Ai, M., Chen, X., Yu, Q., 2024. Spatial correlation analysis between human disturbance intensity (HDI) and ecosystem services value (ESV) in the Chengdu-Chongqing urban agglomeration. Ecol. Indic. 158, 111555.
Badgley, C., Smiley, T. M., Terry, R., et al., 2017. Biodiversity and topographic complexity: modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211-226.
Beck, J., Ballesteros-Mejia, L., Nagel, P., et al., 2013. Online solutions and the “Wallacean shortfall”: what does GBIF contribute to our knowledge of species' ranges? Divers. Distrib. 19, 1043-1050.
Borcard, D., Legendre, P., 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51-68.
Bordelon, A., 2022. Herbarium: the quest to preserve and classify the world's plants. J. Bot. Res. Inst. Tex. 16, 448.
Bowler, D. E., Callaghan, C. T., Bhandari, N., et al., 2022. Temporal trends in the spatial bias of species occurrence records. Ecography 2022, e06219.
Canhos, V., Souza, S., De Giovanni, R., et al., 2004. Global biodiversity informatics: setting the scene for a “new world” of ecological modeling. Biodivers. Inform. 1, 1-13.
Chapman, A. D., Busby, J. R., 1994. Linking plant species information to continental biodiversity inventory, climate modeling and environmental monitoring. In: Miller, R.I. (Ed.), Mapping the Diversity of Nature. Springer, Dordrecht, pp. 179-195.
Chen, C., 1994. History of plant taxonomy in China. In: Wang, Z. (Ed.), History of Chinese Botany. Science Press, Beijing, pp. 121-144.
China Cartographic Publishing House, 1962. China Transportation and Travel Map. China Cartographic Publishing House, Beijing.
China Cartographic Publishing House, 1974. China Transportation Map. China Cartographic Publishing House, Xi’an.
China Cartographic Publishing House, 1986. China Transportation Atlas. China Cartographic Publishing House, Beijing.
Coelho, M. T. P., Barreto, E., Rangel, T. F., et al., 2023. The geography of climate and the global patterns of species diversity. Nature 622, 537-544.
Cosentino, F., Maiorano, L., 2021. Is geographic sampling bias representative of environmental space? Ecol. Inform. 64, 101369.
Crisp, M. D., Laffan, S., Linder, H. P., et al., 2001. Endemism in the Australian flora. J. Biogeogr. 28, 183-198.
Daru, B. H., Park, D. S., Primack, R. B., et al., 2018. Widespread sampling biases in herbaria revealed from large-scale digitization. New Phytol. 217, 939-955.
Department of Comprehensive Statistics of National Bureau of Statistics, 2010. China Compendium of Statistics 1949-2008. China Statistics Press, Beijing.
Diniz-Filho, J. A. F., Bini, L. M., 2005. Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecol. Biogeogr. 14, 177-185.
Diniz-Filho, J. A. F., Bini, L. M., Hawkins, B. A., 2003. Spatial autocorrelation and red herrings in geographical ecology. Global Ecol. Biogeogr. 12, 53-64.
Freitag, S., Hobson, C., Biggs, H. C., et al., 1998. Testing for potential survey bias: the effect of roads, urban areas and nature reserves on a southern African mammal data set. Anim. Conserv. 1, 119-127.
Grilo, C., Bissonette, J., Cramer, P., 2010. Mitigation measures to reduce impacts on biodiversity. In: Jones, S. R. (Ed.), Highways: Construction, Management, and Maintenance. Nova Science Publishers, Inc, Hauppauge, pp. 73-114.
Hu, X., Wu, C., Wang, J., et al., 2018. Identification of spatial variation in road network and its driving patterns: economy and population. Reg. Sci. Urban Econ. 71, 37-45.
Huang, J., Chen, J., Ying, J., et al., 2011. Features and distribution patterns of Chinese endemic seed plant species. J. Systemat. Evol. 49, 81-94.
Husak, G., Michaelsen, J., Funk, C., 2007. Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications. Int. J. Climatol. 27, 935-944.
Irl, S. D. H., Harter, D. E. V., Steinbauer, M. J., et al., 2015. Climate vs. topography - spatial patterns of plant species diversity and endemism on a high-elevation island. J. Ecol. 103, 1621-1633.
Johnson, J. B., Omland, K. S., 2004. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101-108.
Kadmon, R., Farber, O., Danin, A., 2004. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol. Appl. 14, 401-413.
Kleinschroth, F., Laporte, N., Laurance, W. F., et al., 2019. Road expansion and persistence in forests of the Congo Basin. Nat. Sustain. 2, 628-634.
Lituma, C., Buehler, D., 2016. Minimal bias in surveys of grassland birds from roadsides. Condor 118, 715-727.
Liu, D., Liu, L., You, Q., et al., 2022a. Development of a landscape-based multi-metric index to assess wetland health of the Poyang Lake. Remote Sens. 14, 1082.
Liu, H., Qin, H., Bao, B., et al., 2022b. Analysis of digitized specimens of higher plants in China. Guihaia 42, 29-45.
Loiselle, B. A., Joergensen, P. M., Consiglio, T., et al., 2008. Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J. Biogeogr. 35, 105-116.
Meyer, C., Kreft, H., Guralnick, R., et al., 2015. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221.
Ministry of Transport of the People's Republic of China, 2005. The Second National Road Census Data Compilation. China Communications Press, Beijing.
National Bureau of Statisitics of China, 2009-2020. China Statistical Yearbook. China Statistics Press, Beijing.
Nualart, N., Ibanez, N., Soriano, I., et al., 2017. Assessing the relevance of herbarium collections as tools for conservation biology. Bot. Rev. 83, 303-325.
Oliveira, U., Paglia, A. P., Brescovit, A. D., et al., 2016. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers. Distrib. 22, 1232-1244.
Pan, J., Zhao, X., Guo, W., et al., 2024. Characterizing China's road network development from a spatial entropy perspective. J. Transport Geogr. 116, 103848.
Parnell, J. A. N., Simpson, D. A., Moat, J., et al., 2003. Plant collecting spread and densities: their potential impact on biogeographical studies in Thailand. J. Biogeogr. 30, 193-209.
Peng, H. E., Chen, J., Kong, H., et al., 2021. Important supporting role of biological specimen in biodiversity conservation and research. Bull. Chin. Acad. Sci. 36, 425-435.
Petersen, T. K., Speed, J. D. M., Groetan, V., et al., 2021. Species data for understanding biodiversity dynamics: the what, where and when of species occurrence data collection. Ecol. Solut. Evid. 2, e12048.
Qian, H., Kissling, W. D., 2010. Spatial scale and cross-taxon congruence of terrestrial vertebrate and vascular plant species richness in China. Ecology 91, 1172-1183.
Rahbek, C., Graves, G. R., 2001. Multiscale assessment of patterns of avian species richness. Proc. Natl. Acad. Sci. U.S.A. 98, 4534-4539.
Reddy, S., Davalos, L. M., 2003. Geographical sampling bias and its implications for conservation priorities in Africa. J. Biogeogr. 30, 1719-1727.
Sipek, M., Sajna, N., 2024. Lowland forest fragment characteristics and anthropogenic disturbances determine alien plant species richness and composition. Biol. Invasions 26, 1595-1614.
U.S. Geological Survey (USGS), 1996. GTOPO30. DOI: 10.5066/F7DF6PQS. Accessed July 19, 2023. https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-hydro1k#overview. Data repository: USGS Earth Resources Observation and Science (EROS) Center.
Valavi, R., Elith, J., Lahoz-Monfort, J. J., et al., 2021. Modelling species presence-only data with random forests. Ecography 44, 1731-1742.
Van Buskirk, J., Jansen van Rensburg, A., 2020. Relative importance of isolation-by-environment and other determinants of gene flow in an alpine amphibian. Evolution 74, 962-978.
van der Maaten, E., Hamann, A., van der Maaten-Theunissen, M., et al., 2017. Species distribution models predict temporal but not spatial variation in forest growth. Ecol. Evol. 7, 2585-2594.
Vargas, C. A., Bottin, M., Sarkinen, T., et al., 2022. Environmental and geographical biases in plant specimen data from the Colombian Andes. Bot. J. Linn. Soc. 200, 451-464.
Venables, W. N., Ripley, B. D., 2002. Modern Applied Statistics with S, fouth ed., Springer, New York.
Wang, Z., Fang, J., Tang, Z., et al., 2012. Geographical patterns in the beta diversity of China's woody plants: the influence of space, environment and range size. Ecography 35, 1092-1102.
Wong, H. L., Wang, Y., Luo, R., et al., 2017. Local governance and the quality of local infrastructure: evidence from village road projects in rural China. J. Publ. Econ. 152, 119-132.
Yang, W., Liu, D., You, Q., et al., 2021. Taxonomic bias in occurrence information of angiosperm species in China. Sci. China Life Sci. 64, 584-592.
Yang, W., Ma, K., Kreft, H., 2013. Geographical sampling bias in a large distributional database and its effects on species richness-environment models. J. Biogeogr. 40, 1415-1426.
Yang, W., Ma, K., Kreft, H., 2014. Environmental and socio-economic factors shaping the geography of floristic collections in China. Global Ecol. Biogeogr. 23, 1284-1292.
Zhang, J., Xiao, C., Duan, X., et al., 2024. Species' geographical range, environmental range and traits lead to specimen collection preference of dominant plant species of grasslands in Northern China. Plant Divers. 46, 353-361.
Zhang, T., Sun, Y., Guan, M., et al., 2022. Human activity intensity in China under multi-factor interactions: spatiotemporal characteristics and influencing factors. Sustainability 14, 3113.
Zhuang, H., Wang, C., Wang, Y., et al., 2021. Native useful vascular plants of China: a checklist and use patterns. Plant Divers. 43, 134-141.
Zhou, Y., Tong, C., Wang, Y., 2022. Road construction, economic growth, and poverty alleviation in China. Growth Change 53, 1306-1332.
Zizka, A., Antonelli, A., Silvestro, D., 2021. sampbias, a method for quantifying geographic sampling biases in species distribution data. Ecography 44, 25-32.
