Ecology

AbstractEnvironmental heterogeneity plays a crucial role in shaping the distribution and composition of natural vegetation, including understory ferns. This study investigated the influence of environmental variation on understory fern communities within a one-hectare permanent plot in a low-altitude mountain forest in central Taiwan. Twenty-two environmental factors, including topographic, soil, and biotic variables, were recorded. Multiple regression, cluster (two-way indicator species analysis, TWINSPAN), and ordination (detrended correspondence analysis, DCA, and canonical correspondence analysis, CCA) analyses were conducted. A total of 51 fern species (including Lycophytes) belonging to 20 families and 30 genera were recorded. Among these, 43 were terrestrial, and eight were epiphytic; however, only terrestrial species were analyzed because of the limited representation of epiphytes. Multiple regression analyses revealed that environmental variables significantly affected fern richness, abundance, and community composition. Specifically, stream distance and the importance value (IV) of the saplings significantly influenced fern richness; herb/vine IV affected abundance; and the carbon-to-nitrogen ratio (C/N), manganese concentration (Mn), and herb/vine IV impacted the first axis of the DCA. Furthermore, the elevation, curvature, slope, and topographic wetness index (TWI) significantly influenced the second axis of the DCA. In all the models, topographic variables—particularly stream distance—were one of the most influential drivers. TWINSPAN categorized the ferns into four distinct groups (Diplazium donianum var. donianum [DIPLDO], D. donianum var. aphanoneuron [DIPLAP], Blechnopsis orientalis [BLECOR], and Angiopteris lygodiifolia [ANGILY]), and CCA revealed that environmental factors structured the community compositions in line with the TWINSPAN grouping. The DIPLDO and DIPLAP groups were associated with ridges and upper slope habitats characterized by higher elevations and drier conditions. In contrast, the BLECOR and ANGILY groups were associated with lower elevations, stream proximity, steeper slopes, and higher humidity. This study highlights the role of topographic and soil C/N heterogeneity in structuring fern communities in fine-scale plots for future ecological monitoring in subtropical forest ecosystems.

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IntroductionEnvironmental gradients strongly influence the diversity and spatial distribution of plant communities. At regional scales, climate variables such as temperature and precipitation are dominant drivers1,2,3. In contrast, at local scales, fine-scale topography, soil characteristics, and biological interactions play significant roles4,5,6,7,8,9. Among biotic factors, canopy structure and density are widely recognized for their influence on understory plant communities10,11. Ferns, the second largest group of vascular plants, are a dominant component of understory vegetation in tropical and subtropical forests3,12 and are particularly sensitive to environmental heterogeneity.Topography—such as elevation, slope, aspect, and stream proximity—shapes microhabitats by altering light, temperature, and moisture regimes13,14. Topographic variation at the local scale is known to affect both fern diversity10 and abundance15. Stream proximity, in particular, is a strong predictor of fern assemblages16.Soil characteristics are also closely tied to fern distribution. Soil moisture (or humidity) is critical for the growth and development of ferns5,17. Variables such as nutrient content (e.g., N, P, K, Ca, and Mg), pH, and organic matter significantly influence fern performance5,16,18. The carbon-to-nitrogen (C/N) ratio, in particular, serves as a proxy for soil fertility and has been linked to fern richness in several tropical studies19,20. However, soil properties are partially influenced by topographic variations21,22, which in turn may affect the distribution of ferns.Understory ferns depend on canopy-mediated light availability for growth and reproduction23,24. Canopy openness not only alters photosynthetically active radiation but also modulates temperature and humidity in the understory25. Research in Southeast Asia and Taiwan suggests that canopy openness is an important factor influencing fern richness and cover10,26. Furthermore, ferns interact with other plant groups. Dense fern layers can suppress tree seedling recruitment27,28, whereas the diversity of co-occurring understory taxa appears to influence fern richness in varying ways29,30. These biotic interactions may result in mutual inhibition or facilitation depending on local environmental conditions.Ferns reproduce via spores that are readily dispersed by wind. Despite the fact that many ferns may produce spores capable of travelling long distances, chances of establishing new populations are low31. Allopatric differentiation may be associated with gametophytes that are highly sensitive to microclimatic and edaphic parameters32,33. In addition, environmental factors at the mesoscale—such as soil moisture, humidity, temperature, wind speed, rainfall, vegetation type, and canopy openness—significantly influence fern distribution by affecting sporophytes’ water requirements, temperature tolerance, and photosynthetic capacity5,26,34,35,36. Therefore, the diversity of forest microenvironments varies across regions and is reflected in corresponding differences in fern diversity and composition. We surveyed the relationship between environmental heterogeneity and fern diversity within a one-hectare plot embedded in a broader 25-hectare permanent plot in the Lienhuachih region, central Taiwan. We addressed the following questions: (1) Which environmental factors most strongly influence fern richness, abundance, and composition? (2) How do ferns cluster into ecological groups on the basis of these factors?Materials and methodsStudy areaThe study site is located in the Lienhuachih Experimental Forest (23°55’N, 120°52’E), which is located in a low-altitude mountainous area of central Taiwan (Fig. 1). A one-hectare permanent plot was established within a natural forest and represents the northwestern section of a broader 25-hectare forest dynamics plot initiated in 2007. The plot encompasses both mid-slope and riparian habitats, with an elevation range spanning from 755 m to 814 m (Table S1). On the basis of earlier tree surveys (DBH ≥ 1 cm), two forest types were identified: one dominated by Diospyros morrisiana and Cryptocarya chinensis and the other by Machilus japonica var. kusanoi and Helicia formosana37. The region experiences a mean annual temperature of 21.2℃ and receives approximately 2,178 mm of precipitation annually, with rainfall concentrated from March to September and a dry season from October to November26. The soil properties of the 25-hectare plot are partially influenced by topography21.Fig. 1Map of Taiwan (left) and a topographic map (right) of the one-hectare natural forest plot in Lienhuachih within the Houloun stream catchment, a mountainous region in central Taiwan.Full size imageSampling designThe one-hectare plot (100 m × 100 m) was divided into 100 subplots (10 m × 10 m) used as survey units. Vegetation data were collected per subplot, within which all ferns, herbs, vines, and tree saplings (< 1 cm in DBH, > 30 cm in height) were recorded. Fern abundance was assessed by counting individual clumps (treating each clump as one individual) and by estimating percent cover. Epiphytic ferns and vines were measured by the horizontal projection of their canopy cover. Tree data (DBH ≥ 1 cm) were also recorded. Ferns were categorized as either terrestrial or epiphytic, with the former defined as those growing on soil or rocks and the latter as those occurring on tree trunks. Taxonomy follows Volume 6 of the Flora of Taiwan38 and the classification by Kuo et al. (2019)39. Surveys were conducted from July 13 to 15, 2023.Environmental variablesThe topographic variables included elevation, plan curvature, slope, aspect, topographic wetness index (TWI), and distance to the stream. These data were collected from the center of each subplot. Soil variables (data were collected by Chang et al. (2013)40 (2023)41 ) included pH, the carbon-to-nitrogen ratio (C/N), nitrogen, phosphorus, potassium, calcium, magnesium, manganese, zinc, iron, and copper. Soil data were collected at a 20 m × 20 m resolution, with each 10 m × 10 m subplot assigned the values of its nearest soil sample point. Soil moisture was measured at a depth of 5 cm using a RiXEN M-700 S meter between February 26 and March 3, 2024, and the data were averaged from three diagonal points per subplot. Canopy openness was measured from March 15 to 22, 2024, using spherical crown densiometers at a height of 1.3 m at the center of each 10 m × 10 m subplot.Statistical analysisSince the soil properties of the 25-hectare plot are influenced by topography, a two-factor Pearson correlation coefficient analysis was conducted to examine the relationships between topography and soil properties in this study plot. Forward stepwise multiple regression (FSMR) with Poisson and linear models was employed to examine the effects of environmental factors on fern diversity. The abundance data for each species were quantified using the importance value (IV), which was calculated as the sum of its relative density (individuals per species/total individuals, unit: %) and relative cover (cover per species/total cover, unit: %). Twenty-two environmental variables—spanning topography (elevation, plan curvature, slope, aspect, TWI, and stream distance), soil properties (pH, C/N, N, P, K, Ca, Mg, Mn, Zn, Fe, Cu, and soil moisture), and biotic factors (canopy openness, tree density, sapling IV, and herb/vine IV)—were included in the analyses (Supplementary Table S1). The dependent variables included fern richness (species count), abundance (IV), and community composition (first two DCA axes). FSMR was performed for model selection using the “MuMIn” package in R. Significant predictors were selected (p < 0.05, chi-square test [Poisson] for fern richness and F test for fern abundance and composition [linear]), and then collinearity was assessed using the variance inflation factor (VIF). Variables with a VIF > 5 were iteratively excluded. Finally, Poisson regression was used to analyze fern richness and its selected predictor variables, whereas linear regression was applied to abundance, composition, and their respective selected predictors.Community classification was performed using two-way indicator species analysis (TWINSPAN) (dissimilarity metric = total inertia)42. Detrended correspondence analysis (DCA)43 was used to ordinate species and subplots. Canonical correspondence analysis (CCA)44 related species distributions to environmental gradients. The raw data utilized in the aforementioned analysis were derived from the species-subplot matrix, with the data comprising the previously described importance value (IV). All analyses were performed in R v4.3.1.ResultsA total of 51 fern species representing 20 families and 30 genera were recorded within the one-hectare plot. Of these, 43 species were terrestrial, and eight were epiphytic. Diplazium dilatatum was the most abundant species, with 1,011 individuals observed in 98 subplots, followed by Pleocnemia winitii, with 567 individuals in 90 subplots. These two species accounted for 55.8% of the total abundance of terrestrial ferns (Table 1). In contrast, ten terrestrial species were found in only one subplot (Supplementary Table S2), representing 23.3% of the total terrestrial fern richness.Table 1 Number of subplots, relative density, and relative coverage for terrestrial species of fern in the one-hectare plot of the low-altitude natural forest of central Taiwan. The species did not occur in fewer than 2 subplots.Full size tableIn addition to ferns, 37 herb, 43 vine and 76 sapling species were recorded, resulting in a total of 207 understory species (including 8 epiphytic fern species). Owing to their limited abundance and patchy distribution, epiphytic ferns were excluded from further analyses but are documented in Supplementary Table S2. Among the environmental variables, elevation was most significantly correlated with soil properties (11), followed by slope (nine) and stream distance (seven) (Supplementary Table S3).The regression models (Table 2) revealed that among the six selected variables, fern richness was significantly influenced by stream distance (negatively) and sapling abundance (positively). Fern abundance was most strongly associated with herb/vine IVs. With respect to fern composition, DCA1 was associated with stream distance, the C/N ratio, manganese, and herb/vine IV—with only stream distance being not significant. The best model of DCA2 included eight variables, among which elevation, curvature, slope, and TWI were significant.Table 2 Environmental factor models in a one-hectare natural forest plot in Lienhuachih used Poisson regression for fern richness and linear regression for fern abundance and composition (two DCA axes). “VIF” indicates the variance inflation factor test. *: p < 0.05; **: p < 0.01; and ***: p < 0.001.Full size tableTWINSPAN classified the fern community into four groups (Fig. 2a; Supplementary Figure S1): the Diplazium donianum var. donianum group (DIPLDO; n = 52), the D. donianum var. aphanoneuron group (DIPLAP; n = 21), the Blechnopsis orientalis group (BLECOR; n = 8), and the Angiopteris lygodiifolia group (ANGILY; n = 19). The mean fern richness was lowest in DIPLDO (4.2 ± 1.9) and highest in ANGILY (6.4 ± 2.5) (Supplementary Figure S2). Fern abundance (log-transformed) was positively correlated with richness (r = 0.46, p < 0.001), a pattern that was consistent across groups (Fig. 3). However, significant correlations were observed only in the DIPLDO and ANGILY groups, whereas the other two groups showed no significant correlation.Fig. 2TWINSPAN and CCA from a one-hectare natural forest plot in the Lienhuachih area of central Taiwan. (a) TWINSPAN identified four fern groups: DIPLDO (□), DIPLAP (△), BLECOR (○), and ANGILY (●). (b) The figure of the first two axes from the CCA; the words beside the lines represent environmental and biological factors, and the direction indicates the trend in which the value increases. (c) The same analysis as in b, with the letters representing the fern species (see Table 1).Full size imageFig. 3Relationships between fern abundance (log-transformed) and richness (r = 0.46, p < 0.001). The Pearson correlation in the four fern groups was r = 0.48 (p < 0.001, DIPLDO), 0.26 (p = 0.264, DIPLAP), 0.50 (p = 0.205, BLECOR), and 0.65 (p = 0.002, ANGILY).Full size imageThe cumulative explained variance of the first three CCA axes was 9.7%, 16.5%, and 21.2%, respectively. On the first axis of the CCA, herb/vine IV had the highest absolute score (0.80), followed by C/N (0.54), elevation (–0.50), and stream distance (–0.46); on the second axis of the CCA, stream distance (–0.61) had the highest absolute score, followed by Ca (–0.58), elevation (–0.50), and slope (0.43). In addition, herb/vine IV, stream distance, Ca and C/N were the most important determinants of one of the first two CCA axes (Table 3). As shown in Fig. 2b, the BLECOR group is more distinct, whereas the ANGILY group somewhat overlaps with the other groups, and the DIPLDO and DIPLAP groups exhibit greater overlap. Our results showed that these fern groups have adapted to different environments (Fig. 4).Table 3 Scores of the first two CCA axes with the environmental factors in the 1 ha plot of Lienhuachih in the low-altitude natural forest of central Taiwan. * shows the significance test (p < 0.05) for Pearson correlation between these factors and the CCA axes.Full size tableFig. 4Variation in elevation (a), stream distance (b), slope (c), and C/N (d) among different fern communities. Different letters denote statistically significant differences among the different types of fern vegetation (p < 0.05).Full size imageThe species ordination (Fig. 2c) revealed dominant ferns (Diplazium dilatatum and Pleocnemia winitii) near the plot center, whereas the species of named TWINSPAN groups aligned with the environmental characteristics of their respective groups. For example, DIPLDO’s D. donianum var. donianum was located in a topographic and edaphic space that is indicative of drier, upland sites.DiscussionTopographic effectsTopography has long been recognized as a key determinant of forest vegetation patterns8,45,46. In this study, elevation and stream distance emerged as primary predictors of fern richness and composition, despite the modest elevation range (~ 59 m) within the plot. These gradients reflect moisture availability: ridges with higher elevations and well-drained soils tend to be drier, whereas lower streamside zones retain more moisture. The strong correlation between elevation and stream distance (r = 0.40, p < 0.001) reinforces this interpretation. Our findings align with those of previous studies5,10,16 in montane forests where even fine-scale topographic variation influences fern diversity. For the other plant taxa in the 25-ha plot (of which our 1-ha plot was a part), topography was the most important factor affecting the changes in the plant community and species composition37.Soil effectsSoil properties such as nutrient concentrations and organic matter content often covary with topography because of erosion, leaching, and deposition21,47. While some studies have indicated a positive correlation between soil fertility and fern richness49,50, others have shown that lower fertility results in more fern species15,20. In our plot, C/N was significantly associated with fern composition, particularly along the first two CCA axes. Stream-adjacent soils, which are rich in organic matter and nitrogen, presented elevated carbon-to-nitrogen (C/N) ratios because of the greater accumulation of organic matter than that associated with decomposition in moist areas. Although fern richness was not directly correlated with C/N, its indirect effects via topographic mediation were evident. Calcium and manganese were also included in the regression models, although their contributions were relatively modest.Water serves as a critical determinant of both fern richness and distribution4,5,24. Water availability, inferred through the topographic wetness index (TWI), slope, and stream distance, likely exerts a dominant control on fern distributions. While soil moisture in the 25-ha plot (6 transects) was an important factor for the understory plants, including fern species30, it did not emerge as a significant predictor in the models in this study plot. The strong influence of hydrologically relevant topographic variables suggests their overriding importance in determining local fern composition.Biotic influencesLight availability is a well-documented driver of fern performance and affects morphology, abundance, and richness10,24. Although canopy openness was not retained in the final regression models, its significant correlation with tree density (r = − 0.22, p < 0.05) implies indirect effects. Denser tree canopies may reduce understory light, thus constraining fern growth.Interestingly, this study revealed positive associations between fern richness (or abundance) and both sapling and herb/vine cover, contrary to previous findings that emphasized competitive suppression28,30,48. The factors contributing to this outcome are likely multifaceted. The underlying mechanism may involve moisture availability, which is influenced by proximity to the stream. Although herb and vine cover are strongly correlated with fern abundance and composition, the primary determinant appears to be the distance from streams, as areas closer to streams generally exhibit higher moisture levels. Tuomisto et al. (2002) similarly reported that fern and Melastomataceae diversity co-occurred with tree richness in fertile tropical soils29.Fern community grouping and habitat differentiationTWINSPAN and CCA revealed clear compositional differentiation among the four fern groups, corresponding to distinct habitat types. The DIPLDO and DIPLAP groups were associated with ridges and upper slope habitats characterized by higher elevations and drier conditions. In contrast, the BLECOR and ANGILY groups were associated with lower elevations, stream proximity, steeper slopes, and higher humidity. These habitat preferences support the role of environmental filtering in fern assembly and align with prior vegetation classifications within the same forest37.In terms of the correlation between fern richness and abundance, compared with the DIPLDO group, the ANGILY group exhibited communities with greater evenness. These findings suggest that the environment inhabited by the ANGILY group is more conducive to the survival of a diverse range of ferns.ConclusionThis study highlights the significant role of environmental heterogeneity in shaping fern diversity and community composition in a low-altitude subtropical forest. Among the examined factors, topographic variables—particularly stream distance—exerted one of the most influential drivers of fern richness, abundance, and species assemblage. Soil properties, especially C/N, further mediated these relationships and reflected microhabitat variation. The classification of ferns into four ecological groups on the basis of environmental gradients highlights the structuring effect of habitat differentiation. The DIPLDO and DIPLAP groups occupied ridges and upper slope habitats characterized by higher elevations and drier conditions, whereas the BLECOR and ANGILY groups were associated with lower elevations, greater proximity to streams, steeper slopes, and higher humidity. This study highlights the role of topographic and soil-related heterogeneity in structuring fern communities in fine-scale plots. Long-term monitoring incorporating both abiotic and biotic variables is essential for understanding how fern communities respond to environmental change and for informing conservation strategies in subtropical forest ecosystems.

Data availability

The datasets utilized and/or analyzed during the current study are available from the first author upon reasonable request.
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Download referencesAcknowledgementsWe thank the students of the Department of Forestry at National Chung Hsing University for their assistance in the wild investigation.Author informationAuthors and AffiliationsDepartment of Forestry, National Chung Hsing University, No. 145, Xingda Rd., South Dist, Taichung, 402202, TaiwanPei-Hsuan Lee, Yen-Hsueh Tseng & Hsy-Yu TzengTaiwan Forestry Research Institute, No. 53, Nanhai Rd, Taipei, 100051, TaiwanPei-Hsuan Lee, Yao-Moan Huang, Li-Wan Chang, Jian-Hong Yang, Wen-Liang Chiou & Yen-Hsueh TsengAuthorsPei-Hsuan LeeView author publicationsSearch author on:PubMed Google ScholarYao-Moan HuangView author publicationsSearch author on:PubMed Google ScholarLi-Wan ChangView author publicationsSearch author on:PubMed Google ScholarJian-Hong YangView author publicationsSearch author on:PubMed Google ScholarWen-Liang ChiouView author publicationsSearch author on:PubMed Google ScholarYen-Hsueh TsengView author publicationsSearch author on:PubMed Google ScholarHsy-Yu TzengView author publicationsSearch author on:PubMed Google ScholarContributionsConception or design of the work: Pei-Hsuan Lee, Hsy-Yu Tzeng. Data collection: Pei-Hsuan Lee, Li-Wan Chang, Jian-Hong Yang. Data analysis and interpretation: Pei-Hsuan Lee. Drafting the article: Pei-Hsuan Lee. Critical revision of the article: Wen-Liang Chiou, Yao-Moan Huang, Hsy-Yu Tzeng, Yen-Hsueh Tseng. All the authors reviewed the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleLee, PH., Huang, YM., Chang, LW. et al. Environmental heterogeneity and its influence on fern diversity in a low-altitude mountain forest in central Taiwan.
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AbstractThe seal louse (Echinophthirius horridus) is a remarkable example of evolutionary adaptation, thriving as an obligate ectoparasite on deep-diving marine mammals under extreme environmental conditions, including high hydrostatic pressure, extreme drag force, salinity, and fluctuating temperatures. To investigate the anatomical and functional specializations enabling this lifestyle, we compared the leg morphology and musculature of E. horridus with its terrestrial relative, the human head louse (Pediculus humanus capitis), using synchrotron-based 3D microtomography and confocal laser scanning microscopy. Our findings reveal that the seal louse has developed a highly compact and robust leg structure with a fused tibiotarsus, an additional set of leg muscles, and a shortened claw tendon—an unprecedented adaptation among insects. These features allow for greater force transmission and reduced metabolic cost during sustained attachment. Behavioral assays further show that E. horridus can only move effectively on hair-like substrates, underscoring its complete reliance on host fur. These findings suggest a highly specialized muscular control system enabling strong, reliable, and reversible attachment in a challenging aquatic environment.

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All data is provided in the Supplementary Material of the manuscript. Synchrotron data and histological sectioning series90 can be provided upon request or online under: [https://doi.org/10.6084/m9.figshare.28596953.v2] (https:/doi.org/https://doi.org/10.6084/m9.figshare.28596953.v2).
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Preuss, A., Schwaha, T., et al. (2025a) ‘Nano-CT data and histological sections of Pediculus humanus capitis and Echinophthirius horridus’, figshare. https://doi.org/10.6084/m9.figshare.28596953.v2.Download referencesAcknowledgementsWe express our profound gratitude to Dr. Thies Büscher, Dr. Helen Gorges, Fabian Bäumler, Julian Thomas, Simon Züger, and Benedikt Josten for their invaluable guidance and assistance throughout this study. We also extend our appreciation to Esther Appel and Dr. Alexander Kovalev for their technical support during the experiments. We would also like to thank the TiHo Hannover, especially Dr. Insa Herzog and Dr. Kristina Lehnert, for supplying us with seal louse samples. Additionally, we acknowledge Marta Tischer for generously supplying head louse samples preserved in ethanol. We are indebted to Dr. Angelica Cecilia for her assistance during beamtime and to Dr. Tomáš Faragó for his work on tomographic reconstructions. We express our gratitude to the KIT Light Source for providing instruments at their beamlines and to the Institute for Beam Physics and Technology (IBPT) for operating the storage ring, the Karlsruhe Research Accelerator (KARA). Furthermore, we acknowledge the funding provided to S.N.G. by the grant GO 995 46-1 from the German Science Foundation (DFG) within the Special Priority Program (SPP 2332) “Physics of Parasitism.” The funders had no involvement in the study design, data collection and analysis, publication decisions, or manuscript preparation.FundingOpen Access funding enabled and organized by Projekt DEAL. Funding for this study was granted to S.N.G by the German Science Foundation (DFG) under grant number GO 995 46–1, as part of the Special Priority Program (SPP 2332) known as “Physics of Parasitism”.Author informationAuthors and AffiliationsDepartment of Functional Morphology and Biomechanics, Zoological Institute, Kiel University, Am Botanischen Garten 1–9, 24118, Kiel, GermanyAnika Preuss, Lina Ornowski & Stanislav N. GorbInstitute for Photon Science and Synchrotron Radiation (IPS), Karlsruhe Institute of Technology (KIT), Hermann-Von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, GermanyThomas van de Kamp, Elias Hamann & Marcus ZuberLaboratory for Applications of Synchrotron Radiation (LAS), Karlsruhe Institute of Technology (KIT), Kaiserstr.12, 76131, Karlsruhe, GermanyThomas van de KampAuthorsAnika PreussView author publicationsSearch author on:PubMed Google ScholarThomas van de KampView author publicationsSearch author on:PubMed Google ScholarElias HamannView author publicationsSearch author on:PubMed Google ScholarMarcus ZuberView author publicationsSearch author on:PubMed Google ScholarLina OrnowskiView author publicationsSearch author on:PubMed Google ScholarStanislav N. GorbView author publicationsSearch author on:PubMed Google ScholarContributionsA.P., S.N.G.—Conceptualization; A.P., T.v.d.K., S.N.G.—Data curation; A.P., T.v.d.K., S.N.G.—Formal analysis; S.N.G.—Funding acquisition; A.P., T.v.d.K., L.O.—Investigation; A.P., T.v.d.K., E.H., M.Z., S.N.G.—Methodology; S.N.G.—Project administration; A.P.—Software; S.N.G.—Supervision; A.P., S.N.G.—Validation; A.P., L.O.—Visualization; A.P.—Writing—original draft; T.v.d.K., E.H., M.Z., L.O., S.N.G.—Writing – review & editing.Corresponding authorCorrespondence to
Anika Preuss.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethical approval
Ethical review and approval were not required for this study, as all host animals were either found dead, died naturally, or were euthanized on welfare grounds, with none being killed specifically for this research. The authors were not involved in the euthanasia of the hosts, which was carried out by certified seal rangers for reasons unrelated to this study. All regulations regarding animal use were strictly followed.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7Supplementary Material 8Supplementary Material 9Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articlePreuss, A., van de Kamp, T., Hamann, E. et al. Functional morphology of the leg musculature in the marine seal louse: adaptations for high-performance attachment to diving hosts.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32804-2Download citationReceived: 16 September 2025Accepted: 12 December 2025Published: 23 December 2025DOI: https://doi.org/10.1038/s41598-025-32804-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsParasitismSeal louseHuman head louseMarine mammalsBiomechanicsExtremitiesSkeleton-muscle organizationSupplementary Material 4Supplementary Material 5Supplementary Material 6 More

AbstractHeavy-metal (HM) contamination undermines soil functions and food safety, while risk appraisals often rely on chemical indices that can be unstable in the presence of extremes and only indirectly reflect biological integrity. We present an integrative framework that couples standardized contamination metrics with soil microbiome profiling to deliver stable, interpretable classifications and actionable bioindicators. Twelve peri-urban soils from Southern Italy were analysed for potentially toxic elements, including Arsenic (As), Cadmium (Cd), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) and profiled by shotgun metagenomics. We introduce a Standardized Ecological Risk index (SPERI) that preserves the ranking conveyed by conventional composites yet reduces outlier leverage. SPERI strongly agreed with Improved Potential Ecological Risk Index (IPERI) while stabilizing variance (R² = 0.896) and improved between-site comparability. Along the contamination gradient, community structure shifted consistently: families such as Pseudomonadaceae, Xanthomonadaceae and Rhodospirillaceae increased with risk, whereas Geodermatophilaceae and Nocardiaceae declined. Simple decision-tree models trained on family-level relative abundances reliably separated SPERI classes and repeatedly selected Zn- and Cd-enriched sites as primary split drivers, aligning microbial signals with chemical risk. By combining open, reproducible analytics with jointly chemical- and microbiome-informed endpoints, this workflow improves the interpretability and transferability of ecological risk assessment and supports targeted remediation and monitoring in contaminated agro-ecosystems.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Download referencesFundingNo funding received for this research.Author informationAuthors and AffiliationsDepartment of Agriculture, Food Natural Resources and Engineering, University of Foggia, Foggia, ItalyAlessandro De Santis, Antonio Bevilacqua, Maria Rosaria Corbo, Barbara Speranza, Matteo Francavilla, Giuseppe Gatta & Milena SinigagliaDepartment of Agricultural and Forestry scieNcEs (DAFNE), University of Tuscia, Viterbo, ItalyFederica CarucciAuthorsAlessandro De SantisView author publicationsSearch author on:PubMed Google ScholarAntonio BevilacquaView author publicationsSearch author on:PubMed Google ScholarMaria Rosaria CorboView author publicationsSearch author on:PubMed Google ScholarBarbara SperanzaView author publicationsSearch author on:PubMed Google ScholarMatteo FrancavillaView author publicationsSearch author on:PubMed Google ScholarGiuseppe GattaView author publicationsSearch author on:PubMed Google ScholarFederica CarucciView author publicationsSearch author on:PubMed Google ScholarMilena SinigagliaView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization, A.B., M.S., M.R.C. and A.D.S.; methodology, A.B., M.S. and M.R.C.; investigation, A.D.S., B.S., G.G., F.C., and M.F.; data curation, A.B. and A.D.S.; software, A.D.S., and A.B.; writing original draft preparation, A.D.S. and A.B.; writing—review and editing, all authors; supervision, A.B. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
Antonio Bevilacqua.Ethics declarations

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The authors declare no competing interests.

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This research is an exploratory action focusing on soil samples collected solely for methodological purposes. Therefore, according to national and local guidelines and regulations a preliminary approval is not required.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleDe Santis, A., Bevilacqua, A., Corbo, M.R. et al. A statistical approach to model soil microbiota versus heavy metals: a case study on soil samples from Foggia, Southern Italy.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32485-xDownload citationReceived: 03 September 2025Accepted: 10 December 2025Published: 23 December 2025DOI: https://doi.org/10.1038/s41598-025-32485-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsClassification and regression treesShotgun metagenomicsStandardized ecological risk indexCorrelationEcological risk assessmentMicrobial communities More

AbstractDenitrification, a major source of gaseous nitrogen emissions from agricultural soils, is influenced by management. Practices promoting belowground diversity are suggested to support sustainable agriculture, but how they modulate nitrogen losses via denitrification remains inconclusive. Here we sampled 106 cereal fields spanning a 3000 km North-South gradient across Europe and compiled 56 associated climatic, soil, microbial and management variables. We show that increased denitrification potential was associated with higher proportion of time with crop cover over the last ten years and was best predicted by microbial biomass and microbial functional guilds involved in nitrogen cycling, in particular denitrification. We also demonstrate that several diversification practices affect the variation in denitrification potential predictors, suggesting a trade-off between agricultural diversification and nitrogen losses via denitrification. However, increased crop diversity in rotations improved yield-scaled denitrification, highlighting the potential of this practice to minimize nitrogen losses while contributing to sustainable food production.

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Data availability

Data and OTU tables used in this study as well as source data for the figures are available at Zenodo (https://doi.org/10.5281/zenodo.14760398).
Code availability

The R code used in this study is available at Zenodo (https://doi.org/10.5281/zenodo.14760398).
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Download referencesAcknowledgementsThe Digging Deeper project was funded through the 2015–2016 BiodivERsA call, with national funding from the Swiss National Science Foundation (grant 31BD30-172466 to M.G.A.v.d.H), the Deutsche Forschungsgemeinschaft (grant 317895346 to M.C.R.), the Swedish Research Council Formas (grant 2016-0194 to S.H. and 2018-02321 to R.B.), the Spanish Ministerio de Economía y Competitividad (grant PCIN-2016-028 to F.T.M.) and the Agence Nationale de la Recherche (grant ANR-16-EBI3-0004-01 to L.P.). We thank Claudia von Brömssen (Swedish University of Agricultural Sciences) for advice on the generalized additive models.FundingOpen access funding provided by Swedish University of Agricultural Sciences.Author informationAuthor notesThese authors contributed equally: Aurélien Saghaï, Monique E. Smith.Authors and AffiliationsDepartment of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, Uppsala, SwedenAurélien Saghaï & Sara HallinDepartment of Ecology, Swedish University of Agricultural Sciences, Uppsala, SwedenMonique E. Smith, Giulia Vico & Riccardo BommarcoAgroscope, Plant-Soil Interactions Group, Zurich, SwitzerlandSamiran Banerjee, Anna Edlinger, Gina Garland, Marcel G. A. van der Heijden & Chantal HerzogDepartment of Microbiological Sciences, North Dakota State University, Fargo, ND, USASamiran BanerjeeDepartment of Plant and Microbial Biology, University of Zurich, Zurich, SwitzerlandAnna Edlinger, Pablo García-Palacios, Marcel G. A. van der Heijden & Chantal HerzogWageningen Environmental Research, Wageningen University & Research, Wageningen, The NetherlandsAnna EdlingerInstituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, Madrid, SpainPablo García-PalaciosSoil Quality and Use Group, Agroscope, Zurich, SwitzerlandGina GarlandDepartment of Environmental System Sciences, Soil Resources Group, ETH Zurich, Zurich, SwitzerlandGina GarlandEnvironmental Sciences and Engineering, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi ArabiaFernando T. MaestreDepartamento de Biología y Geología, Física y Química Inorgánica, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, Móstoles, SpainDavid S. PescadorInstitut Agro Dijon, Agroecologie, INRAE, Université de Bourgogne, Dijon, FranceLaurent Philippot & Sana RomdhaneInstitute of Biology, Freie Universität Berlin, Berlin, GermanyMatthias C. RilligBerlin-Brandenburg Institute of Advanced Biodiversity Research, Berlin, GermanyMatthias C. RilligAuthorsAurélien SaghaïView author publicationsSearch author on:PubMed Google ScholarMonique E. SmithView author publicationsSearch author on:PubMed Google ScholarGiulia VicoView author publicationsSearch author on:PubMed Google ScholarSamiran BanerjeeView author publicationsSearch author on:PubMed Google ScholarAnna EdlingerView author publicationsSearch author on:PubMed Google ScholarPablo García-PalaciosView author publicationsSearch author on:PubMed Google ScholarGina GarlandView author publicationsSearch author on:PubMed Google ScholarMarcel G. A. van der HeijdenView author publicationsSearch author on:PubMed Google ScholarChantal HerzogView author publicationsSearch author on:PubMed Google ScholarFernando T. MaestreView author publicationsSearch author on:PubMed Google ScholarDavid S. PescadorView author publicationsSearch author on:PubMed Google ScholarLaurent PhilippotView author publicationsSearch author on:PubMed Google ScholarMatthias C. RilligView author publicationsSearch author on:PubMed Google ScholarSana RomdhaneView author publicationsSearch author on:PubMed Google ScholarRiccardo BommarcoView author publicationsSearch author on:PubMed Google ScholarSara HallinView author publicationsSearch author on:PubMed Google ScholarContributionsS.H., M.G.A.v.d.H., F.T.M., L.P., and M.C.R. initiated the study, planned the field work, and contributed materials. A.S., S.B., F.D., A.E., P.G-P., G.G., C.H., D.S.P., and S.R. contributed to data collection. A.S. and M.E.S. performed the analyses, and A.S., M.E.S., G.V., R.B., and S.H. interpreted the results. A.S., M.E.S., and S.H. drafted the manuscript. All authors commented on and approved the final manuscript.Corresponding authorCorrespondence to
Sara Hallin.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleSaghaï, A., Smith, M.E., Vico, G. et al. Diverse crop rotations offset yield-scaled nitrogen losses via denitrification.
Commun Earth Environ (2025). https://doi.org/10.1038/s43247-025-03116-0Download citationReceived: 26 July 2025Accepted: 08 December 2025Published: 23 December 2025DOI: https://doi.org/10.1038/s43247-025-03116-0Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractIn response to global climate change and China’s “dual carbon” goals, forest carbon sinks, as a key nature-based solution, have gained importance in balancing human-induced carbon emissions and ecological restoration. This study examines the effectiveness of forest carbon sinks across 31 Chinese provinces from 2003 to 2018, using the forest stock expansion method to quantify the validity of carbon sinks. We explore the spatiotemporal evolution and regional disparities of forest carbon sink validity and identify the influence of research and development intensity, industrial structure upgrading, urbanisation level, government intervention degree, and economic development level factors. A spatial Durbin model is employed to assess both direct and indirect effects of natural and policy factors on the carbon sink’s effectiveness in both local and neighbouring provinces. Our findings reveal that forest carbon sink effectiveness follows a pattern of “higher in the west, faster in the east, and catching up in the central region”. The results indicate that increased research and development investment and optimised industrial structure positively influence carbon sink growth, whereas excessive government intervention hampers development. Urbanisation and economic development were found to have no significant direct effect. The spatial analysis shows that research and development intensity and industrial optimisation yield positive spillover effects on neighbouring provinces’ carbon sink growth, whereas government intervention and urbanisation yield negative, non-significant spillover effects. These findings suggest the need for strengthened regional innovation policies, improved forestry governance, and optimised forestry services to support the high-quality development of the forestry sector.

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Download referencesFunding1. This research was funded by the Sichuan Police Law Enforcement Research Centre (grant number: JCZFQN202402). 3. This research was funded by the Research Centre for Social Governance Innovation (grant number: SHZLQN2404).Author informationAuthors and AffiliationsSchool of National Security, Southwest University of Political Science and Law, Chongqing, ChinaLidong Shi, Ming Xu & Jiahui ZhaoCollege of Business Administration, Chongqing Technology and Business University, Chongqing, ChinaYuntao TanLegal Affairs Department, Chongqing People’s Hospital, Chongqing, ChinaYucen WuAuthorsLidong ShiView author publicationsSearch author on:PubMed Google ScholarMing XuView author publicationsSearch author on:PubMed Google ScholarYuntao TanView author publicationsSearch author on:PubMed Google ScholarYucen WuView author publicationsSearch author on:PubMed Google ScholarJiahui ZhaoView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualisation, L.S. and J.Z.; methodology, M.X.; software, Y.T.; validation, M.X. and Y.W.; formal analysis, L.S.; investigation, J.Z.; resources, Y.T.; data curation, L.S. and Y.T.; writing—original draft preparation, Y.W., M.X., J.Z.; writing—review and editing, Y.T.; visualisation, L.S.; supervision, J.Z.; project administration, J.Z.; funding acquisition, L.S. All authors reviewed the manuscript.Corresponding authorCorrespondence to
Jiahui Zhao.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleShi, L., Xu, M., Tan, Y. et al. Responding to climate change: assessing the current situation and influencing factors of forest carbon sinks in China.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33255-5Download citationReceived: 05 September 2025Accepted: 17 December 2025Published: 23 December 2025DOI: https://doi.org/10.1038/s41598-025-33255-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsForest carbon sink validitySpatiotemporal heterogeneitySpatial Durbin modelSpatial spillover effect More

AbstractMarine turtles, such as green (Chelonia mydas) and hawksbill (Eretmochelys imbricata), face numerous threats across their habitats. While they nest in the Red Sea, limited knowledge exists about their inter-nesting habitats and post-breeding movements. The NEOM Islands support ~ 95% of all documented nesting in the northeastern Red Sea, are crucial for turtle conservation. This study tracked 17 turtles (11 hawksbills, six greens) using platform terminal transmitters during two nesting seasons on Shusha and Walah Islands. We identified inter-nesting areas, migratory pathways, and foraging grounds, with displacements to foraging areas ranging from 34.8 to 501.7 km. Six feeding grounds were identified, four within Saudi Arabia and two in Egyptian waters. Hawksbills used broader inter-nesting habitats, particularly reef systems, emphasizing the need for conservation strategies targeting both nesting beaches and adjacent reefs. Our findings highlight the shared migratory routes of NEOM turtles with other Red Sea rookeries and support the importance of NEOM’s feeding and migration habitats for long-term conservation. The establishment of NEOM as a nature reserve will further enhance protection efforts for these species.

Data availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request and with permission from NEOM.
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Download referencesAcknowledgementsWe would like to thank the staff from Lotek Wireless Inc. for their support during this study. Authors would also like to thank Dr Natalie Wildermann (Marine Science Program at KAUST) for her advise during in the data analysis. Finally, we also want to express our gratitude to the staff of the King Abdullah University of Science and Technology (KAUST) Institutional Animal Care and Use Committee (IACUC) for their support to obtain our research authorizations.Author informationAuthors and AffiliationsKAUST Beacon Development, KAUST National Transformation Institute, Innovation Cluster, King Abdullah University of Science and Technology, 4700, 23955, Thuwal, Makkah, Kingdom of Saudi ArabiaHector Barrios-Garrido, Abdulrazaq Alatawi, Abdulaziz Alkaboor, Abhishekh Palaparambil Vijaya, August Santillan & Ricardo O. RamalhoNEOM Nature Reserve, Sharma 49631, Tabuk, Kingdom of Saudi ArabiaMishari Alghrair, Enjey Ghazzawi, Abdulqader Khamis, Brett Lyons, Paul Marshall, Deni Porej & Winston CowieCenter for Ecology and Conservation, University of Exeter, Penryn, United KingdomEnjey Ghazzawi TropWATER – Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Queensland, 4811, Townsville, AustraliaHector Barrios-Garrido & Paul MarshallAuthorsHector Barrios-GarridoView author publicationsSearch author on:PubMed Google ScholarAbdulrazaq AlatawiView author publicationsSearch author on:PubMed Google ScholarMishari AlghrairView author publicationsSearch author on:PubMed Google ScholarAbdulaziz AlkaboorView author publicationsSearch author on:PubMed Google ScholarEnjey GhazzawiView author publicationsSearch author on:PubMed Google ScholarAbdulqader KhamisView author publicationsSearch author on:PubMed Google ScholarBrett LyonsView author publicationsSearch author on:PubMed Google ScholarPaul MarshallView author publicationsSearch author on:PubMed Google ScholarAbhishekh Palaparambil VijayaView author publicationsSearch author on:PubMed Google ScholarAugust SantillanView author publicationsSearch author on:PubMed Google ScholarDeni PorejView author publicationsSearch author on:PubMed Google ScholarWinston CowieView author publicationsSearch author on:PubMed Google ScholarRicardo O. RamalhoView author publicationsSearch author on:PubMed Google ScholarContributionsHBG: Validation, Formal analysis, Data curation, Supervision, Writing – Original Draft, Visualisation.AAW: Methodology, Investigation, Data curation, Review.MA: Conceptualisation, Investigation, Data curation, Writing – Review & Editing.AAB: Methodology, Investigation, Data curation, Review.EG: Conceptualisation, Methodology, Investigation, Data curation, Review.AK: Conceptualisation, Writing – Review & Editing, Project administration.BL: Conceptualisation, Writing – Review & Editing, Project administration.PM: Conceptualisation, Writing – Review & Editing, Project administration.AP: Investigation, Data curation, Review, Visualisation.AS: Methodology, Investigation, Data curation, Review.DP: Conceptualisation, Resources, Data curation, Writing – Review & Editing, Project administration.WC: Conceptualisation, Resources, Data curation, Writing – Review & Editing, Project administration.ROR: Original Research Concept and Design, Resources, Writing – Review & Editing, Supervision, Project management.Corresponding authorCorrespondence to
Hector Barrios-Garrido.Ethics declarations

Competing interests
The authors declare that they have a competing interest due to the work performed by KAUST Beacon Department personnel (as consultant service provider) with NEOM; however, this relationship did not influence the study design, data collection, analysis, or interpretation of the results.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleBarrios-Garrido, H., Alatawi, A., Alghrair, M. et al. Post-breeding dispersal of nesting marine turtles from the NEOM Islands, Saudi Arabia.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31237-1Download citationReceived: 05 September 2024Accepted: 01 December 2025Published: 23 December 2025DOI: https://doi.org/10.1038/s41598-025-31237-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsMigratory pathwaysCritical habitatsHabitat useMarine protected areasRed Sea More

AbstractThis study focuses on the nine provinces along the Yellow River, and establishes a water resources system resilience evaluation framework consisting of 34 indicators based on a meteorology-hydrology-socioeconomy-ecology-engineering multidimensional system. By applying a TOPSIS for assessing water resources system resilience, that incorporates combination weighting approach based on game theory, this study investigates the spatiotemporal evolution of the water resources system resilience from 2009 to 2022.The resilience of water resources system in the provinces along the Yellow River exhibited an overall fluctuating upward trend. Since 2019, resilience levels generally increased, with the lowest values of 0.36 in 2009 and 2010, and the highest value of 0.59 in 2021. Some provinces, including Shanxi, Gansu, and Qinghai experienced significant fluctuations in resilience due to climate variability and the implementation of local policies, whereas regions such as Shaanxi and Shandong maintained relatively stable resilience levels. From 2009 to 2022, the resilience levels of water resources system in the provinces along the Yellow River were ranked in descending order: Sichuan > Henan > Shaanxi > Inner Mongolia > Qinghai > Shandong > Ningxia > Gansu > Shanxi. Sichuan and Henan achieved Level II (higher resilience), with the rest at Level III (moderate resilience).

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Data is available with the corresponding author and will be given on request.
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Download referencesFundingThe research was supported by the National Key Research and Development Program of china (2022YFC3202300), National Natural Science Foundation of China General Program (52479014), Talent support project of Henan province (254000510001), The Belt and Road Special Foundation of National Key Laboratory of Water Disaster Prevention (2023nkzd02), Research Fund of Key Laboratory of Water Management and Water Security for Yellow River Basin, Ministry of Water Resources (under construction) (2023-SYSJJ-05), Yellow River Conservancy Commission Outstanding Youth Talent Science and Technology Project (HQK-202301).Author informationAuthors and AffiliationsNorth China University of Water Resources and Electric Power, Zhengzhou, 450046, ChinaFang Wan, Yuze Kang, Hui Guo, Panpan Zhao, Jiawen Zhao & Mingran LiNational Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing, 210029, ChinaFang WanYellow River Conservancy Commission of the Ministry of Water Resources, Zhengzhou, 450003, ChinaYu WangState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, ChinaWeihao WangKey Laboratory of Water Safety for Beijing-Tianjin-Hebei Region of Ministry of Water Resources, Beijing, 100038, ChinaWeihao WangEstuary Administration Bureau, Yellow River Administration Bureau of Shandong Province, Dongying, 257091, ChinaZhen ZhouAuthorsFang WanView author publicationsSearch author on:PubMed Google ScholarYuze KangView author publicationsSearch author on:PubMed Google ScholarYu WangView author publicationsSearch author on:PubMed Google ScholarWeihao WangView author publicationsSearch author on:PubMed Google ScholarHui GuoView author publicationsSearch author on:PubMed Google ScholarPanpan ZhaoView author publicationsSearch author on:PubMed Google ScholarJiawen ZhaoView author publicationsSearch author on:PubMed Google ScholarZhen ZhouView author publicationsSearch author on:PubMed Google ScholarMingran LiView author publicationsSearch author on:PubMed Google ScholarContributionsFW: Software, Conceptualization. YK: Writing–original draft, Methodology. YW: Conceptualization. WW: Writing–review & editing, Writing–original draft, Methodology. HG: Writing–review & editing. PZ: Conceptualization. JZ: Writing–review & editing, Formal analysis. ZZ: Writing–review & editing, Investigation. ML: Writing–review & editing.Corresponding authorCorrespondence to
Yu Wang.Ethics declarations

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We declare that we have no conflict of interest or the authors do not have any possible conflicts of interest, the authors are not affiliated with or involved with any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this paper.

Ethical approval
This study does not involve any experiments or data collection involving human participants, animals, or human tissues. Therefore, ethical approval was not required. The authors affirm that all methods and procedures adhered to the relevant guidelines and regulations.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleWan, F., Kang, Y., Wang, Y. et al. Resilience assessment and spatiotemporal evolution analysis of water resources system in the provinces along the Yellow River.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31512-1Download citationReceived: 12 October 2025Accepted: 03 December 2025Published: 23 December 2025DOI: https://doi.org/10.1038/s41598-025-31512-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsWater resources system resilienceTOPSIS modelSpatiotemporal evolutionProvinces along the Yellow River More

AbstractNatural forests in Ethiopia are significant biodiversity repositories and climate change regulators, but they are under growing pressure from anthropogenic activities. The purpose of this study was to evaluate the composition, structure, and regeneration status of the Mugere Zala natural forest in Asagrit District, Central Ethiopia. A systematic sampling strategy was utilised to collect samples from 20 m × 20 m sample plots for trees, 10 m × 10 m for shrubs, and 5 m × 5 m subplots for saplings and seedlings. The sample plots were placed 400 m apart along transects laid at 200 m intervals. The composition and population structure data for woody species (diameter at breast height (DBH) > 2.5 cm and height > 2 m) were documented. To assess plant diversity, quantitative species diversity, richness, and evenness were calculated. A total of 62 species were identified in 47 genera and 43 families. With 5 (11.6%) species, the Rosaceae family had the highest species richness. In total, 557 mature trees, 747 saplings, and 1036 seedlings ha-1 were observed in the sampled plots. Clutia abyssinica, Olea europaea subsp. cuspidata, Maesa lanceolata, Allophylus abyssinicus, Carissa spinarum, Phytolacca dodecandra, and Jasminum abyssinicum were associated with about 49.98% of the importance values. Compared to other similar forests in Ethiopia, the vegetation structure is similar and distinct. The forest is dominated by small plant species and largest native tree species despite its poor regeneration status. This is because highland forests are particularly difficult to manage due to population pressure, which is associated with grazing effects.

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Download referencesAcknowledgementsWe would like to express our deepest gratitude to the Asagirt District Agriculture Office and Mension Furt Mension for their support and cooperation during data collection. We are grateful to the Meti village Administrators and Security officials for their assistance during our stay in the field. The contributions and assistants of Solomon Bahiru, Dirsha Getachew and Zinash Shewangizaw, who were development agents of the study area, were unforgatble. The national Herbarium workers of Addis Ababa University are highly thankful for their cooperation during species identification.Author informationAuthors and AffiliationsDepartment of Fisheries and Aquatic Sciences, College of Agriculture and Environmental Sciences, Bahir Dar University, PO Box 79, Bahir Dar, EthiopiaMengistu Asmamaw Department of Natural Resource Management, College of Agriculture and Natural Resource Sciences, Debre Berhan University, Debre Berhan, EthiopiaGebremicael FisahaDepartment of Plant Sciences, College of Agriculture and Environmental Sciences, Bahir Dar University, PO Box 79, Bahir Dar, EthiopiaKindye Belaye WassieAuthorsMengistu AsmamawView author publicationsSearch author on:PubMed Google ScholarGebremicael FisahaView author publicationsSearch author on:PubMed Google ScholarKindye Belaye WassieView author publicationsSearch author on:PubMed Google ScholarContributionsM. A., G.F., and K.B. W. wrote the main manuscript and prepared Figs. 1, 2, 3, 4, 5, 6, 7 and 8. All authors reviewed the manuscript.Corresponding authorCorrespondence to
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The authors state that this publication does not contain any information concerning human experiments or the use of human tissue samples. Plant species data was collected from forests in collaboration with the Asagirt District Agriculture Office. The research followed the IUCN policy guidelines and restrictions.

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Reprints and permissionsAbout this articleCite this articleAsmamaw, M., Fisaha, G. & Wassie, K.B. Woody plants composition, structure and regeneration status of Muger Zala natural forest, Central Ethiopia.
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KeywordsDry afromontaneDiversityRegenerationStructureWoody vegetation More