Ecology

AbstractThe elevated heavy metal concentrations and acidic pH conditions characteristic of Acid Mine Drainage (AMD) pose significant environmental challenges, necessitating the development of effective remediation strategies. This study systematically evaluated the performance of a surface-flow constructed wetland system in Ma’anshan, China, for AMD treatment through comprehensive physicochemical characterization and microbial community analysis. The multi-stage wetland system, comprising four interconnected units vegetated with Phragmites australis, Typha angustifolia, and Arundo donax, was monitored for water quality parameters, sediment characteristics, and microbial diversity. Results demonstrated significant improvements in water quality, with pH increasing from 5.18 to 7.41 and substantial removal efficiencies observed for heavy metals: complete (100.00%) removal of Fe, 92.00% removal of Zn, 61.80% removal of Mn, and 63.60% removal of chemical oxygen demand (COD). Sequential extraction analysis revealed that residual fractions constituted the predominant form of sediment-bound heavy metals (65.70%-70.20% for Fe). Proteobacteria make up 58% of the microbial community. Sulfuricurvum combines denitrification and sulfide oxidation, while SRB like Desulfuromonas convert sulfate to sulfide. Nitrospinae oxidizes ammonia to nitrate, Thaumarchaeota oxidizes ammonia to nitrite, and Parkl vularcula facilitates denitrification. In order to maintain cycles and support wetland function and AMD treatment, hydrolytic bacteria break down organic materials. These findings establish the effectiveness of constructed wetlands (CWS) in AMD remediation through synergistic phytoremediation and microbial metabolic processes, offering an ecologically sustainable approach for the restoration of mining-impacted areas.

Similar content being viewed by others

Effects of acid mine drainage on microbial community development and physicochemical properties of mine contaminated sites in Southwest China

Article
Open access
01 July 2025

Demystifying the impacts of anthropogenic activities on physicochemical characteristics of soil in four wetlands of Kashmir Valley, India

Article
Open access
21 October 2025

Microbial carbon, sulfur, iron, and nitrogen cycling linked to the potential remediation of a meromictic acidic pit lake

Article

19 September 2022

Data availability

All data generated or analysed during this study are included in this published article.
References Xu, S. et al. Biogenic iron mineral formation and the fate of arsenic driven by its coupling with ferrous iron in acid mine drainage environment. J. Hazard. Mater. 485 (1-136940.11), 136940 (2024).
Google Scholar 
Skousen, J. G., Ziemkiewicz, P. F. & Mcdonald, L. M. Acid mine drainage formation, control and treatment: approaches and strategies – ScienceDirect. Extractive Industries Soc. 6 (1), 241–249 (2019).
Google Scholar 
Karagüzel, C. et al. Prediction of acid mine drainage potential of dump sites by using static tests: an application on lignite mine. Arab. J. Geosci. 20, 12517. https://doi.org/10.1007/s12517-020-06220-x (2020).
Google Scholar 
Okeleji, O. A. & Ioannidou, V. G. Seasonal variation of iron removal in coal mine water discharge lagoons and constructed wetlands. Environ. Process. 12(2), 40710. https://doi.org/10.1007/s40710-025-00768-0 (2025).
Google Scholar 
Wei, X., Chen, H., Zhu, F. & Li, J. Microbial community structure in an uranium-rich acid mine drainage site: implication for the biogeochemical release of uranium. Front. Microbio 15, 1412599. https://doi.org/10.3389/fmicb.2024.1412599 (2024).
Google Scholar 
Ighalo, J. O. et al. A review of treatment technologies for the mitigation of the toxic environmental effects of acid mine drainage (AMD).Transactions of the institution of chemical engineers. Process Saf. Environ. Prot. Part. B, 157–168 (2022).Sekarjannah, F. A., Mansur, I. & Abidin, Z. Selection of organic materials potentially used to enhance bioremediation of acid mine drainage[J]. J. Degraded Min. Lands Manage. 3, 83–94 (2021).
Google Scholar 
Pines Pozo, M. T. et al. Metal recovery from wastes: A review of recent advances in the use of bioelectrochemical systems. Appl. Sci. 15.3, 2076–3417 (2025).
Google Scholar 
Li, X. et al. Suppressive effects of ferric-catecholate complexes on pyrite oxidation. Chemosphere 214, 70–78 (2019).
Google Scholar 
Hennebel, T., Boon, N., Maes, S. & Lenz, M. Biotechnologies for critical Raw material recovery from primary and secondary sources: r&d priorities and future perspectives. N Biotechno. 32, 121–127 (2015).
Google Scholar 
Fenwu, L., Xingxing, Q., Lixiang, Z. & Jian, Z. Migration and fate of acid mine drainage pollutants in calcareous soil. Int. J. environ. res. public. Health. 1759, 15–23 (2018).
Google Scholar 
Sun, H. et al. Nutrient limitations on primary productivity and phosphorus removal by biological carbon pumps in dammed karst rivers: Implications for eutrophication control. J. Hydrol. 607, 127480. https://doi.org/10.1016/j.jhydrol.2022.127480 (2022).
Google Scholar 
Zhong, J. et al. Biooxidation of arsenopyrite during the coalescence of iron and sulfur oxidizing microbial communities. Environ. Earth Sci. 84, 429–444 (2025).
Google Scholar 
Tabelin, C. B. et al. Acid mine drainage formation and arsenic mobility under strongly acidic conditions: importance of soluble phases, iron oxyhydroxides/oxides and nature of oxidation layer on pyrite. J. Hazard. Mater. 399, 122844 (2020).
Google Scholar 
Tabelin, C. B., Veerawattananun, S., Ito, M., Hiroyoshi, N. & Igarashi T.Pyrite oxidation in the presence of hematite and alumina: I. Batch leaching experiments and kinetic modeling calculations. Sci. Total Environ. 580, 687–698 (2017).
Google Scholar 
Papirio, S., Villa-Gomez, D. K., Esposito, G., Pirozzi, F. & Lens, P. N. L. Acid mine drainage treatment in fluidized-bed bioreactors by sulfate-reducing bacteria: a critical review. Environ. Sci. Tech. 43, 2545–2580 (2013).
Google Scholar 
Fan, N., Shu, Y., Xu, Z. & Zhao, D. Research progress of acid mine wastewater treatment bybioremediation method with sulfate reducingbacteriaas the core. Fresenius Environ. Bull. 7, 30–41 (2021).
Google Scholar 
Pat-Espadas, A. et al. Review of constructed wetlands for acid mine drainage treatment. Water 11, 211–236 (2018).
Google Scholar 
Igarashi, T. et al. The two-step neutralization ferrite-formation process for sustainable acid mine drainage treatment: removal of copper, zinc and arsenic, and the influence of coexisting ions on ferritization. Sci. Total Environ. 715, 1368771–13687712 (2020).
Google Scholar 
Pocaan, J. et al. A mixed media approach using locally available neutralizing materials for the passive treatment of acid mine drainage. Heliyon 11(3), 41984. https://doi.org/10.1016/j.heliyon.2025.e41984 (2025).
Google Scholar 
Jabbar, K. A. et al. Development of Artificial Geochemical Filter to Treat Acid Mine Drainage for Safe Disposal of Mine Water in Salt Range Portion of Indus Basin—A Lab to Pilot Scale Study. Sustainability 14(13), 7693. https://doi.org/10.3390/su14137693 (2022).
Google Scholar 
Rapilly, M. et al. Impacts of acid mine drainage remediation in the largest gold mine of Latin America on natural water bodies in the Dominican Republic. Environ. Sci. Pollut R. 32.3, 1272–1292 (2025).
Google Scholar 
Wibowo, Y. G., Wijaya, C., Halomoan, P., Yudhoyono, A. & Safri, M. Constructed wetlands for treatment of acid mine drainage: A review. Jurnal Presipitasi: Media Komunikasi dan. Pengembangan Teknik Lingkungan. 19 (2), 436–450 (2022).
Google Scholar 
Pat-Espadas, A. M., Portales, L., Amabilis-Sosa, R. & Gómez, L. E. Review of Constructed Wetlands for Acid Mine Drainage Treatment. Water 10(11), 1685. https://doi.org/10.3390/w10111685 (2018).
Google Scholar 
Soda, S. & Nguyen, T. T. Classification of Mine Drainages in Japan Based on Water Quality: Consideration for Constructed Wetland Treatments. Water 15(7), 1258. https://doi.org/10.3390/w15071258 (2023).
Google Scholar 
Hu, X. et al. Continuous and effective treatment of heavy metal in acid mine drainage based on reducing barrier system: A case study in North China. J. Hazard. Mater. Adv. 8, 100152. https://doi.org/10.1016/j.hazadv.2022.100152 (2022).
Google Scholar 
Charity, E. B. C. & Bolutife, M. J. Transmission of antibiotic-resistant bacteria through laptop keyboard among students of a tertiary institution in Lagos, Nigeria and the associated risk factors. Bio Res. 22(2), 4314. https://doi.org/10.4314/br.v22i2.5 (2024).
Google Scholar 
Salihu, M. M., Musa, M. A., Ibrahim, A. G., Usman, J. & Salisu, A. S. Weight and structural considerations of potential green roof growth: media compositions for the Nigerian Building industry. Archit. Papers Fac. Archit. Des. STU. 29.2, 24–29 (2024).
Google Scholar 
Huang, Y. et al. Key factors governing microbial community in extremely acidic mine drainage (ph < 3). Front. Microbiol. 12, 761579. https://doi.org/10.3389/fmicb.2021.761579 (2021).
Google Scholar 
Khan, S. et al. Phytoremediation Prospects for Restoration of Contamination in the Natural Ecosystems. Water 15(8), 1498. https://doi.org/10.1007/s12517-020-06220-x (2023).
Google Scholar 
Jin, D. C., Wang, X. M., Liu, L. L., Liang, J. R. & Zhou, L. X. A novel approach for treating acid mine drainage through forming schwertmannite driven by a mixed culture of Acidiphilium multivorum and Acidithiobacillus ferrooxidans prior to lime neutralization. J. Hazard. Mater. 400, 123108. https://doi.org/10.1016/j.jhazmat.2020.123108 (2020).
Google Scholar 
Song, Y. W. et al. A novel approach for treating acid mine drainage by forming schwertmannite driven by a combination of biooxidation and electroreduction before lime neutralization. Water Res. 221, 118748. https://doi.org/10.1016/j.watres.2022.118748 (2022).
Google Scholar 
Sun, R., Zhang, L., Wang, X., Ou, C. & Jiang, F. Elemental sulfur-driven sulfidogenic process under highly acidic conditions for sulfate-rich acid mine drainage treatment: performance and microbial community analysis. Water Res. 185, 116230. https://doi.org/10.1016/j.watres.2020.116230 (2020).
Google Scholar 
Zeng, Q., Hao, T. W., Mackey, H. R., van Loosdrecht, M. C. M. & Chen, G. H. Recent advances in dissimilatory sulfate reduction: from metabolic study to application. Water Res. 150, 162–181 (2019).
Google Scholar 
Niu, S., Fang, Q., Yu, J. & Wu, J. Heavy metals present in the soils from extremely large opencast iron mine Pit. Bull. Environ. Contam. Toxicol. 107, 984–989 (2021).
Google Scholar 
Guo, J., Xuan, F. Q., Li, D. M., Wang, J. Q. & Zhang, B. C. A. Variations of Soil Chemical Properties and Microbial Community around the Acid Reservoir in the Mining Area. Sustainability 14, 10746. https://doi.org/10.3390/su141710746 (2022).
Google Scholar 
Wu, F., Liu, J. & He, S. Effect of operating parameters of surface flow constructed wetland on nitrogen and phosphorus removal and sensory quality of river water. CET J. – Chem. Eng. Trans. 83, 313–330 (2021).
Google Scholar 
Song, X., Ehde, P. M. & Weisner, S. E. B. Effects of water depth and phosphorusavailability on nitrogen removal in agricultural wetlands. Water 11, 2626. https://doi.org/10.3390/w11122626 (2019).
Google Scholar 
Patyal, V., Jaspal, D. & Khare, K. Performance enhancement of constructed wetlands for wastewater remediation by modifications and integration of technologies: a review. Environ. Prog Sustain. Energy 42, 13951. https://doi.org/10.1002/ep.13951 (2023).
Google Scholar 
Shelef, O., Golan-Goldhirsh, A., Gendler, T. & Rachmilevitch, S. Physiological parameters of plants as indicators of water quality in a constructed wetland. Environ. Sci. Pollution Res. 18, 1234–1242 (2011).
Google Scholar 
Yang, Y., Kim, J., Chung, J. O., Roh, J. H. & Hong, Y. D. Variations in the composition of tea leaves and soil microbial community. Biol. Fert Soils. 58, 167–179 (2022).
Google Scholar 
Voelkner, A., Holthusen, D. & Horn, R. Determination of soil dispersion caused by anaerobic digestates: interferences of pH and soil charge with regard to soil texture and water content. J. SOIL. SEDIMENT. 15, 1491–1499 (2015).
Google Scholar 
Pansu, M. & Gautheyrou, J. Handbook of soil analysis: mineralogical, organic and inorganic methods. Int. J. Cancer, (2006).Tessier, A., Campbell, P. G. C. & Bisson, M. Sequential extraction procedure for the speciation of particulate trace metals. Anal. Chem. 51, 844–851 (1979).
Google Scholar 
Muyzer, G., Dewaal, E. C. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700 (1993).
Google Scholar 
Daghio, M. et al. Anodes stimulate anaerobic toluene degradation via sulfur cycling in marine sediments. Appl. Environ. Microbiol. 23, 297–307 (2015).
Google Scholar 
Ighalo, J. O. et al. A review of treatment technologies for the mitigation of the toxic environmental effects of acid mine drainage (AMD). Process. Saf. Environ. 157, 37–58 (2022).
Google Scholar 
Christel, S., Yu, C., Wu, X., Josefsson, S. & Dopson, M. Comparison of boreal acid sulfate sediment microbial communities in oxidative and reductive environments. Res. Microbiol. 170 (6–7), 288–295 (2019).
Google Scholar 
Oksana, C., Peter, K., Naomi, S. W., Gerhard, S. & Kay K.Microbial nitrogen transformation in constructed wetlands treating contaminated groundwater. Environ. SciPollution Res. https://doi.org/10.1007/s11356-014-3575-3 (2015).
Google Scholar 
Mohamed, A., El-Shatoury, S., Aboulfotoh, A., El-Rahem, K. A. A. & Shahawy, A. E. Synergistic effects (adsorption and biodegradation) of streptomyces hydrogenans immobilization on nano-reed biochar for further application in upflow anaerobic sludge blanket. RSC Adv. 14, 19. https://doi.org/10.1039/D4RA02864C (2024).
Google Scholar 
Kartal, L., En, A. & Timur, S. The removal of heavy metals from refinery effluents by sulfide precipitation. Acta Montanist. Slovaca. 28 (1), 214–231 (2023).
Google Scholar 
Gibert, O., Pablo, J. D., José, L. C. & Ayora, C. Municipal compost-based mixture for acid mine drainage bioremediation: metal retention mechanisms. Appl. Geochem. 20 (9), 1648–1657 (2005).
Google Scholar 
Wang, E. et al. Low carbon loss from Long-Term Manure-Applied soil during abrupt warming is realized through soil and Microbiome interplay. Environ. Sci. Technol. 58 (22), 9658–9668 (2024).
Google Scholar 
Zhang, W., Zhang, I. & Deng, J. Comparison of retention and output of sulfur in limestone soil and yellow soil and their responses to acid deposition in a small karst catchment of Guizhou Province, Southwest China. Environ. Sci. Pollut R 28, 60993–61007 (2021).
Google Scholar 
Shamshuddin, J., Muhrizal, S., Fauziah, I. & Husni, M. H. A. Effects of adding organic materials to an acid sulfate soil on the growth of cocoa (Theobroma Cacao L.) seedlings. Sci. Total Environ. 323 (1/3), 33–45 (2004).
Google Scholar 
Fu, Q. et al. How does triclocarban affect sulfur transformation in anaerobic Systems?[J]. Environ. Sci. Technol. 58 (40), 959–969 (2024).
Google Scholar 
Pantoja, L. & Garelick, H. A critical review of the quantification, analysis and detection of radionuclides in the environment using diffusive gradients in thin films (DGT): advances and perspectives. Pure Appl. Chem. 96 (7), 2023–0809 (2024).
Google Scholar 
Liu, J., Wang, H., Liu, R., Wang, S. & Liu, Y. Sequential Spectral-Spatial feature Convolution network with Self-Attention for remote sensing hyperspectral image classification. IEEE T Geosci. Remote. 63, 1–15 (2025).
Google Scholar 
Li, Y. F., Yang, Y. F., Wang, Y. Y., Liu, W. J. & Zhang, P. J. Characteristics of soil iron forms in wetlands with hvarious restoration ages around the Caizi lake, Anhui Province. Acta Sci. Circum. 35 (10), 3234–3241 (2015).
Google Scholar 
Dai, W. et al. Structural and property analysis of different components in coal liquefaction residue raffinate based on density separation. Chem. Eng. Res. Des. 214, 144–153 (2025).
Google Scholar 
Martinez, C. E. & Motto, H. L. Solubility of lead, zinc and copper added to mineral soils. Environ. Pollut. 107 (1), 153–158 (2000).
Google Scholar 
Liu, X. L. et al. Long-term effects of Biochar addition and straw return on N 2 O fluxes and the related functional gene abundances under wheat-maize rotation system in the North China plain. Appl. Soil. Ecol. 135, 44–55 (2019).
Google Scholar 
Liu, C., Liu, W. S., Ent, A., Morel, J. L. & Qiu, R. L. Simultaneous hyperaccumulation of rare Earth elements, manganese and aluminum in phytolacca Americana in response to soil properties. Chemosphere 282, 1310961–1310969 (2021).
Google Scholar 
Stumn, M. Aquatic chemistry[M] (Academic Internet, 2007).Sirous, S. & Mahboub, S. The status of chemical forms of iron and manganese in various orders of calcareous soils and their relationship with some physicochemical and mineralogical properties. Commun. Soil. Sci. Plan. 15, 2054–2068 (2020).
Google Scholar 
Ali, Q. et al. Self-assembled crown ether (15cr5) with alkaline earth metals to stimulate nonlinear optical properties: the comprehensive study by silico technique. Comput. Theor. Chem. 1236, 114611. https://doi.org/10.1016/j.comptc.2024.114611 (2024).
Google Scholar 
Zhang, Y. L. et al. Treating synthetic mine leaching wastewater by sulfate-reducingbacteria with sugarcane Bagasse as carbon source. Chin. J. Environ. Eng. 10 (5), 2355–2360 (2016).
Google Scholar 
Wu, S. C., Luo, Y. M., Cheung, K. C. & Wong, M. H. Influence of bacteria on Pb and Zn speciation, mobility and bioavailability in soil: A laboratory study. Environ. Pollut. 144 (3), 765–773 (2006).
Google Scholar 
Brallier, S., Harrison, R. B., Henry, C. L. & Xue, D. S. Liming effects on availability of Cd, Cu, Ni and Zn in a soil amended with sewage sludge 16 years previously[J]. Water Air Soil. Pollut. 86, 195–206 (1996).
Google Scholar 
Lukovic, J. et al. Quantitative assessment of effects of cadmium on the histological structure of Poplar and Willow Leaves[J]. Water Air Soil. Pollution. 223, 2979–2993 (2012).
Google Scholar 
Zhang, X. et al. Multi-stage hybrid subsurface flow constructed wetlands for treating piggery and dairy wastewater in cold climate. Environ. Technol. 38 (1–4), 183–191 (2016).
Google Scholar 
Kraková, L. et al. Investigation of bacterial and archaeal communities: novel protocols using modern sequencing by illumina miseq and traditional DGGE-cloning. Extrem Life Extrem Cond. 20, 795–808 (2016).
Google Scholar 
Zhong, F. et al. Bacterial community analysis by PCR-DGGE and 454-pyrosequencing of horizontal subsurface flow constructed wetlands with front aeration. Appl. Microbiol. Biotechnol. 99 (3), 1499–1512 (2015).
Google Scholar 
Retka, J. et al. The Use of Mining Waste Materials for the Treatment ofA cid and Alkaline Mine Wastewater. Minerals. 10(12), 1061 (2020). https://doi.org/10.3390/min10121061Di, L., Li, Y., Nie, L., Wang, S. & Kong, F. Influence of plant radial oxygen loss in constructed wetland combined with microbial fuel cell on nitrobenzene removal from aqueous solution. J. Hazard Mater. 394, 122542 (2020). https://doi.org/10.1016/j.jhazmat.2020.122542Wang, Q. et al. Sulfate removal performance and co-occurrence patterns of microbial community in constructed wetlands treating saline wastewater. J. Water Process Eng. 43, 102266: 10.1016/j.jwpe.2021.102266 (2021).Villegas-Plazas, M., Sanabria, J. & Junca, H. A composite taxonomical and functional framework of microbiomes under acid mine drainage bioremediation systems. J. Environ. Manage. 251, 109581. https://doi.org/10.1016/j.jenvman.2019.109581 (2019).
Google Scholar 
Aoyagi, T. et al. Hydraulic retention time and pH affect the performance and microbial communities of passive bioreactors for treatment of acid mine drainage. AMB Express 7, 42. https://doi.org/10.1186/s13568-017-0440-z (2017).
Google Scholar 
Cho, S. L. et al. Lactococcus Chungangensis sp. nov., a lactic Acidbacterium isolated Fom activated sludge foam. Int. Jounal SystematicEvolutionary Microbiol. 58 (8), 1844–1849 (2008).
Google Scholar 
Daims, H., Lücker, S. & Wagner, M. A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol. 24, 699–712 (2016).
Google Scholar 
Juottonen, H. Disentangling the effects of methanogen community and environment on peatland greenhouse gas production by a reciprocal transplant experiment. Funct. Ecol. 34 (6), 1268–1279 (2020).
Google Scholar 
Thongbunrod, N. & Chaiprasert, P. Efficient methane production from agro-industrial residues using anaerobic fungal-rich consortia. World J. Microb. Biot. 40 (8), 50–57 (2024).
Google Scholar 
Qiu, M. H., Zhang, R. F., Xue, S. S. & Shen, Q. R. Application of bio-organic fertilizer can control fusarium wilt of cucumber plants by regulating microbial community of rhizosphere soil. BIOL. FERT SOILS. 48 (7), 807–816 (2012).
Google Scholar 
Lena, S., Ekberg, A., Mastepanov, M. & Torben, R. C. The effect of vascular plants on carbon turnover and methane emissions from a tundra wetland. Global Change Biol. 9 (8), 1185–1192 (2010).
Google Scholar 
Vinson, D. S. et al. Microbial methane from in situ biodegradation of coal and shale: A review and Reevaluation of hydrogen and carbon isotope signatures. Chem. Geol. 453, 128–145 (2017).
Google Scholar 
Bao, J. et al. Nature AND nurture: enabling formate-dependent growth in methanosarcina acetivorans. FEBS J. 292, 2251–2271 (2025).
Google Scholar 
Wang, C. et al. An additive effect of elevated atmospheric CO 2 and rising temperature on methane emissions related to methanogenic community in rice paddies. Agr Ecosyst. Environ. 257, 165–174 (2018).
Google Scholar 
Kong, X., Chen, J. & Zhang, Z. Y. When polyethylene terephthalate microplastics Meet perfluorooctane sulfonate in thermophilic biogas upgrading system: their effect on methanogenesis. J. Hazard. Mater. 466 (15), 1336261–13362610 (2024).
Google Scholar 
Oladele, P., Ngo, J., Chang, T. & Johnson, T. A. Temporal dynamics of fecal microbiota community succession in broiler chickens, calves, and piglets under aerobic exposure. Microbiol. Spectr. 12 (6), 04084–04023. https://doi.org/10.1128/spectrum.04084-23 (2024).
Google Scholar 
Quiros, C., Herrero, M., Garcia, L. A. & Diaz, M. Application of flow cytometry to segregated kinetic modeling based on the physiological States of microorganisms. Appl. Environ. Microb. 73 (12), 3993–4000 (2007).
Google Scholar 
Silva, C. C. et al. Investigation of bacterial diversity in membrane bioreactor and conventional activated sludge processes from petroleum refineries using phylogenetic and statistical approaches. J. Microb. Biot. 20 (3), 447–459 (2010).
Google Scholar 
Zhang, Z., Li, J., Ren, Z., Li, H. & Zhang, X. Carbothermal synthesis of sulfurized nano zero-valent iron from sulfate-reducing bacteria biomass for mercury removal: The first application of biomass sulfur source. Sci. Total Environ. 931, 172846. https://doi.org/10.1016/j.jece.2019.103537 (2024).
Google Scholar 
Izadi, P. & Schrder, U. What is the Role of Individual Species within Bidirectional Electroactive Microbial Biofilms: A Case Study on Desulfarculus baarsii and Desulfurivibrio alkaliphilus[J]. Chem. Electro Chem. 9, 2. https://doi.org/10.1002/celc.202101116 (2022).
Google Scholar 
Ahmed, M., Saup, C. M., Wilkins, M. J. & Lin, L. S. Continuous Ferric Iron-dosed Anaerobic Wastewater Treatment: Treatment Performance, Sludge Characteristics, and Microbial Composition. J. Environ Chem. Engineer 8, 103537. https://doi.org/10.1016/j.jece.2019.103537 (2019).
Google Scholar 
Petrova, D., Koopman, S. J., Ballester, J. & Rodo, X. Improving the long-lead predictability of El Nio using a novel forecasting scheme based on a dynamic components model. Clim. Dynam. 48 (3–4), 1–28 (2017).
Google Scholar 
Wang, Y. Peracetic acid (PAA)-based pretreatment effectively improves medium-chain fatty acids (MCFAs) production from sewage sludge. Environ. Sci. Ecotechnology 20, 100355. https://doi.org/10.1016/j.ese.2023.100355 (2024).
Google Scholar 
Gilberto et al. Methane production and conductive materials: a critical review. Environ. Sci. Technol. 52 (18), 10241–10253 (2018).
Google Scholar 
Maisch, M. et al. Contribution of microaerophilic Iron(Ⅱ)-Oxidizers to Iron(Ⅲ) mineral formation. Environ. Sci. Technol. 53 (14), 8197–8204 (2019).
Google Scholar 
Bloomfield, C. J. The oxidation of iron sulphides in soils in relation to the formation of acid sulphate soils, and of ochre deposits in field drains. Eur. J. Soil. Sci. 23 (1), 1–16 (2006).
Google Scholar 
Woodward, K. B. & Hofstra, D. Rhizosphere metabolism and its effect on phosphorus pools in the root zone of a submerged macrophyte, Isoetes kirkii. Sci. Total Environ. 809, 151087. https://doi.org/10.1016/j.scitotenv.2021.151087 (2022).
Google Scholar 
Oko, B. J., Tao, Y. & Stuckey, D. C. Dynamics of two methanogenic microbiomes incubated in polycyclic aromatic hydrocarbons, naphthenic acids, and oil field produced water. Biotechnol. Biofuels 10(1), 123. https://doi.org/10.1186/s13068-017-0812-2 (2017).
Google Scholar 
Forget, P. The bacterial nitrate reductases. Solubilization, purification and properties of the enzyme A of Escherichia coli K 12. Eur. J. Biochem. 42 (2), 325–332 (2010).
Google Scholar 
Snoeyenbos-West, O. L., Nevin, K. P., Anderson, R. T. & Lovley, D. R. Enrichment of geobacter species in response to stimulation of fe(iii) reduction in sandy aquifer sediments. Micro Ecol. 39 (2), 153–167 (2000).
Google Scholar 
Download referencesFundingThis research work was supported by the Natural Science Foundation of Anhui Province (2023AH04019, 2024AH0509086, 2024AH040132) and the University Natural Science Foundation (2024xjyq02).Author informationAuthors and AffiliationsSchool of Environment and Life Health, Anhui University of Applied Technology, Hefei, 230009, Anhui Province, P. R. ChinaJing Guo, Lei Cheng, Miao Yang & Zhenli WangAuthorsJing GuoView author publicationsSearch author on:PubMed Google ScholarLei ChengView author publicationsSearch author on:PubMed Google ScholarMiao YangView author publicationsSearch author on:PubMed Google ScholarZhenli WangView author publicationsSearch author on:PubMed Google ScholarContributionsJing Guo.Lei Cheng and Miao Yang wrote the main manuscript text and Zhenli Wang prepared Figs. 3 and 4. All authors reviewed the manuscript.Corresponding authorsCorrespondence to
Jing Guo or Zhenli Wang.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
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 articleGuo, J., Cheng, L., Yang, M. et al. Physicochemical characteristics and microbial community analysis of a wetland system treating acid mine drainage.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33303-0Download citationReceived: 30 August 2025Accepted: 17 December 2025Published: 25 December 2025DOI: https://doi.org/10.1038/s41598-025-33303-0Share 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsAcid mine drainageConstructed wetlandsHeavy-metal removalMicrobial remediationPhytoremediation More

AbstractThe family Pieridae (Lepidoptera: Pieridae) is known for its ecological and conservation significance; however, little is known about its spatial distribution pattern and climate vulnerability in mainland China, complicating the formulation of effective conservation strategies. Pierinae and Coliadinae are widely distributed across most parts of the research zone, especially in the southern regions. Conversely, Dismorphiinae is mainly distributed in the west-central and northeastern parts. Pierinae and Coliadinae flourished over a wider range of elevations in open environments with warmer and more humid habitats, whereas Dismorphiinae is restricted to a narrow elevation range in forested areas with cooler and drier habitats. Therefore, it was necessary to study their distribution patterns separately. The MaxEnt model was applied to analyze the influence of bioclimatic variables on their distribution throughout three historical eras: the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Current (1970–2000). Pierinae and Coliadinae showed a uniform increase in overall highly suitable habitats, while Dismorphiinae showed an initial increase and then a decrease. Due to global warming, all three subfamilies might experience contraction in highly suitable habitats. Most Pieridae species are projected to experience shrinkage in highly suitable habitats, leading to decreased species diversity. These findings highlight divergent historical distribution patterns and habitat preferences among Pieridae subfamilies, yet project a shared vulnerability to future habitat contraction under climate warming.

Data availability

The data presented in this study are available on request from both corresponding authors.
ReferencesWang, W. L. et al. Butterfly Conservation in China: From Science to Action. Insects 11, 661. https://doi.org/10.3390/insects11100661 (2020).Thomas, J. Monitoring change in the abundance and distribution of insects using butterflies and other indicator groups. Philos. Trans. R Soc. B. 360, 339–357. https://doi.org/10.1098/rstb.2004.1585 (2005).
Google Scholar 
Yu, X. T. et al. Species richness of papilionidae butterflies (Lepidoptera: Papilionoidea) in the Hengduan mountains and its future shifts under climate change. Insects 14, 259. https://doi.org/10.3390/insects14030259 (2023).
Google Scholar 
Min, W. et al. Species diversity of butterflies in Shimentai nature Reserve, Guangdong. Biodiv Sci. 11, 441–453. https://doi.org/10.17520/biods.2003052 (2003).
Google Scholar 
Pollard, E. Temperature, rainfall and butterfly numbers. J. Appl. Ecol. 25, 819–828. https://doi.org/10.2307/2403748 (1988).
Google Scholar 
Svancara, L., Abatzoglou, J. & Waterbury, B. Modeling current and future potential distributions of milkweeds and the monarch butterfly in Idaho. Front. ecol. evol. 7, 168. https://doi.org/10.3389/fevo.2019.00168 (2019).
Google Scholar 
Minter, M. et al. Past, current, and potential future distributions of unique genetic diversity in a cold-adapted mountain butterfly. Ecol. Evol. 10, 11155–11168. https://doi.org/10.1002/ece3.6755 (2020).
Google Scholar 
Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Chang. Biol. 13, 1860–1872. (2007).
Google Scholar 
Gibbs, M., Wiklund, C. & Van Dyck, H. Temperature, rainfall and butterfly morphology: does life history theory match the observed pattern? Ecography 34, 336–344. https://doi.org/10.1111/j.1600-0587.2010.06573.x (2010).
Google Scholar 
Bartonova, A. et al. Range dynamics of palaearctic steppe species under glacial cycles: the phylogeography of Proterebia Afra (Lepidoptera: nymphalidae: Satyrinae). Biol. J. Linn. Soc. 125, 867–884. https://doi.org/10.1093/biolinnean/bly136 (2018).
Google Scholar 
Taberlet, P., Fumagalli, L., Wust-Saucy, A. G. & Cosson, J. F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464. https://doi.org/10.1046/j.1365-294x.1998.00289.x (2002).
Google Scholar 
Schoville, S., Stuckey, M. & Roderick, G. Pleistocene origin and population history of a neoendemic alpine butterfly. Mol. Ecol. 20, 1233–1247. https://doi.org/10.1111/j.1365-294X.2011.05003.x (2011).
Google Scholar 
Sherpa, S. et al. Population decline at distribution margins: assessing extinction risk in the last glacial relictual but still functional metapopulation of a European butterfly. Divers. Distrib. 28, 271–290. https://doi.org/10.1111/ddi.13460 (2022).
Google Scholar 
Kebaili, C., Sherpa, S., Rioux, D. & Després, L. Demographic inferences and Climatic niche modelling shed light on the evolutionary history of the emblematic cold-adapted Apollo butterfly at regional scale. Mol. Ecol. 31, 448–466. https://doi.org/10.1111/mec.16244 (2021).
Google Scholar 
Kumar, P. Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using maxent modelling: limitations and challenges. Biodivers. Conserv. 21, 1251–1266. https://doi.org/10.1007/s10531-012-0279-1 (2012).
Google Scholar 
Wu, C. S. & Hsu, Y. F. Butterflies of ChinaVol. 41442–2036 (The Straits Publishing & Distributing Group, 2017).Simon, L. Encyclopedia of Biodiversity. Vol. 1, 943Princeton university, Academic Press, (2013).Powell, J. A., Resh, V. H. & Cardé, R. T. Lepidoptera. In Encyclopedia of Insects 2nd edn (Academic, 2009).Vane-Wright, D. & de Jong, R. The butterflies of sulawesi: annotated checklist for a critical Island fauna. Zool. Verh Leiden. 343, 3–267 (2003).
Google Scholar 
Ding, C. & Zhang, Y. L. Phylogenetic relationships of Pieridae (Lepidoptera: Papilionoidea) in China based on seven gene fragments: phylogenetic relationships of Pieridae. Entomol. Sci. 20, 15–23. https://doi.org/10.1111/ens.12214 (2016).
Google Scholar 
Opler, P. A. A Field Guide To Western Butterflies 2nd edn (Houghton Mifflin Company, 1998).Brock, J. P. & Kaufman, K. Kaufman Field Guide To Butterflies of North America (Houghton Mifflin Company, 2003).Shapiro, A. M. The biology and zoogeography of the legume-feeding Patagonian-Fuegian white butterfly Tatochila Theodice (Lepidoptera: Pieridae). J. N. Y. Entomol. Soc. 251–260 (1991).Shapiro, A. M., Forister, M. L. & Fordyce, J. A. Extreme high-altitude Asian and Andean Pierid butterflies are not each others’ closest relatives. Arct. Antarct. Alp. Res. 39, 137–142 (2007).
Google Scholar 
Shapiro, A. M. Behavioral and ecological observations of Peruvian High-Andean Pierid butterflies (Lepidoptera). Stud. Neotrop. Fauna Environ. 20, 1–13. https://doi.org/10.1080/01650528509360 (1985).
Google Scholar 
Shapiro, A. M. Developmental and phenotypic responses to photoperiod and temperature in an Equatorial montane butterfly, Tatochila xanthodice (Lepidoptera: Pieridae). Biotropica 297–301. https://doi.org/10.2307/2387682 (1978).Tshikolovets, V. V., Yakovlev, R. V. & Bálint, Z. The Butterflies of Mongolia (Tshikolovets, 2009).Tshikolovets, V. V., Yakovlev, R. V. & Kosterin, O. E. The Butterflies of Altai, Sayans and Tuva (Tshikolovets, 2009). (South Siberia.Laiho, J. & Ståhls, G. DNA barcodes identify central Asian Colias butterflies (Lepidoptera, Pieridae). Zookeys 30, 175–196. https://doi.org/10.3897/zookeys.365.5879 (2013).
Google Scholar 
Yata, O. Butterflies of the South East Asian IslandsVol. 3 (Plapac Co. Ltd., 1985).Leong, J. V. et al. Around the world in 26 million years: diversification and biogeography of Pantropical Grass-Yellow Eurema butterflies (Pieridae: Coliadinae). J. Biogeogr. 52, e15107. https://doi.org/10.1111/jbi.15107 (2025).
Google Scholar 
Lukhtanov, V. A. Karyotype evolution and systematics of higher taxa of Pieridae (Lepidoptera) of the world. Ent Obozr. 70, 619–636 (1991).
Google Scholar 
Lamas, G. Twenty-five new Neotropical dismorphiinae (Lepidoptera-Pieridae). Rev. Peru Entomol. 44, 17–36 (2004).
Google Scholar 
Soberón, J. M., Llorente, J. B. & Oñate, L. The use of specimen-label databases for conservation purposes: an example using Mexican papilionid and Pierid butterflies. Biodivers. Conserv. 9, 1441–1466. https://doi.org/10.1023/A:1008987010383 (2000).
Google Scholar 
Zheng, P. China’s Geography142 (China Intercontinental, 2010).Machine, W. Regions of Chinese food-styles/flavors of cooking. (2024). https://web.archive.org/web/20090416203009/http://www.kas.ku.edu:80/service-learning/projects/chinese_food/regional_cuisine. html.Zhu, H. The tropical forests of Southern China and conservation of biodiversity. Bot. Rev. 83, 87–105. https://doi.org/10.1007/s12229-017-9177-2 (2017).
Google Scholar 
Wang, Y. et al. Macro-evolutionary dynamics dominated by dispersal promote the formation of regional biodiversity hotspot‐insights from hawkmoths (Lepidoptera: Sphingidae) in South China. Divers. Distrib. 31, e13916. https://doi.org/10.1111/ddi.13916 (2025).
Google Scholar 
Forti, L. R. & Szabo, J. K. The iNaturalist platform as a source of data to study amphibians in Brazil. Acad. Bras. Ciênc. 95, e20220828. https://doi.org/10.1590/0001-3765202320220828 (2023).
Google Scholar 
Su, J. et al. The distribution pattern and species richness of scorpionflies (Mecoptera: Panorpidae). Insects 14, 332. https://doi.org/10.3390/insects14040332 (2023).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km Spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).
Google Scholar 
Chen, Y., Guo, D., Cao, W. & Li, Y. Changes in net primary productivity and factor detection in china’s yellow river basin from 2000 to 2019. Remote Sens. 15, 2798. https://doi.org/10.3390/rs15112798 (2023).
Google Scholar 
Bai, W. et al. Analysis of Spatial and Temporal variation of vegetation NPP in Daning river basin and its driving forces. Int. J. Remote Sens. 44, 6194–6218. (2023).
Google Scholar 
Wei, T. et al. Package ‘corrplot’. Statistician 56, e24 (2017).
Google Scholar 
Moss, R. et al. Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies: IPCC Expert Meeting Report (Noordwijkerhout). http://www.ipcc.ch/pdf/supporting-material/expert-meeting-ts-scenarios.pdf.IP (2008) .John, W. et al. Report of 2.6 Versus 2.9 Watts/m2 RCPP Evaluation Panel (University of Maryland, Joint Global Change Research Institute, 2009).Vilela, B., Villalobos, F. & LetsR A new R package for data handling and analysis in macroecology. Methods Ecol. Evol. 6, 1229–1234. https://doi.org/10.1111/2041-210X.12401 (2015).
Google Scholar 
Santana, P. A. Jr, Kumar, L., Da Silva, R. S., Pereira, J. L. & Picanço, M. C. Assessing the impact of climate change on the worldwide distribution of Dalbulus Maidis (DeLong) using maxent. Pest Manag. Sci. 75, 2706–2715. https://doi.org/10.1002/ps.5379 (2019).
Google Scholar 
Wei, J., Peng, L., He, Z., Lu, Y. & Wang, F. Potential distribution of two invasive pineapple pests under climate change. Pest Manag. Sci. 76, 1652–1663. https://doi.org/10.1002/ps.5684 (2020).
Google Scholar 
Warren, D. L., Wright, A. N., Seifert, S. N. & Shaffer, H. B. Incorporating model complexity and Spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Divers. Distrib. 20, 334–343. https://doi.org/10.1111/ddi.12160 (2014).
Google Scholar 
Zhou, B. J., Ma, C. L., Gao, C., Han, D. N. & Chai, Y. Impacts of climate change on species distribution patterns of Polyspora sweet in China. Ecol. Evol. 12, e9516. https://doi.org/10.1002/ece3.9516 (2022).
Google Scholar 
Yang, X., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia Adhatoda L. in lesser Himalayan foothills. Ecol. Eng. 51, 83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004 (2013).
Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).
Google Scholar 
Hijmans, R. J. & Elith, J. Spatial distribution models. Vol. 10, (2019).Zhang, X. F., Nizamani, M. M., Jiang, C., Fang, F. Z. & Zhao, K. K. Potential planting regions of Pterocarpus Santalinus (Fabaceae) under current and future climate in China based on maxent modeling. Ecol. Evol. 14, e11409. https://doi.org/10.1002/ece3.11409 (2024).
Google Scholar 
Lu, S. et al. Patterns of tree species richness in Southwest China. Environ. Monit. Assess. 193, 97. https://doi.org/10.1007/s10661-021-08872-y (2021).
Google Scholar 
Xie, D. et al. Diversity of higher plants in China. J. Syst. Evol. 59, 1111–1123. https://doi.org/10.1111/jse.12758 (2021).
Google Scholar 
Ômura, H. Plant secondary metabolites in host selection of butterfly. In Chemical Ecology of Insects. 3–27 (CRC, Boca Raton, Florida, (2018).
Google Scholar 
Castro-Gerardino, D. J. & Llorente-Bousquets, J. Comparative exploration of antennae in Pseudopontia, and antennal clubs of the tribes leptideini and dismorphiini (Lepidoptera: Pieridae). Zootaxa 4347, 401–445 (2017).
Google Scholar 
Scott, J. A. The Butterflies of North America: A Natural History and Field Guide584 (Stanford University Press, 1992).Shanks, K., Senthilarasu, S., ffrench-Constant, R. H. & Mallick, T. K. White butterflies as solar photovoltaic concentrators. Sci. Rep. 5, 12267. https://doi.org/10.1038/srep12267 (2015).
Google Scholar 
Bladon, A. J. et al. How butterflies keep their cool: physical and ecological traits influence thermoregulatory ability and population trends. J. Anim. Ecol. 89, 2440–2450. https://doi.org/10.1111/1365-2656.13319 (2020).
Google Scholar 
Lamas, G. The dismorphiinae (Pieridae) of Mexico, central America and the Antilles. Mexi Soc. Lepi. 5, 3–37 (1979).
Google Scholar 
Hill, G. M., Kawahara, A. Y., Daniels, J. C., Bateman, C. C. & Scheffers, B. R. Climate change effects on animal ecology: butterflies and moths as a case study. Biol. Rev. 96, 2113–2126. https://doi.org/10.1111/brv.12746 (2021).
Google Scholar 
Oberhauser, K. & Peterson, A. Modelling current and future potential wintering distributions of Eastern North American monarch butterflies. Proc. Natl. Acad. Sci. U S A. 100, 14063–14068. https://doi.org/10.1073/pnas.2331584100 (2003).
Google Scholar 
Zavaleta, E. S., Shaw, M. R., Chiariello, N. R., Mooney, H. A. & Field, C. B. Additive effects of simulated climate changes, elevated CO2, and nitrogen deposition on grassland diversity. Proc. Natl. Acad. Sci. U S A. 100, 7650–7654. https://doi.org/10.1073/pnas.0932734100 (2003).
Google Scholar 
Trotter, R. T., Cobb, N. S. & Whitham, T. G. Arthropod community diversity and trophic structure: a comparison between extremes of plant stress. Ecol. Entomol. 33, 1–11. https://doi.org/10.1111/j.1365-2311.2007.00941.x (2008).
Google Scholar 
Kishimoto-Yamada, K. & Itioka, T. Consequences of a severe drought associated with an El Niño-Southern Oscillation on a light-attracted leaf-beetle (Coleoptera, Chrysomelidae) assemblage in Borneo. J. Trop. Ecol. 24, 229–233. https://doi.org/10.1017/S0266467408004811 (2008).
Google Scholar 
Nielsen, E. T. & Nielsen, H. T. Temperatures preferred by the Pierid Ascia Monuste L. Ecology 40, 181–185. https://doi.org/10.2307/1930027 (1959).
Google Scholar 
Advani, N. K., Parmesan, C. & Singer, M. C. Takeoff temperatures in Melitaea Cinxia butterflies from latitudinal and elevational range limits: A potential adaptation to solar irradiance. Ecol. Entomol. 44, 389–396. https://doi.org/10.1111/een.12714 (2019).
Google Scholar 
Hayes, M. P., Hitchcock, G. E., Knock, R. I., Lucas, C. B. H. & Turner, E. C. Temperature and territoriality in the Duke of Burgundy butterfly, Hamearis Lucina. J. Insect Conserv. 23, 739–750. https://doi.org/10.1007/s10841-019-00166-6 (2019).
Google Scholar 
Wang, X. et al. Greater impacts from an extreme cold spell on tropical than temperate butterflies in Southern China. Ecosphere 7, e01315. https://doi.org/10.1002/ecs2.1315 (2016).
Google Scholar 
Ehrlich, P. R. et al. Extinction, reduction, stability and increase: the responses of checkerspot butterfly (Euphydryas) populations to the California drought. Oecologia 46, 101–105. https://doi.org/10.1007/BF00346973 (1980).
Google Scholar 
Huntley, B. et al. Explaining patterns of avian diversity and endemicity: climate and biomes of Southern Africa over the last 140,000 years. J. Biogeogr. 43, 874–886. https://doi.org/10.1111/jbi.12714 (2016).
Google Scholar 
Alves, W. F. et al. Connectivity and climate influence diversity–stability relationships across Spatial scales in European butterfly metacommunities. Glob Ecol. Biogeogr. 33, 1–16. https://doi.org/10.1111/geb.13896 (2024).
Google Scholar 
Chowdhury, S., Fuller, R. A., Dingle, H., Chapman, J. W. & Zalucki, M. P. Migration in butterflies: a global overview. Biol. Rev. 96, 1462–1483. https://doi.org/10.1111/brv.12714 (2021).
Google Scholar 
Kühne, G., Kosuch, J., Hochkirch, A. & Schmitt, T. Extra-Mediterranean glacial refugia in a mediterranean faunal element: the phylogeography of the chalk-hill blue Polyommatus Coridon (Lepidoptera, Lycaenidae). Sci. Rep. 7, 43533. https://doi.org/10.1038/srep43533 (2017).
Google Scholar 
Habel, J. C., Lens, L., Rödder, D. & Schmitt, T. From Africa to Europe and back: refugia and range shifts cause high genetic differentiation in the marbled white butterfly Melanargia Galathea. BMC Evol. Biol. 11, 1–15. https://doi.org/10.1186/1471-2148-11-215 (2011).
Google Scholar 
Foster, P. The potential negative impacts of global climate change on tropical montane cloud forests. Earth Sci. Rev. 55, 73–106. https://doi.org/10.1016/S0012-8252(01)00056-3 (2001).
Google Scholar 
Cannone, N., Sgorbati, S. & Guglielmin, M. Unexpected impacts of climate change on alpine vegetation. Front. Ecol. Environ. 5, 360–364. https://doi.org/10.1890/1540-9295 (2007).
Google Scholar 
Wilson, R. J. et al. Changes to the elevational limits and extent of species ranges associated with climate change. Ecol. lett. 8, 1138–1146. https://doi.org/10.1111/j.1461-0248.2005.00824.x (2005).
Google Scholar 
Liang, Q. et al. Shifts in plant distributions in response to climate warming in a biodiversity hotspot, the Hengduan mountains. J. Biogeogr. 45, 1334–1344. https://doi.org/10.1111/jbi.13229 (2018).
Google Scholar 
Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110. https://doi.org/10.1126/sciadv.abe1110 (2021).
Google Scholar 
Hu, R. et al. Shifts in bird ranges and conservation priorities in China under climate change. PLoS One. 15, e0240225. https://doi.org/10.1371/journal.pone.0240225 (2020).
Google Scholar 
Lamsal, P., Kumar, L., Aryal, A. & Atreya, K. Future climate and habitat distribution of Himalayan musk deer (Moschus chrysogaster). Ecol. Inf. 44, 101–108. https://doi.org/10.1016/j.ecoinf.2018.02.004 (2018).
Google Scholar 
Yang, F., Hu, J. & Wu, R. Combining endangered plants and animals as surrogates to identify priority conservation areas in Yunnan, China. Sci. Rep. 6, 30753. https://doi.org/10.1038/srep30753 (2016).
Google Scholar 
Ma, F. Z. et al. Progress in construction of China butterfly diversity observation network (China BON-Butterflies). J. Ecol. Rural Environ. 34, 27–36. https://doi.org/10.11934/j.issn.1673-4831.2018.01.004 (2018).
Google Scholar 
Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., & Ram, K. rgbif: Interface to the global biodiversity informationfacility API. R package version 3.7.2. https://CRAN.R-project.org/package=rgbif, (2022).Barve, V. & Hart, E. rinat: Access ‘iNaturalist’ Data Through APIs. R package version 0.1.9. https://docs.ropensci.org/rinat/ (2025)Maclean, I. M. D. & Wilson, R. J. Recent ecological responses to climate change support predictions of high extinction risk. Proc. Natl. Acad. Sci. 108,12337–12342. https://doi.org/10.1073/pnas.1017352108 (2011).Download referencesAcknowledgementsWe would like to express our sincere gratitude to Dr. Rehan Zafar for providing invaluable guidance in the analysis of this study. We also thank Dr. Hashmat Khan and Dr. Haroon Khan for their thoughtful review and constructive feedback on the manuscript.FundingThis work was funded by the National Natural Science Foundation of China (grant number 32471563).Author informationAuthors and AffiliationsCollege of Life Sciences, Northwest University, Xi’an, 710069, People’s Republic of ChinaRiaz Hussain, Panpan Miao, Lianxi Xing & Yuan HuaCollege of Geographical Sciences, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, 450046, People’s Republic of ChinaAdnanul RehmanXi’an Botanical Garden of Shaanxi Province (Institute of Botany of Shaanxi Province), Xi’an, 710061, People’s Republic of ChinaLijun FangShaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, 710069, People’s Republic of ChinaLianxi XingKey Laboratory of Resource Biology and Biotechnology in Western China, Northwest University, Ministry of Education, Xi’an, 710069, People’s Republic of ChinaLianxi XingAuthorsRiaz HussainView author publicationsSearch author on:PubMed Google ScholarPanpan MiaoView author publicationsSearch author on:PubMed Google ScholarAdnanul RehmanView author publicationsSearch author on:PubMed Google ScholarLijun FangView author publicationsSearch author on:PubMed Google ScholarLianxi XingView author publicationsSearch author on:PubMed Google ScholarYuan HuaView author publicationsSearch author on:PubMed Google ScholarContributionsR.H. and Y.H: Conceptualization; R.H, Y.H, L.F, A.R, and P.M: methodology and experiments; R.H. and Y.H: data analysis; R.H: writing—original draft preparation; Y.H, L.F, and A.R: writing—review and editing; R.H: visualization; L.X. and Y.H: supervision; L.X: project administration; L.X: funding acquisition. All authors have read and agreed to the published version of the manuscript.Corresponding authorsCorrespondence to
Lianxi Xing or Yuan Hua.Ethics declarations

Competing interests
The authors declare no competing interests.

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 1Rights and permissions
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 articleHussain, R., Miao, P., Rehman, A. et al. Species richness and Spatial distribution of three Pieridae subfamilies across mainland China under past and future climates.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-21555-9Download citationReceived: 27 February 2025Accepted: 22 September 2025Published: 25 December 2025DOI: https://doi.org/10.1038/s41598-025-21555-9Share 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsPierinaeColiadinaeDismorphiinaeMaxEnt modelSpecies richnessDistribution patternClimate change More

Abstract

This study examined the synergistic and independent effects of soil properties, vegetation cover, conservation practices, and slope on the spatial distribution characteristics of soil erosion in the Abu-Ghraibat watershed in 2024. Soil samples have been collected and analyzed in the laboratory, along with high-resolution satellite imagery, meteorological data, and digital elevation model (DEM) data. The findings indicate that soil erosion in the Abu-Ghraibat watershed in 2024 was minimal, with a progressively increasing severity from north to south. In the studied area, grassland accounts for over 50% of soil erosion, with regions with vegetation coverage > 30% as the primary contributors, all of which are influenced by slope. Moreover, the enhancement of vegetation in the lower strata of the basin and in grasslands, especially on slopes ranging from 10° to 45°, along with the conversion of sloping woodlands and grasslands into terraces, has proven an effective strategy for mitigating soil erosion in the Abu-Ghraibat watershed. The present study has demonstrated that the RUSLEGIS integrated model may serve as an effective instrument for quantitatively and spatially mapping soil erosion at the watershed level in the Abu-Ghraibat, while accounting for the provision of landscape services.

Data availability

Data is available by contacting the corresponding author.
ReferencesMallick, J. et al. Evaluating soil erosion zones in the Kangsabati river basin using a stacking framework and SHAP model: a comparative study of machine learning approaches. Environ. Sci. Eur. 37 (1), 34 https://doi.org/10.1186/s12302-025-01079-9   (2025).
Google Scholar 
Mathewos, M., Wosoro, D. & Wondrade, N. Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central rift Valley basin of Ethiopia. Heliyon 10 (10), https://doi.org/10.1016/j.heliyon.2024.e31246  (2024).Gao, G., Liang, Y., Liu, J., Dunkerley, D. & Fu, B. A modified RUSLE model to simulate soil erosion under different ecological restoration types in the loess hilly area. Int. Soil. Water Conserv. Res. 12 (2), 258–266 (2024). https://doi.org/10.1016/j.iswcr.2023.08.007
Google Scholar 
Getahun, Y. S., Tesfay, F., Kassegne, A. B. & Moges, A. S. Geospatial based soil loss rate and land degradation assessment in Debre Berhan Regio-Politan city, Upper Blue Nile Basin, Central Ethiopia, Geomatics. Nat. Hazards Risk, 15(1), https://doi.org/10.1080/19475705.2024.2359993 (2024).Djoukbala, O. et al. A Geospatial approach-based assessment of soil erosion impacts on the dams silting in the semi-arid region. Geomatics Nat. Hazards Risk. 15 (1). https://doi.org/10.1080/19475705.2024.2375543 (2024).Biswas, S. S. & Pani, P. Estimation of soil erosion using RUSLE and GIS techniques: a case study of Barakar river basin, Jharkhand, India. Model. Earth Syst. Environ. 1 (4). https://doi.org/10.1007/s40808-015-0040-3 (2015).Wei, Z. et al. Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning. Remote Sens. Environ. 313, 114371. https://doi.org/10.1016/j.rse.2024.114371 (2024).
Google Scholar 
Kadam, A., Umrikar, B. N. & Sankhua, R. N. Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach. Remote Sens. Land. 2 (1), 65–75 . https://doi.org/10.21523/gcj1.18020105 (2018)
Google Scholar 
Ahmad Bhat, S. et al. Soil erosion modeling using RUSLE & GIS on micro watershed of J&K. ~ 838 ~ J. Pharmacognosy Phytochemistry. 6, 5 (2017).
Google Scholar 
Yuan, S. et al. Dynamic analyses of soil erosion and improved potential combining topography and socio-economic factors on the loess plateau. Ecol. Indic. 160 https://doi.org/10.1016/j.ecolind.2024.111814 (2024).Dash, S. S. & Maity, R. Effect of climate change on soil erosion indicates a dominance of rainfall over LULC changes. J. Hydrol. Reg. Stud. 47 https://doi.org/10.1016/j.ejrh.2023.101373 (2023).Saha, M., Sauda, S. S., Real, H. R. K. & Mahmud, M. Estimation of annual rate and Spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A Geospatial approach. Environ. Challenges. 8, https://doi.org/10.1016/j.envc.2022.100524 (2022).Su, Y., Cui, Y. J., Dupla, J. C. & Canou, J. Soil-water retention behaviour of fine/coarse soil mixture with varying coarse grain contents and fine soil dry densities. Can. Geotech. J. 59 (2), 291–299. https://doi.org/10.1139/cgj-2021-0054 (2022).
Google Scholar 
Maaroof, B. F. Fluvial Landforms Classification Using Geospatial Modeling of Al-Jazeera Eastern Region at Misan Governorate, Iraq, Iraqi National. J. Earth Sci. 25 (2), 199–218 https://doi.org/10.33899/earth.2024.146564.1228 (2025).
Google Scholar 
Maaroof, B. F. & Kareem, H. H. Geomorphological Analysis of Chemical Weathering Features in Al-Band Hills Area, Eastern of Misan Governorate, Iraq. Iraqi National Journal of Earth Science. 23(1), 67–84 https://doi.org/10.33899/earth.2023.137382.1034 (2023).Maaroof, B. F. Geomorphometric assessment of the river drainage network at al-shakak basin (Iraq). J. Geographical Inst. Jovan Cvijic SASA. 72 (1), 1–13. https://doi.org/10.2298/IJGI2201001M (2022).Al-Hasani, B. et al. Assessing climate change impacts on Rainfall-Runoff in Northern iraq: A case study of Kirkuk Governorate, a Semi-Arid region. In: Handbook of Environmental Chemistry, vol. 136, 93–111. https://doi.org/10.1007/698_2024_1154 (Springer Science and Business Media Deutschland GmbH, 2024)Maaroof, B. F. & Kareem, H. H. Water erosion of the slopes of Tayyar drainage basin in the desert of muthanna in Southern Iraq. Indian J Ecology 47(3), 638–644 (2020).Maaroof, B. et al. Classifying fluvial landforms using Geospatial modeling in al-ashaali watershed, Iraqi Southern desert. Bull. Iraq Nat. History Museum. 18 (3), 739–763. https://doi.org/10.26842/binhm.7.2025.18.3.0739 (2025).Maaroof, B. F., Al-Abdan, R. H. & Kareem, H. H. Geographical assessment of natural resources at Abu-Hadair drainage basin in Al-Salman desert. Indian J Ecology 48(3), 796–801 (2021).Sissakian, V. K. et al. Geomorphology of the Mesopotamian Plain: A Critical Review Geology of Iraq View project Mosul dam View project Geomorphology of the Mesopotamian Plain: A Critical Review, online) Scientific Press International Limited, [Online]. Available: https://www.researchgate.net/publication/339952803 (2020).Ali Al-Zubaydi, J. H. & Al-Turaihi, A. S. Slope stability analysis some selected sites at Bajalia anticline in Missan Governorate, Eastern Iraq. Iraqi J. Sci. 65 (7), 3824–3833. https://doi.org/10.24996/ijs.2024.65.7.22 (2024).
Google Scholar 
Sabah, Y. & Yacoub Geomorphology of the Mesopotamian plain: A critical review. J. Earth Sci. Geotech. Eng. 10 (4), 1–25 (2010).
Google Scholar 
Yacoub, S. Y. Geomorphology of the mesopotamia plain. Iraqi Bull. Geol. Min.     (4), (2011).Mustafa, M. M. Mineral resources and industrial deposits in the mesopotamia plain. Geology of the Mesopotamia Plain, p. 7-32 (2011).Al-Hasani, B. et al. Integrated geospatial approach for adaptive rainwater harvesting site selection under the impact of climate change. Stoch. Env. Res. Risk Assess. 38 (3), 1009–1033 https://doi.org/10.1007/s00477-023-02611-0 (2024).Maaroof, B. et al. Environmental assessment of Al-Hillah River pollution at Babil Governorate (Iraq). J. Geographical Inst. Jovan Cvijic SASA. 73 (1), 1–16 https://doi.org/10.2298/IJGI2301001M (2023).
Google Scholar 
Hasani, B. A. L. et al. Rainwater harvesting site assessment using Geospatial technologies in a Semi-Arid region: toward water sustainability. Water (Basel). 17 (15), 2317 https://doi.org/10.3390/w17152317 (2025).
Google Scholar 
Maaroof, B. F. Geomorphological Assessment Using Geoinformatics Applications of the Sloping System of Al-Ashaali Drainage Basin at Iraqi Southern Desert. Iraqi Natl. J. Earth Sci. 22 (1), 38–54 https://doi.org/10.33899/earth.2022.133146.1009 (2022).
Google Scholar 
Maaroof, B. F. Quantitative analysis using Geospatial modeling of al-rahimawi watershed’s shape properties in the Iraqi Southern desert. Bull. Iraq Nat. History Museum. 18 (2), 277–295. https://doi.org/10.26842/binhm.7.2024.18.2.0277 (2024).
Google Scholar 
Getu, L. A., Nagy, A. & Addis, H. K. Soil loss Estimation and severity mapping using the RUSLE model and GIS in Megech watershed, Ethiopia. Environ. Challenges. 8 https://doi.org/10.1016/j.envc.2022.100560 (2022).Prasannakumar, V., Vijith, H., Abinod, S. & Geetha, N. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using revised universal soil loss equation (RUSLE) and geo-information technology. Geosci. Front. 3 (2), 209–215. https://doi.org/10.1016/j.gsf.2011.11.003 (2012).
Google Scholar 
Chuenchum, P., Xu, M. & Tang, W. Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang–Mekong River by using the modified RUSLE model. Int. Soil. Water Conserv. Res. 8 (3), 213–227 https://doi.org/10.1016/j.iswcr.2020.06.006 (2020).
Google Scholar 
Schmidt, S., Tresch, S. & Meusburger, K. Modification of the RUSLE slope length and steepness factor (LS-factor) based on rainfall experiments at steep alpine grasslands. MethodsX 6, 219–229 https://doi.org/10.1016/j.mex.2019.01.004 (2019).
Google Scholar 
Mahgoub, M., Elalfy, E., Soussa, H. & Abdelmonem, Y. Relation between the soil erosion cover management factor and vegetation index in semi-arid basins. Environ. Earth Sci. 83 (10), 337 https://doi.org/10.1007/s12665-024-11593-3 (2024).
Google Scholar 
Shekar, P. R. & Mathew, A. GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India. HydroResearch 7, 315–325 https://doi.org/10.1016/j.hydres.2024.05.003 (2024).
Google Scholar 
Joshi, P. et al. Himalayan watersheds in Nepal record high soil erosion rates estimated using the RUSLE model and experimental erosion plots. Heliyon 9 (5), https://doi.org/10.1016/j.heliyon.2023.e15800 (2023).Zhang, Y. et al. Dynamic analysis of soil erosion in the affected area of the lower yellow river based on RUSLE model. Heliyon 10 (1). https://doi.org/10.1016/j.heliyon.2023.e23819 (2024).Wiltshire, C. et al. Evaluating erosion risk models in a Scottish catchment using organic carbon fingerprinting. J. Soils Sediments. https://doi.org/10.1007/s11368-024-03850-6 (2024).
Google Scholar 
Naipal, V., Reick, C., Pongratz, J. & Van Oost, K. Improving the global applicability of the RUSLE model – Adjustment of the topographical and rainfall erosivity factors. Geosci. Model. Dev. 8 (9), 2893–2913 https://doi.org/10.5194/gmd-8-2893-2015 (2015).
Google Scholar 
Terefe, B. et al. Comparative analysis of RUSLE and SWPT for sub-watershed conservation prioritization in the Ayu watershed, Abay basin Ethiopia. Heliyon 10 (15). https://doi.org/10.1016/j.heliyon.2024.e35132 (2024).Olika, G., Fikadu, G. & Gedefa, B. GIS based soil loss assessment using RUSLE model: A case of Horo district, Western Ethiopia. Heliyon 9 (2), https://doi.org/10.1016/j.heliyon.2023.e13313 (2023).Pandey, R., Mehta, D., Kumar, V. & Prakash Pradhan, R. Quantifying soil erosion and soil organic carbon conservation services in Indian forests: A RUSLE-SDR and GIS-based assessment. Ecol. Indic. 163 https://doi.org/10.1016/j.ecolind.2024.112086 (2024).Yan, R., Zhang, X., Yan, S. & Chen, H. Estimating soil erosion response to land use/cover change in a catchment of the Loess Plateau, China. Int. Soil. Water Conserv. Res. 6 (1), 13–22 https://doi.org/10.1016/j.iswcr.2017.12.002 (2018).
Google Scholar 
Ganasri, B. P. & Ramesh, H. Assessment of soil erosion by RUSLE model using remote sensing and GIS – A case study of Nethravathi basin. Geosci. Front. 7, 953–961 https://doi.org/10.1016/j.gsf.2015.10.007 (2016).
Google Scholar 
Brychta, J., Podhrázská, J. & Šťastná, M. Review of methods of spatio-temporal evaluation of rainfall erosivity and their correct application. https://doi.org/10.1016/j.catena.2022.106454 (Elsevier B V, 2022)Dai, E., Lu, R. & Yin, J. Identifying the effects of landscape pattern on soil conservation services on the Qinghai-Tibet plateau. Glob Ecol. Conserv. 50 https://doi.org/10.1016/j.gecco.2024.e02850 (2024).Sinshaw, B. G. et al. Watershed-based soil erosion and sediment yield modeling in the rib watershed of the upper blue nile Basin, Ethiopia. Energy Nexus. 3 https://doi.org/10.1016/j.nexus.2021.100023 (2021).Talebi, A. & Karimi, Z. Incorporation of management responses in the direction of soil erosion changes from the past to the future based on the RUSLE and DPSIR model. https://doi.org/10.1016/j.indic.2024.100412 (Elsevier B V, 2024)George, J., Kumar, K. S. & Hole, R. M. Geospatial modelling of soil erosion and risk assessment in Indian Himalayan region—A study of Uttarakhand state. Environ. Adv. 4 https://doi.org/10.1016/j.envadv.2021.100039 (2021).Li, P., Xie, Z., Yan, Z., Dong, R. & Tang, L. Assessment of vegetation restoration impacts on soil erosion control services based on a biogeochemical model and RUSLE. J. Hydrol. Reg. Stud. 53 https://doi.org/10.1016/j.ejrh.2024.101830 (2024).Download referencesAcknowledgementsThe authors thank the Department of Applied Geology at the University of Babylon and the Department of Civil Engineering and Built Environment at Liverpool John Moores University for their scientific support throughout this study.FundingOpen access funding provided by Lulea University of Technology.Author informationAuthors and AffiliationsDepartment of Geography, University of Babylon, Hillah, 51001, Babil, IraqBashar F. MaaroofDepartment of General Sciences, University of Misan, Amarah, 62001, Misan, IraqHashim H. KareemDepartment of Applied Geology, University of Babylon, Hillah, 51001, Babil, IraqJaffar H. Al-ZubaydiDepartment of Civil, Environmental, and Natural Resources Engineering, Lulea University of Technology, Lulea, 97187, SwedenNadhir Al-AnsariDepartment of Geography, University of Fayoum, Fayoum, 63511, EgyptMohamed Alkhuzamy AzizDepartment of Geography, University of Wasit, Wasit, 52001, IraqDhia Alden A. AL-QuraishyFaculty of Engineering Technology, Civil Engineering and Built Environment Department, Liverpool John Moores University, Liverpool, L3 5UX, UKBan AL-Hasani, Mawada Abdellatif & Iacopo CarnacinaRemote Sensing Center, University of Mosul, Mosul, 6231, AZ, IraqRayan G. ThannounDepartment of Geology, University of Baghdad, Baghdad, 10001, IraqManal Sh. Al-KubaisiFaculty of Science, Department of Environmental Sustainability, Lakehead University, 500 University Avenue, Orillia, ON, L3V 0B9, CanadaSama S. Al-MaarofiAuthorsBashar F. MaaroofView author publicationsSearch author on:PubMed Google ScholarHashim H. KareemView author publicationsSearch author on:PubMed Google ScholarJaffar H. Al-ZubaydiView author publicationsSearch author on:PubMed Google ScholarNadhir Al-AnsariView author publicationsSearch author on:PubMed Google ScholarMohamed Alkhuzamy AzizView author publicationsSearch author on:PubMed Google ScholarDhia Alden A. AL-QuraishyView author publicationsSearch author on:PubMed Google ScholarBan AL-HasaniView author publicationsSearch author on:PubMed Google ScholarMawada AbdellatifView author publicationsSearch author on:PubMed Google ScholarIacopo CarnacinaView author publicationsSearch author on:PubMed Google ScholarRayan G. ThannounView author publicationsSearch author on:PubMed Google ScholarManal Sh. Al-KubaisiView author publicationsSearch author on:PubMed Google ScholarSama S. Al-MaarofiView author publicationsSearch author on:PubMed Google ScholarContributionsBashar F. Maaroof: Project administration, conceptualization, data curation, formal analysis, investigation, methodology, supervision, validation, visualization, software, writing – original draft. Hashim H. Kareem: Supervise, visualize, methodology, resources, validate, write, review, and edit. Jaffar H. Al-Zubaydi: Supervision, data curation, formal analysis, validation, visualization, methodology, writing – review and editing. Nadhir Al-Ansari: Supervision, data curation, formal analysis, methodology, software, writing, review, and editing. Mohamed Alkhuzamy Aziz: Supervision, data curation, formal analysis, methodology, software, writing, review, and editing. Dhia Alden A. AL-Quraishy: Visualization, data curation, formal analysis, methodology, software, writing, review, and editing. Ban AL-Hasani: Formal analysis, methodology, validation. Mawada Abdellatif: Formal analysis, methodology, validation. Iacopo Carnacina: Formal analysis, methodology, validation. Rayan G. Thannoun: Data curation, formal analysis, methodology, validation. Manal Sh. Al-Kubaisi: Formal analysis, methodology, validation. Sama Al-Maarofi: Formal analysis, methodology, validation.Corresponding authorCorrespondence to
Nadhir Al-Ansari.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights 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 articleMaaroof, B.F., Kareem, H.H., Al-Zubaydi, J.H. et al. Estimating soil erosion utilizing geospatial method and revised universal soil loss equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33403-xDownload citationReceived: 16 July 2025Accepted: 18 December 2025Published: 24 December 2025DOI: https://doi.org/10.1038/s41598-025-33403-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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsGeohazardsSoil degradationGISRUSLEAbu ghraibat watershed More

AbstractChelonid exploitation – including tortoises and freshwater turtles – has been increasingly recognised as a significant element of Palaeolithic subsistence in the Mediterranean and Iberian Peninsula. This study offers an experimental assessment of fire’s role in processing these reptiles, contrasting raw and roasted specimens to evaluate impacts on butchery efficiency, surface modifications, skeletal representation and lithic use-wear. The roasting process markedly reduced disarticulation effort and time, irrespective of the operator’s experience. Cut marks and percussion traces were more frequent in raw-processed individuals, while burnt specimens displayed extensive thermal damage, particularly on carapace plates. However, Fourier-Transform Infrared Spectroscopy (FTIR) revealed limited diagnostic potential for low-intensity thermal exposure. Conversely, lithic tools used in processing exhibited macroscopic edge damage and minor polishes, paralleling wear patterns documented in the butchery of other small fauna. These results align with archaeological evidence from multiple Iberian and Mediterranean sites, suggesting a culturally structured practice of in-shell roasting and anatomical disarticulation. The finds highlight fire’s role in labour optimisation and knowledge transmission, supporting broader discussions on small game exploitation and cognitive planning in early human behaviour.

Data availability

The datasets generated during and/or analysed during the current study are available in the CORA repository and can be accessed through the following links https:/dataverse.csuc.cat/dataset.xhtml?persistentId = doi%3A10.34810%2Fdata2413 and https:/dataverse.csuc.cat/dataset.xhtml?persistentId = doi%3A10.34810%2Fdata2412 Our study does not involve human participants in the usual sense. All individuals who appear in the images are the authors of the study and no other identifiable persons or patient data are included. For this reason, we consider a formal informed-consent statement from study participants not applicable. All authors explicitly agree to the publication of these images and any associated information in an online open-access format.
ReferencesBiton, R., Sharon, G., Oron, M., Steiner, T. & Rabinovich, R. Freshwater turtle or tortoise? The exploitation of testudines at the Mousterian site of Nahal mahanayeem outlet, hula valley, israel. J. Archaeol. Sci. Rep. 14, 409–419. https://doi.org/10.1016/j.jasrep.2017.05.058 (2017).
Google Scholar 
Blasco, R. et al. The earliest evidence for human consumption of tortoises in the european early pleistocene from sima del elefante, sierra de atapuerca, spain. J. Hum. Evol. 61, 503–509. https://doi.org/10.1016/j.jhevol.2011.06.002 (2011).
Google Scholar 
Boneta I. Chelonians in the Iberian Peninsula Archaeological Record: Approximation to its study from the ensemble of the Chalcolithic site of Camino de las Yeseras. Unpublished PhD thesis submitted to Universidad Autónoma de Madrid, (Spain, 2022).Martin, L., Edwards, Y. & Garrard, A. Broad spectrum or specialised activity? Birds and tortoises at the epipalaeolithic site of wadi jilat 22 in the eastern jordan steppe. Antiquity 87, 649–665. https://doi.org/10.1017/S0003598X00049371 (2013).
Google Scholar 
Morales, J. V. & Sanchis, A. The quaternary fossil record of the genus Testudo in the Iberian Peninsula. Archaeological implications and diachronic distribution in the western Mediterranean. J. Archaeol. Sci. 36, 1152–1162. https://doi.org/10.1016/j.jas.2008.12.019 (2009).
Google Scholar 
Moya, R., Sanchis, A. & Fernández-López, J. Freshwater turtles and mesolithic human subsistence: Taxonomic and taphonomic study of the assemblages from El Collado (Oliva, Eastern Iberian Peninsula). J. Archaeol. Sci. Rep. 66, 105234. https://doi.org/10.1016/j.jasrep.2025.105234 (2025).
Google Scholar 
Real, C. et al. Abrigo de la quebrada level IV (Valencia, Spain): Interpreting a middle palaeolithic palimpsest from a zooarchaeological and lithic perspective. J. Paleolit. Archaeol. 3, 187–224. https://doi.org/10.1007/s41982-018-0012-z (2020).
Google Scholar 
Real, C. et al. Estudio de la fauna del nivel IV del Abrigo de la Quebrada y su aportación al conocimiento de la economía y el comportamiento humano en el Paleolítico medio de la vertiente Mediterránea Ibérica. Spal 28(2), 17–49. https://doi.org/10.12795/spal.2019.i28.13 (2019).
Google Scholar 
Sanchis, A., Morales, J.V., Pérez, L.J., Hernández, C.M., Galván, B. La tortuga mediterránea en yacimientos valencianos del Paleolítico medio: Distribución, origen de las acumulaciones y nuevos datos procedentes del Abric del Pastor (Alcoi, Alacant). In Preses petites i grups humans en el passat. II Jornades d’arqueozologia del Museu de Prehistòria de València. València: Museu de Prehistòria de València, pp. 97-120 (2015).Blasco, R. et al. Tortoises as a dietary supplement: A view from the middle pleistocene site of qesem cave, israel. Quatern. Sci. Rev. 133, 165–182. https://doi.org/10.1016/j.quascirev.2015.12.006 (2016).
Google Scholar 
Nabais, M. & Zilhão, J. The consumption of tortoise among Last Interglacial Iberian Neanderthals. Quatern. Sci. Rev. 217, 225–246. https://doi.org/10.1016/j.quascirev.2019.03.024 (2019).
Google Scholar 
Speth, J. D. & Tchernov, E. Middle Paleolithic Tortoise Use at Kebara Cave (Israel). J. Archaeol. Sci. 29, 471–483. https://doi.org/10.1006/jasc.2001.0740 (2002).
Google Scholar 
Stiner M. The Faunas of the Hayonim Cave Israel A 20.000-Year Record of Paleolithic Diet, Demography and Society. In Peabody Museum of Archaeology and Ethnology. (Harvard University, Cambridge, Massachusetts, 2005)Munro, N. D. & Grosman, L. Early evidence (ca. 12,000 B.P.) for feasting at a burial cave in Israel. Proc. Natl. Acad. Sci. U.S.A. 107, 15362–15366. https://doi.org/10.1073/pnas.1001809107 (2010).
Google Scholar 
Radu, V., Mărgărit, M., Voinea, V., Boroneant, A. & Dulama, I. D. Processing the Testudo carapace in prehistoric romania (8th and 5th millennia BC). Archaeol. Anthropol. Sci. 14, 60. https://doi.org/10.1007/s12520-022-01523-4 (2022).
Google Scholar 
Barkai, R., Rosell, J., Blasco, R. & Gopher, A. Fire for a reason: barbecue at middle pleistocene qesem Cave, israel. Curr. Anthropol. 58, S314–S328. https://doi.org/10.1086/691211 (2017).
Google Scholar 
Angelucci, D. E., Nabais, M. & Zilhão, J. Formation processes, fire use, and patterns of human occupation across the Middle Palaeolithic (MIS 5a–5b) of gruta da oliveira (Almonda karst system, Torres Novas, Portugal). PLoS ONE 18, e0292075. https://doi.org/10.1371/journal.pone.0292075 (2023).
Google Scholar 
MacDonald, K., Scherjon, F., Van Veen, E., Vaesen, K. & Roebroeks, W. Middle Pleistocene fire use: The first signal of widespread cultural diffusion in human evolution. Proc. Natl. Acad. Sci. U.S.A. 118, e2101108118. https://doi.org/10.1073/pnas.2101108118 (2021).
Google Scholar 
Roebroeks, W. & Villa, P. On the earliest evidence for habitual use of fire in Europe. Proc. Natl. Acad. Sci. U.S.A. 108, 5209–5214. https://doi.org/10.1073/pnas.1018116108 (2011).
Google Scholar 
Blasco, R. Human consumption of tortoises at Level IV of Bolomor Cave (Valencia, Spain). J. Archaeol. Sci. 35, 2839–2848. https://doi.org/10.1016/j.jas.2008.05.013 (2008).
Google Scholar 
Boneta, I., Pérez-García, A. & Liesau, C. Chelonians from the middle palaeolithic site of mealhada (Coimbra, Portugal): An update. Diversity 15, 243. https://doi.org/10.3390/d15020243 (2023).
Google Scholar 
Rhodin, A.G.J., Iverson, J.B., Bour, R., Fritz, U., Georges, A., Shaffer, H.B., van Dijk P.P. Turtles of the world: Annotated checklist and atlas of taxonomy, synonymy, distribution, and conservation status. In Conservation Biology of Freshwater Turtles and Tortoises: A Compilation Project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group Chelonian Research Monographs 9th edn, Vol. 8 (Eds. Rhodin, A.G.J., Iverson, J.B., van Dijk, P.P., Stanford, C.B., Goode, E.V., Buhlmann, K.A., & Mittermeier, R.A.) 1–472 (2021).Nicholson, R. A morphological investigation of burnt animal bone and an evaluation of its utility in archaeology. J. Archaeol. Sci. 20, 411–428. https://doi.org/10.1006/jasc.1993.1025 (1993).
Google Scholar 
Shipman, P., Foster, G. & Schoeninger, M. Burnt bones and teeth: An experimental study of color, morphology, crystal structure and shrinkage. J. Archaeol. Sci. 11, 307–325. https://doi.org/10.1016/0305-4403(84)90013-X (1984).
Google Scholar 
Simonsen, K. P., Rasmussen, A. R., Mathisen, P., Petersen, H. & Borup, F. A fast preparation of skeletal materials using enzyme maceration. J. Forensic Sci. 56, 480–484. https://doi.org/10.1111/j.1556-4029.2010.01668.x (2011).
Google Scholar 
Kamminga, J. The Nature of Use Polish and Abrasive Smoothing on Stone Tools. In Lithic Use-wear Analysis (ed. Hayden, B.) 143–158 (Academic Press, 1979).
Google Scholar 
Keeley, L. H. Experimental Determination of Stone Tool Uses (University of Chicago Press, 1980).
Google Scholar 
Plisson, H. Etude fonctionnelle d’outillages lithiques prehistoriques par l’analyse des micro-usures: Recherche méthodologique et archéologique. PhD thesis, Université Paris I (1985).Tringham, R., Cooper, G., Odell, G., Voytek, B. & Whitman, A. Experimentation in the formation of edge damage: A new approach to lithic analysis. J. Field Archeol. 1, 171–196. https://doi.org/10.1179/jfa.1974.1.1-2.171 (1974).
Google Scholar 
Andrefsky W. Lithics: Macroscopic approaches to analysis. In 2nd edition (Cambridge University Press, 2005).Claud, E. et al. Le référentiel des outils lithiques. Palethnologie. https://doi.org/10.4000/palethnologie.5142 (2019).
Google Scholar 
Claud, E. et al. The practices used by the neanderthals in the acquisition and exploitation of plant and animal resources and the function of the sites studied: Summary and discussion. Palethnologie https://doi.org/10.4000/palethnologie.4179 (2019).
Google Scholar 
McPherron, S. et al. An experimental assessment of the influences on edge damage to lithic artifacts: a consideration of edge angle, substrate grain size, raw material properties, and exposed face. J. Archaeol. Sci. 49, 70–82. https://doi.org/10.1016/j.jas.2014.04.003 (2014).
Google Scholar 
González Urquijo, J. E. & Ibáñez Estévez, J. J. Metodologia de análisis functional de instrumentos tallados en sílex (Universidad de Deusto, 1994).
Google Scholar 
Semenov, S. A. Prehistoric Technology: An Experimental Study of the Oldest Tools and Artefacts from Traces of Manufacture and Wear (Cory, Adams & Mackay, London, 1964).
Google Scholar 
Costamagno, S., Claud, E., Thiébaut, C., Chacón, M. & Soulier, M. C. L’exploitation des ressources végétales et animales au paléolithique: quels outils méthodologiques pour quelles questions?. Palethnologie https://doi.org/10.4000/palethnologie.3866 (2019).
Google Scholar 
Greiner, M. et al. Bone incineration: An experimental study on mineral structure, colour and crystalline state. J. Archaeol. Sci. Rep. 25, 507–518. https://doi.org/10.1016/j.jasrep.2019.05.009 (2019).
Google Scholar 
Lebon, M. et al. New parameters for the characterization of diagenetic alterations and heat-induced changes of fossil bone mineral using Fourier transform infrared spectrometry. J. Archaeol. Sci. 37, 2265–2276. https://doi.org/10.1016/j.jas.2010.03.024 (2010).
Google Scholar 
Jia, X. & Sit, M. M. Laminated tortoise scepter. Gems Gemology 52, 419–420 (2016).
Google Scholar 
Alashwal, B.Y., Gupta, A., Husain, M.S.B., Characterization of dehydrated keratin protein extracted from chicken feather. In IOP Conference Series: Materials Science and Engeneering, 702 012033 https://doi.org/10.1088/1757-899X/702/1/012033 (2019).Hainschwang, T. & Leggio, L. The characterization of tortoise shell and its imitations. Gem. Gemol. 42, 36–52 (2006).
Google Scholar 
Hua, K. et al. Assessment of the defatting efficacy of mechanical and chemical treatment for allograft cancellous bone and its effects on biomechanics properties of bone. Orthop. Surg. 12, 617–630. https://doi.org/10.1111/os.12639 (2020).
Google Scholar 
Mattiello, S. et al. Physico-Chemical characterization of keratin from wool and chicken feathers extracted using refined chemical methods. Polymers (Basel) 15, 181. https://doi.org/10.3390/polym15010181 (2022).
Google Scholar 
Scaggion, C. et al. An FTIR-based model for the diagenetic alteration of archaeological bones. J. Archaeol. Sci. 161, 105900. https://doi.org/10.1016/j.jas.2023.105900 (2024).
Google Scholar 
Koon, H. E. C., Nicholson, R. A. & Collins, M. J. A practical approach to the identification of low temperature heated bone using TEM. J. Archaeol. Sci. 30, 1393–1399. https://doi.org/10.1016/S0305-4403(03)00034-7 (2003).
Google Scholar 
Mamede, A. et al. The potential of bioapatite hydroxyls for research on archaeological burned bone. Anal. Chem. https://doi.org/10.1021/acs.analchem.8b02868 (2018).
Google Scholar 
Alibardi, L. & Toni, M. Skin structure and cornification proteins in the soft-shelled turtle Trionyx spiniferus. Zoology (Jena) 109, 182–195. https://doi.org/10.1016/j.zool.2005.11.005 (2006).
Google Scholar 
Rosa, J. et al. The effects of exogenous substances on the color of heated bones. Am. J. Biol. Anthropol. 184, e24905. https://doi.org/10.1002/ajpa.24905 (2024).
Google Scholar 
Asaikkutti, A., Bhavan, P. S., Vimala, K., Karthik, M. & Cheruparambath, P. Dietary supplementation of green synthesized manganese-oxide nanoparticles and its effect on growth performance, muscle composition and digestive enzyme activities of the giant freshwater prawn Macrobrachium rosenbergii. J. Trace Elem. Med. Biol. 35, 7–17. https://doi.org/10.1016/j.jtemb.2016.01.005 (2016).
Google Scholar 
Meza, E., Ferreira, L M. Agencia Indígena y Colonialismo Una arqueología de contacto sobre la producción de aceite de tortuga en el Orinoco Medio, Venezuela (siglos XVIII y XIX). Amazônica: Revista de Antropologia 7 377 402 (2015).Guevara Chumacero, M., Pichardo Fragoso, A. & Martínez, Cornelio M. La tortuga en tabasco: comida, identidad y representación. Estud. De Cultura Maya 49(97), 122. https://doi.org/10.19130/iifl.ecm.2017.49.758 (2016).
Google Scholar 
Ferronato, B. & Georges, A. Distribution of freshwater turtle rock art and archaeological sites in australia: A glimpse into aboriginal use of chelonians. Herpetol. Conserv. Biol. 18, 374–391 (2023).
Google Scholar 
Sudiana, I.G. N., Ardika, I. W., Parimartha, I. G., Titib, I. M. Exploitation and protection of turtles at Serangan and Tanjung Benoa villages South Bali in the perspective of cultural studies. E-J. Cult. Stud. ISSN 2338–2449. Available at: https://ojs.unud.ac.id/index.php/ecs/article/view/3577 (2012).Susilo, E., Isdianto, A., Parawangsa, I. N. Y., Fathah, A. L. & Putri, B. M. Balancing tradition and conservation: The use of turtles in Balinese ceremonies and its environmental implications. Int. J. Environ. Impact. 7, 233–243. https://doi.org/10.18280/ijei.070208 (2024).
Google Scholar 
Charnov, E. L. Optimal foraging, the marginal value theorem. Theor. Popul. Biol. 9, 129–136. https://doi.org/10.1016/0040-5809(76)90040-X (1976).
Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory Monographies in Behaviour and Ecology (Princeton University Press, 1987).
Google Scholar 
Stiner, M. Thirty years on the “Broad Spectrum Revolution” and paleolithic demography. PNAS 98, 6993–6996. https://doi.org/10.1073/pnas.121176198 (2001).
Google Scholar 
Stiner, M. & Munro, N. Approaches to prehistoric diet breadth, demography, and prey ranking systems in time and space. J. Archaeol. Method. Theor. 9, 181–214. https://doi.org/10.1023/A:1016530308865 (2002).
Google Scholar 
Stiner, M. C., Munro, N. D., Surovell, T. A., Tchernov, E. & Bar-Yosef, O. Paleolithic population growth pulses evidenced by small animal exploitation. Science 283, 190–194. https://doi.org/10.1126/science.283.5399.190 (1999).
Google Scholar 
Laland, K., Matthews, B. & Feldman, M. W. Ahn introduction to niche construction theory. Evol. Ecol. 30, 191–202. https://doi.org/10.1007/s10682-016-9821-z (2016).
Google Scholar 
Odling-Smee, F. J., Laland, K. N., Feldman, M. W., Niche construction: The neglected process in evolution. In Monographies in Population Biology Vol. 37 (Princeton University Press 2003).Zeder, M. The broad spectrum revolution at 40: Resource diversity, intensification, and an alternative to optimal foraging explanations. J. Anthropol. Archaeol. 31, 241–264. https://doi.org/10.1016/j.jaa.2012.03.003 (2012).
Google Scholar 
Quintanilha, L. et al. Carcass traits of amazon turtles (Podocnemis Expansa) reared in captivity in Brazil. Int. J. Plant Anim. Environ. Sci. 3, 60–67 (2013).
Google Scholar 
Sorensen, A. C., Claud, E. & Soressi, M. Neanderal fire-making technology inferred from microwear analysis. Sci. Rep. 8, 10065. https://doi.org/10.1038/s41598-018-28342-9 (2018).
Google Scholar 
Stout, D. & Khreisheh, N. Skill learning and human brain evolution: An experimental approach. Camb. Archaeol. J. 25, 867–875. https://doi.org/10.1017/S0959774315000359 (2015).
Google Scholar 
Gowlett, J. The discovery of fire by humans: A long and convoluted process. Philos. Trans. Royal Soc. B: Biol. Sci. https://doi.org/10.1098/rstb.2015.0164 (2016).
Google Scholar 
Lew-Levy, S., Reckin, R., Lavi, N., Cristóbal-Azkarate, J. & Ellis-Davies, K. How do hunter-gatherer children learn subsistence skills? A meta-ethnographic review. Hum. Nat. 28, 367–394. https://doi.org/10.1007/s12110-017-9302-2 (2017).
Google Scholar 
MacDonald, K. Cross-cultural comparison of learning in human hunting. Hum. Nat. 18, 386–402. https://doi.org/10.1007/s12110-007-9019-8 (2007).
Google Scholar 
Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. & Holekamp, K. Brain size predicts problem-solving ability in mammalian carnivores. Proc. Natl. Acad. Sci. 113, 2532–2537. https://doi.org/10.1073/pnas.1505913113 (2016).
Google Scholar 
Download referencesAcknowledgementsWe acknowledge the use of the Vertebrate Reference Collection (Osteoteca) of the Archaeosciences Laboratory of the Património Cultural I.P, in Lisbon, Portugal. We also extend our gratitude to the PRISC infrastructure (Portuguese Research Infrastructure of Scientific Collections) for their support.FundingFinancial support has been provided by the research project “PALAEO.WEST.IBERIA – Contrasting Dietary Patterns and Adaptive Responses among Modern Humans, Neanderthals and Pre-Neanderthals in the Atlantic Façade of Iberia” funded through the Fundação para a Ciência e a Tecnologia (FCT) Scientific Research and Technological Development (IC&DT) call across all scientific domains, Call Reference MPr-2023–12, project number 2023.16301.ICDT. This research was also funded by Mariana Nabais’ postdoc contract for project “SMALLPREY – Neanderthal and Anatomically Modern Human interactions with small prey in Atlantic Iberia throughout the changing environments of the Pleistocene”, as part of the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034349, and from the State Research Agency of the Spanish Ministry of Science and Innovation through the Program Maria de Maeztu Unit of Excellence (CEX2019-000945-M). Additional support has been given by Portuguese funds through FCT – Fundação para a Ciência e a Tecnologia in the framework of the project “UID/00698/2025 (doi.org/https://doi.org/10.54499/UID/00698/2025): Centre for Archaeology. University of Lisbon”. Ruth Blasco develops her work within the project PID 2022-138590NB-C41 funded by MCIN/AEI/https://doi.org/10.13039/501100011033/FEDER, UE and the projects 2021-SGR-01237 and CLT009/22/000045 funded by the Generalitat de Catalunya. Ruth Blasco is supported by a Ramon y Cajal research contract by the Spanish Ministry of Science and Innovation (RYC 2019–026386-I). Valentina Lubrano is beneficiary of a FCT Doctoral Grant (reference: 2021.05263.BD). Anna Rufà is currently a beneficiary of a CEEC – 3rd Edition research contract promoted by the Portuguese FCT (reference: 2020. 00877.CEECIND) and her research is also funded in part by the Fundação para a Ciência e Tecnologia, I.P. (FCT, https://ror.org/00snfqn589) under the grant UID/0421. Mariana Nabais, Ruth Blasco, Valentina Lubrano and Anna Rufà also participate in the Spanish MICINN project NEANDIVERSITY2, PID2022-138590NB-C41.Author informationAuthors and AffiliationsUNIARQ – Centre for Archaeology, University of Lisbon, Lisbon, PortugalMariana Nabais & Marina IgrejaSWAD – South-West Archaeology Digs, Moura, PortugalMariana NabaisIPHES-CERCA – Catalan Institute of Human Palaeoecology and Social Evolution, Tarragona, SpainRuth BlascoDepartment of History and Art History, Área de Prehistòria, Rovira i Virgili University, Tarragona, SpainRuth BlascoAutónoma University of Madrid, Madrid, SpainIratxe BonetaArqueozoo S.L, Madrid, SpainIratxe BonetaLARC – Archaeosciences Laboratory (LARC), Património Cultural I.P, Lisbon, PortugalDavid Gonçalves & Marina IgrejaResearch Centre for Anthropology and Health, Department of Life Sciences, University of Coimbra, Coimbra, PortugalDavid GonçalvesCentre for Functional Ecology, Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, Coimbra, PortugalDavid GonçalvesCIBIO – Research Centre in Biodiversity and Genetic Resources, University of Porto, Porto, PortugalMarina IgrejaICArEHB – Interdisciplinary Center for Archaeology and the Evolution of Human Behaviour, University of Algarve, Faro, PortugalValentina Lubrano & Anna RufàPACEA UMR 5199, University of Bordeaux, Bordeaux, FranceAnna RufàAuthorsMariana NabaisView author publicationsSearch author on:PubMed Google ScholarRuth BlascoView author publicationsSearch author on:PubMed Google ScholarIratxe BonetaView author publicationsSearch author on:PubMed Google ScholarDavid GonçalvesView author publicationsSearch author on:PubMed Google ScholarMarina IgrejaView author publicationsSearch author on:PubMed Google ScholarValentina LubranoView author publicationsSearch author on:PubMed Google ScholarAnna RufàView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualisation: MN Data curation: MN Formal analysis: MN, MI, DG Funding acquisition: MN, AR, RB Investigation: MN, MI, DG Methodology: MN, IB, MI, DG Resources: MN, AR, MI, DG Visualisation: MN, MI, DG Writing – original draft: MN, MI, DG Writing – review & editing: MN, AR, IB, MI, VL, RB, DG.Corresponding authorCorrespondence to
Mariana Nabais.Ethics declarations

Competing interests
The authors declare no competing interests.

Informed consent
Our study does not involve human participants in the usual sense. All individuals who appear in the images are the authors of the study and no other identifiable persons or patient data are included. For this reason, we consider a formal informed-consent statement from study participants not applicable. All authors explicitly agree to the publication of these images and any associated information in an online open-access format.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Rights 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 articleNabais, M., Blasco, R., Boneta, I. et al. Experimental analysis of roasted and raw turtle butchery and implications for early human cognition and behaviour.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31738-zDownload citationReceived: 03 July 2025Accepted: 04 December 2025Published: 24 December 2025DOI: https://doi.org/10.1038/s41598-025-31738-zShare 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsExperimental archaeologyTaphonomySubsistence strategiesLithic use-wearFTIRChelonids More

AbstractWithin the Tabei Uplift of the Tarim Basin, Ordovician reservoirs in both the northern Halahatang (N-Halahatang) and western Lunnan (W-Lunnan) areas experienced extensive biodegradation during the Late Hercynian (Permian). Subsequent Himalayan (Neogene–Quaternary) tectonism induced divergent burial-thermal histories: the N-Halahatang reservoirs underwent intensive maturation (> 6,500 m depth; 1.02–1.22% Ro), while the W-Lunnan reservoirs experienced milder maturation (< 5,800 m depth; 0.70–0.85% Ro). Despite similar δ13Coil values indicating genetic affinity, the relatively deeply buried biodegraded oils from the N-Halahatang area contain abundant C6–C8 light hydrocarbons (LHs), while the biodegraded oils from the W-Lunnan area exhibit only trace amounts of C6–C8 LHs. To elucidate the evolution of LHs compositions and fingerprints in biodegraded oils under thermal maturation, and to determine whether the more enriched C6–C8 LHs in the N-Halahatang oils can be attributed to enhanced burial-thermal maturation, two relatively shallower-burial biodegraded oils (Well LG40: slight to moderate biodegradation‌; Well LG7: heavy to severe biodegradation) from the W-Lunnan area were artificially pyrolyzed to various maturities. Subsequently, LH parameters of the pyrolyzed oils were compared with those of the naturally matured, deeply buried oils (heavy to severe biodegradation) from the N-Halahatang area. The results indicated that both biodegraded oils generated C6–C8 LHs through thermal cracking, and the more severely biodegraded oil (Well LG7) exhibited a lower LH maximum yield than that from Well LG40. Certain parameters for organic matter type classification (n-C7–DMCP–MCH and 3RP–5RP–6RP diagrams) generally remained applicable during thermal maturation, whereas most parameters for secondary alteration identification and maturity assessment were significantly compromised. Additionally, LH parameters of the N-Halahatang oils (1.02–1.22% Ro) matched those of the LG7 pyrolyzed oils at EasyRo = 1.00–1.20%, confirming that the enriched C6–C8 LHs in the N-Halahatang oils can be attributed to cracking of biodegraded oils (with ‌biodegradation levels equivalent to Well LG7‌) under intense burial-thermal maturation. Furthermore, the potential C6–C13 LHs derived from biodegraded oil cracking constitute 11–16 wt% of N-Halahatang’s liquid hydrocarbon resources.

Data availability

Datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
ReferencesChang, X. C., Wang, T. G., Li, Q. M., Cheng, B. & Tao, X. W. Geochemistry and possible origin of petroleum in palaeozoic reservoirs from Halahatang depression. J. Asian Earth Sci. 74, 129–141. https://doi.org/10.1016/j.jseaes.2013.03.024 (2013).
Google Scholar 
Chang, X. C., Wang, T. G., Li, Q. M. & Ou, G. X. Charging of ordovician reservoirs in the Halahatang depression (Tarim Basin, NW China) determined by oil geochemistry. J. Petrol. Geol. 36, 383–398. https://doi.org/10.1111/jpg.12562 (2013).
Google Scholar 
Wang, Y. F. et al. Phase change of the ordovician hydrocarbon in the Tarim basin: A case study from the Halahatang-Shunbei area. Open. Geosci. 16, 20220629. https://doi.org/10.1515/geo-2022-0629 (2024).
Google Scholar 
Yang, P. et al. Petroleum accumulation history of deeply buried carbonate reservoirs in the Northern Tarim Basin, Northwestern China. AAPG Bull. 108, 1193–1229. https://doi.org/10.1306/06212321210 (2024).
Google Scholar 
Zhang, S. C. et al. Geochemistry of paleozoic marine oils from the Tarim Basin, NW China. Part 4: paleobiodegradation and oil charge mixing. Org. Geochem. 67, 41–57. https://doi.org/10.1016/j.orggeochem.2013.12.008 (2014).
Google Scholar 
Li, M. J. et al. Paleo-heat flow evolution of the Tabei uplift in Tarim Basin, Northwest China. J. Asian Earth Sci. 37, 52–66. https://doi.org/10.1016/j.jseaes.2009.07.007 (2010).
Google Scholar 
Wang, T. G. et al. Stratigraphic thermohistory and its implications for regional geoevolution in the Tarim Basin, NW China. Sci. China Earth Sci. 53, 1495–1505. https://doi.org/10.1007/s11430-010-4069-x (2010).
Google Scholar 
He, D. F., Jia, C. Z., Liu, S. B., Pan, W. Q. & Wang, S. J. Dynamics for multistage pool formation of Lunnan low uplift in Tarim basin. Chin. Sci. Bull. 47, 128–138. https://doi.org/10.1007/bf02902829 (2002).
Google Scholar 
Cai, Z. X., Wu, N., Yang, H. J., Gu, Q. Y. & Han, J. F. Mechanism of evaporative fractionation in condensate gas reservoirs in Lunnan low salient. Natur. Gas Ind. 29, 21–24 (2009).
Google Scholar 
Wu, N. et al. Hydrocarbon charging of the ordovician reservoirs in Tahe-Lunnan area, China. Sci. China Earth Sci. 56, 763–772. https://doi.org/10.1007/s11430-013-4598-1 (2013).
Google Scholar 
Zhu, G. Y., Liu, X. W., Zhu, Y. F., Su, J. & Wang, K. The characteristics and the accumulation mechanism of complex reservoirs in the Hanicatam area, Tarim basin. Bull. Mineral. Petrol. Geochem. 32, 231–242 (2013).
Google Scholar 
Zhu, G. Y., Wang, M. & Zhang, T. W. Identification of polycyclic sulfides hexahydrodibenzothiophenes and their implications for heavy oil accumulation in ultra-deep strata in Tarim basin. Mar. Pet. Geol. 78, 439–447. https://doi.org/10.1016/j.marpetgeo.2016.09.027 (2016).
Google Scholar 
Zhu, G. Y. et al. Alteration and multi-stage accumulation of oil and gas in the ordovician of the Tabei Uplift, Tarim Basin, NW china: implications for genetic origin of the diverse hydrocarbons. Mar. Pet. Geol. 46, 234–250. https://doi.org/10.1016/j.marpetgeo.2013.06.007 (2013).
Google Scholar 
Zhu, G. Y. et al. The complexity, secondary geochemical process, genetic mechanism and distribution prediction of deep marine oil and gas in the Tarim Basin, China. Earth Sci. Rev. 198, 102930. https://doi.org/10.1016/j.earscirev.2019.102930 (2019).
Google Scholar 
Chang, X. C., Wang, T. G., Li, Q. M., Cheng, B. & Zhang, L. P. Maturity assessment of severely biodegraded marine oils from the Halahatang depression in Tarim basin. Energy Explor. Exploit. 30, 331–350. https://doi.org/10.1260/0144-5987.30.3.331 (2012).
Google Scholar 
Li, M. J. et al. Practical application of reservoir geochemistry in petroleum exploration: case study from a paleozoic carbonate reservoir in the Tarim basin (Northwestern China). Energy Fuels. 32, 1230–1241. https://doi.org/10.1021/acs.energyfuels.7b03186 (2018).
Google Scholar 
Wang, J. B., Guo, R. T., Xiao, X. M., Liu, Z. F. & Shen, J. G. Timing and phases of hydrocarbon migration and accumulation of the formation of oil and gas pools in Lunnan low uplift of Tarim basin. Acta Sedimentol. Sin. 20, 320–325 (2002).
Google Scholar 
Zhao, W. Z., Zhu, G. Y., Su, J., Yang, H. J. & Zhu, Y. F. Study on multi stage filling and accumulation model of marine oil and gas reservoirs: A case study of the Lungudong area, Tarim basin. Acta Petrol. Sin. 28, 709–721 (2012).
Google Scholar 
Cheng, B., Wang, T. G. & Chang, X. C. Application of C5–C7 light hydrocarbons in geochemical studies: A case study of ordovician crude oils from the Halahatang depression, Tabei uplift. Nat. Gas Geosci. 24, 398–405 (2013).
Google Scholar 
Wang, Y. P., Chang, X. C., Cheng, B. & Shi, S. B. Comparison of C5-C7 light hydrocarbons in Halahatang ordovician oil analyzed by comprehensive 2-D and conventional gas chromatography. Nat. Gas Geosci. 26, 1814–1822 (2015).
Google Scholar 
Wu, X. Q., Liu, Q. Y., Tao, X. W. & Hu, G. Y. Geochemical characteristics of natural gas from Halahatang Sag in the Tarim basin. Geochimica 43, 477–488. https://doi.org/10.19700/j.0379-1726.2014.05.006 (2014).
Google Scholar 
Tissot, B. P. & Welte, D. H. Petroleum Formation and Occurence (Springer-Verlag, 1984).Hossain, M. A., Suzuki, N., Matsumoto, K., Sakamoto, R. & Takeda, N. In-reservoir fractionation and the accumulation of oil and condensates in the surma Basin, NE Bangladesh. J. Petrol. Geol. 37, 269–286. https://doi.org/10.1111/jpg.12583 (2014).
Google Scholar 
Thompson, K. F. M. Fractionated aromatic petroleums and the generation of gas-condensates. Org. Geochem. 11, 573–590. https://doi.org/10.1016/0146-6380(87)90011-8 (1987).
Google Scholar 
Thompson, K. F. M. Gas-condensate migration and oil fractionation in deltaic systems. Mar. Pet. Geol. 5, 237–246. https://doi.org/10.1016/0264-8172(88)90004-9 (1988).
Google Scholar 
Zhang, S. C. et al. Geochemistry of palaeozoic marine petroleum from the Tarim Basin, NW china: part 3. Thermal cracking of liquid hydrocarbons and gas washing as the major mechanisms for deep gas condensate accumulations. Org. Geochem. 42, 1394–1410. https://doi.org/10.1016/j.orggeochem.2011.08.013 (2011).
Google Scholar 
Zhu, X. J. et al. Effects of evaporative fractionation on diamondoid hydrocarbons in condensates from the Xihu Sag, East China sea shelf basin. Mar. Pet. Geol. 126, 104929. https://doi.org/10.1016/j.marpetgeo.2021.104929 (2021).
Google Scholar 
Cai, J., Lü, X. X. & Li, B. Y. Tectonic fracture and its significance in hydrocarbon migration and accumulation: a case study on middle and lower ordovician in Tabei uplift of Tarim Basin, NW China. Geol. J. 51, 572–583. https://doi.org/10.1002/gj.2656 (2016).
Google Scholar 
Chen, J. Q., Ma, K. Y., Pang, X. Q. & Yang, H. J. Secondary migration of hydrocarbons in ordovician carbonate reservoirs in the Lunnan area, Tarim basin. J. Petrol. Sci. Eng. 188, 106962. https://doi.org/10.1016/j.petrol.2020.106962 (2020).
Google Scholar 
Li, S. M., Zhang, B. S., Xing, L. T., Sun, H. & Yuan, X. Y. Geochemical feathers of deep hydrocarbon migration and accumulation in Halahatang-Yingmaili area of the Northern Tarim basin. Acta Petrol. Sin. 36, 92–101. https://doi.org/10.7623/syxb2015S2008 (2015).
Google Scholar 
Hill, R. J., Tang, Y. C. & Kaplan, I. R. Insights into oil cracking based on laboratory experiments. Org. Geochem. 34, 1651–1672. https://doi.org/10.1016/s0146-6380(03)00173-6 (2003).
Google Scholar 
Peters, K. E. & Moldowan, J. M. The Biomarker Guide: Interpreting Molecular Fossils in Petroleum and Ancient Sediments (Prentice Hall, 1993).Jiang, B., Liu, W. M., Liao, Y. H. & Peng, P. A. Molecular transformations of heteroatomic organic compounds in crude oils caused by biodegradation and subsequent thermal maturation: insights from ESI FT-ICR MS. Org. Geochem. 188, 104741. https://doi.org/10.1016/j.orggeochem.2024.104741 (2024).
Google Scholar 
Liao, Y. H., Shi, Q., Hsu, C. S., Pan, Y. H. & Zhang, Y. H. Distribution of acids and nitrogen-containing compounds in biodegraded oils of the Liaohe basin by negative ion ESI FT-ICR MS. Org. Geochem. 47, 51–65. https://doi.org/10.1016/j.orggeochem.2012.03.006 (2012).
Google Scholar 
Pan, Y. H., Liao, Y. H. & Peng, X. Z. Variations in chemical and stable carbon isotopic compositions of Liaohe crude oil during aerobic biodegradation simulation. Geochimica 44, 581–589. https://doi.org/10.19700/j.0379-1726.2015.06.007 (2015).
Google Scholar 
Huang, Y. Y., Liao, Y. H., Xu, T., Wang, Y. P. & Peng, P. A. Characteristics of light hydrocarbons under the superimposed influence of biodegradation and subsequent thermal maturation. Org. Geochem. 177, 104557. https://doi.org/10.1016/j.orggeochem.2023.104557 (2023).
Google Scholar 
Liao, Y. H. et al. Superimposed secondary alteration of oil reservoirs. Part I: influence of biodegradation on the gas generation behavior of crude oils. Org. Geochem. 142, 103965. https://doi.org/10.1016/j.orggeochem.2019.103965 (2020).
Google Scholar 
Liu, W. M. et al. Superimposed secondary alteration of oil reservoirs. Part II: the characteristics of biomarkers under the superimposed influences of biodegradation and thermal alteration. Fuel 307, 121721. https://doi.org/10.1016/j.fuel.2021.121721 (2022).
Google Scholar 
Chai, Z. & Chen, Z. H. Biomarkers, light hydrocarbons, and diamondoids of petroleum in deep reservoirs of the Southeast Tabei Uplift, Tarim basin: implication for its origin, alteration, and charging direction. Mar. Pet. Geol. 147, 106019. https://doi.org/10.1016/j.marpetgeo.2022.106019 (2023).
Google Scholar 
Li, F. et al. The disputes on the source of paleozoic marine oil and gas and the determination of the cambrian system as the main source rocks in Tarim basin. Acta Petrol. Sin. 42, 1417–1436. https://doi.org/10.7623/syxb202111002 (2021).
Google Scholar 
Liu, Z. et al. The hydrocarbon generation process of the deeply buried cambrian yuertusi formation in the Tabei uplift, Tarim Basin, Northwestern china: constraints from calcite veins hosting oil inclusions in the source rock. Geol. Soc. Am. Bull. 136, 3810–3824. https://doi.org/10.1130/b37295.1 (2024).
Google Scholar 
Wu, L. et al. Long-lived paleo-uplift controls on Neoproterozoic-Cambrian black shales in the Tarim basin. Mar. Pet. Geol. 155, 106343. https://doi.org/10.1016/j.marpetgeo.2023.106343 (2023).
Google Scholar 
Wang, S. et al. Genetic mechanism of multiphase States of ordovician oil and gas reservoirs in Fuman oilfield, Tarim Basin, China. Mar. Pet. Geol. 157, 106449. https://doi.org/10.1016/j.marpetgeo.2023.106449 (2023).
Google Scholar 
Xiao, X. M., Liu, Z. F., Liu, D. H., Shen, J. G. & Fu, J. M. A new method to reconstruct hydrocarbon-generating histories of source rocks in a petroleum-bearing basin – the method of geological and geochemical sections. Chin. Sci. Bull. 45, 35–40. https://doi.org/10.1007/bf02893782 (2000).
Google Scholar 
Zhan, Z. W., Tian, Y. K., Zou, Y. R., Liao, Z. W. & Peng, P. A. De-convoluting crude oil mixtures from palaeozoic reservoirs in the Tabei Uplift, Tarim Basin, China. Org. Geochem. 97, 78–94. https://doi.org/10.1016/j.orggeochem.2016.04.004 (2016).
Google Scholar 
Tian, F. et al. Multiscale geological-geophysical characterization of the epigenic origin and deeply buried paleokarst system in Tahe Oilfield, Tarim basin. Mar. Pet. Geol. 102, 16–32. https://doi.org/10.1016/j.marpetgeo.2018.12.029 (2019).
Google Scholar 
Zhu, G. Y. et al. Hydrocarbon accumulation mechanisms and industrial exploration depth of large-area fracture-cavity carbonates in the Tarim Basin, Western China. J. Petrol. Sci. Eng. 133, 889–907. https://doi.org/10.1016/j.petrol.2015.03.014 (2015).
Google Scholar 
Zhu, Z. J. et al. Analysis of the filling patterns and reservoir development models of the ordovician paleokarst reservoirs in the Tahe oilfield. Mar. Pet. Geol. 161, 106690. https://doi.org/10.1016/j.marpetgeo.2024.106690 (2024).
Google Scholar 
Jacob, H. Classification, structure, genesis and practical importance of natural solid oil bitumen (‘migrabitumen’). Int. J. Coal Geol. 11, 65–79. https://doi.org/10.1016/0166-5162(89)90113-4 (1989).
Google Scholar 
Parnell, J., Monson, B. & Geng, A. Maturity and petrography of bitumens in the carboniferous of Ireland. Int. J. Coal Geol. 29, 23–38. https://doi.org/10.1016/0166-5162(95)00023-2 (1996).
Google Scholar 
Karweil, J. Die metamorphose der Kohlen vom standpunkt der physikalischen chemie. Z. Dtsch. Geol. Ges. 107, 132–139 (1956).
Google Scholar 
Liao, Y. H., Geng, A. S. & Huang, H. P. The influence of biodegradation on resins and asphaltenes in the Liaohe basin. Org. Geochem. 40, 312–320. https://doi.org/10.1016/j.orggeochem.2008.12.006 (2009).
Google Scholar 
Sweeney, J. J. & Burnham, A. K. Evaluation of a simple model of vitrinite reflectance based on chemical kinetics. AAPG Bull. 74, 1559–1570 (1990).
Google Scholar 
Huang, Y. Y. et al. Improved light hydrocarbons collection method for the pyrolysis of crude oil in gold tube closed system experiments. Org. Geochem. 168, 104432. https://doi.org/10.1016/j.orggeochem.2022.104432 (2022).
Google Scholar 
Odden, W., Patience, R. L. & van Graas, G. W. Application of light hydrocarbons (C4-C13) to oil/source rock correlations: a study of the light hydrocarbon compositions of source rocks and test fluids from offshore Mid-Norway. Org. Geochem. 28, 823–847. https://doi.org/10.1016/s0146-6380(98)00039-4 (1998).
Google Scholar 
Leythaeuser, D., Schaefer, R. G., Cornford, C. & Weiner, B. Generation and migration of light hydrocarbons (C2–C7) in sedimentary basins. Org. Geochem. 1, 191–204. https://doi.org/10.1016/0146-6380(79)90022-6 (1979).
Google Scholar 
Thompson, K. F. M. Classification and thermal history of petroleum based on light hydrocarbons. Geochim. Cosmochim. Acta. 47, 303–316. https://doi.org/10.1016/0016-7037(83)90143-6 (1983).
Google Scholar 
Dai, J. X. Identification of various alkane gases. Sci. China-Chem‌. 35, 1246–1257 (1992).
Google Scholar 
ten Haven, H. L. Applications and limitations of Mango’s light hydrocarbon parameters in petroleum correlation studies. Org. Geochem. 24, 957–976. https://doi.org/10.1016/s0146-6380(96)00091-5 (1996).
Google Scholar 
Mango, F. D. An invariance in the isoheptanes of petroleum. Science 237, 514–517. https://doi.org/10.1126/science.237.4814.514 (1987).
Google Scholar 
Mango, F. D. The origin of light cycloalkanes in petroleum. Geochim. Cosmochim. Acta. 54, 23–27. https://doi.org/10.1016/0016-7037(90)90191-m (1990).
Google Scholar 
Mango, F. D. The origin of light hydrocarbons in petroleum: A kinetic test of the steady-state catalytic hypothesis. Geochim. Cosmochim. Acta. 54, 1315–1323. https://doi.org/10.1016/0016-7037(90)90156-f (1990).
Google Scholar 
Mango, F. D. The origin of light hydrocarbons in petroleum: ring preference in the closure of carbocyclic rings. Geochim. Cosmochim. Acta. 58, 895–901. https://doi.org/10.1016/0016-7037(94)90513-4 (1994).
Google Scholar 
Mango, F. D. The light hydrocarbons in petroleum: a critical review. Org. Geochem. 26, 417–440. https://doi.org/10.1016/s0146-6380(97)00031-4 (1997).
Google Scholar 
Akinlua, A., Ajayi, T. R. & Adeleke, B. B. Niger delta oil geochemistry: insight from light hydrocarbons. J. Petrol. Sci. Eng. 50, 308–314. https://doi.org/10.1016/j.petrol.2005.12.003 (2006).
Google Scholar 
Obermajer, M., Osadetz, K. G., Fowler, M. G. & Snowdon, L. R. Light hydrocarbon (gasoline range) parameter refinement of biomarker-based oil-oil correlation studies: an example from Williston basin. Org. Geochem. 31, 959–976. https://doi.org/10.1016/s0146-6380(00)00114-5 (2000).
Google Scholar 
Smith, M. & Bend, S. Geochemical analysis and Familial association of red river and Winnipeg reservoired oils of the Williston Basin, Canada. Org. Geochem. 35, 443–452. https://doi.org/10.1016/j.orggeochem.2004.01.008 (2004).
Google Scholar 
Philippi, G. T. The deep subsurface temperature controlled origin of the gaseous and gasoline-range hydrocarbons of petroleum. Geochim. Cosmochim. Acta. 39, 1353–1373. https://doi.org/10.1016/0016-7037(75)90115-5 (1975).
Google Scholar 
Thompson, K. F. M. Light hydrocarbons in subsurface sediments. Geochim. Cosmochim. Acta. 43, 657–672. https://doi.org/10.1016/0016-7037(79)90251-5 (1979).
Google Scholar 
Mackenzie, A. S. & McKenzie, D. Isomerization and aromatization of hydrocarbons in sedimentary basins formed by extension. Geol. Mag. 120, 417–470. https://doi.org/10.1017/s0016756800027461 (1983).
Google Scholar 
Peters, K. E. & Moldowan, J. M. Effects of source, thermal maturity, and biodegradation on the distribution and isomerization of Homohopanes in petroleum. Org. Geochem. 17, 47–61. https://doi.org/10.1016/0146-6380(91)90039-m (1991).
Google Scholar 
Peters, K. E., Walters, C. C. & Moldowan, J. M. The Biomarker Guide (Cambridge University Press, 2005).van Graas, G. W. Biomarker maturity parameters for high maturities: calibration of the working range up to the oil/condensate threshold. Org. Geochem. 16, 1025–1032. https://doi.org/10.1016/0146-6380(90)90139-q (1990).
Google Scholar 
Kissin, Y. V. Catagenesis and composition of petroleum: origin of n-alkanes and isoalkanes in petroleum crudes. Geochim. Cosmochim. Acta. 51, 2445–2457. https://doi.org/10.1016/0016-7037(87)90296-1 (1987).
Google Scholar 
Behar, F., Lorant, F. & Mazeas, L. Elaboration of a new compositional kinetic schema for oil cracking. Org. Geochem. 39, 764–782. https://doi.org/10.1016/j.orggeochem.2008.03.007 (2008).
Google Scholar 
Chang, C. T., Lee, M. R., Lin, L. H. & Kuo, C. L. Application of C7 hydrocarbons technique to oil and condensate from type III organic matter in Northwestern Taiwan. Int. J. Coal Geol. 71, 103–114. https://doi.org/10.1016/j.coal.2006.06.011 (2007).
Google Scholar 
Li, F. L. et al. Fault system dynamics and their impact on ordovician carbonate karst reservoirs: outcrop analogs and 3D seismic analysis in the Tabei region, Tarim Basin, NW China. Mar. Pet. Geol. 167, 106923. https://doi.org/10.1016/j.marpetgeo.2024.106923 (2024).
Google Scholar 
Download referencesAcknowledgementsThe authors thank Dr. Jinzhong Liu, Mr. Yong Li, Dr. Zewen Liao, and Dr. Yankuan Tian for their assistance in laboratory analyses. The authors are also grateful to the anonymous reviewers for their constructive suggestions.FundingThis work was supported by the National Natural Science Foundation of China (Grant Nos. 42173056 and 42572184), the project Theory of Hydrocarbon Enrichment under Multi-Spheric Interactions of the Earth (Grant No. THEMSIE04010104). This is also a contribution to the Special Fund for the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA14010103).Author informationAuthors and AffiliationsState Key Laboratory of Deep Earth Processes and Resources, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, ChinaYuwei Yang, Yuhong Liao, Yueyi Huang, Bin Cheng, Huanyu Lin & Yunpeng WangState Key Laboratory of Advanced Environmental Technology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, ChinaYijun Zheng & Ping’An PengUniversity of Chinese Academy of Sciences, Yuquan Road, Beijing, 100049, ChinaYuwei Yang, Yuhong Liao, Yijun Zheng, Bin Cheng, Huanyu Lin, Yunpeng Wang & Ping’An PengCAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, ChinaYuwei Yang, Yuhong Liao, Yijun Zheng, Bin Cheng, Huanyu Lin, Yunpeng Wang & Ping’An PengBiogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu, 610041, ChinaYueyi HuangAuthorsYuwei YangView author publicationsSearch author on:PubMed Google ScholarYuhong LiaoView author publicationsSearch author on:PubMed Google ScholarYueyi HuangView author publicationsSearch author on:PubMed Google ScholarYijun ZhengView author publicationsSearch author on:PubMed Google ScholarBin ChengView author publicationsSearch author on:PubMed Google ScholarHuanyu LinView author publicationsSearch author on:PubMed Google ScholarYunpeng WangView author publicationsSearch author on:PubMed Google ScholarPing’An PengView author publicationsSearch author on:PubMed Google ScholarContributionsYuwei Yang: Investigation, Methodology, Formal analysis, Writing-Original Draft; Yuhong Liao: Supervision, Conceptualization, Funding acquisition, Validation, Writing-Reviewing and Editing; Yueyi Huang: Data Curation; Yijun Zheng: Writing-Reviewing and Editing; Bin Cheng: Resources; Huanyu Lin: Visualization; Yunpeng Wang: Project administration, Funding acquisition; Ping’An Peng: Funding acquisition.Corresponding authorCorrespondence to
Yuhong Liao.Ethics declarations

Competing interests
The authors declare no competing interests.

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 1Rights and permissions
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 articleYang, Y., Liao, Y., Huang, Y. et al. Thermal evolution of light hydrocarbon fingerprints in biodegraded oils from Ordovician reservoirs, Tabei Uplift, Tarim Basin.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33256-4Download citationReceived: 28 October 2025Accepted: 17 December 2025Published: 24 December 2025DOI: https://doi.org/10.1038/s41598-025-33256-4Share 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsBiodegradationBurial-thermal maturationLight hydrocarbonsOrdovician reservoirsTabei UpliftTarim Basin More

AbstractGardenia jasminoides, a widely distributed resource rich in Crocin, has generated substantial market demand due to its potential value as a saffron substitute. This necessitates the exploration of efficient and sustainable cultivation strategies to obtain target compounds for specific purposes. To enhance cultivation efficiency and secure supply chains, we integrated MaxEnt modeling, spatial interpolation, and geodetector analysis. This framework aimed to predict suitable habitats for G. jasminoides across China, map spatial variation in bioactive compounds including Crocin, Gardenia Yellow, and Geniposide, and identify environmental drivers influencing their distribution. MaxEnt achieved high predictive accuracy (AUC = 0.960), identifying Jiangxi, Zhejiang, and Guangdong as key high-suitability regions. Precipitation of the driest month and human population density emerged as dominant factors shaping species distribution. Spatial gradients revealed that Crocin and Gardenia Yellow decrease from southwest to northeast, whereas Geniposide exhibits latitudinal differentiation characterized by higher concentrations in northern regions. Geodetector analysis highlighted vegetation type as the primary driver of compound variation, with q values of 0.618 for Crocin, 0.606 for Gardenia Yellow, and 0.639 for Geniposide. These results indicate that the accumulation of target compounds is strictly modulated by ecological niches, where specific vegetation types drive metabolic differentiation through microclimate regulation and interspecific competition. Based on these findings, we advocate for an industry-oriented divergent cultivation strategy. Southwestern China should be prioritized for Crocin-rich germplasm to support the natural pigment industry, whereas northern regions are designated as premium zones for pharmaceutical-grade Geniposide sourcing. Furthermore, recognizing vegetation type as a critical driver facilitates the implementation of targeted habitat management techniques. These findings provide a direct guide for designating priority cultivation zones and optimizing harvest timing to maximize the yield of target compounds for specific industrial uses.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
ReferenceState Pharmacopoeia Committee. Pharmacopoeia of the People’s Republic of China (China Medical Science and Technology Press, 2020).
Google Scholar 
Tian, J., Qin, S., Han, J., Meng, J. & Liang, A. A review of the ethnopharmacology, phytochemistry, pharmacology and toxicology of Fructus Gardeniae (Zhi-zi). J. Ethnopharmacol. 289, 114984. https://doi.org/10.1016/j.jep.2022.114984 (2022).
Google Scholar 
Li, Z., Shen, J., Bi, W., He, C. & Peng, Y. Progress of research on the resources and utilization of Gardenia jasminoides in China. J. Chin. Med. Mater. 40, 498–503. https://doi.org/10.13863/j.issn1001-4454.2017.02.055 (2017).
Google Scholar 
Sommano, S. R., Suppakittpaisarn, P., Sringarm, K., Junmahasathien, T. & Ruksiriwanich, W. Recovery of crocins from floral tissue of Gardenia jasminoides ellis. Front. Nutr. 7, 106. https://doi.org/10.3389/fnut.2020.00106 (2020).
Google Scholar 
Liu, Q., Huang, L., Fu, C., Gu, Z. & Yang, C. Effects of environmental factors on crocin accumulation and related genes in Gardenia jasminoides. J. Chinese Med. Mater. 45, 516–523. https://doi.org/10.13863/j.issn1001-4454.2022.03.002 (2022).
Google Scholar 
Pironon, S. et al. The global distribution of plants used by humans. Science 383, 293–297. https://doi.org/10.1126/science.adg8028 (2024).
Google Scholar 
Namgung, H., Kim, M.-J., Baek, S., Lee, J.-H. & Kim, H. Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea. J. Asia-Pacific Entomol. 23, 291–297. https://doi.org/10.1016/j.aspen.2020.01.009 (2020).
Google Scholar 
Wang, G. et al. Integrating Maxent model and landscape ecology theory for studying spatiotemporal dynamics of habitat: Suggestions for conservation of endangered Red-crowned crane. Ecol. Indic. 116, 106472. https://doi.org/10.1016/j.ecolind.2020.106472 (2020).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. https://doi.org/10.1111/j.1472-4642.2010.00725.x (2010).
Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography https://doi.org/10.1111/j.0906-7590.2008.5203.x (2008).
Google Scholar 
Miao, Q. et al. Study on ecological suitability of Gardenia jasminoides based on ArcGIS and Maxent model. China J. Chin. Materia Medica 41, 3181–3185. https://doi.org/10.4268/cjcmm20161711 (2016).
Google Scholar 
Inderjit, Catford, J. A., Kalisz, S., Simberloff, D., and Wardle, D. A. A framework for understanding human-driven vegetation change. Oikos 126 1687–1698. https://doi.org/10.1111/oik.04587 (2017)Fristoe, T. S. et al. Evolutionary imbalance, climate and human history jointly shape the global biogeography of alien plants. Nat. Ecol. Evol. 7, 1633–1644. https://doi.org/10.1038/s41559-023-02172-z (2023).
Google Scholar 
Cao, Q. et al. Phenotypic diversity of Gardenia jasminoides and Its correlations with environmental fators. J. Yunnan Agric. Univ. (Nat. Sci.) 38(587), 596. https://doi.org/10.12101/j.issn.1004-390X(n).202202009 (2023).
Google Scholar 
Zhang, Y. X., Peng S. W., Wang, C. H., and Zhu X. Q. Standardized cultivation of a traditional typical medicinal Gardenia jasminoides Ellis in Hunan Province (2016). Modern Agricultural Science and Technology 106-107 109. https://doi.org/10.3969/j.issn.1007-5739.2016.12.067Hu, Y., Lei, X., Li, H., Zhang, Y. & Li, L. Determination of effective components of Gardenia jasminenoides. Chin. Agric. Sci. Bullet. 39, 126–130 (2023).
Google Scholar 
Wu, W., Zhao, Y. & Liu, Y. Determination of the content of Gardenia jasminoides Ellis from different producing areas by HPLC. China Surfactant Deterg. Cosmet. 52, 451–456. https://doi.org/10.3969/j.issn.1001-1803.2022.04.016 (2022).
Google Scholar 
Wang, L. & Jackson, D. A. Effects of sample size, data quality, and species response in environmental space on modeling species distributions. Landsc. Ecol. 38, 4009–4031. https://doi.org/10.1007/s10980-023-01771-2 (2023).
Google Scholar 
Majkowska-Gadomska, J., Kaliniewicz, Z., Francke, A., Sałata, A. & Jadwisieńczak, K. K. An evaluation of the biometric parameters and chemical composition of the florets, leaves, and stalks of broccoli plants grown in different soil types. Appl. Sci. 14, 4411. https://doi.org/10.3390/app14114411 (2024).
Google Scholar 
Ogundola, A. F., Bvenura, C., Ehigie, A. F. & Afolayan, A. J. Effects of soil types on phytochemical constituents and antioxidant properties of Solanum nigrum. S. Afr. J. Bot. 151, 325–333. https://doi.org/10.1016/j.sajb.2022.09.048 (2022).
Google Scholar 
Dimoudi, A. & Nikolopoulou, M. Vegetation in the urban environment: microclimatic analysis and benefits. Energy Build. 35, 69–76. https://doi.org/10.1016/S0378-7788(02)00081-6 (2003).
Google Scholar 
Gao, J. et al. Study of the effects of ten-year microclimate regulation based on different vegetation type combinations in a city riparian zone. Water 14, 1932. https://doi.org/10.3390/w14121932 (2022).
Google Scholar 
Wang, C. et al. Effects of vegetation restoration on local microclimate on the loess plateau. J. Geogr. Sci 32, 291–316. https://doi.org/10.1007/s11442-022-1948-y (2022).
Google Scholar 
Wang, D. et al. Global assessment of the distribution and conservation status of a key medicinal plant (Artemisia annua L.): The roles of climate and anthropogenic activities. Sci. Total Environ. 821, 153378 (2022).
Google Scholar 
Giaccone, E. et al. Influence of microclimate and geomorphological factors on alpine vegetation in the Western Swiss Alps. Earth Surf. Process. Landf. 44, 3093–3107. https://doi.org/10.1002/esp.4715 (2019).
Google Scholar 
Li, K. et al. Unveiling molecular mechanisms of pigment synthesis in gardenia (gardenia jasminoides) fruits through integrative transcriptomics and metabolomics analysis. Food Chem.: Sci. 9, 100209. https://doi.org/10.1016/j.fochms.2024.100209 (2024).
Google Scholar 
Shahrajabian, M. H., Kuang, Y., Cui, H., Fu, L. & Sun, W. Metabolic changes of active components of important medicinal plants on the basis of traditional chinese medicine under different environmental stresses. Curr. Org. Chem. 27, 782–806. https://doi.org/10.2174/1385272827666230807150910 (2023).
Google Scholar 
Bouwmeester, H., Dong, L., Wippel, K., Hofland, T. & Smilde, A. The chemical interaction between plants and the rhizosphere microbiome. Trends Plant Sci. 30, 1002–1019. https://doi.org/10.1016/j.tplants.2025.06.001 (2025).
Google Scholar 
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & van der Putten, W. H. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799. https://doi.org/10.1038/nrmicro3109 (2013).
Google Scholar 
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633. https://doi.org/10.1126/science.1094875 (2004).
Google Scholar 
Ou, Y. Y. et al. Effects of plants-associated microbiota on cultivation and quality of chinese herbal medicines. Chin. Herb. Med. 16, 190–203. https://doi.org/10.1016/j.chmed.2022.12.004 (2024).
Google Scholar 
Pang, Z. et al. Linking plant secondary metabolites and plant microbiomes: a review. Front. Plant Sci. https://doi.org/10.3389/fpls.2021.621276 (2021).
Google Scholar 
Tullus, A. et al. Climate and competitive status modulate the variation in secondary metabolites more in leaves than in fine roots of betula pendula. Front. Plant Sci. https://doi.org/10.3389/fpls.2021.746165 (2021).
Google Scholar 
Zeng, X. et al. Differences in response of tree species at different succession stages to neighborhood competition. Forests 15, 435. https://doi.org/10.3390/f15030435 (2024).
Google Scholar 
Peng, Z. et al. Interspecific rhizosphere interactions enhance secondary metabolite synthesis and volatile oil content in atractylodes lancea through root exudates. Ind. Crops Prod. 236, 122084. https://doi.org/10.1016/j.indcrop.2025.122084 (2025).
Google Scholar 
Divekar, P. A. et al. Plant secondary metabolites as defense tools against herbivores for sustainable crop protection. Int. J. Mol. Sci. 23, 2690. https://doi.org/10.3390/ijms23052690 (2022).
Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x (2010).
Google Scholar 
Glor, R. E. & Warren, D. Testing ecological explanations for biogeographic boundaries. Evolution 65, 673–683. https://doi.org/10.1111/j.1558-5646.2010.01177.x (2011).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. https://doi.org/10.1002/joc.5086 (2017).
Google Scholar 
Xu, X. China population spatial distribution kilometer grid dataset. https://doi.org/10.12078/2017121101 (2017)FAO, and IIASA Harmonized world soil database version 2.0. FAO International Institute for Applied Systems Analysis (IIASA) Accessed January 14 2025 https://openknowledge.fao.org/handle/20.500.14283/cc3823en (2023)Abdelaal, M., Fois, M., Dakhil, M. A., Bacchetta, G. & El-Sherbeny, G. A. Predicting the potential current and future distribution of the endangered endemic vascular plant primula boveana Decne. ex Duby in Egypt. Plants 9, 957. https://doi.org/10.3390/plants9080957 (2020).
Google Scholar 
Jenks, G. F. The data model concept in statistical mapping. Int. Yearb. Cartogr. 7, 186–190 (1967).
Google Scholar 
Venne, S. & Currie, D. J. Can habitat suitability estimated from MaxEnt predict colonizations and extinctions?. Diversity Distrib. 27, 873–886. https://doi.org/10.1111/ddi.13238 (2021).
Google Scholar 
Zhou, C. et al. Study on the determination of the contents of fourteen terpenoids in gardeniae fructus by UPLC-MS/MS. J. Chin. Med. Mater. 45, 1644–1649. https://doi.org/10.13863/j.issn1001-4454.2022.07.022 (2022).
Google Scholar 
Mollalo, A., Alimohammadi, A. & Khoshabi, M. Spatial and spatio-temporal analysis of human brucellosis in Iran. Trans. R. Soc. Trop. Med. Hyg. 108, 721–728. https://doi.org/10.1093/trstmh/tru133 (2014).
Google Scholar 
Tang, F. et al. Spatio-temporal trends and risk factors for Shigella from 2001 to 2011 in Jiangsu Province People’s Republic of China. PLOS ONE 9, e83487. https://doi.org/10.1371/journal.pone.0083487 (2014).
Google Scholar 
Peng, J., Yang, M., Liang, H., Wang, S. & Dai, C. The trend-surface analysis of childhood tuberculosis in Maoxian based on Geographic Information System(GIS). Chinese J. Child Health Care 17(422–424), 427. https://doi.org/10.3969/j.issn.1672-0504.2005.04.024 (2009).
Google Scholar 
Cha, W. et al. Spatial autocorrelation and trend surface analysis of bacillary dysentery by geographic information system (GIS) in Hunan Province in 2013. Chin. J. Dis. Control Prev. 19, 1096–1100. https://doi.org/10.16462/j.cnki.zhjbkz.2015.11.005 (2015).
Google Scholar 
Li, X., Cheng, G. & Lu, L. Comparison of spatial interpolation methods. Adv. Earth Sci. 15, 260–265. https://doi.org/10.3321/j.issn:1001-8166.2000.03.004 (2000).
Google Scholar 
Wang, J. & Xu, C. Geodetector: Principle and prospective. Acta Geographica Sinica 72, 116–134. https://doi.org/10.11821/dlxb201701010 (2017).
Google Scholar 
Wang, J., Zhang, T. & Fu, B. A measure of spatial stratified heterogeneity. Ecol. Indic. 67, 250–256. https://doi.org/10.1016/j.ecolind.2016.02.052 (2016).
Google Scholar 
Song, Y., Wang, J., Ge, Y. & Xu, C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GISci. Remote Sens. 57(5), 593–610. https://doi.org/10.1080/15481603.2020.1760434 (2020).
Google Scholar 
Download referencesAcknowledgmentsWe sincerely thank all the scholars and farmers who have helped us during the sample collection process. We would like to express our gratitude to all previous researchers who helped and references for this study.FundingThis research was supported by the National Natural Science Foundation of China (No. 82274052), CACMS Innovation Fund (No.CI2023E002, CI2024E003), Special Project on Survey of Scientific and Technological Basic Resources (No. 2022FY101000), National Key R&D Program: Intergovernmental Cooperation in International Science and Technology Innovation (No. 2022YFE0119300).Author informationAuthors and AffiliationsState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, ChinaMingxu Zhang, Cong Zhou, Suhua Huang, Hui Wang, Tingting Shi, Meng Li, Zhixian Jing & Xiaobo ZhangAuthorsMingxu ZhangView author publicationsSearch author on:PubMed Google ScholarCong ZhouView author publicationsSearch author on:PubMed Google ScholarSuhua HuangView author publicationsSearch author on:PubMed Google ScholarHui WangView author publicationsSearch author on:PubMed Google ScholarTingting ShiView author publicationsSearch author on:PubMed Google ScholarMeng LiView author publicationsSearch author on:PubMed Google ScholarZhixian JingView author publicationsSearch author on:PubMed Google ScholarXiaobo ZhangView author publicationsSearch author on:PubMed Google ScholarContributionsM.Z.: Writing – original draft, Conceptualization, Methodology, Validation, Data curation, Writing – review & editing; C.Z.: Writing – original draft, Conceptualization, Methodology, Validation, Data curation, Writing – review & editing; S.H.: Conceptualization, Writing – review & editing; H.W.: Writing-review and editing, Methodology; T.S.: Writing – review & editing, Data curation; Z.J.: Writing – review & editing, Data curation; M.L.: Writing – review & editing; X.Z.: Writing – review & editing, Conceptualization, Methodology.Corresponding authorCorrespondence to
Xiaobo Zhang.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethics
Information on the voucher specimens, including the deposition location, deposition number, and specimen identifier, is provided in Additional Table 1. We confirm that all research involving field studies and the collection of Gardenia jasminoides was conducted in strict compliance with relevant institutional, national, and international guidelines and legislation. Given the Least Concern status of Gardenia jasminoides and that collection occurred outside of protected areas, specific collection licenses were not required; all collection adhered strictly to local regulations. Furthermore, we adhere to the principles outlined in the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. We note that Gardenia jasminoides was most recently assessed for The IUCN Red List of Threatened Species in 2023 and is listed as Least Concern. This classification is consistent with its national assessment in China, where the species is also categorized as Least Concern.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Information.Rights and permissions
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 articleZhang, M., Zhou, C., Huang, S. et al. Integrated geographical and ecological analysis reveals environmental drivers of Gardenia jasminoides distribution and chemical variation.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32876-0Download citationReceived: 01 September 2025Accepted: 12 December 2025Published: 24 December 2025DOI: https://doi.org/10.1038/s41598-025-32876-0Share 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
Provided by the Springer Nature SharedIt content-sharing initiative
Keywords
Gardenia jasminoides
Suitable distributionSpatial differentiationQuality variationEnvironmental drivers More

AbstractTo explore the structure and assembly of the rare bacterial community within sediment samples, as well as their responses their responses to environmental influencing factors, we collected surface sediment and overlying water samples from Sancha Lake across four seasons. MiSeq high-throughput sequencing was applied to the V3-V4 hypervariable regions of the 16 S rRNA genes, and the β – Nearest Taxon index (βNTI) was utilized to analyze the bacterial community assembly in the sediment samples. Our findings uncovered abundant bacterial diversity within the sediment samples of Sancha Lake, with 9314 operational taxonomic units (OTUs) identified, encompassing 59 phyla, 198 classes, 279 orders, 447 families, and 758 genera of bacteria. Proteobacteria and Chloroflexi were the dominant rare bacteria at the phylum level, whereas Coxiella and hgcl_clade were the principal rare bacteria at the genus level. The variety index of rare communities across diverse seasons was notably higher than that of abundant ones (P < 0.01). Bacterial community structure differed between spring and other seasons, and the rare bacterial community exhibited substantial seasonal alterations during non-spring periods. pH, dissolved oxygen (DO), total phosphorus (TP), and soluble reactive phosphorus (SRP) were the predominant environmental factors, exerting an even greater influence on rare bacteria. Within the co-occurrence network, rare bacteria constituted the majority of nodes and connections and were the dominant key species throughout all seasons. The assembly of their community was chiefly deterministic in autumn and random in other seasons. This study indicated that rare bacteria in Sancha Lake were diverse. They were keystone taxa for maintaining community interactions and stable operation, and their assembly process was influenced by both stochastic and deterministic factors.

Similar content being viewed by others

Discovery of a novel bacterial class with the capacity to drive sulfur cycling and microbiome structure in a paleo-ocean analog

Article
Open access
18 August 2023

Seasonal changes of prokaryotic microbial community structure in Zhangjiayan Reservoir and its response to environmental factors

Article
Open access
06 March 2024

A long-read sequencing approach to high-resolution profiling of bacterioplankton diversity in a shallow freshwater lake

Article
Open access
10 April 2025

Data availability

The datasets analysed during the current study are available in the NCBI repository (https://www.ncbi.nlm.nih.gov/). The BioProject accession number is PRJNA1336117.
ReferencesAlexander, T. J., Vonlanthen, P. & Seehausen, O. Does eutrophication-driven evolution change aquatic ecosystems? Philos. Trans. R. Soc. B-Biol. Sci. 372 (1712), 20160041 (2017).
Google Scholar 
Yang, Y., Gao, B., Hao, H., Zhou, H. & Lu, J. Nitrogen and phosphorus in sediments in China: a national-scale assessment and review. Sci. Total Environ. 576, 840–849 (2017).
Google Scholar 
Jin, X. et al. Environmental factors influencing the spatial distribution of sediment bacterial community structure and function in Poyang lake. Res. Environ. Sci. 30 (4), 529–536 (2017).
Google Scholar 
Zhong, M. et al. Spatial-temporal distribution of bacterial communities and main nutrients driver of sediment in a shallow lake. Earth Environ. Sci. 51 (4), 377–387 (2023).
Google Scholar 
Shao, K., Gao, G., Wang, Y., Tang, X. & Qin, B. Vertical diversity of sediment bacterial communities in two different trophic States of the eutrophic lake Taihu, China. J. Environ. Sci. 25 (6), 1186–1194 (2013).
Google Scholar 
Xue, Y. et al. The diversity of bacterial communities in the sediment of different lake zones of lake Taihu in winter. China Environ. Sci. 38 (2), 719–728 (2018).
Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6 (2), 298–308 (2012).
Google Scholar 
Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81 (4), 1–32 (2017).
Google Scholar 
Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8 (4), 732–740 (2006).
Google Scholar 
Qi, R. et al. Distinct composition and assembly processes of bacterial communities in a river from the arid area: ecotypes or habitat types? Microb. Ecol. 84 (3), 769–779 (2021).
Google Scholar 
Zeng, J. et al. Patterns and assembly processes of planktonic and sedimentary bacterial community differ along a trophic gradient in freshwater lakes. Ecol. Indic. 106, 105491 (2019).
Google Scholar 
Jia, X., DiniAndreote, F. & Salles, J. F. Unravelling the interplay of ecological processes structuring the bacterial rare biosphere. ISME Commun. 2 (1), 1–11 (2022).
Google Scholar 
Qiu, Z. et al. Large scale exploration reveals rare taxa crucially shape microbial assembly in alkaline lake sediments. Npj Biofilms Microbiomes. 10 (1), 62 (2024).
Google Scholar 
Ren, Z., Luo, W. & Zhang, C. Rare bacterial biosphere is more environmental controlled and deterministically governed than abundant one in sediment of thermokarst lakes across the Qinghai-Tibet plateau. Front. Microbiol. 13, 944646 (2022).
Google Scholar 
Song, Y. et al. Antibiotic pollution and its effects on the Spatiotemporal variation in microbial community structure and functional genes in sediment of Baiyangdian lake. Environ. Sci. 45 (8), 4904–4914 (2024).
Google Scholar 
Haglund, A. L., Lantz, P., Törnblom, E. & Tranvik, L. Depth distribution of active bacteria and bacterial activity in lake sediment. FEMS Microbiol. Ecol. 46 (1), 31–38 (2003).
Google Scholar 
Zhou, T. X. et al. Spatial distribution of composition and diversity of aquatic bacterial communities in lake fuxian during vertical stratification period. J. Lake Sci. 34 (5), 1642–1655 (2022).
Google Scholar 
Jones, I. D., Winfield, I. J. & Carse, F. Assessment of long-term changes in habitat availability for Arctic charr(Salvelinus alpinus) in a temperate lake using oxygen profiles and hydroacoustic surveys. Freshw. Biol. 53 (2), 393–402 (2008).
Google Scholar 
Chen, Y. et al. Spatiotemporal variation characteristics of mixed layer depth and hypoxic zone during the thermal stratification decay period in a Southern Chinese reservoir: a case study of Tianshuiku reservoir in Nanning City. J. Lake Sci. 35 (5), 1623–1634 (2023).
Google Scholar 
Jia, B., Fu, W., Yu, J., Zhang, C. & Tang, Y. Relationship among sediment characteristics, eutrophication process and human activities in the Sancha Lake, Sichuan, Southwestern China. China Environ. Sci. 33 (9), 1638–1644 (2013).
Google Scholar 
Ruban, V., Brigault, S., Demare, D. & Philippe, A. M. An investigation of the origin and mobility of phosphorus in freshwater sediments from Bort-Les-Orgues Reservoir, France. J. Environ. Monit. 1 (4), 403–407 (1999).
Google Scholar 
Ruban, V. et al. Harmonized protocol and certified reference material for the determination of extractable contents of phosphorus in freshwater sediments — a synthesis of recent works. Fresenius J. Anal. Chem. 370 (2), 224–228 (2001).
Google Scholar 
Xu, N., Tan, G. C., Wang, H. Y. & Gai, X. Effect of Biochar additions to soil on nitrogen leaching, microbial biomass and bacterial community structure. Eur. J. Soil. Biol. 74, 1–8 (2016).
Google Scholar 
Schloss, P. D., Gevers, D. & Westcott, S. L. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One 6 (12), e27310 (2011).Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41 (D1), D590–D596 (2013).
Google Scholar 
Jiao, S. & Lu, Y. Abundant fungi adapt to broader environmental gradients than rare fungi in agricultural fields. Glob. Chang. Biol. 26 (8), 4657–4668 (2020).
Google Scholar 
Hair, J. F. Multivariate Data Analysis: An Overview (Springer, 2011).Wu, P. et al. Unraveling the spatial-temporal distribution patterns of soil abundant and rare bacterial communities in china’s subtropical mountain forest. Front. Microbiol. 15, 1323887 (2024).
Google Scholar 
Ling, N. et al. Insight into how organic amendments can shape the soil Microbiome in long-term field experiments as revealed by network analysis. Soil. Biol. Biochem. 99, 137–149 (2016).
Google Scholar 
Chen, C., Shen, Q., Zhong, J., Liu, C. & Fan, C. Distribution characteristics of phosphorus in surface sediments during seasons of algae blooms in bloom-accumulation area in the Taihu lake. Resour. Environ. Yangtze River Basin 23 (9), 1258–1264 (2014).
Google Scholar 
Zhang, X., Zhao, Y. & Jin, Y. Diversity and phylogenetic analysis of bacteria in sediments of Xiaokou sites in lake Wuliangsuhai. J. Inner Mongolia Agric. Univ. (Nat. Sci. Ed.) 32 (4), 206–212 (2011).
Google Scholar 
Welch, D. B. M. & Huse, S. M. Microbial diversity in the deep sea and the underexplored rare biosphere. In Handbook of Molecular Microbial Ecology II: Metagenomics in Different Habitats. 243–252 (2011).Li, Y. et al. Divergent adaptation strategies of abundant and rare bacteria to salinity stress and metal stress in polluted Jinzhou Bay. Environ. Res. 245 (Mar.), 118030 (2024).
Google Scholar 
Chu, B. T. T. et al. Metagenomics reveals the impact of wastewater treatment plants on the dispersal of microorganisms and genes in aquatic sediments. Appl. Environ. Microbiol. 84 (5), e02168–e02117 (2018).
Google Scholar 
Yuan, W. et al. The vertical distribution of bacterial and archaeal communities in the water and sediment of lake Taihu. FEMS Microbiol. Ecol. 71 (2), 263–276 (2010).
Google Scholar 
Keshri, J., Pradeep Ram, A. S. & Sime-Ngando, T. Distinctive patterns in the taxonomical resolution of bacterioplankton in the sediment and pore waters of contrasted freshwater lakes. Microb. Ecol. 75 (3), 662–673 (2017).
Google Scholar 
Cheng, H. et al. Changes of bacterial communities in response to prolonged hydrodynamic disturbances in the eutrophic water-sediment systems. Int. J. Environ. Res. Public. Health. 16 (20), 3868 (2019).
Google Scholar 
Liu, L., Yang, J., Yu, Z. & Wilkinson, D. M. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J. 9 (9), 2068–2077 (2015).
Google Scholar 
Jiao, C. et al. Abundant and rare bacterioplankton in freshwater lakes subjected to different levels of tourism disturbances. Water 10 (8), 16 (2018).
Google Scholar 
Jiao, S., Chen, W. & Wei, G. Biogeography and ecological diversity patterns of rare and abundant bacteria in oil-contaminated soils. Mol. Ecol. 26 (19), 5305–5317 (2017).
Google Scholar 
Zhang, W. et al. The diversity and biogeography of abundant and rare intertidal marine microeukaryotes explained by environment and dispersal limitation. Environ. Microbiol. 20 (2), 462–476 (2018).
Google Scholar 
Dang, C. et al. Rare biosphere regulates the planktonic and sedimentary bacteria by disparate ecological processes in a large source water reservoir. Water Res. 216, 118296 (2022).
Google Scholar 
Yu, X. et al. Seasonal changes of prokaryotic microbial community structure in Zhangjiayan reservoir and its response to environmental factors. Sci. Rep. 14 (1), 1–14 (2024).
Google Scholar 
Wan, Y., Ruan, X., Zhang, Y. & Li, R. Illumina sequencing-based analysis of sediment bacteria community in different trophic status freshwater lakes. Microbiol. Open 6(4), e00450 (2017).Yin, X. et al. Composition and predictive functional analysis of bacterial communities in surface sediments of the Danjiangkou reservoir. J. Lake Sci. 30 (4), 1052–1053 (2018).
Google Scholar 
Winters, A. D., Marsh, T. L., Brenden, T. O. & Faisal, M. Molecular characterization of bacterial communities associated with sediments in the Laurentian great lakes. J. Gt Lakes Res. 40 (3), 640–645 (2014).
Google Scholar 
Goberna, M. & Verdú, M. Cautionary notes on the use of co-occurrence networks in soil ecology. Soil. Biol. Biochem. 166, 108534 (2022).
Google Scholar 
De Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9 (1), 1–12 (2018).
Google Scholar 
Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13 (4), 217 (2015).
Google Scholar 
Li, X. et al. Comparing diversity patterns and processes of microbial community assembly in water column and sediment in lake Wuchang, China. PeerJ 11, e14592 (2023).
Google Scholar 
Download referencesAcknowledgementsThis research was funded by Student Research Training Program(242005).FundingThis research was funded by Student Research Training Program(242005).Author informationAuthors and AffiliationsSchool of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, ChinaYong Li, Yajie Li, Yihan Wu, Zuguang Liu, Shiqi Luo & Sidan GongAuthorsYong LiView author publicationsSearch author on:PubMed Google ScholarYajie LiView author publicationsSearch author on:PubMed Google ScholarYihan WuView author publicationsSearch author on:PubMed Google ScholarZuguang LiuView author publicationsSearch author on:PubMed Google ScholarShiqi LuoView author publicationsSearch author on:PubMed Google ScholarSidan GongView author publicationsSearch author on:PubMed Google ScholarContributionsY.L. writing – review & editing, investigation, conceptualization. Y.L. and Y.W. writing – original draft, visualization, project administration. Z.L. and S.L. writing – original draft, visualization. S.G. and Y.L. methodology, investigation. All authors read and approved the final manuscript.Corresponding authorCorrespondence to
Yong Li.Ethics declarations

Competing interests
The authors declare no competing interests.

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 1Rights and permissions
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 articleLi, Y., Li, Y., Wu, Y. et al. Structure and community assembly of rare bacterial community in sediments of Sancha Lake.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31889-zDownload citationReceived: 08 April 2025Accepted: 05 December 2025Published: 24 December 2025DOI: https://doi.org/10.1038/s41598-025-31889-zShare 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsSancha Lake sedimentsEutrophicationRare bacteriaDiversityCommunity assembly More

AbstractJaguars (Panthera onca) are highly sensitive to persecution, habitat loss, and fragmentation, making the identification of suitable habitat critical for conservation planning. Using GPS telemetry data from 172 individuals across seven countries – the largest jaguar dataset to date – we developed multiscale Resource Selection Functions (RSFs) incorporating 15 environmental covariates to model habitat suitability across the species’ historic range. Jaguars selected productive habitats near water and strongly avoided human-modified landscapes, including areas with high human population density and livestock presence. The resulting habitat suitability surface showed strong predictive performance (AUC = 0.88; Boyce Index = 0.91) and correlated with known density estimates and distribution models. Jaguar Conservation Units (JCUs) and Protected Areas (PAs) contained 68.7% and 53.9% of predicted suitable habitat, respectively, while occupying only a third of the range. Non-designated lands, though comprising just 4% of the range, held nearly 10% of total suitability. The Amazon and Mayan Forests were identified as core strongholds, while ecoregion-based modelling revealed additional areas of high suitability in the Pantanal, Gran Chaco, Cerrado, and coastal Mexico. While Brazil encompassed the largest extent of highly suitable habitat, countries such as Paraguay, Argentina, and the United States gained conservation relevance under the ecoregion-stratified scenario.

Similar content being viewed by others

Impending anthropogenic threats and protected area prioritization for jaguars in the Brazilian Amazon

Article
Open access
15 February 2023

Reconciling biome-wide conservation of an apex carnivore with land-use economics in the increasingly threatened Pantanal wetlands

Article
Open access
23 November 2021

Wildfires disproportionately affected jaguars in the Pantanal

Article
Open access
13 October 2022

Data availability

GPS telemetry data for 117 of the 172 jaguar individuals used in this study are publicly available via Morato et al. (2018)82. The remaining 55 individuals were provided by collaborators and remain under the stewardship of their respective research groups; these data are not publicly available due to ongoing research use and data-sharing agreements. However, access to these data may be granted upon reasonable request to the corresponding authors, pending approval from the original data providers. The resulting habitat suitability surface generated by this study will be made openly available on the Zenodo repository upon manuscript acceptance (currently accessible for peer review at: https://zenodo.org/records/15824344?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjY3YTJjNzhjLTVmMzItNDFhZi04YmY1LTk0NTQzZmFkYjgyZSIsImRhdGEiOnt9LCJyYW5kb20iOiJiYTI3YTBjNDRhOGRkNjk3NWI1ZGI1OWEyMDRkYWU3NCJ9._5XjPvxWOw4azyxXv4Ww-eaoHm1FG54BexND5TEEsmBnBTahFRBpbdScnwD_8McXtH-eNHyVaqA6hoWbieufCQ).
ReferencesSunquist, M. E. & Sunquist, F. C. Ecological constraints on predation by large felids. Carnivore Behav. Ecol. Evol. https://doi.org/10.1007/978-1-4757-4716-4_11 (1989).
Google Scholar 
Seymour, K. L. Panthera Onca. Mammalian Species. 340, 1–9 (1989).
Google Scholar 
Quigley, H. et al. Pantera Onca. IUCN Red List. Threatened Species. 8235, 8 (2017).
Google Scholar 
Ceballos, G. et al. Beyond words: From Jaguar population trends to conservation and public policy in Mexico. PLoS ONE. 16, 1–22 (2021).
Google Scholar 
de la Torre, J. A., González-Maya, J. F., Zarza, H., Ceballos, G. & Medellín, R. A. The jaguar’s spots are darker than they appear: Assessing the global conservation status of the jaguar Panthera Onca. Oryx 52, 300–315 (2018).
Google Scholar 
Alvarenga, G. C. et al. Jaguar (Panthera onca) density and population size across protected areas and Indigenous lands in the Amazon biome, its largest stronghold. Biol. Conserv. 303, 111010 (2025).
Google Scholar 
Berzins, R. et al. Distribution and status of the jaguar in the Guiana Shield. Cat News Special Issue 16, 1–22 (2023).Jędrzejewski, W. et al. Estimating species distribution changes due to human impacts: The 2020’s status of the jaguar in South America. CATnews Special Issue 16, 44–55 (2023).Meyer, C. B. & Thuiller, W. Accuracy of resource selection functions across spatial scales. Divers. Distrib. 12, 288–297 (2006).
Google Scholar 
Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: A review. Landsc. Ecol. 27, 777–797 (2012).
Google Scholar 
Mateo Sánchez, M. C., Cushman, S. A. & Saura, S. Scale dependence in habitat selection: The case of the endangered brown bear (Ursus arctos) in the Cantabrian range (NW Spain). Int. J. Geogr. Inf. Sci. 28, 1531–1546 (2014).
Google Scholar 
McGarigal, K., Zeller, K. A. & Cushman, S. A. Multi-scale habitat selection modeling: Introduction to the special issue. Landsc. Ecol. 31, 1157–1160 (2016).
Google Scholar 
Cushman, S. A. et al. Simulating multi-scale optimization and variable selection in species distribution modeling. Ecol. Inf. 83, 102832 (2024).
Google Scholar 
Macdonald, D. W. et al. Multi-scale habitat modelling identifies Spatial conservation priorities for Mainland clouded leopards (Neofelis nebulosa). Divers. Distrib. 00, 1–16 (2019).
Google Scholar 
Sanderson, E. W. et al. Planning to save a species: The jaguar as a model. Conserv. Biol. 16, 58–72 (2002).
Google Scholar 
Alvarenga, G. C. et al. Multi-scale path-level analysis of jaguar habitat use in the Pantanal ecosystem. Biol. Conserv. 253, 108900 (2021).Balbuena-Serrano, Á. et al. Connectivity of priority areas for the conservation of large carnivores in Northern Mexico. J. Nat. Conserv. 65, 126116 (2022).Ferraz, K. M. P. M. et al. Distribuição potencial e adequabilidade ambiental dos biomas brasileiros à ocorrência da onça-pintada. in Plano Nacional para Conservação da Onça-Pintada (eds. Paula, R. C. de, Desdiez, A. & Cavalcanti., S.) 125–206 (Instituto Chico Mendes de Conservação da Biodiversidade, ICMBio, Brasília, 2013).Petracca, L. S. et al. Robust inference on large-scale species habitat use with interview data: The status of jaguars outside protected areas in Central America. J. Appl. Ecol. 55, 723–734 (2018).
Google Scholar 
Figel, J. J., Botero-Cañola, S., Lavariega, M. C. & Luna-Krauletz, M. D. Overlooked jaguar guardians: Indigenous Territories and range-wide conservation of a cultural icon. Ambio https://doi.org/10.1007/s13280-022-01754-8 (2022).
Google Scholar 
Paviolo, A. et al. A biodiversity hotspot losing its top predator: The challenge of jaguar conservation in the Atlantic forest of South America. Sci. Rep. 6, 37147 (2016).Sollmann, R., Torres, N. M. & Silveira, L. Jaguar conservation in Brazil: The role of protected areas. CAT News 4, 15–20 (2008).De Angelo, C., Paviolo, A., Wiegand, T., Kanagaraj, R. & Di Bitetti, M. S. Understanding species persistence for defining conservation actions: A management landscape for jaguars in the Atlantic Forest. Biol. Conserv. 159, 422–433 (2013).
Google Scholar 
Gese, E. M., Terletzky, P. A., Cavalcanti, S. M. C. & Neale, C. M. U. Influence of behavioral state, sex, and season on resource selection by jaguars (Panthera onca): Always on the prowl? Ecosphere 9, e02341 (2018).
Google Scholar 
Craighead, K., Yacelga, M., Wan, H. Y., Vogt, R. & Cushman, S. A. Scale-dependent seasonal habitat selection by jaguars (Panthera onca) and pumas (Puma concolor) in Panama. Landsc. Ecol. 37, 129–146 (2022).
Google Scholar 
Conde, D. A. et al. Sex matters: Modeling male and female habitat differences for jaguar conservation. Biol. Conserv. 143, 1980–1988 (2010).
Google Scholar 
Colchero, F. et al. Jaguars on the move: Modeling movement to mitigate fragmentation from road expansion in the Mayan Forest. Anim. Conserv. 14, 158–166 (2011).
Google Scholar 
Rodríguez-Soto, C., Monroy-Vilchis, O. & Zarco-González, M. M. Corridors for jaguar (Panthera onca) in Mexico: Conservation strategies. J. Nat. Conserv. 21, 438–443 (2013).
Google Scholar 
Harmsen, B. J. et al. Long term monitoring of jaguars in the Cockscomb Basin Wildlife Sanctuary, Belize; implications for camera trap studies of carnivores. PLoS ONE. 12, 1–19 (2017).
Google Scholar 
Thompson, J. J. et al. Environmental and anthropogenic factors synergistically affect space use of jaguars. Curr. Biol. 31, 3457–3466e4 (2021).
Google Scholar 
Srbek-Araujo, A. C. Do female jaguars (Panthera onca Linnaeus, 1758) deliberately avoid camera traps? Mammalian Biol. 88, 26–30 (2018).
Google Scholar 
Cerqueira, R. C., de Rivera, O. R., Jaeger, J. A. G. & Grilo, C. Direct and indirect effects of roads on space use by jaguars in Brazil. Sci. Rep. 11, 1–9 (2021).
Google Scholar 
Alegre, V. B. et al. The effect of anthropogenic features on the habitat selection of a large carnivore is conditional on sex and circadian period, suggesting a landscape of coexistence. J. Nat. Conserv. 73, 126412 (2023).Jędrzejewski, W. et al. Estimating large carnivore populations at global scale based on spatial predictions of density and distribution – Application to the Jaguar (Panthera onca). PLoS ONE. 13, 1–25 (2018).
Google Scholar 
Tôrres, N. M. et al. Can species distribution modelling provide estimates of population densities? A case study with jaguars in the neotropics. Divers. Distrib. 18, 615–627 (2012).
Google Scholar 
Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American Marten (Martes americana) in the Northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).
Google Scholar 
Wan, H. Y. et al. Meta-replication reveals nonstationarity in multi-scale habitat selection of Mexican spotted Owl. Condor: Ornithological Appl. 119, 641–658 (2017).
Google Scholar 
Morato, R. G. et al. Resource selection in an apex predator and variation in response to local landscape characteristics. Biol. Conserv. 228, 233–240 (2018).
Google Scholar 
Villalva, P., Palomares, F. & Zanin, M. Effect of uneven tolerance to human disturbance on dominance interactions of top predators. Conserv. Biol. 39, 1–10 (2024).
Google Scholar 
Eriksson, C. E. et al. Extensive aquatic subsidies lead to territorial breakdown and high density of an apex predator. Ecology (2021). https://doi.org/10.1002/ecy.3543
Google Scholar 
Azevedo, F. C. C. & Verdade, L. M. Predator-prey interactions: Jaguar predation on Caiman in a floodplain forest. J. Zool. 286, 200–207 (2012).
Google Scholar 
Schaller, G. B. & Crawshaw, P. G. Movement patterns of jaguar. Biotropica 12, 161–168 (1980).
Google Scholar 
Khamila, C. N. et al. There is a trade-off between forest productivity and animal biodiversity in Europe. Biodivers. Conserv. 32, 1879–1899 (2023).
Google Scholar 
Peña-Mondragón, J. L., Castillo, A., Hoogesteijn, A. & Martínez-Meyer, E. Livestock predation by jaguars Panthera onca in South-Eastern Mexico: The role of local peoples’ practices. Oryx 51, 254–262 (2016).
Google Scholar 
Stryker, J. A. Thermoregulatory Behaviour Assessment and Thermal Imaging of Large Felids (The University of Guelph, 2016).
Google Scholar 
Rabinowitz, A. & Zeller, K. A. A range-wide model of landscape connectivity and conservation for the jaguar, Panthera Onca. Biol. Conserv. 143, 939–945 (2010).
Google Scholar 
Astete, S. et al. Living in extreme environments: Modeling habitat suitability for jaguars, pumas, and their prey in a semiarid habitat. J. Mammal. 98, 464–474 (2017).
Google Scholar 
Morato, R. G., Ferraz, K. M. P. M. D. B., De Paula, R. C. & Campos, C. B. De. Identification of priority conservation areas and potential corridors for jaguars in the Caatinga Biome, Brazil. PLoS ONE 9, e92950 (2014).de la Torre, J. A., Núñez, J. M. & Medellín, R. A. Habitat availability and connectivity for jaguars (Panthera onca) in the Southern Mayan forest: Conservation priorities for a fragmented landscape. Biol. Conserv. 206, 270–282 (2017).
Google Scholar 
Ramirez-Reyes, C., Bateman, B. L. & Radeloff, V. C. Effects of habitat suitability and minimum patch size thresholds on the assessment of landscape connectivity for jaguars in the Sierra Gorda, Mexico. Biol. Conserv. 204, 296–305 (2016).
Google Scholar 
Penjor, U., Kaszta, Ż., Macdonald, D. W. & Cushman, S. A. Prioritizing areas for conservation outside the existing protected area network in Bhutan: The use of multi-species, multi-scale habitat suitability models. Landsc. Ecol. 36, 1281–1309 (2021).
Google Scholar 
Chiaverini, L. et al. Multi-scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands. Anim. Conserv. 25, 660–679 (2022).
Google Scholar 
Macdonald, D. W. et al. Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation? Biodivers. Conserv. 29, 4035–4057 (2020).
Google Scholar 
Zeller, K. A., Vickers, T. W., Ernest, H. B. & Boyce, W. M. Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study. PLoS ONE. 12, e0179570 (2017).
Google Scholar 
Calderón, A. P. et al. Occupancy models reveal potential of conservation prioritization for Central American jaguars. Anim. Conserv. https://doi.org/10.1111/acv.12772 (2022).
Google Scholar 
Redo, D. J., Grau, H. R., Aide, T. M. & Clark, M. L. Asymmetric forest transition driven by the interaction of socioeconomic development and environmental heterogeneity in Central America. Proc. Natl. Acad. Sci. U S A. 109, 8839–8844 (2012).
Google Scholar 
Wultsch, C. et al. Genetic diversity and population structure of Mesoamerican jaguars (Panthera onca): Implications for conservation and management. PLoS ONE. 11, 1–25 (2016).
Google Scholar 
Meyer, N. F. V. et al. Effectiveness of Panama as an intercontinental land bridge for large mammals. Conserv. Biol. 0, 1–13 (2019).
Google Scholar 
Motlagh, J. A. Terrifying Journey Through the World’s Most Dangerous Jungle,. Outside Online (2016).Obinna, D. N. Camino de La muerte: Crossing the Darién gap and migration in the Americas. Migr. Dev. 13, 7–24 (2024).
Google Scholar 
Jędrzejewski, W. et al. Jaguars (Panthera onca) in the Llanos of Colombia and Venezuela: Estimating distribution and population size by combining different modeling approaches. in Neotropical Mammals (eds Mandujano, S., Naranjo, E. J. & Ponce, A. G. P. et al.) (Springer, Cham, 2023). https://doi.org/10.1007/978-3-031-39566-6_9
Google Scholar 
Hyde, M. et al. Tourism-supported working lands sustain a growing jaguar population in the Colombian Llanos. Sci. Rep. 13, 1–11 (2023).
Google Scholar 
Boron, V. et al. Jaguar densities across human-dominated landscapes in Colombia: The contribution of unprotected areas to long term conservation. PLoS ONE. 11, e0153973 (2016).
Google Scholar 
Seidl, A. F., De Silva, J. D. S. V. & Moraes, A. S. Cattle ranching and deforestation in the Brazilian Pantanal. Ecol. Econ. 36, 413–425 (2001).
Google Scholar 
Soisalo, M. K. & Cavalcanti, S. M. C. Estimating the density of a jaguar population in the Brazilian Pantanal using camera-traps and capture-recapture sampling in combination with GPS radio-telemetry. Biol. Conserv. 129, 487–496 (2006).
Google Scholar 
Devlin, A. L. et al. Drivers of large carnivore density in non-hunted, multi-use landscapes. Conserv. Sci. Pract. 5, 1–13 (2023).
Google Scholar 
de Barros, A. E. et al. Wildfires disproportionately affected jaguars in the Pantanal. Commun. Biol. 5, 1–12 (2022).
Google Scholar 
Santos, F. C., Chaves, F. M., Negri, R. G. & Massi, K. G. Fires in Pantanal: The link to agriculture conversions in Cerrado, and hydrological changes. Wetlands 44, 1–11 (2024).
Google Scholar 
Romero-Muñoz, A. et al. Habitat loss and overhunting synergistically drive the extirpation of jaguars from the Gran Chaco. Divers. Distrib. 25, 176–190 (2019).
Google Scholar 
Thompson, J. J. et al. Jaguar (Panthera onca) population density and landscape connectivity in a deforestation hotspot: The Paraguayan Dry Chaco as a case study. Perspect. Ecol. Conserv. https://doi.org/10.1016/j.pecon.2022.09.001 (2022).
Google Scholar 
Quiroga, V. A., Boaglio, G. I., Noss, A. J. & Di Bitetti, M. S. Critical population status of the jaguar Panthera onca in the Argentine Chaco: Camera-trap surveys suggest recent collapse and imminent regional extinction. Oryx 48, 141–148 (2014).
Google Scholar 
Haag, T. et al. The effect of habitat fragmentation on the genetic structure of a top predator: Loss of diversity and high differentiation among remnant populations of Atlantic Forest jaguars (Panthera onca). Mol. Ecol. 19, 4906–4921 (2010).
Google Scholar 
Roques, S. et al. Effects of habitat deterioration on the population genetics and conservation of the jaguar. Conserv. Genet. 17, 125–139 (2016).
Google Scholar 
de Paula, R. C., Campos, C. B. & Oliveira, T. G. Red List assessment for the jaguar in the Caatinga biome. Cat News Special Issue 7, 19–24 (2012).Portugal, M. P., Morato, R. G., Ferraz, K. M. P. M. D. B., Rodrigues, F. H. G. & Jacobi, C. M. Priority areas for jaguar Panthera onca conservation in the Cerrado. Oryx 54, 854–865 (2019).
Google Scholar 
Moraes, E. A. Jr. The status of the jaguar in the Cerrado. CATnews Special Issue 7, 25–28 (2012).Soterroni, A. C. et al. Expanding the soy moratorium to Brazil’s Cerrado. Sci. Adv. 5, eaav7336 (2019).
Google Scholar 
Segura-Garcia, C. et al. The fire regimes of the Cerrado and their changes through time. Philos. Trans. R. Soc. B: Biol. Sci. 380, 20230460 (2025).Pompeu, J., Assis, T. O. & Ometto, J. P. Landscape changes in the cerrado: Challenges of land clearing, fragmentation and land tenure for biological conservation. Sci. Total Environ. 906, 167581 (2024).Pessôa, A. C. M. et al. Protected areas are effective on curbing fires in the Amazon. Ecol. Econ. 214, 107983 (2023).
Google Scholar 
Qin, Y. et al. Forest conservation in Indigenous Territories and protected areas in the Brazilian Amazon. Nat. Sustain. 6, 295–305 (2023).
Google Scholar 
Moutinho, P. & Azevedo-Ramos, C. Untitled public forestlands threat Amazon conservation. Nat. Commun. 14, 1152 (2023).
Google Scholar 
Morán-López, R., Cortés Gañán, E., Uceda Tolosa, O. & Sánchez Guzmán, J. M. The umbrella effect of natura 2000 Annex species spreads over multiple taxonomic groups, conservation attributes and organizational levels. Anim. Conserv. 23, 407–419 (2020).
Google Scholar 
Zanin, M. et al. The differential genetic signatures related to climatic landscapes for jaguars and pumas on a continental scale. Integr. Zool. 16, 2–18 (2021).
Google Scholar 
Morato, R. G. et al. Jaguar movement database: A GPS-based movement dataset of an apex predator in the Neotropics. Ecology 99, 1691 (2018).
Google Scholar 
Rondinini, C. & Boitani, L. Mind the map: Trips and pitfalls in making and reading maps of carnivore distribution. in Carnivore Ecology and Conservation: A Handbook of Techniques (eds Boitani, L. & Powell, R. A.) 506 (Oxford University Press, New York, USA, (2012).
Google Scholar 
Crego, R. D., Stabach, J. A. & Connette, G. Implementation of species distribution models in Google Earth Engine. Divers. Distrib. 28, 904–916 (2022).
Google Scholar 
Zeller, K. A. et al. Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: Pumas as a case study. Landsc. Ecol. 29, 541–557 (2014).
Google Scholar 
Sillero, N. & Barbosa, A. M. Common mistakes in ecological niche models. Int. J. Geogr. Inf. Sci. 35, 213–226 (2021).
Google Scholar 
Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83, 2049–2055 (2002).
Google Scholar 
Johnson, D. H. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71 (1980).
Google Scholar 
Rodríguez-Soto, C. et al. Predicting potential distribution of the jaguar (Panthera onca) in Mexico: Identification of priority areas for conservation. Divers. Distrib. 17, 350–361 (2011).
Google Scholar 
Searle, C. E. et al. Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data. J. Appl. Ecol. 59, 2346–2359 (2022).
Google Scholar 
Ávila–Nájera, D. M., Chávez, C., Pérez–elizalde, S., Palacios–pérez, J. & Tigar, B. Coexistence of jaguars (Panthera onca) and pumas (Puma concolor) in a tropical forest in South–Eastern Mexico. Anim. Biodivers. Conserv. 43, 55–66 (2020).
Google Scholar 
Schoen, J. M., DeFries, R. & Cushman, S. Open-source, environmentally dynamic machine learning models demonstrate behavior-dependent utilization of mixed-use landscapes by jaguars (Panthera onca). Biol. Conserv. 302, 110978 (2025).
Google Scholar 
Bogoni, J. A., Peres, C. A. & Ferraz, K. M. P. M. B. Extent, intensity and drivers of mammal defaunation: A continental-scale analysis across the neotropics. Sci. Rep. 10, 1–16 (2020).
Google Scholar 
Gilbert, M. et al. Global cattle distribution in 2010 (5 minutes of arc). Harv. Dataverse https://doi.org/10.7910/DVN/GIVQ75 (2018).
Google Scholar 
Gilbert, M. et al. Global goats distribution in 2010 (5 minutes of arc). Harv. Dataverse https://doi.org/10.7910/DVN/OCPH42 (2018).
Google Scholar 
Gilbert, M. et al. Global sheep distribution in 2010 (5 minutes of arc). Harv. Dataverse https://doi.org/10.7910/DVN/BLWPZN (2018).Montalvo, V. H., Sáenz-Bolaños, C., Carrillo, E. & Fuller, T. K. A review of environmental and anthropogenic variables used to model jaguar occurrence. Neotropical Biology Conserv. 18, 31–51 (2023).
Google Scholar 
Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).
Google Scholar 
Jenness, J. Topographic Position Index (tpi_jen.avx) Extension for ArcView 3.x, v.1.-3a. Preprint at (2006). http://www.jennessent.com/arcview/tpi.htmAmatulli, G., McInerney, D., Sethi, T., Strobl, P. & Domisch, S. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Sci. Data. 7, 1–18 (2020).
Google Scholar 
Weill, R. R. & Brady, N. C. The Nature and Properties of Soils (Pearson, 2016).
Google Scholar 
Cushman, S. A., Elliot, N. B., Macdonald, D. W. & Loveridge, A. J. A multi-scale assessment of population connectivity in African lions (Panthera leo) in response to landscape change. Landsc. Ecol. 31, 1337–1353 (2016).
Google Scholar 
Macdonald, D. W. et al. Multi-scale habitat selection modeling identifies threats and conservation opportunities for the Sunda clouded Leopard (Neofelis diardi). Biol. Conserv. 227, 92–103 (2018).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. Preprint at. (2018).QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://Preprint at (2016). http://www.qgis.org/en/site/Gorelick, N. et al. Google Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Google Scholar 
Morato, R. G. et al. Space use and movement of a Neotropical top predator: The endangered jaguar. PLoS ONE. 11, 1–17 (2016).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6
Google Scholar 
De Jay, N. et al. mRMRe: An R package for parallelized mRMR ensemble feature selection. Bioinformatics 29, 2365–2368 (2013).
Google Scholar 
Fletcher, R. & Fortin, M. J. Spatial ecology and conservation modeling. Springer Nat. Switz. https://doi.org/10.1007/978-3-030-01989-1 (2018).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Hijmans, R. terra: Spatial Data Analysis. Preprint at (2024).Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Modell. 157, 281–300 (2002).
Google Scholar 
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Modell. 199, 142–152 (2006).
Google Scholar 
Nagy-Reis, M. et al. NEOTROPICAL CARNIVORES: A data set on carnivore distribution in the Neotropics. Ecology 101, 1–5 (2020).
Google Scholar 
Robin, X. et al. pROC: An open-source package for R and S + to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).
Google Scholar 
MMAMC, M. do M. A. e M. C. Cadastro Nacional de Florestas Públicas (CNFP). (2022).Nijhawan, S. Conservation units, priority areas and dispersal corridors for jaguars in Brazil. Cat News Special Issue 7, 43–47 (2012).UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). (UNEP-WCMC and IUCN www.protectedplanet.net., 2022).Griffith, D. M., Nivelo-Villavicencio, C., Rodas, F., Puglla, B. & Cisneros, R. New altitudinal records of Panthera onca (Carnivora: Felidae) in the Andean region of Ecuador. Mammalia 86, 190–195 (2021).
Google Scholar 
Zeller, K. Jaguars in the New Millennium Data Set Update: The State of the Jaguar in 2006 1–77 (Wildlife Conservation Society, Bronx, 2007).Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baurch-Murdo, S. & Kiesecker, J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob Chang. Biol. 00, 1–16 (2019).
Google Scholar 
Sorichetta, A. et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Sci. Data https://doi.org/10.1038/sdata.2015.45 (2015).
Google Scholar 
Li, X., Zhou, Y., Zhao, M. & Zhao, X. A harmonized global nighttime light dataset 1992–2018. Sci. Data. 7, 1–9 (2020).
Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Giglio, L., Justice, C., Boschetti, L. & Roy, D. MCD64A1 MODIS/Terra + Aqua burned area monthly L3 global 500m SIN grid V006. NASA EOSDIS Land. Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.006 (2015).
Google Scholar 
Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-Day L3 global 250m SIN grid V006. NASA EOSDIS Land. Processes Distrib. Act. Archive Cent. https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).
Google Scholar 
Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra gross primary productivity 8-Day L4 global 500m SIN grid V006. NASA EOSDIS Land. Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).
Google Scholar 
DiMiceli, C. et al. MOD44B MODIS/Terra vegetation continuous fields yearly L3 global 250m SIN grid V006. (2015). https://doi.org/10.5067/MODIS/MOD44B.006Potapov, P. et al. Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2020.112165 (2020).
Google Scholar 
Wan, Z., Hook, S. & Hulley, G. MODIS/Terra land surface Temperature/Emissivity 8-Day L3 global 1km SIN grid V061. NASA EOSDIS Land. Processes DAAC. https://doi.org/10.5067/MODIS/MOD11A2.061 (2021).
Google Scholar 
Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L. & Karger, D. CHELSA-BIOCLIM + A novel set of global climate-related predictors at kilometre-resolution. EnviDat https://doi.org/10.16904/envidat.332 (2022).
Google Scholar 
Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4. CGIAR-CSI SRTM 90m (2008). https://srtm.csi.cgiar.orgHengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).
Google Scholar 
Download referencesAcknowledgementsGCA doctorate is supported by a grant to David Macdonald at WildCRU from the Robertson Foundation. We are extremely grateful to the Alianza WWF-Fundación, Telmex/Telcel, the Universidad Nacional Autónoma de México (project DGAPA, PAPIT IN208017), and Amigos de Calakmul A.C. for their financial support. We extend special thanks to the ejidos Caobas and Laguna Om, as well as the Calakmul Biosphere Reserve, for granting permission to conduct our research on their lands. The Instituto Homem Pantaneiro is grateful to ICMBio/CENAP, Panthera Brasil, and Onçafari for their valuable partnerships. The Mamirauá Institute thanks CNPq for the scholarships, and the communities of Mamirauá Sustainable Development Reserve for their essential field support – especially Lázaro Pinto dos Santos (Lazinho) and Railgler Gomes dos Santos (Raí), in memoriam. ACSA thanks the Espírito Santo Research and Innovation Support Foundation (FAPES) for funding the project (FAPES 510/2016), as well as for the Capixaba Researcher Fellowship (FAPES 404/2022). SAC thanks NASA for the project grant 80NSSC25K7244. All figures were created by GCA using QGIS v3.36.0 (https://qgis.org). Editorial assistance was provided by ChatGPT (OpenAI) to improve clarity and language use.FundingGCA doctorate is supported by a grant to David Macdonald at WildCRU from the Robertson Foundation. ACSA was supported by the Espírito Santo Research and Innovation Support Foundation (FAPES) for funding the project (FAPES 510/2016), as well as for the Capixaba Researcher Fellowship (FAPES 404/2022). SAC was supported by a NASA project grant number 80NSSC25K7244.Author informationAuthors and AffiliationsWildlife Conservation Research Unit (WildCRU), Department of Biology, University of Oxford, Life and Mind Building, South Parks Road, Oxford, OX1 3EL, UKGuilherme Costa Alvarenga, Caroline C. Sartor, Samuel A. Cushman, Alexandra Zimmermann & David W. MacdonaldGrupo de Pesquisa em Ecologia e Conservação de Felinos na Amazônia, Mamirauá Institute for Sustainable Development (MISD), Estrada do Bexiga, nº 2584, Tefé, AM, BrazilGuilherme Costa Alvarenga, Diogo Maia Gräbin, Emiliano E. Ramalho & Marcos Roberto Monteiro de BritoDepartment of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USAŻaneta KasztaPrograma de Pós-Graduação em Ciência Animal e Programa de Pós-Graduação em Ecologia de Ecossistemas, Universidade Vila Velha, Av. Comissário José Dantas de Melo, 21, Boa Vista, Vila Velha, Espírito Santo, 29102-920, BrazilAna Carolina Srbek-AraujoLaboratório de Ecologia e Zoologia de Vertebrados (LABEV/ICB), Universidade Federal do Pará, Belém, PA, BrazilAna Cristina Mendes-Oliveira & Leonardo SenaPanthera, 104 West 40th Street, 5th Floor, New York, NY, 10018, USABart Harmsen, Rebecca J. Foster & Ronaldo G. MoratoInstituto de Ciencias de la Tierra, Biodiversidad y Ambiente (IBCIA), Universidad Nacional de Río Cuarto and National Scientific and Technical Research Council (CONICET), Ruta Nacional 36 Km 601, Río Cuarto, ArgentinaCarlos De AngeloInstitute for the Conservation of Neotropical Carnivores, Avenida Horácio Neto, 1030, Parque Edmundo Zanoni, Atibaia, SP, BrazilCarolina Franco Esteves, Claudia B. de Campos, Daiana Jeronimo Polli, Emiliano E. Ramalho & Fernando C. C. AzevedoPrograma de Pós-Graduação em Ecologia e Conservação, Instituto de Biociências – Inbio, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, 79070-900, BrazilDiego F. Passos VianaFundación Rewilding Argentina, Scalabrini Ortiz 3355, 1425, Buenos Aires, ArgentinaEmiliano DonadioWCS Big Cat Program, New York, USAEsteban PayánDepartamento de Ciências Naturais, Universidade Federal de São João del Rei, São João del Rei, MG, 36301-160, BrazilFernando C. C. AzevedoDepartment of Conservation Biology, Doñana Biological Station, CSIC, Avda. Américo Vespucio 26, 41092, Isla de la Cartuja, Seville, SpainFrancisco PalomaresWildlife Protection Solutions, 2501 Welton Street, Denver, CO, 80205, USAGeorge V. N. PowellLaboratorio de Ecología y Conservación de Fauna Silvestre, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, Ciudad de México, MexicoGerardo CeballosInstituto Homem Pantaneiro, Corumbá, Mato Grosso do Sul, BrazilGrasiela Porfirio & Wener Hugo Arruda MorenoDepartamento de Ciencias Ambientales, Universidad Autónoma Metropolitana, Unidad Lerma, CBS, Lerma de Villada, MexicoHeliot ZarzaPrimero Conservation, Box 158885935, Pinetop, AZ, USAIvonne CassaigneCentro de Pesquisa de Limnologia, Biodiversidade e Etnobiologia do Pantanal; Laboratório de Mastozoologia; Programa de Pós-graduação em Ciências Ambientais, Universidade do Estado de Mato Grosso-UNEMAT, Cáceres, MT, 78217-900, BrazilJuliano A. BogoniMamirauá Institute for Sustainable Development (MISD), Estrada do Bexiga, nº 2584, Tefé, AM, BrazilLouise MaranhãoAssociação Onçafari, São Paulo, SP, BrazilMarcos Roberto Monteiro de BritoSan Diego Zoo Wildlife Alliance, Conservation Science and Wildlife Health, 15600 San Pasqual Valley Road, Escondido, CA, 92027, USAMathias W. ToblerNatural History Museum, University of Oslo, POB 1172 Blindern, 0318, Oslo, NorwayØystein WiigCentro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros (CENAP-ICMBio), Estrada Municipal Hisaichi Takebayashi, 8600, Atibaia, SP, 12952-011, BrazilRicardo SampaioAlianza Jaguar AC, Lab. Vida Silvestre, Fac. Biol. Universidad Michoacana, Morelia, Michoacán, MexicoRodrigo NuñezWorld Wide Fund for Nature (WWF) UK, The Living Planet Centre, Brewery Road, Woking, GU214LL, UKValeria BoronEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, UKYadvinder MalhiLeverhulme Centre for Nature Recovery, University of Oxford, South Parks Road, Oxford, UKYadvinder MalhiAuthorsGuilherme Costa AlvarengaView author publicationsSearch author on:PubMed Google ScholarCaroline C. SartorView author publicationsSearch author on:PubMed Google ScholarSamuel A. CushmanView author publicationsSearch author on:PubMed Google ScholarAlexandra ZimmermannView author publicationsSearch author on:PubMed Google ScholarAna Carolina Srbek-AraujoView author publicationsSearch author on:PubMed Google ScholarAna Cristina Mendes-OliveiraView author publicationsSearch author on:PubMed Google ScholarBart HarmsenView author publicationsSearch author on:PubMed Google ScholarCarlos De AngeloView author publicationsSearch author on:PubMed Google ScholarCarolina Franco EstevesView author publicationsSearch author on:PubMed Google ScholarClaudia B. de CamposView author publicationsSearch author on:PubMed Google ScholarDaiana Jeronimo PolliView author publicationsSearch author on:PubMed Google ScholarDiego F. Passos VianaView author publicationsSearch author on:PubMed Google ScholarDiogo Maia GräbinView author publicationsSearch author on:PubMed Google ScholarEmiliano DonadioView author publicationsSearch author on:PubMed Google ScholarEmiliano E. RamalhoView author publicationsSearch author on:PubMed Google ScholarEsteban PayánView author publicationsSearch author on:PubMed Google ScholarFernando C. C. AzevedoView author publicationsSearch author on:PubMed Google ScholarFrancisco PalomaresView author publicationsSearch author on:PubMed Google ScholarGeorge V. N. PowellView author publicationsSearch author on:PubMed Google ScholarGerardo CeballosView author publicationsSearch author on:PubMed Google ScholarGrasiela PorfirioView author publicationsSearch author on:PubMed Google ScholarHeliot ZarzaView author publicationsSearch author on:PubMed Google ScholarIvonne CassaigneView author publicationsSearch author on:PubMed Google ScholarJuliano A. BogoniView author publicationsSearch author on:PubMed Google ScholarLeonardo SenaView author publicationsSearch author on:PubMed Google ScholarLouise MaranhãoView author publicationsSearch author on:PubMed Google ScholarMarcos Roberto Monteiro de BritoView author publicationsSearch author on:PubMed Google ScholarMathias W. ToblerView author publicationsSearch author on:PubMed Google ScholarØystein WiigView author publicationsSearch author on:PubMed Google ScholarRebecca J. FosterView author publicationsSearch author on:PubMed Google ScholarRicardo SampaioView author publicationsSearch author on:PubMed Google ScholarRodrigo NuñezView author publicationsSearch author on:PubMed Google ScholarRonaldo G. MoratoView author publicationsSearch author on:PubMed Google ScholarValeria BoronView author publicationsSearch author on:PubMed Google ScholarWener Hugo Arruda MorenoView author publicationsSearch author on:PubMed Google ScholarYadvinder MalhiView author publicationsSearch author on:PubMed Google ScholarDavid W. MacdonaldView author publicationsSearch author on:PubMed Google ScholarŻaneta KasztaView author publicationsSearch author on:PubMed Google ScholarContributionsGuilherme Costa Alvarenga: Conceptualization, Investigation, Data curation, Methodology, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing. Caroline C. Sartor: Methodology, Formal analysis, Writing – review & editing. Samuel (A) Cushman: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision, Project administration. Alexandra Zimmermann: Writing – review & editing. Ana Carolina Srbek-Araujo: Investigation, Resources, Writing – review & editing. Ana Cristina Mendes-Oliveira: Investigation, Resources, Writing – review & editing. Bart Harmsen: Investigation, Resources, Writing – review & editing. Carlos De Angelo: Investigation, Resources, Writing – review & editing. Carolina Franco Esteves: Investigation, Resources, Writing – review & editing. Claudia (B) de Campos: Investigation, Resources, Writing – review & editing. Daiana Jeronimo Polli: Investigation, Resources, Writing – review & editing. Diego F. Passos Viana: Investigation, Resources, Writing – review & editing. Diogo Maia Gräbin: Investigation, Resources, Writing – review & editing. Emiliano Donadio: Investigation, Resources, Writing – review & editing. Emiliano E. Ramalho: Investigation, Resources, Writing – review & editing. Esteban Payán: Investigation, Resources, Writing – review & editing. Fernando (C) C. Azevedo: Investigation, Resources, Writing – review & editing. Francisco Palomares: Investigation, Resources, Writing – review & editing. George V. N. Powell: Investigation, Resources, Writing – review & editing. Gerardo Ceballos: Investigation, Resources, Writing – review & editing. Grasiela Porfirio: Investigation, Resources, Writing – review & editing. Heliot Zarza: Investigation, Resources, Writing – review & editing. Ivonne Cassaigne: Investigation, Resources, Writing – review & editing. Juliano A. Bogoni: Investigation, Resources, Writing – review & editing. Leonardo Sena: Investigation, Resources, Writing – review & editing. Louise Maranhão: Investigation, Resources, Writing – review & editing. Marcos Roberto Monteiro de Brito: Investigation, Resources, Writing – review & editing. Mathias W. Tobler: Investigation, Resources, Writing – review & editing. Øystein Wiig: Investigation, Resources, Writing – review & editing. Rebecca J. Foster: Investigation, Resources, Writing – review & editing. Ricardo Sampaio: Investigation, Resources, Writing – review & editing. Rodrigo Nuñez: Investigation, Resources, Writing – review & editing. Ronaldo G. Morato: Investigation, Resources, Writing – review & editing. Valeria Boron: Investigation, Resources, Writing – review & editing. Wener Hugo Arruda Moreno: Investigation, Resources, Writing – review & editing. Yadvinder Malhi: Writing – review & editing. David W. Macdonald: Resources, Writing – review & editing, Funding acquisition. Zaneta Kaszta: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision, Project administration.Corresponding authorCorrespondence to
Guilherme Costa Alvarenga.Ethics declarations

Competing interests
The authors declare no competing interests.

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 1Rights 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 articleAlvarenga, G.C., Sartor, C.C., Cushman, S.A. et al. Range-wide assessment of habitat suitability for jaguars using multiscale species distribution modelling.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-30512-5Download citationReceived: 06 July 2025Accepted: 25 November 2025Published: 24 December 2025DOI: https://doi.org/10.1038/s41598-025-30512-5Share 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsHabitat suitabilityIndigenous landsJaguar conservation unitsMultiscale modellingPanthera oncaProtected areas More