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Vegetation composition and structure influencesbird species community assemblages in the highlandagricultural landscape of Nyandarua, KenyaP Kariuki Ndang'ang'aabc, John BM Njorogeb & Mwangi Githiruc

a BirdLife International – Africa Partnership Secretariat, PO Box 3502 – 00100, Nairobi,Kenyab Jomo Kenyatta University of Agriculture and Technology, PO Box 62000 – 00200,Nairobi, Kenyac Ornithology Section, National Museums of Kenya, PO Box 40658 – 00100, Nairobi, KenyaPublished online: 13 Nov 2013.

To cite this article: P Kariuki Ndang'ang'a, John BM Njoroge & Mwangi Githiru (2013) Vegetation composition and structureinfluences bird species community assemblages in the highland agricultural landscape of Nyandarua, Kenya, Ostrich:Journal of African Ornithology, 84:3, 171-179, DOI: 10.2989/00306525.2013.860929

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Vegetation composition and structure influences bird species community assemblages in the highland agricultural landscape of Nyandarua, Kenya

P Kariuki Ndang’ang’a1,2,3*, John BM Njoroge2 and Mwangi Githiru3

1 BirdLife International – Africa Partnership Secretariat, PO Box 3502 – 00100, Nairobi, Kenya2 Jomo Kenyatta University of Agriculture and Technology, PO Box 62000 – 00200, Nairobi, Kenya3 Ornithology Section, National Museums of Kenya, PO Box 40658 – 00100, Nairobi, Kenya* Corresponding author, e-mail: kariuki.ndanganga@birdlife.org

We examined the effects of vegetation composition and structure on bird species diversity and richness of foraging guilds in the highland agricultural landscape of Nyandarua, Kenya. Bird point counts and vegetation surveys were undertaken during four sampling periods. Linear mixed models were used to examine the effects of vegetation variables on bird species diversity and richness of respective foraging guilds, and logistic generalised linear models used to examine vegetation effects on the presence/absence of the 17 most common bird species. Bird species diversity increased with increasing density of woody plant species and vegetation structural hetero-geneity. Two gradients of increasing vegetation structural heterogeneity were most important in influencing bird community composition and had positive effects on species diversity and the presence of most of the species assessed: (1) increasing closed cover due to woody plant density, which also had positive effects on species richness of frugivores and nectarivores, but negative effects on carnivores, and (2) increasing fallow and cultivation versus decreasing grassland/pasture cover, which also had a positive effect on species richness of granivores and omnivores. This study reaffirms the need to maintain a structurally rich agricultural landscape for it to support agrobiodiversity.

Keywords: birds, East Africa, farmland, forest, grassland, heterogeneity

Online supplementary material: Supplementary data for this article are available at http://dx.doi.org/10.2989/00306525.2013.860929

Complex, often traditional, agricultural ecosystems can support significant species richness, but many of these systems are threatened by modern agricultural practices (Kellermann et al. 2008). Agricultural intensification results in simplification of agricultural landscapes by the expansion of agricultural land and removal of non-crop habitat (Benton et al. 2003, Bianchi et al. 2006). Even so, some traditional crops still host high biodiversity (Donald 1994). The simplifi-cation of landscape composition and the decline of biodiver-sity may affect the functioning of natural pest and weed control because non-crop habitats provide requisites for a broad spectrum of natural enemies, and the exchange of natural enemies between crop and non-crop habitats is likely to be diminished in landscapes dominated by arable cropland (Bianchi et al. 2006).

In addition to hosting beneficial wild species, non-crop habitats within agricultural landscapes are crucial for general biodiversity conservation since increasingly large areas of human-modified habitats are being superimposed on natural areas, greatly modifying the landscape (Gardner et al. 2009). The protected area network in Kenya alone will be insufficient to conserve biodiversity in the long term, because much of Kenya’s biodiversity is not represented in these protected areas (Bennun and Njoroge 1999). Indeed, globally, the future of many indigenous plants and animals

species is closely tied to land use in human-dominated landscapes: their long-term persistence will depend on a capacity to survive within the ‘countryside’ occupied and managed by humans (Bennett et al. 2006).

Frequently, conservation planning is based on a binary view of ‘habitat’ and ‘non-habitat’ components, whereas agricultural landscapes are diverse and their spatial hetero-geneity is an important determinant of some aspects of biodiversity (Benton et al. 2003). Agricultural landscapes are mosaics of different land uses and the knowledge base for managing them will be significantly enhanced by a better understanding of the properties of ‘whole’ land mosaics and how they influence species assemblages and ecolog-ical processes (Bennett et al. 2006). It is therefore important to undertake studies to understand how changes in the agroecosystems affect the capacity of the land mosaic to support natural flora and fauna and maintain ecological processes. While such studies are common in other parts of the world, they are still limited in Africa. In Kenya, a few bird community studies have been undertaken on the agroeco-system surrounding Kakamega Forest, a mid-altitude tropical rainforest (Eshiamwata et al. 2006, Laube et al. 2008, Otieno et al. 2011, Mulwa et al. 2012), whereas in other parts of Africa other studies have been undertaken in Burkina Faso (Söderström et al. 2003), Nigeria (Hulme

Introduction

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Ndang’ang’a, Njoroge and Githiru172

2007, Usieta et al. 2013) and Uganda (Nalwanga 2011). Generally, the studies emphasised the importance of retaining structurally rich farmland for high bird species diversity and richness. In particular, the following features within farmland were demonstrated as important: horizontal and vertical heterogeneity at different scales (Söderström et al. 2003), diversity of habitat structure (Laube et al. 2008, Mulwa et al. 2012), retaining of natural features (Hulme 2007), volume of hedges (Otieno et al. 2011, Usieta et al. 2013) and planting of indigenous trees (Eshiamwata et al. 2006). The current study attempts to fill the gap in knowledge as it relates to African highland farmland landscapes and offers a unique opportunity of simultane-ously studying bird communities in farmlands that were historically derived from both grasslands and forests.

This study focused on the high-altitude agricultural landscapes of Nyandarua County, central Kenya. Birds were used as the study taxon because they make excellent flagships and indicators for biodiversity, the environment and the sustainability of human activities (Gregory et al. 2003). They occur in a range of habitats in considerable numbers, reflect changes in other biodiversity (e.g. animals and plants) and are responsive and sensitive to environ-mental change. The conclusions made from this avian study will therefore have implications for what might be happening to non-avian taxa in the study area and beyond.

The main objective of this study was to assess the overall bird species diversity and species richness of various avian foraging guilds of the high-altitude agricultural landscape of Nyandarua in relation to vegetation composition and structure. The specific objectives were to: (a) determine bird species composition in the Nyandarua agricultural landscape as a function of grassland- or forest-derived vegetation matrix that historically dominated parts of the landscape, and (b) examine the effects of vegetation composition and structure on bird species diversity and species richness within various foraging guilds. It was predicted that (1) bird communities in grassland- and forest-derived agricultural landscapes will be distinct, and (2) bird species diversity and the species richness of respective foraging guilds will increase with increasing heterogeneity of vegetation cover types. The results of the study are discussed vis a vis the implications of managing vegetation cover and structure in the agricultural landscape on the occurrence of avian foraging guilds and common species known to be important as predators of crop pests, post-dispersal weed seed predators, and as crop pests themselves.

Materials and methods

Study areaThe study was conducted in Nyandarua county (0°08′ N– 0°50′ S, 35°13′–36°42′ E; 2 000–3 000 m above sea level) of central Kenya, East Africa (Figure 1a). Nyandarua County, with an area of 3 528 km2, constitutes 0.6% of the Kenyan land area (Republic of Kenya 1997) and is divided into two subdistricts, Nyandarua North and Nyandarua South. Before the 1960s most of Nyandarua North was covered by forest, whereas Nyandarua South was originally covered by grasslands. Apart from the protected Aberdare Forest, Lake Olborrosat, settlement and urban areas, the rest of

the district is mostly covered by cultivation and pasture. The main food crops grown in the district are potatoes Solanum tuberosum, cabbages Brassica oleracea L. var. capitata,

Figure 1: (a) Map of the study area. Bird count plots are indicated as small dark circles along transects within the sampling/study sites. (b) Layout of point count plots and transects within a study site

Paths/Roads Point count

plots ontransects

LEGEND

AFRICAKenyaKenya

NYANDARUA

NYANDARUA

NAKURU

NYERI

Bahati

Aberdare

Aberdares

Kipipiri

Sampling sites

County boundary

Forest reserves

0 7.5 15 km

0°15 S0°15 S

0°30 S0°30 S

36°15 E

36°15 E 36°30 E

36°30 E

(a)

(b)

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Ostrich 2013, 84(3): 171–179 173

kales B.o. acephala, peas Pisum sativum, maize Zea mays, beans Phaseolus vulgaris, onions Allium cepa, carrots Daucus carota and fruits (apples Malus domestica, pears Pyrus spp. and plums Prunus salicina), while cash crops include pyrethrum Chrysanthemum cinerariaefolium, wheat Triticum aestivum and cut flowers. Livestock production (dairy cattle Bos primigenius, sheep Ovis aries and poultry) is widespread in the area.

Annual rainfall varies between 750 and 1 500 mm, which falls between March and May (long rains) and again between October and December (short rains). However, in recent years rainfall has been less predictable and during the two study years (2010 and 2011) the rain was contin-uous, without a clear break between the long and short rains. The human population density is about 184 individ-uals km−2, the rural density being lower at about 169 compared to urban at about 288 individuals km−2 (Kenya National Bureau of Statistics 2010).

Study designWithin each of the two subdistricts, 5 km 5 km study sites (three in Nyandarua south and two in Nyandarua north) were systematically selected from a map to represent varying human population densities (Figure 1a). Within each study site, 30-metre-radius sampling plots were identified along 1 km transects (Table 1). Transects were semi-randomly located through first identifying one or two access roads or paths in a study site that as much as possible covered a long distance across the site with any two roads/paths spaced at least 2 km from each other. Second, the first transect would be placed perpendicular to the path or road and at least 250 m from the boundary of the site. The first sampling plot on the transect would be placed 100 m away from the path/road and all plots spaced 250 m apart along a 1 km transect. The transects were 1 km apart and extended from one or both sides of the path /road (Figure 1b). The location of each sampling station was marked using a GPS. Bird counts and assessment of vegetation structure and composition were conducted during four sampling periods, two in the wet season (May to September 2010, May to August 2011) and two in the dry season (October 2010 to February 2011, January to February 2012).

Bird communityBirds were counted using the standard point count method as described by Bibby et al. (2000). On arriving at the point count station, the observer allowed the birds time to settle for 1 min, and then recorded all the birds seen or heard within the 30 m radius plot for a period of 10 min. Birds flying over sample areas were excluded, as well as swallows, swifts and martins. The counts were conducted between dawn and 11:00 on fair weather days. Diet classifi-cation for African birds (Kissling et al. 2007), in which up to three ‘major’ and three ‘minor’ diets taken by every species are listed, was used as a basis for placing all recorded bird species into seven foraging guilds. Only the ‘major’ diets were used in placing birds into guild classifications that followed Gray et al. (2007): carnivores (vertebrates), frugivores (fruits), granivores (seeds), herbivores (vegetable materials, e.g. leaves, shoots, roots, flowers and bulbs),

insectivores (insects and other invertebrates), nectarivores (nectar) and omnivores (more than one major food item composing of both plant and animal materials). Further diet information was obtained from Hockey et al. (2005).

Vegetation structure and compositionThe vegetation composition and structure were quanti-fied at both the 30-metre-radius plot (hereafter ‘local’) and the surrounding 100-metre-radius scale. It was assumed that this latter scale would be a surrogate for individual farm scale because the farm size in the study area mostly is below 10 ha (Ndang’ang’a et al. 2002). At the local scale, the following variables were recorded: (1) type of species and number of woody plant individuals. Tree and shrub species were identified after Beentje (1994) and classed as either indigenous or exotic; (2) extent of respective of cover types, namely fallow, cultivated, grassland or pasture, shrub, woodland, bare, wetland and settlement expressed in percentage to the nearest 5%; (3) the total length of hedgerows and tree lines; (4) index of vegetation cover diversity calculated from estimates of the percentage cover of the different cover types and calculating the Shannon–Wiener diversity index (Bibby et al. 2000); (5) vertical vegeta-tion heterogeneity within each plot, obtained by estimating plant cover over the whole study plot to the nearest 5% at heights of 0, 1, 2, 4, 8 and 16 m. Vertical vegetation hetero-geneity was then defined as the diversity of vegetation layers using the Shannon–Wiener diversity index.

The surrounding vegetation at 100 m radius (‘farm’ scale) was recorded as the extent (%) of respective of cover types (cultivated or fallow, grassland, shrub, woodland, bare, wetland and settlement). Based on this, the dominant vegetation type (covering 50%), the surrounding vegeta-tion was classified as (1) cultivated or fallow, (2) grassland, (3) woodland or shrub, or (4) mixed (with no single vegeta-tion type covering 50%). Throughout the four sampling periods, only a few of the plots were surrounded by vegeta-tion dominated by woodland (n 24) or shrub (n 9) and thus these were not included in the analysis for effects of dominant surrounding vegetation type.

Statistical analysisSpecies accumulation curves were plotted for both the North and South subdistricts to assess whether the study recorded close to complete species sets for each of the subdistricts. The Shannon–Wiener index of bird species diversity per point count plot was calculated and species richness was expressed as the number of species per plot. Percentages were used to assess and describe the

Table 1: Location of study sites, number of transects and point count plots

Subdistrict and historical vegetation matrix Site name No. of

transectsNo. of point count plots

Nyandarua South Murungaru 11 53(grassland) Ndunyu Njeru 12 60

Njabini 12 59Nyandarua North Ol-Joro-Orok 11 55

(forest/woodland) Lesirko 23 115

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relative abundance of respective species, habitat-based and foraging guilds of birds. The commonness of respec-tive species was assessed as a percentage of the number of point count plots in which a species was recorded throughout the study period.

Using R (R Development Core Team 2012), principal components analysis (PCA) was done to compress the set of vegetation composition and structure variables recorded at the local scale into a smaller number of axes of habitat variability having the benefit of being orthogonal and uncorrelated. The components were then interpreted using the component loadings (correlations between the principal component and each original residual variable). The extracted PCA factors that accounted for most of the variability within the variables were retained as measures of vegetation composition and structure for the subsequent multivariate modeling analysis. Before performing the PCA the values of variables were standardised in relation to each other to convert them into a similar scale. This was done through calculating z-scores for each variable so that it had a mean of zero and a standard deviation of one (Quinn and Keough 2002). The variables analysed were number of indigenous trees, number of exotic trees, vegetation-cover diversity index, vertical heterogeneity index, tree species diversity, tree line/hedge length, and percentage cover of cultivation, fallow and grassland. Woodland cover was highly correlated to tree (exotic and indigenous) numbers and thus was not entered into the analysis.

The associations between all possible explanatory habitat variables (the extracted PCA factors that explained the majority of variation from the PCA analysis) and bird species diversity as well as species richness of respective foraging guilds were examined using linear mixed models using SPSS Statistics 17.0 (SPSS, Chicago, USA).

Fixed factors in the models were subdistrict (North or South), season (dry or wet) and the dominant surrounding vegetation at 100 m radius (cultivation, grassland or mixed). Study site and transect were the random factors. The first four most important principal components (explaining 70.4% of the variation) were entered as the covariates. The four sampling periods were considered as repeats. Data per count plot on species diversity (Shannon–Wiener diversity index) and species richness (number of species) of each of the foraging guilds were entered as the response variables. There were insufficient detections for herbivores to build valid models.

Logistic generalised linear models with binomial distri-bution and log link (Crawley 2007, Zuur et al. 2009) were used to test for the effects of subdistrict, season, dominant surrounding vegetation cover at 100 m radius and all possible explanatory habitat variables (those that explained the majority of variation from the PCA analysis) on the presence or absence of the 17 respective most common bird species. Common bird species were considered as those present in at least 10 of the sample plots during each of the four sampling periods.

Results

A total of 195 bird species representing 5 501 individuals were recorded throughout the study that covered 342

point count plots along 69 transects during four sampling periods. More species (161) were recorded in the North compared to the South (105) subdistrict (Supplementary Table S1). Species accumulation curves for both subdis-tricts almost leveled off, indicating that almost complete lists of species for the subdistricts and the entire study areas were recorded (Supplementary Figure S1). Four of the species are listed in the IUCN Red List as globally threatened or near-threatened: Martial Eagle Polemaetus bellicosus (Vulnerable), Grey Crowned Crane Balearica regulorum (Endangered), Sharpe’s Longclaw Macronyx sharpei (Endangered) and Jackson’s Widowbird Euplectes jacksoni (Near-threatened). Insectivores were represented by the highest number of species (45%, n 195), whereas granivores were represented by the highest number of individuals (38%, n 5 501).

In terms of species composition by habitat preference, most species (25%, n 195) were forest species and the proportions of species preferring grassland (18%), bush (16%), wetland (16%) and diverse (14%) habitats occurred in almost equal proportions (Supplementary Table S1). However, species preferring diverse habitats were the most abundant (48%, n 5 501) followed by those prefer-ring bush (26%). The three most abundant species were Streaky Seedeater Serinus striolatus (a granivore making up 14% of all individuals), Rufous Sparrow Passer rufocinctus (granivore, 11%) and Baglafecht Weaver Ploceus baglafecht (omnivore, 11%).

Effects of vegetation composition and structure at the local (30-metre-radius plot) scalePrincipal components analysis extracted four factors, which together accounted for 70.4% of the variability within the 10 vegetation composition and structure variables (Table 2). PC1 corresponded to decreasing cover of woody species and decreasing structural heterogeneity (high negative scores for tree species diversity, vegetation cover diversity, shrub cover and the number of exotic and indigenous trees), explaining 27.3% of the variation. PC2 corresponded to increasing grassland and decreasing cultivation cover,

Table 2: Loadings from principal components analysis of vegetation composition and structure variables at the 30-metre-radius plot scale. Factor loadings greater than 0.2 are highlighted in bold

Variable PC1 PC2 PC3 PC4Tree species diversity −0.471 0.067 0.077 −0.045Vegetation cover diversity −0.430 −0.124 −0.079 0.190Percent cultivation cover 0.049 −0.632 −0.167 −0.431Percent fallow cover −0.011 −0.245 0.317 0.762Percent grassland cover 0.149 0.705 −0.082 −0.063Percent shrub cover −0.383 0.110 0.383 −0.152Tree line/hedge length −0.085 −0.011 −0.479 0.295No. of exotic trees −0.334 0.093 −0.466 0.176No. of indigenous trees −0.433 0.049 0.346 −0.189Vertical vegetation

heterogeneity−0.340 0.016 −0.374 −0.125

Importance of componentsSD 1.65 1.30 1.20 1.09Variance (%) 27.33 16.87 14.43 11.78Cumulative variance (%) 27.33 44.19 58.63 70.41

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Ostrich 2013, 84(3): 171–179 175

explaining 16.9% of the variation. PC3 corresponded to increasing indigenous trees, shrub and fallow cover and decreasing exotic trees, tree line/hedge length and vertical heterogeneity, explaining 14.4% of the variation. PC4 corresponded to increasing fallow and decreasing cultiva-tion cover, explaining 11.8% of the variation.

Overall bird species diversity per count plot was positively associated with increasing abundance and cover of woody plant species (trees and shrubs) and vegetation structural heterogeneity (negative to PC1; Table 3). On the other hand it was associated with increasing cultiva-tion and fallow cover and was negatively affected by increasing grassland cover (negative to PC2). On the cultivation–fallow gradient, species diversity was favoured by increasing cultivation and decreasing fallow cover and exotic trees (negative to PC4).

The species richness of carnivores was associated with open habitats with decreasing abundance and cover of woody plant species (trees and shrubs) and vegetation structural heterogeneity (positive to PC1; Table 3). This is unlike the species richness of frugivores and nectarivores, which was strongly associated with increasing woody plants and structural heterogeneity. In addition, on a gradient of indigenous trees, shrubs and fallow cover versus exotic trees and tree line/hedge, frugivores were associated with increasing indigenous trees, shrubs and fallow cover (positive to PC3). Granivore species richness was, on one hand, associated with increasing cultivation and fallow and decreasing grassland cover (negative to PC2, positive to PC4). On a gradient of indigenous trees, shrubs and fallow cover versus exotic trees and tree line/hedge, granivores were associated with tree line/hedge length, abundance of exotic trees and vertical heterogeneity (negative to PC3). Omnivore species richness was associated with increasing

cultivation and fallow cover and decreasing grassland cover (negative to PC2).

The presence of seven of the 17 common bird species assessed was associated with closed vegetation with increasing abundance and cover of woody plant species (trees and shrubs) and vegetation structural heteroge-neity (negative to PC1, Table 4). Six of the 17 species were associated with increasing cultivation and fallow and decreasing grassland cover (negative to PC2), and only one (Grassland Pipit) was favoured by increasing grassland cover and decreasing cultivation and fallow (positive to PC2). Seven species were associated with increasing exotic trees, tree line/hedge length and vertical heteroge-neity and decreasing indigenous trees, fallow and shrub cover (negative to PC3). Only the presence of Ring-necked Dove Streptopelia capicola was highly associated with increasing fallow and tree line/hedge against decreasing cultivation (positive to PC4).

Effects of subdistrict/historical vegetation matrix, surrounding vegetation and seasonThe species diversity per point count plot was signifi-cantly higher in the South than the North subdistrict and lower in plots surrounded by vegetation dominated by grasslands compared to those surrounded by cultivated and mixed vegetation (Figure 2, Table 3). Species richness of carnivores and insectivores was significantly higher in the South, whereas that of frugivores, granivores and omnivores was higher in the North subdistrict (Figure 3, Table 3). Grasslands as the dominant surrounding vegeta-tion type had significant negative effects on the species richness of granivores and nectarivores, but a positive effect on the species richness of insectivores. The other surrounding vegetation types did not have significant effects

Table 3: Linear mixed models for overall species diversity and species richness of respective foraging guilds on the effects of subdistrict, season, dominant vegetation surrounding point count plots and vegetation composition and structure variables at the point count plot level, as summarised using PCA factors that accounted for most of the variability within the variables

Parameter Speciesdiversity

Species richness of foraging guildsCarnivores Frugivores Granivores Insectivores Nectarivores Omnivores

Intercept 0.621*** 0.924*** 0.203*** 2.402*** 3.149*** 0.349*** 1.606***Subdistrict: North −0.082* −0.602*** 0.248*** 0.428*** −0.275** 0.028 0.379***Subdistrict: Southa 0 0 0 0 0 0 0Season: Dry −0.056. 0.031 0.018 −0.305*** −2.179*** −0.185*** −0.186**Season: Weta 0 0 0 0 0 0 0Surrounding Vegetation:

Cultivated0.042 −0.103. −0.020 0.168. −0.182 0.060. −0.062

Surrounding Vegetation: Grassland

−0.095* 0.032 −0.016 −0.342*** 0.301* −0.076* −0.015

Surrounding Vegetation: Mixeda

0 0 0 0 0 0 0

PC1b −0.083*** 0.083*** −0.094*** −0.037 0.056. −0.056*** −0.030PC2b −0.052** −0.006 −0.017 −0.088* −0.024 0.015 −0.087**PC3b −0.022 0.025 0.071*** −0.099** 0.070. −0.025* 0.039PC4b −0.031* 0.008 −0.036* 0.071* −0.057 −0.004 0.014Estimated coefficients and respective level of significance are presented with asterisks indicating significance of habitat component in the model: . P 0.1, * P 0.05, ** P 0.01, *** P 0.001a Indicates a parameter that is set to zero

b PC1 decreasing cover of woody species and decreasing structural heterogeneity; PC2 increasing grassland and decreasing cultivation cover; PC3 increasing indigenous trees, shrub and fallow cover and decreasing exotic trees, tree line/hedge length and vertical heterogeneity; PC4 increasing fallow and decreasing cultivation cover

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on species richness of foraging guilds. Significantly higher species richness of granivores, insectivores and omnivores was recorded during the wet than the dry season.

Of the 17 commonest species assessed, the presence of four was significantly higher in the North while the presence of eight was significantly higher in the South (Table 4). Only the presence of four of these 17 species was significantly affected by the dominant surrounding vegetation: Cape Robin-chat Cossypha caffra was negatively affected by grasslands, Olive Thrush Turdus olivaceus was favoured by mixed vegetation, and Hunter’s Cisticola Cisticola hunteri and Rufous Sparrow were favoured by cultivation. Only for two of the 17 species were there significant effects of season on their presence: Brimstone Canary Serinus sulphuratus, with significantly higher presence in the wet season, and Speckled Mousebird Colius striatus with signifi-cantly higher presence in the dry season.

Discussion

This study provides insights into how vegetation composi-tion and structure influences bird species and community assemblages in an East African highland agricultural landscape. Indeed, landscape composition is an important

influence on bird communities on farmland (Pärt and Söderström 2000, Herzon and O’Hara 2007, Haslem and Bennet 2008a, 2008b). The study’s focus on foraging guilds is quite relevant since many of the most important ecosystem services that birds provide result from their foraging behavior (Wenny et al. 2011).

Effects of historical habitat matrix/subdistrictSome distinction in species diversity, species richness of some foraging guilds and the occurrence of some common species between the North and South subdistricts was confirmed. This possibly indicated that the effects of the differences in the historical vegetation matrix in the subdis-tricts still has an influence on the current bird species composition. Apparently, the North is still relatively more structurally complex and has higher cover of woodland and shrubs as well high density of indigenous woody plant species, thus hosting the higher overall number of species and forest-dependent species. This is addition to frugivores, a guild which is known to be dependent on woody species for foraging and nesting. On the other hand, the South subdistrict still has high grassland cover but cultivations and exotic trees species cover large areas too. These habitat elements are less structurally complex thus attract

Table 4: Logistic generalised linear model coefficients for the presence/absence of 17 common bird species at plots on the effects of subdistrict, season, dominant vegetation surrounding point count plots and vegetation composition and structure variables at the point count plot level, as summarised using PCA factors that accounted for most of the variability within the variables

Baglafecht Weaver

Brimstone Canary

Bronze Sunbird

Cape Robin-chat

CapeRook

Common Fiscal

Common Stonechat

Grassland Pipit

Hunter’s Cisticola

Foraging guild Omnivore Granivore Nectarivore Omnivore Carnivore Insectivore Insectivore Insectivore Insectivore(Intercept) −1.27*** −5.408*** −2.86*** −1.786*** −20.966 −2.151*** −3.322*** −5.377*** −2.167***Subdistrict: South −0.462** 2.38*** −0.256 −0.907*** 17.416 0.591** 1.256*** 2.334*** 0.543*Season: Wet 0.074 1.123*** 0.353 0.194 0.514 −0.124 −0.184 −0.18 −0.07Surrounding Vegetation:

Grassland0.056 0.092 −0.318 −0.985* 0.179 0.23 0.073 0.441 −1.856***

Surrounding Vegetation: Mixed

−0.273 0.435 0.113 −0.37 0.341 −0.029 −0.211 −0.462 −0.582*

PC1a −0.191*** −0.321** −0.134 −0.351*** 0.186 −0.044 0.059 0.727** −0.026PC2a −0.361*** −0.157 −0.139 −0.125 −0.027 0.025 −0.411*** −0.074 −0.04PC3a −0.201** 0.067 −0.309** −0.065 −0.073 −0.05 0.076 −0.166 −0.112PC4a 0.075 0.207 −0.06 0.068 0.316 0.134. 0.034 0.027 −0.138

OliveThrush

Ring-necked Dove

RufousSparrow

Speckled Mousebird

Speke’s Weaver

Streaky Seedeater

White-eyed Slaty Flycatcher

Yellow-crowned Canary

Diet group Omnivore Granivore Granivore Frugivore Omnivore Granivore Insectivore Granivore(Intercept) −3.533*** −2.665*** −0.987*** −2.828*** −3.694*** −1.46*** −4.594*** −5.319***Subdistrict: South −0.778* −0.479 −0.147 −0.29 0.895** 0.362* −1.61** 2.652***Season: Wet −0.056 −0.265 0.191 −0.668* 0.175 0.258. 0.832. −0.526.Surrounding Vegetation:

Grassland0.215 −0.731 −0.96*** −0.207 0.48 −0.494. −0.568 0.349

Surrounding Vegetation: Mixed

0.854* −0.343 −0.566** −0.355 0.067 −0.15 1.358* 0.034

PC1a −0.381*** −0.041 −0.062 −0.548*** −0.086 −0.312*** −0.306* −0.231PC2a −0.331* −0.083 −0.231** −0.271 −0.325** −0.39*** −0.024 −0.142PC3a −0.062 −0.376** −0.289*** −0.32** −0.022 −0.176** 0.303. −0.451**PC4a 0.068 0.34** −0.119. −0.017 0.085 −0.077 0.155 −0.341.Estimated coefficients and respective level of significance are presented with asterisks indicating significance of habitat component in the model: . P 0.1,* P 0.05, ** P 0.01, *** P 0.001 a PC1 decreasing cover of woody species and decreasing structural heterogeneity; PC2 increasing grassland and decreasing cultivation cover; PC3 increasing indigenous trees, shrub and fallow cover and decreasing exotic trees, tree line/hedge length and vertical heterogeneity; PC4 increasing fallow and decreasing cultivation cover

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few species, but higher densities of grassland species, especially granivores due to their generalised habitat requirements as well as other species that prefer open habitats e.g. some raptors and waterbird species.

Effects of vegetation at the local scale and surrounding 100-metre-radius ‘farm’ scaleConsistent with Mulwa et al. (2012) and Vickery and Arlettaz (2012), structural heterogeneity of the vegetation at the local and surrounding farm scale seemed to have a strong positive influence on overall species diversity, species richness of most of the foraging guilds and common species assessed. This was demonstrated by the increasing overall species diversity and species richness with foraging guilds associated with cover of woody plant species and vegetation heterogeneity at the local level as opposed to decreases associated with grasslands cover at both local and surrounding farm scales. Surroundings dominated by mixed vegetation were particularly important for frugivores, probably due to their relatively high cover of woody plant species compared to those dominated by cultivations and grasslands. Structural heterogeneity associated with cultivations and fallow also seemed to have an influence on overall species diversity and species richness of some generalist foraging guilds (granivores and omnivores) and more than a third of common species assessed. These observations coincide with the prediction of Benton et al. (2003) and Tscharntke et al. (2005) that biodiversity should increase with increasing heterogeneity of vegetation cover types. Landscape heterogeneity is generally associated with increased biodiversity (Benton et al. 2003), particularly the inclusion of non-cropped habitat. This could be attrib-uted to the added value of heterogeneity due to landscape complementation (Tscharntke et al. 2005), in which different cover types are needed or preferred at different times in an organism’s life cycle, or provide complementary resources such as food and nest sites at a particular time (Tscharntke et al. 2011). Insectivore species richness was, however, not associated with structural heterogeneity at the local scale, possibly due to the large number of species composing

this foraging guild leading to a diversity of species-specific habitat requirements.

The presence of almost each of the 17 common species assessed was observed to be positively influenced by at least one of the natural habitat measures (shrub, fallow, trees and grassland), emphasising the need for cultiva-tion to occur in proximity to these natural cover types due to their stability and ability to provide critical resources (e.g. nest sites and cover) for respective species to productive cover types (Tscharntke et al. 2011). Indeed, elsewhere, declines in farmland birds have been attrib-uted to decreases in area of natural or semi-natural habitat leading to reduced carrying capacity for these habitats (Fuller et al. 1995) and reduced foraging habitats close to safe nesting sites (Pärt and Söderström 1999).

The strong positive relations of granivore species richness to increased cover of cultivation and fallow could be attributed to increasing food (seed) resources associated with these cover types. Substantial amounts of weed seeds grains are held in cultivations and fallow providing food especially for seedeaters, canaries, doves, sparrows and weavers. They are also a source of grains/seeds from crops commonly grown in the study area, e.g. maize, wheat, oat and green peas, commonly fed upon especially by the larger granivores and omnivores, e.g. weavers.

Management implicationsThe study provides knowledge on how functional birds are influenced by agricultural landscape structure and composi-tion in a Kenyan highland agroecosystem. This may help farmers in knowing how they may modify their manage-ment activities to minimise negative impacts on birds and other biodiversity.

Within cultivation and fallow areas, vertical vegeta-tion heterogeneity and cover type diversity needs to be maintained. This could be through practicing mixed cropping, planting fruit trees and leaving out fallow areas. Extensive monocrops, e.g. of wheat, oat and maize, may attract large numbers of granivores because they provide locally

Figure 2: Mean species diversity per plot (SE) in the North and South subdistricts at point count plots with different types of surrounding vegetation at 100 m radius (‘farm’ scale)

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DOMINANT SURROUNDING VEGETATION(at 100m radius)

Cultivated Grassland Mixed

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Ndang’ang’a, Njoroge and Githiru178

abundant and closely spaced food, and this may lend avian granivory to pest status as has been exemplified elsewhere with the Red-billed Quelea Quelea quelea (Maclean 1990). As shown by Ndang’ang’a et al. (2013), Speckled Mousebird, Speke’s Weaver Ploceus spekei and Baglafecht Weaver are the bird species that are most often associated with crop damage in Nyandarua. Given that the Speckled Mousebird has been shown to prefer cover from shrubs, it would be advisable to plant crops that are susceptible to this species some distance away from wooded vegetation, thick hedges and orchards. Speke’s Weaver on the other hand has a strong preference for cultivated areas and it may probably be preferable to ensure that there are substan-tial non-crop cover types in the agricultural landscape to ensure that the species doesn’t occur in numbers that could lend it to be a pest. Baglafecht Weaver, a species that has been shown to prefer a combination of wooded vegeta-tion and cultivated areas is known to contribute to signifi-cant crop damage only in the South, a subdistrict that was originally covered by grassland but has recently also been planted with exotic trees. Crop damage by this species could therefore be avoided by not planting susceptible crops close to these exotic tree plantations or tree lines.

Apart from cultivation and fallow, the smaller granivores and doves that have been shown to make significant contri-bution to weed seed predation (Ndang’ang’a et al. 2013) seem also to be favoured by occurrence of non-crop cover types: grasslands for the smaller granivores and indigenous woody vegetation for doves. This further emphasises the need to maintain significant grassland cover in the South and indigenous woody vegetation in the North subdistrict. Maintenance of these cover types would also have positive implications for insectivores that could potentially be providing pest control services and frugivores that could be tree seed dispersers. Movements of forest birds into agricul-tural land can be enhanced by riparian corridors, living fences, tree islands and isolated fruiting trees.

Overall, consistent with recommendations from other studies (Eshiamwata et al. 2006, Laube et al. 2006, Otieno et al. 2011, Mulwa et al. 2012), it is important to maintain structurally rich agricultural landscape in Nyandarua in order to retain a high species richness and abundance of species from various functional groups. In areas that were originally forest, this may be enhanced through inclusion of non-crop woody habitat elements, e.g. live fences (Harvey et al. 1995, Pulido-Santacruz and Renjifo 2011), field margins (Jobin et al. 2001, Vickery et al. 2009) and planting of indigenous trees dotted across landscape (e.g. figs, Eshiamwata et al. 2006) or on a line along fences (hedge rows).

Conclusions

As predicted, bird community composition in the agricultural landscapes of Nyandarua County that had historically been derived from grassland had distinctions from forest-derived landscapes. The forest-derived landscape of the North subdistrict was structurally more heterogenous and hosted more species and higher richness of frugivores, a group that is associated with higher cover of woody species. Second, as predicted, bird species diversity and the species richness of respective foraging guilds, except insectivores, increased

with increasing heterogeneity of both natural (e.g. woodland/trees and shrubs) and productive (e.g. cultivation) vegeta-tion cover types. However, there were four distinct gradients of increasing heterogeneity that influenced species diversity, species richness of different foraging guilds and species occurrence in different ways: low to high density of woody species; increasing cultivation as opposed to decreasing grasslands/pasture cover; increasing indigenous trees/shrub cover and decreasing exotic trees and treelines/hedge; and increasing fallow versus decreasing cultivation cover. The first two were the most important in explaining bird community composition. Third, it is important to maintain a structurally rich agricultural landscape in Nyandarua in order to retain a high species richness and abundance of species from various functional groups, including those that may provide important ecosystem services such as pest and weed control.

Acknowledgements — This study was part of research that was financially supported by grants from the National Geographic Committee for Research and Exploration (8926-11), International Foundation for Science (D 3530-2) and the Rufford Small Grants Scheme (9082-1). We thank farmers, local administrators and the Kenya Agricultural Research Institute for allowing and facilitating access to properties. The following assisted in field data collection: Jack Kiiru, Dominic Kimani, George Muigai, James Gichia, John Ndaire, Amina Njoki, Justus Thiong’o, Justus Macharia and Eustus Nginga. Juliet Vickery, Phil Atkinson, Kamau Ngamau and Wariara Kariuki provided invaluable advice in design of this study. Geoffrey Wambugu assisted with drawing the study area map.

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Received April 2013, accepted September 2013Editor: A Monadjem

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