Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Human activities and species biological traits drive the long-term persistence of old trees in human-dominated landscapes


Old trees have many ecological and socio-cultural values. However, knowledge of the factors influencing their long-term persistence in human-dominated landscapes is limited. Here, using an extensive database (nearly 1.8 million individual old trees belonging to 1,580 species) from China, we identified which species were most likely to persist as old trees in human-dominated landscapes and where they were most likely to occur. We found that species with greater potential height, smaller leaf size and diverse human utilization attributes had the highest probability of long-term persistence. The persistence probabilities of human-associated species (taxa with diverse human utilization attributes) were relatively high in intensively cultivated areas. Conversely, the persistence probabilities of spontaneous species (taxa with no human utilization attributes and which are not cultivated) were relatively high in mountainous areas or regions inhabited by ethnic minorities. The distinctly different geographic patterns of persistence probabilities of the two groups of species were related to their dissimilar responses to heterogeneous human activities and site conditions. A small number of human-associated species dominated the current cohort of old trees, while most spontaneous species were rare and endemic. Our study revealed the potential impacts of human activities on the long-term persistence of trees and the associated shifts in species composition in human-dominated landscapes.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Distribution of study counties and photos of two representative old trees.
Fig. 2: Current composition characteristics and distribution of old trees.
Fig. 3: RFR across species.
Fig. 4: Geographic patterns and determinants of SRR.

Data availability

The distribution data of old-tree species are available in Atlas of Woody Plants in China: Distribution and Climate38 and the National Specimen Information Infrastructure ( The main source of old-tree species biological traits data is accessible through the ‘Flora of China’ ( The species list and tree abundance data of old trees in China are available in Figshare (


  1. Lindenmayer, D. B. Conserving large old trees as small natural features. Biol. Conserv. 211, 51–59 (2017).

    Google Scholar 

  2. Lindenmayer, D. B. & Laurance, W. F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. 92, 1434–1458 (2017).

    PubMed  Google Scholar 

  3. Blicharska, M. & Mikusiński, G. Incorporating social and cultural significance of large old trees in conservation policy. Conserv. Biol. 28, 1558–1567 (2014).

    Article  PubMed  Google Scholar 

  4. Cannon, C. H., Piovesan, G. & Munné-Bosch, S. Old and ancient trees are life history lottery winners and vital evolutionary resources for long-term adaptive capacity. Nat. Plants 8, 136–145 (2022).

    PubMed  Google Scholar 

  5. Liu, J. et al. Age and spatial distribution of the world’s oldest trees. Conserv. Biol. 36, e1390 (2022).

    Google Scholar 

  6. Lindenmayer, D. B., Laurance, W. F. & Franklin, J. F. Global decline in large old trees. Science 338, 1305–1306 (2012).

    CAS  PubMed  Google Scholar 

  7. Patrut, A. et al. The demise of the largest and oldest African baobabs. Nat. Plants 4, 423–426 (2018).

    PubMed  Google Scholar 

  8. Liu, J., Yang, B. & Lindenmayer, D. B. The oldest trees in China and where to find them. Front. Ecol. Environ. 17, 319–322 (2019).

    Google Scholar 

  9. Huang, L. et al. Biogeographic and anthropogenic factors shaping the distribution and species assemblage of heritage trees in China. Urban For. Urban Green. 50, 126652 (2020).

    Google Scholar 

  10. Jin, C. et al. Co-existence between humans and nature: heritage trees in China’s Yangtze River region. Urban For. Urban Green. 54, 126748 (2020).

    Google Scholar 

  11. Locosselli, G. M. et al. Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature. Proc. Natl Acad. Sci. USA 117, 33358–33364 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Liu, J. et al. Diversity and density patterns of large old trees in China. Sci. Total Environ. 655, 255–262 (2019).

    CAS  PubMed  Google Scholar 

  13. Lindenmayer, D. B. & Laurance, W. F. The unique challenges of conserving large old trees. Trends Ecol. Evol. 31, 416–418 (2016).

    PubMed  Google Scholar 

  14. Huang, L. et al. Local cultural beliefs and practices promote conservation of large old trees in an ethnic minority region in southwestern China. Urban For. Urban Green. 49, 126584 (2020).

    Google Scholar 

  15. Zhou, L. et al. Fengshui forests as a conservation paradigm of the golden larch in China. For. Ecol. Manage. 520, 120358 (2022).

    Google Scholar 

  16. Tang, C. Q. et al. Evidence for the persistence of wild Ginkgo biloba (Ginkgoaceae) populations in the Dalou Mountains, southwestern China. Am. J. Bot. 99, 1408–1414 (2012).

    PubMed  Google Scholar 

  17. Frascaroli, F., Bhagwat, S., Guarino, R., Chiarucci, A. & Schmid, B. Shrines in Central Italy conserve plant diversity and large trees. Ambio 45, 468–479 (2016).

    PubMed  Google Scholar 

  18. Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).

    CAS  PubMed  Google Scholar 

  19. Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 1–5 (2015).

    Google Scholar 

  20. Laanisto, L., Sammul, M., Kull, T., Macek, P. & Hutchings, M. J. Trait‐based analysis of decline in plant species ranges during the 20th century: a regional comparison between the UK and Estonia. Glob. Change Biol. 21, 2726–2738 (2015).

    Google Scholar 

  21. Osborne, C. P. et al. Human impacts in African savannas are mediated by plant functional traits. New Phytol. 220, 10–24 (2018).

    PubMed  Google Scholar 

  22. Xu, W.-B. et al. Human activities have opposing effects on distributions of narrow-ranged and widespread plant species in China. Proc. Natl Acad. Sci. USA 116, 26674–26681 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Cámara-Leret, R. et al. Fundamental species traits explain provisioning services of tropical American palms. Nat. Plants 3, 1–7 (2017).

    Google Scholar 

  24. Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).

    CAS  PubMed  Google Scholar 

  25. Lu, M. & He, F. Estimating regional species richness: the case of China’s vascular plant species. Glob. Ecol. Biogeogr. 26, 835–845 (2017).

    Google Scholar 

  26. Zhang, H., Lai, P. Y. & Jim, C. Y. Species diversity and spatial pattern of old and precious trees in Macau. Landsc. Urban Plan. 162, 56–67 (2017).

    Google Scholar 

  27. Qian, S. et al. Biotic homogenization of China’s urban greening: a meta-analysis on woody species. Urban For. Urban Green. 18, 25–33 (2016).

    Google Scholar 

  28. Tang, C. Q. et al. Identifying long-term stable refugia for relict plant species in East Asia. Nat. Commun. 9, 1–14 (2018).

    Google Scholar 

  29. Tang, C. Q. et al. Survival of a tertiary relict species, Liriodendron chinense (Magnoliaceae), in southern China, with special reference to village fengshui forests. Am. J. Bot. 100, 2112–2119 (2013).

    PubMed  Google Scholar 

  30. Coggins, C. & Minor, J. Fengshui forests as a socio-natural reservoir in the face of climate change and environmental transformation. Asia Pac. Perspect. 15, 4–29 (2018).

    Google Scholar 

  31. Yuan, J. & Liu, J. Fengshui forest management by the Buyi ethnic minority in China. For. Ecol. Manage. 257, 2002–2009 (2009).

    Google Scholar 

  32. Tang, C. Q. et al. Population structure of relict Metasequoia glyptostroboides and its habitat fragmentation and degradation in south-central China. Biol. Conserv. 144, 279–289 (2011).

    Google Scholar 

  33. Lôbo, D., Leao, T., Melo, F. P., Santos, A. M. & Tabarelli, M. Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers. Distrib. 17, 287–296 (2011).

    Google Scholar 

  34. Zhao, Y.-P. et al. Resequencing 545 ginkgo genomes across the world reveals the evolutionary history of the living fossil. Nat. Commun. 10, 1–10 (2019).

    Google Scholar 

  35. Cubino, J. P. et al. Taxonomic, phylogenetic, and functional composition and homogenization of residential yard vegetation with contrasting management. Landsc. Urban Plan. 202, 103877 (2020).

    Google Scholar 

  36. Piovesan, G., Cannon, C. H., Liu, J. & Munné-Bosch, S. Ancient trees: irreplaceable conservation resource for ecosystem restoration. Trends Ecol. Evol. 37, 1025–1028 (2022).

    PubMed  Google Scholar 

  37. Lu, L.-M. et al. Evolutionary history of the angiosperm flora of China. Nature 554, 234–238 (2018).

    CAS  PubMed  Google Scholar 

  38. Fang, J., Wang, Z. & Tang, Z. Atlas of Woody Plants in China: Distribution and Climate Vol. 1 (Springer, 2011).

  39. Wang, Z., Fang, J., Tang, Z. & Lin, X. Patterns, determinants and models of woody plant diversity in China. Proc. R. Soc. B 278, 2122–2132 (2011).

    PubMed  Google Scholar 

  40. Li, Y. et al. Leaf size of woody dicots predicts ecosystem primary productivity. Ecol. Lett. 23, 1003–1013 (2020).

    PubMed  PubMed Central  Google Scholar 

  41. Peng, S. et al. Preserving the woody plant tree of life in China under future climate and land-cover changes. Proc. R. Soc. B 289, 20221497 (2022).

    PubMed  PubMed Central  Google Scholar 

  42. Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).

    Google Scholar 

  43. Wang, Y. et al. Drivers of large‐scale geographical variation in sexual systems of woody plants. Glob. Ecol. Biogeogr. 29, 546–557 (2020).

    Google Scholar 

  44. Molina-Venegas, R., Rodríguez, M. Á., Pardo-de-Santayana, M., Ronquillo, C. & Mabberley, D. J. Maximum levels of global phylogenetic diversity efficiently capture plant services for humankind. Nat. Ecol. Evol. 5, 583–588 (2021).

    PubMed  Google Scholar 

  45. ArcGIS v.10.1 (ESRI, 2010).

  46. Kissling, W. D. & Carl, G. Spatial autocorrelation and the selection of simultaneous autoregressive models. Glob. Ecol. Biogeogr. 17, 59–71 (2008).

    Google Scholar 

  47. Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach (Springer, 2002).

  48. Bivand, R. et al. spatialreg: Spatial Regression Analysis. The R Project for Statistical Computing (2019)

  49. Jin, Y. & Qian, H. V. PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).

    Google Scholar 

  50. Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).

    PubMed  Google Scholar 

  51. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Google Scholar 

  52. Wood, S. Package ‘mgcv’. The R Project for Statistical Computing (2015)–38.tar.gz

Download references


We thank M. Zheng, J. Wang, R. Liao and L. Tian for help in data collection; our colleagues and local forestry departments that generously provided the original data of old trees; J. Liu for disccussing many sections of the paper; and G. Wheeler for assistance with the English language and grammatical editing of the paper. This study was supported by the Chongqing Technology Innovation and Application Demonstration Major Theme Special Project (cstc2018jszxzdyfxmX0007) to Y.Y., the National Natural Science Foundation of China (32071652, 32025025 and 31988102) to Y.Y and Z.T. and the China Postdoctoral Science Foundation (2022M720254) to L.H.

Author information

Authors and Affiliations



L.H., Y.Y., Z.T. and D.B.L. conceived the paper. L.H., L.Z., C.J. and S.H. established the database. L.H. and Y.P. analysed the data. L.H. wrote the manuscript. All authors, including Y.G., Y.M., K.S., M.P., H.L., D.L., X.X., J.M., C.C., C.Y.J. and E.Y., contributed substantially to the writing and discussion of the paper.

Corresponding authors

Correspondence to Yongchuan Yang, Zhiyao Tang or David B. Lindenmayer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Charles Cannon, Grzegorz Mikusiński and Fangliang He for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Table 1 Determinants of tree proportion of human-associated species. Summary results of simultaneous autoregressive models explaining the relationships between the explanatory variables and tree proportion of human-associated species at the spatial scale of 100 km × 100 km. A total of 384 grids were used in the analysis. (pseudo-R2 = 0.43)
Extended Data Table 2 Determinants of spatial recruitment rate (SRR). Summary results of simultaneous autoregressive models explaining the relationships between the explanatory variables and SRR at the spatial scale of 100 km ×100 km. A total of 384 grids were used in the analysis

Extended Data Fig. 1 Comparison of species richness and individual counts among the three groups of old trees.

a, Comparison of species richness for the three groups of old trees at the national scale. b, Comparison of individual counts for the three groups of old trees at the national scale. HS, human-associated species; SS, semi-spontaneous species; S, spontaneous species.

Extended Data Fig. 2 Ordering of old tree species by tree abundance and species observed range size.

a, Ordering of old tree species by tree abundance. b, Ordering of old tree species by species observed range size (number of study grids in which a species occurred).

Extended Data Fig. 3 Comparison of potential and observed range size for the three groups of old trees.

The observed range size refers to the number of grid cells in which a species has been observed to occur. Boxplots in show the median (centre line), 25th and 75th quartiles (hinges), 1.5 times the interquartile range from the hinges (whiskers) and values outside 1.5 times the interquartile range (points).

Extended Data Fig. 4 Variations in range filling rate (RFR) among family.

Comparison of the mean RFR between the families with more than ten species. Data are presented as mean values +/− SE.

Extended Data Fig. 5 Difference of spatial recruitment rate (SRR) between human-associated species and spontaneous species.

a, Histogram of SRR of human-associated species and spontaneous species. b, Comparison of the SRR of human-associated species (n = 206) and spontaneous species (n = 931) at the grid scale. In (B), boxplots in show the median (centre line), 25th and 75th quartiles (hinges), 1.5 times the interquartile range from the hinges (whiskers) and values outside 1.5 times the interquartile range (points). Significance test was performed using the Wilcoxon rank-sum test.

Extended Data Fig. 6 Administrative provinces and topography of China.

a, China’s administrative provinces. b, Topography with annotations of key landform features of China.

Extended Data Fig. 7 Distribution of study counties.

Counties (round dots) with species-abundance data of old trees in our database. The red line indicates the Hu Huanyong Line, which separates China into the northwestern and southeastern halves based on human population density. Background data show the distribution of vegetation types in China.

Extended Data Fig. 8 Methods for calculating the range filling rate and spatial recruitment rate.

a, Methods for calculating the range filling rate. b, Methods for calculating the spatial recruitment rate.

Extended Data Fig. 9 Distribution of species human utilization index.

Ordering of old tree species by human utilization index. Red vertical dashed line represents the 75th quartile.

Extended Data Fig. 10 Correlation among explanatory variables.

Spearman’s rank correlation coefficients among the explanatory variables.

Supplementary information

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

Huang, L., Jin, C., Pan, Y. et al. Human activities and species biological traits drive the long-term persistence of old trees in human-dominated landscapes. Nat. Plants 9, 898–907 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing