CIFOVIS - Artículos y ponencias con arbitraje

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https://hdl.handle.net/11117/1530

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Examinando CIFOVIS - Artículos y ponencias con arbitraje por tema "Agricultural Science"

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    Climatic and edaphic-based predictors of normalized difference vegetation index in tropical dry landscapes: A pantropical analysis
    (Wiley, 2022-06) DeLaPeña-Domene, Marinés; Rodríguez-Tapia, Gerardo; Mesa-Sierra, Natalia; Rivero-Villar, Anaitzi; Giardina, Christian P.; Johnson, Nels G.; Campo, Julio
    Aim: Spatial patterns in resource supply drive variability in vegetation structure and function, yet quantification of this variability for tropical dry forests (TDFs) remains rudimentary. Several climate-driven indices have been developed to classify and delineate TDFs globally, but there has not been a climo-edaphic synthesis of these indices to assess and delineate the extent of TDFs. A statistical climo-edaphic synthesis of these indices is therefore required. Location: Pantropical. Time period: Modern. Major taxa studied: Vascular plants. Methods: We assembled most known prior descriptions of TDFs into a single data layer and assessed statistically how the TDF biome, which we call tropical dry landscapes (TDLs) composed of forest and non-forest vegetation, varied with respect to the normalized difference vegetation index (NDVI) sensed by MODIS (250 m pixel resolution). We examined how the NDVI varied with respect to mean annual temperature (MAT) and rainfall (MAR), precipitation regime, evapotranspiration and the physical, chemical and biological properties of TDL soils. Results: Overall, the NDVI varied widely across TDLs, and we were able to identify five principal NDVI categories. A regression tree model captured 90% of NDVI variation across TDLs, with 14 climate and soil metrics as predictors. The model was then pruned to use only the three strongest metrics. These included the Lang aridity index, total evapotranspiration (ET) and MAT, which aligned with identified NDVI thresholds and accounted for 70% of the variation in NDVI. We found that across a global TDL distribution, ET was the strongest positive predictor and MAT the strongest negative predictor of the NDVI. Main conclusions: The remote sensing-based approach described here provides a comprehensive and quantitative biogeographical characterization of global TDL occurrence and the climatic and edaphic drivers of these landscapes.