Open access peer-reviewed chapter

Avifauna in Relation to Habitat Disturbance in Wildlife Management Areas of the Ruvuma Miombo Ecosystem, Southern Tanzania

Written By

Ally K. Nkwabi, John K. Bukombe, Hamza K. Kija, Steven D. Liseki, Sood A. Ndimuligo and Pius Y. Kavana

Submitted: December 7th, 2020 Reviewed: March 19th, 2021 Published: May 6th, 2021

DOI: 10.5772/intechopen.97332

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Abstract

Understanding of relative distribution of avifauna provides insights for the conservation and management of wildlife in the community managed areas. This study examined relative diversity, abundance, and distribution of avifauna in selected habitat types across five Wildlife Management Areas of the Ruvuma landscape in miombo vegetation, southern Tanzania. Five habitat types were surveyed during the study: farmland, swamps, riverine forest, dense and open woodland. Transect lines, mist-netting, and point count methods were used to document 156 species of birds in the study sites. Descriptive statistics and Kruskal-Wallis tests were used to compare species richness and diversity across habitat types. We found differences in avifaunal species distribution in the study area whereby farmland had the highest abundance of avifauna species and lowest in the riverine forest. These results suggest that variations of avifauna species abundance, diversity, and distribution could be attributed by human activities across habitat types; due to the reason that habitats with less human encroachment had good species diversity and richness. Therefore, to improve avitourism and avoid local extinction of species, we urge for prompt action to mitigate species loss by creating awareness in the adjacent community through conservation education on the importance of protecting such biodiversity resources.

Keywords

  • Avifauna
  • diversity
  • conservation
  • habitat destruction
  • wildlife management areas
  • miombo

1. Introduction

The miombo ecosystems are known worldwide for their higher biodiversity [1, 2]. Woodlands in the miombo ecosystems are dominated by trees of the genera Brachystegia, Julbernardia, and Isoberlinia Leguminosae, subfamily Caesalpinioideae [1, 3]. The woodlands cover between 2.7 and 3.6 million km2 in 11 African countries [2, 4, 5, 6]. In Tanzania, this vegetation type covers more than 90% of forested land [4, 7, 8, 9, 10], and some of the miombo woodlands are found within several of the iconic protected areas including Selous Game Reserves and the Mikumi, Ruaha, Nyerere National Park as well as the Ruvuma Landscape in southern Tanzania. The ecological services it provides include: the provision of forage for wild and domestic animals, nesting sites for birds, water catchments, carbon sequestration, and biodiversity conservation in general and is archived due to the presence of habitat heterogeneity in particular flora diversity that exists in the miombo areas [3, 4].

Floral species compositions are a very important component to determine the distribution and diversity of avifauna communities [11]. Bird species diversity in savannah landscapes increases with an increase in vegetation/habitat heterogeneity in the miombo woodlands [5, 6]. In heterogeneous habitats, some avian species tend to show preference on certain habitat types, which also influence avifaunal diversity, abundance, and distribution across landscapes [7, 8, 12]. For example, miombo pied barbet (Tricholaema frontata), miombo rock thrush (Monticola angolensis), stierling’s wren warbler (Calamonastes stierlingi), racket-tailed roller (Coracias spatulatus) and white-tailed blue-flycatcher (Elminia albicauda) prefer miombo woodland, only stierling’s wren warbler and racket-tailed roller were observed during data collection other species listed here were not recorded during this study possibly due to habitat degradation.

The Ruvuma landscape in Tunduru District, in southern Tanzania encompasses five Wildlife Management Areas (WMAs) namely: Mbarang’andu, Kimbanda, and Kisungule in Namtumbo District, Nalika and Chingoli WMAs in Tunduru District (Figure 1). It borders the Selous Game Reserve and Nyerere National Park in the north and the Niassa National Reserve (Mozambique) to the south. The Ruvuma River forms an international boundary between Tanzania and Mozambique within Namtumbo and Tunduru districts [13]. The two protected areas rely on the presence of the five Wildlife Management Areas as they provide dispersal and movement area (corridor) to Niassa National Reserve in Mozambique and to Nyerere National Park. Habitat destruction by humans is a serious threat that alters the integrity of ecosystems [8], also affects vegetation cover. It is possible that human activities occurring in the miombo woodland resulted in land cover change [7, 9, 10, 14, 15]. Currently, the Wildlife Management Areas (WMAs) of the Ruvuma region in southern Tanzania undergo fragmentations caused by human activities which include uncontrolled wildfires, collection of fuel wood, charcoal, timber, illegal hunting, cattle grazing, and agriculture. In this area, communities have formulated the Wildlife Management Area (WMA), which is the form of community-based conservation which ensures villagers or communities rich in wildlife sustainably conserve, utilize and benefit from wildlife. Wildlife Management Areas are formed within village land from which villagers set aside a piece of land purposely for sustainable conservation and utilization of wildlife resources. The Tanzania government actualized WMAs for the local community to participate in wildlife management and conserve wildlife habitats in the communal land.

Figure 1.

Map of Ruvuma WMAs showing the location of the sampling sites.

Apart from the study investigated on abundance, nesting and habitat of the white-browed sparrow-weaver (Plocepasser mahali) conducted by Ngongolo and Mtoka [16] no other study attempted to describe the diversity, abundance, and distribution of avifaunal species across the habitat gradient, and assess the implication of ongoing human activities to the conservation of avifauna species across the Ruvuma Landscape. This gives an opportunity to assess avifauna diversity and distribution in relation to habitat disturbance and how avifauna responded to this habitat destruction. Studying avifauna in Ruvuma landscape will open a room for avitourim activities and conserve from habitat degradation. Therefore, this chapter aimed at presenting the diversity of avifauna species in the Wildlife Management Areas in the Ruvuma Landscape in relation to human activities. It is predicted that avian species diversity and abundance would be higher in protected habitats inside WMA than in areas dominated by human activities namely farmlands.

In this study we treated the presence of farmlands in WMAs where they are not supposed to be as disturbance, because all WMAs in Tanzania have land use planning. The land use planning in all WMAs provides guidelines by zoning communal land where different activities can be conducted, such cattle grazing, settlements, farming and wildlife conservation area (tourist areas). All plots selected in this study were from wildlife conservation zones where also farms existed. Potential actions for intervention have been highlighted.

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2. Methods and materials

2.1 Climate and vegetation types

The rainfall pattern is unimodal spanning from late November to May with a mean annual rainfall of 800–1200 mm in a north–south gradient. The mean annual temperature is 21°C, following the Köppen system [17]. The area consists of extensive miombo woodland, including Brachystegia sp., Julbernardia sp., Isoberlinia sp., Afzelia quanzensis, Pterocarpus angolensis, and rare and threatened plant species such as Dalbergia melanoxylon, which forms dense miombo along the hills and rivers [18]. Also, there are seasonal and permanent wetlands (swamps), riverine forests along numerous perennial and seasonal streams. Due to the increasing anthropogenic activities, the area currently has farmlands and patches of wooded with scattered trees and grazing land.

2.2 Sampling design

Five sites of 200 m x 200 m were established in each WMA, making a total of 25 sites. We selected different habitat types for each of the five sites, namely miombo woodland (open and dense), farmland, swamps, and riverine forest.

2.3 Avifauna survey

Each site was sampled using three complementary methods to maximize the sample size. First, in each habitat type, avifauna counts were carried out using the point transects technique [6, 19]. This method consists of standing at a particular point or walking slowly across the site back and forth several times, to detect cryptic and skulking species in the area. These counts were repeated for 3 days, based on results from our pilot study, and the numbers for each site were averaged. A 20-minute counting period was used at each site, and the starting time (between 6:30 and 10:30 h) was rotated among the sites to reduce bias. Avifauna was identified by both sight and call, and numbers were recorded [20].

Secondly, the transect method was used. Three transects 40 km in length each were established in every WMA using existing roads. The locations of all transects were based on accessibility and were sampled using a vehicle driven at a speed of 20 km/hr. or less that stopped for each individual or group of birds encountered [21]. Two observers sighted and recorded all avifauna on either side of the vehicle and notes on habitat type were also taken [21].

Thirdly, mist-netting was used to the targeted cryptic, understory, and lower canopy avian species. Nets were erected and checked every 15 min in the early morning (between 6:30–10:30 h) and late afternoon (between 16:00–18:00 h). The total number of each species caught, and the associated habitat type was recorded. Each bird was marked with a drop of red permanent spray paints at the base of its toes on the right tarsi for verification, if recaptured, to avoid double counting [22].

2.4 Statistical analysis

The biodiversity indices in different habitats or within these WMAs were obtained following Magurran [23]. This index uses three biodiversity indices including, diversity, richness, and abundance. A non-parametric Kruskal-Wallis test was used to assess whether there were significant differences in mean species abundance among five WMAs, and across each habitat type [24]. Differences in mean bird numbers between habitats in each WMA were tested using Mann–Whitney tests to assess whether the number of species was significantly lower in human-encroached habitat (farmland), i.e., farmland, compared to riverine forest, and dense and open miombo woodland habitats. Statistical tests were computed using the software package PAST [24]. For all these analyses, farmland habitat in this study represented human encroachment into protected areas and was used to compare with other habitat types found in the WMAs. We further calculated the Jaccard similarity index (Ji) between different habitat types to determine the level of similarities in species composition using the formulae [24]:

Jaccard similarity coefficientJ;J=A/A+B+CE1

Where A = number of species found in both communities, B = number of species only found in community 1 and C = number of species found in community 2. The equation returns a number between 0 and 1, where a number close to 1 indicates a higher similarity in species composition [23]. We then multiplied J by 100 to obtain a percent, to easily interpret the results.

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3. Results

3.1 Avian species diversity, distribution, and richness

A total of 156 avian species representing 18 orders and 61 families were recorded in the five WMAs. The overall avian species Shannon diversity (H′) for all the habitat types ranged from 2.28–4.08, except for dense miombo woodland which had H′ = 1.69 (Table 1). Riverine forest habitat had higher species richness (n = 101 species), representing almost 45% of the total recorded individuals (Table 1). Avian species diversity was highest in riverine forest and lowest in dense miombo woodlands (Table 1; Figure 2). The Shannon Index of diversity revealed that species evenness for the five habitats surveyed was relatively low ranging from 0.29–0.59 (Table 1).

Habitat typeNumber of avian speciesOverall abundanceMean abundanceShannon diversity (H)Shannon evenness (EH)
Dense miombo141057.50 ± 3.911.690.39
Farmland4058014.50 ± 5.822.460.29
Open miombo98133813.65 ± 2.083.90.51
Riverine forest1017597.52 ± 0.974.080.59
Swamp areas201889.40 ± 3.262.280.49

Table 1.

Avian species diversity, abundance, and evenness in different habitats of WMAs in Ruvuma landscape (± standard error).

Figure 2.

Avian species diversity in different habitats.

Values bearing different letters within column are significantly different (p < 0.05) and values with similar letters within column are not significantly different (p > 0.05; Table 2). Dense miombo woodland, farm and swamp exhibited higher number of birds per point count than in open miombo woodland and riverine forest implying that the avian species were more scattered in open miombo woodlands and riverine forests.

HabitatsAverage bird count
Dense miombo woodland6.18a
Farm6.11a
Open miombo woodland3.71b
Riverine forest3.45b
swamp6.48a

Table 2.

Average number of birds per point count in different habitats.

The overall mean abundance of avifauna in the WMAs differed significantly (Kruskal-Wallis test, χ2 = 50.13, df = 4, P = 0.03). Kimbanda had the highest mean abundance of species followed by Kisungule (Figure 3). There was a significant difference in the mean abundance of avifauna across the five habitats (Kruskal-Wallis test, χ2 = 13.18, df = 4, P = 0.010). Mean abundance of species was significantly higher in farmland than in dense miombo (Mann–Whitney tests, U = 19, P < 0.0001), open miombo woodland (U = 66.5, P < 0.0003), riverine forest (U = 157, P < 0.019) and swamps (U = 93.5, P < 0.004) (Figure 3).

Figure 3.

Avian abundance in different habitats of wildlife management areas in southern Tanzania.

The distribution of the 2970 avifauna species recorded in the five habitat types is given in (Table 1 above; Figure 4). Some species were found in more than one habitat type, a total of six species with bronze mannikin (Lonchura cucullata) the most abundant (Figure 5). Tawny-flanked prinia (Prinia subflava), blue-spotted wood dove (Turtur afer), common bulbul (Pycnonotus barbatus), violet-backed starling (Cinnyricinclus leucogaster), and Jameson’s firefinch (Lagonosticta rhodopareia) were observed in four habitat types, except swamp habitat (see Figure 5; Appendix Table A1). Southern cordon-bleu (Uraeginthus bengalus) was observed in three habitat types and was the second most abundant species recorded during this study (Figure 5). Other species including pied crow (Corvus albus), brown-headed parrot (Poicephalus cryptoxanthus), and red-necked francolin (Pternistis afer) were observed in three habitat types (see Appendix Table A1) whereas black-faced waxbill (Estrilda erythronotos) and African pied wagtail (Motacilla aguimp), were observed only in farmland areas.

Figure 4.

Avifauna species observed foraging in different habitats. Definition of abbreviation used (Demiwo = dense miombo woodland, riverfore = riverine forest, farmland = farmland habitat, opemiwo = open miombo woodland).

Figure 5.

Distribution of avian species in different habitats within WMAs of the Ruvuma landscape in southern Tanzania.

Cryptic species like African broadbill (Smithornis capensis) and red-capped robin-chat (Cossypha natalensis) and understory bird species including red-throated twinspot (Hypargos niveoguttatus) were observed only in the riverine forest using mist-nets and point count methods (Appendix Table A1). Palearctic migrants including European nightjar (Caprimulgus europaeus), European swift (Apus apus), and European bee-eater (Merops apiaster) were also recorded. Trumpeter hornbill (Bycanistes bucinator) is a bird of conservation status that was observed during the study in forest patches.

3.2 Species composition and similarities between different habitat types

We found strong contrast in species composition among habitat types (Table 3). The highest species similarities were between open woodland vs. Riverine forest (41%), Farmland vs. Open woodland (24%) and Farmland vs. Riverine forest (21%) while dense woodland vs. Swamp areas had no similarity in composition (0%), Open woodland vs. Swamp area (1%) and Farmland vs. Swamp area (2%; Table 3). The Jaccard similarity indices among various pairs of habitat types compared (Table 3; Figure 6).

Habitat typesDense woodlandOpen woodlandFarmlandRiverineSwamp area
Open woodland11
Farmland1524
Riverine forest84121
Swamp area0125

Table 3.

Jaccard species composition similarity index (J) between habitat types of the WMAs in Ruvuma landscape, in southern Tanzania. In this table the similarity presented in percentage (%).

Figure 6.

Plotted trend line to show species composition similarities between habitat types of the WMAs in Ruvuma landscape, in southern Tanzania. Definition of abbreviation used (dw vs. sw = dense woodland vs. swamp area; dw vs. fm = dense woodland vs. farmland; dw vs. ow = dense woodland vs. open woodland; dw vs. rf = dense woodland vs. riverine forest; fm vs.ow = farmland vs. open woodland; fm vs. rf = farmland vs. riverine forest; fm vs. sw = farmland vs. swamp area; ow vs. rf = open woodland vs. riverine forest; ow vs. sw = open woodland vs. swamp area; rf vs. sw = riverine forest vs. swamp area).

From the results, avian species adapted to open miombo woodlands and those adapted to riverine forest were very closely related and far from avian species adapted to swamps (Figure 7). Avian species adapted to swamps were separated from all other avian species adapted other habitats (Figure 7). Indeed, this entails a need for conservation of swamps to avoid local distinction of swamp adapted species.

Figure 7.

Cluster analysis of different habitat types based on bird species composition (presence/absence). Definition of abbreviation used (Demiwo = dense miombo woodland, riverfore = riverine forest, farmland = farmland habitat, opemiwo = open miombo woodland).

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4. Discussion and conclusion

4.1 Avian species diversity, distribution, and richness

Farmland habitats were observed in all WMAs except in Mbarang’andu where we did not encounter cultivated areas inside the core WMA. Possibly due to the presence of an anti-poaching office established inside WMA by Tanzania Wildlife Management Authority (TAWA, formerly Wildlife Division). In our study, we predicted that there would be higher avian diversity, richness, and abundance in WMAs than in human-modified areas named here as farmlands. We found strong support for this prediction for the species diversity and richness of avifauna but not for abundance. This suggested that the differing occurrence of avifauna species across given habitats could be attributed to some reasons including food requirement as well as heat tolerance [25].

The richness and diversity imply a variety of taxa that exist in an area, many taxa should, therefore, survive in habitats that have a variety of favorable conditions and resources such as the presence of food, nesting areas, shade and water that might contribute to higher species richness and diversity. Therefore, low species diversity in the farmland might be contributed by the insufficient supply of food as well as insufficient cover for birds to hide against predators, lack of shade to hide from diurnal temperature [12, 26] low food supply compared to forests and woodlands. Suggesting that farmlands have reached maximum disturbance, as in lower farmlands heterogeneous vegetation offer foods and shelter for birds encouraging higher diversity and abundance [8]. Thus the granivores which are largely seed eaters such as the bronze mannikin, southern cordon-bleu, and red-billed quelea were dominant in farmlands than in other habitats because farmlands were rich in seed types vegetation, in line with the findings of others [12, 26]. Furthermore, for similar reasons, the abundance of the granivores species was also higher in open miombo where grassland patches are dominant than in forest areas. Birds that preferred mixed habitat of tree-covered vegetation and open areas chose forest and woodlands but are not water-bound and avoided farmlands such as red-throated twinspot, pygmy kingfisher and red-capped robin-chat, they co-existed in riverine forest and woodland, together with birds that prefer evergreen or lowland forest, dense deciduous thickets, or other dense woodlands such as black-throated wattle-eye and the African broadbill.

4.2 Species composition and similarities between different habitat types

The presence of higher species composition and similarities among habitat types suggests that miombo woodlands harbor unique avifauna species. Some avian species are observed to occur in more than one habitat type indicating that avian species are not habitat specialists. In this study, such patterns were observed; some species existed in more than 4 habitat types suggesting areas visited they provide similar resource abundance, types, and habitat heterogeneity.

Therefore, under no intervention strategies, the Ruvuma Landscape will result in a marked loss of avian richness and diversity. This suggests that measures that will reduce land clearance for agriculture need to be promptly implemented to reduce the ecological impacts on avifauna. Wildlife management areas should involve adjacent communities that are the key stakeholders of the habitats and species biodiversity conservation. Such measures can enhance the resilience of wildlife management areas and complement the goals of community-based conservation measures [27, 28]. Unfortunately, any proposed measures may be challenged by increasing human pressure due to agricultural intensification needs as well as a rapidly changing climate that may be beyond the WMA’s management control. Examining the links of these threats to avian biodiversity and addressing such in an urgent manner is likely to abate current human disturbance in the WMAs of Ruvuma region.

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Acknowledgments

We thank the District Game Officers of Namtumbo and Tunduru as well as Community Based Conservation Training Centre (CBCTC) staff for their assistance and positive cooperation they have rendered for the success of this project. We thank the Village Game Scouts (VGSs) and all WMA leaders for their guidance during data collection. Furthermore, we recognize the materials and technical support offered by the Tanzania Wildlife Research Institute (TAWIRI). This study was funded by the WWF Tanzania grant to Geo Network Ltd. based at Dar es Salaam.

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Conflict of interest

The authors have not declared any conflict of interests.

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No.English nameSpecies nameHabitat type
Dense miombo woodlandFarmlandOpen miombo woodlandRiverine forestSwamp areasGrand TotalRatio
1Bronze mannikinSpermestes cuculiata56222567204060.137
2Southern (Blue-breasted) cordon-bleuUraeginthus angolensis385123002110.071
3Red-billed queleaQuelea quelea015115001300.044
4Tawny-flanked priniaPrinia subflava1827383401170.039
5Common waxbillEstrilda astrild0963200920.031
6Common bulbulPycnonotus goiavier5137330760.026
7Ring-necked doveStreptopelia capicola0215100720.024
8European bee-eaterMerops apiaster035186680.023
9Violet-backed starlingCinnyricinclus leucogaster2445170680.023
10White-faced whistling-duckDendrocygna viduata000062620.021
11Helmeted guineafowlNumida meleagris005920610.021
12Blue-spotted wood-doveTurtur afer5726110490.016
13African green-pigeonTreron calvus024240480.016
14Pied crowCorvus albus0301050450.015
15Fork-tailed drongoDicrurus adsimilis023550420.014
16Arrow-marked babblerTurdoides jardineii0012260380.013
17Gray-backed (bleating) camaropteraCamaroptera brevicaudata006320380.013
18Little greenbulEurillas virens002360380.013
19African jacanaActophilornis africanus000233350.012
20Black-crowned tchagraTchagra senegalus042650350.012
21Lesser striped swallowCecropis abyssinica0130210340.011
22Wire-tailed swallowHirundo smithii034000340.011
23Rufous-naped larkMirafra africana003200320.011
24Brown-headed parrotPoicephalus cryptoxanthus202520290.010
25Lesser blue-eared starlingLamprotornis chloropterus002530280.009
26Black-backed puffbackDryoscopus cubla0010160260.009
27Black-headed orioleRiolus larvatus011860250.008
28Collared sunbirdHedydipna collaris109150250.008
29Mosque swallowCecropis senegalensis061260240.008
30Pied kingfisherCeryle rudis000618240.008
31Mottled spinetailTelacanthura ussheri000230230.008
32Purple-crested turacoGallirex porphyreolophus001580230.008
33Pennant-winged nightjarCaprimulgus vexillarius001920210.007
34Rattling cisticolaCisticola chiniana400170210.007
35Tropical boubouLaniarius aethiopicus007140210.007
36White-headed black chatMyrmecocichla arnotti001740210.007
37African paradise-flycatcherTerpsiphone viridis031250200.007
38Gray-headed bush-shrikeMalaconotus blanchoti006130190.006
39African palm-swiftCypsiurus parvus019000190.006
40Brown-crowned tchagraTchagra australis04770180.006
41Flappet larkMirafra rufocinnamomea021060180.006
42Pale-billed hornbillLophoceros pallidirostris002160180.006
43Red-throated twinspotHypargos niveoguttatus000180180.006
44Gray-headed kingfisherHalcyon leucocephala001520170.006
45Jameson’s frefinchLagonosticta rhodopareia22580170.006
46Red-necked francolinPternistis afer06380170.006
47Yellow bishopEuplectes capensis00890170.006
48African golden orioleOriolus auratus021400160.005
49Black-faced waxbillEstrilda erythronotos015000150.005
50White-rumped swiftApus caffer030120150.005
51Yellow-breasted apalisApalis flavida003120150.005
52Black-throated wattle-eyePlatysteira peltata000140140.005
53African firefinchLagonosticta rubricata06430130.004
54Green woodhoopoePhoeniculus purpureus001300130.004
55Spotted flycatcherMuscicapa striata001210130.004
56Orange-breasted bush-shrikeChlorophoneus sulfureopectus00850130.004
57White-backed duckThalassornis leuconotus000012120.004
58White-browed sparrow-weaverPlocepasser mahali001200120.004
59Yellow-fronted canaryCrithagra mozambica001200120.004
60African darterAnhinga rufa000011110.004
61Kurrichane thrushTurdus libonyana00920110.004
62African gray hornbillLophoceros nasutus02350100.003
63Böhm’s spinetailNeafrapus boehmi000100100.003
64Common squacco heronArdeola ralloides000010100.003
65Coqui francolinPeliperdix coqui001000100.003
66Shelley’s sunbirdCinnyris shelleyi00370100.003
67Reichenow’s woodpeckerCampethera scriptoricauda10900100.003
68African broadbillSmithornis capensis0009090.003
69Black crakeZapornia flavirostra0002790.003
70Green-capped eremomelaEremomela scotops0063090.003
71Striped kingfisherHalcyon chelicuti0072090.003
72Little bee-eaterMerops pusillus0062080.003
73Little swiftApus affinis0800080.003
74Pied wagtailMotacilla aguimp0800080.003
75Senegal lapwingVanellus lugubris0080080.003
76Amethyst sunbirdChalcomitra amethystina0007070.002
77Greater honeyguideIndicator indicator0070070.002
78Racket-tailed rollerCoracias spatulatus0160070.002
79Red-faced cisticolaCisticola erythrops0004370.002
80Rufous-bellied titMelaniparus rufiventris0052070.002
81Broad-billed rollerEurystomus glaucurus0051060.002
82Brown-hooded kingfisherHalcyon albiventris0060060.002
83Dark chanting-goshawkMelierax metabates0141060.002
84Eastern bearded scrub-robinTychaedon quadrivirgata0024060.002
85Great white egretArdea alba0000660.002
86Southern ground-hornbillBucorvus leadbeateri0060060.002
87Livingstone’s turacoTauraco livingstonii0060060.002
88Red-cheeked cordon-bleuUraeginthus bengalus0006060.002
89Southern gray-headed sparrowPasser diffusus0060060.002
90Swallow-tailed bee-eaterMerops hirundineus0060060.002
91Trumpeter hornbillBycanistes bucinator0006060.002
92White-crested helmetshrikePrionops plumatus0060060.002
93Willow warblerPhylloscopus trochilus0060060.002
94Common hoopoeUpupa epops0005050.002
95Black cuckooCuculus clamosus0005050.002
96Black kiteMilvus migrans0230050.002
97Common sandpiperActitis hypoleucos0003250.002
98Golden-tailed woodpeckerCampethera abingoni0041050.002
99Little sparrowhawkAccipiter minullus0041050.002
100Pale (East coast) batisBatis soror0023050.002
101Pygmy kingfisherIspidina picta0003250.002
102Red-chested cuckooCuculus solitarius0005050.002
103Miombo wren warblerCalamonastes stierlingi0050050.002
104Wattled lapwingVanellus senegallus0003250.002
105White-bellied sunbirdCinnyris talatala0032050.002
106White-breasted cuckoo-shrikeCeblepyris pectoralis0050050.002
107Yellow-bellied greenbulChlorocichla flaviventris0023050.002
108Cardinal woodpeckerDendropicos fuscescens1030040.001
109African pipitAnthus richardi0030250.002
110HamerkopScopus umbretta0004040.001
111Lilac-breasted rollerCoracias caudatus0022040.001
112Pearl-spotted owletGlaucidium perlatum0040040.001
113Red-capped robin-chatCossypha natalensis0004040.001
114White-browed coucalCentropus superciliosus0022040.001
115White-browed robin-chatCossypha heuglini0004040.001
116Black cuckoo-shrikeCampephaga flava0012030.001
117Böhm’s bee-eaterMerops boehmi0003030.001
118BrubruNilaus afer0030030.001
119Cabanis’s buntingEmberiza cabanisi0210030.001
120Crested barbetTrachyphonus vaillantii0012030.001
121Crowned hornbillLophoceros alboterminatus0300030.001
122European swiftApus apus0003030.001
123African fish eagleHaliaeetus vocifer0001230.001
124Hadada ibisBostrychia hagedash0000330.001
125Harlequin quailCoturnix delegorguei0003030.001
126Namaqua doveOena capensis3000030.001
127Speckle-throated woodpeckerCampethera scriptoricauda0030030.001
128Parasitic weaverAnomalospiza imberbis0030030.001
129Red-fronted tinkerbirdPogoniulus pusillus0003030.001
130Red-headed weaverAnaplectes rubriceps0030030.001
131Speckled mousebirdColius striatus0003030.001
132Stripe-breasted seedeaterCrithagra striatipectus0030030.001
133White-browed scrub-robinCercotrichas leucophrys0012030.001
134Wood sandpiperTringa glareola0000330.001
135Black-headed heronArdea melanocephala0101020.001
136Black-winged stiltHimantopus himantopus0002020.001
137Brimstone canaryCrithagra sulphurata0020020.001
138Egyptian gooseAlopochen aegyptiaca0000220.001
139Fiscal shrikeLanius collaris0200020.001
140Golden-breasted buntingEmberiza flaviventris2000020.001
141Retz’s helmet shrikePrionops retzii0002020.001
142Scarlet-chested sunbirdChalcomitra senegalensis0002020.001
143Tambourine doveTurtur tympanistria0002020.001
144African barred owletGlaucidium capense0010010.000
145Piping cisticolaCisticola fulvicapilla0030030.001
146Red-eyed doveStreptopelia semitorquata0003030.001
147Beautiful sunbirdCinnyris pulchellus0100010.000
148Black coucalCentropus grillii0001010.000
149Brown snake-eagleCircaetus cinereus0001010.000
150European nightjarCaprimulgus europaeus0010010.000
151Gray heronArdea cinerea0000110.000
152Olive sunbirdCyanomitra olivacea0001010.000
153SaddlebillEphippiorhynchus senegalensis0000110.000
154Spectacled weaverPloceus ocularis0010010.000
155Spotted creeperSalpornis salvadori0001010.000
156Woodland kingfisherHalcyon senegalensis0001010.000
Grand Total10558013387591882970

Table A1.

List of avifauna species observed in different habitats of WMAs in Ruvuma.

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Written By

Ally K. Nkwabi, John K. Bukombe, Hamza K. Kija, Steven D. Liseki, Sood A. Ndimuligo and Pius Y. Kavana

Submitted: December 7th, 2020 Reviewed: March 19th, 2021 Published: May 6th, 2021