---
_id: '6936'
abstract:
- lang: eng
  text: "A key challenge for community ecology is to understand to what extent observational
    data can be used to infer the underlying community assembly processes. As different
    processes can lead to similar or even identical patterns, statistical analyses
    of non‐manipulative observational data never yield undisputable causal inference
    on the underlying processes. Still, most empirical studies in community ecology
    are based on observational data, and hence understanding under which circumstances
    such data can shed light on assembly processes is a central concern for community
    ecologists. We simulated a spatial agent‐based model that generates variation
    in metacommunity dynamics across multiple axes, including the four classic metacommunity
    paradigms as special cases. We further simulated a virtual ecologist who analysed
    snapshot data sampled from the simulations using eighteen output metrics derived
    from beta‐diversity and habitat variation indices, variation partitioning and
    joint species distribution modelling. Our results indicated two main axes of variation
    in the output metrics. The first axis of variation described whether the landscape
    has patchy or continuous variation, and thus was essentially independent of the
    properties of the species community. The second axis of variation related to the
    level of predictability of the metacommunity. The most predictable communities
    were niche‐based metacommunities inhabiting static landscapes with marked environmental
    heterogeneity, such as metacommunities following the species sorting paradigm
    or the mass effects paradigm. The most unpredictable communities were neutral‐based
    metacommunities inhabiting dynamics landscapes with little spatial heterogeneity,
    such as metacommunities following the neutral or patch sorting paradigms. The
    output metrics from joint species distribution modelling yielded generally the
    highest resolution to disentangle among the simulated scenarios. Yet, the different
    types of statistical approaches utilized in this study carried complementary information,
    and thus our results suggest that the most comprehensive evaluation of metacommunity
    structure can be obtained by combining them.\r\n"
article_processing_charge: No
article_type: original
author:
- first_name: Otso
  full_name: Ovaskainen, Otso
  last_name: Ovaskainen
- first_name: Joel
  full_name: Rybicki, Joel
  id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
  last_name: Rybicki
  orcid: 0000-0002-6432-6646
- first_name: Nerea
  full_name: Abrego, Nerea
  last_name: Abrego
citation:
  ama: Ovaskainen O, Rybicki J, Abrego N. What can observational data reveal about
    metacommunity processes? <i>Ecography</i>. 2019;42(11):1877-1886. doi:<a href="https://doi.org/10.1111/ecog.04444">10.1111/ecog.04444</a>
  apa: Ovaskainen, O., Rybicki, J., &#38; Abrego, N. (2019). What can observational
    data reveal about metacommunity processes? <i>Ecography</i>. Wiley. <a href="https://doi.org/10.1111/ecog.04444">https://doi.org/10.1111/ecog.04444</a>
  chicago: Ovaskainen, Otso, Joel Rybicki, and Nerea Abrego. “What Can Observational
    Data Reveal about Metacommunity Processes?” <i>Ecography</i>. Wiley, 2019. <a
    href="https://doi.org/10.1111/ecog.04444">https://doi.org/10.1111/ecog.04444</a>.
  ieee: O. Ovaskainen, J. Rybicki, and N. Abrego, “What can observational data reveal
    about metacommunity processes?,” <i>Ecography</i>, vol. 42, no. 11. Wiley, pp.
    1877–1886, 2019.
  ista: Ovaskainen O, Rybicki J, Abrego N. 2019. What can observational data reveal
    about metacommunity processes? Ecography. 42(11), 1877–1886.
  mla: Ovaskainen, Otso, et al. “What Can Observational Data Reveal about Metacommunity
    Processes?” <i>Ecography</i>, vol. 42, no. 11, Wiley, 2019, pp. 1877–86, doi:<a
    href="https://doi.org/10.1111/ecog.04444">10.1111/ecog.04444</a>.
  short: O. Ovaskainen, J. Rybicki, N. Abrego, Ecography 42 (2019) 1877–1886.
date_created: 2019-10-08T13:01:24Z
date_published: 2019-11-01T00:00:00Z
date_updated: 2023-08-30T06:57:25Z
day: '01'
ddc:
- '577'
department:
- _id: DaAl
doi: 10.1111/ecog.04444
ec_funded: 1
external_id:
  isi:
  - '000486348700001'
file:
- access_level: open_access
  checksum: 6c9fbbd5ea8ce10ae93e55ad560a7bf9
  content_type: application/pdf
  creator: jrybicki
  date_created: 2019-10-08T13:07:44Z
  date_updated: 2020-07-14T12:47:45Z
  file_id: '6937'
  file_name: ecog.04444.pdf
  file_size: 1682718
  relation: main_file
file_date_updated: 2020-07-14T12:47:45Z
has_accepted_license: '1'
intvolume: '        42'
isi: 1
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 1877-1886
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '754411'
  name: ISTplus - Postdoctoral Fellowships
publication: Ecography
publication_identifier:
  eissn:
  - 1600-0587
  issn:
  - 0906-7590
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: What can observational data reveal about metacommunity processes?
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 42
year: '2019'
...