Evidence map›Paper›PMID 25960188›Full record

ArticleEcology letters2015

The ecological forecast horizon, and examples of its uses and determinants.

Owen L Petchey, Mikael Pontarp, Thomas M Massie, Sonia Kéfi, Arpat Ozgul, Maja Weilenmann, Gian Marco Palamara, Florian Altermatt, Blake Matthews, Jonathan M Levine and 8 more

Abstract read
In one paragraph

Article in Ecology letters, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

64 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Plasticity in climate change responses.Biological reviews of the Cambridge Philosophical Society · 2025
    Review
  7. Article
  8. Revealing unseen dynamical regimes of ecosystems from population time-series data.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. Article
  10. Article
  11. What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge.Ecological applications : a publication of the Ecological Society of America · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Constraining nonlinear time series modeling with the metabolic theory of ecology.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Review
  16. Review
  17. Review
  18. Article
  19. Genetic architecture of dispersal and local adaptation drives accelerating range expansions.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  20. Article

4 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

18 authors.

Owen L PetcheyInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-7724-1633
Mikael PontarpInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Thomas M MassieInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Sonia KéfiInstitut des Sciences de l'Evolution, Université de Montpellier, CNRS, IRD, EPHE, CC065, Place Eugène Bataillon, 34095, Montpellier Cedex 05, France.
Arpat OzgulInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Maja WeilenmannInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Gian Marco PalamaraInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Florian AltermattInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Blake MatthewsDepartment of Aquatic Ecology, Center for Ecology, Evolution, and Biogeochemistry, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Seestrasse 79, 6047 Luzern, Switzerland.
Jonathan M LevineInstitute of Integrative Biology, ETH Zurich, Universitätstrasse 16, 8092, Zurich, Switzerland.
Dylan Z ChildsAnimal and Plant Sciences, Sheffield University, Sheffield, Western Bank. S10 2TN South Yorkshire, UK.
Brian J McGillSchool of Biology and Ecology and Mitchel Center for Sustainability Solutions, University of Maine, Orono, 5751 Murray Hall, ME 04469, USA.
Michael E SchaepmanUniversity of Zurich, Department of Geography, Remote Sensing Laboratories, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
Bernhard SchmidInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Piet SpaakDepartment of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland.
Andrew P BeckermanAnimal and Plant Sciences, Sheffield University, Sheffield, Western Bank. S10 2TN South Yorkshire, UK.
Frank PennekampInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Ian S PearseThe Illinois Natural History Survey, Champaign, 1816 South Oak Street, MC 652, IL 61820, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.

Indexed as

ForecastingBiological EvolutionEcologyEcosystemModels, StatisticalPhylogenyDynamicsecosystemsenvironmental changeforecastingfuturespredictionscenarios

Identifiers

PMID25960188
PMCPMC4676300

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.