Skip to main content
SpringerLink
Log in

Potentially suitable distribution areas of Populus euphratica and Tamarix chinensis by MaxEnt and random forest model in the lower reaches of the Heihe River, China

  • Research
  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Populus euphratica and Tamarix chinensis play a vital role in windbreak and sand fixation, maintaining species diversity and ensuring community stability. Managing and protecting the P. euphratica and T. chinensis forests in the Heihe River’s lower reaches is an urgent issue to maintain the desert region’s ecological balance. In this study, based on the distribution points of P. euphratica and T. chinensis species and environmental data, MaxEnt and random forest (RF) models were used to characterize the potential distribution areas of P. euphratica and T. chinensis in the lower reaches of the Heihe River. The results showed that the accuracy of the RF model was much higher than that of the MaxEnt model. Both the RF and MaxEnt models showed that the distance to the river greatly influenced the distribution of P. euphratica and T. chinensis. Furthermore, the RF model predicted significantly larger highly suitable areas for both P. euphratica and T. chinensis than the MaxEnt model. Our study enhances the understanding of the species’ spatial distribution, offering valuable insights for practical management and conservation strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Availability of data and material

Data are available from the authors upon reasonable request.

References

  • Anderson, R. P., Peterson, A. T., & Gomez-Laverde, M. (2002). Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos, 98(1), 3–16. https://doi.org/10.1034/j.1600-0706.2002.t01-1-980116.x

    Article  Google Scholar 

  • Bar Massada, A., Syphard, A. D., Stewart, S. I., & Radeloff, V. C. (2013). Wildfire ignition-distribution modelling: A comparative study in the Huron–Manistee National Forest, Michigan, USA. International Journal of Wildland Fire, 22(2). https://doi.org/10.1071/wf11178

  • Bertrand, R., Lenoir, J., Piedallu, C., Riofrio-Dillon, G., de Ruffray, P., Vidal, C., . . . Gegout, J. C. (2011). Changes in plant community composition lag behind climate warming in lowland forests. Nature, 479(7374), 517–520. https://doi.org/10.1038/nature10548

  • Booth, T. H. (2018). Species distribution modelling tools and databases to assist managing forests under climate change. Forest Ecology and Management, 430, 196–203. https://doi.org/10.1016/j.foreco.2018.08.019

    Article  Google Scholar 

  • Brown, J. L., Bennett, J. R., & French, C. M. (2017). SDMtoolbox 2.0: The next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ, 5, e4095. https://doi.org/10.7717/peerj.4095

    Article  Google Scholar 

  • Bystriakova, N., Peregrym, M., & Dragicevic, S. (2015). Effect of environment on distributions of rock ferns in the Mediterranean climate: The case of the genus Asplenium in Montenegro. Flora - Morphology, Distribution, Functional Ecology of Plants, 215, 84–91. https://doi.org/10.1016/j.flora.2015.07.003

    Article  Google Scholar 

  • Chang, Y., Bertola, L. V., & Hoskin, C. J. (2022). Species distribution modelling of the endangered mahogany glider (Petaurus gracilis ) reveals key areas for targeted survey and conservation. Austral Ecology, 48(2), 289–312. https://doi.org/10.1111/aec.13266

    Article  Google Scholar 

  • Chen, Y., Sharma, S., Zhou, X., Yang, K., Li, X., Niu, X., . . . Khadka, N. (2021). Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya. Atmospheric Research, 250. https://doi.org/10.1016/j.atmosres.2020.105365

  • Cheng, G. D., Li, X., Zhao, W. Z., Xu, Z. M., Feng, Q., Xiao, S. C., & Xiao, H. L. (2014). Integrated study of the water-ecosystem-economy in the Heihe River Basin. National Science Review, 1(3), 413–428. https://doi.org/10.1093/nsr/nwu017

    Article  Google Scholar 

  • Damaneh, J. M., Ahmadi, J., Rahmanian, S., Sadeghi, S. M. M., Nasiri, V., & Borz, S. A. (2022). Prediction of wild pistachio ecological niche using machine learning models. Ecological Informatics, 72. https://doi.org/10.1016/j.ecoinf.2022.101907

  • Elith, J., & Graham, C. H. (2009). Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32(1), 66–77. https://doi.org/10.1111/j.1600-0587.2008.05505.x

    Article  Google Scholar 

  • Fang, Y., Zhang, X., Wei, H., Wang, D., Chen, R., Wang, L., & Gu, W. (2021). Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae. Science of the Total Environment, 756, 143841. https://doi.org/10.1016/j.scitotenv.2020.143841

    Article  CAS  Google Scholar 

  • Fern, R. R., & Morrison, M. L. (2017). Mapping critical areas for migratory songbirds using a fusion of remote sensing and distributional modeling techniques. Ecological Informatics, 42, 55–60. https://doi.org/10.1016/j.ecoinf.2017.09.007

    Article  Google Scholar 

  • Ganglo, J. C. (2023). Ecological niche model transferability of the white star apple (Chrysophyllum albidum G. Don) in the context of climate and global changes. Scientific Reports, 13(1), 2430. https://doi.org/10.1038/s41598-023-29048-3

    Article  CAS  Google Scholar 

  • Guisan, A., Tingley, R., Baumgartner, J. B., Naujokaitis-Lewis, I., Sutcliffe, P. R., Tulloch, A. I., . . . Buckley, Y. M. (2013). Predicting species distributions for conservation decisions. Ecology Letters, 16(12), 1424–1435. https://doi.org/10.1111/ele.12189

  • Guo, Y., Wei, H., Lu, C., Gao, B., & Gu, W. (2016). Predictions of potential geographical distribution and quality of Schisandra sphenanthera under climate change. PeerJ, 4, e2554. https://doi.org/10.7717/peerj.2554

    Article  Google Scholar 

  • Guo, Y., Li, X., Zhao, Z., & Wei, H. (2018). Modeling the distribution of Populus euphratica in the Heihe River Basin, an inland river basin in an arid region of China. Science China Earth Sciences, 61(11), 1669–1684. https://doi.org/10.1007/s11430-017-9241-2

    Article  Google Scholar 

  • He, Z., & Zhao, W. (2006). Characterizing the spatial structures of riparian plant communities in the lower reaches of the Heihe River in China using geostatistical techniques. Ecological Research, 21(4), 551–559. https://doi.org/10.1007/s11284-006-0160-3

    Article  Google Scholar 

  • Hosseini, S. P., Amiri, M., & Senn, J. (2022). The effect of environmental and human factors on the distribution of brown bear (Ursus Arctos Isabellinus) in Iran. Applied Ecology and Environmental Research, 20(1), 153–170. https://doi.org/10.15666/aeer/2001_153170

    Article  Google Scholar 

  • Iverson, L. R., Rebbeck, J., Peters, M. P., Hutchinson, T., & Fox, T. (2019). Predicting Ailanthus altissima presence across a managed forest landscape in southeast Ohio. Forest Ecosystems, 6(1). https://doi.org/10.1186/s40663-019-0198-7

  • Khajoei Nasab, F., Mehrabian, A., Mostafavi, H., & Neemati, A. (2022). The influence of climate change on the suitable habitats of Allium species endemic to Iran. Environmental Monitoring and Assessment, 194(3), 169. https://doi.org/10.1007/s10661-022-09793-0

    Article  Google Scholar 

  • Kim, S. J., Lim, C.-H., Kim, G. S., Lee, J., Geiger, T., Rahmati, O., . . . Lee, W.-K. (2019). Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sensing, 11(1). https://doi.org/10.3390/rs11010086

  • Kong, F., Tang, L., He, H., Yang, F., Tao, J., & Wang, W. (2021). Assessing the impact of climate change on the distribution of Osmanthus fragrans using Maxent. Environmental Science and Pollution Research International, 28(26), 34655–34663. https://doi.org/10.1007/s11356-021-13121-3

    Article  Google Scholar 

  • Li, X., Zhou, W., & Ouyang, Z. (2013). Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution? Landscape and Urban Planning, 114, 1–8. https://doi.org/10.1016/j.landurbplan.2013.02.005

    Article  Google Scholar 

  • Li, Z., Zhang, X., Zheng, Y., Qiu, A., & Zhang, L. (2019b). Effects of temperature on flowering phenological traits of Populus euphratica Oliv. and Populus pruinosa Schrenk populations, Xinjiang, China. Journal of Arid Land, 11(5), 754–763. https://doi.org/10.1007/s40333-019-0026-5

    Article  Google Scholar 

  • Li, G., Cheng, Z., Lu, D., Lu, W., Huang, J., Zhi, J., & Li, S. (2019a). Examining hickory plantation expansion and evaluating suitability for it using multitemporal satellite imagery and ancillary data. Applied Geography, 109.https://doi.org/10.1016/j.apgeog.2019.102035

  • Luoto, M., & Heikkinen, R. K. (2008). Disregarding topographical heterogeneity biases species turnover assessments based on bioclimatic models. Global Change Biology, 14(3), 483–494. https://doi.org/10.1111/j.1365-2486.2007.01527.x

    Article  Google Scholar 

  • Ma, T., Wang, J., Zhou, G., Yue, Z., Hu, Q., Chen, Y., . . . Liu, J. (2013). Genomic insights into salt adaptation in a desert poplar. Nature Communications, 4, 2797. https://doi.org/10.1038/ncomms3797

  • Mi, C., Huettmann, F., Guo, Y., Han, X., & Wen, L. (2017). Why choose random forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. PeerJ, 5, e2849. https://doi.org/10.7717/peerj.2849

    Article  Google Scholar 

  • Mtengwana, B., Dube, T., Mudereri, B. T., & Shoko, C. (2021). Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa. Giscience & Remote Sensing, 58(4), 483–500. https://doi.org/10.1080/15481603.2021.1903281

    Article  Google Scholar 

  • Nath, A., Sinha, A., Lahkar, B. P., & Brahma, N. (2019). In search of aliens: Factors influencing the distribution of Chromolaena odorata L. and Mikania micrantha Kunth in the Terai grasslands of Manas National Park, India. Ecological Engineering, 131, 16–26. https://doi.org/10.1016/j.ecoleng.2019.02.012

    Article  Google Scholar 

  • Newete, S. W., Allem, S. M., Venter, N., & Byrne, M. J. (2020). Tamarix efficiency in salt excretion and physiological tolerance to salt-induced stress in South Africa. International Journal of Phytoremediation, 22(1), 3–9. https://doi.org/10.1080/15226514.2019.1633997

    Article  CAS  Google Scholar 

  • Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

    Article  Google Scholar 

  • Phillips, S. J., Dudík, M., Schapire, R. E.. [Internet] Maxent software for modeling species niches and distributions (version 3.4.1). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2023–10–13

  • Rochlin, I., Egizi, A., Narvaez, Z., Bonilla, D. L., Gallagher, M., Williams, G. M., . . . Fonseca, D. M. (2023). Microhabitat modeling of the invasive Asian longhorned tick (Haemaphysalis longicornis) in New Jersey, USA. Ticks and Tick-borne Diseases, 14(2), 102126. https://doi.org/10.1016/j.ttbdis.2023.102126

  • Salas, E. A. L., Seamster, V. A., Boykin, K. G., Harings, N. M., & Dixon, K. W. (2017). Modeling the impacts of climate change on species of concern (birds) in South Central U.S. based on bioclimatic variables. AIMS Environmental Science, 4(2), 358–385. https://doi.org/10.3934/environsci.2017.2.358

    Article  Google Scholar 

  • Shi, S., Yu, J., Wang, F., Wang, P., Zhang, Y., & Jin, K. (2021). Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau. Science of the Total Environment, 755(Pt 2), 142419.

    Article  CAS  Google Scholar 

  • Singh, M., Arunachalam, R., & Kumar, L. (2021). Modeling potential hotspots of invasive Prosopis juliflora (Swartz) DC in India. Ecological Informatics, 64. https://doi.org/10.1016/j.ecoinf.2021.101386

  • Sun, L., Liu, G., Lu, Y., Zhang, B., & Zhang, G. (2020). Molecular data and ecological niche modelling reveal the phylogeographic pattern of the widespread shrub Tamarix chinensis Lour. (Tamaricaceae) in China. Kew Bulletin, 75(3). https://doi.org/10.1007/s12225-020-09899-z

  • Tang, S.-L., Song, Y.-B., Zeng, B., & Dong, M. (2021). Potential distribution of the extremely endangered species Ostrya rehderiana (Betulaceae) in China under future climate change. Environmental Science and Pollution Research, 29(5), 7782–7792. https://doi.org/10.1007/s11356-021-16268-1

    Article  Google Scholar 

  • Tarnian, F., Kumar, S., Azarnivand, H., Chahouki, M. A. Z., & Mossivand, A. M. (2021). Assessing the effects of climate change on the distribution of Daphne mucronata in Iran. Environmental Monitoring and Assessment, 193(9), 562. https://doi.org/10.1007/s10661-021-09311-8

    Article  CAS  Google Scholar 

  • Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). BIOMOD - A platform for ensemble forecasting of species distributions. Ecography, 32(3), 369–373. https://doi.org/10.1111/j.1600-0587.2008.05742.x

    Article  Google Scholar 

  • Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2021). Predictive performance of presence‐only species distribution models: A benchmark study with reproducible code. Ecological Monographs, 92(1). https://doi.org/10.1002/ecm.1486

  • Wen, G., Ye, X., Lai, W., Shi, C., Huang, Q., Ye, L., & Zhang, G. (2021). Dynamic analysis of mixed forest species under climate change scenarios. Ecological Indicators, 133. https://doi.org/10.1016/j.ecolind.2021.108350

  • Williams, J. N., Seo, C., Thorne, J., Nelson, J. K., Erwin, S., O’Brien, J. M., & Schwartz, M. W. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15(4), 565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x

    Article  Google Scholar 

  • Xia, J. B., Zhang, S. Y., Zhao, X. M., Liu, J. H., & Chen, Y. P. (2016). Effects of different groundwater depths on the distribution characteristics of soil-Tamarix water contents and salinity under saline mineralization conditions. CATENA, 142, 166–176. https://doi.org/10.1016/j.catena.2016.03.005

    Article  CAS  Google Scholar 

  • Xiao, S.-C., Xiao, H.-L., Peng, X.-M., & Tian, Q.-Y. (2013). Daily and seasonal stem radial activity of Populus euphratica and its association with hydroclimatic factors in the lower reaches of China’s Heihe River basin. Environmental Earth Sciences, 72(2), 609–621. https://doi.org/10.1007/s12665-013-2982-y

    Article  Google Scholar 

  • Xiao, J., Eziz, A., Zhang, H., Wang, Z., Tang, Z., & Fang, J. (2019). Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China. Ecology and Evolution, 9(23), 13596–13607. https://doi.org/10.1002/ece3.5817

    Article  Google Scholar 

  • Xu, X., Zhang, H., Yue, J., Xie, T., Xu, Y., & Tian, Y. (2018). Predicting shifts in the suitable climatic distribution of walnut (Juglans regia L.) in China: Maximum entropy model paves the way to forest management. Forests, 9(3). https://doi.org/10.3390/f9030103

  • Zhang, D. Y., Yin, L. K., & Pan, B. R. (2002). Biological and ecological characteristics of Tamarix L. and its effect on the ecological environment. Science in China Series D-Earth Sciences, 45, 18–22. https://doi.org/10.1007/bf02878384

    Article  Google Scholar 

  • Zhang, S., Ye, Z., Chen, Y., & Xu, Y. (2017). Vegetation responses to an ecological water conveyance project in the lower reaches of the Heihe River basin. Ecohydrology, 10(6). https://doi.org/10.1002/eco.1866

  • Zheng, K., Wei, J. Z., Pei, J. Y., Cheng, H., Zhang, X. L., Huang, F. Q., . . . Ye, J. S. (2019). Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Science of the Total Environment, 660, 236–244. https://doi.org/10.1016/j.scitotenv.2019.01.022

Download references

Funding

This work was supported by the National Natural Science Foundation of China (51779209, 52379025).

Author information

Authors and Affiliations

  1. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an, China

    Yichi Zhang, Xiaohui Jiang, Yuxin Lei, Quanlong Wu, Yihan Liu & Xiaowei Shi

  2. Department of Physical Geography, College of Urban and Environmental Sciences, Northwest University, Xi’an, China

    Yichi Zhang & Xiaohui Jiang

Authors
  1. Yichi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuxin Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quanlong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yihan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaowei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ: validation, formal analysis, writing, review and editing. XJ: supervision, funding acquisition, project administration. YL: methodology, software investigation. QW: methodology. YL: validation. XS: editing.

Corresponding author

Correspondence to Xiaohui Jiang.

Ethics declarations

Ethics approval and consent to participate

All authors have read, have understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Jiang, X., Lei, Y. et al. Potentially suitable distribution areas of Populus euphratica and Tamarix chinensis by MaxEnt and random forest model in the lower reaches of the Heihe River, China. Environ Monit Assess 195, 1519 (2023). https://doi.org/10.1007/s10661-023-12122-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-023-12122-8

Keywords

Search

Navigation