DOI: 10.5061/dryad.bcc2fqzjc
title: Plant water limitation and its impact on the oviposition preferences of the monarch butterfly, Danaus plexippus (Lepidoptera: Nymphalidae)

description: Intensifying drought conditions across the western United States due to
 global climate change are altering plant-insect interactions. Specialist
 herbivores must find their host plants within a matrix of nonhosts, and
 thus often rely upon specific plant secondary chemistry for host location
 and oviposition cues. Climate-induced alterations to plant chemistry could
 thus affect female selection of larval food-plants. Here, we investigated
 whether host-plant water limitation influenced oviposition preference in a
 threatened invertebrate: the monarch butterfly (Danaus plexippus). We
 found that females deposited more eggs on reduced-water than on
 well-watered narrowleaf milkweed plants (Asclepias fascicularis), but we
 could not attribute this change to any specific change in plant chemistry.
 Specialist herbivores, such as the monarch butterfly, which are tightly
 linked to specific plant cues, may experience a shift in preferences under
 global-change conditions. Understanding oviposition preferences will be
 important to directing ongoing habitat restoration activities for this
 declining insect.
description: Seeds of <em>A. fascicularis</em> were collected from
 Reno, NV (39.49361, -119.85459) in 2018 and 2019 and germinated in May
 2020. Plants were grown in 164 mL treepots with 50% peat moss: 34%
 vermiculite: 16% perlite. To manipulate water availability, soil
 saturation was maintained at 70% field capacity in control (well-watered)
 plants and at 30% in reduced-water plants for 1–3 weeks using a
 gravitational dry-down method following Diethelm et al. (2022). The
 variation in dry-down time is due to initially high dieback in the
 reduced-water group, which led us to add more plants to that treatment
 group, with the treatment maintained for 1 week. The 70% control level
 reflects what plants typically experience at agricultural field edges,
 whereas 30% represents a dry treatment that does not induce wilting
 (Diethelm et al. 2022). We avoided wilting because females may
 discriminate against wilted plants, independent of plant chemistry (Aikins
 et al. 2023). To allow female monarchs to select a
 mate, each female was initially kept in a mesh caging (40 cm x 40 cm x 61
 cm) with three male butterflies and two other females. Male-female pairs
 that were observed mating were moved, still linked, to separate cages, and
 mated females were isolated the following day. Once males were observed to
 mate twice, they were removed from the study. Each oviposition preference
 trial presented a single, mated female butterfly (n = 15) with a choice
 between one control and one reduced-water plant box within a flight cage.
 In an attempt to isolate the effects of plant chemistry and account for
 potential oviposition bias toward larger plants (Cohen and Brower 1982),
 we selected experimental plants of similar size and presented only the top
 10 cm of the stem to the butterflies. Each oviposition trial occurred in a
 20 m<sup>3</sup> flight cage for 3 h between 9:30 and 18:30.
 To allow females to eat ad libitum, a cotton pad of 1:1 ratio of red
 Gatorade®:deionized water was placed at the middle of the flight cage.
 After each trial, the exposed biomass of milkweeds per treatment was
 clipped, the number of monarch eggs per treatment was recorded before eggs
 were removed, and the plant section was weighed. All but two of the
 females were used twice, with trials &gt; 6 d apart. At the end of
 each 3-hour trial, the exposed sections of the plants were transferred to
 a -80 ℃ freezer for storage until chemical analysis. To
 investigate how our water treatments affected plant secondary metabolites,
 we performed a non-targeted analysis of UV-absorbent metabolites following
 Diethelm et al. (2022). We estimated the concentration of each metabolite
 using ultrahigh-performance liquid chromatography (UPLC; Waters
 Corporation, Milford, MA). <strong>Methods for
 processing the data:</strong> To calculate the concentrations of
 plant secondary compounds in digitoxin equivalents, we used a digitoxin
 internal standard (Sigma Chemical Company, St. Louis, MO) and corrected
 peak areas by sample dry mass and the 0.15mg/ml concentration of the
 digitoxin standard. We also calculated metabolite diversity, using the
 exponential term of the Shannon index (q = 1; Chao et al.
 2014).  For female choice response variables, we used
 generalized linear mixed models (GLMMs) with Gaussian or negative binomial
 distributions in R version 3.6.1.  For secondary chemistry response
 variables, we modeled the data with linear regressions. We report beta
 coefficients (β) with standard errors as effect sizes (Bischl et al.
 2017). Marginal and conditional R2 values were calculated in the MuMIn R
 package (Barton 2019). To establish the predictors of
 the number of monarch eggs, we started with a saturated model treating
 water and the concentration of flavonol glycosides as fixed effects and
 the number of leaves, female age, time since female mated, and female
 identity as covariates. Initially, our global model had two predictors and
 four co-variates. The co-variates were used based on other research that
 suggested they could be important. However, to prevent over-fitting, we
 used backward model selection with the adjusted Akaike Information
 Criterion and likelihood ratio tests. We selected this method because it
 penalizes models with more parameters. We were able to eliminate all of
 the co-variates based on this method. We included both
 flavonol glycosides and water treatment as fixed effects in the model
 because preliminary tests suggested that the water treatment did not
 affect chemistry (t = -0.5, df = 50, P = 0.6). To account for repeated
 trials of a given female, we included female identity as a random
 intercept effect. To control for non-independence between plants within a
 single trial, the trial number was also as a random intercept effect,
 nested within female identity. We then used backward selection (Zuur et
 al. 2009) in MuMIn, and the best-fit model was selected based on the
 Akaike Information Criterion (AICc). Marginal predictors were evaluated
 using log-likelihood ratio tests (LRT) in lmtest (Zeileis and Hothorn
 2002). To evaluate the predictors of plant secondary metabolites,
 including the total concentration of UV-absorbent metabolites, the
 concentration of flavonol glycosides, and the exponential of
 Shannon's entropy index (q = 1) for metabolite diversity, we modeled
 water as a fixed effect and the duration of dry-down as a covariate. To
 measure the strength of the relationship between total concentration and
 the concentration of flavonol glycosides, we calculated a Pearson’s
 correlation value. <strong>Standards and
 calibration information</strong>: We used a digitoxin internal
 standard (Sigma Chemical Company, St. Louis, MO).
 <strong>Environmental/experimental
 conditions</strong>: We used a flight cage inside a greenhouse under
 controlled temperatures but with natural lighting.
 <strong>Quality-assurance procedures performed on the
 data</strong>: We ensured data accuracy by verifying values and
 identifying any missing or erroneous entries.
description: Instrument- or software-specific information needed to interpret
 the data: <br>-R Core Team (2023). R: A language and environment for
 statistical computing. R Foundation for Statistical Computing, Vienna,
 Austria. URL https://www.R-project.org/.<br>-RStudio (2023) URL
 http://www.rstudio.com/<br>-Rstudio packages: tidyvers, ggplot,
 ggforce (for jitter plot), Hmisc, Rmisc (for SE summary), lmtest (for
 model comparisions), glmmTMB (for mixed models), MuMin, DHARMa, BBmisc,
 hillR, ggpubr