Spatial modeling of Biodiversity and Carbon storage along the inhabited slopes of Mount Kilimanjaro (Tanzania) and Taita Hills (Kenya)

Abstract

The inhabited montane areas in Mount Kilimanjaro and Taita Hills are threatened by expansion and intensification of agriculture. Due to poor cropland management and destruction of remnant indigeneous forests on the slopes, soil conditions and micro-climate are deteriorating. This has ultimately compromised resilience of the plant species and carbon sequestration in the areas. The resilience will further be risked by climate change in the region which shows montane areas will be more vulnerable. Thus, the study aimed at determining model relationships of vegetation structures and carbon storage to environmental variables on the inhabited slopes of montane areas. One hectare plot was used for sampling Woody Plant Species (WPS), tree biometries (diameter>10 cm) along transects. Biodiversity indices, richness and diversity indices were used to determine distribution of WPS in different sites and types of cropland. Allometric model was used for estimating the above-ground carbon storage (AGCS) from tree biometries. Ground based Remote Sensing, hemispherical photography and SunScan canopy analyzer for measuring LAI in VALERI plots. Univariate and test statistics was performed on WPS, AGCS, LAI between site and types of cropland. Generalized Linear Model (GLM) was used for predicting response variable from spatial predictors physical, edaphic variables, vegetation index (EVI) and population density in R programme. GLM prediction models were used for spatial upscaling of response variables using maths algebra tool in ArcGIS 10.2. Impact of climate change on distributions of selected WPS Albizia gummifera (Albizia), Mangifera indica (Mango) and Persea americana (Avocado) was analysed under RCP 4.5 and 8.5 projections for peak periods of 2055 and 2085 with a machine-learning technique. Woody Plant Species Richness significantly differ between sites (t=3.06, p=0.002) and types of cropland with only 32% of the species shared between the two sites. The spatial distribution of WPSR is significantly explained by multivariate model with predictors SOC + I(Elev.2) in Kilimanjaro (R2=0.78 , p=0.00 , AIC=67.42) and predictors I(Elev.2) + Slope + Population Density in Taita Hills (R2=0.97 , p=0.00 , AIC=36.91). Spatial model for AGCS in Kilimanjaro is better explained by multivariate predictor SOC + CEC + pH + BD) (R2=0.94, p=0.00, AIC=91.33) and in Taita Hills model predictor Elev. + Slope + Population Density) shows a better spatial model distribution (R2=0.79, p=0.01, AIC=71.11). LAI spatial distribution in Kilimanjaro is strong and significantly varies mostly with elevation but no significant distribution is observed in Taita Hills with all variables. Projection of species distribution under baseline climate condition shows Taita Hills has significantly higher proportion of suitable areas for Albizia, Mango and Avocado than in Kilimanjaro (F=153.17, p=0.01). Avocado will experience upshift in minimum elevation range in Kilimanjaro under all RCPs except RCP 8.5, 2085 which will decrease in proportion of suitable area and fragmentation under RCP4.5 (2055) and RCP8.5 (2055). Mango will experience downshift, which will increase proportion of suitable areas in Kilimanjaro under RCP 8.5, 2085 with fragmentation of the areas occurring under RCP4.5 (2055 & 2085) and RCP8.5 (2055). Downshift in Albizia and Mango will occur which will increase proportion of suitable areas in Taita Hills under RCP 8.5, 2085. The distributions of biodiversity in montane areas are explained by multivariate predictors, which however differ on sites. Climate change projections will cause varied response of some species shifting upslope and other downslope with habitat fragmentation occurring in the montane areas. Effective monitoring of the inhabited montane areas should use WPSR, AGCS and LAI developed models for sustainable conservation of biodiversity and improved carbon sequestration. While, mitigation measures for climate change should have different choices of plant species for downslope and upslope in order contribute high AGC sequestration and sustainable livelihood on the slopes.