Evidence map›Paper›PMID 40557120›Full record

ArticleEcology and evolution2025

Pedology and Plant Provenance Can Improve Species Distribution Predictions of Australian Native Flora: A Calibrated and Validated Modeling Exercise on 5033 Species.

Farzin Shabani, Mohsen Ahmadi, Niloufar Lorestani, Shazia Bibi, Atefeh Esmaeili, Tessa Lane, Martin F Breed, John Llewelyn, Craig Liddicoat, Philip Kibet Langat and 7 more

Abstract read
In one paragraph

Article in Ecology and evolution, 2025. 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

17 authors.

Farzin ShabaniCollege of Arts and Sciences Qatar University Doha Qatar.ORCID https://orcid.org/0000-0002-5100-8921
Mohsen AhmadiDepartment of Natural Resources Isfahan University of Technology Isfahan Iran.
Niloufar LorestaniCollege of Arts and Sciences Qatar University Doha Qatar.ORCID https://orcid.org/0009-0003-1652-0844
Shazia BibiCollege of Arts and Sciences Qatar University Doha Qatar.
Atefeh EsmaeiliCollege of Arts and Sciences Qatar University Doha Qatar.
Tessa LaneCollege of Science and Engineering Flinders University Bedford Park South Australia Australia.
Martin F BreedCollege of Science and Engineering Flinders University Bedford Park South Australia Australia.ORCID https://orcid.org/0000-0001-7810-9696
John LlewelynCollege of Science and Engineering Flinders University Bedford Park South Australia Australia.
Craig LiddicoatCollege of Science and Engineering Flinders University Bedford Park South Australia Australia.
Philip Kibet LangatSchool of Engineering and Technology Central Queensland University Rockhampton Queensland Australia.
Bahareh KalantarRIKEN Center for Advanced Intelligence Project Goal-Oriented Technology Research Group, Disaster Resilience Science Team Tokyo Japan.
Nadiezhda Ramírez-CabralINIFAP National Research Institute of Agriculture, Forestry and Livestock Zacatecas México.
Pooja SinghDepartment of Plant Sciences University of California Davis Davis California USA.
Ricardo Siqueira da SilvaDepartment of Ecological Modelling Helmholtz Centre for Environmental Research-UFZ Leipzig Germany.
Mohammed Abu-DieyehCollege of Arts and Sciences Qatar University Doha Qatar.
Masoud NazarizadehFaculty of Science University of South Bohemia České Budějovice Czech Republic.
Alessandro OssolaDepartment of Plant Sciences University of California Davis Davis California USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Species distribution models (SDMs) are valuable tools for assessing species' responses to environmental factors and identifying areas suitable for their survival. The careful selection of input variables is critical, as their interactions and correlations with other environmental factors can affect model performance. This study evaluates the influence of climate and soil variables on the performance of SDMs for 5033 Australian terrestrial vascular plant species, representing the largest phylogenetic diversity of native flora assessed in such an analysis. Using an ensemble of correlative models, we assessed the predictive performance of climate and soil variables, individually and in combination, across four distinct ecoregions: Desert (

Indexed as

ecoregion‐specific dataensemble modelshabitat suitabilityprecipitation seasonalitypredictor variablesspecies distributions

Identifiers

PMID40557120
PMCPMC12185943

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.