Evidence map›Paper›PMID 33710619›Full record

ReviewEcology2021

A practical guide to selecting models for exploration, inference, and prediction in ecology.

Andrew T Tredennick, Giles Hooker, Stephen P Ellner, Peter B Adler

Abstract readReview
In one paragraph

Review in Ecology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.

0numbers the graph read from it
0cells of the map it votes in
102citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

102 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Limited capacity of habitat features to buffer grassland bird declines.Ecological applications : a publication of the Ecological Society of America · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. A practical guide to characterising ecological coexistence.Biological reviews of the Cambridge Philosophical Society · 2026
    Review
  20. Article

42 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Andrew T TredennickWestern EcoSystems Technology, Inc., 1610 East Reynolds Street, Laramie, Wyoming, 82072, USA.ORCID 0000-0003-1254-3339
Giles HookerDepartment of Statistics and Data Science, Cornell University, Ithaca, New York, 14853, USA.
Stephen P EllnerDepartment of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, 14853, USA.
Peter B AdlerDepartment of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main Hill, Logan, Utah, 84322, USA.ORCID 0000-0002-4216-4009

Funding

Forecasting tipping points in emerging and re-emerging infectious diseasesU01GM110744 · NIGMS · UNIVERSITY OF GEORGIA · PI DRAKE, JOHN M, EPUREANU, BOGDAN · 2014 to 2018
$3.2M
Division of Environmental Biology 1353039Division of Environmental Biology 1353078NIGMS NIH HHS U01 GM110744NIGMS NIH HHS U01GM110744
6 · The paper itself

Abstract

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.

Indexed as

EcologyModels, Statisticalmodel selectionpredictionvalidationvariable selection

Identifiers

PMID33710619
PMCPMC8187274

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.