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Plant beta-diversity across biomes captured by imaging spectroscopy

  • Díaz, S. et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3553579 (2019).

  • Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).

    ADS 
    Article 

    Google Scholar 

  • Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).

    Article 

    Google Scholar 

  • HyspIRI Mission Concept Team. HyspIRI Final Report. https://hyspiri.jpl.nasa.gov/downloads/reports_whitepapers/HyspIRI_FINAL_Report_1October2018_20181005a.pdf. Jet Propulsion Laboratories, California Institute of Technology, Pasadena, CA, USA (2018).

  • Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).

    Article 

    Google Scholar 

  • Cawse-Nicholson, K. et al. NASA’s surface biology and geology designated observable: a perspective on surface imaging algorithms. Remote Sens. Environ. 257, 112349 (2021).

    ADS 
    Article 

    Google Scholar 

  • Stavros, E. N. et al. ISS Observations Offer Insights Into Plant Function. Nature Ecology and Evolution 1, https://doi.org/10.1038/s41559-017-0194 (2017).

  • Rast, M., Nieke, J., Adams, J., Isola, C. & Gascon, F. Copernicus Hyperspectral Imaging Mission for the Environment (Chime). IEEE International Geoscience and Remote Sensing Symposium IGARSS, 108–111, https://doi.org/10.1109/IGARSS47720.2021.9553319 (2021).

  • Cogliati, S. et al. The PRISMA imaging spectroscopy mission: overview and first performance analysis. Remote Sens. Environ. 262, 112499 (2021).

    ADS 
    Article 

    Google Scholar 

  • Asner, G. P. et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355, 385–389 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. N. Phytologist 228, 485–493 (2020).

    Article 

    Google Scholar 

  • Schweiger, A. K. et al. Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evolution https://doi.org/10.1038/s41559-018-0551-1 (2018).

    Article 

    Google Scholar 

  • Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).

    Article 

    Google Scholar 

  • Laliberté, E., Schweiger, A. K. & Legendre, P. Partitioning plant spectral diversity into alpha and beta components. Ecol. Lett. 23, 370–380 (2020).

    Article 

    Google Scholar 

  • Rocchini, D. et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol. Inform. 5, 318–329 (2010).

    Article 

    Google Scholar 

  • Gholizadeh, H. et al. Detecting prairie biodiversity with airborne remote sensing. Remote Sens. Environ. 221, 38–49 (2019).

    ADS 
    Article 

    Google Scholar 

  • Wang, R. et al. Influence of species richness, evenness, and composition on optical diversity: a simulation study. Remote Sens. Environ. 211, 218–228 (2018).

    ADS 
    Article 

    Google Scholar 

  • Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high‐fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).

    Article 

    Google Scholar 

  • Draper, F. C. et al. Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. J. Ecol. 107, 696–710 (2019).

    Article 

    Google Scholar 

  • Wang, R., Gamon, J. A., Cavender‐Bares, J., Townsend, P. A. & Zygielbaum, A. I. The spatial sensitivity of the spectral diversity–biodiversity relationship: an experimental test in a prairie grassland. Ecol. Appl. 28, 541–556 (2018).

    Article 

    Google Scholar 

  • Rossi, C. et al. Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species-rich grasslands. Remote Sens. Ecol. Conserv. https://doi.org/10.1002/rse2.244 (2021).

    Article 

    Google Scholar 

  • Finderup Nielsen, T., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).

    Article 

    Google Scholar 

  • McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evolution 14, 450–453 (1999).

    CAS 
    Article 

    Google Scholar 

  • Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).

    ADS 
    Article 

    Google Scholar 

  • Rocchini, D. et al. Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring. Methods Ecol. Evolution 9, 1787–1798 (2018).

    Article 

    Google Scholar 

  • Chadwick, K. D. & Asner, G. P. Landscape evolution and nutrient rejuvenation reflected in Amazon forest canopy chemistry. Ecol. Lett. 21, 978–988 (2018).

    Article 

    Google Scholar 

  • Felsenstein, J. Phylogenies and the comparative method. American Naturalist, 1-15, https://doi.org/10.1086/284325 (1985).

  • Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).

    ADS 
    Article 

    Google Scholar 

  • Schimel, D. S., Asner, G. P. & Moorcroft, P. Observing changing ecological diversity in the Anthropocene. Front. Ecol. Environ. 11, 129–137 (2013).

    Article 

    Google Scholar 

  • NEON (National Ecological Observatory Network). Spectrometer orthorectified surface directional reflectance—mosaic, RELEASE-2021 (DP3.30006.001). https://doi.org/10.48443/qeae-3×15. Dataset accessed from https://data.neonscience.org on March (2021).

  • Richter, R. & Schläpfer, D. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. Int. J. Remote Sens. 23, 2631–2649 (2002).

    Article 

    Google Scholar 

  • Asner, G. P. & Martin, R. E. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front. Ecol. Environ. 7, 269–276 (2009).

    Article 

    Google Scholar 

  • Rüfenacht, D., Fredembach, C. & Süsstrunk, S. Automatic and accurate shadow detection using near-infrared information. IEEE Trans. pattern Anal. Mach. Intell. 36, 1672–1678 (2013).

    Article 

    Google Scholar 

  • NEON (National Ecological Observatory Network). High-resolution orthorectified camera imagery mosaic, RELEASE-2021 (DP3.30010.001). https://doi.org/10.48443/4e85-cr14. Dataset accessed from https://data.neonscience.org on March 3 (2021).

  • Feilhauer, H., Asner, G. P., Martin, R. E. & Schmidtlein, S. Brightness-normalized partial least squares regression for hyperspectral data. J. Quant. Spectrosc. Radiat. Transf. 111, 1947–1957 (2010).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • NEON (National Ecological Observatory Network). Plant presence and percent cover, RELEASE-2021 (DP1.10058.001). https://doi.org/10.48443/abge-r811. Dataset accessed from https://data.neonscience.org on March 3 (2021).

  • NEON (National Ecological Observatory Network). Woody plant vegetation structure, RELEASE-2021 (DP1.10098.001). https://doi.org/10.48443/e3qn-xw47. Dataset accessed from https://data.neonscience.org on March 3 (2021).

  • Schweiger, A. K. NEON_crown_area (1.0.0). https://doi.org/10.5281/zenodo.6383923 (2022).

  • R Foundation for Statistical Computing. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2019).

  • Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).

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

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  • Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

    CAS 
    Article 

    Google Scholar 

  • NEON (National Ecological Observatory Network). Plant foliar traits, RELEASE-2021 (DP1.10026.001). https://doi.org/10.48443/za0d-wn97. Dataset accessed from https://data.neonscience.org on March 3 (2021).

  • Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).

    Article 

    Google Scholar 

  • Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

    Article 

    Google Scholar 

  • Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. nlme: Linear and nonlinear mixed effects models. R package version 3.1-152 (2021).

  • NEON (National Ecological Observatory Network). LAI—spectrometer—mosaic, RELEASE-2021 (DP3.30012.001). https://doi.org/10.48443/h2rb-pj34. Dataset accessed from https://data.neonscience.org on March 3 (2021).


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