zengeya et al 2014_austral ecol

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Seasonal habitat selection and space use by a semi-free

range herbivore in a heterogeneous savanna landscape

FADZAI M. ZENGEYA,1* AMON MURWIRA1 ANDMICHEL DE GARINE-WICHATITSKY2,3

1Department of Geography and Environmental Science, University of Zimbabwe, Harare, Zimbabwe(Email:[email protected]),2UPR AGIRs, Centre International de Recherche Agricole pour leDvelopement, Montpellier, France; and 3Research Platform Production and Conservation in

Partnership, Department of Biological Sciences, University of Zimbabwe, Harare, Zimbabwe

Abstract Understanding factors that influence habitat selection in heterogeneous landscapes is fundamental for

establishing realistic models on animal distribution to inform rangeland management. In this study, we tested

whether seasonal variation in habitat selection within the home range of a large herbivore was influenced by

constraints such as, distances from water and central place using semi-free range cattle (Bos taurus) as a case study.

We also tested whether shifts in space use over time were dependent on spatial scale and on the overall abundance

of resources.We predicted that distance from water significantly influenced dry season habitat selection while the

influence of the central place on habitat selection was season-independent.We also predicted that shifts in space useover time were spatial scale-dependent, and that large herbivores would include more diverse habitats in their home

ranges during the dry season, when water and food resources are less abundant. Multinomial logit models were

used to construct habitat selection models with distances from water and central place as habitat-specific

constraints. Results showed significant variations in habitat selection between the dry and wet season. As predicted,

the effect of distance from central place was season-independent, while the effect of water was not included in the

top dry season models contrary to expectation.A diverse range of habitats were also selected during the dry season

including agricultural fields. Results also indicated that shifts in space use were spatial scale dependent, with core

areas being more sensitive to changes than the home range. In addition, shifts in space use responded to temporal

changes in habitat composition. Overall, our results suggest that semi-free range herbivores adopt different foraging

strategies in response to spatial-temporal changes in habitat availability.

Key words: agricultural landscape, central place, home range, multinomial logit model, semi-arid.

INTRODUCTION

Agricultural landscapes in arid and semi-arid savannas

are spatially and temporally heterogeneous and herbi-

vores have to cope with this heterogeneity for their

survival (Scoones 1992, 1995). In addition to coping,

heterogeneous environments provide opportunities for

herbivores to persist under a range of conditions.

Improved knowledge of herbivore response to spatial

and temporal heterogeneity is therefore important for

understanding herbivore habitat selection in theselandscapes (Beasley et al. 2007). While the effect of

landscape heterogeneity on animal behaviour has been

a core focus of ecology (Wu 2013), understanding

herbivore habitat selection in heterogeneous agricul-

tural landscapes remains poorly explored.

Agricultural landscapes are widespread in Africa

and this necessitates improved understanding of the

effects of heterogeneity on habitat selection. Further-

more, due to the fragmented nature of agricultural

landscapes they are often subject to changes in the

abundance and temporal availability of forage

resources (Beasley et al. 2007). Consequently, tempo-

ral and spatial changes in the availability of forage

resources may in turn influence selection of some

habitats that offer substitutable resources (Dunning

et al. 1992), thereby influencing both habitat selection

and space use patterns at various spatial and temporal

scales (Rosenzweig 1981; Pulliam & Danielson 1991).

Thus, variable habitat selection can be expected tooccur when resources become limited for example,

during the dry season as herbivores search for scarce

quality resources while selection stabilizes when

resources are abundant for example, during the wet

season. Agricultural landscapes therefore provide a

good opportunity to study the effects of both spatial

and temporal heterogeneity on habitat selection.

Although acquisition of forage is the likely driving

force in herbivore habitat selection in agricultural land-

scapes, herbivores are often faced with several limiting

factors including access to water. Herbivores are*Corresponding author.

Accepted for publication March 2014.

Austral Ecology(2014) ,

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2014 The Authors doi:10.1111/aec.12137

Austral Ecology 2014 Ecological Society of Australia
mailto:[email protected]:[email protected]


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METHODS

Study area

The study was carried out in the Southeast Lowveld of

Zimbabwe located between (22523.50S and 31223.

16E) to the west and (22257.66S and 312658.81E) tothe east at an altitude between 300 and 600 m above mean

sea level (Chenje et al. 1998). The area is semi-arid with a

mean annual rainfall of 300600 mm and a mean annual

temperature of 2527C (Chenje et al. 1998). The primary

land use activities are largely livestock production and to a

lesser extent rainfed and irrigated cropping (Nyamudeza

et al. 2001). Rainfed agriculture is mostly for drought

resistant crops such as sorghum (Sorgum bicolor) and millet

(Pennisetum glaucum) and occasionally maize (Zea mays).

Livestock production involves cattle, goats (Capra aegagrus

hircus), sheep (Ovis aries) and donkeys (Equus asinus).

Large parts of the study area are occupied by Mopane-

dominated woodland/shrub (Colophospermum mopane).

Other dominant vegetation types in the study area includeCombretum-dominated woodland/shrub, Acacia-dominated

shrub and riparian woodland. Open grasslands also occupy a

smaller areal extent and agricultural fields substantially span

across the study area creating a heterogeneous landscape.

Cattle data

We randomly selected 12 semi-free range cattle herds within

the study area. Semi-free range cattle are cattle that are

normally left to graze during the day with minimal human

interference especially during the dry season. Based on this

system, cattle are only driven back to the kraal at night for

safe keeping against predators and theft. One adult lead cow

was selected as a representative of each herd and fitted witha GPS collar (African Wildlife Tracking collars, Pretoria,

South Africa).The GPS collars were programmed to auto-

matically log the cattle position every hour for the period

August 2008 to July 2009 and had an average success fix rate

of 94%. About 99.5% of the GPS locations were three-

dimensional.For this reason, we deemed the GPS data a true

representation of the spatial distribution of the animals.

The GPS data were captured in geographic coordinates

(latitude/longitude) based on theWGS84 reference spheroid.

The geographic coordinates were then re-projected to Uni-

versalTransverse Mercator (UTM) in metres based on WGS

84 Spheroid Zone 36K South. Next, we removed GPS read-

ings that were obtained when thecattle were in the kraal using

ArcGIS GIS 9.2 (ESRI 2005) as these tended to mask theintensity of use of the rangeland during the grazing period.

GPS data for only nine of the twelve herds were used for

analysis as these had complete data for the study period.The

GPS data were used to assessmonthly distributionduring the

AugustJuly period as well as seasonal distribution. We

selected four seasons that were correlated with the crop

growing and non-crop growing season within the study area

and these included the early wet season (initial planting of

crops in fields, NovemberJanuary), late wet season (growth

and maturation of crops, FebruaryApril), early dry season

(crop residues available after harvesting, MayJuly) and late

dry season (fewcropresiduesavailable,SeptemberOctober).

Habitat composition

Habitat types within the study area were classified from an

IKONOS (4 m) satellite image of April 2008. The IKONOS

image was classified by integrating spectral information with

measures of texture to improve spectral separability between

classes and thus improve on the overall accuracy. We used

texture measures based on Haralicks grey tone spatialdependency matrix (see Haralick et al. 1973). A supervised

Maximum Likelihood classification technique was then

applied using ENVI 4.7 (ITT Visual Information Solutions

2009) to produce eight habitat types based on dominant

floristic-physiognomic classes including water. Validation of

habitat types was based on a combination of visual interpre-

tation of easily identifiable classes and field based data col-

lected in March 2009. The classified habitat types included

Mopane-dominated woodland/shrub, Combretum-dominated

woodland/shrub, Acacia-dominated shrub, riparian wood-

land,bare areas, agricultural fields and opengrasslands. Since

the habitat types were based on broad floristic-physiognomic

vegetation types, they were deemed fairly stable and were

unlikely to change across the temporal scaleof this study. Postclassification processing involved applying a majority filter

(77) to the classified image (Zhou et al. 2008) based on

exploratory analysis, in order to remove salt and pepper

effects. The classified habitat map had an accuracy of 88%

(kappa=0.86), where the kappa coefficient assesses the clas-

sification accuracy as a proportion of agreement obtained

after removing the chance effect (Congalton 1991). For a

detailed description of kappa see Congalton (1991).

Habitat selection

We considered habitat selection at the scale of the home

range. We calculated the home ranges for each individualusing the fixed kernel method (Worton 1989) as described in

Zengeya et al. (2011). The home range was defined as 90%

and the core area as 50% of the space use (Brger et al.

2006). However, the area that is available to each individual

is usually larger than the home range. We therefore buffered

the area of the home range based on the average distance

between consecutive GPS locations for instance,

1350 m=late dry, 1230 m=early dry, 1200 m =late wet

and 1550 m=early wet season. We then overlayed the new

home ranges that included the buffered area on the habitat

map in a Geographic Information System (GIS) to deter-

mine the available habitats. Thereafter, we calculated the

proportion of each habitat that was available within the new

home ranges.We then plotted the proportion of habitat typesby season to visually assess any changes in habitat composi-

tion within the new home ranges.

Next, we built a spreadsheet with the single GPS location

for each of the nine cows as records (early dry,n=5906; late

dry, n=5881; early wet, n=6232; late wet, n=4800) and

the proportion of available habitats for each season. Water

and kraal distance were also considered as important con-

straints in habitat selection.Thus, we determined the average

distance from water and kraal for individual animal locations

per habitat type in a GIS.We particularly selected permanent

water sources because of their significant influence on animal

distribution especially during the dry season when water

HABITAT SELECTION AND SPACE USE 3




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availability becomes limited. Ephemeral sources were not

included because observations made in the field showed that

cattle herds were only using permanent water sources in the

dry season. The calculated distances from water and kraal

were then incorporated into the habitat selection models as

habitat-specific covariates. However, we did not include

water as a constraint in the wet season models since water is

not limiting during this period. In addition, agricultural fieldswere also not included in the wet season models as they were

not available to the animals.

We also considered how food availability for instance, land-

scape productivity varied with distance from the kraal as this

may influence habitat selection. We used a remotely sensed

vegetation index, the enhanced vegetation index (EVI) which

is directly related with plant productivity (Hueteet al. 2002)

as a proxy of landscape productivity. We thus downloaded

MODIS 16 day (250 m) EVI data that covered the study

period(September 2008 to July 2009) from the USGS EROS

Data Center (http://glovis.usgs.gov/) . EVI data were

re-projected from sinusoidal projection to Universal Trans-

verse Mercator (UTM) in metres based onWGS 84 Spheroid

Zone 36K South in ENVI 4.7 (ITT Visual InformationSolutions2009).Next, wecalculated theaverageEVI foreach

season in a GIS. We then constructed concentric circles

aroundthe kraal ina GIS.The minimum radiusfrom thekraal

was at a conservative distance of 300 m which was based on

the minimum mean distance from the kraal withina particular

habitat (350 m).We thendoubled the radius thereafter result-

ingin concentric circlesof radius600,1200,2400 and4800 m

(which represented the larger landscape). We then extracted

the maximum EVI within different concentric circles around

the kraal for each of the individuals.We used the maximum

EVIbased on theassumption that during this time herbivores

search for scarce high quality forage.

Analysis of shifts in space use

We calculated monthly changes in space use by obtaining

Minta index values (Minta 1992), which quantify the average

change in spatial overlap as follows:

Percent overlap area A area B

area A area B=

( ) ( )( )

( )

100 (1)

where areas A and B represent home range or core area used

by the same animal in two different months and the numera-

tor represents the area of overlap between area A and B

(Janmaat et al. 2009). Minta index values were then averaged

across the nine animals to assess the temporal changes in

space use. Overlap values range between 0 and 1 with an

overlap value of 1 representing 100% overlap. Core areaestimates for the analysis of space use were determined using

two methods, the Local Convex hull (LoCoH) which pro-

vides consistent core area estimates (50% contour) (Getz &

Wilmers 2004) and the kernel method.

Further, analysis involved determining whether home

range and core area expanded or contracted in size as space

use changed.We calculated the area of the home range and

core area for all the nine individuals in a GIS and then

plotted the mean area (MeanSE) for the monthly home

range and core areas.

Finally, we tested whether changes in space use were

related to changes in habitat composition within the home

range and core area. Changes in habitat composition per pair

of months was calculated using the BrayCurtis index (Bray

& Curtis 1957). For habitats that were not available for a

particular month we substituted zero with 0.001.The index

measures the dissimilarity between habitats of each pair of

home ranges. When BrayCurtis distance equals 1 it indi-

cates that habitat composition is different, while a Bray

Curtis distance value of zero indicates that habitatcomposition is similar.

Statistical analysis

We analysed habitat selection within the home ranges across

seasons using multinomial logit models (McCracken et al.

1998). Multinomial logit models offer several advantages in

habitat selection studies as they allow for changing habitat

availability within the home range and also incorporate both

animal and habitat-specific covariates (Kneib et al. 2011).

Habitattype wastreatedas a categorical responsevariable that

could be related to habitat-specificcovariates suchas distance

from water and kraal.The varying availability of habitat typesis taken into account in the models by inclusionof offsetterms

(Kneib et al. 2011). Forhabitat selection analysis,we selected

Mopanewoodland/shrub and open grassland as the reference

categories for the dry season and wet season respectively.This

was basedon thereasoning that the referencecategory should

always be available at each point in time (during each season)

for all the animals (Kneib et al. 2011). Note that a detailed

description of multinomial logitmodels is coveredextensively

elsewhere (see McCrackenet al. 1998;Kneib et al. 2011).The

individual cattle identifier (id) was taken as a randomeffect in

the model.

We constructed seasonal models which incorporated

both habitat specific covariates (kraal +water distance) and

habitat types, as well as models which considered habitattypes with either kraal or water distance. Finally we consid-

ered the model with habitat only. We did not consider first

order interactions as all effects of category-specific covariates

are restricted to the global effect for instance the effect of

distance from water is irrespective of habitat type. Model

selection was based on conditional Akaike Information

Criteria (AICc) (Burnham & Anderson 1998). We therefore

retained the model with the lowest AICc score. Relative

strength of evidence for each model was assessed using

Akaike weights (wi). Based on the best linear model with

significant covariates, we checked whether the continuous

covariates had nonlinear effects in the model (Kneib et al.

2011). Selection for a particular habitat (r) occurs when the

confidence intervals of positive parameter estimates () aregreater than zero (95% >0) relative to the reference habitat.

We performed all analyses in Bayes X for Windows version

2.1 (07.05.2012; Belitz et al. 2012).

Next, we tested whether shifts in space use significantly

differed between successively paired months and whether

productivity differed with increasing distances from the kraal

using linear mixed models (Pinheiro & Bates 2000) from

package lme4 (Bates et al. 2011) in R for windows version

2.15.0 (R Development Core Team 2012). Minta index

values were arcsine transformed while distance from the kraal

was log transformed.The individual animals were added as a

random factor to account for within-individual dependency

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between MayJune with


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Temporal changes in home range

and core area size

Home range and core area size varied greatly across

the monthly scale.The smallest home range and core

area size occurred in January with a mean home range

of 141.716.3 ha and core area size of 26.1 2.3 ha.The largest home range size was observed in Decem-

ber with 449.649.2 ha and core area size of 104.815.7 ha. Observable changes in area between thehome range and core areas were detected (Fig. 5).The

home range experienced larger changes in area com-

pared with the core areas.The largest expansion of the

home range and core area occurred between October

November followed by AprilMay while December

January had the largest reduction in area. MayJune,

which experienced the largest spatial shift, had a

smaller reduction in area compared with December

January.

DISCUSSION

Results of this study indicated that habitat selection

within the home range of semi-free range cattle signifi-cantly differed between seasons. However, intra-

seasonal habitat selection remained fairly constant

particularly during the wet season. Similar selection

patterns were observed for other herbivores. For

example, van Beestet al. (2010) reported stable moose

(Alces alces) habitat selection patterns within the home

range during summer, while variable habitat selection

patterns were observed for winter. This implies that

during the wet season when there is re-growth of veg-

etation, habitat selection is stable while the opposite

occurs during the dry season when resources become

scarce. Seasonal variation in habitat selection by semi-

free range cattle indicated the different foraging strat-

egies that herbivores adopt in response to the temporal

changes in habitat availability in heterogeneous semi-

arid landscapes.

Cattle generally selected a more diverse range of

habitats during the dry season compared with the wetseason. For instance, during the early dry season, there

was increased selection for Acaciashrub, agricultural

fields, bare areas and open grasslands. During the late

dry season, results indicated a similar pattern except

that there was increased use of riparian woodlands

while agricultural fields and bare areas were least used

relative to their availability. This illustrated how the

diversity of habitat types within a landscape can influ-

ence opportunistic switching by herbivores between

different habitat types (Scoones 1995; Bennett et al.

2007). Although the selected dry season habitat types

offered different food resources they provided supple-

mental food resources that were generally not availablein the wet season. This is consistent with Abbaset al.

(2011) who demonstrated that a variety of habitats

provided by forest fragmentation benefit generalist

herbivores by increasing access to various substitutable

food resources. Furthermore, the variation in resource

quality across habitats, although not quantified in this

study, encourages flexibility in habitat selection (Smith

et al. 2013). In our case, the increased selection of

Acaciaduring the dry season may be linked to nutri-

tious pods which have been reported to contain high

protein content (Timberlakeet al. 1999). In addition,

agricultural fields offered nutritious crop residues

during the dry season only as they were not accessibleto animals in the wet season. Although cattle are

mainly grazers, preferring grass species, the quality of

grass during the dry season is generally low (Zengeya

et al. 2013) unlike during the wet season.Thus, habi-

tats selected by cattle in the dry season in our study

site likely offered nutritional advantages to animals

when grass declined in quality (Woodward & Coppock

1995). Consequently, the divergence in habitat selec-

tion between the dry and wet seasons could be linked

to the abundance in high quality grass during the wet

season compared with the dry season.

The effect of kraal distance on habitat selection

showed a negative but non-significant effect during thelate dry and late wet season. This result partly sup-

ported the prediction that the effect of kraal location

on habitat selection was season independent. Land-

scape productivity around the kraal was lower during

the dry season compared with the wet season, thus

animals were subjected to low food quality especially

at distances of 300 and 600 m. However, this effect

was likely to be more pronounced during the dry

season as resource quality levels are often lower.None-

theless, cattle were able to select a diverse range of

habitats which offer alternative sources of browse feed,

Fig. 3. Temporal changes in space use of semi-free range

cattle as determined by mean percent overlap of monthly

(September 2008 to July 2009) home ranges and core areas

using the Minta index.





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such as Acaciapods or Mopaneleaves during the dry

season. The results were consistent with the predic-

tions for a central place forager (Orians & Pearson

1979), for which animals are likely to be subjected to

lower productive patches at shorter distances from the

central place (in our case the kraal) than at larger

distances from the central place.

Selection for the most abundant habitat type variedacross seasons. Selection was consistent with previous

findings which linked selection of Mopane with sea-

sonal variation in chemical composition of browse

species. For example, Kelly and Walker (1976)

reported that dry season use of Mopane woodland/

shrub may result from the high dry leaf protein

content, whereas late dry season use may be linked

with the early emergence of new green leaves that

contain high protein levels approximately 17.5%

(Owen-Smith & Coopers 1989). Styles and Skinner

(1997) also suggested that low preference/avoidance of

Mopanein the wet season was linked to increased levels

of tannin which discouraged browsing.As predicted, we found shifts in space use to be

spatial scale specific, with core areas being relatively

unstable compared with the home range. For

instance, a steep negative gradient was noted between

monthly core area overlaps from the late dry season

months up to the early wet season months. Similarly,

it has also been reported that herbivores alter their

space use patterns during periods of scarce and low

quality forage (Edenius 1991), increasing within

season space use shifts (Wittmer et al. 2006). Our

results also indicated that significant shifts in space

use occurred at the end of the early wet season

(DecemberJanuary) and at the beginning of theearly dry season (MayJune) which was accompanied

by extensive contraction of both home range and

core area size. This phenomenon was most pro-

nounced at the end of the early wet season when

resource quality and quantity such as grass was

expected to increase, thus resulting in reduced home

range and core area.This is consistent with Harestad

and Bunnell (1979) who reported that home range

size was a function of habitat productivity, with

smaller ranges being associated with highly

productive areas. Although the MayJune overlap

resulted in a smaller reduction in core area and home

range size, it had the largest spatial shift which weattributed to the accessibility of agricultural fields.

This period also coincided with low availability and

quality of forage available for herbivores within the

rangeland (Katjiua & Ward 2006; Bennett et al.

2007), making crop residues made available within

agricultural fields a critical supplement for cattle. In

a similar study on white-tailed deer (Odocoileus

virginianus), Brinkman et al. (2005) noted that deer

home ranges significantly shifted to agricultural fields

once they became accessible especially during

periods of low forage availability.

Fig. 4. Relationship between shifts in space use (Minta index values) with changes in habitat composition estimated using

BrayCurtis index at the core area (a) and home range (b). The dashed curves indicate 1SE limits, both xand yaxes aretransformed see text.

Fig. 5. Temporal changes in home range (90% contour)

and core area (50% contour) size estimated using the kernel

density estimator.

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In addition, shifting of home ranges and core areas

by herbivores occurred as a response to the temporal

variation in habitat composition within home ranges

and core area. Consequently, small changes in space

use were associated with minimal changes in habitat

composition whereas large changes in space use were

associated with increased changes in habitatcomposition. Thus, during periods of low abundance

of foraging resource, cattle could have modified their

space use in order to accommodate new habitats as a

coping strategy.

The modelling approach used in this study enabled

direct incorporation of constraining factors that can

influence habitat selection of herbivores. Such an

approach allowed better insights into processes under-

lying habitat selection. Although our models were

limited in quantifying interactions for categorical vari-

ables, multinomial logit models still offer the added

advantages of incorporating random effects and char-

acteristics of the individual animal as well as habitat.Based on the cattle data, we showed that herbivore

habitat selectionvaried over time andwas influenced by

different constraints. Furthermore, we established that

temporal shifts in space use were a function of varying

habitat composition. We demonstrated that distances

from water and kraal were key factors constraining

habitat selection of semi-free range cattle in a semi-arid

agro-ecological landscape. We however acknowledge

the fact that several other factors characterizing the

animal (physiological or reproductive status) and avail-

able habitats which include forage resources also affect

habitat selection in such a landscape. Incorporation of

these and other factors could improve resource selec-tion modelling in similar landscapes.

ACKNOWLEDGEMENTS

We thank Thomas Kneib for assistance in the running

of Bayes X codes and Nicolas Morellet forgivinguseful

suggestions to improve the manuscript.We also wish to

thank Alexander Caron for his assistance in data col-

lection andfor providing some useful commentsduring

the preparation of this manuscript.We are grateful to

the Malipati farmers and the Department of Veterinary

Service who allowed us to conduct this study in their

area. This work was conducted within the frameworkof the Research Platform Production and Conserva-

tion in Partnership, RP-PCP (RP-PCP grant/project

CC#2).The project was also supported by the DAAD

Zimbabwe In-Country scholarship.

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