Browsing by Autor "Monica Moraes R"

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Biotic and Abiotic Drivers of Phenotypic Diversity in the Genus Lupinus (Fabaceae)
(Multidisciplinary Digital Publishing Institute, 2026) Mateo Burke Irazoque; Monica Moraes R; Sissi Lozada-Gobilard
The genus Lupinus (Fabaceae) represents an exceptional model system for studying evolutionary processes mediated by pollinators and environmental factors. This review synthesizes evidence on phenotypic variability of floral traits, trait selection by biotic and abiotic factors, and the eco-evolutionary implications of these interactions. Pollinators shape floral traits through specialized selection that optimizes pollination efficiency while maintaining plasticity toward autogamy under pollinator scarcity. At the same time, abiotic pressures drive adaptations to climate and elevation, which Lupinus achieves through phenotypic plasticity, such as adjusting flowering time, and through mutualisms with soil microbes that reduce abiotic stress. Ecological implications reveal contrasting dynamics, where native species sustain specialized pollination networks, while invasive species such as L. polyphyllus Lindl. disrupt these interactions through competitive exclusion. Thus, these two factors collectively drive Lupinus phenotypic diversity through specialized adaptations and plasticity. Finally, we emphasize the need for integrated studies combining genomics and ecological modeling to decipher the adaptive mechanisms of this genus.
Characterizing Tree Species Richness in Indigenous Peoples’ Lands: Addressing Often-Overlooked Data Limitations and Biases
(2026) Sven Kock; Jorge C. Llopis; Miguel Fernández; Monica Moraes R; Álvaro Fernández-Llamazares
<title>Abstract</title> Several studies have characterized species richness distribution in Indigenous Peoples’ Lands (IPL) using global biodiversity datasets. However, these datasets contain substantial limitations and biases which may compromise the accuracy of spatial biodiversity assessments. In this study, we characterize tree species richness across IPL in Bolivia based on three different datasets to examine how spatial and taxonomic biases, as well as the data’s institutional provenance, shape characterization outcomes. Results closely reflect identified spatial and taxonomic biases, and as data generally come from various sources, the individual collections’ scope and our characterization do not align. We also notice that most data derive from international institutions without information about contributions from Indigenous Peoples, complicating sensitive interpretation and weakening visibility of Indigenous knowledge systems. Our findings underscore the limitations of relying solely on global biodiversity datasets to map species distributions in IPL and emphasize the need for locally grounded, ethically framed data collection practices.