Building clone-consistent ecosystem models.

Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the...

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Main Authors: Gerrit Ansmann, Tobias Bollenbach
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-02-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1008635
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spelling doaj-cdefd701c4584abf957166bd7079fbc92021-07-09T04:32:09ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-02-01172e100863510.1371/journal.pcbi.1008635Building clone-consistent ecosystem models.Gerrit AnsmannTobias BollenbachMany ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency. Although clone consistency can always be achieved with sufficient assumptions, we argue that it is important to explicitly name and consider the assumptions made: They may not be justified or limit the applicability of models and the generality of the results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.https://doi.org/10.1371/journal.pcbi.1008635
collection DOAJ
language English
format Article
sources DOAJ
author Gerrit Ansmann
Tobias Bollenbach
spellingShingle Gerrit Ansmann
Tobias Bollenbach
Building clone-consistent ecosystem models.
PLoS Computational Biology
author_facet Gerrit Ansmann
Tobias Bollenbach
author_sort Gerrit Ansmann
title Building clone-consistent ecosystem models.
title_short Building clone-consistent ecosystem models.
title_full Building clone-consistent ecosystem models.
title_fullStr Building clone-consistent ecosystem models.
title_full_unstemmed Building clone-consistent ecosystem models.
title_sort building clone-consistent ecosystem models.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2021-02-01
description Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency. Although clone consistency can always be achieved with sufficient assumptions, we argue that it is important to explicitly name and consider the assumptions made: They may not be justified or limit the applicability of models and the generality of the results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.
url https://doi.org/10.1371/journal.pcbi.1008635
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