Evidence map›Paper›PMID 42396882›Full record

ArticleEcology2026

Consequences of phenological shifts are determined by the number of generations per season.

Heng-Xing Zou, Volker H W Rudolf

Abstract read
In one paragraph

Article in Ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Heng-Xing ZouProgram in Ecology and Evolutionary Biology, Department of BioSciences, Rice University, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-1965-6910
Volker H W RudolfProgram in Ecology and Evolutionary Biology, Department of BioSciences, Rice University, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-9214-2000

Funding

National Science Foundation DEB-1655626
6 · The paper itself

Abstract

The order and timing of species' arrival in a community can often change species interactions. Termed priority effects, this phenomenon arises in communities with drastically different life histories (e.g., bacteria, insects, and amphibians), yet how their life history traits affect the consequences of priority effects in seasonal systems remains understudied. Here, we test how the length of one growing season, which can accommodate different numbers of overlapping generations, interacts with priority effects using two competing flour beetles, Tribolium castaneum and Tribolium confusum. We manipulated the sequence and timing of arrivals and collected all adults after one, two, or three generations, simulating phenological shifts in communities with different growing season lengths with less or more overlapping generations. The early-arriving species always reached a higher population, and in general, its per capita competition towards the late arriver was stronger, indicating priority effects. However, fitting competition models to the data revealed that for both species, longer experimental durations (which included more overlapping generations) led to less dramatic changes in competitive outcomes with differences in arrival time, indicating that overlapping generations of each species provided a buffer against strong priority effects from different arrival times. These results suggest that changes in the order and timing of species arrival (e.g., due to climate change) can impact species coexistence, but such effects could be contingent on the life history, such as the number of generations of interacting species, or abiotic factors, such as the duration of a growing season.

Indexed as

SeasonsTriboliumAnimalsTime Factorscoexistencelife historypriority effectsseasonal dynamicsTribolium

Identifiers

PMID42396882
PMCPMC13371149

What OpenQuestion holds

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Registered trials

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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.