Evidence map›Paper›PMID 42340333›Full record

ArticleJournal of phycology2026

Environmental constraints on freshwater red algae in Estonian streams: Insights from long-term observations and machine-learning models.

Kai Piirsoo, Sirje Vilbaste, Peeter Pall, Kalle Olli

Abstract read
In one paragraph

Article in Journal of phycology, 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

4 authors.

Kai PiirsooHydrobiology and Fisheries, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia.ORCID https://orcid.org/0009-0009-4399-1346
Sirje VilbasteHydrobiology and Fisheries, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia.ORCID https://orcid.org/0000-0002-6745-7780
Peeter PallHydrobiology and Fisheries, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia.
Kalle OlliHydrobiology and Fisheries, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia.ORCID https://orcid.org/0000-0002-7943-5714

Funding

Eesti Teadusagentuur PRG1266Eesti Teadusagentuur TARISTU24-TK11European Commission 101147477European Commission 951963
6 · The paper itself

Abstract

The division Rhodophyta (red algae) includes an inconspicuous and often overlooked component of benthic freshwater biota. In many cases, freshwater red algae are considered indicators of good ecological status, yet their environmental preferences remain poorly understood. Species richness is low globally, and the distribution of many taxa remains poorly known. Here, we report the occurrence of 12 taxa of benthic red algae in 272 Estonian streams, based on 734 sampling sites and 1263 sampling occasions. The aim of this study is to identify the key environmental drivers of freshwater red algae occurrence and to evaluate their potential as indicators of water quality. Red algae were recorded in 20% of the samples. The influence of 37 environmental variables on the occurrence of red algae was assessed using machine-learning algorithms (random forest and gradient boosting trees). Hydrological and hydrochemical variables were by far the most important variable groups, followed by bottom substrate, watershed land cover and land use, and geological bedrock type. Among the hydrochemical variables, biological oxygen demand was the single most important predictor, indicating a high sensitivity of freshwater red algae to labile organic pollution. In contrast, the effect sizes of mineral and total nutrients were lower and more nuanced, suggesting that freshwater red algae are not particularly sensitive indicators of eutrophication. Some division-level responses may have been obscured by contrasting species-specific patterns. Species-level effects were often difficult to verify using machine-learning approaches due to low occurrence frequencies.

Indexed as

Machine LearningRhodophytaRiversEstoniaFresh WaterBatrachospermaceaefreshwater red algaehabitat preferencesmachine learningriver ecosystemsspecies distribution

Identifiers

PMID42340333
PMCPMC13485182

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.