Expanded Statistical Analysis and Results

Electronic Supplementary Material:

Expanded statistical analysis and results

From endosymbionts to host communities: factors determining the reproductive success of arthropod vectors

Irit Messikaa1, Mario Garridob1, Hadar Kedema , Victor Chinac,d, Yoni Gavishe, Qunfeng Dongf,

Clay Fuquag, Keith Clayg, and Hadas Hawlenab*2

aDepartment of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; bMitrani

Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Jacob

Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion,

Israel; cDepartment of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel; dThe

Inter-University Institute for Marine Sciences, Eilat, Israel; eSchool of Biology, Faculty of Biological

Sciences, University of Leeds, UK; fCenter for Biomedical Informatics, Department of Public Health

Sciences, Stritch School of Medicine, Loyola University Chicago, Illinois, U.S.A; gDepartment of

Biology, Indiana University, Bloomington, IN, USA

*Corresponding author name and address Hadas Hawlena Mitrani Department of Desert Ecology Jacob Blaustein Institutes for Desert Research Ben-Gurion University of the Negev Midreshet Ben-Gurion 84990 ISRAEL Email: [email protected] phone: (+972) 08-6596775

1 Equal contribution

2 Author contributions: IM, HK, KC, CF, QD and HH conceived and designed the study. IM and HK performed the study. IM, MG, YG, and VC analyzed the data. IM, MG and HH wrote the manuscript; other authors provided editorial advice. First stage of data analysis: detecting the most important factors determining the female flea quality and reproductive success

We explored 13 sets of models, including 10 for predicting the current reproductive success of the female fleas and three for predicting the quality of the female fleas (Table 2). Since female fleas are larger and develop faster than males (Krasnov et al. 2003; Khokhlova et al. 2010), we ran separate sets of models for female and male offspring. The 10 reproductive success variables that were used as dependent variables were not highly correlated (Spearman |r| < 0.5 for all pairwise tests); therefore, we explored each separately. The three other model sets were devoted to exploring the determinants of the mass and the asymmetry level of the female flea, and the presence or absence of Mycoplasma in it (Table 2). All the competing models were based on generalized linear models (GLM) with either a normal distribution, a Poisson distribution for the count-dependent variables, or a binomial distribution for the binary-dependent variables. Since the offspring sex ratio, the synchrony in offspring emergence, the mean development time, and the variability in offspring mass did not have normal or Poisson distributions, we transformed them to binary variables, by setting values lower than or equal to the median to “0” and values above the median to “1.” The explanatory factors associated with our five hypotheses are presented in Table 2. To control for the possible effects of litter size (e.g., detecting a general quantity-quality trade-off that is not associated with any of the hypotheses), we included this variable in the offspring quality and variability analyses as well. Since the sample size was different among the 13 model sets, we had to exclude the explanatory factors that were not linked with a specific hypothesis for some of the model sets (Table 2). Second stage of data analysis: calculation of the overall female reproductive success (RS) index

In stage 2, we had to merge all the data subsets since some of them included the whole set (the number of offspring, and the female’s mass, asymmetry level, and its infection by Mycoplasma), while others included only subsets (the offspring’s sex ratio and the separate measurements for males and females; Table 2). Therefore, we created an index for overall female reproductive success (RS), following equation 1 in the main text. This index is often used in evaluating the overall reproductive success of insects as it is more meaningful than each of its components alone (Tessier and Consolatti 1991; Yanagi and Miyatake 2002; Moreau et al. 2006; Herreras et al. 2007). The values of this index increase with the number of offspring and mean body masses, and are affected by the offspring sex ratio due to the sex-biased flea mass, thus accounting for the main dependent variables evaluated during stage 1. Table S1. Descriptions of the path models and their weights (in percentages) employed in stage 2. The models were set to predict the index of the female fleas’ reproductive success. Weights (wi) are for the Akaike information criterion corrected for sample size (AICc). Models include the saturated model (bottom; Fig. S1) and its derivatives. The derivative models were selected to test different hypotheses about the direct and indirect paths that may explain the most important associations revealed in stage 1 (marked with bold in Table 2). Each model includes the associations marked in gray, for which letters correspond to the associations described in Fig. S1. All models are based on the same AIC metric (i.e., using the same mediators and dependent variables) and, therefore, are comparable. The combination of the four upper models has strong support of the data (). Associations # model w i a b c d e f g h i j k l m n o p q r s t u v 1 23 2 17 3 14 4 12 5 9 6 6 7 5 8 2 9 2 10 1 11 1 12 1 13 1 14 1 15 1 16 1 17 1 18 1 19 1 20 1 21 0 22 0 23 0 24 0 25 0 26 0 27 0 28 0 29 0 30 0 31 0 32 0 33 0 34 0 35 0 36 0 37 0 38 0 39 0 40 0 41 0 Saturated 0 Fig. S1. Saturated path analysis model. The model includes all of the biologically meaningful paths that may form the important associations revealed in stage 1 (marked with bold in Table 2) among the various intrinsic and extrinsic factors and the index of the female fleas’ reproductive success. The model was used to construct the set of competing models described in Table S1. “GA” = G. andersoni; “GP” = G. pyramidum References for Electronic Supplementary Material

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