Abstract
Termite mounds are major sites of functional heterogeneity in the tropical ecosystems globally; through their prodigious influence on vegetation and soil perturbation. They aid soil aeration, water infiltration and catabolism of vegetative matter into nutrient rich humus. There is no documentation of a model for prediction of vegetation lifeforms with respect to mound basal radii, heights and altitude. Objective of this study was therefore to develop a model for rapid prediction of vegetation lifeforms (trees, shrubs, lianas and grass) abundance based on physiography (basal radii and heights) and altitude of the termite mounds. Study population of the mounds was unknown. Cross sectional research design was used. Saturated sampling was done where sixty accessible termite mounds were studied. Both basal radii and heights of termite mounds were measured using 50 m tape measure or hand-held inclinometer. Altitude data were captured by hand-held Global Positioning System (GPS). Trees, shrubs and lianas were identified visually and counted on the mounds while grass abundance was estimated using 0.3 m by 0.3 m quadrat on every termitarium. Multiple Linear Regressions were done to model vegetation lifeforms abundance based on termite mound basal radius, height and altitude. Results indicated that predicted MLR significantly (p ≤ 0.05) predicted trees, shrubs and lianas but not grass abundance. Predicted trees abundance = -89.2587 + 10.46157 (radius (m)) - 4.96989 (height (m)) + 0.074074 (altitude (m)), predicted shrubs abundance = 19.26065 + 6.780626 (radius (m)) - 6.09157 (height (m)) - 0.00822 (altitude (m)) and predicted lianas abundance = -24.9345 + 5.881659 (radius (m)) - 0.68423 (height (m)) + 0.020729 (altitude (m)). This study demonstrated significant effect of termite mound physiography on vegetation lifeforms abundance as well as developed a model for rapid prediction of their abundance on termite mounds.
Highlights
IntroductionMultiple linear regressions is a statistical tool for understanding relationship between a dependent variable and one or more independent variables [1]-[6]
Multiple linear regressions is a statistical tool for understanding relationship between a dependent variable and one or more independent variables [1]-[6].Normally, regression model is given in the form in Equation (1) below:Y = β0 + β1 X1 + β2 X 2 + + βm X m + ε (1)where Y represent the dependent variable; X1, ∙∙∙, Xm represent the several independent variables; β0, ∙∙∙, βm represent the regression coefficients and; ε represent the random error.Regression model has been used in studies of termite mounds including explanation of termite mounds occurrence with canopy cover, distance from forest edge and logging and stump removal as multiple explanatory variables in Nepal [7]
Four multiple linear regression models with vegetation lifeforms abundance as dependent variables and termite mounds physiography as independent variables were developed in Statistical Package for Social Sciences (SPSS) (Version 16.0 Release 16.0.0) programme
Summary
Multiple linear regressions is a statistical tool for understanding relationship between a dependent variable and one or more independent variables [1]-[6]. In another study, Cox regression models were used to determine mortality rates of termites as a function of time and dose rates of fungal isolates [9]. Multiple linear regression models were run elsewhere to determine the number of termite taxa as a function of latitude, altitude, mean annual precipitation and Simpson index of vascular plants [10]. Clay content of mound soils was determined using regression model with Aluminium, Natrium, Kalium, Magnesium, Phosphorus, Titanium, Iron and Manganese as the independent variables [10]. Results showed up to 78% of overall variation in the clay content being explained by Al, Mg, Ti, Fe and Mn in an equation as shown in Equation (2) below:
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