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Research Article
13 December 2018

Assessing Transmission of Antimicrobial-Resistant Escherichia coli in Wild Giraffe Contact Networks


There is growing evidence that anthropogenic sources of antibiotics and antimicrobial-resistant bacteria can spill over into natural ecosystems, raising questions about the role wild animals play in the emergence, maintenance, and dispersal of antibiotic resistance genes. In particular, we lack an understanding of how resistance genes circulate within wild animal populations, including whether specific host characteristics, such as social associations, promote interhost transmission of these genes. In this study, we used social network analysis to explore the forces shaping population-level patterns of resistant Escherichia coli in wild giraffe (Giraffa camelopardalis) and assess the relative importance of social contact for the dissemination of resistant E. coli between giraffe. Of 195 giraffe sampled, only 5.1% harbored E. coli isolates resistant to one or more tested antibiotics. Whole-genome sequencing on a subset of resistant isolates revealed a number of acquired resistance genes with linkages to mobile genetic elements. However, we found no evidence that the spread of resistance genes among giraffe was facilitated by interhost associations. Giraffe with lower social degree were more likely to harbor resistant E. coli, but this relationship was likely driven by a correlation between an individual’s social connectedness and age. Indeed, resistant E. coli was most frequently detected in socially isolated neonates, indicating that resistant E. coli may have a selective advantage in the gastrointestinal tracts of neonates compared to other age classes. Taken together, these results suggest that the maintenance of antimicrobial-resistant bacteria in wild populations may, in part, be determined by host traits and microbial competition dynamics within the host.
IMPORTANCE Antimicrobial resistance represents a significant threat to human health, food security, and the global economy. To fully understand the evolution and dissemination of resistance genes, a complete picture of antimicrobial resistance in all biological compartments, including natural ecosystems, is required. The environment and wild animals may act as reservoirs for anthropogenically derived resistance genes that could be transferrable to clinically relevant bacteria of humans and domestic animals. Our study investigated the possible transmission mechanisms for antimicrobial-resistant bacteria within a wild animal population and, more broadly, contributes to our understanding of how resistance genes are spread and maintained in natural ecosystems.


Antimicrobial resistance (AMR) is a major global threat to public health and animal agriculture (1, 2). Ongoing misuse and overprescription of antibiotics creates selective pressure for AMR-encoding genes (ARGs) that impact the prevention and treatment of infections in both humans and domestic animals. Of further concern is the growing evidence that residual antibiotics and resistant bacteria may spread from clinical and agricultural settings into natural ecosystems, creating reservoirs of ARGs that could undergo horizontal gene transfer into novel opportunistic pathogens (36). Indeed, clinically relevant antibiotic-resistant bacteria have been identified in a wide range of natural habitats and wild animal species, including those at the human-agriculture-environment interface (for examples, see references 5 and 7 to 10). However, there are still large gaps in our understanding of the potential role natural ecosystems play in the emergence, maintenance, and dispersal of ARGs. We also know relatively little about what forces shape observed patterns of AMR in wild animal populations (3, 1113), yet this information is critical for determining if intervention is required and subsequently how control strategies could be implemented (12).
Understanding ARG dissemination in wild animals requires knowledge, in part, of commensal bacterial transmission. Commensal bacteria in the host gastrointestinal (GI) tract can acquire and transfer ARGs with relative ease, and resistant bacterial clones are often used as indicators of AMR prevalence in GI bacterial populations (1416). Recent work with humans and other animals suggests that these GI bacteria can spread between hosts via interactions, such as physical contact or shared environmental resources (17, 18). Thus, individuals with direct or indirect associations may share specific bacterial strains (1921) or have more similar microbial communities (2224).
By extension, physical contact or common environments may similarly promote the spread of antibiotic-resistant bacteria among human and animal hosts. Indeed, there is growing evidence that shared use of environmental resources may facilitate the transmission of resistant bacteria between domestic animals, human populations, environmental reservoirs, and wildlife (2527). Within populations, however, there are still unanswered questions regarding the relative importance of AMR “spillover” events from humans or domestic animals compared to independent circulation of resistant bacteria within wild animals. Specifically, are repeated spillovers required to sustain AMR in a wild animal population or, after the initial introduction of an ARG from an anthropogenic source, does interhost transmission within the wild population contribute to AMR maintenance and dispersal? Research on within-species ARG transmission is still rare and primarily limited to studies with livestock (28, 29) and humans (30, 31). Comparable studies in wildlife systems would be of significant value for understanding the transmission dynamics of ARGs within natural ecosystems and in the absence of antibiotic use.
In this study, we used wild giraffe (Giraffa camelopardalis) to investigate the drivers shaping population-level patterns of antibiotic-resistant Escherichia coli. Giraffe sociality is characterized by fission-fusion social organization, where social group membership changes daily or even hourly as animals move through their environment (3235). Variation in patterns of direct or indirect association could influence how antibiotic-resistant bacteria are spread through the population. Commensal E. coli is commonly found in the GI tracts of a wide range of animals and is disseminated via host contact with fecally contaminated environmental resources or direct host-to-host contact (3639). Further, both commensal and pathogenic E. coli organisms are thought to be particularly important contributors to the global spread of antimicrobial resistance genes via mobile genetic elements (MGEs) (40, 41). Prior research in the same giraffe population found that social contact networks predicted E. coli sharing between individuals; giraffe that were more strongly connected in the co-occurrence network were more likely to harbor the same E. coli strains than giraffe rarely observed together (21). As a follow-up to this work, we investigated whether giraffe contact networks similarly predict patterns of antibiotic-resistant E. coli among individuals. First, we identified which host characteristics, including metrics of both host social and spatial connectivity, influence the likelihood of a giraffe harboring resistant E. coli. We then tested whether giraffe contact networks could promote the dissemination of ARGs between individuals. If social or spatial contact networks predict patterns of observed ARGs within the giraffe population, this would suggest that social associations may facilitate the circulation of resistant bacteria within wildlife populations. Taken together, these results advance our understanding of ARG transmission dynamics, including the forces responsible for the distribution of AMR in natural ecosystems.


Antimicrobial resistance was rare in giraffe Escherichia coli and unrelated to subtype.

We conducted antibiotic susceptibility testing on 765 E. coli isolates from 195 giraffe inhabiting the Ol Pejeta Conservancy (OPC) wildlife reserve in Kenya. Of these isolates, antibiotic resistance was detected in only 2.7% of isolates (n = 21 isolates) from 5.1% of giraffe (n = 10 individuals) (Table 1). Multidrug-resistant isolates (resistant to three or more antibiotic classes [42]) were found in 30% of AMR-positive giraffe (n = 3), with the most common phenotype consisting of resistance to amoxicillin, oxytetracycline, and co-trimoxazole (Table 1). Three isolates from a single giraffe (G670) exhibited cefotaxime MICs above the epidemiological cutoff value (ECOFF) but not the clinical breakpoint, suggesting resistance to cefotaxime may be emerging in wild-type E. coli but is not yet clinically relevant. Similarly, isolate G6844 exhibited a co-trimoxazole MIC above the ECOFF but not the clinical breakpoint. None of the 765 E. coli isolates exhibited resistance to florfenicol or gentamicin.
TABLE 1 Antibiotic resistance phenotype of E. coli isolates from giraffe
a The isolates are labeled by giraffe identification number with the isolation number in the subscript.
b Abbreviations: AMX, amoxicillin; CTX, cefotaxime; OXY, oxytetracycline; SXT, co-trimoxazole.
c The MIC was below the clinical breakpoint defined by EUCAST but above the ECOFF.
Within an individual giraffe, resistant isolates exhibited the same AMR profiles and were typically the same E. coli genomic subtype (mean pairwise genetic similarity based on BOX-PCR: 97.8% ± 1.6%). This suggests that multiple clones of a single E. coli strain were likely collected and screened from individual giraffe. However, between giraffe, genetic similarity was not an indication of matching AMR profiles (mean genetic similarity: 55.6% ± 30.5%).

Acquired resistance genes were common and found in proximity to mobile genetic elements.

Given that resistant isolates within an individual giraffe were the same E. coli genomic subtype, a single representative resistant isolate was selected from each AMR-positive giraffe for whole-genome sequencing and de novo assembly (n = 10 isolates). The average read depth per isolate was 28×, with a mean of 248 scaffolds of ≥500 bp after assembly (see Table S1 in the supplemental material). In silico phylotyping revealed that resistant isolates belonged to all four main phylogroups and accessory group E (Table 2). Phylotypes B1 and D accounted for 40% (n = 4) and 30% (n = 3) of the isolates, respectively, while phylotypes A, B2, and E were each represented by one isolate (Table 2). In silico multilocus sequence type (MLST) analysis identified seven E. coli sequence types (STs), including ST1670, which accounted for all three of the phylotype D isolates (Table 2).
TABLE 2 Genomic characteristics of E. coli isolates displaying phenotypic resistance to at least one antibiotic
Size (bp)Acquired resistance gene(s)
(GenBank accession no.)
Genetic contextbScaffold
G894ST1670DNODE_1308,626catA1 (V00622), dfrA7 (JF806498), sul1 (various)intI1, qacEΔ1, Tn3 transposase, tnpM1
   NODE_1564,584strA (AF321551), strB (M28829), sul2 (HQ840942)IncQ1 plasmid2
   NODE_1624,066tet(A) (AJ517790)Unknown3
   NODE_1932,029blaTEM-1 (JF910132)tnpR4
G3631ST5281ENODE_1156,198strA (M96392), strB (M28829), sul2 (AY034138)Unknown5
   NODE_1254,928tet(B) (AP000342)Clustered with Tn10; tetR, tet(C), tet(D)6
G3662ST1670DNODE_2465,775catA1 (V00622), dfrA7 (JF806498), sul1 (various)intI1, qacEΔ1, insA, IS1 orfA, IS1 transposase, intS1
   NODE_1404,640strA (AF321551), strB (M28829), sul2 (HQ840942)IncQ1 plasmid2
   NODE_4836,946tet(A) (AJ517790)tnsB3
   NODE_1662,085blaTEM-1 (JF910132)tnpR4
G6741ST95B2NODE_825,615tet(A) (AJ517790)Unknown 
G6782ST1433ANODE_3163,379blaTEM-1 (JF910132)IncK plasmid, tnpA, tnpR4
G6844ST947B1NODE_1236,217strA (M96392), strB (M28829), sul2 (AY034138)Unknown5
   NODE_1324,910tet(B) (AP000342)Clustered with Tn10; tetR, tet(C), tet(D)6
G6853ST337B1NODE_7314,011dfrA5 (X12868), tet(A) (AJ517790)Plasmid (unclassified), intI1, Tn3 transposase, tnpM 
   NODE_1122,952strA (AF321551), strB (M28829), sul2 (HQ840942)Unknown2
   NODE_1142,017blaTEM-1 (JF910132)Plasmid (unclassified), tnpR4
G6881ST937B1NODE_855,632strA (M96392), strB (M28829), sul2 (AY034138)Unknown5
   NODE_904,902tet(B) (AP000342)Clustered with Tn10; tetR, tet(C), tet(D)6
G6891ST1670DNODE_1198,682catA1 (V00622), dfrA7 (JF806498), sul1 (various)intI1, qacEΔ1, Tn3 transposase, tnpM1
   NODE_1474,640strA (AF321551), strB (M28829), sul2 (HQ840942)IncQ1 plasmid2
   NODE_4043,030tet(A) (AJ517790)tnsB3
   NODE_1812,085blaTEM-1 (JF910132)tnpR4
The isolates are labeled by giraffe identification number, with the isolation order number as the subscript.
Lists any genes identified via the INTEGRALL (115), ISfinder (116), PlasmidFinder (117), and NCBI nucleotide ( databases associated with mobile genetic elements, including integrons, transposons, insertion sequences, plasmids, and phages.
Scaffold groups were determined from BLAST alignments of ≥99% similarity across ≥90% of the shorter scaffold sequence.
A total of 10 different acquired ARGs were identified, with an average of 4 ARGs per isolate (range: 0 to 8 ARGs [Table 2]). Genes conferring resistance to sulfonamides had the highest prevalence (sul1 and sul2; n = 9 isolates), followed by genes conferring resistance to tetracyclines [tet(A) and tet(B); n = 8], aminoglycosides (strA and strB; n = 7), and beta-lactams (blaTEM-1; n = 5). In general, ARGs identified in each isolate reflected the observed phenotypic AMR profiles (Tables 1 and 2). However, we did not identify any acquired ARGs in isolate G6701 that could be responsible for observed resistance to amoxicillin or the elevated cefotaxime MIC. Additionally, isolate G3662 carried genes that typically confer resistance to oxytetracycline [tet(A)] and co-trimoxazole (sul1, sul2, and dfrA7) but did not exhibit phenotypic resistance to either antibiotic. Finally, although the presence of sul2 in isolate G6844 could account for a co-trimoxazole MIC above the ECOFF, the same gene was also identified in two other isolates that did not exhibit elevated co-trimoxazole MICs (G6881 and G3631) (Tables 1 and 2).
Sequence alignments of ARG-carrying scaffolds identified six scaffold groups where pairwise scaffold similarity was ≥99% for at least 90% of the shorter scaffold’s length (Table 2). Two of these scaffold groups were characterized by the presence of a sul2-strAB gene cluster (scaffold groups 2 and 5), while scaffold group 1 carried the genes catA1, dfrA7, and sul1 (Table 2). The remaining three scaffold groups (3, 4, and 6) carried one ARG each: tet(A), tet(B), and blaTEM-1, respectively (Table 2). Genes associated with mobile genetic elements (MGEs), such as those encoding transposases, resolvases, integrases, and plasmid replicons, were found in 71% of all ARG-carrying scaffolds (n = 17) across 50% of isolates (n = 5) (Table 2). Of particular note was the presence of the class 1 integron-integrase gene, intI1, on all members of scaffold group 1, in addition to a unique scaffold carrying ARGs tet(A) and dfrA5 (Table 2). Interestingly, the group 1 scaffold from isolate G3662 also carried the phage-related integrase gene, intS, suggesting the presence of a phage. Six of the 17 ARG-carrying scaffolds were identified as plasmids across five isolates, including the IncQ1 plasmid in three sul2-strAB isolates and the IncK plasmid in one blaTEM-1 isolate (Table 2). All members of the blaTEM-1-positive scaffold group also carried the gene tnpR, which encodes a transposon gamma-delta resolvase typically found in Tn3 family transposons.

Neonates showed higher antimicrobial resistance than other age classes.

We investigated the importance of host-level social and ecological factors for predicting antimicrobial-resistant E. coli in giraffe using univariate and multivariate Firth bias-reduced logistic regressions (Tables 3 and 4). In both univariate and multivariate models, social degree (i.e., number of social contacts) was the single most important predictor of AMR risk, accounting for more than 90% of Akaike’s information criterion corrected for small sample size (AICc) weight in univariate models and included in all three best-fit multivariate models (Tables 3 and 4). Specifically, giraffe with lower social degree were significantly more likely to carry at least one resistant E. coli isolate (P value ≤ 0.001). Age class was the second most important predictor in univariate models, with 5% of AICc weight (Table 3). Neonates were significantly more likely to carry at least one resistant E. coli isolate than juveniles, subadults, and adults (Tables 3 and 5). However, in multivariate models, only one of the three best-fit models contained age (Table 4). This result was somewhat surprising given that 71% of all neonates (5/7 giraffe) carried at least one resistant isolate and neonates accounted for 50% of all AMR-positive giraffe (Table 5). One possible explanation for the minimal importance of age in regression models is that other factors, such as social degree, may have reflected additional information about age variation not captured by age classes. For example, among giraffe with known ages (i.e., neonates and juveniles), social degree was highly correlated with age (Pearson’s r = 0.80; Pearson’s r = 0.93 if restricted to <150 days of age) (Fig. 1), and younger individuals were more likely to carry resistant E. coli (Wilcoxon rank sum test: W = 3; P value < 0.0001).
TABLE 3 Univariate model results for potential social and ecological predictors of E. coli antimicrobial resistance in giraffe
CovariateCoefficientP valueΔAICcAICc weight
Social degree−0.19<0.0010.00.93
Age  6.00.05
Home range size−0.030.0038.60.01
Spatial degree−0.060.00410.1<0.01
Sex: male0.030.5912.7<0.01
Social betweenness−0.0010.1313.1<0.01
Spatial betweenness−0.0010.1813.7<0.01
Intercept only−2.87<0.00123.5<0.01
River side: west−0.140.3124.7<0.01
TABLE 4 Coefficients of the best-fit models for antibiotic-resistant E. coli risk in giraffea
Home range
C2.330.16−0.21d0.005b−0.004    −0.03
Only models receiving >5% AICc weight are shown. AICc weights were calculated relative to all univariate and multivariate models.
P ≤ 0.05.
P ≤ 0.01.
P ≤ 0.001.
TABLE 5 Prevalence of antibiotic-resistant E. coli in giraffe fecal samples by age class
AntibioticNo. of resistant samples (%)a
(n = 7)
(n = 19)
(n = 17)
(n = 152)
Any5 (71.4)1 (5.3)04 (2.6)
Amoxicillin3 (42.9)003 (2.0)
Oxytetracycline4 (57.1)1 (5.3)02 (1.3)
Co-trimoxazole2 (28.6)001 (0.7)
For each antibiotic, the first number represents the number of giraffe with ≥1 resistant E. coli isolate and the related percentage for the given age class is indicated in parentheses.
FIG 1 Relationship between social degree and age in days. Each point represents one giraffe. Red points indicate giraffe harboring at least one antimicrobial-resistant E. coli isolate.
AMR risk was also related to small home range size and low spatial degree in univariate models (Table 3), but neither factor was consistently present or significant in the best-fit multivariate models (Table 4). Although social and spatial betweenness accounted for less than 1% of AICc weight in univariate model comparisons (Table 3), both factors were included in all three best-fit multivariate models (Table 4). Specifically, giraffe AMR risk increased with increasing social betweenness and decreasing spatial betweenness (Table 4). However, the values of betweenness regression coefficients were small (Table 4), suggesting that the effect of social and spatial betweenness on AMR risk was at most minor compared to other factors.

Contact networks were unrelated to patterns of antimicrobial resistance.

We next assessed the relevance of giraffe contact networks for the transmission of ARGs between giraffe. Plots of both social and spatial networks suggest there was relatively little clustering of AMR-positive giraffe (Fig. 2). Network k-tests and path tests (43) were conducted on social and spatial networks for each scaffold group identified in at least two AMR-positive giraffe. All tests failed to reject the null hypothesis that ARG-positive giraffe were distributed randomly in the networks (Bonferroni adjusted P values > 0.99).
FIG 2 Networks of giraffe association strength (a) and home range overlap (b). Enlarged nodes outlined in red indicate giraffe yielding at least one antimicrobial-resistant E. coli isolate.


To date, considerable research has focused on ARG dissemination between natural ecosystems and humans or domestic animals (for examples, see references 27 and 44 to 46). However, less attention has been paid to understanding whether ARGs circulate independently within wild animal populations after an introductory anthropogenic spillover event (but see references 27 and 47). In this study, we investigated the potential drivers shaping patterns of antimicrobial-resistant E. coli within a population of wild giraffe. While we did find clinically resistant E. coli in this population, social network analyses provided little evidence for the spread of acquired ARGs via interhost associations. Instead, the presence of AMR in this population may be primarily driven by repeated spillover of resistant bacteria from anthropogenic sources or related to other potential selection pressures for ARGs.
Giraffe home ranges overlap extensively with herds of domestic cattle, and all animals within OPC share common water sources that may act as environmental reservoirs of ARGs and hotspots of transmission from cattle to wild animals. While our current results alone cannot establish whether the resistant E. coli organisms were directly transmitted from the local cattle population, a number of our findings do provide more general evidence that AMR in this giraffe population was primarily derived from anthropogenic sources. First, 9 of the 10 E. coli isolates we sequenced carried at least one acquired ARG, and all of these ARGs have been previously identified in E. coli from both humans and domestic cattle in East Africa (4852). Further, prior research on AMR at human-livestock-wildlife interfaces suggests that the presence of these ARGs in wild animals is closely associated with anthropogenic land usage (27, 47, 5355). Second, although the dominant B1 phylogroup of our sequenced isolates is typical for E. coli in both domestic and wild herbivorous animals (36, 56), two other isolates belonged to phylogroups A and B2, the dominant phylogroups of human populations (36, 57). Both isolates also had MLST sequence types (ST1433 and ST95) previously isolated from humans and frequently associated with extraintestinal diseases, including urinary tract and bloodstream infections (58, 59). Third, we found that four of our sequenced isolates carried the class 1 integron-integrase gene, intI1. This MGE-associated gene plays a major role in the global spread of ARGs and is thought to be a reliable genetic marker of anthropogenic pollution (60, 61). Taken together, these results suggest that giraffe AMR is, in large part, derived from anthropogenic sources. However, the specific local sources of AMR—whether cattle or another unknown source—are yet to be determined.
The genetic characterization of our resistant isolates also provides evidence for both the horizontal transfer of ARGs between bacterial strains and the clonal spread of ARG-carrying E. coli. On the one hand, we observed multiple instances where isolates with distinct genetic backgrounds harbored the same ARGs in similar genetic contexts. For example, scaffold group 5, which carried the sul2-strAB gene cluster, was found in three isolates with three different MLST sequence types. While we could not identify any MGEs associated with this scaffold group, prior studies found that the sul2-strAB gene cluster is part of a cassette and typically found on plasmids in a wide range of Gram-negative bacteria, including E. coli (62, 63). On the other hand, the ST1670 genomic subtype found in three different giraffe shared matching ARGs that were carried by the same four scaffold groups, which suggests that they may have descended from the same recent ancestor. This is in contrast to prior research on a sympatric population of cattle and wild buffalo in southern Africa that found the spread of ARGs was primarily independent of E. coli strain sharing between individuals (27). Together, our findings regarding both horizontal gene transfer and clonal transmission of ARGs suggest that both mechanisms play a role in the maintenance of AMR in this wild animal population and highlight the potential complexity of ARG spread.
The overall low prevalence of AMR identified in this population suggests that spillover from local humans and livestock may be a relatively rare occurrence or that there are simply low reservoirs of resistance to be shared between populations. This is somewhat surprising given reports of high antimicrobial use and AMR in both humans and livestock in Kenya (6468) and high prevalence of resistant bacteria observed in previous studies of East African wildlife (8, 9, 69). In part, this variability in AMR prevalence between studies may be explained by differences in dietary niche between host species. Specifically, the type and location of food consumed may affect the level of exposure to residual antibiotics and resistant bacteria. For example, carnivores typically exhibit the highest AMR risk, possibly due to trophic accumulation of ARGs through the food chain, while herbivores have the lowest risk (3, 8, 70). Giraffe exposure to AMR may be particularly low because their diet primarily consists of leaves from trees and shrubs, areas where antibiotic contamination and resistant bacteria associated with the host GI tract are less likely to exist.
Alternatively, the low AMR prevalence in this study compared to findings for other African wildlife may simply be an unintended consequence of our sampling design. The GI tract typically harbors a single dominant strain of E. coli that constitutes more than half of the total isolated colonies (71, 72). Since many AMR mutations or plasmids can reduce fitness in the absence of antibiotic selection pressures, resistant strains may be outcompeted by nonresistant strains with higher fitness (73, 74). Thus, finding dominant strains with AMR phenotypes may be relatively rare in wild animals where selection for AMR is low (27). Genotyping of E. coli used in this study identified, on average, only 1.7 unique subtypes per giraffe (21). Consequently, the majority of susceptibility testing was probably conducted on the dominant E. coli strains, where resistance may have been present but rare. A sampling design that includes susceptibility testing of both dominant and subdominant isolates might have revealed higher levels of AMR within the giraffe population. For example, work with wild African buffalo found AMR in only one dominant E. coli strain but identified resistance in many subdominant isolates (27). Similar findings have also been reported for both livestock (75) and humans (76, 77). Assessment of these rare, subdominant strains could be more effectively achieved either by initially culturing fecal samples in the presence of antibiotics or through metagenomic sequencing approaches.
AMR prevalence may also be low if the antibiotics we selected were not representative of AMR selection pressures in humans, livestock, and wildlife within the study area. However, it is worth noting that the two antibiotics most frequently used to treat cattle and wildlife (amoxicillin and oxytetracycline; G. Omondi, unpublished data) were also the two most common AMR phenotypes we identified in the giraffe population. Other antibiotics with higher rates of improper usage, such as those obtained over the counter by nonprofessionals near OPC, would be worth including in susceptibility testing for future surveys of AMR in this area (68).
Despite the relatively low prevalence of giraffe AMR, regression model results indicated that there was a compelling relationship between AMR risk and individual-level measures of social connectedness. In particular, individuals with fewer social links to other giraffe were more likely to harbor resistant E. coli. However, it is unclear whether this relationship reflects a real effect of social connectivity on AMR or whether it captures variation in another host characteristic not accounted for in our data set. The latter explanation seems more likely given that socially mediated transmission should result in giraffe with higher social degree experiencing higher AMR risk. Indeed, at the network level, neither social nor spatial contact networks explained the observed pattern of AMR-positive giraffe. Instead, we observed strong age-related patterns of AMR, with the highest AMR risk occurring in giraffe younger than 3 months. During the first few months of life, and particularly during the first 2 weeks, calves are kept relatively isolated from other giraffe and consequently have few social associations and small home ranges (78, 79). Thus, our observed relationship between social and spatial connectedness and AMR risk may simply reflect these early months when associations are less frequent and AMR is high. Future work disentangling age-related social and ranging behaviors from other aspects of social and spatial association will provide a more complete picture of the host traits that predispose certain groups to acquire, maintain, and spread ARGs.
The high likelihood of identifying resistant E. coli in neonate giraffe compared to other age classes suggests that age may be an important predictor of AMR risk in giraffe. In young giraffe, exposure to residual antibiotics is presumably low, particularly while neonates are exclusively nursing. Therefore, the high prevalence of AMR in neonates compared to other age classes is unlikely to be caused by differential selection pressures related to antibiotic exposure. Instead, our results suggest that resistant E. coli strains may have a selective advantage over susceptible strains within the neonate GI tract. Some ARGs may be genetically linked to advantageous genes that enhance the fitness of resistant strains, such as encoding mechanisms of colonization, adhesion, or reproduction (80, 81). For example, the milk diet of giraffe neonates could create a niche for bacterial strains that carry genes for enhanced acquisition or utilization of milk-associated nutrients and antimicrobial resistance (82, 83). As neonate feeding behavior shifts from nursing to extensive browsing between 2 and 4 months of age (78, 79), the selective advantage of these strains would be lost and the abundance of resistant bacteria would subsequently decrease (84). Indeed, our observation that AMR prevalence was higher in neonates (0 to 3 months) than in other giraffe age classes provides support for this hypothesis, but further culture and genome-based work would be required to identify the underlying mechanism(s) responsible for the apparent age-related differences in AMR. It is also worth noting that similar age-related distributions of AMR have also been observed in both humans and domestic animals (for examples, see references 85 to 88)—even in the absence of antibiotic selection pressures (82)—but the mechanisms underlying these patterns remain largely unknown.
Overall, our results indicate that the presence of AMR in this wild giraffe population is primarily due to dissemination of resistant E. coli strains and/or ARGs from local anthropogenic sources and not independent circulation of ARGs among giraffe. However, our finding that AMR risk differs between giraffe age classes suggests that ARG spread is not a completely random process and that the persistence of resistant strains may be dependent on host traits and competition dynamics of the gut microbial community. To date, the potential hazards of wild animal reservoirs of acquired ARGs have yet to be determined. Yet the mere presence of clinically resistant E. coli in natural ecosystems warrants concern and investigation into the potential consequences for both humans and domestic animals. Future AMR research should therefore include studies of wildlife and environmental reservoirs, with a focus on the role that natural ecosystems may play in the emergence, maintenance, and global spread of AMR.


Study population and field observations.

This study was conducted at Ol Pejeta Conservancy (OPC), a wildlife reserve located in Laikipia, Kenya (0˚N, 36˚56′E) that integrates commercial cattle ranching with wildlife conservation. The reserve is bisected by the Ewaso Ng’iro river, with the western side home to wide-ranging OPC cattle herds and the eastern side featuring small clusters of cattle and proximity to local villages and their livestock. All giraffe within OPC at the time of the study (n = 212) were individually recognized based on unique spot patterns on their necks. Age class (neonate, <3 months; juvenile, 3 months to 1.5 years; subadult, 1.5 to 4 years; and adult, >4 years) for each giraffe was established according to physical attributes and age-associated behaviors (21, 78, 79). Approximate birth dates were known for all neonates and juveniles. The population exhibited a 50:50 sex ratio, although the sex of two neonates born at the end of the observation period was not determined. At the end of the study period, the giraffe population of OPC consisted of 160 adults, 20 subadults, 21 juveniles, and 11 neonates.
Behavioral observations were carried out in OPC from 21 January to 2 August 2011 as described elsewhere (21). Briefly, giraffe were located by driving daily routes through different regions of OPC. Giraffe social group membership was determined by proximity to other group members and/or movement of individuals in a common direction (35, 89, 90). In total, there were 1,089 sightings of giraffe groups during the study period, with each giraffe observed an average of 31.1 ± 7.6 (mean ± standard deviation [SD]) times (approximately once per week).
This research was approved by Kenya’s National Council for Science and Technology (permit NCST/RRI/12/1/MAS/147) and the UC Davis Institutional Animal Care and Use Committee (protocol no. 15887).

Contact network construction.

We constructed two giraffe contact networks based on (i) social associations and (ii) spatial overlap of home ranges, with the same set of individuals included in both networks (n = 193). For the social network, patterns of association were established from group membership, with pairs of giraffe linked if they were observed in the same social group at least once. Network connections were weighted according to association strength (AS), which was calculated as the number of sightings where a giraffe pair was observed in the same group divided by the total number of times they were seen together or apart (21). Because giraffe mothers tend to isolate their calves from other giraffe for the first 1 to 3 weeks postpartum (78, 79), two neonates born at the end of field observations were linked only to their mothers.
The spatial network was constructed based on the extent of home range overlap between individuals (21). Network connections were weighted according to the proportion of overlap. Home range boundaries of each giraffe were mapped using a fixed-kernel utilization distribution of Global Positioning System (GPS) coordinates recorded during sightings. Each giraffe’s core home range was calculated using a 75% kernel density estimation (91). Average home range size ranged from 16.9 ± 13.4 km2 for neonates to 95.7 ± 3.3 km2 for adult males.
For both social and spatial networks, we calculated two standard measures of network connectivity for each individual: weighted degree and weighted betweenness (92). Degree is defined as the number of individuals to which the focal individual is connected (93). Weighted degree (here social/spatial degree) is the extension of degree for weighted networks and accounts for both the number of linked individuals and the weight of those links (92). Betweenness measures the extent a focal individual falls on the shortest paths between other pairs of individuals in the network (93). For weighted networks, the shortest paths used to calculate betweenness are based on the sum of connection weights (92). Previous work has shown that both metrics positively correlate with microbial diversity and bacterial subtype sharing (21, 22, 94), as well as pathogen infection risk (95, 96), suggesting that individuals with high degree or betweenness may have elevated opportunities for exposure to antibiotic-resistant bacteria compared to those with low network connectivity.

Fecal sample collection and DNA fingerprinting.

Detailed methods relating to fecal sample collection, E. coli isolation, and genetic analysis are described elsewhere (21). Briefly, fecal samples from 195 giraffe were collected between 10 August and 11 September 2011 (after the completion of behavioral observations). Collected samples were streaked onto E. coli selective CHROMagar EC agar (CHROMagar, Paris, France) and incubated overnight at 37˚C. Up to six E. coli colonies (range, 2 to 6; median, 4) were randomly selected from each incubated sample, subcultured, and frozen for transport to the United States. To determine the genetic profile of each isolate, densitometric curves were generated using the banding patterns from BOX-PCR and gel electrophoresis (9799). Genetic similarity of each isolate to all others was subsequently calculated through pairwise comparisons of densitometric curves using the Pearson product-moment correlation coefficient. Two isolates were interpreted as the same E. coli subtype if they were at least 90% similar (21).

Antimicrobial susceptibility testing.

All E. coli isolates (n = 765) were tested for sensitivity to six antibiotics: amoxicillin (beta-lactam: penicillin), cefotaxime (beta-lactam: third-generation cephalosporin), florfenicol (phenicol), gentamicin (aminoglycoside), oxytetracycline (tetracycline), and co-trimoxazole (sulfonamide/dihydrofolate reductase [DHFR] inhibitor). These antibiotics were selected based on reports of antibiotic use in Kenya and recent research in eastern and southern Africa demonstrating acquired resistance in E. coli from local livestock and wild animals (8, 27, 48, 49, 65, 68). In addition, amoxicillin and oxytetracycline were the primary antimicrobial treatments used by OPC in cattle and wild animals (G. Omondi, unpublished data).
Sensitivity testing was performed using broth microdilution in a two-step process following methods established by the Clinical and Laboratory Standards Institute (CLSI) (100) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) (101). First, isolates were tested against a single concentration of each antibiotic based on the epidemiological cutoff values (ECOFFs) determined for Enterobacteriaceae by EUCAST (102). ECOFFs represent the MICs that separate wild-type bacterial populations from strains with acquired resistance mechanisms (102). While these values do not provide information on the clinical relevance of resistance per se, they are useful for detection of AMR emergence in wild-type populations (102). Thus, an isolate was described as “tentatively resistant” if bacterial growth was observed in the presence of the antibiotic at the ECOFF concentration. In step two, all isolates tentatively classified as resistant from step one were tested for sensitivity across a range of 10 antibiotic concentrations using standard broth microdilution methods described by Wiegand et al. (103). Any isolate with a MIC above the established EUCAST clinical breakpoint was considered resistant (104). E. coli ATCC 25922 was used as a quality control strain in both steps of the sensitivity screen, as recommended by the CLSI and EUCAST (100, 101).

Whole-genome sequencing and analyses.

DNA was extracted from one resistant isolate per giraffe using the Qiagen DNeasy Blood & Tissue kit (Qiagen, Hilden, Germany). DNA libraries were prepared and multiplexed with the NexteraXT library preparation kit (Illumina, San Diego, CA) and subjected to paired-end whole-genome sequencing on the Illumina MiSeq platform with 300-bp read lengths. Raw sequences were trimmed and quality filtered using Trimmomatic v 0.33 (105), and sequence quality was assessed with FastQC v 0.11.7 ( Average read depth and genome coverage were determined by mapping reads to the E. coli K-12 strain MG1655 chromosome (NCBI reference sequence NC_000913.3) using the Burrows-Wheeler Aligner v 0.7.17 (106) and SAMtools v 1.5 (107). De novo genome assemblies of high-quality reads were performed with SPAdes v 3.10.0 (108) following the assembly selection procedure of Ahlstrom et al. (47).
Prokka v 0.7.17 (109) was used to identify open reading frames (ORFs) on assembly scaffolds of ≥500 bp. In silico phylotyping of resistant isolates was based on the Clermont scheme (110) and performed using the web-based service ClermonTyper ( (111). In silico MLST was used to identify isolate sequence types and performed using the Center of Genomic Epidemiology web-based MLST 2.0 method (112) ( Acquired resistance genes were identified by aligning ORFs to the ResFinder database (downloaded 22 April 2018) (113) using BLASTN (114) with an E value threshold of 1e−10, query identity of ≥99%, and query coverage of ≥85%. The similarity between ARG-carrying scaffolds from different isolates was assessed by pairwise BLASTN alignments. Scaffolds were assigned to the same scaffold cluster if their alignment exhibited ≥99% similarity across ≥90% of the length of the shorter scaffold sequence. To determine the genetic context of identified acquired ARGs (e.g., on a plasmid, within a transposon, etc.), we identified genes associated with mobile genetic elements on each ARG-carrying scaffold using the INTEGRALL (115), ISfinder (116), PlasmidFinder (117), and NCBI nucleotide ( databases.

Statistical analyses.

To investigate whether any host traits influenced the risk of carrying antibiotic-resistant E. coli, we performed univariable and multivariable logistic regression models using Firth's penalized likelihood approach implemented in the R package logistf (118120). This method corrects for biases in maximum likelihood estimation due to rare events and small sample sizes while still producing finite and consistent parameter estimates (118, 119). The dependent variable (0/1) was whether an individual carried at least one E. coli isolate resistant to at least one antibiotic (i.e., AMR risk). Covariates included age class, sex, home range size, river side (west versus east), social and spatial degree, and social and spatial betweenness. River side was included due to different management strategies for cattle on each side of the river, as well as because previous research has shown that E. coli population structure is in part influenced by the river (121). In a separate univariable logistic regression, we also tested for a relationship between AMR risk and the total number of isolates collected per giraffe to confirm that more isolates did not increase the likelihood of detecting antibiotic-resistant E. coli (P = 0.49). Due to the nonindependence of network data, univariable and multivariable regression P values were calculated via permutation methods where the dependent variable was randomized relative to the covariates (10,000 total permutations) (122, 123). P values were defined as the proportion of random permutations that yielded a coefficient as extreme as the observed value. Covariates with P values of <0.2 in univariable regressions were included in multivariable models. We used forward selection for inclusion of covariates to determine the best-fit multivariate models. To account for potential collinearity among covariates, we calculated generalized variance inflation factor (GVIF) values for each multivariable model and rejected models exhibiting GVIFs of >2.0 (124). GVIFs are recommended over traditional variance inflation factor values when any covariate in the model has more than 1 degree of freedom (125). Candidate models were compared using Akaike’s information criterion corrected for small sample size (AICc) (126). Models with a ΔAICc of <2.0 were considered equally parsimonious (126). All analyses were conducted in R v 3.4.2.
To determine whether giraffe social or spatial association networks represent potential transmission pathways for antimicrobial-resistant E. coli between individuals, we used the network k-test procedure implemented in R (43). This method involves calculating the mean number of infected individuals occurring within one step of an infected individual in a network (i.e., mean infected degree), called the k-statistic. The network location of infected individuals is then randomized (node-label swap) and the k-statistic is recalculated after each data permutation (10,000 total permutations). The P value is calculated by comparing the observed k-statistic to the distribution of null k-statistics. If the mean number of infected nodes within one step is significantly greater than expected if cases were randomly distributed in the network, this suggests that the pattern of infection cases may have resulted from transmission via network links. Due to the large number of connections between individuals in both the social and spatial networks (many of which had low weight and probably represent transient interactions) and because the k-test does not account for link weight, pairs of giraffe were connected only if the link weight (i.e., AS or home range overlap) was greater than the median weight of all network pairs. For weighted networks, we also performed an extension of the k-test procedure that uses the average weighted inverse path-length between each infected node and the nearest other infected node (which we term the “path test”), with the assumption that infected nodes will be closer together (shorter path lengths) than random expectations if the network represents potential transmission pathways. For this study, we defined an infected case as a giraffe harboring an E. coli isolate with both an ARG-positive scaffold and the corresponding resistance phenotype. Separate k-tests and path tests were conducted on both social and spatial networks for each scaffold group that was identified in at least two AMR-positive giraffe. To correct for multiple hypothesis testing on the same association network, we applied Bonferroni adjustments to all k-test and path test P values within each network.


This work was supported by the National Science Foundation (doctoral dissertation improvement grant IOS-1209338), Phoenix Zoo, Oregon Zoo, Sigma Xi, Animal Behavior Society, American Society of Mammalogists, Explorer’s Club, Northeastern Wisconsin Zoo, Cleveland Metroparks Zoo, Cleveland Zoological Society, UC Davis Wildlife Health Center, and UC Davis Faculty Research Grant program. K.V. was supported by a National Science Foundation Graduate Research Fellowship during the field phase of the project, and E.A.M. was supported in part by the University of Minnesota MnDrive program.
We thank Bonnie Weber and the Atwill laboratory for assistance in laboratory work and K. Gitahi of the University of Nairobi, OPC staff, and the Office of the President of the Republic of Kenya for enabling various facets of the research.

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WHO. 2014. Antimicrobial resistance: global report on surveillance.WHO, Geneva, Switzerland.
OIE. 2016. OIE annual report on the use of antimicrobial agents in animals: better understanding of the global situation.OIE, Paris, France.
Vittecoq M, Godreuil S, Prugnolle F, Durand P, Brazier L, Renaud N, Arnal A, Aberkane S, Jean-Pierre H, Gauthier-Clerc M, Thomas F, Renaud F. 2016. Antimicrobial resistance in wildlife. J Appl Ecol 53:519–529.
Allen HK, Donato J, Wang HH, Cloud-Hansen KA, Davies J, Handelsman J. 2010. Call of the wild: antibiotic resistance genes in natural environments. Nat Rev Microbiol 8:251–259.
Fondi M, Karkman A, Tamminen MV, Bosi E, Virta M, Fani R, Alm E, McInerney JO. 2016. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol Evol 8:1388–1400.
Berglund B. 2015. Environmental dissemination of antibiotic resistance genes and correlation to anthropogenic contamination with antibiotics. Infect Ecol Epidemiol 5:28564.
Bondo KJ, Pearl DL, Janecko N, Boerlin P, Reid-Smith RJ, Parmley J, Jardine CM. 2016. Epidemiology of antimicrobial resistance in Escherichia coli isolates from raccoons (Procyon lotor) and the environment on swine farms and conservation areas in Southern Ontario. PLoS One 11:e0165303.
Jobbins SE, Alexander KA. 2015. From whence they came—antibiotic-resistant Escherichia coli in African wildlife. J Wildl Dis 51:811–820.
Rwego IB, Isabirye-Basuta G, Gillespie TR, Goldberg TL. 2008. Gastrointestinal bacterial transmission among humans, mountain gorillas, and livestock in Bwindi Impenetrable National Park, Uganda. Conserv Biol 22:1600–1607.
Wheeler E, Hong PY, Bedon LC, Mackie RI. 2012. Carriage of antibiotic-resistant enteric bacteria varies among sites in Galapagos reptiles. J Wildl Dis 48:56–67.
American Academy of Microbiology. 2009. Antibiotic resistance: an ecological perspective on an old problem. American Academy of Microbiology, Washington, DC.
Arnold KE, Williams NJ, Bennett M. 2016. ‘Disperse abroad in the land’: the role of wildlife in the dissemination of antimicrobial resistance. Biol Lett 12:20160137.
Wellington EMH, Boxall ABA, Cross P, Feil EJ, Gaze WH, Hawkey PM, Johnson-Rollings AS, Jones DL, Lee NM, Otten W, Thomas CM, Williams AP. 2013. The role of the natural environment in the emergence of antibiotic resistance in Gram-negative bacteria. Lancet Infect Dis 13:155–165.
Caprioli A, Busani L, Martel JL, Helmuth R. 2000. Monitoring of antibiotic resistance in bacteria of animal origin: epidemiological and microbiological methodologies. Int J Antimicrob Agents 14:295–301.
Radhouani H, Silva N, Poeta P, Torres C, Correia S, Igrejas G. 2014. Potential impact of antimicrobial resistance in wildlife, environment and human health. Front Microbiol 5:23.
van den Bogaard AE, Stobberingh EE. 2000. Epidemiology of resistance to antibiotics—links between animals and humans. Int J Antimicrob Agents 14:327–335.
Archie EA, Theis KR. 2011. Animal behaviour meets microbial ecology. Anim Behav 82:425–436.
Archie EA, Tung J. 2015. Social behavior and the microbiome. Curr Opin Behav Sci 6:28–34.
Blyton MDJ, Banks SC, Peakall R, Lindenmayer DB, Gordon DM. 2014. Not all types of host contacts are equal when it comes to E. coli transmission. Ecol Lett 17:970–978.
Bull CM, Godfrey SS, Gordon DM. 2012. Social networks and the spread of Salmonella in a sleepy lizard population. Mol Ecol 21:4386–4392.
VanderWaal KL, Atwill ER, Isbell LA, McCowan B. 2014. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis). J Anim Ecol 83:406–414.
Tung J, Barreiro LB, Burns MB, Grenier JC, Lynch J, Grieneisen LE, Altmann J, Alberts SC, Blekhman R, Archie EA. 2015. Social networks predict gut microbiome composition in wild baboons. Elife 4:e05224.
Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM, Gibbons SM, Larsen P, Shogan BD, Weiss S, Metcalf JL, Ursell LK, Vazquez-Baeza Y, Van Treuren W, Hasan NA, Gibson MK, Colwell R, Dantas G, Knight R, Gilbert JA. 2014. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345:1048–1052.
Inoue R, Ushida K. 2003. Vertical and horizontal transmission of intestinal commensal bacteria in the rat model. FEMS Microbiol Ecol 46:213–219.
Blaak H, Hamidjaja RA, van Hoek AHAM, de Heer L, Husman AMD, Schets FM. 2014. Detection of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli on flies at poultry farms. Appl Environ Microbiol 80:239–246.
Deng Y, Zeng Z, Chen S, He L, Liu Y, Wu C, Chen Z, Yao Q, Hou J, Yang T, Liu JH. 2011. Dissemination of IncFII plasmids carrying rmtB and qepA in Escherichia coli from pigs, farm workers and the environment. Clin Microbiol Infect 17:1740–1745.
Mercat M, Clermont O, Massot M, Ruppe E, de Garine-Wichatitsky M, Miguel E, Fox HV, Cornelis D, Andremont A, Denamur E, Caron A. 2016. Escherichia coli population structure and antibiotic resistance at a buffalo/cattle interface in southern Africa. Appl Environ Microbiol 82:1459–1467.
Levy SB, Fitzgerald GB, Macone AB. 1976. Spread of antibiotic resistance plasmids from chicken to chicken and from chicken to man. Nature 260:40–42.
Berge ACB, Epperson WB, Pritchard RH. 2005. Assessing the effect of a single dose florfenicol treatment in feedlot cattle on the antimicrobial resistance patterns in faecal Escherichia coli. Vet Res 36:723–734.
Fornasini M, Reves RR, Murray BE, Morrow AL, Pickering LK. 1992. Trimethoprim-resistant Escherichia coli in households of children attending day care centers. J Infect Dis 166:326–330.
Eisenberg JNS, Goldstick J, Cevallos W, Trueba G, Levy K, Scott J, Percha B, Segovia R, Ponce K, Hubbard A, Marrs C, Foxman B, Smith DL, Trostle J. 2012. In-roads to the spread of antibiotic resistance: regional patterns of microbial transmission in northern coastal Ecuador. J R Soc Interface 9:1029–1039.
Shorrocks B, Croft DP. 2009. Necks and networks: a preliminary study of population structure in the reticulated giraffe (Giraffa camelopardalis reticulata de Winston). Afr J Ecol 47:374–381.
Bercovitch FB, Berry PSM. 2013. Herd composition, kinship and fission-fusion social dynamics among wild giraffe. Afr J Ecol 51:206–216.
Carter KD, Seddon JM, Frere CH, Carter JK, Goldizen AW. 2013. Fission-fusion dynamics in wild giraffes may be driven by kinship, spatial overlap and individual social preferences. Anim Behav 85:385–394.
VanderWaal KL, Wang H, McCowan B, Fushing H, Isbell LA. 2014. Multilevel social organization and space use in reticulated giraffe (Giraffa camelopardalis). Behav Ecol 25:17–26.
Tenaillon O, Skurnik D, Picard B, Denamur E. 2010. The population genetics of commensal Escherichia coli. Nat Rev Microbiol 8:207–217.
Hartl DL, Dykhuizen DE. 1984. The population genetics of Escherichia coli. Annu Rev Genet 18:31–68.
McGee P, Scott L, Sheridan JJ, Earley B, Leonard N. 2004. Horizontal transmission of Escherichia coli O157:H7 during cattle housing. J Food Prot 67:2651–2656.
Johnson JR, Clabots C, Kuskowski MA. 2008. Multiple-host sharing, long-term persistence, and virulence of Escherichia coli clones from human and animal household members. J Clin Microbiol 46:4078–4082.
von Wintersdorff CJ, Penders J, van Niekerk JM, Mills ND, Majumder S, van Alphen LB, Savelkoul PH, Wolffs PF. 2016. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front Microbiol 7:173.
Woolhouse ME, Ward MJ. 2013. Sources of antimicrobial resistance. Science 341:1460–1461.
Schwarz S, Silley P, Simjee S, Woodford N, van Duijkeren E, Johnson AP, Gaastra W. 2010. Editorial: assessing the antimicrobial susceptibility of bacteria obtained from animals. J Antimicrob Chemother 65:601–604.
VanderWaal K, Enns EA, Picasso C, Packer C, Craft ME. 2016. Evaluating empirical contact networks as potential transmission pathways for infectious diseases. J R Soc Interface 13:1–10.
Rybarikova J, Dolejska M, Materna D, Literak I, Cizek A. 2010. Phenotypic and genotypic characteristics of antimicrobial resistant Escherichia coli isolated from symbovine flies, cattle and sympatric insectivorous house martins from a farm in the Czech Republic (2006-2007). Res Vet Sci 89:179–183.
Guo J, Li J, Chen H, Bond PL, Yuan Z. 2017. Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. Water Res 123:468–478.
Nesme J, Cecillon S, Delmont TO, Monier JM, Vogel TM, Simonet P. 2014. Large-scale metagenomic-based study of antibiotic resistance in the environment. Curr Biol 24:1096–1100.
Ahlstrom CA, Bonnedahl J, Woksepp H, Hernandez J, Olsen B, Ramey AM. 2018. Acquisition and dissemination of cephalosporin-resistant E. coli in migratory birds sampled at an Alaska landfill as inferred through genomic analysis. Sci Rep 8:7361.
Madoshi BP, Kudirkiene E, Mtambo MM, Muhairwa AP, Lupindu AM, Olsen JE. 2016. Characterisation of commensal Escherichia coli isolated from apparently healthy cattle and their attendants in Tanzania. PLoS One 11:e0168160.
Seni J, Falgenhauer L, Simeo N, Mirambo MM, Imirzalioglu C, Matee M, Rweyemamu M, Chakraborty T, Mshana SE. 2016. Multiple ESBL-producing Escherichia coli sequence types carrying quinolone and aminoglycoside resistance genes circulating in companion and domestic farm animals in Mwanza, Tanzania, harbor commonly occurring plasmids. Front Microbiol 7:142.
Mainda G, Bessell PB, Muma JB, McAteer SP, Chase-Topping ME, Gibbons J, Stevens MP, Gally DL, Bronsvoort BMD. 2015. Prevalence and patterns of antimicrobial resistance among Escherichia coli isolated from Zambian dairy cattle across different production systems. Sci Rep 5:12439.
Kikuvi GM, Schwarz S, Ombui JN, Mitema ES, Kehrenberg C. 2007. Streptomycin and chloramphenicol resistance genes in Escherichia coli isolates from cattle, pigs, and chicken in Kenya. Microb Drug Resist 13:62–68.
Musicha P, Feasey NA, Cain AK, Kallonen T, Chaguza C, Peno C, Khonga M, Thompson S, Gray KJ, Mather AE, Heyderman RS, Everett DB, Thomson NR, Msefula CL. 2017. Genomic landscape of extended-spectrum beta-lactamase resistance in Escherichia coli from an urban African setting. J Antimicrob Chemother 72:1602–1609.
Allen SE, Boerlin P, Janecko N, Lumsden JS, Barker IK, Pearl DL, Reid-Smith RJ, Jardine C. 2011. Antimicrobial resistance in generic Escherichia coli isolates from wild small mammals living in swine farm, residential, landfill, and natural environments in Southern Ontario, Canada. Appl Environ Microbiol 77:882–888.
Cole D, Drum DJV, Stallknecht DE, White DG, Lee MD, Ayers S, Sobsey M, Maurer JJ. 2005. Free-living Canada geese and antimicrobial resistance. Emerg Infect Dis 11:935–938.
Skurnik D, Ruimy R, Andremont A, Amorin C, Rouquet P, Picard B, Denamur E. 2006. Effect of human vicinity on antimicrobial resistance and integrons in animal faecal Escherichia coli. J Antimicrob Chemother 57:1215–1219.
Carlos C, Pires MM, Stoppe NC, Hachich EM, Sato MIZ, Gomes TAT, Amaral LA, Ottoboni LMM. 2010. Escherichia coli phylogenetic group determination and its application in the identification of the major animal source of fecal contamination. BMC Microbiol 10:161.
Smati M, Clermont O, Le Gal F, Schichmanoff O, Jaureguy F, Eddi A, Denamur E, Picard B, Coliville Group. 2013. Real-time PCR for quantitative analysis of human commensal Escherichia coli populations reveals a high frequency of subdominant phylogroups. Appl Environ Microbiol 79:5005–5012.
Alikhan NF, Zhou Z, Sergeant MJ, Achtman M. 2018. A genomic overview of the population structure of Salmonella. PLoS Genet 14:e1007261.
Doumith M, Day M, Ciesielczuk H, Hope R, Underwood A, Reynolds R, Wain J, Livermore DM, Woodford N. 2015. Rapid identification of major Escherichia coli sequence types causing urinary tract and bloodstream infections. J Clin Microbiol 53:160–166.
Gillings MR, Gaze WH, Pruden A, Smalla K, Tiedje JM, Zhu YG. 2015. Using the class 1 integron-integrase gene as a proxy for anthropogenic pollution. ISME J 9:1269–1279.
Gillings M, Boucher Y, Labbate M, Holmes A, Krishnan S, Holley M, Stokes HW. 2008. The evolution of class 1 integrons and the rise of antibiotic resistance. J Bacteriol 190:5095–5100.
Bean DC, Livermore DM, Hall LMC. 2009. Plasmids imparting sulfonamide resistance in Escherichia coli: implications for persistence. Antimicrob Agents Chemother 53:1088–1093.
Radstrom P, Swedberg G, Skold O. 1991. Genetic analyses of sulfonamide resistance and its dissemination in gram-negative bacteria illustrate new aspects of R plasmid evolution. Antimicrob Agents Chemother 35:1840–1848.
Mitema E, Kikuvi G. 2004. Surveillance of the overall use of antimicrobial drugs in humans over a 5 year period (1997–2001) in Kenya. J Antimicrob Chemother 54:966–967.
Mitema E, Kikuvi G, Wegener H, Stohr K. 2001. An assessment of antimicrobial consumption in food processing animals in Kenya. J Vet Pharmacol Ther 24:385–390.
Irungu P, Bett B, Mbogoh S, Nyamwaro S, Murilla G, Randolph T. 2007. Evidence of improper usage of veterinary drugs in cattle in Maasailand, Kenya. Bull Anim Health Prod Afr 55:210–225.
Kikuvi G, Ole-Mapenay I, Mitema E, Ombui J. 2006. Antimicrobial resistance in Escherichia coli isolates from faeces and carcass samples of slaughtered cattle, swine and chickens in Kenya. Israel J Vet Med 61:82–88.
GARP. 2011. Situation analysis and recommendations: antibiotic use and resistance in Kenya. Center for Disease Dynamics, Economics & Policy, Washington, DC.
Rolland RM, Hausfater G, Marshall B, Levy SB. 1985. Antibiotic-resistant bacteria in wild primates: increased prevalence in baboons feeding on human refuse. Appl Environ Microbiol 49:791–794.
Livermore DM, Warner M, Hall LMC, Enne VI, Projan SJ, Dunman PM, Wooster SL, Harrison G. 2001. Antibiotic resistance in bacteria from magpies (Pica pica) and rabbits (Oryctolagus cuniculus) from west Wales. Environ Microbiol 3:658–661.
Sears HJ, Brownlee I, Uchiyama JK. 1950. Persistence of individual strains of Escherichia coli in the intestinal tract of man. J Bacteriol 59:293–301.
Sears HJ, Brownlee I. 1952. Further observations on the persistence of individual strains of Escherichia coli in the intestinal tract of man. J Bacteriol 63:47–57.
Andersson DI. 2003. Persistence of antibiotic resistant bacteria. Curr Opin Microbiol 6:452–456.
Andersson DI, Hughes D. 2010. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol 8:260–271.
Haenni M, Chatre P, Metayer V, Bour M, Signol E, Madec JY, Gay E. 2014. Comparative prevalence and characterization of ESBL-producing Enterobacteriaceae in dominant versus subdominant enteric flora in veal calves at slaughterhouse, France. Vet Microbiol 171:321–327.
Nicolas-Chanoine MH, Gruson C, Bialek-Davenet S, Bertrand X, Thomas-Jean F, Bert F, Moyat M, Meiller E, Marcon E, Danchin N, Noussair L, Moreau R, Leflon-Guibout V. 2013. 10-Fold increase (2006–11) in the rate of healthy subjects with extended-spectrum beta-lactamase-producing Escherichia coli faecal carriage in a Parisian check-up centre. J Antimicrob Chemother 68:562–568.
Bartoloni A, Pallecchi L, Rodriguez H, Fernandez C, Mantella A, Bartalesi F, Strohmeyer M, Kristiansson C, Gotuzzo E, Paradisi F, Rossolini GM. 2009. Antibiotic resistance in a very remote Amazonas community. Int J Antimicrob Agents 33:125–129.
Langman VA. 1977. Cow-calf relationships in giraffe (Giraffa Camelopardalis Giraffa). Z Tierpsychol 43:264–286.
Pratt DM, Anderson VH. 1979. Giraffe cow-calf relationships and social-development of the calf in the Serengeti. Z Tierpsychol 51:233–251.
Alonso A, Sanchez P, Martinez JL. 2001. Environmental selection of antibiotic resistance genes. Environ Microbiol 3:1–9.
Beceiro A, Tomas M, Bou G. 2013. Antimicrobial resistance and virulence: a successful or deleterious association in the bacterial world? Clin Microbiol Rev 26:185–230.
Khachatryan AR, Hancock DD, Besser TE, Call DR. 2004. Role of calf-adapted Escherichia coli in maintenance of antimicrobial drug resistance in dairy calves. Appl Environ Microbiol 70:752–757.
Khachatryan AR, Besser TE, Hancock DD, Call DR. 2006. Use of a nonmedicated dietary supplement correlates with increased prevalence of streptomycin-sulfa-tetracycline-resistant Escherichia coli on a dairy farm. Appl Environ Microbiol 72:4583–4588.
Edrington TS, Farrow RL, Carter BH, Islas A, Hagevoort GR, Callaway TR, Anderson RC, Nisbet DJ. 2012. Age and diet effects on fecal populations and antibiotic resistance of a multi-drug resistant Escherichia coli in dairy calves. Agric Food Anal Bacteriol 2:162–174.
Mathew AG, Saxton AM, Upchurch WG, Chattin SE. 1999. Multiple antibiotic resistance patterns of Escherichia coli isolates from swine farms. Appl Environ Microbiol 65:2770–2772.
Nollet N, Houf K, Dewulf J, Catry B, De Zutter L, De Kruif A, Maes D. 2006. Variability in antimicrobial resistance among Salmonella enterica strains from fattening pigs and sows. Microb Drug Resist 12:74–81.
Linton KB, Lee PA, Richmond MH, Gillespie WA, Rowland AJ, Baker VN. 1972. Antibiotic resistance and transmissible R-factors in the intestinal coliform flora of healthy adults and children in an urban and a rural community. J Hyg (Lond) 70:99–104.
Nam HM, Lee HS, Byun JW, Yoon SS, Jung SC, Joo YS, Lim SK. 2010. Prevalence of antimicrobial resistance in fecal Escherichia coli isolates from stray pet dogs and hospitalized pet dogs in Korea. Microb Drug Resist 16:75–79.
Leuthold BM. 1979. Social organization and behavior of giraffe in Tsavo East National Park. Afr J Ecol 17:19–34.
Fennessy JT. 2004. Ecology of desert-dwelling giraffe Giraffa camelopardalis angolensis in northwestern Namibia. PhD thesis. University of Sydney, Sydney, Australia.
Harris S, Cresswell WJ, Forde PG, Trewhella WJ, Woollard T, Wray S. 1990. Home-range analysis using radio-tracking data—a review of problems and techniques particularly as applied to the study of mammals. Mammal Rev 20:97–123.
Opsahl T, Agneessens F, Skvoretz J. 2010. Node centrality in weighted networks: generalizing degree and shortest paths. Soc Networks 32:245–251.
Wasserman S, Faust K. 1994. Social network analysis: methods and applications.Cambridge University Press, Cambridge, United Kingdom.
Perofsky AC, Lewis RJ, Abondano LA, Di Fiore A, Meyers LA. 2017. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc Biol Sci 284:20172274.
Drewe JA. 2010. Who infects whom? Social networks and tuberculosis transmission in wild meerkats. Proc Biol Sci 277:633–642.
Alvarez LG, Webb CR, Holmes MA. 2011. A novel field-based approach to validate the use of network models for disease spread between dairy herds. Epidemiol Infect 139:1863–1874.
Cesaris L, Gillespie BE, Srinivasan V, Almeida RA, Zecconi A, Oliver SP. 2007. Discriminating between strains of Escherichia coli using pulsed-field gel electrophoresis and BOX-PCR. Foodborne Pathog Dis 4:473–480.
Goldberg TL, Gillespie TR, Singer RS. 2006. Optimization of analytical parameters for inferring relationships among Escherichia coli isolates from repetitive-element PCR by maximizing correspondence with multilocus sequence typing data. Appl Environ Microbiol 72:6049–6052.
Johnson JR, Clabots C. 2000. Improved repetitive-element PCR fingerprinting of Salmonella enterica with the use of extremely elevated annealing temperatures. Clin Diagn Lab Immunol 7:258–264.
CLSI. 2015. Performance standards for antimicrobial susceptibility testing. Twenty-fifth informational supplement. CLSI, Wayne, PA.
ESCMID. 2003. Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin Microbiol Infect 9:ix–xv.
Kahlmeter G, Brown DFJ, Goldstein FW, MacGowan AP, Mouton JW, Osterlund A, Rodloff A, Steinbakk M, Urbaskova P, Vatopoulos A. 2003. European harmonization of MIC breakpoints for antimicrobial susceptibility testing of bacteria. J Antimicrob Chemother 52:145–148.
Wiegand I, Hilpert K, Hancock REW. 2008. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat Protoc 3:163–175.
EUCAST. 2017. Breakpoint tables for interpretation of MICs and zone diameters, version 8.1.
Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120.
Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477.
Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069.
Clermont O, Christenson JK, Denamur E, Gordon DM. 2013. The Clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylo-groups. Environ Microbiol Rep 5:58–65.
Beghain J, Bridier-Nahmias A, Le Nagard H, Denamur E, Clermont O. 2018. ClermonTyping: an easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb Genom doi:
Larsen MV, Cosentino S, Rasmussen S, Friis C, Hasman H, Marvig RL, Jelsbak L, Sicheritz-Ponten T, Ussery DW, Aarestrup FM, Lund O. 2012. Multilocus sequence typing of total-genome-sequenced bacteria. J Clin Microbiol 50:1355–1361.
Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. 2012. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 67:2640–2644.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10:421.
Moura A, Soares M, Pereira C, Leitao N, Henriques I, Correia A. 2009. INTEGRALL: a database and search engine for integrons, integrases and gene cassettes. Bioinformatics 25:1096–1098.
Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. 2006. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 34:D32–D36.
Carattoli A, Zankari E, Garcia-Fernandez A, Larsen MV, Lund O, Villa L, Aarestrup FM, Hasman H. 2014. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 58:3895–3903.
Firth D. 1993. Bias reduction of maximum likelihood-estimates. Biometrika 80:27–38.
Heinze G, Schemper M. 2002. A solution to the problem of separation in logistic regression. Stat Med 21:2409–2419.
Heinze G, Ploner M. 2016. logistf: Firth’s bias-reduced logistic regression, vR package version 1.22.
VanderWaal KL, Atwill ER, Isbell LA, McCowan B. 2014. Quantifying microbe transmission networks for wild and domestic ungulates in Kenya. Biol Conserv 169:136–146.
Hanneman RA, Riddle M. 2005. Introduction to social network methods. University of California, Riverside, Riverside, CA.
Croft DP, Madden JR, Franks DW, James R. 2011. Hypothesis testing in animal social networks. Trends Ecol Evol 26:502–507.
Graham MH. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815.
Fox J, Monette G. 1992. Generalized collinearity diagnostics. J Am Stat Assoc 87:178–183.
Burnham KP, Anderson DR. 1998. Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York, NY.

Information & Contributors


Published In

cover image Applied and Environmental Microbiology
Applied and Environmental Microbiology
Volume 85Number 11 January 2019
eLocator: e02136-18
Editor: Andrew J. McBain, University of Manchester
PubMed: 30413480


Received: 31 August 2018
Accepted: 11 October 2018
Published online: 13 December 2018


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  1. Escherichia coli
  2. antibiotic resistance
  3. social network analysis
  4. transmission dynamics
  5. wildlife



Elizabeth A. Miller
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
Timothy J. Johnson
Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
George Omondi
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
Edward R. Atwill
Department of Veterinary Health & Reproduction, School of Veterinary Medicine, University of California, Davis, California, USA
Lynne A. Isbell
Department of Anthropology and Animal Behavior Graduate Group, University of California, Davis, California, USA
Brenda McCowan
Department of Veterinary Health & Reproduction, School of Veterinary Medicine, University of California, Davis, California, USA
Kimberly VanderWaal
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA


Andrew J. McBain
University of Manchester


Address correspondence to Elizabeth A. Miller, [email protected].

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