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Hyperspectral proximal sensing shows clear relation between Spatial pattern of leaf traits and bacterial alpha diversity

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Abstract

The phyllosphere bacteria play a crucial role in global greenhouse gas emissions and sequestration, but the spatial interactions between phyllosphere bacterial diversity, host leaf traits and environmental variation remain poorly understood. This gap is mainly due to methodological limitations in linking the spatial pattern of bacterial diversity to leaf traits. Here, we present machine learning models based on visible and near-infrared (VIS-NIR) leaf hyperspectral proximal sensing that are used to independently predict the phyllosphere bacterial alpha-diversity indices and leaf traits under different abiotic conditions for both sides of the leaf. We demonstrate that the models can effectively represent leaf traits and bacterial alpha-diversity indices for different abiotic environmental conditions. The cross-correlation of the spatial patterns as a result of the spatial application of the independent models reveals fine-scale associations between leaf traits and bacterial colonization patterns. Our findings highlight the great potential of hyperspectral proximal sensing for understanding the relationship between leaf bacterial richness and leaf resources within the leaf microecosystem. Ultimately, this will enhance our capacity to quantify the contribution of the leaf bacteria to the greenhouse gas balance of the forest canopy in a changing climate.

Data availability

The dataset used during the current study is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank Anke Becker for valuable input and productive discussions on the design of the field campaigns. We are grateful to Fiona Ullmann, Ramona Zülch, Sarah Gilles, Rebecca Dannoritzer, Merle Munoz Andres, Dominik Greif, Marco Göttig, and Nils Jansen for their support in processing leaves in the field and lab. We also thank Anjaharinony Andry Ny Aina Rakotomalala, Tobias Müller, Stefan Pinkert, and Nina Farwig as well as Christian Lampei and Mona Schreiber for their contributions to campaign design, planning, and execution, including valuable climbing support. Special thanks to Andreas Kautz for logistical support in the forest, Esther Meißner for help with field and lab work, Xiangbo Yin for assistance with fieldwork preparations, and Stella Drechsel and Olga Schechtel for conducting the C/N analyses of leaf samples. L.O. acknowledges co-funding for the position of Susanne Walden by Deutsche Forschungsgemeinschaft (DFG, GermanResearch Foundation) RU 5571, 507084794, OP 219/20-1.

Funding

Open Access funding enabled and organized by Projekt DEAL. This work was funded by the LOEWE research initiative of the State of Hesse, Germany, through the Ministry of Science and Arts (HMWK), as part of the LOEWE research cluster Tree-M (LOEWE/2/ 15/519/03/08.001(0002)/88).

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Authors and Affiliations

  1. Laboratory for Climatology and Remote Sensing, Department of Geography, Marburg University, 35032, Marburg, Germany

    Fanhao Kong, Sebastian Achilles & Jörg Bendix

  2. Marburg University, Department of Biology, Evolutionary Ecology of Plants, 35043, Marburg, Germany

    Annabell Rosemarie Wagner, Lucy Saueressig, Robert R. Junker & Hamed Azarbad

  3. Department of Biology, Plant Ecology and Geobotany, Marburg University, 35043, Marburg, Germany

    Susanne Walden, Eric Martiné, Lars Opgenoorth & Mona Schreiber

  4. Department of Geography, Ecological Plant Geography, Marburg University, 34032, Marburg, Germany

    Maaike Y. Bader

  5. Department of Biology, Marburg University, 35043, Marburg, Germany

    Stella Drechsler

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  1. Fanhao Kong
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Contributions

F.K. and J.B. wrote the manuscript with input from all authors. S.A. designed the hyperspectral scanning frame, and F.K. developed the hyperspectral data analysis pipeline. A.W. performed the bacterial data analysis. S.W. and E.M. conducted the Dualex-based leaf trait measurements. S.D. carried out the C/N analyses of leaf samples. S.W., E.M., F.K., A.W., and L.S. conducted the field campaigns and sample collection. All authors reviewed and approved the final manuscript.

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Fanhao Kong.

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Kong, F., Wagner, A.R., Walden, S. et al. Hyperspectral proximal sensing shows clear relation between Spatial pattern of leaf traits and bacterial alpha diversity.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33183-4

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