 2021. Surveys lasted three minutes per plot and were conducted
 randomly from 9h to 15h under optimal weather conditions (no gusts of wind
 or precipitation). Flower visitors were identified at the order level. We
 used presence-absence data to analyze the effects of CR and chemotype
 presence on occurrence. However, as CR and individual chemotypes influence
 reproductive traits in our system (Ojeda‐Prieto et al., 2024), flower
 visitor abundance per flower head (visitation rate) assessed effects on
 abundance. Predatory arthropods and ants on individual
 plants were recorded during herbivore counting for 2021, 2022, and the day
 after in 2023. Predators were classified into taxa/feeding guilds
 (Araneae, Coleoptera, Dermaptera, Diptera, Hemiptera, and Hymenoptera),
 specifying aphid parasitoids and parasitized aphids (counted as mummies
 found). Ant presence on plants, but not abundance, was noted. Predator and
 ant occurrences and parasitized aphid abundance were used for analyses for
 the three seasons. In 2023, predator and ant abundances were also
 analyzed. <strong>Statistical
 analyses</strong> Statistical analyses were
 conducted using R version 4.1.2 and RStudio 2023.09.1 (R Core Team, 2021).
 All statistics were performed at the plot level. The effects of chemotype
 presence and CR on the occurrence of herbivores, flower visitors,
 predators, and ants were assessed through binomial Generalized Linear
 Mixed-effect Models (GLMM) using the <em>‘lme4’</em> package
 (Bates et al., 2015). Linear Mixed-Effect Models (LMM) and Zero-Inflated
 Negative Binomial Models (ZINB), using the
 <em>'glmmTMB'</em> package (Brooks et al., 2017),
 modeled the effect on abundances. P-values were estimated by type II
 Wald-Chi-Squared tests using the Anova() function
 (‘<em>car</em>’ package; Fox & Weisberg, 2019). Data
 visualization used academic-licensed Biorender® and
 <em>'ggplot2’</em> (Wickham, 2009),
 <em>‘grid’</em> (R Core Team, 2021),
 <em>‘gridExtra’</em> (Auguie et al., 2017),
 <em>‘ggpubr’</em> (Kassambara, 2020), and
 <em>’DHARMa’</em> (Hartig, 2022) packages.
 <em>Effects of chemotype richness and chemotype presence on
 arthropod taxa occurrence and abundance</em>
 Binomial GLMMs assessed the effect of CR and chemotype presence
 on occurrence. LMMs and ZINB tested the effects on abundance. Data were
 analyzed at the arthropod group and lower taxonomic levels (when
 indicated). Data were analyzed cumulatively for each year and for each
 independent sampling time.
 <strong>References</strong>
 Auguie, B., Antonov, A., & Auguie, M. B. (2017).
 <em>gridExtra: Miscellaneous functions for “grid”
 graphics</em>. Bates, D., Maechler, M., Bolker,
 B., & Walker, S. (2015). Fitting linear mixed-effects models using
 lme4. <em>Journal of Statistical Software</em>,
 <em>67</em>(1), 1–48. Brooks, M. E.,
 Kristensen, K., Van Benthem, J., Magnusson, A., Berg, C. W., Nielsen, A.,
 Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). glmmTMB
 balances speed and flexibility among packages for zero-inflated
 generalized linear mixed modeling. <em>The R Journal</em>,
 <em>9</em>(2), 378–400. Fox, J., &
 Weisberg, S. (2019). <em>An {R} companion to applied
 regression</em> (Third Edition). Thousand Oaks CA: Sage.
 Hartig, F. (2022). <em>DHARMa: Residual diagnostics for
 hierarchical (multi-level / mixed) regression model</em>. https://cran.r-hub.io/web/packages/DHARMa/DHARMa.pdf Kassambara, A. (2020). <em>ggpubr: “ggplot2” based publication ready plots</em>. Ojeda-Prieto, L., Medina-van Berkum, P., Unsicker, S. B., Heinen, R., & Weisser, W. W. (2025). Intraspecific chemical variation of Tanacetum vulgare affects plant growth and reproductive traits in field plant communities. <em>Plant biology (Stuttgart, Germany)</em>, <em>27</em>(5), 785–801. https://doi.org/10.1111/plb.13646 R Core Team. (2021). <em>R: A language and environment for statistical computing</em>. R Foundation for Statistical Computing. Wickham, H. (2009). <em>ggplot2: Elegant graphics for data analysis</em>. Springer New York. https://doi.org/10.1007/978-0-387-98141-3
description: # README for: Increasing plant chemical diversity at plot level has
 contrasting effects on arthropod functional groups This README.txt file
 was generated on 20240613 by Lina Ojeda-Prieto ## GENERAL INFORMATION Raw
 data used for the following paper: Ojeda-Prieto et al. (2025). *Increasing
 plant chemical diversity at plot level has contrasting effects on
 arthropod functional groups* **Author Information** **Investigator Contact
 Information**\ Name: Lina Ojeda-Prieto\ Institution: Technical University
 of Munich\ Address: Hans-Carl-von-Carlowitz Platz 2. Freising, Germany\
 Email: [lina.ojeda@tum.de](mailto:lina.ojeda@tum.de);
 [lina.ojedaprieto@gmail.com](mailto:lina.ojedaprieto@gmail.com) Date of
 data collection: 2021, 2022 ,and 2023\ Geographic location of data
 collection: Jena, Germany\ Data collected by Lina Ojeda-Prieto and Elicer
 L. Moreno --- ## DATA-SPECIFIC INFORMATION FOR: ### 1._Herbivores_2021.csv
 A. Number of variables: 28\ B. Number of cases/rows: 6552 C. Variable List
 1. Plant – Assigned plant number (1 to 504) 2. Block – Block number (1 to
 6) 3. Plot – Assigned plot number (1 to 84) 4. Pos – Position of the plant
 within each plot (1 to 6) 5. Level – Plot-level chemotype richness (1, 2,
 3, 6) 6. Chemotype – Plant chemotype ID (6 different chemotypes) 7.
 Daughter – Plant daughter ID (18 different daughters) 8. Date – Date of
 survey in 2021 9. MF – *M. fuscoviride* absence/presence (0/1) 10. MF_W –
 Number of winged *M. fuscoviride* 11. MF_U – Number of unwinged *M.
 fuscoviride* 12. MF_T – Total number of *M. fuscoviride* 13. MT – *M.
 tanacetaria* absence/presence (0/1) 14. MT_W – Number of winged *M.
 tanacetaria* 15. MT_U – Number of unwinged *M. tanacetaria* 16. MT_T –
 Total number of *M. tanacetaria* 17. UT – *U. tanaceti* absence/presence
 (0/1) 18. UT_W – Number of winged *U. tanaceti* 19. UT_U – Number of
 unwinged *U. tanaceti* 20. UT_T – Total number of *U. tanaceti* 21. AF –
 *A. fabae* absence/presence (0/1) 22. AF_W – Number of winged *A. fabae*
 23. AF_U – Number of unwinged *A. fabae* 24. AF_T – Total number of *A.
 fabae* 25. BC – *B. cardui* absence/presence (0/1) 26. BC_W – Number of
 winged *B. cardui* 27. BC_U – Number of unwinged *B. cardui* 28. BC_T –
 Total number of *B. cardui* --- ### 2._Herbivores_2022.csv A. Number of
 variables: 28\ B. Number of cases/rows: 4366 C. Variable List 1. Plant –
 Assigned plant number (1 to 504) 2. Block – Block number (1 to 6) 3. Plot
 – Assigned plot number (1 to 84) 4. Pos – Position of the plant within
 each plot (1 to 6) 5. Level – Plot-level chemotype richness (1, 2, 3, 6)
 6. Chemotype – Plant chemotype ID (6 different chemotypes) 7. Daughter –
 Plant daughter ID (18 different daughters) 8. Date – Date of survey in
 2022 9. MF – *M. fuscoviride* absence/presence (0/1) 10. MF_W – Number of
 winged *M. fuscoviride* 11. MF_U – Number of unwinged *M. fuscoviride* 12.
 MF_T – Total number of *M. fuscoviride* 13. MT – *M. tanacetaria*
 absence/presence (0/1) 14. MT_W – Number of winged *M. tanacetaria* 15.
 MT_U – Number of unwinged *M. tanacetaria* 16. MT_T – Total number of *M.
 tanacetaria* 17. UT – *U. tanaceti* absence/presence (0/1) 18. UT_W –
 Number of winged *U. tanaceti* 19. UT_U – Number of unwinged *U. tanaceti*
 20. UT_T – Total number of *U. tanaceti* 21. AF – *A. fabae*
 absence/presence (0/1) 22. AF_W – Number of winged *A. fabae* 23. AF_U –
 Number of unwinged *A. fabae* 24. AF_T – Total number of *A. fabae* 25. BC
 – *B. cardui* absence/presence (0/1) 26. BC_W – Number of winged *B.
 cardui* 27. BC_U – Number of unwinged *B. cardui* 28. BC_T – Total number
 of *B. cardui* --- ### 3._Herbivores_2023.csv A. Number of variables: 28\
 B. Number of cases/rows: 1512 C. Variable List 1. Plant – Assigned plant
 number (1 to 504) 2. Block – Block number (1 to 6) 3. Plot – Assigned plot
 number (1 to 84) 4. Pos – Position of the plant within each plot (1 to 6)
 5. Level – Plot-level chemotype richness (1, 2, 3, 6) 6. Chemotype – Plant
 chemotype ID (6 different chemotypes) 7. Daughter – Plant daughter ID (18
 different daughters) 8. Date – Date of survey in 2023 9. MF – *M.
 fuscoviride* absence/presence (0/1) 10. MF_W – Number of winged *M.
 fuscoviride* 11. MF_U – Number of unwinged *M. fuscoviride* 12. MF_T –
 Total number of *M. fuscoviride* 13. MT – *M. tanacetaria*
 absence/presence (0/1) 14. MT_W – Number of winged *M. tanacetaria* 15.
 MT_U – Number of unwinged *M. tanacetaria* 16. MT_T – Total number of *M.
 tanacetaria* 17. UT – *U. tanaceti* absence/presence (0/1) 18. UT_W –
 Number of winged *U. tanaceti* 19. UT_U – Number of unwinged *U. tanaceti*
 20. UT_T – Total number of *U. tanaceti* 21. AF – *A. fabae*
 absence/presence (0/1) 22. AF_W – Number of winged *A. fabae* 23. AF_U –
 Number of unwinged *A. fabae* 24. AF_T – Total number of *A. fabae* 25. BC
 – *B. cardui* absence/presence (0/1) 26. BC_W – Number of winged *B.
 cardui* 27. BC_U – Number of unwinged *B. cardui* 28. BC_T – Total number
 of *B. cardui* --- ### 4._PA_chemotypes.csv A. Number of variables: 4\ B.
 Number of cases/rows: 504 C. Variable List 1. Plant – Assigned plant
 number (1 to 504) 2. Chemotype – Plant chemotype ID (6 different
 chemotypes) 3. Daughter – Plant daughter ID (18 different daughters) 4.
 Block – Block number (1 to 6) --- ### 5._Flower_visitors_2021.csv A.
 Number of variables: 5\ B. Number of cases/rows: 251 C. Variable List 1.
 Block – Block number (1 to 6) 2. Plot – Assigned plot number (1 to 84) 3.
 Level – Plot-level chemotype richness (1, 2, 3, 6) 4. Date – Date of
 survey in 2021 5. Abundance – Number of flower visitors observed --- ###
 6._Predators_2021.csv A. Number of variables: 6\ B. Number of cases/rows:
 6552 C. Variable List 1. Plant – Assigned plant number (1 to 504) 2. Block
 – Block number (1 to 6) 3. Plot – Assigned plot number (1 to 84) 4. Pos –
 Position of the plant within each plot (1 to 6) 5. Date – Date of survey
 in 2021 6. Predators – Number of predators observed --- ###
 7._Predators_2022.csv A. Number of variables: 6\ B. Number of cases/rows:
 4366 C. Variable List 1. Plant – Assigned plant number (1 to 504) 2. Block
 – Block number (1 to 6) 3. Plot – Assigned plot number (1 to 84) 4. Pos –
 Position of the plant within each plot (1 to 6) 5. Date – Date of survey
 in 2022 6. Predators – Number of predators observed --- ###
 8._Predators_2023.csv A. Number of variables: 6\ B. Number of cases/rows:
 1512 C. Variable List 1. Plant – Assigned plant number (1 to 504) 2. Block
 – Block number (1 to 6) 3. Plot – Assigned plot number (1 to 84) 4. Pos –
 Position of the plant within each plot (1 to 6) 5. Date – Date of survey
 in 2023 6. Predators – Number of predators observed --- ## Notes on
 Missing Data Cells containing `NA` indicate that data were **not
 available**. Missing values occur for two main reasons: 1. **Weather
 interruptions**: Insects are highly sensitive to weather. On some sampling
 dates, data collection was interrupted (e.g., due to strong rain). In such
 cases, incomplete block counts were excluded, and only data from completed
 blocks were used. 2. **Plant availability**: Not all plants were present
 or had regrown at the same rate, particularly early in the season after
 cutting from the previous year. If a plant was not available for sampling
 on a given date, its values are recorded as `NA`. Thus, `NA` in the
 dataset means “data not available due to weather or plant absence,” and
 not “not applicable.”