Predicting the distribution of <i>Paspalum guenoarum</i> (Poaceae) using environmental variables and ensemble species distribution models

Francisco M. Luna; Florencia Marcón; Diógenes Cecchin Silveira; Miguel Dall’Agnol; Carlos A. Acuña

doi:10.30972/bon.3529466

Autores/as

Francisco M. Luna Instituto de Botánica del Nordeste, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Agrarias, Universidad Nacional del Nordeste https://orcid.org/0009-0003-7222-7298
Florencia Marcón Instituto de Botánica del Nordeste, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Agrarias, Universidad Nacional del Nordeste https://orcid.org/0000-0002-2555-948X
Diógenes Cecchin Silveira Universidade Federal do Rio Grande do Sul, Faculdade de Agronomia https://orcid.org/0000-0001-6653-4839
Miguel Dall’Agnol Universidade Federal do Rio Grande do Sul, Faculdade de Agronomia https://orcid.org/0000-0001-5471-1850
Carlos A. Acuña Instituto de Botánica del Nordeste, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Agrarias, Universidad Nacional del Nordeste https://orcid.org/0000-0002-8878-086X

DOI:

https://doi.org/10.30972/bon.3529466

Palabras clave:

BIOMOD2, conservación de germoplasma, modelado de nicho ecológico, modelos ensamblados, Paspalum guenoarum

Resumen

Paspalum guenoarum Arechav. es una gramínea perenne nativa de América del Sur, altamente valorada por su producción continua de forraje durante todo el año y su notable tolerancia al frío. En este estudio, caracterizamos su distribución, identificamos las principales variables ambientales que la determinan y evaluamos el rendimiento predictivo de diferentes algoritmos utilizados para estimar su distribución potencial. A partir de registros de herbario y accesiones de germoplasma junto con variables ambientales, modelamos el nicho ecológico de la especie mediante un enfoque de modelado de consenso. Nuestros resultados indican que la especie se distribuye principalmente en el noreste de Argentina, el sur de Brasil, el norte de Uruguay y Paraguay, y se asocia a regiones subtropicales sin estación seca. Las variables bioclimáticas, particularmente la precipitación del mes más húmedo y el rango diurno de temperatura promedio, fueron los predictores más importantes, mientras que las variables edáficas y la elevación tuvieron una influencia secundaria. Los modelos de consenso produjeron predicciones más robustas que los algoritmos individuales; sin embargo, MaxEnt mostró un alto rendimiento predictivo y facilidad de implementación. En general, estos hallazgos proporcionan una base sólida para comprender la distribución y las preferencias ambientales de la especie, y ofrecen un marco de referencia para futuros estudios de modelado de distribución dentro del género Paspalum.

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