Evidence map›Paper›PMID 42903863›Full record

ArticleRoyal Society open science2026

Mapping groundwater-dependent vegetation in temperate climates on the example of Central Germany.

Léonard El-Hokayem, David Emanuel Schulz, Christopher Conrad

Abstract read
In one paragraph

Article in Royal Society open science, 2026. 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
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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

3 authors.

Léonard El-HokayemInstitute of Geosciences and Geography, Martin Luther University Halle Wittenberg, Halle (Saale), Germany.ORCID https://orcid.org/0000-0002-0552-3949
David Emanuel SchulzInstitute of Geosciences and Geography, Martin Luther University Halle Wittenberg, Halle (Saale), Germany.
Christopher ConradInstitute of Geosciences and Geography, Martin Luther University Halle Wittenberg, Halle (Saale), Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Groundwater-dependent ecosystems are biodiversity hotspots that provide habitat for specialized species. The European Union (EU) Water Framework Directive (WFD) stresses the importance of identifying and protecting these ecosystems. So far, most studies focus on (semi-) arid regions, where groundwater use by ecosystems is both more prevalent and easier to detect. In this study, we adapt groundwater-dependent vegetation (GDV) mapping approaches developed for dry climates to temperate regions by replacing seasonal drought-based indicators with contrasts between dry and wet years. We implement this concept by integrating for the first time to our knowledge, ECOSTRESS evapotranspiration data with high-resolution remote sensing, regional geospatial data and botanical field observations. To test our framework, we trained and validated random forest models with eight predictor variables using 166 vegetation plots to identify GDV in Central Germany. Ground-truth GDV plots prefer cooler, wetter, nutrient-richer and more acidic to alkaline environments compared to non-GDV. The final model using a phreatophyte cover threshold of 25% representing GDV plots achieved a testing accuracy of 0.97, identifying 2067 km² of GDV. Currently, only 19% is protected under the EU WFD. The proposed mapping framework offers a solution for identifying GDV in temperate regions. The new GDV maps can contribute to managing groundwater resources and preserving biodiversity hotspots in regions facing increasing droughts, ultimately supporting the implementation of the EU WFD.

Indexed as

biodiversityEU Water Framework Directivegroundwater-dependent vegetationmachine learningphreatophytesremote sensing

Identifiers

PMID42903863
PMCPMC13682426

What OpenQuestion holds

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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.