Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites
Yingyi Zhao
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Search for more papers by this authorZhihui Wang
Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070 China
Search for more papers by this authorZhengbing Yan
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093 China
Search for more papers by this authorMinkyu Moon
Department of Earth and Environment, Boston University, Boston, MA, 02215 USA
School of Natural Resources and Environmental Science, Kangwon National University, Chuncheon, 24341 Korea
Search for more papers by this authorDedi Yang
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830 USA
Search for more papers by this authorLin Meng
Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, TN, 37240 USA
Search for more papers by this authorSolveig Franziska Bucher
Institute of Ecology and Evolution with Herbarium Haussknecht and Botanical Garden, Department of Plant Biodiversity, Friedrich Schiller University Jena, Jena, D-07743 Germany
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, D-04103 Germany
Search for more papers by this authorJing Wang
School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 510006 Guangdong, China
Search for more papers by this authorGuangqin Song
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Search for more papers by this authorZhengfei Guo
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Search for more papers by this authorYanjun Su
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093 China
Search for more papers by this authorCorresponding Author
Jin Wu
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Author for correspondence:
Jin Wu
Email:[email protected]
Search for more papers by this authorYingyi Zhao
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Search for more papers by this authorZhihui Wang
Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070 China
Search for more papers by this authorZhengbing Yan
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093 China
Search for more papers by this authorMinkyu Moon
Department of Earth and Environment, Boston University, Boston, MA, 02215 USA
School of Natural Resources and Environmental Science, Kangwon National University, Chuncheon, 24341 Korea
Search for more papers by this authorDedi Yang
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830 USA
Search for more papers by this authorLin Meng
Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, TN, 37240 USA
Search for more papers by this authorSolveig Franziska Bucher
Institute of Ecology and Evolution with Herbarium Haussknecht and Botanical Garden, Department of Plant Biodiversity, Friedrich Schiller University Jena, Jena, D-07743 Germany
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, D-04103 Germany
Search for more papers by this authorJing Wang
School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 510006 Guangdong, China
Search for more papers by this authorGuangqin Song
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Search for more papers by this authorZhengfei Guo
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Search for more papers by this authorYanjun Su
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093 China
Search for more papers by this authorCorresponding Author
Jin Wu
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
Author for correspondence:
Jin Wu
Email:[email protected]
Search for more papers by this authorSummary
- Land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored.
- In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability.
- Our results indicate that PFTs alone explain only 0.8–23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites.
- These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.
Open Research
Data availability
Multi-Source Land Surface Phenology (MSLSP) product is available at https://lpdaac.usgs.gov/products/mslsp30nav011/, foliar functional trait data are available at https://tinyurl.com/neontraits1, LiDAR point cloud data are available at doi: 10.48443/xxby-5a18, the land cover map from United States Geological Survey's (USGS) National Land Cover Database (NLCD) for the year 2019 is available at doi: 10.5066/P9KZCM54.
Supporting Information
| Filename | Description |
|---|---|
| nph19684-sup-0001-Supinfo.pdfPDF document, 2.6 MB |
Fig. S1 Maps illustrating the mean of yearly mean temperatures for the 6-yr period spanning from 2016 to 2021. Fig. S2 Partial dependency plots for the relationship between start of season and functional traits of boosted regression trees at the Dead Lake site. Fig. S3 Partial dependency plots for the relationship between end of season and functional traits of boosted regression trees at the Dead Lake site. Fig. S4 Partial dependency plots for the relationship between start of season and functional traits of boosted regression trees at the Smithsonian Environmental Research Center site. Fig. S5 Partial dependency plots for the relationship between end of season and functional traits of boosted regression trees at the Smithsonian Environmental Research Center site. Fig. S6 Partial dependency plots for the relationship between start of season and functional traits of boosted regression trees at the Treehaven site. Fig. S7 Partial dependency plots for the relationship between end of season and functional traits of boosted regression trees at the Treehaven site. Fig. S8 Partial dependency plots for the relationship between start of season and functional traits of boosted regression trees at the University of Notre Dame Environmental Research Center site. Fig. S9 Partial dependency plots for the relationship between end of season and functional traits of boosted regression trees at the University of Notre Dame Environmental Research Center site. Fig. S10 Principal component analysis for 12 plant functional traits and two phenological metrics. Fig. S11 Correlation coefficients of all pairwise combinations of functional traits across different sites. Methods S1 Generating structural traits from Light Detection and Ranging data. Methods S2 Process of conducting data quality control. Table S1 The mean, 5th percentile, and 95th percentile of mean start of season and end of season from 2016 to 2021 for different plant functional types at each site. Table S2 Correlation coefficients between main principal components and phenological metrics of different plant functional types at each study site. Table S3 Number of sample pixels along with the fraction of each plant functional type and the proportion of valid sample pixels to the total pixels of all plant functional types. Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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