Revealing floristic variation and map uncertainties for different plant groups in western Amazonia
| Autor: | Kalle Ruokolainen, Jasper Van doninck, Gabriel M. Moulatlet, Henrik Balslev, Thaise Emilio, Hanna Tuomisto, Gabriela Zuquim, Pablo Pérez Chaves |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Área of applicability 010504 meteorology & atmospheric sciences Juruá river vegetation mapping Melastomataceae Niche Zingiberales Species-environmental relationships Amazonian biogeography Plant Science Plant community 010603 evolutionary biology 01 natural sciences area of applicability Floristics Machine learning 0105 earth and related environmental sciences 2. Zero hunger tropical forests Ecology biology Amazon rainforest 15. Life on land Remote sensing biology.organism_classification Tropical forests niche Variation (linguistics) Geography Remote sensing (archaeology) Ferns species–environmental relationships Vegetation mapping Palms |
| Zdroj: | Repositorio Universidad Regional Amazónica Universidad Regional Amazónica instacron:IKIAM Zuquim, G, Tuomisto, H, Chaves, P P, Emilio, T, Moulatlet, G M, Ruokolainen, K, Van doninck, J & Balslev, H 2021, ' Revealing floristic variation and map uncertainties for different plant groups in western Amazonia ', Journal of Vegetation Science, vol. 32, no. 5, e13081 . https://doi.org/10.1111/jvs.13081 |
| DOI: | 10.1111/jvs.13081 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|