Savanna woody plants responses to mammalian herbivory and implications for management of livestock-wildlife landscape
Kibet, Staline; Nyangito, Moses; MacOpiyo, Laban; Kenfack, David (2021), Savanna woody plants responses to mammalian herbivory and implications for management of livestock-wildlife landscape, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf21h
1. The need to address wildlife conservation outside of protected areas has become more urgent than ever before to meet environmental and socio-economic goals. However, there is limited knowledge about how woody plants respond to herbivory within landscapes shared by wildlife and domestic herbivores in African savanna, thus management decisions might be based on inaccurate information and ultimately be ineffective.
2. We compared woody vegetation dynamics between two adjacent ranches with different management objectives and subjected to varying levels of herbivory by both wildlife and domesticated mammals using 421 square plots of 400 m2 nested on three transects, each 3 km long and purposively selected to minimize bio-physical differences.
3. Both species and structural diversity were significantly higher (P < 0.05) in the site with lower levels of herbivory. Conversely, the site with higher levels of herbivory recorded enhanced biomass production for a selection of palatable forage species, perhaps due to compensatory re-growth. This enhanced biomass however dampens as trees grow taller than the browsing zone.
4. A higher intensity of herbivory seems to promote increases in browsing- tolerant Acacia mellifera as well as homogenization of the vegetation architecture and lower structural diversity. Conversely, low intensity of browsing modified by environmental factors seems to promote proliferation of encroaching unpalatable species which are increasingly becoming a major rangeland management challenge in the study region.
5 Managing landscapes for the co-existence of both wildlife and livestock demands critical analysis of how vegetation responses to herbivory to ensure suitable ecological niches are maintained. To increase browse biomass for livestock within the landscape would demand that dominant palatable browse –tolerant species are suppressed within the browsing zone of majority of browsing livestock kept by promoting appropriate browsing intensity. On the other hand, if the management objective is to promote coexistence of both wildlife and livestock then, the strategy would be to promote structural diversity by varying livestock stocking rate. Given the movement of wildlife between properties, livestock stocking rates should be considered within a wider landscape than just individual private lands.
1. Study Site
The study was conducted in Mpala private commercial Ranch (hereafter refer to PR) and adjacent Ilmotiok communal pastoral Group Ranch (hereafter referred to as GR) in Laikipia County, Kenya centred at 37053’E and 0017’N in a semi-arid savanna ecosystem (Fig. 1). The area receives mean annual rainfall of 450 – 600 mm in a weakly trimodal pattern with rain expected in April –May, August and October with January-March being a dry season (Augustine and McNaughton, 2006). Mean annual temperatures for the County is estimated at 16 - 26˚C. The soils in the region are variable from poorly drained black cotton (vertisols) and plunosol to well drained ferric and chromic luvisols (Ngigi 2006). Topographically, the region has undulating and non-undulating surface with the highest and lowest point having 1800 and 1500 m a.s.l
Livestock stocking rates as well as native herbivore densities per site was used as surrogates for different level of browsing intensity. Herbivore densities were derived from published and grey literature largely wildlife long-term surveys from the study region. The PR has maintained low livestock stocking rate of 10 – 12 TLU/km2 mainly cattle, camels and sheep over the past three decades. The GR stocking rates on the other hand fluctuates depending on prevailing weather conditions with high stocking rates during rainy seasons when pastures and water are plenty and low shortly after major droughts. The GR is estimated to be stocking at higher rate than PR (see Georgiadis et al., 2007; Kinnaird and O’Brien, 2012; Kinnaird et al., 2012; Kaye-zwiebel and King. 2014). In the GR the grazers kept included cattle, sheep, and donkeys and browsers include goats and camels.
Average estimates of combined grazing and browsing wildlife and domestic animals in the region (1985 -2012) indicates that GR had densities of 43 TLU/Km2 compared to 28 TLU/Km2 for PR. Domestic and wildlife browsers considered the most effective in modifying woody plant structure and perhaps its composition, was estimated at 28 TLU/Km2 and 17 TLU/Km2 for GR and PR respectively (Augustine, 2003b; Kinnaird and O’Brien, 2012; Kinnaird et al., 2012; Ngene et al., 2013; Kaye-zwiebel and King, 2014).
To corroborate on the use of fire as a management tool in the study sites in the last 40 years since the group ranches were established, ten key informant interviews targeting senior persons who have lived in the region for more than 35 years were conducted. Physical checks for burn stumps as well as burnt scars from older trees were also done.
Sampling design for Structural data
Three transects lines; 3 km long, approximately 200 m apart were purposively established per site to ensure some similarity in soils types and topography. In each transect, subplots measuring 20 x 20 m were systematically laid in alternating manner at 20 m intervals to make 75 subplots per transect and a total of 225 per site. Sites that indicated signs of human disturbances such as abandoned kraal, charcoal kilns, trees harvesting were avoided and alternative nearby site was selected. Twenty subplots were discarded in PR due to wildlife threats during fieldwork. In each subplot, percent vegetation cover, bare ground, percent slope, elevation, and soil texture were recorded. Soil texture was determined in the field based on feel flow chart protocol (see Vagen et al., 2010).
In each subplot all woody species with stem diameter of 1.0 cm at approximately 50 cm above ground (hereafter referred to as diameter at knee height - DKH) were enumerated. Bedside DKH, height, canopy depth and canopy diameter (CRWN) were measured and recorded. Non woody invader species were also recorded and their abundance cover estimated. Botanical nomenclature followed Flora of Tropical East Africa, (1954). Duplicate copies of specimen for each species were collected for re-distribution between East African Herbarium, and Mpala Research Centre.
Structural diversity and biomass production estimates were based on four dominant palatable species, Acacia brevispica, Acacia tortilis, Acacia mellifera and Acacia etbaica based on Lusigi et al. (1984) rating and from interviewing knowledgeable local herders. A. mellifera and A. etbaica overlapped in PR and GR and therefore provided basis for comparing browsing effects at species level.
Canopy volumes, canopy area, and canopy densities per subplot were then calculated based on structural data collected using the formulas provided below.
Canopy Area (CA) = π(D1D24) ………………………………..Eq (1)
Where D1 and D2 are the two perpendicular diameter measurements when projected on the ground. This formula does not assume symmetry of the canopy.
Canopy volume (Canvol) = π23Hd(D1D24)………………………………………………Eq (2)
Based on ellipsoid volume formula, where Hd is the length of canopy depth, while D1 and D2 are the two canopy diameter readings (Thorne et al., 2002).
Canopy density (CD) per subplot = πD1D216…………………………………………Eq (3)
This is summation of individual trees canopy area (equation 1) divided by subplot area of 400 m2 then multiply by 100 to make it into a percentage(Manila, 2007).
Coefficient of variation (CV) = δπ ……………………………………………………Eq (4)
Where δ is standard deviation and π is the mean
Vegetation piospheric effects were tested in GR with apparent grazing/browsing gradient based on two focal points; settlement area (homesteads) and a temporary watering point at the other extreme end. Often livestock spend more time in early mornings, late afternoon and night near homesteads similarly at watering points during the day. The two focal points were 3 Km apart. We hypothesized that higher browsing and grazing occur near homesteads and close to watering points. To test this hypothesis, we compared tree densities, tree canopy area and percent bare ground using 20 x 20 m subplots along the transects. It was anticipated that lower tree and canopy densities and, higher percent bare ground on subplots near homesteads and at the watering point compared to subplots in the middle of the transects.
The browse biomass was estimated using double sampling method as described by Foroughbakch et al. (2008). Regression equations based on basal branch diameter and browse biomass was derived and used to estimate edible biomass per hectare per site based on number of stems/branches. Foroughbakch et al. (2008) recommended the use of 15 individuals per species however, in this study; we took 48, 32, 12 and 37 individuals for Acacia mellifera, Acacia brevispica, Acacia tortilis, and Acacia etbaica respectively. All individuals per species were then measured using a diameter tape thereafter all leaves and young shoots were harvested. The values obtained per individual were paired with their basal diameters and then regression analyses carried out
The following equations were derived: A. etbaica y = 56.24x-78.981, r² = 0.685; A. mellifera, y = 131.76x – 265.6, r² = 0.714; A. brevispica y = 48.74x - 41.17, r² = 0.24 and A. tortilis y = 53.5x – 103.5; r² = 0.75 where y is the edible (browsable) biomass and x are branch base diameter. As a result of low coefficient of determination for A. brevispica (r2 = 0.24) and absence of A. tortilis in sampled plots in PR, the two species were therefore excluded in biomass estimates discussions.
To isolate effects of soil properties, 45 soil samples were collected per site picked in a stratified random manner from the subplots described above. Plots were stratified by soil formation (black cotton, transition and sandy) and randomly selecting subplots within each formation. In each sub-plot, 5 subsamples were augured 0-30 cm deep from four corners and at the centre and lumped into a composite sample. The composite samples were sun-dried and later transported in labelled zip-lock bags to National Agricultural Research Laboratories, Nairobi for further processing and analysis. Standard methods were followed in pH and macro and micro-nutrients analysis as indicated;
Available nutrient elements (P, K, Na, Ca, Mg and Mn): The Mehlich Double Acid Method was used (Mehlich, 1984). The oven - dry soil samples were extracted in a 1:5 ratio (w/v) with a mixture of 0.1 N HCl and 0.025 N H2SO4. The elements; Na, Ca and K were determined using a flame photometer and P, Mg and Mn using a spectrophotometer.
Total organic carbon: Calorimetric method was used (Murphy and Riley, 1962): All organic C in the soil sample was oxidized by acidified dichromate at 1500C for 30 minutes to ensure complete oxidation. Barium chloride was then added to the cool digests. After mixing thoroughly digests were left to stand overnight. The C concentration was then read on the spectrophotometer at 600 nm.
Total nitrogen: Kjeldahl method was used (Benton, 1991); Soil samples was digested with concentrated sulphuric acid containing potassium sulphate, selenium and copper sulphate hydrated at approximately 3500C. Total N was determined by distillation followed by titration with H2SO4.
Soil pH and EC was determined in a 1:1 (w/v) soil – water suspension with pH – meter and conductivity meter respectively.
Available trace elements: Extraction with 0.1 M HCl: The oven - dry soil samples were extracted for trace elements (Fe, Zn & Cu) in a 1:10 ratio (w/v) with 0.1 M HCl. Elements amounts available were determined with Atomic Absorption Spectrophotometer (Black et al., 1965).
Extractable Phosphorus: Olsen method (Olsen et al., 1954) (for soils with pH 7.0 and above was used): The dried soil samples were extracted in a 1:5 ratio (w/v) with 0.5M sodium bicarbonate solution at pH 8.5. Extractable phosphorus was determined spectrophotometrically.
Vegetation data was subjected to Canonical Correspondence Analysis (CCA) to decipher possible linkages between species and environmental variables from those associated with browsing. This analysis was performed using PC-ORD version 5.19 (McCune and Mefford, 2006). Variation within transects (vegetation and soil properties) were tested using Kruskal-Wallis test. Piospheric effects from intense grazing and browsing near homesteads and near watering point at GR, were analysed using polynomial regression (Gardener, 2012). Mann-Whitney U-tests were used to compare structural data while coefficients of variation (CV) were calculated to estimate structural diversity between sites. Correlation analysis between browse biomass and vegetation structure was done. Q1 Macros Software version 2014.12 was used in all significance tests (KnowWare International Inc. 1996-2014).
Legends for Datasets
- Columns in MasterData file
A Transect Line: L1 to L3 refers to Transect 1 to 3 for il Motiok Ranch, M1 -M3 refers to Mpala Ranch transect 1 to 3
B Quadrat – Sampling plots for Il Motiok and Mpala
C SpCode – Species Code (ACACME – Acacia mellifera, ACACET = Acacia etbaica, ACACBR – Acacia brevispica, ACACTO – Acacia tortilis), LECIU – Lecium , IPOMKI – Ipomoea kituensis, SANSVO – Sansenvieria volkensii, ACACSE – Acacia seyal, ACACRE – Acacia reficiens, ACACDR – Acacia drepanolobium, EUCLDI – Euclea divinorum,
D DKH – Diameter at Knee height (approx. 50 cm from ground level)
E Code – M – main branch, B – base multiple stem(s) with DKH readings (NB this is not branches)
F Similar to E above
G Basal area calculated using formula A=πr2
H Hmin – Lowest canopy height
I Hmax – Highest canopy height
J CanDepth – Canopy depth calculated as Hmax – Hmin
K CRWN1 – Crown diameter 1 – longer distance
L CRWN 2 – Crown Diameter 2 – shorter distance
M CRWNDIAM – Crown diameter – consolidated diameter calculated by averaging CRWN1 and CRWN 2
N Slope – estimated; 1 – less than 5%, 2 – more than 5 but less than 10%, 3 – More than 10% but less than 15%, 4 - more than 15%
O Soil texture (1 – Clay; 2 – clay loam; 3- Loam; 4 – Sandy; 5- Sandy clay
P Elev – Elevation measured in meters above sea level
Q TrCov – Tree Cover percentage estimated based on ground covered by shade at noon per plot
R Shcov – Shrub cover percentage estimated based on ground covered by shade at noon per plot
S HCov – Herbaceous Cover estimated based on ground covered by herbs as percent of total plot area
T Bare – Percent bare ground as percent of total plot area
U CanArea – Canopy area calculated using the formula Canopy Area=πr2
V CanArea – Canopy area calculated using the formula Canopy Area = π(D1D24). This is summation of individual trees canopy area (equation 1) divided by subplot area of 400 m2 then multiply by 100 to make it into a percentage(Manila, 2007)
W The difference in canopy area based on the formula in U and V above
X CanVol – Canopy volume calculated based on the formula (Canvol) = π 2 3 Hd( D1D2 4 )
Based on ellipsoid volume formula, where Hd is the length of canopy depth, while D1 and D2 are the two canopy diameter readings (Thorne et al., 2002)
STRI, Award: Levinson award
STRI, Award: Levinson award