Fragment size, vegetation structure and physical environment control grassland functioning: a test based on artificial neural networks

• Autores: Herrera, Lorena Paola; Texeira González, Marcos Alexis; Paruelo, José
• Año de publicación: 2013
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Enhanced Vegetation Index; Fragmentation; Landscape Structure; Modis Data
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Questions: How do fragment-level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks (ANNs) a better statistical tool to model variations in grassland functioning compared to linear regression models (LRMs)? Location: Tandilia Range, Southern Pampa, Buenos Aires Province, Argentina. Methods: We characterized the dynamics of the vegetation functioning in 60 remnant grasslands by means of the Enhanced Vegetation Index (EVI) data provided by MODIS/Terra images from July 2000 to June 2005. First, we performed a principal component analysis on the fragments mean monthly values of EVI in order to obtain synthetic measures (i.e. the PCA axes) of grassland functioning. Grassland fragments were also characterized by their size, vegetation structure (abundance of the tall-tussock grass Paspalum quadrifarium), and physical environment (soil type -abundance of litholitic soils-, elevation, aspect and slope). The relationship between grassland functioning and these explanatory variables was explored by means of linear regression models (LRMs) and artificial neural networks (ANNs). Results: The first and second PCA axes were related to the annual integral of EVI (EVI-I) and EVI seasonality (EVI-S), respectively; and explained jointly approximately 80% of total variability in mean EVI values. ANNs captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in Southern Pampa. Results showed EVI-I variability was related to all independent variables except aspect. While fragment-size, litholitic soils and slope were negatively related to EVI-I, the abundance of P. quadrifarium showed a positive effect on the spectral index. Grasslands with high seasonality were large, and had high slope and aspect, low abundance of P. quadrifarium and more abundance of litholitic soils. Conclusions: Our results showed that grassland functioning in Southern Pampa, as estimated by EVI, depends on fragment-size, vegetation structure and physical factors (soil type, aspect and slope). Paspalum quadrifarium may be performing an important functional role in this grassland system.
Fil: Herrera, Lorena Paola. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Departamento de Producción Vegetal; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires. Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Texeira González, Marcos Alexis.
Fil: Paruelo, José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina