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Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks

Diogenis A. Kiziridis, Lynne Boddy, Dan Eastwood Orcid Logo, Chenggui Yuan, Mike Fowler Orcid Logo

Methods in Ecology and Evolution, Volume: 11, Issue: 12, Pages: 1663 - 1672

Swansea University Authors: Dan Eastwood Orcid Logo, Mike Fowler Orcid Logo

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DOI (Published version): 10.1111/2041-210x.13493

Abstract

1. Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, that is, their ‘minimum dimensionality’. Existing methods for estimating minimum dimensionality often lack thre...

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Published in: Methods in Ecology and Evolution
ISSN: 2041-210X 2041-210X
Published: Wiley 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa55365
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It is important, therefore, to identify the minimum number of traits that characterise ecological networks, that is, their &#x2018;minimum dimensionality&#x2019;. Existing methods for estimating minimum dimensionality often lack three features associated with in- creased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated &#x2018;forbidden links&#x2019; and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a &#x2018;mini- mum mechanistic dimensionality&#x2019; measure, accounting for these three features.2. The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey spe- cies via a typical predation mode, in each step of the predation sequence, the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.3. We applied our method to 658 published empirical ecological networks includ- ing primary consumption, predator&#x2013;prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), &#x2018;forbidden interactions&#x2019; as trait-mediated interaction outcomes (92%) or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.4. Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics.</abstract><type>Journal Article</type><journal>Methods in Ecology and Evolution</journal><volume>11</volume><journalNumber>12</journalNumber><paginationStart>1663</paginationStart><paginationEnd>1672</paginationEnd><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2041-210X</issnPrint><issnElectronic>2041-210X</issnElectronic><keywords>cyclic rock&#x2013;paper&#x2013;scissors intransitive game; food web intervality; multilayer ecological networks; mutualism; niche space; phenotype space; social networks; trophic interactions</keywords><publishedDay>7</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-10-07</publishedDate><doi>10.1111/2041-210x.13493</doi><url/><notes/><college>COLLEGE NANME</college><department>Biosciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SBI</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>N/A</funders><lastEdited>2021-11-30T15:19:29.9763041</lastEdited><Created>2020-10-07T19:39:40.2152885</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Biosciences</level></path><authors><author><firstname>Diogenis A.</firstname><surname>Kiziridis</surname><order>1</order></author><author><firstname>Lynne</firstname><surname>Boddy</surname><order>2</order></author><author><firstname>Dan</firstname><surname>Eastwood</surname><orcid>0000-0002-7015-0739</orcid><order>3</order></author><author><firstname>Chenggui</firstname><surname>Yuan</surname><order>4</order></author><author><firstname>Mike</firstname><surname>Fowler</surname><orcid>0000-0003-1544-0407</orcid><order>5</order></author></authors><documents><document><filename>55365__18367__59b173446a004378a440c0c36e0b58bc.pdf</filename><originalFilename>Kiziridis_etal_2020_MEE.pdf</originalFilename><uploaded>2020-10-07T19:53:23.7624248</uploaded><type>Output</type><contentLength>2270650</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2021-09-23T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2021-11-30T15:19:29.9763041 v2 55365 2020-10-07 Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks 4982f3fa83886c0362e2bb43ce1c027f 0000-0002-7015-0739 Dan Eastwood Dan Eastwood true false a3a29027498d4b43a3f082a0a5ba16b4 0000-0003-1544-0407 Mike Fowler Mike Fowler true false 2020-10-07 SBI 1. Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, that is, their ‘minimum dimensionality’. Existing methods for estimating minimum dimensionality often lack three features associated with in- creased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated ‘forbidden links’ and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a ‘mini- mum mechanistic dimensionality’ measure, accounting for these three features.2. The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey spe- cies via a typical predation mode, in each step of the predation sequence, the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.3. We applied our method to 658 published empirical ecological networks includ- ing primary consumption, predator–prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), ‘forbidden interactions’ as trait-mediated interaction outcomes (92%) or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.4. Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics. Journal Article Methods in Ecology and Evolution 11 12 1663 1672 Wiley 2041-210X 2041-210X cyclic rock–paper–scissors intransitive game; food web intervality; multilayer ecological networks; mutualism; niche space; phenotype space; social networks; trophic interactions 7 10 2020 2020-10-07 10.1111/2041-210x.13493 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University N/A 2021-11-30T15:19:29.9763041 2020-10-07T19:39:40.2152885 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Diogenis A. Kiziridis 1 Lynne Boddy 2 Dan Eastwood 0000-0002-7015-0739 3 Chenggui Yuan 4 Mike Fowler 0000-0003-1544-0407 5 55365__18367__59b173446a004378a440c0c36e0b58bc.pdf Kiziridis_etal_2020_MEE.pdf 2020-10-07T19:53:23.7624248 Output 2270650 application/pdf Accepted Manuscript true 2021-09-23T00:00:00.0000000 true eng
title Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
spellingShingle Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
Dan Eastwood
Mike Fowler
title_short Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
title_full Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
title_fullStr Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
title_full_unstemmed Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
title_sort Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks
author_id_str_mv 4982f3fa83886c0362e2bb43ce1c027f
a3a29027498d4b43a3f082a0a5ba16b4
author_id_fullname_str_mv 4982f3fa83886c0362e2bb43ce1c027f_***_Dan Eastwood
a3a29027498d4b43a3f082a0a5ba16b4_***_Mike Fowler
author Dan Eastwood
Mike Fowler
author2 Diogenis A. Kiziridis
Lynne Boddy
Dan Eastwood
Chenggui Yuan
Mike Fowler
format Journal article
container_title Methods in Ecology and Evolution
container_volume 11
container_issue 12
container_start_page 1663
publishDate 2020
institution Swansea University
issn 2041-210X
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doi_str_mv 10.1111/2041-210x.13493
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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description 1. Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, that is, their ‘minimum dimensionality’. Existing methods for estimating minimum dimensionality often lack three features associated with in- creased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated ‘forbidden links’ and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a ‘mini- mum mechanistic dimensionality’ measure, accounting for these three features.2. The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey spe- cies via a typical predation mode, in each step of the predation sequence, the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.3. We applied our method to 658 published empirical ecological networks includ- ing primary consumption, predator–prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), ‘forbidden interactions’ as trait-mediated interaction outcomes (92%) or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.4. Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics.
published_date 2020-10-07T04:09:31Z
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score 11.013619

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