Revealing floristic variation and map uncertainties for different plant groups in western Amazonia

Gabriela Zuquim, Hanna Tuomisto, Pablo P. Chaves, Thaise Emilio, Gabriel M. Moulatlet, Kalle Ruokolainen, Jasper Van doninck, Henrik Balslev*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Questions: Understanding spatial variation in floristic composition is crucial to quantify the extent, patchiness and connectivity of distinct habitats and their spatial relationships. Broad-scale variation in floristic composition and the degree of uniqueness of different regions remains poorly mapped and understood in several areas across the globe. We here aim to map vegetation heterogeneity in Amazonia. Location: Middle Juruá river region, Amazonas State, Brazil. Methods: We mapped four plant groups by applying machine learning to scale up locally observed community composition and using environmental and remotely sensed variables as predictors, which were obtained as GIS layers. To quantify how reliable our predictions were, we made an assessment of model transferability and spatial applicability. We also compared our floristic composition map to the official Brazilian national-level vegetation classification. Results: The overall performance of our floristic models was high for all four plant groups, especially ferns, and the predictions were found to be spatially congruent and highly transferable in space. For some areas, the models were assessed not to be applicable, as the field sampling did not cover the spectral or environmental characteristics of those regions. Our maps show extensive habitat heterogeneity across the region. When compared to the Brazilian vegetation classification, floristic composition was relatively homogeneous within dense forests, while floristic heterogeneity in rainforests classified as open was high. Conclusion: Our maps provide geoecological characterization of the regions and can be used to test biogeographical hypotheses, develop species distribution models and, ultimately, aid science-based conservation and land-use planning.

Original languageEnglish
Article numbere13081
JournalJournal of vegetation science
Volume32
Issue number5
DOIs
Publication statusPublished - 1 Sept 2021
Externally publishedYes

Keywords

  • Amazonian biogeography
  • area of applicability
  • ferns
  • Juruá river
  • machine learning
  • Melastomataceae
  • niche
  • palms
  • plant community
  • remote sensing
  • species–environmental relationships
  • tropical forests
  • vegetation mapping
  • Zingiberales
  • ITC-CV

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