www.ccsenet.org/ijb International Journal of Biology Vol. 3, No. 3; July 2011 Published by Canadian Center of Science and Education 73 Multivariate Analysis of Soil-Vegetation Interrelationships in a South-Southern Secondary Forest of Nigeria A.I. Iwara (Corresponding author) Dept. of Geography, University of Ibadan, Ibadan, Nigeria Tel: 234-803-945-1970 E-mail: iwaradream2010@yahoo.com F.O. Ogundele Dept. of Geography & Planning, Lagos State University, Nigeria U.W. Ibor Dept. of Geography, University of Ibadan, Ibadan, Nigeria T.N. Deekor Dept. of Geography & Environmental Management, University of Port Harcourt, Nigeria Received: April 12, 2011 Accepted: April 30, 2011 doi:105539/ijb.v3n3p73 Abstract Multivariate statistical techniques were employed to study soil-vegetation interrelationships in a secondary forest of South-Southern Nigeria. The grid system of vegetation sampling was used to randomly collect vegetation and soil data from fifteen quadrats of 10m x 10m. The result of principal components analysis identified seven basic sets of soil-vegetation variables that enhanced the interrelationships. Canonical correlation result indicated a positive association between organic matter and tree size, while the linear association between organic matter and tree density revealed an inverse relationship. The result of redundancy coefficient indicated that 18 percent of the variance in vegetation characteristics was accounted for by the variability in soil properties whereas, 81 percent of the variance in soil properties was accounted for by the variability in vegetation characteristics. The regression analyses on the other hand indicated that exchangeable sodium positively influenced tree species composition and richness; and that tree size as well as tree density exerted substantial influence on the contents of organic matter and total nitrogen of the soil. However, drawing inference from results of canonical correlation analysis and those of multiple regression analysis, it was concluded that soil and vegetation components of the secondary forest vegetation were mutually dependent and therefore exerted joint influences on each another. Keywords: Canonical correlation, Redundancy coefficient, Multiple regression, Soil properties, Vegetation characteristics 1. Introduction Vegetation and soil are interrelated and exert reciprocal effects on each other. This is because soil gives support in terms of moisture, nutrient and anchorage to vegetation to grow effectively on the one hand, and on the other, vegetation provides protective cover for soil, suppresses soil erosion as well as helps to maintain soil nutrient through litter accumulation and subsequent decay (Eni et al., 2011). Both soil and vegetation are multivariate in nature, as such, the relationships of soil and vegetation components can be analysed by multivariate statistical methods (Aweto, 1981; Ukpong, 1994). This reciprocal relationship between soil and vegetation demands a multivariate approach in order to determine critical vegetation and soil properties that sustain this integrative association. On this premise, multivariate analytical techniques (principal component analysis, canonical correlation analysis, factor analysis, and canonical correspondence analysis among others) are very useful in the analysis of soil and vegetation as each consists of data corresponding to a large number of variables. Thus, analysis via these techniques produces easily interpretable results.