Why network analysis is often disconnected from community ecology: A critique and an ecologist's guide
Network analyses of mutualistic or antagonistic interactions between species are very popular, but their biological interpretations are often unclear and incautious. Here I propose to distinguish two possible implications of network patterns in conjunction with solutions to avoid misinterpretations....
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Published in | Basic and applied ecology Vol. 11; no. 3; pp. 185 - 195 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Elsevier GmbH
01.05.2010
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Subjects | |
Online Access | Get full text |
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Abstract | Network analyses of mutualistic or antagonistic interactions between species are very popular, but their biological interpretations are often unclear and incautious. Here I propose to distinguish two possible implications of network patterns in conjunction with solutions to avoid misinterpretations. Interpretations can be either
(1)
niche-based, describing specialisation, trait (mis-)matching between species, niche breadth and niche overlap and their relationship to interspecific competition and species coexistence, or
(2)
impact-based, focusing on frequencies of interactions between species such as predation or infection rates and mutualistic services, aiming to quantify each species’ relative contribution to an ecological effect.
For niche-based implications, it is crucial to acknowledge the sampling limitations of a network and thus control for the number of observations of each species. This is particularly important for those kinds of networks that summarise observed interactions in communities (e.g. bipartite host–parasitoid or plant–animal networks), rather than compile information from different sources or experiments (as in many food webs). Variation in total observation frequencies may alone explain network patterns that have often been interpreted as ‘specialisation asymmetries’ (nestedness, dependence asymmetries). I show analytically that ‘dependence asymmetries’ between two species (or two guilds) only reflect variation in their total observation frequencies. To depict true asymmetries in niche breadth, independent data are required for both species.
Moreover, simulated co-extinction scenarios assume that each species ‘depends’ on its associated partners in the network (again niche-based), but species that appear most endangered are simply those with one or very few observations and are not necessarily specialised. Distinguishing niche-based and impact-based interpretations may help to bridge terminological and conceptual gaps between network pattern analyses and traditional community ecology.
Mutualistische oder antagonistische Beziehungen zwischen Arten einer Gemeinschaft werden derzeit häufig mit Hilfe von Netzwerkanalysen beschrieben. Da die biologische Deutung solcher Analysen oft missverständlich ist, wird in diesem Artikel vorgeschlagen, zwei Interpretationsarten zu unterscheiden:
(1)
Ökologische Nische, z.B. Spezialisierung, Nischenbreite und überlappung, sowie Kompatibilität von Merkmalen zwischen Arten.
(2)
Interaktionseffekte, die von der relativen Häufigkeit der Wechselwirkungen abhängig sind, z.B. Prädations- und Infektionsraten oder mutualistische Funktionen.
Bei nischenbezogenen Deutungen von Netzwerken, die auf beobachteten Interaktionen basieren, muss jedoch berücksichtigt werden, dass die Gesamtzahl der Beobachtungen pro Art limitiert ist und sich zwischen Arten stark unterscheidet. Allein diese Variation kann viele Netzwerkmuster erklären, beispielsweise “Nestedness”, was oft als asymmetrische Spezialisierung missverstanden wurde. Hier wird analytisch bewiesen, dass eine mutmaßliche “Spezialisierungs-Asymmetrie” zwischen zwei Arten allein auf deren unterschiedliche Beobachtungshäufigkeit zurückgeführt werden kann. Unhabhängig erhobene Daten für beide Arten sind notwendig, um diesen Trugschluss zu vermeiden.
Das Aussterben von Arten durch Verlust des Assoziationspartners (Koextinktion) wurde in mehreren publizierten Studien modelliert. Solche Simulationen basieren auf der Annahme, dass jede Art von seinen beobachteten Assoziationspartnern abhängig ist (nischenbasierte Deutung). Hier kann jedoch gezeigt werden, dass vor allem solche Arten scheinbar gefährdet sind, die nur ein- oder wenige Male beobachtet wurden, also nicht notwendigerweise Spezialisten darstellen. Die explizite Unterscheidung zwischen nischen- und effektbasierter Interpretation könnte demnach eine hilfreiche konzeptionelle Brücke darstellen, um Netzwerkanalysen und klassische Gemeinschaftsökologie zusammenzuführen. |
---|---|
AbstractList | Network analyses of mutualistic or antagonistic interactions between species are very popular, but their biological interpretations are often unclear and incautious. Here I propose to distinguish two possible implications of network patterns in conjunction with solutions to avoid misinterpretations. Interpretations can be either (1) niche-based, describing specialisation, trait (mis-)matching between species, niche breadth and niche overlap and their relationship to interspecific competition and species coexistence, or (2) impact-based, focusing on frequencies of interactions between species such as predation or infection rates and mutualistic services, aiming to quantify each species' relative contribution to an ecological effect. For niche-based implications, it is crucial to acknowledge the sampling limitations of a network and thus control for the number of observations of each species. This is particularly important for those kinds of networks that summarise observed interactions in communities (e.g. bipartite host-parasitoid or plant-animal networks), rather than compile information from different sources or experiments (as in many food webs). Variation in total observation frequencies may alone explain network patterns that have often been interpreted as a~specialisation asymmetries' (nestedness, dependence asymmetries). I show analytically that a~dependence asymmetries' between two species (or two guilds) only reflect variation in their total observation frequencies. To depict true asymmetries in niche breadth, independent data are required for both species. Moreover, simulated co-extinction scenarios assume that each species a~depends' on its associated partners in the network (again niche-based), but species that appear most endangered are simply those with one or very few observations and are not necessarily specialised. Distinguishing niche-based and impact-based interpretations may help to bridge terminological and conceptual gaps between network pattern analyses and traditional community ecology. Mutualistische oder antagonistische Beziehungen zwischen Arten einer Gemeinschaft werden derzeit hA[curren]ufig mit Hilfe von Netzwerkanalysen beschrieben. Da die biologische Deutung solcher Analysen oft missverstA[curren]ndlich ist, wird in diesem Artikel vorgeschlagen, zwei Interpretationsarten zu unterscheiden: (1) A-kologische Nische, z.B. Spezialisierung, Nischenbreite und ueberlappung, sowie KompatibilitA[curren]t von Merkmalen zwischen Arten. (2) Interaktionseffekte, die von der relativen HA[curren]ufigkeit der Wechselwirkungen abhA[curren]ngig sind, z.B. PrA[curren]dations- und Infektionsraten oder mutualistische Funktionen. Bei nischenbezogenen Deutungen von Netzwerken, die auf beobachteten Interaktionen basieren, muss jedoch beruecksichtigt werden, dass die Gesamtzahl der Beobachtungen pro Art limitiert ist und sich zwischen Arten stark unterscheidet. Allein diese Variation kann viele Netzwerkmuster erklA[curren]ren, beispielsweise "Nestedness", was oft als asymmetrische Spezialisierung missverstanden wurde. Hier wird analytisch bewiesen, dass eine mutmassliche "Spezialisierungs-Asymmetrie" zwischen zwei Arten allein auf deren unterschiedliche BeobachtungshA[curren]ufigkeit zurueckgefuehrt werden kann. UnhabhA[curren]ngig erhobene Daten fuer beide Arten sind notwendig, um diesen Trugschluss zu vermeiden. Das Aussterben von Arten durch Verlust des Assoziationspartners (Koextinktion) wurde in mehreren publizierten Studien modelliert. Solche Simulationen basieren auf der Annahme, dass jede Art von seinen beobachteten Assoziationspartnern abhA[curren]ngig ist (nischenbasierte Deutung). Hier kann jedoch gezeigt werden, dass vor allem solche Arten scheinbar gefA[curren]hrdet sind, die nur ein- oder wenige Male beobachtet wurden, also nicht notwendigerweise Spezialisten darstellen. Die explizite Unterscheidung zwischen nischen- und effektbasierter Interpretation koennte demnach eine hilfreiche konzeptionelle Bruecke darstellen, um Netzwerkanalysen und klassische Gemeinschaftsoekologie zusammenzufuehren. Network analyses of mutualistic or antagonistic interactions between species are very popular, but their biological interpretations are often unclear and incautious. Here I propose to distinguish two possible implications of network patterns in conjunction with solutions to avoid misinterpretations. Interpretations can be either (1) niche-based, describing specialisation, trait (mis-)matching between species, niche breadth and niche overlap and their relationship to interspecific competition and species coexistence, or (2) impact-based, focusing on frequencies of interactions between species such as predation or infection rates and mutualistic services, aiming to quantify each species’ relative contribution to an ecological effect. For niche-based implications, it is crucial to acknowledge the sampling limitations of a network and thus control for the number of observations of each species. This is particularly important for those kinds of networks that summarise observed interactions in communities (e.g. bipartite host–parasitoid or plant–animal networks), rather than compile information from different sources or experiments (as in many food webs). Variation in total observation frequencies may alone explain network patterns that have often been interpreted as ‘specialisation asymmetries’ (nestedness, dependence asymmetries). I show analytically that ‘dependence asymmetries’ between two species (or two guilds) only reflect variation in their total observation frequencies. To depict true asymmetries in niche breadth, independent data are required for both species. Moreover, simulated co-extinction scenarios assume that each species ‘depends’ on its associated partners in the network (again niche-based), but species that appear most endangered are simply those with one or very few observations and are not necessarily specialised. Distinguishing niche-based and impact-based interpretations may help to bridge terminological and conceptual gaps between network pattern analyses and traditional community ecology. Mutualistische oder antagonistische Beziehungen zwischen Arten einer Gemeinschaft werden derzeit häufig mit Hilfe von Netzwerkanalysen beschrieben. Da die biologische Deutung solcher Analysen oft missverständlich ist, wird in diesem Artikel vorgeschlagen, zwei Interpretationsarten zu unterscheiden: (1) Ökologische Nische, z.B. Spezialisierung, Nischenbreite und überlappung, sowie Kompatibilität von Merkmalen zwischen Arten. (2) Interaktionseffekte, die von der relativen Häufigkeit der Wechselwirkungen abhängig sind, z.B. Prädations- und Infektionsraten oder mutualistische Funktionen. Bei nischenbezogenen Deutungen von Netzwerken, die auf beobachteten Interaktionen basieren, muss jedoch berücksichtigt werden, dass die Gesamtzahl der Beobachtungen pro Art limitiert ist und sich zwischen Arten stark unterscheidet. Allein diese Variation kann viele Netzwerkmuster erklären, beispielsweise “Nestedness”, was oft als asymmetrische Spezialisierung missverstanden wurde. Hier wird analytisch bewiesen, dass eine mutmaßliche “Spezialisierungs-Asymmetrie” zwischen zwei Arten allein auf deren unterschiedliche Beobachtungshäufigkeit zurückgeführt werden kann. Unhabhängig erhobene Daten für beide Arten sind notwendig, um diesen Trugschluss zu vermeiden. Das Aussterben von Arten durch Verlust des Assoziationspartners (Koextinktion) wurde in mehreren publizierten Studien modelliert. Solche Simulationen basieren auf der Annahme, dass jede Art von seinen beobachteten Assoziationspartnern abhängig ist (nischenbasierte Deutung). Hier kann jedoch gezeigt werden, dass vor allem solche Arten scheinbar gefährdet sind, die nur ein- oder wenige Male beobachtet wurden, also nicht notwendigerweise Spezialisten darstellen. Die explizite Unterscheidung zwischen nischen- und effektbasierter Interpretation könnte demnach eine hilfreiche konzeptionelle Brücke darstellen, um Netzwerkanalysen und klassische Gemeinschaftsökologie zusammenzuführen. |
Author | Blüthgen, Nico |
Author_xml | – sequence: 1 givenname: Nico surname: Bluethgen fullname: Bluethgen, Nico |
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