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Fragment size, vegetation structure and physical environment control grassland functioning: a test based on artificial neural networks
Author(s) -
Herrera Lorena P.,
Texeira M.,
Paruelo J.M.
Publication year - 2013
Publication title -
applied vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.096
H-Index - 64
eISSN - 1654-109X
pISSN - 1402-2001
DOI - 10.1111/avsc.12009
Subject(s) - grassland , enhanced vegetation index , vegetation (pathology) , principal component analysis , linear regression , physical geography , environmental science , regression , abundance (ecology) , regression analysis , grassland ecosystem , ecology , statistics , mathematics , normalized difference vegetation index , geography , biology , leaf area index , vegetation index , medicine , pathology
Questions How do fragment‐level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks ( ANN s) a better statistical tool to model variations in grassland functioning compared to linear regression models ( LRM s)? Location Tandilia R ange, S outhern P ampa, B uenos A ires P rovince, A rgentina. Methods We characterized the dynamics of the vegetation functioning in 60 remnant grasslands using enhanced vegetation index ( EVI ) data provided by MODIS / T erra images from J uly 2000 to J une 2005. First, we performed a principal components analysis ( PCA ) on the fragment 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 size, vegetation structure (abundance of the tall‐tussock grass P aspalum 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 using linear regression models ( LRM s) and artificial neural networks ( ANN s). Results The first and second PCA axes were related to the annual integral of EVI ( EVI ‐I) and EVI seasonality ( EVI ‐S), respectively; these explained jointly ca. 80% of total variability in mean EVI values. ANN s captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in S outhern P ampa. Results showed that 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 had 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 increased abundance of litholitic soils. Conclusions Our results showed that grassland functioning in S outhern P ampa, as estimated by EVI , depends on fragment size, vegetation structure and physical factors (soil type, aspect and slope). P aspalum quadrifarium may have an important functional role in this grassland system.