ArticleScientific reports2025
Effects of forest structure from graph theory connectivity indicators on river water quality in the Caspian Sea Basin.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Exploring the potential forage quality of some halophytic species at different phenological stages.BMC plant biology · 2025Article
- Modeling potential of halophytes in the production of biofuel and edible oil using linear regression and an adaptive neuro-fuzzy inference system.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The structure of a landscape is important as it affects the sources of food for humans and other animals and quality and amount of organic matter in water bodies. The spatial arrangement of landscape features, especially the variations in patch size and the physical space between them, influences the quantity and quality of materials that flow into water bodies and watercourses. In this study located in the Greater Caspian Sea Basin, we investigated how the connectivity of forest patches and the resulting landscape corridors may influence the quality of water. From geographic databases and graph theory we developed 10 landscape metrics and 11 water quality indicators and used these to estimate whether continuous, unbroken landscapes might be influential in improving water quality. We employed Pearson's and Spearman's correlation coefficients to examine the potential relationships between forest-patch connectivity and water quality metrics. Additionally, we employed stepwise, non-linear regression to develop allometry-based power, exponential, and logarithmic models for estimating water quality metrics. The analysis revealed a significant negative correlation between certain forest-patch connectivity indicators (such as landscape coincidence probability and the integral index of connectivity) and certain water quality metrics, suggesting that increased forest connectivity may be associated with improvements in water quality. The modeling results indicated that logarithmic, power and exponential models (or non-simple regression models) with acceptable AIC coefficients (the model selection criteria explained in the materials and methods section) were the most suitable for describing these relationships. As the connectivity of forest patches decreases and subsequently the fragmentation of forest patches increases, water pollution parameters increase while water quality decreases. The models developed included high R
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