Evidence map›Paper›PMID 31871191›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Hidden similarities in the dynamics of a weakly synchronous marine metapopulation.

Tanya L Rogers, Stephan B Munch

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Climate change could amplify weak synchrony in large marine ecosystems.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. 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
  6. Decomposing predictability to identify dominant causal drivers in complex ecosystems.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  7. Review
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

2 authors.

Tanya L RogersMarine Science Center, Department of Marine and Environmental Sciences, Northeastern University, Nahant, MA 01908; tanya.rogers@noaa.gov.
Stephan B MunchSouthwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA 95060.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Populations of many marine species are only weakly synchronous, despite coupling through larval dispersal and exposure to synchronous environmental drivers. Although this is often attributed to observation noise, factors including local environmental differences, spatially variable dynamics, and chaos might also reduce or eliminate metapopulation synchrony. To differentiate spatially variable dynamics from similar dynamics driven by spatially variable environments, we applied hierarchical delay embedding. A unique output of this approach, the "dynamic correlation," quantifies similarity in intrinsic dynamics of populations, independently of whether their abundance is correlated through time. We applied these methods to 17 populations of blue crab (

Indexed as

Models, BiologicalSpatio-Temporal AnalysisAnimalsAtlantic OceanBrachyuraEcological Parameter MonitoringLarvaPopulation DynamicsTemperatureUnited StatesCallinectes sapidusenvironmental gradientshierarchical modelssynchronytime-delay embedding

Identifiers

PMID31871191
PMCPMC6955368

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.