Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC)
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences. Department of Biology, Lund University, Lund, Sweden; Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Evolution and Ecology Program,International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.ORCID iD: 0000-0001-9862-816x
2019 (English)In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210X, Vol. 10, no 4, p. 450-460Article, review/survey (Refereed) Published
Abstract [en]

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as a mechanistic and causal link between process and pattern is often lacking, and evolutionary processes and trophic interactions are ignored.

Here, we revisit the current knowledge on community assembly across scales and, in line with several reviews that have outlined challenges associated with current inference techniques, we identify a discrepancy between the current paradigm of eco-evolutionary community assembly and current inference techniques that focus mainly on competition and habitat filtering. We argue that trait-based dynamic eco-evolutionary models in combination with recently developed model fitting and model evaluation techniques can provide avenues for more accurate, reliable, and inclusive inference. To exemplify, we implement a trait-based, spatially explicit eco-evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources.

Through a case study on inference of prey and predator niche width in an eco-evolutionary context, we demonstrate how inclusive and mechanistic approaches-eco-evolutionary modelling and Approximate Bayesian Computation (ABC)-can enable inference of assembly processes that have been largely neglected by traditional techniques despite the ubiquity of such processes.

Much literature points to the limitations of current inference techniques, but concrete solutions to such limitations are few. Many of the challenges associated with novel inference techniques are, however, already to some extent resolved in other fields and thus ready to be put into action in a more formal way for inferring processes of community assembly from signals in various data sources.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 10, no 4, p. 450-460
Keywords [en]
biogeography, community assembly, community structure, ecology, evolution, process inference
National Category
Bioinformatics (Computational Biology) Evolutionary Biology
Identifiers
URN: urn:nbn:se:umu:diva-158737DOI: 10.1111/2041-210X.13129ISI: 000463036400001OAI: oai:DiVA.org:umu-158737DiVA, id: diva2:1316863
Available from: 2019-05-21 Created: 2019-05-21 Last updated: 2019-05-21Bibliographically approved

Open Access in DiVA

fulltext(1273 kB)51 downloads
File information
File name FULLTEXT01.pdfFile size 1273 kBChecksum SHA-512
af631ec831a7a54be952d52a59247db71304f71b365132083b9e61e836562b89ce6e081c5805352bc6f8a728408b0308b5ac05899921e6da344b8a5f808d867d
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Pontarp, MikaelBrännström, ÅkePetchey, Owen L.
By organisation
Department of Ecology and Environmental SciencesDepartment of Mathematics and Mathematical Statistics
In the same journal
Methods in Ecology and Evolution
Bioinformatics (Computational Biology)Evolutionary Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 51 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 137 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf