Evaluating the frequency and common drivers of within-host priority effects during coinfection

Fletcher W Halliday1†, Tuomas Aivelo†, Pablo Beldomenico†, Aaron Blackwell†, Vanessa Ezenwa†, Anna Jolles†, Erin Gorsich†, Claudia Fichtel†, Charlotte Defolie†, Anni M Hämäläinen†, Josue H Rakotoniaina†, Eva Kaesler†, Corey Freeman-Gallant†, Michael Huffman†, Natalie Olifers†, Arnaldo M Júnior†, Elisa P de Araujo†, Rita de Cassia Bianchi†,  Ana PN Gomes†, Paulo HD Cançado†, Matthew E Gompper†, & Paulo S D’Andrea†, Miroslava Soldanova†, Christopher Taylor†, Jason R Rohr

† contributed dataset

1Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, CH

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A current frontier in disease ecology is understanding how interactions among parasite species influence their epidemics. Interactions among parasites can result when prior infection by one parasite alters host susceptibility to a second parasite, generating priority effects among parasite species within host individuals. The increasing number of laboratory studies that aim to measure priority effects highlights growing interest among disease ecologists to understand these processes. Yet, laboratory studies, which are implemented at the scale of host individuals, are poorly suited to understand parasite epidemics, which occur at the scale of host populations, posing a key challenge for disease ecologists. One way to overcome this challenge is to mark and then repeatedly recapture sentinel hosts in the field, then analyze the resulting data using longitudinal regression models (Fenton et al. 2014; Hellard et al. 2015; Halliday et al. 2017, 2018). The purpose of this project was to implement this approach across empirical systems to test two broad questions:

  1. Is disease risk influenced by infection sequence during natural epidemics (i.e., do parasites generally experience/exhibit priority effects)?
  2. To what extent do these within-host priority effects depend on traits of individual parasite species?

To evaluate the role of within-host priority effects during parasite epidemics, we compiled longitudinal datasets of coinfection in host individuals across 13 host and 110 parasite species, including over 25,000 observations of host plants, primates, ungulates, small mammals, birds, and invertebrates. To evaluate the role of within-host priority effects, we performed time-until event analysis, specifically testing whether infection sequence among co-occurring parasites influenced their risk of infection (e.g., Halliday et al. 2017, 2019).  Surprisingly, the sequence of infection predicted fewer than 10% of the more than 1000 pairwise combinations of potentially interacting parasites. This rarity of within-host priority effects may result from a lack of natural variation in infection sequence, indicating that within-host priority effects may be a less common driver of parasite epidemics than previously thought, and highlighting the need for experimental manipulations of parasite infection in the field (e.g., Ezenwa & Jolles 2015; Pedersen & Fenton 2015; Halliday et al. 2017). Showing weak support for ecological theory, parasites with a high degree of niche overlap were slightly more sensitive to priority effects than parasites with a low degree of niche overlap, and this effect was stronger for parasites with narrow niche requirements than with wide niche-requirements. However, in contrast with theory, the frequency of priority effects could not be explained by a parasite’s impact on its host. Together, these results indicate that priority effects may be less commonly detected during natural epidemics than previously expected, and that within-host priority effects among parasites may not necessarily follow the same ecological rules of community assembly as their free-living counterparts.

 The database:

We compiled a database of longitudinal surveys of host individuals (i.e., mark-recapture data), where hosts were unmanipulated (sentinels were ok), and surveyed for infection by multiple parasites. This study focused on interspecific interactions among parasites rather than intraspecific interactions among strains of the same parasite species. The final database includes plants, invertebrate, and vertebrate hosts (Table 1).

Table 1. Contributing authors and associated host datasets
Contributors Host species Host taxon
T. Aivelo Microcebus rufus Primate
P. Beldomenico Hydrochoerus hydrochaeris Rodent
A. Blackwell Homo sapiens Human
V. Ezenwa, A. Jolles, E. Gorsich Syncerus caffer Wild ungulate
AM Hämäläinen, JH Rakotoniaina, E. Kaesler, C. Kraus, P. Kappeler Microcebus murinus Primate
C. Fichtel, C. Defolie Eulemur rufifrons Primate
C. Freeman-Gallant Geothlypis trichas Bird
F. Halliday Festuca arundinacea Plant
M. Huffman Pan troglodytes Primate
N. Olifers, AM Júnior, EP de Araujo , R de Cassia Bianchi,  APN Gomes, PHD Cançado, ME Gompper, & PS D’Andrea Nasua nasua &              Cerdocyon thous Carnivore
M. Soldanova Lymnaea stagnalis Invertebrate
C. Taylor Microtus agrestis Rodent

 

A standard measurement of within-host priority effects across systems:

To facilitate comparisons across systems, data were analyzed using a common analytical method (described in Halliday et al 2017). Briefly, to detect priority effects, we recorded the sequence of infection by each parasite on each host individual. Time in days since the first survey of each host individual was used as a proxy for exposure to parasite propagules. To model within-host priority effects, we constructed a series of models following Halliday et al (2017). These models explicitly measure priority effects by testing whether the sequence of infection on an individual host influences the rate of infection by each parasite. Each model included one dependent variable pertaining to one parasite species (“the focal parasite”).  To explicitly model priority effects, we used Cox Regression with Firth’s Penalized Likelihood to reduce bias from factors exhibiting (quasi)complete separation (Heinze & Schemper 2002) from the R package, coxphf (Ploner & Heinze 2015), to estimate the probability of a host transitioning from uninfected to infected. Specifically, the dependent variable in each model was time to infection, modeled as the transition rate from uninfected to infected as a function of exposure time. We modeled time to infection resulting from a baseline rate of infection shared by all individuals and modified by the infection status of the host by each other parasite during the previous survey (treated as a time-varying coefficient). Exponentiated coefficients are interpreted as multiplicative changes in infection rate, providing a standardized estimate of priority effects across study systems. Statistically significant (p<0.05) positive and negative coefficients were interpreted as evidence of facilitative and antagonistic priority effects, respectively, whereas non-significant coefficients were interpreted as lacking sufficient evidence of a priority effect occurring.

Our hypotheses:

Hypothesis 1 – Niche overlap. Priority effects are expected to occur more commonly among species with a high degree of niche overlap (Vannette & Fukami 2014). A host comprises the entire niche available to parasites during infection (Kuris et al. 1980; Rynkiewicz et al. 2015), and thus coinfecting parasites often exhibit high niche overlap (Sousa & Zoologist 1992; Graham 2008; Seabloom et al. 2015). The degree of niche overlap among parasites may additionally vary depending on whether parasites share common vectors, employ similar feeding strategies, or share infection sites. We hypothesized that such parasites would therefore more commonly experience priority effects.

Hypothesis 2 – Parasite virulence. Priority effects are expected to occur when early arriving species have a high impact on shared resources with later arriving species (Vannette & Fukami 2014). Parasites require host resources for survival, growth, and reproduction (Lafferty & Kuris 2002). We hypothesized that early-arriving parasites that were more damaging to their hosts would more commonly experience priority effects.

Hypothesis 3 – Host breadth. Priority effects are expected occur when the late arriving species have high requirements for shared resources with early arriving species (Vannette & Fukami 2014). Late arriving species with narrow niche requirements may therefore experience the strongest priority effects. For free-living species, the breadth of niche requirements may manifest as generality in a species’ use of habitat types or tolerance to environmental conditions. For parasites, the breadth of niche requirements may be related to host specificity, defined as the ability to infect multiple unrelated host taxa (Barrett et al. 2009; Park et al. 2018). We hypothesized that late-arriving host specialists would therefore more commonly experience priority effects.

Analytical methods

Data analysis was performed using R version 3.5.2 (R Core Team 2015). To test whether parasites that shared similar traits more commonly experienced priority effects (Hypothesis 1), we used a series of generalized linear mixed models using the lme4 package, version 1.1-20 (Bates et al. 2014), treating the source dataset and host species (Table 1) as random intercepts in each model. The dependent variable in each model was whether or not a pairwise combination of parasites exhibited a priority effect (0,1), and the independent variables where whether or not parasites were the same type (e.g., both macroparasites), shared a common infection site (e.g., both gastrointestinal), or shared the same transmission mode (e.g., both tick-vectored).  Data for every trait was not available for every parasite species (Table S1), so in order to maximize the number of studies contributing to the analysis, each independent variable was therefore tested in a separate analysis. We used inverse-variance weighting based on the number of surveys per host to give more explanatory weight to studies with a greater number of surveys of each host individual. However, because this weighting strongly influenced the results of the analyses, we report results from both weighted and unweighted analyses.

To test whether early-arriving parasites that were more damaging to their hosts would more commonly experience priority effects, we again tested whether or not a pairwise combination of parasites exhibited a priority effect, this time as a function of host virulence. We coded host virulence as 1 if the parasite had few reported adverse effects on the host, 2 if the parasite was known to moderately reduce host fitness, and 3 if the parasite was known to seriously impact host survival (e.g., infections frequently leading to host mortality).

To test whether late-arriving host specialists more commonly experienced priority effects, we used a similar, weighted generalized linear mixed model in lme4, testing whether or not a pairwise combination of parasites exhibited a priority effect as a function of the host specificity of the late-arriving parasite (generalist = known to infect more than one host species; specialist = known to infect only a single host species).

We finally tested whether niche-overlap lead to stronger priority effects among late-arriving host specialists by analyzing the interaction coefficient for each pairwise combination of parasites as a dependent variable.  Because more closely related species tend to exhibit higher niche-overlap, we used the phylogenetic relatedness of parasites as a metric of niche-overlap, and modeled its interaction with host specificity of the late-arriving parasite and their main effects as independent variables in this analysis. Parasites were defined as “closely related” if they shared at least the same order, and “distantly related” if they were from different orders or more distantly related.

Results

Contrary to expectations, we found surprisingly limited evidence of priority effects across our database. Specifically, out of 1128 potential pairwise interactions, we detected only 98 interactions resulting from prior infection (i.e., priority effects). 64 were facilitative (prior infection by one parasite species facilitated subsequent infection by another species), 34 interactions were antagonistic (prior infection by one parasite species inhibited infection by a subsequent species), and the remaining 1030 lacked sufficient evidence to support a priority effect occurring. This surprising result may stem from a lack of variation in infection sequence among study systems. Early infections were often dominated by one or a few parasite species (Fig. 1), potentially resulting from variation in parasite phenology (Halliday et al. 2017) or variation in parasite transmission rates (Clay et al. 2019). Together, these results point to the need for studies that systematically manipulate infection sequence during natural epidemics, potentially through the use of pesticides or anti-parasite treatments of hosts (Pedersen & Fenton 2015).

 

first_infection_plot_4col
Fig 1. In most study systems, early infections were dominated by one or a few parasites. This lack of variation in infection sequence may explain why fewer priority effects were observed than expected based on laboratory studies. Each point represents a single parasite species, and the y-axis represents the proportion of hosts whose first infection was associated with that parasite. Values on the y-axis sum to >1 due to simultaneous infections.

We found weak support for the hypothesis that parasites with high niche overlap would more commonly experience priority effects. Specifically, when weighted by the number of samples per host individual, parasites of the same type (e.g., both macroparasites), parasites that infected the same site (e.g., gastrointestinal parasites), and parasites with the same transmission mode (e.g., both tick vectored), all experienced significantly more priority effects than parasites of different types, different sites, and different transmission modes (p= 0.006, p<0.001, and p = 0.004, respectively; Fig 2). However, these effects were of small magnitude and were not statistically significant when the same analyses were performed with unweighted data (p = 0.98, p = 0.48, p = 0.68, respectively). We found no support for the hypothesis that early-arriving parasites that were more damaging to their hosts would more commonly experience priority effects. Even though the weighted regression indicated that parasite virulence significantly predicted the priority effects (p < 0.001), the direction of this effect was not consistent with our prediction, with moderately virulent parasites exhibiting the lowest rate of priority effects (Fig 2). The effect of host virulence on the probability of observing a priority effect was not significant when the analysis was performed with unweighted data (p = 0.25).

 

hypothesis_1_3_short
Fig 2. Priority effects were somewhat more commonly observed among parasites of the same type, infection site, or transmission mode (p0.05 when unweighted), lending some support to hypothesis one. In contrast with hypothesis one, priority effects were no more commonly observed when early arriving species had greater or smaller impacts on their hosts, though weighted regression indicates that moderately viulent parasites may exhibit fewer priority effects than low- or highly virulent parasites.

We found strong evidence that later arriving parasites with narrow host breadth (e.g., specialists) more commonly experienced priority effects than parasites with wider host breadth (e.g., generalists; weigthed p < 0.001, unweighted p = 0.007; Fig. 3 – inset). Furthermore, among host specialists, more closely related parasites tended to exhibit more antagonistic interactions (p = 0.003). Together, these results indicate that priority effects among parasites may be less common than expected based on laboratory studies, and that priority effects are rarely consistent with predictions from ecological theory.

 

plot_with_inset_no_annotation
Fig 3. Priority effects were more commonly observed when late-arriving parasites were host specialists, supporting hypothesis two. Among specialists, more closely related parasites tended to experience more competition. Black circles are model estimated means, error bars are 95% confidence intervals; colored points are estimates from each individual pairwise combination of studies, with size corresponding to the number of samples per host individual. Blue indicates significant facilitation, red indicates significant antagonism, and grey indicates insufficient evidence for significant priority effect. The inset shows the model estimated probability of a priority effect on the y-axis as a function of host specificity.

 

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Ezenwa, V. & Jolles, A. (2015). Opposite effects of anthelmintic treatment on microbial infection at individual versus population scales. Science (80-. ).

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Table S1. Parasites and their associated traits

Parasite Type Virulence Specialist Route Mode Helminth Intracellular
Anaplasma centrale microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Ascaris lumbricoides macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Anaplasma marginale microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Anaplasma phagocytophilum microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Acanthocephala macroparasite Low Complex Ingestion Helminth Not intracellular
Amblyomma cajennense ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Amblyomma ovale ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Amblyomma parvum ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Apicomplexa microparasite Low Complex Not Helminth Intracellular
Ascaridae macroparasite Low Direct Ingestion Helminth Not intracellular
Ascaridida macroparasite Low Direct Ingestion Helminth Not intracellular
Aspidoderidae macroparasite Low Direct Ingestion Helminth Not intracellular
Australapatemon burti macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Australapatemon minor macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Babesia microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Ballantidium microparasite Low Direct Ingestion Not Helminth Not intracellular
Bartonella microparasite Medium Generalist Complex Flea vectored Not Helminth Intracellular
Brucella abortus microparasite Medium Generalist Direct Ingestion Not Helminth Intracellular
Callistoura macroparasite Low Direct Ingestion Helminth Not intracellular
Capillaria macroparasite Low Complex Ingestion Helminth Not intracellular
Cestoda macroparasite Low Complex Ingestion Helminth Not intracellular
Conoidasida microparasite Low Direct Ingestion Not Helminth Intracellular
Conoidasida microparasite Low Direct Ingestion Not Helminth Intracellular
Colletotrichum cereale microparasite Low Generalist Direct Rain splash Not Helminth Not intracellular
Cooperia macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Cotylurus cornutus macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Cowpox virus microparasite Medium Generalist Direct Direct contact Not Helminth Intracellular
Dicrocoeliidae macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Diphyllobothriidae macroparasite Low Complex Ingestion Helminth Not intracellular
Diplostomum pseudospathaceum macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Dipylidiidae macroparasite Low Complex Ingestion Helminth Not intracellular
Ehrlichia ruminantium microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Anaplasma microparasite Low Specialist Complex Tick vectored Not Helminth Intracellular
Echinocleus hydrochaeri macroparasite Low Specialist Complex Ingestion Helminth Not intracellular
Echinoparyphium aconiatum macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Echinoparyphium recurvatum macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Echinostoma revolutum macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Eimeria boliviensis microparasite Medium Specialist Direct Ingestion Not Helminth Intracellular
Eimeria hydrochaeri microparasite Medium Specialist Direct Ingestion Not Helminth Intracellular
Eimeria microparasite Medium Specialist Direct Ingestion Not Helminth Intracellular
Entamoeba microparasite Medium Direct Ingestion Not Helminth Not intracellular
Fasciolidae macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Siphonaptera ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Giardia lamblia microparasite Medium Generalist Direct Ingestion Not Helminth Not intracellular
Haemonchus macroparasite Medium Direct Ingestion Helminth Not intracellular
Ancylostomatidae macroparasite Low Specialist Direct Free-living Helminth Not intracellular
Hymenolepis macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Hypoderaeum conoideum macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Laelapidae ectoparasite Low Direct Free-living Not Helminth Not intracellular
Lemuricola macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Lemuricola macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Lemurostrongylus macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Leucocytozoon microparasite Low Generalist Complex Blackfly vectored Not Helminth Intracellular
Listrophorus ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Phthiraptera ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Metagonymus macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Hystrichopsyllidae ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Moliniella anceps macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Myobiidae ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Neotrichodectes pallidus ectoparasite Low Specialist Direct Free-living Not Helminth Not intracellular
Notocotylus attenuatus macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Oesophagostomum macroparasite Medium Generalist Direct Ingestion Helminth Not intracellular
Opisthioglyphe ranae macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Opisthorchis macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Oesophagostomum stephanostomum macroparasite Medium Generalist Direct Ingestion Helminth Not intracellular
Oxyuridae macroparasite Low Direct Ingestion Helminth Not intracellular
Oxyuridae macroparasite Low Direct Ingestion Helminth Not intracellular
Oxyuridae macroparasite Low Direct Ingestion Helminth Not intracellular
Pararhabdonema macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Paryphostomum radiatum macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Physalopteridae macroparasite Low Complex Ingestion Helminth Not intracellular
Plagiorchis elegans macroparasite Low Specialist Complex Ingestion Helminth Not intracellular
Plasmodium microparasite Medium Specialist Complex Mosquito vectored Not Helminth Intracellular
Protozoophaga obesa macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Strongyloides macroparasite Low Generalist Direct Free-living Helminth Not intracellular
Caenorhabditis macroparasite Low Direct Ingestion Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Chromadorea macroparasite Low Helminth Not intracellular
Enterobius macroparasite Low Direct Ingestion Helminth Not intracellular
Panagrellus macroparasite Low Direct Ingestion Helminth Not intracellular
Puccinia coronata microparasite Low Specialist Complex Airborne Not Helminth Not intracellular
Rhizoctonia solani macroparasite Medium Generalist Direct Rain splash Not Helminth Not intracellular
Schistosoma macroparasite Medium Generalist Complex Free-living Helminth Not intracellular
Schistosomatidae macroparasite Medium Generalist Complex Free-living Helminth Not intracellular
Strongyloides fuelleborni macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Strongiloides macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongiloides chapini macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Subulura macroparasite Low Complex Ingestion Helminth Not intracellular
Subulura macroparasite Low Complex Ingestion Helminth Not intracellular
Theileria mutans microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Theileria parva microparasite High Generalist Complex Tick vectored Not Helminth Intracellular
Theileria sable microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Theileria sp (buffalo) microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Trichuris trichiura macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Theileria velifera microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Mycobacterium tuberculosis microparasite Medium Specialist Direct Airborne Not Helminth Intracellular
Thysanotaenia macroparasite Low Specialist Complex Ingestion Helminth Not intracellular
Ixodida ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Trematoda macroparasite Low Complex Helminth Not intracellular
Trichinellidae macroparasite Low Complex Ingestion Helminth Not intracellular
Trichobilharzia szidati macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Trichodectes canis ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Trichostrongyloidea macroparasite Low Direct Ingestion Helminth Not intracellular
Trichuris macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Trichuris cutillasae macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Trichuris macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Trypanosoma microparasite Medium Generalist Complex Tsetse fly vectored Not Helminth Intracellular
Trichuris trichiura macroparasite Low Specialist Direct Ingestion Helminth Not intracellular

 

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