Evidence map›Paper›PMID 41719360›Full record

ArticlePloS one2026

AquaX: An enhanced and revised AquaMaps framework to model marine species distributions and biodiversity.

Gabriel Reygondeau, Yulia Egorova, Kristina Boerder, Derek P Tittensor, Kristin Kaschner, Kathleen Kesner-Reyes, Nicolas Bailly, William W L Cheung

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Gabriel ReygondeauRosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, Florida, United States of America.ORCID https://orcid.org/0000-0001-9074-625X
Yulia EgorovaRosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, Florida, United States of America.ORCID https://orcid.org/0000-0002-8712-7789
Kristina BoerderFuture of Marine Ecosystems Lab, Dalhousie University, Halifax, Canada.
Derek P TittensorFuture of Marine Ecosystems Lab, Dalhousie University, Halifax, Canada.
Kristin KaschnerDepartment of Biometry and Environmental Systems Analysis, Albert-Ludwigs University, Freiburg im Breisgau, Germany.ORCID https://orcid.org/0000-0002-4061-626X
Kathleen Kesner-ReyesQuantitative Aquatics, Inc., Los Baños, Laguna, Philippines.
Nicolas BaillyQuantitative Aquatics, Inc., Los Baños, Laguna, Philippines.
William W L CheungInstitute for the Oceans and Fisheries, University of British Columbia, Vancouver, British Columbia, Canada.ORCID https://orcid.org/0000-0001-9998-0384

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Marine biodiversity underpins ecosystem health and is critical for the provision of essential ecological services. Global efforts to mitigate biodiversity loss are underway but require comprehensive knowledge on the biogeography of species to be effective. However, key challenges limit comprehensive mapping of species distributions, including the ecosystem complexity and difficulty of sampling the marine realm. Global initiatives such as AquaMaps pioneered large-scale marine species mapping using species distribution models or ecological niche models and provided the knowledge base for effective marine conservation and management. Recently, methodological and data advances have enabled a more modern and robust approach that enables higher resolution outputs more suited to conservation applications at all scales. Building on AquaMaps, we developed a next-generation marine species habitat suitability modelling platform called AquaX, providing a suite of advances that include an ensemble of ten machine learning algorithms, enabling spatial uncertainty assessments, validation indices, and ecological niche representation at a ten-fold improved spatial resolution of 0.05°. Furthermore, AquaX integrates (i) accepted taxonomy from the World Register of Marine Species, (ii) species-specific ecological, physiological, and biogeographical information (D3-Ocean system), (iii) updated occurrence records validated through expert input, and (iv) refined species range maps using expert knowledge and biogeographical divisions. AquaX also projects species' habitat suitability for both present and future conditions based on two time periods and three climate scenarios. This work provides species range maps for numerous species compared to previously available datasets and improves the accurate use of observational data. The approaches described here improve predictive accuracy at scales more relevant to marine biodiversity conservation and offer an openly accessible tool to support marine biodiversity research and conservation planning under accelerating environmental change. AquaX represents an important step forward in species distribution modeling, enabling researchers and policymakers to better understand marine biodiversity patterns and develop more effective conservation strategies.

Indexed as

Aquatic OrganismsBiodiversityModels, BiologicalAnimalsConservation of Natural ResourcesEcosystemMarine BiologyOceans and Seas

Identifiers

PMID41719360
PMCPMC12923144

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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.