Aedes egg laying behavior Erika Mudrak, CSCU November 7, 2018

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1 Aedes egg laying behavior Erika Mudrak, CSCU November 7, 2018 Introduction The current study investivates whether the mosquito species Aedes albopictus preferentially lays it s eggs in water in containers that already have mosquito eggs, and if so, does the amount of eggs or sepcies of eggs matter. The study took place at three houses, where 6 mosquito traps were deployed each week. Each of the 6 mosquito traps had a different treatment: - Control just fish food (2 traps) - 20 Ae. aegypti larvae + fish food - 20 Ae. albopictus larvae + fish food Ae. aegypti larvae + fish food Ae. albopictus larvae + fish food At the begining of the week, blood-fed, gravid mosquitos that were marked with day-glo dust were released into the house. Resarchers returned for the following 4 days too record the number of marked mosquitos in each of the 6 traps. Hypothesis: Ae. albopictus prefers to lay eggs in containers with Ae. aegypti larvae because it is proof of high-quality habitat, and this increases competition between the two species. Data The data consist of 312 observations of 10 variables. Each row of data represents a trap at a given day. For each row of data, we are provided with the release number (week), the date of collection, the date of release, the time of release, the House, the trapid, the number of larvae in the trap, the species of larvae in the trap, the age of the trap larvae, and how many marked mosquitos were found. Research Question The research question is whether the number of mosquitos layed in each trap type varies. If so, is mosquito species more important or is the number of eggs more important? 1

2 Descriptive Statistics and Data manipulation 0.4 Marked A. albopictus recaptured by trap larval species and density Marked albopictus recaptured Species.of.trap.larvae aegypti albopictus none Number of larvae in traps The predictors of interest are Number.of.trap.larvae and Species.of.trap.larvae. aegypti albopictus none Note these variables are not fully crossed. There are in fact 5 unique treatments, but they are not wellexplained bynumber.of.trap.larvae and Species.of.trap.larvae. There is no way to explain these treatments in a fully crossed way using two variables. I added a new treatment variable which defines the 5 unique treatments in one variable. 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Note that the 0_none treatment has twice as many observations because two traps at each house had that treatment. How does this treatment variable distribute among houses and release numbers? 6/11/2018 6/15/2018 7/15/2018 7/2/2018 7/9/

3 _none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Lucas Bueno Lucas Malo Mia _none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Lucas Bueno Lucas Malo Mia Release number is synonymous with release date. We will use Release number because it is shorter and sorts correctly as entered. House Mia had an extra release date (3) which neither of the other houses had. This causes some imbalance in sample size, but there is sufficient sampling done for all houses. Consider the response variable Histogram of mark$marked.albo.fem Frequency mark$marked.albo.fem

4 The response variable is highly zero-inflated. This could cause modeling issues. As per discussion on XXX date, it is not of interest to keep track of the counts on specific days following a release. We will aggregate the data by summing the counts on all four days following a release. This results 78 observations, where each observation is all the mosquitos collected for a release date at a given trap at a given house. By summing the counts, we reduce the number of zeros in the data. Histogram of markagg$marked.albo.fem Frequency markagg$marked.albo.fem With the aggregated data, we have a single observation for a given trap, house and release.number,, House = Lucas Bueno Release.number Treatment _none _aegypti _albopictus _aegypti _albopictus ,, House = Lucas Malo Release.number Treatment _none _aegypti _albopictus _aegypti _albopictus ,, House = Mia Release.number Treatment

5 0_none _aegypti _albopictus _aegypti _albopictus The below plot shows how number of recaptured mosquitos varies by all important factors- Treatment, House and Release Date. marked albopictus recaptured by trap larval species and density Marked albopictus recaptured Lucas Bueno Lucas Malo Mia 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Treatment 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Model The main predictor of interest is Treatment. To address non-independence of the observations, we should account for House, Release.number and trapid. Since House only has three levels, we can add it in as a block (fixed) effect. Release.number and trapid have 6 levels each, we can include them as crossed random effects. We also need to nest trapid within house because the traps were numbered within a house, and trap 1 at Mia doesn t relate to trap 1 at Lucas Bueno. We also need to include a nested effect of house nested within Release.number to group the 6 traps that were measured together for a given release date at a given house. Since the response is a count, we will use a Poisson distribution for a generalized linear mixed model. Possible language for a manuscript: I fit a generalized linear mixed model with a Poisson distribution and a log link, using the R Statistical Software (R Core Team 2018) and the lme4 package (Bates et. al 2015). Fixed effects were Treatment and House, and random effects were Release.number and trap nested within house. Post-hoc analyses were conducted with the package emmeans (Lenth 2018). 5

6 R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), doi: / Russell Lenth (2018). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version We need to check this model for overdispersion. We used the function provided here: io/mixedmodels-misc/glmmfaq.html There did not appear to be strong overdispersion, so we can continue to intepret model. chisq ratio rdf p Interpretation Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmermod] Family: poisson ( log ) Formula: Marked.albo.fem ~ House + Treatment + (1 Release.number) + (1 Release.number:House) + (1 House:trapID) Data: markagg AIC BIC loglik deviance df.resid Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.Dev. House:trapID (Intercept) 0 0 Release.number:House (Intercept) 0 0 Release.number (Intercept) 0 0 Number of obs: 78, groups: House:trapID, 18; Release.number:House, 13; Release.number, 5 Fixed effects: Estimate Std. Error z value Pr(> z ) (Intercept) e-05 *** HouseLucas Malo * HouseMia Treatment20_aegypti *** Treatment20_albopictus e-05 *** Treatment100_aegypti Treatment100_albopictus *** --- Signif. codes: 0 '***' 01 '**' 1 '*' 5 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) HsLcsM HouseM Trtmnt20_g Trtmnt20_l Trtmnt100_g 6

7 HouseLucsMl HouseMia Trtmnt20_gy Trtmnt20_lb Trtmnt100_g Trtmnt100_l We test the significance of the predictors using likelihood ratio tests: Single term deletions Model: Marked.albo.fem ~ House + Treatment + (1 Release.number) + (1 Release.number:House) + (1 House:trapID) Df AIC LRT Pr(Chi) <none> House * Treatment e-05 *** --- Signif. codes: 0 '***' 01 '**' 1 '*' 5 '.' 0.1 ' ' 1 There was a significant effect of Treatment on the number of mosquistos recaptured. There is also a significant effect of House, but since this is a variable that we are only controlling for, we will not interpret this any further. We continue with post-hoc analyses of which Treatments are significantly different from each other. Treatment rate SE df asymp.lcl asymp.ucl 0_none Inf _aegypti Inf _albopictus Inf _aegypti Inf _albopictus Inf Results are averaged over the levels of: House Confidence level used: 0.95 Intervals are back-transformed from the log scale The above table shows the expected (average) number of mosquitos collected for each treatment type, averaged over House, and accounting for slight Release date and trap differences. These values are shown in the below graph. The error bars shown are the asymptotic upper and lower confidence levels. They are not symmetric around the estimate due to the log-transformation used in the model. 7

8 rate _none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Treatment The below table shows the pairwise comparisons among these five groups. P-values have been adjusted with a Tukey correction for a family of 5 estimates. contrast ratio SE df z.ratio p.value 0_none / 20_aegypti Inf _none / 20_albopictus Inf _none / 100_aegypti Inf _none / 100_albopictus Inf _aegypti / 20_albopictus Inf _aegypti / 100_aegypti Inf _aegypti / 100_albopictus Inf _albopictus / 100_aegypti Inf _albopictus / 100_albopictus Inf _aegypti / 100_albopictus Inf Results are averaged over the levels of: House P value adjustment: tukey method for comparing a family of 5 estimates Tests are performed on the log scale These pairwise comparisons are summarized as a compact letter display in the below table. Groups that do not have the same letter label are significantly different at the alpha = 5 level. Here we see that though traps with 100 aegypti larvae do not have significantly more mosquitos captured than the controls, all three of the other treatments captured significantly more mosquitos than control. Treatment rate SE df asymp.lcl asymp.ucl.group 0_none Inf A 100_aegypti Inf AB 100_albopictus Inf B 8

9 20_aegypti Inf B 20_albopictus Inf B Results are averaged over the levels of: House Confidence level used: 0.95 Intervals are back-transformed from the log scale P value adjustment: tukey method for comparing a family of 5 estimates Tests are performed on the log scale significance level used: alpha = 5 We could also make specific contrasts, aimed at determining if there are differences by species or egg number. contrast ratio SE df z.ratio p.value aegypti.v.none Inf albopictus.v.none Inf <.0001 aegypti.v.albopictus Inf twenty.v Inf <.0001 hundred.v Inf twenty.v Inf Results are averaged over the levels of: House Tests are performed on the log scale Conclusion There was a significant effect of the 5-level treatment variable on the mean number of mosquitos recaptured (P < 001). Traps with 100 A. aegypti larvae do not have significantly more mosquitos captured than the control traps (P = ), all three of the other treatments captured significantly more mosquitos than control (P < 05 for all). There were no differences in the number of mosquitos captured among these three treatments, however. 9

10 B 1.5 B B rate 1.0 AB 0.5 A 0_none 20_aegypti 20_albopictus 100_aegypti 100_albopictus Treatment 10