12 missing values (n=323), we fit the phylogenetic GLMMs using the brms package, default priors, 235 and infection status as a Bernoulli-distributed response. We included random effects for bat 236 species and phylogeny, the latter of which used the phylogenetic covariance matrix (Bürkner, 237 2017). We ran four chains for 20,000 iterations with a burn-in period of 10,000, thinned every 10 238 steps, for a total 4,000 samples. We compared GLMMs using the leave-one-out cross-validation 239 (LOOIC) and assessed fit with a Bayesian R 2 , including the total modeled variance and that To identify species trait correlates of prevalence, we fit 11 PGLS models (weighted by 258 sampling variance) with body mass, annual fecundity (litters per year * pups per litter), dietary 259 guild, quantitative diet, foraging strata, aspect ratio, roost type, roost flexibility, colony size, 260 geographic range size, and evolutionary distinctiveness as predictors. We also fit PGLS models 261 with only sample size or an intercept. We compared models with Akaike information criterion 262 corrected for small sample sizes (AICc) and estimated R 2 (Burnham & Anderson, 2002) . 263 264
We compared our 16S rRNA sequences to those in GenBank (Volokhov et al., 2017; Volokhov 266 et al., 2011) . Briefly, we aligned sequences using Clustal X, and inter-and intra-species 267 similarity values were generated using BioEdit. Genetic distances were calculated with the 268 Kimura two-parameter and Tamura-Nei models, and the phylogeny was constructed using 269 MEGA X with the minimum evolution algorithm (Kumar, Stecher, Li, Knyaz, & Tamura, 2018). 270
We assigned hemoplasma genotypes to positive bats based on analysis of the 16S rRNA 271 partial gene (860-1000 bp) sequences in GenBank and their clustering on the phylogeny. 272
Genotypes were designated as novel if (i) sequences differed from the closest hemoplasma 273 sequences in GenBank by ≥ 1.5% and/or (ii) if sequence similarity was <1.5% but genotype-274 specific reproducible mutations (at least two per sequence) were observed between hemoplasma 275 sequences from at least two independent bat samples and the nearest GenBank hemoplasma 276 sequences. These genotype-specific mutations were further used to differentiate closely related 277 hemoplasma genotypes from our sample. We caution that genotype is not synonymous with 278 species, as analysis of the 16S rRNA gene alone is insufficient for accurate species identification 279 14 of Mycoplasma spp. (Volokhov et al., 2012) . Future studies using genomics or housekeeping 280 genes may identify independent but closely related hemoplasma species in our genotypes. 281
To assess if hemoplasma genotype assignments were associated with site and year, we 282 used χ 2 tests with p values generated through a Monte Carlo procedure. Prior to our phylogenetic 283 and network analyses of genotype distributions across bat species (see below), we used another 284 χ 2 test to assess the association between hemoplasma genotype identify and bat host identity. 285 286
To determine the degree to which bat hemoplasma genotypes display host specificity and to 288 describe their evolutionary relationships with host species, we used our bat and hemoplasma 289 phylogenies to construct a binary association matrix. To test the dependence of the hemoplasma 290 phylogeny upon the bat phylogeny and thus assess evidence of evolutionary codivergence, we 291 applied the Procrustes Approach to Cophylogeny (PACo) using distance matrices and the paco 292 package ( We used hemoplasma genotype assignments to create a network, with each node representing a 299 bat species and edges representing shared genotypes among bat species pairs. We built an 300 adjacency matrix using the igraph package and used the Louvain method to assess the structure 301 of bat-hemoplasma communities within this network (Csardi & Nepusz, 2006) . To test whether 302 15 the distribution of hemoplasma genotypes across our Neotropical bat species is shaped by host 303 phylogeny, we used two GLMs to predict counts of shared genotypes (Poisson errors) and the 304 presence of sharing (binomial errors) by phylogenetic distance between bat species. We assessed 305 statistical significance with a quadratic assignment procedure via the sna package (Butts, 2008). 306
We calculated two metrics of network centrality to quantify different aspects of how 307 important a node (bat species) is to hemoplasma genotype sharing: degree and eigenvector 308 centrality (Bell, Atkinson, & Carlson, 1999). Whereas degree indicates the number of other 309 species with which a host shares bacterial genotypes (i.e., links per node), eigenvector centrality 310 indicates the tendency for a host to share genotypes with species that also share more genotypes 311 (i.e., connectivity). Eigenvector centrality is thus an extension of degree that can identify hubs of 312 parasite sharing (Gómez, Nunn, & Verdú, 2013). These two metrics were moderately correlated 313 (ρ=0.59), with many non-zero degree species displaying zero eigenvector centrality. To examine 314 spatial and temporal patterns in host centrality, we built separate adjacency networks per each 315 site and year. We fit separate GLMs to ask how hemoplasma sharing centrality was predicted by 316 site, year, and the two-way interaction. Degree was modeled as a Poisson-distributed response, 317 while eigenvector centrality was logit-transformed and used Gaussian errors. We next applied 318 phylogenetic factorization to both metrics and weighted the algorithms by the square-root sample 319 size per species (Garamszegi, 2014). We then fit the same PGLS models used in our prevalence 320 analysis to identify the most competitive trait predictors of bat species centrality to hemoplasma 321 sharing. To lastly assess whether network centrality is associated with hemoplasma prevalence, 322 we fit two weighted PGLS models with each centrality metric as a univariate predictor. 323 324
We detected sequence-confirmed hemoplasma infection in 239 of 469 individuals (51%; 95% 327 CI: 46-55%), with positive individuals in 23 of the 33 sampled bat species (Table S3) Pteronotus mesoamericanus, for which prevalence was greater than 58%. Although these three 346 species were also heavily sampled, other well-sampled species such as Sturnira parvidens and 347
Carollia sowelli had lower prevalence, and sample size did not predict prevalence (Table 1) . 348
Our phylogenetic analysis identified 29 Mycoplasma genotypes in the Belize bat community 351 (Table 2) After controlling for multiple comparisons, our 29 bacterial genotypes were associated 362 with site (χ 2 =47.11, p<0.01) and year (χ 2 =40.40, p<0.01). Genotype composition was more 363 diverse at LAR (Fig. S4) , and KK hemoplasmas were dominated by vampire bat genotypes 364 (VBG1-3). Genotype composition was more idiosyncratic by study year. However, these 29 365 bacterial genotypes were most strongly associated with bat species (χ 2 =3532, p<0.01; Fig. S4 ). 366 367
Although some hemoplasma genotypes were shared between bat species (i.e., VBG1, CS2, PPM, 369 EF1, AH1-2, MYE, PLU, SP; n=9), most showed strong host specificity (n=20; whereas six genotypes were each restricted to a single bat species (Fig. 5A ). GLMs showed that 381 both the frequency and presence of genotype sharing declined with phylogenetic distance 382 between bat species (Poisson: p<0.001, R 2 =0.08; binomial: p<0.001, R 2 =0.51; Fig. 5B ). 383
Bat species shared hemoplasma genotypes with zero to five other species (i.e., degree), 384 and most hosts were not central to the network of genotype sharing (i.e., eigenvector centrality of 385 zero). Six bat species had non-zero eigenvector centrality values that ranged from 37% to 100%, 386 indicating that these hosts generally shared more hemoplasma genotypes with other highly 387 connected hosts. Stratifying our hemoplasma genotype network across sites and years showed 388 that centrality measures varied by space but not time (Fig. S7, Table S5 ). We observed no 389 hemoplasma genotype sharing at KK, likely reflecting lower host diversity (Herrera et al., 2018) . 390
Phylogenetic factorization identified similar bat clades with significantly different 391 centrality compared to the paraphyletic remainder ( Fig. 6A-B Trait-based analyses showed that degree centrality was best predicted by diet (Table S6) ; 400 bat species feeding more heavily on fruit and nectar shared more bacterial genotypes with other 401 species (β=0.004, p<0.001, R 2 =0.20; Fig. 6C ). Similarly, eigenvector centrality was best 402 predicted by bat colony size and diet (Table S7 ); highly central species had small colonies 403 (β large =-1.93, p=0.05, R 2 =0.13) and fed more on plants (β=0.03, p<0.01, R 2 =0.10; Fig. 6D ). 404
As a final analysis, we assessed whether network centrality (i.e., a bat species' role in 405 hemoplasma genotype sharing) predicted contemporary infection prevalence (Fig. S8) . However, 406
we found no associations between species-level infection prevalence and centrality as measured 407 by degree (β=-0.13, R 2 =0.03, p=0.42) or eigenvector centrality (β=-0.20, R 2 <0.01, p=0.79). prevalence were distinct from those in genotype sharing centrality (e.g., large-colony species had 482 higher prevalence but lower connectivity), and prevalence accordingly did not predict centrality. 483 Second, we found general congruence between bat and hemoplasma phylogenies. Although this 484
shows codivergence is a strong evolutionary force, congruence can also stem from preferential cophylogeny plot shows the bat phylogeny on the left and the hemoplasma genotype phylogeny 933 on the right. We used the treespace package to collapse our complete hemoplasma phylogeny 934 ( Fig. S3) 