DOI: 10.5061/dryad.ttdz08m4k
title: Plant secondary metabolite increases the control-effectiveness of natural enemy - based on caffeine and Snellenius manilae

description: The food resources in the field can effectively strengthen the ability of
 natural enemies to control the pest. Certain compounds, in addition to
 carbohydrates and amino acids, may improve the physiological performance
 of insects. Caffeine, for instance, has been shown to enhance pollinator
 memory and physiological reactions. However, little is known about how
 caffeine influences parasitoids. The control effectiveness and survival
 rate of the parasitoid (Snellenius manilae) were tested in this study
 after the parasitoids were fed solutions with different concentrations of
 caffeine. We examined caffeine concentrations of 10-2, 10-4, and
 10-6 (M) mixed with a 25% sucrose solution and a pure sucrose
 solution as a control group. The results show that a concentration of
 10-6 caffeine solution significantly increased the parasitism
 rate of S. manilae by 10.76% when compared to the
 control group. Despite the significantly lower survival rate and male bias
 of S. manilae offspring in the 10-2 treatment,
 no further negative responses in growth performance, development time, or
 cocoon weight were observed. These findings suggest that an appropriate
 concentration of caffeine solution can have a positive impact on the
 control effectiveness of parasitoids in the laboratory. Our results
 highlight the potential of secondary compounds to increase the bio-control
 effectiveness.
description: # Plant Secondary Metabolite Increases the Control-Effectiveness of
 Natural Enemy - Based on Caffeine and *Snellenius manilae*
 [https://doi.org/10.5061/dryad.ttdz08m4k](https://doi.org/10.5061/dryad.ttdz08m4k) The experiment was conducted at the Insect-Plant Interaction Laboratory of the Entomology Department at National Chung Hsing University in Taichung, Taiwan. In the first part, the experiment involved feeding *S. manilae* with different concentrations of caffeine solution and parasitizing late 1st instar *S. litura* larvae for five consecutive days. The experiment was conducted between 8:00 a.m. and 5:00 p.m. In the second part, the longevity of *S. manilae* was tested across genders with four treatments. All the experiments were conducted under controlled conditions (26 ± 2°C, 14:10 hr L:D, 60% RH). To avoid any potential influence on the next generation, all the offspring of tested parasitoids were not used for further experiment or reproduction. ## Description of the data and file structure * Both of the csv files can directly import into R program. * Both of the files can be separated by treatment. In the treatment column, t2 represented the high dose treatment; t4 represented moderate dose and t6 represent the low dose treatment. * The file entitled "caffeine data parasitism rate" contained detailed information about the 10 rounds experiment. The detailed information about the column: column D (number of parasitized *S. litura* larvae), E (number of parasitized *S. litura* larvae, but the cocoon was incompletely), F (number of *S. litura* larvae died during development stage), G (number of *S. litura* larvae disappeared during development stage), H (number of *S. litura* larvae failed to found after the parasitized process), I (the missing data), J (number of *S. litura* larvae not been parasitized), K (number of total left data, (30 - column F, G, H)), L (number of male offspring), M (number of female offspring), N (number of parasitoids eclosion successfully), O (parasitism rate), P (failed cocoon rate), Q (eclosion rate). The parasitism rate were calculated by (column D/ column K); cocoon rate was calculated by (1-(column E/ (column D + column E))); eclosion rate was calculated by (column N/column D); gender ratio was calculated by (column L/(column L + column M)). * The file entitled "caffeine data 1 to 10 V2" contained the detailed information about the individual result of *S. litura* larvae. The letters in column E represented the status of the *S. litura* larvae, s (number of parasitized *S. litura* larvae), hs (number of parasitized *S. litura* larvae, but the cocoon was incompletely), g (number of *S. litura* larvae died during development stage), d (number of *S. litura* larvae disappeared during development stage), unf (number of *S. litura* larvae failed to found after the parasitized process), m (the missing data), x (number of *S. litura* larvae not been parasitized). Column F represented cocoon weight of *S. manilae*; column G (duration day of larval stage); column H (duration day of pupal stage); I (gender of *S. manilae* ). The "NA" cells found in the column G, H, I means the larva of *S. litura* were not growing successfully into the next stage, that is the reason why the NA cells occurred. * Blank cells correspond to missing values. ## Code/Software Statistical analyses were conducted using R version 4.3.0 (R Core Team, 2023). The values of parasitism rate (%), pupation rate (%), eclosion rate (%), duration of the larval stage (day), pupal stage (day), and cocoon weight (mg) utilized the generalized linear model (GLM) with a quasibinomial distribution and error link logit (function “glm”) with a Tukey post-hoc test at a significance level of *p*&lt;0.05 to assess differences between treatments. Sex ratio between the four treatments, Pearson's chi-squared test (function “chisq.test”) was conducted at a significance level of *p*&lt;0.05. Additionally, longevity between caffeine concentrations and gender, the longevity of adult parasitoid was analyzed using Kaplan-Meier at a significance level of *p*&lt;0.05 (function “survfit”). code of glm:   fit1 &lt;- glm(column header of rate\~treatment, data=caffeine\_parasitism,  family = quasibinomial(link = "logit")) summary(fit1) library(multcomp) fit1.t &lt;- glht(fit1, linfct = mcp(treatment = "Tukey")) summary(fit1.t)   code of pearson's chi square:   plot\_gender&lt;-ggbarstats(data = file,  x=gender,  y=treatment,  label = "both",  centrality.label.args=list(size=80)) plot\_gender.f&lt;-plot\_gender+  My\_Theme plot\_gender.f   sig\_level\_table&lt;-table(gender\_select$treatment,gender\_select$gender) sig\_level\_table pairwise\_prop\_test(sig\_level\_table)   code of Kaplan-Meier test:   male\_survival\_curve&lt;- survfit(Surv(male, status) \~ concentration, data =male\_survival)   \<br> \<br>   <br>