Table of parameter abreviations and file names _______________________________ Oddie-VSH-infest-2017.txt _______________________________ 'fecund' - The average number of viable female offspring found in the rpesence of a male as defined by Locke et al 2012. 'trt.col' -treatment colony: The population to which the colony belongs (surviving = R, susceptible = S) 'avg.brood.stage' - The average stage of the brood dissected and included in the analysis. Brood ages are sufficiently variable in this dataset and are known to affect viable female offspring estimates. 'origin.col' - The donor colony ID of the frames provided to both groups (R and S). 'infested' & 'uninfested' - Those cells that were found either infested with mites or uninfested. 'prop.VSH' - The proportion of brood cells that were cleaned out (brood removed) within the 10 day introduction period inside reciever colonies. 'brd.rem' & brd.n.rem' - The number of cells removed by bees or not removed by bees within the sample observed after the 10 day introductory period inside reciever colonies. _______________________________ Oddie-mite-count-2017.txt _______________________________ 'no.days' - The number of days the mite bottom boards were left under the colonies 'date' - Date of collection 'col.typ' - treatment colony: The population to which the colony belongs (surviving = R, susceptible = S) 'colony' - Colony ID number 'mite.count' - total number of mites counted on bottom boards 'daily.drop' - The average number of fallen mites per day _______________________________ Oddie-mite-damage-2017.txt _______________________________ 'apiary' -The apiary in which the colony resided 'col.typ' - treatment colony: The population to which the colony belongs (surviving = R, susceptible = S) 'colony' - Colony ID number 'damaged'& 'undamaged' -The number of mites found to be damaged or not within the samples taken per colony 'total.examined' - The total number of mites taken for the damage sample per colony 'damaged prop' -The proportion of damaged mites in each sample ___________________________________________________________________________ main<-read.table("Oddie-VSH-infest-2017.txt",header=T) attach(main) summary(main) names(main) ____________________________________________________________________________ Parameters affecting mite fecundity: d1<-lmer(fecund~trt.col+avg.brood.stage+(1|origin.col)) summary(d1) d2<-update(d1,~.-avg.brood.stage) anova(d1,d2) _____________________________________________________________________________ Models: d2: fecund ~ trt.col + (1 | origin.col) d1: fecund ~ trt.col + avg.brood.stage + (1 | origin.col) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) d2 4 20.845 24.623 -6.4226 12.845 d1 5 22.468 27.191 -6.2342 12.468 0.3769 1 0.5392 _____________________________________________________________________________ d3<-update(d2,~.-trt.col) anova(d2,d3,test="F") _____________________________________________________________________________ d3: fecund ~ (1 | origin.col) d2: fecund ~ trt.col + (1 | origin.col) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) d3 3 23.748 26.582 -8.8741 17.748 d2 4 20.845 24.623 -6.4226 12.845 4.903 1 0.02681 * _____________________________________________________________________________ summary(d2) dispersion_glmer(d2) [1] 0.3392883 _____________________________________________________________________________ Varroa sensitive hygiene analysis _____________________________________________________________________________ tbl=matrix(c(41,859,37,1063),ncol=2,nrow=2) colnames(tbl) <- c("Surviving","Susceptible") rownames(tbl) <- c("removed","not removed") tbl chisq.test(tbl) _____________________________________________________________________________ Pearson's Chi-squared test with Yates' continuity correction data: tbl X-squared = 1.5718, df = 1, p-value = 0.2099 _____________________________________________________________________________ Infested cell proportion analysis _____________________________________________________________________________ tbl=matrix(c(423,1530,412,1900),ncol=2,nrow=2) colnames(tbl) <- c("Surviving","Susceptible") rownames(tbl) <- c("infested","not infested") tbl chisq.test(tbl) _____________________________________________________________________________ Pearson's Chi-squared test with Yates' continuity correction data: tbl X-squared = 9.667, df = 1, p-value = 0.001876 _____________________________________________________________________________ Accounting for the multiple foundress events within each treatment group (surviving & susceptible) _____________________________________________________________________________ ks.test(R,S) mult<-read.table("mult.foundKS.txt",header=T) attach(mult) summary(mult) mult _____________________________________________________________________________ ks.test(R,S) R S Min. :0.000 Min. :1.000 1st Qu.:1.000 1st Qu.:1.000 Median :1.000 Median :1.000 Mean :1.563 Mean :1.397 3rd Qu.:2.000 3rd Qu.:2.000 Max. :6.000 Max. :4.000 NA's :42 > ks.test(R,S) Two-sample Kolmogorov-Smirnov test data: R and S D = 0.076313, p-value = 0.4919 alternative hypothesis: two-sided Warning message: In ks.test(R, S) : p-value will be approximate in the presence of ties _________________________________________________________________________ Mite count analysis _________________________________________________________________________ count<-read.table("Oddie-mite-counts-2017.txt",header=T) attach(count) summary(count) names(count) logdd<-log(daily.drop) t.test(logdd~col.typ) __________________________________________________________ data: logdd by col.typ t = -3.8216, df = 15.524, p-value = 0.001577 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -3.3978999 -0.9692857 sample estimates: mean in group R mean in group S -0.2870823 1.8965105 _________________________________________________________________________ Mite damage analysis _________________________________________________________________________ mited<-read.table("Oddie-mite-damage-2017.txt",header=T) attach(mited) summary(mited) tbl=matrix(c(95,145,61,100),ncol=2,nrow=2) colnames(tbl) <- c("Surviving","Susceptible") rownames(tbl) <- c("damaged","undamaged") tbl chisq.test(tbl) Pearson's Chi-squared test with Yates' continuity correction data: tbl X-squared = 0.05609, df = 1, p-value = 0.8128 _____________________________________________ Analysis performed using a glm including apiary d1<-glm(cbind(damaged,undamaged)~col.typ+apiary,family=binomial) summary(d1) _____________________________________________ d2<-update(d1,~.-apiary) anova(d1,d2,test="Chi") _____________________________________________ Model 1: cbind(damaged, undamaged) ~ col.typ + apiary Model 2: cbind(damaged, undamaged) ~ col.typ Resid. Df Resid. Dev Df Deviance Pr(>Chi) 1 28 37.320 2 30 38.406 -2 -1.0856 0.5811 _____________________________________________ d3<-update(d2,~.-col.typ) anova(d2,d3,test="Chi") _____________________________________________ Model 1: cbind(damaged, undamaged) ~ col.typ Model 2: cbind(damaged, undamaged) ~ 1 Resid. Df Resid. Dev Df Deviance Pr(>Chi) 1 30 38.406 2 31 38.522 -1 -0.11662 0.7327 _____________________________________________ summary(d3) _____________________________________________ Call: glm(formula = cbind(damaged, undamaged) ~ 1, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -2.4174 -0.8504 0.1956 0.7203 2.2740 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.4514 0.1024 -4.407 1.05e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 38.522 on 31 degrees of freedom Residual deviance: 38.522 on 31 degrees of freedom AIC: 123.98