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Using Multi‐Response Models to Investigate Pathogen Coinfections Across Scales: Insights from Emerging Diseases of Amphibians

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Using Multi‐Response Models to Investigate Pathogen Coinfections Across Scales: Insights from Emerging Diseases of Amphibians University of South Florida Scholar Commons Integrative Biology Faculty and Staff Publications Integrative Biology 4-2018 Using Multi‐Response Models to Investigate Pathogen Coinfections across Scales: Insights from Emerging Diseases of Amphibians William E. Stutz University of Colorado Andrew R. Blaustein Oregon State University Cheryl J. Briggs University of California, Santa Barbara Jason T. Hoverman Purdue University Jason R. Rohr University of South Florida, [email protected] See next page for additional authors Follow this and additional works at: https://scholarcommons.usf.edu/bin_facpub Part of the Integrative Biology Commons Scholar Commons Citation Stutz, William E.; Blaustein, Andrew R.; Briggs, Cheryl J.; Hoverman, Jason T.; Rohr, Jason R.; and Johnson, Pieter T. J., "Using Multi‐Response Models to Investigate Pathogen Coinfections across Scales: Insights from Emerging Diseases of Amphibians" (2018). Integrative Biology Faculty and Staff Publications. 466. https://scholarcommons.usf.edu/bin_facpub/466 This Article is brought to you for free and open access by the Integrative Biology at Scholar Commons. It has been accepted for inclusion in Integrative Biology Faculty and Staff Publications by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Authors William E. Stutz, Andrew R. Blaustein, Cheryl J. Briggs, Jason T. Hoverman, Jason R. Rohr, and Pieter T. J. Johnson This article is available at Scholar Commons: https://scholarcommons.usf.edu/bin_facpub/466 1 2 DR. PIETER JOHNSON (Orcid ID : 0000-0002-7997-5390) 3 4 5 Article type : Research Article 6 7 8 Using multi-response models to investigate pathogen coinfections across scales: insights 9 from emerging diseases of amphibians 10 11 William E. Stutz1, Andrew R. Blaustein2, Cheryl J. Briggs3, Jason T. Hoverman4, Jason R. Rohr5, 12 Pieter T. J. Johnson1* 13 14 1Department of Ecology and Evolutionary Biology, University of Colorado at Boulder, Boulder, 15 CO 80309-0334 ([email protected] and [email protected]) 16 2Integrative Biology, 3029 Cordley Hall, Oregon State University, Corvallis, OR 97331-2914 17 ([email protected]) 18 3Ecology, Evolution and Marine Biology, University of California, Santa Barbara, Santa 19 Barbara, CA 93106-9610 ([email protected]) 20 4Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907- 21 2061 ([email protected]) 22 5Department of Integrative Biology, University of South Florida, 4202 East Fowler Avenue, 23 SCA 110, Tampa, FL 33620 ([email protected]) 24 25 *Corresponding author: Pieter Johnson, [email protected], 303-492-5623 (phone); 26 303-492-8699 (fax) 27 Word count: 6,527 28 Running title: Parasite coinfections across scales Author Manuscript 29 This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/2041-210X.12938 This article is protected by copyright. All rights reserved 2 30 Summary 31 1. Associations among parasites affect many aspects of host-parasite dynamics, but a lack of 32 analytical tools has limited investigations of parasite correlations in observational data that are 33 often nested across spatial and biological scales. 34 2. Here we illustrate how hierarchical, multiresponse modeling can characterize parasite 35 associations by allowing for hierarchical structuring, offering estimates of uncertainty, and 36 incorporating correlational model structures. After introducing the general approach, we apply 37 this framework to investigate coinfections among four amphibian parasites (the trematodes 38 Ribeiroia ondatrae and Echinostoma spp., the chytrid fungus Batrachochytrium dendrobatidis, 39 and ranaviruses) and among >2000 individual hosts, 90 study sites, and five amphibian host 40 species. 41 3. Ninety-two percent of sites and 80% of hosts supported two or more pathogen species. Our 42 results revealed strong correlations between parasite pairs that varied by scale (from among hosts 43 to among sites) and classification (microparasite versus macroparasite), but were broadly 44 consistent across taxonomically diverse host species. At the host-scale, infection by the 45 trematode R. ondatrae correlated positively with the microparasites, B. dendrobatidis and 46 ranavirus, which were themselves positively associated. However, infection by a second 47 trematode (Echinostoma spp.) correlated negatively with B. dendrobatidis and ranavirus, both at 48 the host- and site-level scales, highlighting the importance of differential relationships between 49 micro- and macroparasites. 50 4. Given the extensive number of coinfecting symbiont combinations inherent to natural systems, 51 particularly across multiple host species, multiresponse modeling of cross-sectional field data 52 offers a valuable tool to identify a tractable number of hypothesized interactions for experimental 53 testing while accounting for uncertainty and potential sources of co-exposure. For amphibians 54 specifically, the high frequency of co-occurrence and coinfection among these pathogens – each 55 of which is known to impair host fitness or survival – highlights the urgency of understanding 56 parasite associations for conservation and disease management. 57 Author Manuscript 58 Keywords: coinfection, hierarchical models, Batrachochytrium dendrobatidis, ranavirus, 59 Ribeiroia ondatrae, Echinostoma, multiresponse models, amphibians, emerging infectious 60 diseases This article is protected by copyright. All rights reserved 3 61 62 Tweetable Abstract (120 character limit) 63 Because hosts are infected with multiple parasite species, we use multi-response models to 64 understand diseases of amphibians. 65 Introduction 66 Many, if not all, organisms become infected by multiple parasite species during their lives 67 (Petney and Andrews 1998). Concurrent infections can alter fitness consequences for the host 68 either additively or through interactions between parasites (e.g., Cox 2001). Parasite interactions 69 – which can be positive or negative – occur through a variety of mechanisms, including indirect 70 interactions mediated by the host immune system as well as direct pathways, such as competition 71 for host resources (Graham 2002; Mideo 2009). Within-host interactions have recently been 72 implicated as influential in a number of human and wildlife diseases, including HIV and malaria 73 in humans (Korenromp et al. 2005), bovine tuberculosis in African buffalo (Ezenwa and Jolles 74 2015), colony collapse disorder in honeybees (Ratnieks and Carreck 2010), and emerging 75 infections in coral reefs (Bromenshenk et al. 2010). Coinfection-mediated changes in infection or 76 pathology within hosts can also affect the resulting transmission rates between hosts or among 77 populations (Ezenwa and Jolles 2015; Hellard et al. 2015; Johnson et al. 2015). Given the 78 potential consequences of coinfection for pathology, mortality, and transmission, understanding 79 the strength and extent of parasite associations in natural populations has become a major goal 80 for disease ecologists (Lello et al. 2004; Jolles et al. 2008; Telfer et al. 2010; Tompkins et al. 81 2011; Hellard et al. 2015). 82 Many factors influence parasite exposure and susceptibility both among host individuals 83 (e.g., age, sex, diet, genetics) and across host populations or species (e.g., environmental, spatial, 84 or epidemiological factors) (Wilson et al. 2002; Hawley and Altizer 2011; Hellard et al. 2015). In 85 some cases, parasite correlations observed at one scale (e.g., infections within individual hosts) 86 maybe absent or reversed at other scales (e.g., average infection load among sites), emphasizing 87 the importance of explicitly considering scale (the inappropriate application of correlations at 88 one scale to anotherAuthor Manuscript scale is sometimes referred to as the ‘ecological fallacy’) (Joseph et al. 89 2016). Thus, even in the absence of interspecific parasite interactions, spatial or temporal non- 90 independence of the factors influencing infection can generate correlations in parasite occurrence 91 or abundance. For instance, while infections with two plant viruses, Barley- and Cereal Yellow This article is protected by copyright. All rights reserved 4 92 Dwarf Virus (B/CYDV), often correlate positively within Oregon meadows (Seabloom et al. 93 2010), whether such patterns reflect parasite interaction (e.g., facilitation) or instead stem from 94 shared vectors or other environmental drivers is challenging to determine from surveys alone. 95 This obstacle is particularly relevant to cross-sectional studies in multisymbiont and multihost 96 systems, where the number of potential parasite associations is large and not easily tested 97 experimentally. Thus, rigorous analytical methods that can help to characterize covariation in 98 parasite abundance across different scales (e.g., among individuals, populations, communities, 99 regions) and among multiple host species are becoming increasingly important (Hellard et al. 100 2015). Effective tools to identify pathogen correlations from cross-sectional field data – while 101 characterizing sources of uncertainty and scale-specific covariates that can drive variation in 102 parasite exposure – are essential for understanding the degree
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