Spatio-temporal Variability in Distribution Patterns of Tribulus terrestris: Linking Patterns and Processes

Abstract

Spatial patterns are useful descriptors of the horizontal structure in a plant population and may change over time as the individual components of the population grow or die out. But, whether this is the case for desert woody annuals is largely unknown. In the present investigation, the variations in spatial patterns of Tribulus terrestris during different pulse events in semi-arid area of the Thar Desert, India, was quantified. Further ordination technique and path analysis were utilized to link the pattern and process of spatial distribution of T. terrestris. Dispersal indices like index of dispersal (ID), index of clumping (IC), Green’s Index, Lloyd’s mean crowding and Morisita’s index of dispersion (Iδ ) revealed uniform distribution pattern during non-pulse events, showing intense competition among plants for limited resources. Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity indicated the appropriate use of factor analysis and the significant relationships between variables. Principal Component Analysis (PCA) exhibited the significant correlation of the index of dispersion with the index of clumping and with the Lloyd index, while the Lloyd index correlated with the index of clumping and with the Morisita index. Path analysis suggested the association of soil organic carbon, nitrogen, and C/N ratio with the transition from clumped to uniform pattern. Further, lower soil phosphorus also supported the uniform distribution of this plant. Diversity indices like evenness and Simpson index are associated with uniform and clumped distribution patterns. Higher and intermediate level of percent cover and seed out-put of T. terrestris were also related to uniform and clumped patterns. Path analysis also indicated that salinity tolerance capacity of the species could be utilized for reclamation programme. Keywords: Morisita index and Bartlett’s test, Path analysis, Principal component analysis (PCA), Pulse events.