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

Nature Plants volume 9, pages 898–907 (2023)Cite this article

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Abstract

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.

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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 (www.nsii.org.cn/). The main source of old-tree species biological traits data is accessible through the ‘Flora of China’ (https://www.plantplus.cn/foc). The species list and tree abundance data of old trees in China are available in Figshare (https://doi.org/10.6084/m9.figshare.22545844).

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Acknowledgements

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

  1. Institute of Ecology, College of Urban and Environmental Sciences and Key Laboratory for Earth Surface Processes, Peking University, Beijing, China

    Li Huang, Yanpei Guo, Yuanyuan Meng & Zhiyao Tang

  2. Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment, Ministry of Education, Chongqing University, Chongqing, China

    Li Huang, Cheng Jin, Lihua Zhou, Siwei Hu, Mingyue Pang, Hong Li, Dunmei Lin & Yongchuan Yang

  3. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China

    Yingji Pan

  4. Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai, China

    Kun Song & Enrong Yan

  5. Key Laboratory of Bio-resources and Eco-environment, Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China

    Xiaoting Xu

  6. Department of Geography and Environmental Planning, University of Maine at Farmington, Farmington, ME, USA

    Jesse Minor

  7. Faculty in Geography and Asian Studies, Bard College at Simon’s Rock, Great Barrington, MA, USA

    Chris Coggins

  8. Department of Social Sciences, Education University of Hong Kong, Tai Po, China

    C. Y. Jim

  9. Fenner School of Environment and Society, Australian National University, Canberra, Australian Capital Territory, Australia

    David B. Lindenmayer

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  1. Li Huang
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Contributions

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.

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

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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)
Full size table
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
Full size table

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.

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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). https://doi.org/10.1038/s41477-023-01412-1

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