Dataset Information

Data of plant diversity, spectral reflectance at specie level and satellite spectral variables from the largest dry forest nucleus in South America.

ABSTRACT: The use of satellite remote sensing makes it possible to acquire useful information about the environment, since it presents tools capable of assisting the practical search of information related to species richness. Here we present data on richness and Shannon index from phytosociological researches, vegetation indices and individual bands spectral reflectance from satellite images and leaf-level spectral reflectance from eight Caatinga species. For further interpretation of the data presented in this article, please see the research article "Predicting plant species richness with satellite images in the largest dry forest nucleus in South America" [1].

SUBMITTER: Samara E Silva Medeiros E

PROVIDER: S-EPMC6717164 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Data of plant diversity, spectral reflectance at specie level and satellite spectral variables from the largest dry forest nucleus in South America.

Samara E Silva Medeiros Edna E Machado Célia Cristina Clemente CCC Galvíncio Josiclêda Domiciano JD de Moura Magna Soelma Beserra MSB de Araujo Helder Farias Pereira HFP

Data in brief 20190729

The use of satellite remote sensing makes it possible to acquire useful information about the environment, since it presents tools capable of assisting the practical search of information related to species richness. Here we present data on richness and Shannon index from phytosociological researches, vegetation indices and individual bands spectral reflectance from satellite images and leaf-level spectral reflectance from eight Caatinga species. For further interpretation of the data presented ...[more]

PMID: 31489350

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Project description:Our aims were to quantify and map the plant sub regions of the the Caatinga, that covers 844,453 km2 and is the largest block of seasonally dry forest in South America. We performed spatial analyses of the largest dataset of woody plant distributions in this region assembled to date (of 2,666 shrub and tree species; 260 localities), compared these distributions with the current phytogeographic regionalizations, and investigated the potential environmental drivers of the floristic patterns in these sub regions. Phytogeographical regions were identified using quantitative analyses of species turnover calculated as Simpson dissimilarity index. We applied an interpolation method to map NMDS axes of compositional variation over the entire extent of the Caatinga, and then classified the compositional dissimilarity according to the number of biogeographical sub regions identified a priori using k-means analysis. We used multinomial logistic regression models to investigate the influence of contemporary climatic productivity, topographic complexity, soil characteristics, climate stability since the last glacial maximum, and the human footprint in explaining the identified sub regions. We identified nine spatially cohesive biogeographical sub regions. Current productivity, as indicated by an aridity index, was the only explanatory variable retained in the best model, explaining nearly half of the floristic variability between sub regions. The highest rates of endemism within the Caatinga were in the Core and Periphery Chapada Diamantina sub regions. Our findings suggest that the topographic complexity, soil variation, and human footprint in the Caatinga act on woody plant distributions at local scales and not as determinants of broad floristic patterns. The lack of effect of climatic stability since the last glacial maximum probably results from the fact that a single measure of climatic stability does not adequately capture the highly dynamic climatic shifts the region suffered during the Pleistocene. There was limited overlap between our results and previous Caatinga classifications.

Dataset Information

Data of plant diversity, spectral reflectance at specie level and satellite spectral variables from the largest dry forest nucleus in South America.

Publications

Data of plant diversity, spectral reflectance at specie level and satellite spectral variables from the largest dry forest nucleus in South America.

Similar Datasets

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