ArticleBMC plant biology2025
Shrub mitigate the negative impacts of drought on the ecological adaptability of Syntrichia caninervis in the desert regions of Northwest China.
Article in BMC plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Desert Plant Trait Networks Differ Among Plant Life Forms and Soil Moisture-Salinity Environments.Plants (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundSyntrichia caninervis, a representative stress-tolerant moss, thrives under extreme conditions such as extreme temperatures, drought, and intense radiation, making it a top candidate for extraterrestrial colonization. However, whether the ecological adaptation traits of S. caninervis are consistent across different distribution areas and microhabitats and the interactive relationships among functional traits remain unclear. This study employed a plant trait network (PTN) approach to examine the relationships among 25 functional traits of S. caninervis within and outside shrub habitats to assess its ecological adaptability in temperate desert.
resultShrub microhabitats increased the biomass, leaf morphology, and photosynthetic efficiency of S. caninervis by improving the local hydrothermal environment while reducing the physiological stress response. The trait network of S. caninervis under shrub presented a relatively high edge density and average clustering coefficient and relatively low network diameter, average path length, and degree of modularity. These findings suggest that shrubs enhance trait network connectivity and functional collaboration of S. caninervis, effectively mitigating drought stress and optimizing resource utilization efficiency. Central trait analysis revealed that, under shrub conditions, Fv/Fm and soluble sugars were central traits, whereas exposed area, Fv/Fm and POD were central traits. As precipitation decreased and temperature increased, the leaf area and photosynthetic activity of S. caninervis initially tended to increase but then decreased. Simultaneously, its trait network connectivity strengthened, with both modularity and local tightness gradually increasing, reflecting its adaptation to drought and high-temperature stress through trait synergy and functional modular specialization. Structural equation modeling (SEM) analysis revealed that climate dominated the ecological adaptability of S. caninervis through both direct and indirect pathways, whereas shrubs significantly alleviated the negative impacts of drought stress by improving hydrothermal conditions. This study not only reveals the physiological adaptation strategies of moss species in extreme environments but also provides new insights into the adaptive mechanisms of moss species in desert ecosystems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.