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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.

Multiple linear regressions is a statistical tool for understanding relationship between a dependent variable and one or more independent variables [

Y = β 0 + β 1 X 1 + β 2 X 2 + ⋯ + β m X m + ε (1)

where Y represent the dependent variable; X_{1}, ∙∙∙, X_{m} represent the several independent variables; β_{0}, ∙∙∙, β_{m} represent the regression coefficients and; ε represent the random error (residuals).

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 [

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 [

% Clay = − 7.0 + 6.4 Al − 0.8 Mg − 11.0 Ti + 1.3 Fe − 3.9 Mn . (2)

In order to explain the moisture retention of soils with distance from termite mound, a regression model was developed which showed declining moisture retention ability of soil with distance from the base of termite mound [

Y = 21.219 x − 0.206 , R 2 = 0.871 (3)

Global and regional studies provide indication that regression modeling can be a great effort in supporting prediction of dependent variable values based on several independent variables. Using a single independent variable may not give satisfactory results especially in ecological studies where a dependent variable may have several independent variables.

Accordingly, there has been no attempt in literature to model vegetation lifeforms on termite mounds using various physiographic characteristics of the mounds as predictor variables. Physiography of a termite mound (basal radius and height) as well as its altitudinal location has been surveyed in several studies but none has attempted to use them in explaining abundance of vegetation lifeforms. It would be quite economical to assess abundance of vegetation lifeforms based on variables that can be easily and rapidly measures such as basal radii and heights of termite mounds. This study therefore developed a model to predict vegetation lifeforms abundance on termite mound by relying on physiography of the mounds (basal radius and height) as well as altitudinal location. It is envisaged that this will make it possible to conduct rapid assessment of vegetation lifeforms abundance in tropical savannah ecosystem.

Katolo Sub-Location is located within Kano Plains centered at latitude 0˚14'S and longitude 35˚00'E with elevation ranging from 1175 m on the lowest Western end to 1341 m on top of Orucho hills to the Eastern end bordering Kapsarok Sub-Location of Rift Valley Province (

The area receives long rains in the month of May and short rains normally come in September [

Livestock rearing (cattle, sheep and goats) and crop farming (maize, sorghum, beans and assorted local vegetables) dominate the area [

Being a plain, many studies in this area have focused on rainfall run-off modeling [

savannah surroundings (O. Wyclife, personal communication, March 4, 2016); yet lack in documentation so far. No studies have been done on the effects of termitaria physiography on vegetation lifeforms abundance in the area, yet.

A cross-sectional descriptive research design was used in this study. The design was employed because it allowed for studying of the termite mounds in their natural state (in situ). The design allowed for future detailed investigation on key variables studied and gave opportunity to gather in-depth information about the variables. This was an appropriate method for this study since the units of analysis; termite mounds and vegetation lifeforms were already in the study area and could be studied within a short span of time.

The actual data about the number of termite mounds in the study area was not in existence. A sample size of 60 mounds was used after reviewing literature on appropriate sample sizes that have been used by other researchers in studying termitaria elsewhere. Reference [

Reference [

In this study, we used a total of 60 termite mounds which were arrived at randomly provided they fell within the study area of Katolo Sub-location. Starting termite mound for sampling was chosen randomly based on the convenience to the researchers and its location within the area of study following the work of [

Data used in this study were obtained via primary data collection methods between December, 2016 and March, 2017. Primary data collection methods adopted were observation, measurement, counting and recording. These methods were appropriate for the data collection because the data to be collected could be obtained by measurement (height, radius and altitude) and counting (trees, shrubs, lianas and grass).

Mound basal radius, r b _{,} was estimated from the circumference of the base of the mound, c b . A 50 m tape measure was wound round the base of every termite mound to find out the circumference following the formula adopted from [

r b = c b / 2 π (4)

A 50 m tape measure was used to quantify termite mound height (m) by holding it vertically adjacent to the termite mound walls and vertical rise observed by the researcher. In case where the termite mound was taller than the researcher, inclinometer was used alongside simple trigonometric ratios to determine exact mound heights [

H = h + d tan θ (5)

where H is the termite mound height (m), h is the height of the observer (m), d is the distance from the centre of the termite mound to observer (m) and θ is the angle of elevation (in degrees) of the top of the mound from the horizontal line of sight of the observer. Altitude of the termite mounds was determined using hand-held Global Positioning System (GPS) and given in metres.

Trees growing on-mound were regarded as woody vegetation lifeforms with diameter at the base (10 cm above the mound) greater than 6 cm as determined by sliding Vernier caliper and have heights above 3 m (determined by inclinometer) as applied elsewhere by [

Due to labor intensity of counting individual grass throughout the mound or off-mound plots, a 0.3 m × 0.3 m quadrat was used to estimate the population of grass on every termite mound. Adoption of quadrat method followed the works of [

Four multiple linear regression models with vegetation lifeforms (trees, shrubs, lianas and grass) abundance as dependent variables and termite mounds physiography (basal radius, height and altitude) as independent variables were developed in Statistical Package for Social Sciences (SPSS) (Version 16.0 Release 16.0.0) programme. Independent variables were first tested for collinearity and were noted to have no significant correlation. Models gave the best predictions that were tested for significance at p ≤ 0.05.

Three independent variables (termite mound basal radius, height and altitudinal location of termite mound) were used to develop a model. Initially a test for collinearity was done among the variables. The test for collinearity was meant to leave out independent variables that were significantly correlated. Test results showed that none of the variables were significantly (p ≤ 0.05) correlated with each other (

A multiple regression model to predict trees abundance on termite mounds was developed at 95% confidence level with F(3, 57) = 94.77, p = 0.000) with adjusted r^{2} of 82.66%. The relationship gave the following regression equation (Equation (6)):

Basal radii | Heights | Altitude | |
---|---|---|---|

Basal radii | 1.00 | NS^{a}^{ } | NS^{a}^{ } |

Heights | 0.135 | 1.00 | NS^{a}^{ } |

Altitude | 0.153 | -0.023 | 1.00 |

T a = − 89.2587 + 10.46157 r − 4.96989 h + 0.074074 a (6)

where T_{a} is Tree abundance, r is basal radius (m), h is termite mound height and a is altitude (m).

On the other hand, shrubs abundance on termite mounds could also be modeled with a statistically significant (p ≤ 0.05) linear relationship with physiographic attributes of termite mounds and altitude in a multiple regression model (F(3, 57) = 20.21, p = 0.000) with adjusted r^{2} of 49.41%.The relationship gave the following regression equation (Equation (7)):

S a = 19.26065 + 6.780626 r − 6.09157 h − 0.00822 a (7)

where S_{a} is Shrubs abundance, r is basal radius (m), h is termite mound height and a is altitude (m).

Lianas abundance was also significantly (p ≤ 0.05) explained by variation in termite mound physiographic attributes and altitude (F (3, 56) = 56.18, p = 0.000) and r^{2} = 73.72%. The regression model equation which could help to estimate lianas abundance within the study area was found to be:

L a = − 24.9345 + 5.881659 r − 0.68423 h + 0.020729 a (8)

where L_{a} is Lianas abundance, r is basal radius (m), h is termite mound height and a is altitude (m).

The last vegetation life form investigated was grass abundance which did not show significant response to variation in termite mound physiographic attributes and altitude (F(3, 56) = 0.47, p = 0.706) with an r2 = −2.78%. An equation to support prediction of abundance of grass on the termite mounds studied was:

G a = 676.4303 + 114.0194 r − 45.1003 h + 0.182634 a (9)

where G_{a} is Grass abundance, r is basal radius (m), h is termite mound height and a is altitude (m).

The significant (p < 0.05) multiple regression equations obtained could help in predicting abundance of vegetation lifeforms in tropical savannah ecosystems such as was the case with Katolo Sub-Location in Kisumu County.

This application of multiple linear regression models to predict vegetation lifeforms abundance borrowed from the work by [

This study has however gave a new model to be easily tested and adopted in rapid assessment of various vegetation lifeforms within tropical savannah ecosystems based on basal radius, heights and altitudinal location of termite mounds.

Based on the findings of this study, it was concluded that Multiple Regression Model could be a rapid option for determining abundance of vegetation lifeforms based on physiography of the termite mounds. Trees, shrubs and lianas vegetation lifeforms could be significantly predicted using basal radii, heights and altitude of the mound. However, grass abundance could not be significantly predicted using the model. The study was limited to dry season and it would be important to conduct similar study across seasons in order to arrive at better comparative results. It is recommended that other variables such as soil physical-chemical parameters of termite mounds be investigated in the study area to support development of a robust model for prediction of vegetation lifeforms abundance, especially grass.

Oluoch, W.A., Oindo, B.O. and Abuom, P. (2017) Modeling Vegetation Lifeforms Abundance based on Epigeal Termitaria Physiography and Altitude in Tropical Savannah of Katolo Sub-Location, Kisumu County. Journal of Geoscience and Environment Protection, 5, 22-31. https://doi.org/10.4236/gep.2017.510003