DOI: 10.5061/dryad.0p2ngf21h
title: Savanna woody plants responses to mammalian herbivory and implications for management of livestock-wildlife landscape

description: 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 &lt; 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.
description: 1. <b>Study
 Site</b> 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 37<sup>0</sup>53’E and
 0<sup>0</sup>17’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
 <strong>2. Methods</strong>
 <b><i>Browsing
 intensity</i></b>
 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/km<sup>2</sup> 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/Km<sup>2</sup> compared to 28
 TLU/Km<sup>2</sup> for PR. Domestic and wildlife browsers
 considered the most effective  in modifying woody plant structure and
 perhaps its composition, was estimated at 28
 TLU/Km<sup>2</sup> and 17 TLU/Km<sup>2</sup> 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.  
   <b><i>Sampling design for
 Structural data</i></b> 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<i>.,</i> 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.
   <b><i>Structural
 diversity</i></b> Structural diversity and
 biomass production estimates were based on four dominant palatable
 species, <i>Acacia brevispica, Acacia tortilis, Acacia mellifera and
 Acacia etbaica </i>based on Lusigi <i>et al</i>. (1984)
 rating and from interviewing knowledgeable local herders<i>. A.
 mellifera </i>and<i> A. etbaica</i> 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<i>(</i><i>D1D2</i><i>4</i><i>)</i> ………………………………..Eq (1)   Where <i>D<sub>1</sub></i><sub> </sub>and<sub> </sub><i>D<sub>2</sub></i> are the two perpendicular diameter measurements when projected on the ground. This formula does not assume symmetry of the canopy. Canopy volume (Canvol) = <i>π</i><i>2</i><i>3</i><i>Hd(</i><i>D1D2</i><i>4</i><i>)</i>………………………………………………Eq (2) Based on ellipsoid volume formula, where <i>H<sub>d</sub></i> is the length of canopy depth, while <i>D<sub>1</sub></i> and <i>D<sub>2</sub></i> are the two canopy diameter readings (Thorne et al., 2002).  Canopy density (CD) per subplot =<i> </i><i>πD1D2</i><i>16</i>…………………………………………Eq (3) This is summation of individual trees canopy area (equation 1) divided by subplot area of 400 m<sup>2</sup> then multiply by 100 to make it into a percentage(Manila, 2007). Coefficient of variation (CV) =  <i>δ</i><i>π</i>      ……………………………………………………Eq (4)  Where<i> δ</i> is standard deviation and <i>π </i>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. <b><i>Browse biomass</i></b> The browse biomass was estimated using double sampling method as described by Foroughbakch et al<i>.</i> (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<i>.</i> (2008) recommended the use of 15 individuals per species however, in this study; we took 48, 32, 12 and 37 individuals for <i>Acacia mellifera, Acacia brevispica, Acacia tortilis</i>, and <i>Acacia etbaica</i> 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.<i> etbaica</i> <i>y</i> = 56.24<i>x</i>-78.981, r² = 0.685; <i>A. mellifera</i>, <i>y</i> = 131.76<i>x</i> – 265.6, r² = 0.714; <i>A. brevispica</i> <i>y</i> = 48.74<i>x</i> - 41.17, r² = 0.24 and <i>A. tortilis</i> <i>y</i> = 53.5<i>x</i> – 103.5; r² = 0.75 where <i>y</i> is the edible (browsable) biomass and <i>x</i> are branch base diameter. As a result of low coefficient of determination for <i>A.</i> <i>brevispica</i> (r<sup>2</sup> = 0.24) and absence of <i>A. tortilis</i> in sampled plots in PR, the two species were therefore excluded in biomass estimates discussions. <b><i>Soil measurement</i></b> 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; <i>Available nutrient elements</i> (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 H<sub>2</sub>SO<sub>4</sub>.  The elements; Na, Ca and K were determined using a flame photometer and P, Mg and Mn using a spectrophotometer. <i>Total organic carbon:</i> Calorimetric method was used (Murphy and Riley, 1962): All organic C in the soil sample was oxidized by acidified dichromate at 150<sup>0</sup>C<sub> </sub>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. <i>Total nitrogen:</i><b> </b>Kjeldahl method was used (Benton, 1991); Soil samples was digested with concentrated sulphuric acid containing potassium sulphate, selenium and copper sulphate hydrated at approximately 350<sup>0</sup>C.  Total N was determined by distillation followed by titration with H<sub>2</sub>SO<sub>4</sub>. <i>Soil pH and EC</i> was determined in a 1:1 (w/v) soil – water suspension with pH – meter and conductivity meter respectively. <i>Available trace elements</i>: 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). <i>Extractable Phosphorus</i>: 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. <b>Data Analysis</b> 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).  
description: <b>Legends for Datasets</b> 
 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
 <i>A=π</i><i>r</i><sup><i>2</i></sup> 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 <i>Canopy Area=π</i><i>r</i><sup><i>2 </i></sup> V          CanArea – Canopy area calculated using the formula <i>Canopy Area</i>  = π<i>(</i><i>D1D2</i><i>4</i><i>)</i>. This is summation of individual trees canopy area (equation 1) divided by subplot area of 400 m<sup>2</sup> 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) = <i>π</i> <i>2</i> <i>3</i> <i>Hd(</i> <i>D1D2</i> <i>4</i> <i>)</i>  Based on ellipsoid volume formula, where <i>H<sub>d</sub></i> is the length of canopy depth, while <i>D<sub>1</sub></i> and <i>D<sub>2</sub></i> are the two canopy diameter readings (Thorne et al., 2002)