Nonlinear higher order abiotic interactions explain riverine biodiversity

Aim: Theory and experiments strongly support the importance of interactive effects of multiple factors shaping biodiversity, although their importance rarely has been investigated at biogeographically relevant scales. In particular, the importance of higher order interactions among environmental fac...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of biogeography 2018-03, Vol.45 (3), p.628-639
Hauptverfasser: Ryo, Masahiro, Harvey, Eric, Robinson, Christopher T., Altermatt, Florian
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 639
container_issue 3
container_start_page 628
container_title Journal of biogeography
container_volume 45
creator Ryo, Masahiro
Harvey, Eric
Robinson, Christopher T.
Altermatt, Florian
description Aim: Theory and experiments strongly support the importance of interactive effects of multiple factors shaping biodiversity, although their importance rarely has been investigated at biogeographically relevant scales. In particular, the importance of higher order interactions among environmental factors at such scales is largely unknown. We investigated higher order interactions of environmental factors to explain diversity patterns in a metacommunity of aquatic invertebrates at a biogeographically relevant scale and discuss the findings in an environmental management context. Location: All major drainage basins in Switzerland (Rhine, Rhone, Ticino and Inn; 41,285 km2). Methods: Riverine α-diversity patterns at two taxonomic levels (family richness of all benthic macroinvertebrates and species richness of Ephemeroptera, Plecoptera and Trichoptera) were examined at 518 sites across the basins. We applied a novel machine learning technique to detect key three-way interactions of explanatory variables by comparing the relative importance of 1,140 three-way combinations for family richness and 680 three-way combinations for species richness. Results: Relatively few but important three-way interactions were meaningful for predicting biodiversity patterns among the numerous possible combinations. Specifically, we found that interactions among elevational gradient, prevalence of forest coverage in the upstream basin and biogeoclimatic regional classification were distinctly important. Main conclusion: Our results indicated that a high prevalence of terrestrial forest generally sustains riverine benthic macroinvertebrate diversity, but this relationship varies considerably with biogeoclimatic and elevational conditions likely due to community composition of forests and macroinvertebrates changing along climatic and geographical gradients. An adequate management of riverine ecosystems at relevant biogeographical scales requires the identification of such interactions and a context-dependent implementation.
doi_str_mv 10.1111/jbi.13164
format Article
fullrecord <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_journals_2007896148</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>26626976</jstor_id><sourcerecordid>26626976</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4204-622d043b97e2c6807de66ce4882b9510f240329dfa8507568160223b05b0833f3</originalsourceid><addsrcrecordid>eNp1kD1PwzAQhi0EEqUw8AOQIjExpD1_xh6h4qOoggVmKx8OdRTiYqdA_j0uATZuuLvhee-kB6FTDDMca94UdoYpFmwPTTAVPCVCqX00AQo8BZLBIToKoQEAxSmboOWD61rbmdwna_uyNj5xvoo9L6zrbZnYrjc-L3vrupCYz02b2y7x9t34GEoiVO32YPvhGB3UeRvMyc-coueb66fFXbp6vF0uLldpyQiwVBBSAaOFygwphYSsMkKUhklJCsUx1IQBJaqqc8kh40JiAYTQAngBktKaTtH5eHfj3dvWhF43buu7-FITgEwqgZmM1MVIld6F4E2tN96-5n7QGPTOlI6m9LepyM5H9sO2Zvgf1PdXy9_E2ZhoQu_8X4IIEXVngn4Bg7Zylg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2007896148</pqid></control><display><type>article</type><title>Nonlinear higher order abiotic interactions explain riverine biodiversity</title><source>Jstor Complete Legacy</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Ryo, Masahiro ; Harvey, Eric ; Robinson, Christopher T. ; Altermatt, Florian</creator><creatorcontrib>Ryo, Masahiro ; Harvey, Eric ; Robinson, Christopher T. ; Altermatt, Florian</creatorcontrib><description>Aim: Theory and experiments strongly support the importance of interactive effects of multiple factors shaping biodiversity, although their importance rarely has been investigated at biogeographically relevant scales. In particular, the importance of higher order interactions among environmental factors at such scales is largely unknown. We investigated higher order interactions of environmental factors to explain diversity patterns in a metacommunity of aquatic invertebrates at a biogeographically relevant scale and discuss the findings in an environmental management context. Location: All major drainage basins in Switzerland (Rhine, Rhone, Ticino and Inn; 41,285 km2). Methods: Riverine α-diversity patterns at two taxonomic levels (family richness of all benthic macroinvertebrates and species richness of Ephemeroptera, Plecoptera and Trichoptera) were examined at 518 sites across the basins. We applied a novel machine learning technique to detect key three-way interactions of explanatory variables by comparing the relative importance of 1,140 three-way combinations for family richness and 680 three-way combinations for species richness. Results: Relatively few but important three-way interactions were meaningful for predicting biodiversity patterns among the numerous possible combinations. Specifically, we found that interactions among elevational gradient, prevalence of forest coverage in the upstream basin and biogeoclimatic regional classification were distinctly important. Main conclusion: Our results indicated that a high prevalence of terrestrial forest generally sustains riverine benthic macroinvertebrate diversity, but this relationship varies considerably with biogeoclimatic and elevational conditions likely due to community composition of forests and macroinvertebrates changing along climatic and geographical gradients. An adequate management of riverine ecosystems at relevant biogeographical scales requires the identification of such interactions and a context-dependent implementation.</description><identifier>ISSN: 0305-0270</identifier><identifier>EISSN: 1365-2699</identifier><identifier>DOI: 10.1111/jbi.13164</identifier><language>eng</language><publisher>Oxford: John Wiley &amp; Sons Ltd</publisher><subject>Aquatic ecosystems ; Aquatic insects ; Aquatic organisms ; Basins ; Biodiversity ; Climate change ; Community composition ; conservation ; context dependency ; Diversity drivers ; Drainage basins ; Drainage management ; ecological surprises ; Ecosystem management ; Environmental changes ; Environmental factors ; Environmental management ; Forests ; freshwater ; Invertebrates ; land use ; Learning algorithms ; Machine learning ; Macroinvertebrates ; metacommunity ; meta‐ecosystem ; multiple stressors ; Species richness ; Strategic management ; Terrestrial environments</subject><ispartof>Journal of biogeography, 2018-03, Vol.45 (3), p.628-639</ispartof><rights>Copyright © 2017 John Wiley &amp; Sons Ltd.</rights><rights>2018 John Wiley &amp; Sons Ltd</rights><rights>Copyright © 2018 John Wiley &amp; Sons Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4204-622d043b97e2c6807de66ce4882b9510f240329dfa8507568160223b05b0833f3</citedby><cites>FETCH-LOGICAL-c4204-622d043b97e2c6807de66ce4882b9510f240329dfa8507568160223b05b0833f3</cites><orcidid>0000-0002-5271-3446</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26626976$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26626976$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,1411,27901,27902,45550,45551,57992,58225</link.rule.ids></links><search><creatorcontrib>Ryo, Masahiro</creatorcontrib><creatorcontrib>Harvey, Eric</creatorcontrib><creatorcontrib>Robinson, Christopher T.</creatorcontrib><creatorcontrib>Altermatt, Florian</creatorcontrib><title>Nonlinear higher order abiotic interactions explain riverine biodiversity</title><title>Journal of biogeography</title><description>Aim: Theory and experiments strongly support the importance of interactive effects of multiple factors shaping biodiversity, although their importance rarely has been investigated at biogeographically relevant scales. In particular, the importance of higher order interactions among environmental factors at such scales is largely unknown. We investigated higher order interactions of environmental factors to explain diversity patterns in a metacommunity of aquatic invertebrates at a biogeographically relevant scale and discuss the findings in an environmental management context. Location: All major drainage basins in Switzerland (Rhine, Rhone, Ticino and Inn; 41,285 km2). Methods: Riverine α-diversity patterns at two taxonomic levels (family richness of all benthic macroinvertebrates and species richness of Ephemeroptera, Plecoptera and Trichoptera) were examined at 518 sites across the basins. We applied a novel machine learning technique to detect key three-way interactions of explanatory variables by comparing the relative importance of 1,140 three-way combinations for family richness and 680 three-way combinations for species richness. Results: Relatively few but important three-way interactions were meaningful for predicting biodiversity patterns among the numerous possible combinations. Specifically, we found that interactions among elevational gradient, prevalence of forest coverage in the upstream basin and biogeoclimatic regional classification were distinctly important. Main conclusion: Our results indicated that a high prevalence of terrestrial forest generally sustains riverine benthic macroinvertebrate diversity, but this relationship varies considerably with biogeoclimatic and elevational conditions likely due to community composition of forests and macroinvertebrates changing along climatic and geographical gradients. An adequate management of riverine ecosystems at relevant biogeographical scales requires the identification of such interactions and a context-dependent implementation.</description><subject>Aquatic ecosystems</subject><subject>Aquatic insects</subject><subject>Aquatic organisms</subject><subject>Basins</subject><subject>Biodiversity</subject><subject>Climate change</subject><subject>Community composition</subject><subject>conservation</subject><subject>context dependency</subject><subject>Diversity drivers</subject><subject>Drainage basins</subject><subject>Drainage management</subject><subject>ecological surprises</subject><subject>Ecosystem management</subject><subject>Environmental changes</subject><subject>Environmental factors</subject><subject>Environmental management</subject><subject>Forests</subject><subject>freshwater</subject><subject>Invertebrates</subject><subject>land use</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Macroinvertebrates</subject><subject>metacommunity</subject><subject>meta‐ecosystem</subject><subject>multiple stressors</subject><subject>Species richness</subject><subject>Strategic management</subject><subject>Terrestrial environments</subject><issn>0305-0270</issn><issn>1365-2699</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kD1PwzAQhi0EEqUw8AOQIjExpD1_xh6h4qOoggVmKx8OdRTiYqdA_j0uATZuuLvhee-kB6FTDDMca94UdoYpFmwPTTAVPCVCqX00AQo8BZLBIToKoQEAxSmboOWD61rbmdwna_uyNj5xvoo9L6zrbZnYrjc-L3vrupCYz02b2y7x9t34GEoiVO32YPvhGB3UeRvMyc-coueb66fFXbp6vF0uLldpyQiwVBBSAaOFygwphYSsMkKUhklJCsUx1IQBJaqqc8kh40JiAYTQAngBktKaTtH5eHfj3dvWhF43buu7-FITgEwqgZmM1MVIld6F4E2tN96-5n7QGPTOlI6m9LepyM5H9sO2Zvgf1PdXy9_E2ZhoQu_8X4IIEXVngn4Bg7Zylg</recordid><startdate>201803</startdate><enddate>201803</enddate><creator>Ryo, Masahiro</creator><creator>Harvey, Eric</creator><creator>Robinson, Christopher T.</creator><creator>Altermatt, Florian</creator><general>John Wiley &amp; Sons Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7SS</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><orcidid>https://orcid.org/0000-0002-5271-3446</orcidid></search><sort><creationdate>201803</creationdate><title>Nonlinear higher order abiotic interactions explain riverine biodiversity</title><author>Ryo, Masahiro ; Harvey, Eric ; Robinson, Christopher T. ; Altermatt, Florian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4204-622d043b97e2c6807de66ce4882b9510f240329dfa8507568160223b05b0833f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Aquatic ecosystems</topic><topic>Aquatic insects</topic><topic>Aquatic organisms</topic><topic>Basins</topic><topic>Biodiversity</topic><topic>Climate change</topic><topic>Community composition</topic><topic>conservation</topic><topic>context dependency</topic><topic>Diversity drivers</topic><topic>Drainage basins</topic><topic>Drainage management</topic><topic>ecological surprises</topic><topic>Ecosystem management</topic><topic>Environmental changes</topic><topic>Environmental factors</topic><topic>Environmental management</topic><topic>Forests</topic><topic>freshwater</topic><topic>Invertebrates</topic><topic>land use</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Macroinvertebrates</topic><topic>metacommunity</topic><topic>meta‐ecosystem</topic><topic>multiple stressors</topic><topic>Species richness</topic><topic>Strategic management</topic><topic>Terrestrial environments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ryo, Masahiro</creatorcontrib><creatorcontrib>Harvey, Eric</creatorcontrib><creatorcontrib>Robinson, Christopher T.</creatorcontrib><creatorcontrib>Altermatt, Florian</creatorcontrib><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</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><jtitle>Journal of biogeography</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ryo, Masahiro</au><au>Harvey, Eric</au><au>Robinson, Christopher T.</au><au>Altermatt, Florian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear higher order abiotic interactions explain riverine biodiversity</atitle><jtitle>Journal of biogeography</jtitle><date>2018-03</date><risdate>2018</risdate><volume>45</volume><issue>3</issue><spage>628</spage><epage>639</epage><pages>628-639</pages><issn>0305-0270</issn><eissn>1365-2699</eissn><abstract>Aim: Theory and experiments strongly support the importance of interactive effects of multiple factors shaping biodiversity, although their importance rarely has been investigated at biogeographically relevant scales. In particular, the importance of higher order interactions among environmental factors at such scales is largely unknown. We investigated higher order interactions of environmental factors to explain diversity patterns in a metacommunity of aquatic invertebrates at a biogeographically relevant scale and discuss the findings in an environmental management context. Location: All major drainage basins in Switzerland (Rhine, Rhone, Ticino and Inn; 41,285 km2). Methods: Riverine α-diversity patterns at two taxonomic levels (family richness of all benthic macroinvertebrates and species richness of Ephemeroptera, Plecoptera and Trichoptera) were examined at 518 sites across the basins. We applied a novel machine learning technique to detect key three-way interactions of explanatory variables by comparing the relative importance of 1,140 three-way combinations for family richness and 680 three-way combinations for species richness. Results: Relatively few but important three-way interactions were meaningful for predicting biodiversity patterns among the numerous possible combinations. Specifically, we found that interactions among elevational gradient, prevalence of forest coverage in the upstream basin and biogeoclimatic regional classification were distinctly important. Main conclusion: Our results indicated that a high prevalence of terrestrial forest generally sustains riverine benthic macroinvertebrate diversity, but this relationship varies considerably with biogeoclimatic and elevational conditions likely due to community composition of forests and macroinvertebrates changing along climatic and geographical gradients. An adequate management of riverine ecosystems at relevant biogeographical scales requires the identification of such interactions and a context-dependent implementation.</abstract><cop>Oxford</cop><pub>John Wiley &amp; Sons Ltd</pub><doi>10.1111/jbi.13164</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-5271-3446</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0305-0270
ispartof Journal of biogeography, 2018-03, Vol.45 (3), p.628-639
issn 0305-0270
1365-2699
language eng
recordid cdi_proquest_journals_2007896148
source Jstor Complete Legacy; Wiley Online Library Journals Frontfile Complete
subjects Aquatic ecosystems
Aquatic insects
Aquatic organisms
Basins
Biodiversity
Climate change
Community composition
conservation
context dependency
Diversity drivers
Drainage basins
Drainage management
ecological surprises
Ecosystem management
Environmental changes
Environmental factors
Environmental management
Forests
freshwater
Invertebrates
land use
Learning algorithms
Machine learning
Macroinvertebrates
metacommunity
meta‐ecosystem
multiple stressors
Species richness
Strategic management
Terrestrial environments
title Nonlinear higher order abiotic interactions explain riverine biodiversity
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T21%3A38%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Nonlinear%20higher%20order%20abiotic%20interactions%20explain%20riverine%20biodiversity&rft.jtitle=Journal%20of%20biogeography&rft.au=Ryo,%20Masahiro&rft.date=2018-03&rft.volume=45&rft.issue=3&rft.spage=628&rft.epage=639&rft.pages=628-639&rft.issn=0305-0270&rft.eissn=1365-2699&rft_id=info:doi/10.1111/jbi.13164&rft_dat=%3Cjstor_proqu%3E26626976%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2007896148&rft_id=info:pmid/&rft_jstor_id=26626976&rfr_iscdi=true