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Accounting for Imperfect Detection Reveals Role of Host Traits in Structuring Viral Diversity

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Accounting for Imperfect Detection Reveals Role of Host Traits in Structuring Viral Diversity bioRxiv preprint doi: https://doi.org/10.1101/2020.06.29.178798; this version posted June 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Accounting for imperfect detection reveals role of host traits in structuring viral diversity 2 of a wild bat community 3 4 Anna R. Sjodin1,2,*, Simon J. Anthony3, Michael R. Willig1,4, Morgan W. Tingley1,5 5 6 1 Ecology and Evolutionary Biology, University of Connecticut, 75 N. Eagleville Road, Unit 7 3043, Storrs, Connecticut, USA 06269 8 2 Biological Sciences, University of Idaho, 875 Perimeter Drive, MS 3051, Moscow, Idaho, USA 9 83844 10 3 Center for Infection and Immunity, Columbia University, 722 W. 168th Street, 17th Floor, New 11 York, New York, USA 10032 12 4 Center for Environmental Sciences & Engineering and Institute of the Environment, University 13 of Connecticut, Storrs, Connecticut, USA 06269 14 5 Ecology and Evolutionary Biology, University of California – Los Angeles, 621 Charles E 15 Young Drive South, Los Angeles, California, USA 90095 16 17 18 * Corresponding author 19 email: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.29.178798; this version posted June 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 20 Abstract: Understanding how multi-scale host heterogeneity affects viral community assembly 21 can illuminate ecological drivers of infection and host-switching. Yet, such studies are hindered 22 by imperfect viral detection. To address this issue, we used a community occupancy model – 23 refashioned for the hierarchical nature of molecular-detection methods – to account for failed 24 detection when examining how individual-level host traits affect herpesvirus richness in eight 25 species of wild bats. We then used model predictions to examine the role of host sex and species 26 identity on viral diversity at the levels of host individual, population, and community. Results 27 demonstrate that cPCR and viral sequencing failed to perfectly detect viral presence. 28 Nevertheless, model estimates correcting for imperfect detection show that reproductively active 29 bats, especially reproductively active females, have significantly higher viral richness, and host 30 sex and species identity interact to affect viral richness. Further, host sex significantly affects 31 viral turnover across host populations, as females host more heterogeneous viral communities 32 than do males. Results suggest models of viral ecology benefit from integration of multi-scale 33 host factors, with implications for bat conservation and epidemiology. Furthermore, by 34 accounting for imperfect detection in laboratory assays, we demonstrate how statistical models 35 developed for other purposes hold promising possibilities for molecular and epidemiological 36 applications. 37 38 Key Words: 39 cPCR, Chiroptera, Herpesvirus, Occupancy Modeling, Virus Community 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.29.178798; this version posted June 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 40 1. Introduction: 41 Emerging infectious viruses, such as SARS-CoV 2 or Zika, represent single pathogen 42 species embedded within multi-host, multi-pathogen systems. Community ecology – at the scale 43 of host communities, and also at the scale of viral communities – is increasingly recognized as a 44 productive framework with which to dissect such complex dynamics, illuminating risk factors 45 associated with pathogen transmission, host switching, and spillover from wildlife to humans, 46 among others [1, 2, 3, 4, 5, 6]. However, viral community ecology is limited by two major 47 quantitative obstacles. First, most viruses are rare [7, 8, 9, 10, 11]. This means that large numbers 48 of host individuals must be sampled for population-level detection, and resulting datasets are 49 highly zero-inflated. Second, many wildlife viruses remain unknown to science [12, 13]. 50 Currently, two of the most common methods for characterizing viral communities and 51 finding new viral taxa are unbiased high-throughput sequencing (HTS; e.g. metagenomics) and 52 consensus polymerase chain reaction (cPCR; [10]). HTS is advantageous when the goal is to 53 construct an entire viral genome or when so little is known about the target taxon that primers 54 cannot be designed. However, HTS lacks the sensitivity of PCR methods. cPCR is a method that 55 relies on the use of degenerate primers for the broad amplification of both known and unknown 56 viruses [10, 12]. It targets genome regions that are highly conserved among related viruses (e.g. 57 at the family or genus level) and can detect multiple coinfecting viruses, if present. The 58 degenerate primers bind with different efficiency to different viruses within a targeted taxon, so 59 viruses with lower homology in the primer-binding region will not be detected as efficiently as 60 would viruses with high sequence homology. Additionally, degenerate primers are, by definition, 61 less specific. This reduces the sensitivity of cPCR and increases the probability of false negatives 62 when viral load in the sample is low. This is a particular problem in wildlife surveys, where 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.29.178798; this version posted June 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 63 many samples are taken from healthy or asymptomatic animals [4, 14, 15, 16, 17, 18], and 64 absence of symptoms is often associated with low viral load [19, 20, 21]. As a result, both HTS 65 and cPCR methods have a high possibility of failed detection of true infections (i.e. false 66 negatives). 67 Viral ecological studies using HTS and cPCR methods have mostly failed to account for 68 the issues of false negatives. In general, high rates of false negatives are acknowledged (e.g. [4, 69 22]) but are rarely considered a major problem preventing honest inference. This becomes 70 particularly problematic for studies of viral diversity and community assembly when accurate 71 measures of the number and identity of detected viruses are necessary to infer patterns and 72 mechanisms of co-infecting viruses. As consequent viral OTUs are missed for each sampled 73 individual, the missing data compound when individuals are aggregated at the population and 74 community levels. A handful of recent studies have used occupancy modeling techniques, 75 adapted from wildlife ecology (where sites are surveyed to detect species, rather than where 76 individual animals are assayed to detect viral strains), to better account for imperfect detection in 77 an epidemiological context [23, 24, 25, 26]. However, as best we are aware, no one has yet 78 adapted these methods to the detection process when using HTS or cPCR for biodiversity 79 discovery or characterization of viral community ecology [27], and it is not a common practice to 80 statistically account for failed detection in such a way across molecular biology more generally. 81 Additionally, viral ecologists have struggled to synthesize community dynamics across 82 host scales. For example, infection heterogeneity among individuals is a key element in 83 infectious disease dynamics [28, 29, 30] that is still relatively unexplored in viral communities of 84 wildlife. Pathogen aggregation in host individuals can affect pathogen interactions, transmission, 85 and host fitness, scaling up to alter population and community dynamics of the host. Failure to 4 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.29.178798; this version posted June 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 86 account for such heterogeneity has potentially dangerous or expensive consequences for 87 modelling outbreak probabilities and forecasting disease spread [31, 32, 33]. 88 Bats are an important host to understand from the perspective of viral transmission, as 89 they seem to harbor a high number of viruses capable of infecting humans [34, 35, 36]. Cave- 90 roosting bats in Puerto Rico are abundant and relatively easily captured, making them a practical 91 system for addressing viral dynamics from an ecological perspective. Additionally, much is 92 known about their behavior, biology, and life history that can guide contextual understanding of 93 viral community dynamics [37]. For example, most cave-roosting species in Puerto Rico show 94 sex-related spatial segregation during reproduction [37]. Females often use a centralized 95 maternity colony within the cave, where they frequently and repeatedly come into contact with 96 the same individuals. Males do not use this space. By continually re-exposing themselves to the 97 same individual conspecifics, females likely reduce the variety of viruses to which they are 98 exposed. Thus, host sex likely effects pathogen diversity. Indeed, the role of host sex has shown 99 female-biased effects on ectoparasite and macro-parasite infection in Puerto Rican bats [38, 39]. 100 Furthermore, many Puerto Rican caves are well-studied in terms of bat diversity and abundance.
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