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

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.

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Publication Details

Journal
Journal of Phycology
Published
2026-06-24
DOI
https://doi.org/10.1111/jpy.70188
Primary Topic
Aquatic Ecosystems and Phytoplankton Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Kalle Olli, Sirje Vilbaste, P K Pall, Kai Piirsoo
Journal of Phycology
Aquatic Ecosystems and Phytoplankton Dynamics
article

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

Kalle Olli, Sirje Vilbaste, P K Pall, Kai Piirsoo
article en

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.

Journal of Phycology
Estonian University of Life Sciences (EE), University of Tartu (EE)
European Commission, Eesti Teadusagentuur
Life in Land
Openalex Percentile: Top 11%
Aquatic Ecosystems and Phytoplankton Dynamics
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