Evidence map›Paper›PMID 42801109›Full record

ArticlePlant diversity2026

Scale-dependent evidence for the abundant-center hypothesis and underlying mechanisms.

Jingyue Huang, Dingliang Xing

Abstract read
In one paragraph

Article in Plant diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jingyue HuangZhejiang Zhoushan Island Ecosystem Observation and Research Station, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
Dingliang XingZhejiang Zhoushan Island Ecosystem Observation and Research Station, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whether species are more abundant near the center of their distribution has been intensively debated. Previous studies have largely focused on local sampling units, often overlooking the inherent scale-dependence of ecological patterns. Here, we examine how sampling grain influences the relationship between species abundance and distance to a species' distributional centroid, using inventory data of 212 tree species from the USDA Forest Inventory and Analysis program (FIA). We found scale-dependent support for the abundant-center hypothesis. At the fine spatial grain of individual FIA plots, positive and negative abundance-distance relationships were roughly balanced, with the mean regression slope approaching zero. This pattern reversed at coarser grains (> 0.5°), where the odds of detecting a negative trend more than doubled and the mean regression slope became significantly negative. Additionally, we identified niche overlap as a key factor constraining species abundance at smaller grains, with its impact diminishing at larger scales. Our results underscore the necessity of incorporating biotic interactions in fine-grain species abundance and distribution modeling. Conversely, traditional climate envelope models based on abiotic variables may effectively capture abundance patterns at coarser grains. This scale-explicit perspective helps reconcile previous inconsistent findings and advances our understanding of how ecological processes structure species distributions across scales.

Indexed as

Abundant-center hypothesisClimate envelope modelingInterspecific interactionsNiche centralitySpecies abundance

Identifiers

PMID42801109
PMCPMC13615776

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.