Evidence map›Paper›PMID 33290672›Full record

ArticleProceedings. Biological sciences2020

What processes must we understand to forecast regional-scale population dynamics?

Jesse R Lasky, Mevin B Hooten, Peter B Adler

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  6. Climate biogeography ofJournal of biogeography · 2024
    Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
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

3 authors.

Jesse R LaskyDepartment of Biology, Pennsylvania State University, University Park, PA, USA.
Mevin B HootenU.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO, USA.
Peter B AdlerDepartment of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

Indexed as

Biological EvolutionPopulation DynamicsBayes TheoremBiodiversityBiologyClimate ChangeEcosystemclimate changelocal adaptationmacroecologyrange dynamics

Identifiers

PMID33290672
PMCPMC7739927

What OpenQuestion holds

Textmetadata
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.