Evidence map›Paper›PMID 40891330›Full record

ArticleGlobal change biology2025

Projecting Uncertainty in Ecosystem Persistence Under Climate Change.

Christina A Buelow, Dominic A Andradi-Brown, Thomas A Worthington, Maria F Adame, Rod M Connolly, Catherine E Lovelock, Kerrylee Rogers, Jaramar Villarreal-Rosas, Christopher J Brown

Abstract read
In one paragraph

Article in Global change biology, 2025. 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. 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

9 authors.

Christina A BuelowCoastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia.ORCID 0000-0002-3084-2554
Dominic A Andradi-BrownOcean Conservation, World Wildlife Fund, Washington, District of Columbia, USA.ORCID 0000-0001-5191-5225
Thomas A WorthingtonDepartment of Plant Sciences, University of Cambridge, Cambridge, UK.ORCID 0000-0002-8138-9075
Maria F AdameCoastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia.ORCID 0000-0001-9620-9252
Rod M ConnollyCoastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia.ORCID 0000-0001-6223-1291
Catherine E LovelockThe University of Queensland, School of Environment, St. Lucia, Queensland, Australia.ORCID 0000-0002-2219-6855
Kerrylee RogersSchool of Science, University of Wollongong, Wollongong, New South Wales, Australia.ORCID 0000-0003-1350-4737
Jaramar Villarreal-RosasCoastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia.ORCID 0000-0001-6056-3268
Christopher J BrownCoastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia.ORCID 0000-0002-7271-4091

Funding

Australian Research Council DP210100739Australian Research Council FL200100133Australian Research Council FT210100792Australian Research Council FT240100621Jeremy and Hannelore Grantham Environmental Trust
6 · The paper itself

Abstract

Global projections of ecosystem responses to increasing climatic and anthropogenic pressures are needed to inform adaptation planning. However, data of appropriate spatiotemporal resolution are often not available to parameterize complex environmental processes at the global scale. Modeling approaches that can project the probability of ecosystem persistence when parameter uncertainty is high may offer a way forward. In particular, the conservation of coastal ecosystems with complex dynamics, like mangrove forests, may benefit from knowing where their future persistence is highly probable or, alternatively, cannot be reliably estimated without additional data of appropriate resolution. Here, we simulated network models to make probabilistic projections of the direction of net change in mangrove ecosystems worldwide under the SSP5-8.5 climate emissions scenario by the years 2040-2060. Seaward net loss was the most probable outcome in 77% [37%-78%; 95% confidence interval (CI)] of mangrove forest units, while 30% [15%-59%; CI] were projected to experience landward net gain or stability. In more than 50% of forest units, projections were ambiguous and therefore unreliable, with a near equal probability of net loss or gain. Quantitative models parameterized with locally accurate data could resolve uncertainty in the future persistence of mangroves in places with unreliable probabilistic projections. Projections made under conservation scenarios also showed that, with action to manage or restore, the number of mangrove forest units likely to experience net gain or stability in the future could nearly double. Our approach to simulating ecosystem responses to climatic and anthropogenic pressures provides a clear indication of how certain (or uncertain) ecosystem persistence is and thus can inform conservation planning.

Indexed as

Climate ChangeConservation of Natural ResourcesEcosystemWetlandsModels, TheoreticalUncertaintyclimate changecoastalconservationmanagementmangrovesmodellingprojectionsrestoration

Identifiers

PMID40891330
PMCPMC12402971

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.