Using multi‐response models to investigate pathogen coinfections across scales: Insights from emerging diseases of amphibians

Associations among parasites affect many aspects of host–parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales. Here we illustrate how hierarchical, multiresponse modellin...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Methods in ecology and evolution 2018-04, Vol.9 (4), p.1109-1120
Hauptverfasser: Stutz, William E., Blaustein, Andrew R., Briggs, Cheryl J., Hoverman, Jason T., Rohr, Jason R., Johnson, Pieter T. J., Kembel, Steven
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1120
container_issue 4
container_start_page 1109
container_title Methods in ecology and evolution
container_volume 9
creator Stutz, William E.
Blaustein, Andrew R.
Briggs, Cheryl J.
Hoverman, Jason T.
Rohr, Jason R.
Johnson, Pieter T. J.
Kembel, Steven
description Associations among parasites affect many aspects of host–parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales. Here we illustrate how hierarchical, multiresponse modelling can characterize parasite associations by allowing for hierarchical structuring, offering estimates of uncertainty and incorporating correlational model structures. After introducing the general approach, we apply this framework to investigate coinfections among four amphibian parasites (the trematodes Ribeiroia ondatrae and Echinostoma spp., the chytrid fungus Batrachochytrium dendrobatidis and ranaviruses) and among >2,000 individual hosts, 90 study sites and five amphibian host species. Ninety‐two percent of sites and 80% of hosts supported two or more pathogen species. Our results revealed strong correlations between parasite pairs that varied by scale (from among hosts to among sites) and classification (microparasite versus macroparasite), but were broadly consistent across taxonomically diverse host species. At the host‐scale, infection by the trematode R. ondatrae correlated positively with the microparasites, B. dendrobatidis and ranavirus, which were themselves positively associated. However, infection by a second trematode (Echinostoma spp.) correlated negatively with B. dendrobatidis and ranavirus, both at the host‐ and site‐level scales, highlighting the importance of differential relationships between micro‐ and macroparasites. Given the extensive number of coinfecting symbiont combinations inherent to natural systems, particularly across multiple host species, multiresponse modelling of cross‐sectional field data offers a valuable tool to identify a tractable number of hypothesized interactions for experimental testing while accounting for uncertainty and potential sources of co‐exposure. For amphibians specifically, the high frequency of co‐occurrence and coinfection among these pathogens—each of which is known to impair host fitness or survival—highlights the urgency of understanding parasite associations for conservation and disease management.
doi_str_mv 10.1111/2041-210X.12938
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5978769</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2022940800</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5148-cd6f4c3315e6429b570bd68011b223a66e0d0458a9090b2f42176ebfcd13f15e3</originalsourceid><addsrcrecordid>eNqFkb9uFDEQxi0EItGRmg5ZoqG5xPb-OZsCCUUXiBREk0h0ltc73nO0ay-e3aBU5BF4Rp4EXy6cAk2msEf2bz575iPkNWfHPMeJYCVfCs6-HXOhCvmMHO5Pnj_KD8gR4jXLUUjFRPmSHAglay5ldUh-XqEPHR3mfvK_734lwDEGBDrEFnqkU6Q-3ABOvjMT0NFMm9hBoDb64MBOPsPU2BQRKVrTA76n5wF9t5mQuhQHCgOkbvtE6xEMAtLoqBnGjW-8CfiKvHCmRzh62Bfk6mx9efp5efH10_npx4ulrXgpl7atXWmLgldQl0I11Yo1bS0Z540QhalrYC0rK2kUU6wRrhR8VUPjbMsLl4uKBfmw0x3nZoDWQpiS6fWY_GDSrY7G639vgt_oLt7oSq3kqlZZ4N2DQIrf5zwRPXi00PcmQJxR53ErVTIuy4y-_Q-9jnMKub1MCZEpmb1YkJMddT-9BG7_Gc701t-tZF6yg_re31zx5nEPe_6vmxmod8AP38PtU3r6y3pd7JT_APgxs5E</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2022940800</pqid></control><display><type>article</type><title>Using multi‐response models to investigate pathogen coinfections across scales: Insights from emerging diseases of amphibians</title><source>Wiley Online Library Journals Frontfile Complete</source><source>Alma/SFX Local Collection</source><creator>Stutz, William E. ; Blaustein, Andrew R. ; Briggs, Cheryl J. ; Hoverman, Jason T. ; Rohr, Jason R. ; Johnson, Pieter T. J. ; Kembel, Steven</creator><contributor>Kembel, Steven</contributor><creatorcontrib>Stutz, William E. ; Blaustein, Andrew R. ; Briggs, Cheryl J. ; Hoverman, Jason T. ; Rohr, Jason R. ; Johnson, Pieter T. J. ; Kembel, Steven ; Kembel, Steven</creatorcontrib><description>Associations among parasites affect many aspects of host–parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales. Here we illustrate how hierarchical, multiresponse modelling can characterize parasite associations by allowing for hierarchical structuring, offering estimates of uncertainty and incorporating correlational model structures. After introducing the general approach, we apply this framework to investigate coinfections among four amphibian parasites (the trematodes Ribeiroia ondatrae and Echinostoma spp., the chytrid fungus Batrachochytrium dendrobatidis and ranaviruses) and among &gt;2,000 individual hosts, 90 study sites and five amphibian host species. Ninety‐two percent of sites and 80% of hosts supported two or more pathogen species. Our results revealed strong correlations between parasite pairs that varied by scale (from among hosts to among sites) and classification (microparasite versus macroparasite), but were broadly consistent across taxonomically diverse host species. At the host‐scale, infection by the trematode R. ondatrae correlated positively with the microparasites, B. dendrobatidis and ranavirus, which were themselves positively associated. However, infection by a second trematode (Echinostoma spp.) correlated negatively with B. dendrobatidis and ranavirus, both at the host‐ and site‐level scales, highlighting the importance of differential relationships between micro‐ and macroparasites. Given the extensive number of coinfecting symbiont combinations inherent to natural systems, particularly across multiple host species, multiresponse modelling of cross‐sectional field data offers a valuable tool to identify a tractable number of hypothesized interactions for experimental testing while accounting for uncertainty and potential sources of co‐exposure. For amphibians specifically, the high frequency of co‐occurrence and coinfection among these pathogens—each of which is known to impair host fitness or survival—highlights the urgency of understanding parasite associations for conservation and disease management.</description><identifier>ISSN: 2041-210X</identifier><identifier>EISSN: 2041-210X</identifier><identifier>DOI: 10.1111/2041-210X.12938</identifier><identifier>PMID: 29861885</identifier><language>eng</language><publisher>United States: John Wiley &amp; Sons, Inc</publisher><subject>Amphibians ; Batrachochytrium ; Batrachochytrium dendrobatidis ; coinfection ; Disease control ; Echinostoma ; emerging infectious diseases ; Fitness ; Fungi ; hierarchical models ; Infections ; Investigations ; Modelling ; multiresponse models ; Parasites ; Pathogens ; ranavirus ; Ribeiroia ondatrae ; Species ; Uncertainty</subject><ispartof>Methods in ecology and evolution, 2018-04, Vol.9 (4), p.1109-1120</ispartof><rights>2017 The Authors. Methods in Ecology and Evolution © 2017 British Ecological Society</rights><rights>Methods in Ecology and Evolution © 2018 British Ecological Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5148-cd6f4c3315e6429b570bd68011b223a66e0d0458a9090b2f42176ebfcd13f15e3</citedby><cites>FETCH-LOGICAL-c5148-cd6f4c3315e6429b570bd68011b223a66e0d0458a9090b2f42176ebfcd13f15e3</cites><orcidid>0000-0002-7997-5390</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2F2041-210X.12938$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2F2041-210X.12938$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,778,782,883,1414,27911,27912,45561,45562</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29861885$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kembel, Steven</contributor><creatorcontrib>Stutz, William E.</creatorcontrib><creatorcontrib>Blaustein, Andrew R.</creatorcontrib><creatorcontrib>Briggs, Cheryl J.</creatorcontrib><creatorcontrib>Hoverman, Jason T.</creatorcontrib><creatorcontrib>Rohr, Jason R.</creatorcontrib><creatorcontrib>Johnson, Pieter T. J.</creatorcontrib><creatorcontrib>Kembel, Steven</creatorcontrib><title>Using multi‐response models to investigate pathogen coinfections across scales: Insights from emerging diseases of amphibians</title><title>Methods in ecology and evolution</title><addtitle>Methods Ecol Evol</addtitle><description>Associations among parasites affect many aspects of host–parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales. Here we illustrate how hierarchical, multiresponse modelling can characterize parasite associations by allowing for hierarchical structuring, offering estimates of uncertainty and incorporating correlational model structures. After introducing the general approach, we apply this framework to investigate coinfections among four amphibian parasites (the trematodes Ribeiroia ondatrae and Echinostoma spp., the chytrid fungus Batrachochytrium dendrobatidis and ranaviruses) and among &gt;2,000 individual hosts, 90 study sites and five amphibian host species. Ninety‐two percent of sites and 80% of hosts supported two or more pathogen species. Our results revealed strong correlations between parasite pairs that varied by scale (from among hosts to among sites) and classification (microparasite versus macroparasite), but were broadly consistent across taxonomically diverse host species. At the host‐scale, infection by the trematode R. ondatrae correlated positively with the microparasites, B. dendrobatidis and ranavirus, which were themselves positively associated. However, infection by a second trematode (Echinostoma spp.) correlated negatively with B. dendrobatidis and ranavirus, both at the host‐ and site‐level scales, highlighting the importance of differential relationships between micro‐ and macroparasites. Given the extensive number of coinfecting symbiont combinations inherent to natural systems, particularly across multiple host species, multiresponse modelling of cross‐sectional field data offers a valuable tool to identify a tractable number of hypothesized interactions for experimental testing while accounting for uncertainty and potential sources of co‐exposure. For amphibians specifically, the high frequency of co‐occurrence and coinfection among these pathogens—each of which is known to impair host fitness or survival—highlights the urgency of understanding parasite associations for conservation and disease management.</description><subject>Amphibians</subject><subject>Batrachochytrium</subject><subject>Batrachochytrium dendrobatidis</subject><subject>coinfection</subject><subject>Disease control</subject><subject>Echinostoma</subject><subject>emerging infectious diseases</subject><subject>Fitness</subject><subject>Fungi</subject><subject>hierarchical models</subject><subject>Infections</subject><subject>Investigations</subject><subject>Modelling</subject><subject>multiresponse models</subject><subject>Parasites</subject><subject>Pathogens</subject><subject>ranavirus</subject><subject>Ribeiroia ondatrae</subject><subject>Species</subject><subject>Uncertainty</subject><issn>2041-210X</issn><issn>2041-210X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqFkb9uFDEQxi0EItGRmg5ZoqG5xPb-OZsCCUUXiBREk0h0ltc73nO0ay-e3aBU5BF4Rp4EXy6cAk2msEf2bz575iPkNWfHPMeJYCVfCs6-HXOhCvmMHO5Pnj_KD8gR4jXLUUjFRPmSHAglay5ldUh-XqEPHR3mfvK_734lwDEGBDrEFnqkU6Q-3ABOvjMT0NFMm9hBoDb64MBOPsPU2BQRKVrTA76n5wF9t5mQuhQHCgOkbvtE6xEMAtLoqBnGjW-8CfiKvHCmRzh62Bfk6mx9efp5efH10_npx4ulrXgpl7atXWmLgldQl0I11Yo1bS0Z540QhalrYC0rK2kUU6wRrhR8VUPjbMsLl4uKBfmw0x3nZoDWQpiS6fWY_GDSrY7G639vgt_oLt7oSq3kqlZZ4N2DQIrf5zwRPXi00PcmQJxR53ErVTIuy4y-_Q-9jnMKub1MCZEpmb1YkJMddT-9BG7_Gc701t-tZF6yg_re31zx5nEPe_6vmxmod8AP38PtU3r6y3pd7JT_APgxs5E</recordid><startdate>201804</startdate><enddate>201804</enddate><creator>Stutz, William E.</creator><creator>Blaustein, Andrew R.</creator><creator>Briggs, Cheryl J.</creator><creator>Hoverman, Jason T.</creator><creator>Rohr, Jason R.</creator><creator>Johnson, Pieter T. J.</creator><creator>Kembel, Steven</creator><general>John Wiley &amp; Sons, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7SN</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7997-5390</orcidid></search><sort><creationdate>201804</creationdate><title>Using multi‐response models to investigate pathogen coinfections across scales: Insights from emerging diseases of amphibians</title><author>Stutz, William E. ; Blaustein, Andrew R. ; Briggs, Cheryl J. ; Hoverman, Jason T. ; Rohr, Jason R. ; Johnson, Pieter T. J. ; Kembel, Steven</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5148-cd6f4c3315e6429b570bd68011b223a66e0d0458a9090b2f42176ebfcd13f15e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Amphibians</topic><topic>Batrachochytrium</topic><topic>Batrachochytrium dendrobatidis</topic><topic>coinfection</topic><topic>Disease control</topic><topic>Echinostoma</topic><topic>emerging infectious diseases</topic><topic>Fitness</topic><topic>Fungi</topic><topic>hierarchical models</topic><topic>Infections</topic><topic>Investigations</topic><topic>Modelling</topic><topic>multiresponse models</topic><topic>Parasites</topic><topic>Pathogens</topic><topic>ranavirus</topic><topic>Ribeiroia ondatrae</topic><topic>Species</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stutz, William E.</creatorcontrib><creatorcontrib>Blaustein, Andrew R.</creatorcontrib><creatorcontrib>Briggs, Cheryl J.</creatorcontrib><creatorcontrib>Hoverman, Jason T.</creatorcontrib><creatorcontrib>Rohr, Jason R.</creatorcontrib><creatorcontrib>Johnson, Pieter T. J.</creatorcontrib><creatorcontrib>Kembel, Steven</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Ecology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Methods in ecology and evolution</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stutz, William E.</au><au>Blaustein, Andrew R.</au><au>Briggs, Cheryl J.</au><au>Hoverman, Jason T.</au><au>Rohr, Jason R.</au><au>Johnson, Pieter T. J.</au><au>Kembel, Steven</au><au>Kembel, Steven</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using multi‐response models to investigate pathogen coinfections across scales: Insights from emerging diseases of amphibians</atitle><jtitle>Methods in ecology and evolution</jtitle><addtitle>Methods Ecol Evol</addtitle><date>2018-04</date><risdate>2018</risdate><volume>9</volume><issue>4</issue><spage>1109</spage><epage>1120</epage><pages>1109-1120</pages><issn>2041-210X</issn><eissn>2041-210X</eissn><abstract>Associations among parasites affect many aspects of host–parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales. Here we illustrate how hierarchical, multiresponse modelling can characterize parasite associations by allowing for hierarchical structuring, offering estimates of uncertainty and incorporating correlational model structures. After introducing the general approach, we apply this framework to investigate coinfections among four amphibian parasites (the trematodes Ribeiroia ondatrae and Echinostoma spp., the chytrid fungus Batrachochytrium dendrobatidis and ranaviruses) and among &gt;2,000 individual hosts, 90 study sites and five amphibian host species. Ninety‐two percent of sites and 80% of hosts supported two or more pathogen species. Our results revealed strong correlations between parasite pairs that varied by scale (from among hosts to among sites) and classification (microparasite versus macroparasite), but were broadly consistent across taxonomically diverse host species. At the host‐scale, infection by the trematode R. ondatrae correlated positively with the microparasites, B. dendrobatidis and ranavirus, which were themselves positively associated. However, infection by a second trematode (Echinostoma spp.) correlated negatively with B. dendrobatidis and ranavirus, both at the host‐ and site‐level scales, highlighting the importance of differential relationships between micro‐ and macroparasites. Given the extensive number of coinfecting symbiont combinations inherent to natural systems, particularly across multiple host species, multiresponse modelling of cross‐sectional field data offers a valuable tool to identify a tractable number of hypothesized interactions for experimental testing while accounting for uncertainty and potential sources of co‐exposure. For amphibians specifically, the high frequency of co‐occurrence and coinfection among these pathogens—each of which is known to impair host fitness or survival—highlights the urgency of understanding parasite associations for conservation and disease management.</abstract><cop>United States</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>29861885</pmid><doi>10.1111/2041-210X.12938</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7997-5390</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2041-210X
ispartof Methods in ecology and evolution, 2018-04, Vol.9 (4), p.1109-1120
issn 2041-210X
2041-210X
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5978769
source Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection
subjects Amphibians
Batrachochytrium
Batrachochytrium dendrobatidis
coinfection
Disease control
Echinostoma
emerging infectious diseases
Fitness
Fungi
hierarchical models
Infections
Investigations
Modelling
multiresponse models
Parasites
Pathogens
ranavirus
Ribeiroia ondatrae
Species
Uncertainty
title Using multi‐response models to investigate pathogen coinfections across scales: Insights from emerging diseases of amphibians
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T17%3A31%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20multi%E2%80%90response%20models%20to%20investigate%20pathogen%20coinfections%20across%20scales:%20Insights%20from%20emerging%20diseases%20of%20amphibians&rft.jtitle=Methods%20in%20ecology%20and%20evolution&rft.au=Stutz,%20William%20E.&rft.date=2018-04&rft.volume=9&rft.issue=4&rft.spage=1109&rft.epage=1120&rft.pages=1109-1120&rft.issn=2041-210X&rft.eissn=2041-210X&rft_id=info:doi/10.1111/2041-210X.12938&rft_dat=%3Cproquest_pubme%3E2022940800%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2022940800&rft_id=info:pmid/29861885&rfr_iscdi=true