Liliana Carranza-Becerra
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Liliana Carranza-Becerra has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Alfredo Esparza-Orozco
Doctorado de Ciencias Básicas, Universidad Autónoma de Zacatecas, Calle Preparatoria, s/n, Colonia Hidráulica, Zacatecas 98068, Mexico
1 shared publication - Benjamín Valladares-Carranza
Centro de Investigación y Estudios Avanzados en Salud Animal, Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, Km 15.5 Carretera Panamericana Toluca-Atlacomulco, Toluca 50200, Estado de Mexico, Mexico
1 shared publication - Claudia Yared Michel López
Universidad Autónoma de Baja California
1 shared publication - Cruz Daniel Mandujano-García
Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas, Av. Preparatoria S/N, Col. Agronómica, Zacatecas C.P. 98600, Zacatecas, Mexico
1 shared publication - Eladio Delgadillo-Ruiz
Department of Civil and Environmental Engineering, Division of Engineering, University of Guanajuato, Guanajuato 36000, Mexico
1 shared publication - Héctor Emmanuel Valtierra-Marín
Universidad Autónoma de Zacatecas
1 shared publication
Latest Publications
- Open Access
Environmental Heterogeneity Drives Secondary Metabolite Diversity from Mesquite Pods in Semiarid Regions
Secondary metabolites (SM) in plants play crucial pharmacological, ecological, and nutritional roles for humans, wildlife, and livestock. Environmental Heterogeneity (EH) encompasses the variability of biotic and abiotic factors that influence biological responses of plant species. Advancements in remote sensing have enhanced the ability to assess plant functional traits more affordably and comprehensively by integrating spectral reflectance data with detailed plant metabolomics. However, studies investigating the relationship between EH—quantified using Rao’s Q heterogeneity index from remote sensing data—and SM diversity remain limited. Here, we present the first report demonstrating that the biotic component of EH, measured as Rao’s Q, is positively associated with SM diversity in mesquite pod extracts—higher Rao’s Q values correspond to greater SM diversity. Generalized additive models (GAMs) revealed that Rao’s Q contributed the most explanatory power, accounting for 21.2% of the deviance, compared to pod weight (13.7%) and pod length (2.03%). However, only the relationship between Rao’s Q and SM diversity was statistically significant (p = 0.029). The Rao’s Q index derived from remote sensing serves as a scalable proxy for identifying SM hotspots, facilitating the targeted discovery of regions with high pharmacological or nutritional value.
Open AccessEnvironmental Heterogeneity Drives Secondary Metabolite Diversity from Mesquite Pods in Semiarid Regions
Secondary metabolites (SM) in plants play crucial pharmacological, ecological, and nutritional roles for humans, wildlife, and livestock. Environmental Heterogeneity (EH) encompasses the variability of biotic and abiotic factors that influence biological responses of plant species. Advancements in remote sensing have enhanced the ability to assess plant functional traits more affordably and comprehensively by integrating spectral reflectance data with detailed plant metabolomics. However, studies investigating the relationship between EH—quantified using Rao’s Q heterogeneity index from remote sensing data—and SM diversity remain limited. Here, we present the first report demonstrating that the biotic component of EH, measured as Rao’s Q, is positively associated with SM diversity in mesquite pod extracts—higher Rao’s Q values correspond to greater SM diversity. Generalized additive models (GAMs) revealed that Rao’s Q contributed the most explanatory power, accounting for 21.2% of the deviance, compared to pod weight (13.7%) and pod length (2.03%). However, only the relationship between Rao’s Q and SM diversity was statistically significant (p = 0.029). The Rao’s Q index derived from remote sensing serves as a scalable proxy for identifying SM hotspots, facilitating the targeted discovery of regions with high pharmacological or nutritional value.