Statistical analysis
All statistical analyses were performed using R statistical software v. 3.6.0 (R Core Team 2014). We used Generalized Linear Mixed Models (GLMM) to analyze host development time, host survival (HS), and parasitism rate (PR) as response variables. Replicates were initiated over the course of five blocks of time; thus, we applied a mixed model approach, in which block was used as a random intercept. For host survival and parasitism rates, we included an observation level random effect (OLRE) to meet overdispersion and heteroscedasticity model assumptions. In all further analyses, the alternative host species treatment of D. birchii and D. sulfurigaster were combined to form its own unique species identification (D. birchii-D. sulfurigaster ).
We modeled host development times using data from control vials (i.e., no parasitoids) only, with fixed factors of temperature, competition, and host species combination, and all potential interactions between the three factors. Log transformed development time data improved model fit and helped meet assumptions of normality. We modeled host survival and parasitism rates as a function of the cofactors using a Binomial GLMM with a logit link function. All statistical models included a three-way interaction between temperature, resource competition, and phenological delay. We then compared models that incorporated interactions with host or parasitoid species and included all nested two-way interactions. We selected the model which minimized AICc using the bblme package in R (Bolker & Bolker 2019). We used the DHARMa(Hartig 2019) package to statistically test whether any assumptions of normality, non-constant error variance, and overdispersion were violated. DHARMa simulates scaled (quantile) residuals for mixed models and provides built-in tests to inspect model assumptions. Post-hoc multiple comparisons were performed using the emmeanspackage (Lenth 2019) and P-values were adjusted using the Tukey method when necessary. All figures were generated using the ggplot2 R package (Wickham 2011).