kwakuchinja wildlife corridor ecological viability …

June 2019

Kwakuchinja Wildlife Corridor Region

KWAKUCHINJA WILDLIFE CORRIDOR ECOLOGICAL

VIABILITY ASSESSMENT

Kwakuchinja Wildlife Corridor Ecological Viability Assessment Page ii

Kwakuchinja Wildlife Corridor Ecological Viability Assessment 2019

Conducted by:

Tanzania Wildlife Research Institute, TAWIRI

P.O Box 661

ARUSHA

+255783004048

AND

(Tanzania Natural Resource Forum, TNRF)

P.O. Box 15605

ARUSHA

Copyright 2019

This report is made possible by the generous support of the American people through the United States Agency for

International Development (USAID). The contents are the responsibility of Tanzania Wildlife Research Institute

(TAWIRI) and the Tanzania Natural Resource Forum (TNRF) and do not necessarily reflect the views of USAID or the

United States Government.

Kwakuchinja Wildlife Corridor Ecological Viability Assessment Page iii

CONTENTS

LIST OF FIGURES AND TABLES ................................................................................................................................ iv

ABBREVIATIONS .......................................................................................................................................................... v

1. INTRODUCTION ................................................................................................................................................... 1

1.1 Objective and scope of the study.................................................................................................................. 1

2. METHODS ............................................................................................................................................................ 2

2.1 Study area .................................................................................................................................................... 2

2.2 Mapping and Analysis of Land Use and Land Cover Change ...................................................................... 3

2.2.1 Image acquisition and Pre-processing ................................................................................................. 3

2.2.2 Sampling design: Sample size and distribution of samples ................................................................. 3

2.2.3 Mapping and analysis .......................................................................................................................... 4

2.3 Aerial Wildlife Surveys .................................................................................................................................. 4

2.4 Ground Transect Wildlife Surveys ................................................................................................................ 5

2.5 Camera trap wildlife surveys......................................................................................................................... 6

2.5.1 Camera trap field data collection ......................................................................................................... 7

2.5.2 Camera trap data analysis ................................................................................................................... 8

3. RESULTS .............................................................................................................................................................. 8

3.1 Land cover mapping and analysis ................................................................................................................ 8

3.2 Aerial Wildlife Surveys ................................................................................................................................ 11

3.2.1 Buffalo Distribution in KWC ................................................................................................................ 11

3.2.2 Elephant Distribution in KWC ............................................................................................................. 13

3.2.3 Giraffe Distribution in KWC ................................................................................................................ 14

3.2.4 Grant’s Gazelle Distribution in KWC .................................................................................................. 15

3.2.5 Impala Distribution in KWC ................................................................................................................ 16

3.2.6 Wildebeest distribution in KWC .......................................................................................................... 17

3.2.7 Zebra distribution in KWC .................................................................................................................. 18

3.2.8 Human activities ................................................................................................................................ 19

3.3 Ground transect wildlife surveys ................................................................................................................. 20

3.4 Camera trap wildlife surveys....................................................................................................................... 22

4. DISCUSSION ...................................................................................................................................................... 24

4.1 Landcover classes and change .................................................................................................................. 24

4.2 Wildlife population distribution .................................................................................................................... 24

5. CONCLUSION AND RECOMMENDATION ........................................................................................................ 26

Kwakuchinja Wildlife Corridor Ecological Viability Assessment Page iv

6. REFERENCES .................................................................................................................................................... 28

ANNEXES .................................................................................................................................................................... 34

LIST OF FIGURES AND TABLES

Figures

Figure 1. Kwakuchinja wildlife corridor study area ............................................................................................... 2

Figure 2. Randomly selected ground transects used for distance sampling wildlife surveys in the Kwakuchinja study

area ................................................................................................................................................................ 6

Figure 3. Locations of 21 camera traps in the KWC portion of Burunge WMA……………..……………………………...7

Figure 4. Land use map in the Kwakuchinja Wildlife Corridor, 2008 ...................................................................... 9

Figure 5. Land use map in Kwakuchinja Wildlife Corridor, 2018 .......................................................................... 10

Figure 6. Buffalo population distribution in 2009-2016 at Kwakuchinja study area ................................................. 12

Figure 7. Elephant population distribution in 2009-2016 at Kwakuchinja study area .............................................. 13

Figure 8. Giraffe population distribution in Kwakuchinja wildlife corridor ............................................................... 14

Figure 9. Grant’s gazelle population distribution in Kwakuchinja wildlife corridor ................................................... 15

Figure 10. Impala population distribution in Kwakuchinja wildlife corridor ............................................................. 16

Figure 11. Wildebeest population distribution in Kwakuchinja wildlife corridor....................................................... 17

Figure 12. Distribution of zebra in Kwakuchinja wildlife corridor .......................................................................... 18

Figure 13. Livestock distribution in the Kwakuchinja study area .......................................................................... 19

Figure 14.Settlement distribution in the Kwakuchinja study area ......................................................................... 20

Figure 15. All species distribution and corridor buffer zone in the Kwakuchinja study area..................................... 25

Figure 16. Tarangire-Manyara ecosystem and three important corridors in the region: Kwakuchinja, Makuyuni, and

Tarangire-Simanjiro (Source: Wildlife corridors in Tanzania 2009) ...................................................................... 27

Tables

Table 1. Landcover classes in Kwakuchinja Wildlife Corridor for years 2008 and 2018 .............................................. 11

Table 2. Ground transect species counts and number of observations ....................................................................... 21

Table 3 List of species observed during the camera trap survey in Burunge WMA indicating the number of …….22-23

events and trap rates for each species

Annexes

Annex 1. Buffalo distribution in various surveyed years over Kwakuchinja study area ................................................ 34

Annex 2. Elephant distribution in various surveyed years over the Kwakuchinja study area ....................................... 35

Annex 3. Giraffe distribution in various surveyed years over Kwakuchinja study area ................................................ 36

Annex 4. Grant gazelle distribution in various surveyed years over Kwakuchinja study area ...................................... 37

Annex 5. Impala density in various surveyed years over Kwakuchinja study area ...................................................... 38

Annex 6. Wildebeest distribution in various surveyed years over Kwakuchinja study area ......................................... 39

Annex 7. Zebra distribution in various surveyed years over Kwakuchinja study area .................................................. 40

Kwakuchinja Wildlife Corridor Ecological Viability Assessment Page v

ABBREVIATIONS

GPS Global Position System

KWC Kwakuchinja Wildlife Corridor

GCA Game Controlled Area

WMA Wildlife Management Area

Ha Hectare

Km Kilometer

E East

S South

SRF Systematic Reconnaissance Flight

TC Total Count

n Sample size

m meters

Kwakuchinja Wildlife Corridor Ecological Viability Assessment Page 1

1. INTRODUCTION

The Kwakuchinja wildlife corridor (KWC) connects Lake Manyara National Park to Tarangire National Park

and facilitates wildlife movements between these protected areas. The KWC region is an important

movement corridor and dispersal area for a variety of wildlife species such as antelopes, buffaloes, elephants,

lions, giraffe, warthogs, and zebra. Within the corridor and surrounding region several native ethnic groups

of Barbaig, Iraq, Mbugwe, and Maasai people live in the following villages: Mbuyu wa Mjerumani, Sangaiwe,

Minjingu, Vilima Vitatu, Kazaroho, Kakoi, Olasiti, Mswakini Juu, Mswakini Chini, and Ngolei. People’s

livelihoods in these villages primarily include livestock keeping, subsistence farming, commercial agriculture,

and to a lesser extent other commercial activity such as mining, tourism, livestock, and goods auctions. Most

of the Kwakuchinja wildlife corridor is within the Burunge WMA. Village lands surround the corridor, and the

protected area Manyara Ranch is approximately 10 km to the northeast.

1.1 Objective and scope of the study

This study aims to assess the Kwakuchinja wildlife corridor’s ecological viability to support wildlife and

wildlife movements between Lake Manyara National Park and Tarangire National Park. The study draws

on satellite image analysis to evaluate patterns and trends in land use, land cover, and human activities

in the region, and it presents the results of ecological surveys designed to assess movement patterns

and distributions of a variety of wildlife species in the corridor and surrounding area.1 Although many

more studies could be done to understand the corridor’s ecological and sociological dynamics, the

findings of this report provide key evidence with which to assess the corridor and inform the official

designation of the corridor according to the Wildlife Corridor, Dispersal Areas, Buffer Zones and Migratory

Route regulations, 2018.

1 Most but not all surveys focused on large mammals because of their visibility and because of time and budget constraints.


2. METHODS

2.1 Study area

The Kwakuchinja wildlife corridor is located between 03°35’38’’ and 03°48’02’’ south latitude and 35°48’21’’

and 35°59’25’’ east longitude (TAWIRI 2009) (Figure 1). The corridor is oriented mostly north to south and,

together with Burunge WMA, connects Lake Manyara National Park and Tarangire National Park. Vegetation

in the Kwakuchinja wildlife corridor region is primarily savanna with sparse shrubland and pockets of

woodland dominated by Acacia tortilis. The area has natural rivers and lakes and fertile black cotton soil in

the floodplains. Average annual rainfall is 450–650 mm; short rains occur from November to December and

longer rains occur from February to May. March and April are the wettest months; July and August are the

driest.

Figure 1. Kwakuchinja wildlife corridor study area


2.2 Mapping and Analysis of Land Use and Land Cover Change

2.2.1 Image acquisition and Pre-processing

To analyze land use and land cover changes in the study area over time, Landsat Thematic Mapper (TM5)

and Landsat-8 (Operational Land Imagery) images of the study area were downloaded from Earth Explorer

(https://earthexplorer.usgs.gov/) for the years 2008 and 2018, respectively. Seasonal land cover variation

was minimized by downloading image scenes captured during similar satellite overpass times and seasons

(Dry seasons). Because these types of satellite images are normally somewhat distorted due to sensor

movements, images were corrected for radiometric effects prior to analysis. Corrections were also applied to

compensate for scattering effects due to atmospheric phenomena such as haze. Both radiometric and

atmospheric corrections were conducted with the Dark Object Subtraction (DOS) method (Chavez 1996) in

QGIS and in PCI Geomatica (PCI 2015). Such pre-processing removes false indications of objects, facilitates

comparison of multi-temporal images and field-based data (Franklin and Giles 1995; Chavez 1996), and

ensures that the corrected images are of sufficient high quality for analysis (Pons et al. 2014). Image and/or

sensor differences within and between scenes were normalized by converting the brightness values of each

pixel (Digital Number (DN) to the actual ground reflectance (Top of Atmosphere Reflectance (TOA) to

enhance the representation of the original image and overall accuracy (Jensen 1996; Amro et al. 2011).

2.2.2 Sampling design: Sample size and distribution of samples

To validate the satellite images, selected random samples of images were traced on the ground using hand-

held Garmin CSX GPS, and those locations were assessed in the field to compare them to their satellite

images. Sample points that were located in inaccessible areas (due to terrain features or absence of roads)

and restricted off-road access were replaced with samples from nearby pixels with similar reflectance or

overlaid in high-resolution Google Earth (https://www.google.com/intl/de/earth/) and Bing

(https://www.bing.com/maps) images, and the corresponding object(s) and/or land use and land cover were

identified (Thomlinson et al. 1999). For each major class of land use and land cover identified with the satellite

https://earthexplorer.usgs.gov/

https://www.google.com/intl/de/earth/

https://www.bing.com/maps


images, a total of 30-50 samples were randomly generated for validation on the ground. To minimize the

likelihood of misclassifying the land use and land cover classes identified with satellite images, spectral

signatures for the selected ground validation sample points were inspected in scatter plots. Opportunistic

observations made while traveling in the field from one ground validation sample point to another also

supplemented the testing data sets.

2.2.3 Mapping and analysis

Prior to mapping land use and land cover for the study area, image classification was performed using the

Random Forest (RF) Package in the R software (Breiman, 2001). RF is a powerful machine learning classifier

that has received wide acceptance in land-based remote sensing (Cutler et al. 2007; Frakes et al. 2015).

Statistics for each land cover class in the study were calculated in MS Excel to facilitate summary analysis.

Land use and land cover maps were generated using Arc GIS software.

2.3 Aerial Wildlife Surveys

Since 1987 TAWIRI has conducted aerial wildlife surveys in the study area using two methods—Total

Counts2 (TC) and Systematic Reconnaissance Flight3 (SRF) (following Norton-Griffiths 1978), which

combined cover 12,000 - 16,900 km2 of the study area and surrounding ecosystem. TAWIRI uses TC in this

ecosystem for elephant and buffalo and SRF for other large mammals, including giraffe, Grant’s gazelle,

impala, wildebeest, and zebra. In this KWC ecological viability assessment we report aerial survey data for

each of these species for the following years: buffalo (2009, 2011, 2014, 2016); elephant (2007, 2009, 2011,

2014, 2016); giraffe (2007, 2014, 2016); Grant’s gazelle (2007, 2011, 2016); impala (2007, 2011, 2016);

wildebeest (2007, 2011, 2014, 2016); and zebra (2007, 2011, 2014, 2016). TAWIRI also uses SRF to survey

2 Total Counts rely on searching and enumerating all target species in a survey area. It is appropriate only for highly visible species and small areas that can be counted in a single flight session. 3 Systematic Reconnaissance Flight (SRF) surveys sample narrow strips of land along transects (long flight lines), where the sample is then multiplied to produce an estimate for the total survey area. For SRF sampling to be accurate, samples must be representative of the whole population. SRF sampling does not assume that the animals themselves are evenly distributed, but the sample transects must be located without reference to the distribution of animals, e.g., the samples are allocated systematically according to a predefined map.


human activities in the study area and surrounding ecosystem, including human settlements and livestock

keeping. In this assessment we report aerial survey data for human activities for 2007, 2011, 2014, and 2016.

The objectives of the aerial surveys are: (i) to determine the population status of large mammals, (ii) to map

their distribution patterns, (iii) to derive population trends, (iv) to assess the types and distribution of major

human activities, and (v) to record census results in a centralized wildlife database at TAWIRI, which allows

comparison between censuses.

2.4 Ground Transect Wildlife Surveys

Between January 22nd and 31st 2019 surveyors affiliated with Tanzania Wildlife Research Institute (TAWIRI)

and Tanzania Natural Resource Forum (TNRF) conducted ground transect wildlife surveys to assess

populations of large mammals in the study area. The surveys were designed to count individual animals and

also permit estimates of animal population density and distribution. Prior to conducting the surveys TAWIRI

and TNRF used GIS software and a stratified random sampling design to locate 46 4-km survey transects

across 16 km2 (4 X 4 km grid), which covered all habitat types in the study area to avoid bias in area sampling

(Buckland & Elston 1993; Jimmie R. Parrish, Frank P. Howe, & Russell E. Norvell 2003) (Figure 2). Three

teams, each comprised of three people working as a single observational unit, covered a total of 46 transects

for nine days between 6:00 AM and 6:00 PM each day, when most diurnal mammals are active. Surveyors

walked along standardized, randomly oriented, fixed-width line transects and used established protocols to

count and record animal observations (following James Gibbs 2008; Trolleet et al. 2008; Fewster et al. 2009).

Before field activities commenced the survey team was trained on survey procedures; transects avoided

inaccessible areas such as swamps, thickets, and gullies.


Figure 2. Randomly selected ground transects used for distance sampling wildlife surveys in the Kwakuchinja study area

2.5 Camera trap wildlife surveys

To further assess the number and diversity of animal species that use the KWC corridor, researchers from

TAWIRI and the nonprofit ChemChem Association deployed 21 cameras in the KWC portion of Burunge

WMA (Kissui et al. 2019). These camera “traps” automatically take pictures of animals that pass in front of

them.


2.5.1 Camera trap field data collection

Two models of camera traps were used in this survey: Reconyx HF 2 PRO white, digital and Reconyx

HC500 HyperFire Semi-Covert IR digital. All cameras were located within the Burunge WMA corridor area,

with specific locations randomly selected using a Google Earth map overlaid to the study area. Camera trap

locations are shown in Figure 3. Because the objective of the survey was to maximize species detection,

camera traps were randomly placed at distances ranging between 150m and 650m apart. Camera traps

were left to run for 60 days from 18th December 2018 to 18th February 2019 with fortnightly visits to retrieve

the pictures. Cameras were mounted on trees at a height that varied from 0.5m to 3m above the ground,

depending on the distances between the cameras and targeted wildlife trails. Cameras were programmed

to take photographs without delay between sequential photos and to operate 24hrs/day.

Figure 3. Locations of 21 camera traps in the KWC portion of Burunge WMA (from Kissui et al. 2019)


2.5.2 Camera trap data analysis

Camelot -1.4.5 software (Hendry & Mann 2017) was used to process photographs. Camelot software

extracts information about species identification, keeps track of camera trap locations, and records the

dates and times when the photographs are taken. The data were exported from Camelot into Excel for

analysis. Because the study area contains gregarious wildlife species that move in large herds such as

wildebeest, zebra, and impala, consecutive photos of the same number of individuals of the same species

were considered independent observation events only when (i) photos were spaced for at least 2 minutes

(T=2), and (ii) within the T=2 window, the number of individuals of the same species differed between

consecutive sightings, and (iii) the life stages of the individuals in consecutive sightings were different.

Camera trap sampling effort was calculated by multiplying the number of cameras by the number of camera

trap-nights, i.e., the number of nights that each camera was operational in the field. Trap success rate or

photographic rate was obtained by dividing the number of events (photographs) by the sampling effort.

3. RESULTS

3.1 Land cover mapping and analysis

Land cover mapping and analysis identified six land cover classes for both 2008 and 2018Error!

Reference source not found.. In 2008 grassland was the most common type of land cover (41% of total

area) in the Kwakuchinja study area, followed by water bodies (21%), shrubland (19%), woodland (14%),

bare land (4%), and agriculture (1%) (

Table 1 and Figure 4). In 2018 grassland was again the most common type of land cover (33% of total

area), followed by shrubland (22%), water bodies (14%), woodland (12%), bare land (10%), and agriculture

(9%) (


Table 1 and Figure 55). Of particular note is the fact that from 2008 to 2018 the proportion of grassland,

water bodies, and woodland decreased (by 95 km2, 84 km2, and 29 km2, respectively) while the proportion

of shrubland, bare land, and agriculture increased (by 38 km2, 72 km2, and 97 km2, respectively) (

Table 1).

Figure 4. Land use map in the Kwakuchinja Wildlife Corridor, 2008


Figure 5. Land use map in Kwakuchinja Wildlife Corridor, 2018


Table 1. Landcover classes in Kwakuchinja Wildlife Corridor for years 2008 and 2018

Class name Coverage (km2 and percentages)

2008 2018

Area % Area % Net change (km2)

Bare land 54.52 4.25 127.02 9.89 72.5 Increasing

Water bodies

262.79 20.47 178.53 13.9 -84.26 Declining

Agriculture 16.06 1.25 113.17 8.81 97.11 Increasing

Grassland 522.95 40.72 428.03 33.34 -94.92 Declining

Shrubland 245.57 19.12 283.89 22.11 38.32 Increasing

Woodland 182.18 14.19 153.43 11.95 -28.75 Declining

Total 1284.07 100 1284.07 100

3.2 Aerial Wildlife Surveys

TAWIRI aerial surveys reveal that the wildlife species surveyed are widely distributed around, and at times

within, the Kwakuchinja wildlife corridor. Results for individual species are given below.

3.2.1 Buffalo Distribution in KWC

Buffaloes were observed in the KWC area in 2009, 2011, 2014, and 2016 (Annex 1). In 2009 buffaloes were

well distributed at the southern part of Lolkisale, Kwakuchinja area and at the western part of the corridor

toward Lake Manyara National Park. In 2011 the buffaloes were concentrated mostly inside Tarangire

National Park. This is also true for 2014, although some groups were observed in Manyara Ranch and again

at the western part of the corridor toward Lake Manyara National Park. There were only a few observations

in 2016. Figure 66 depicts observation locations for all four years combined.


Figure 6. Buffalo population distribution in 2009-2016 at Kwakuchinja study area


3.2.2 Elephant Distribution in KWC

Elephants were observed in the KWC area in 2007, 2009, 2011, 2014, and 2016 (Error! Reference source

not found.). In 2007 elephants were observed mostly in Burunge WMA, and a few groups were observed in

Lake Manyara National Park. In 2009 elephants were frequently seen within Tarangire National Park and

outside the border at the southern part of Lolkisale GCA and Manyara ranch. In 2011 a single family of

elephants was observed at Manyara Ranch near Makuyuni Bridge. In 2014 the majority of elephants

observed were along the border between Tarangire National Park and in Burunge WMA. In 2016 only a few

elephants were observed in Manyara Ranch. Error! Reference source not found.7 depicts observation

locations for all five years combined.

Figure 7. Elephant population distribution in 2009-2016 at Kwakuchinja study area


3.2.3 Giraffe Distribution in KWC

Giraffe were observed in the KWC study area in 2007, 2014, and 2016 (Annex 3). In 2007 giraffe were

distributed in Burunge WMA, Lolkisale area, Manyara ranch, and Tarangire National Park. In 2014 giraffe

mostly used Burunge WMA and the northern part of the Kibaoni area. In 2016 only a small group of giraffe

were observed at Manyara Ranch. Figure 88 depicts observation locations for all three years combined.

Figure 8. Giraffe population distribution in Kwakuchinja wildlife corridor


3.2.4 Grant’s Gazelle Distribution in KWC

Grant’s gazelle were observed in the KWC study area in 2007, 2011, and 2016 (

). In 2007 a small group was observed near Naitolia village, Lolkisale area. In 2011 four groups were observed

at Manyara Ranch and again in the Lolkisale area. In 2016 Grant’s gazelle were observed only at Manyara

Ranch. Error! Reference source not found.9 depicts observation locations for all three years combined.


Figure 9. Grant’s gazelle population distribution in Kwakuchinja wildlife corridor


3.2.5 Impala Distribution in KWC

Impala were observed in the KWC study area in 2007, 2011, and 2014

). In 2007 a few groups were distributed around the Burunge WMA. In 2011 a single group was observed at

Manyara Ranch. In 2016 groups were observed at the southeast and northwest margins of the Burunge

WMA. Error! Reference source not found.10 depicts observation locations for all three years combined.


Figure 10. Impala population distribution in Kwakuchinja wildlife corridor

3.2.6 Wildebeest distribution in KWC

Wildebeest were observed in the KWC study area in 2007, 2011, 2014, and 2016 (Error! Reference source

not found.). In 2007 and 2011 groups of wildebeest were observed in the southern regions of the Burunge

WMA. In 2014 wildebeest were widely distributed in the study area, especially in the WMA. However, in 2016

wildebeest were observed only at Manyara Ranch and in the Lolkisale area. Error! Reference source not

found.1 depicts observation locations for all four years combined.


Figure 31. Wildebeest population distribution in Kwakuchinja wildlife corridor

3.2.7 Zebra distribution in KWC

Zebra were observed in the KWC study area in 2007, 2011, 2014, and 2016 (

). In 2007 and 2011 zebra were distributed across the Burunge WMA and Manyara Ranch. In 2014 zebra

were observed primarily in the WMA. In 2016 zebra were concentrated at Manyara Ranch. Error! Reference

source not found.2 depicts observation locations for all four years combined.


Figure 42. Distribution of zebra in Kwakuchinja wildlife corridor

3.2.8 Human activities

Human activities, especially settlements and livestock keeping, are among the greatest threats to wildlife in

the Kwakuchinja study area. Livestock are a significant threat to wildlife in the KWC area because livestock

are abundant (Table 2), and they compete with wildlife for resources. Aerial surveys conducted in 2007, 2009,


2011, 2014, and 2016, found livestock (Error! Reference source not found.3) and human settlements

(Figure 64) in almost all regions of the KWC area, the one exception being a portion of Manyara Ranch.

Figure 53. Livestock distribution in the Kwakuchinja study area


Figure 64. Settlement distribution in the Kwakuchinja study area

3.3 Ground transect wildlife surveys

Surveyors walking ground transects in the Kwakuchinja study area observed 23 species of animals. Among

these, three species were livestock, i.e., cattle, sheep and goats (grouped together and recorded as “shoats”),

and donkeys; the twenty others were wildlife species (Table 2). In most cases surveyors recorded animals

that they directly observed, but surveyors also encountered and recorded signs of elephants, spotted hyenas,

and wild dogs (Table 22). The most abundant species observed were cattle, shoats, zebra, and wildebeest;


the most abundant wildlife species observed were wildebeest, zebra, Thomson gazelles, eland, and giraffe;

the least abundant wildlife species observed were Grant’s gazelle, Blue monkey, Silver-backed jackal,

Steenbok, Vervet monkey, Hare, Olive baboon, Spotted Hyena, Waterbuck, and wild dog (See Table 2).

Table 2. Ground transect species counts and number of observations

Sn Species Number of Encounters

Number of Individual Animals Encountered Remarks

1 Cattle 73 7322

2 Shoats (sheep and goats) 52 4122

3 Zebra 24 622

4 Wildebeest 21 730

5 Thomson gazelle 14 246

6 Donkey 13 80

7 Elephant sign 11 0 Dung and footprints only

8 Giraffe 11 101

9 Dik-dik 8 13

10 Eland 8 241

11 Buffalo 5 19

12 Impala 5 39

13 Warthog 5 11

14 Grant gazelle 3 60

15 Blue Monkey 2 28

16 Silver backed jackal 2 4

17 Steenbok 2 3

18 Vervet monkey 2 8

19 Hare 1 1

20 Olive baboon 1 65

21 Spotted Hyena 1 0 Footprints

22 Waterbuck 1 5

23 Wild dog 1 0 Dung and footprints


3.4 Camera trap wildlife surveys

The camera traps observed 63 wildlife species in the corridor portion of the Burunge WMA as well as cattle,

people, and a few goats, sheep, and domestic dogs. The 10 most commonly photographed species were

wildebeest, zebra, impala, warthog, giraffe, cattle, dik-dik, olive baboon, Bohor reedbuck, and humans.

Results are presented in Table 3.

Table 3. List of species observed during the camera trap survey in Burunge WMA indicating the number of events and trap rates for each species (from Kissui et al. 2019)

Species Common name

Number of events Trap success (RAI)

1 Wildebeest 10004 7.939

2 Zebra 8591 6.818

3 Impala 4080 3.238

4 Warthog 1723 1.367

5 Giraffe 1693 1.343

6 Cattle 902 0.716

7 Dik-dik 554 0.439

8 Olive baboon 483 0.383

9 Bohor Reedbuck 472 0.375

10 Human 355 0.282

11 Bushbuck 285 0.226

12 Vervet monkey 279 0.221

13 Little Egret 251 0.199

14 Blacksmith plover 222 0.176

15 Spotted hyena 203 0.161

16 Helmeted guineafowl 200 0.159

17 Red-billed oxpecker 115 0.091

18 Crowned Plover 104 0.083

19 African bush elephant 98 0.077

20 Crested francolin 88 0.069

21 Yellow-billed oxpecker 87 0.069

22 Red-necked spurfowl 73 0.058

23 Donkey 50 0.039

24 Bush pig 37 0.029

25 Common ostrich 35 0.028

26 Common genet 31 0.025

27 Crested Porcupine 30 0.024

28 Cattle egret 27 0.021

29 Large Spotted Genet 25 0.019


30 Great egret 23 0.018

31 Lesser kudu 18 0.014

32 Superb starling 18 0.014

33 Ring-necked dove 16 0.013

34 Leopard 14 0.011

35 Banded mongoose 13 0.010

36 Leopard tortoise 11 0.009

37 White-tailed Mongoose 11 0.009

38 Black-backed jackal 10 0.008

39 Lion 10 0.008

40 Aardvark 9 0.007

41 African Wild Ass 9 0.007

42 Egyptian goose 9 0.007

43 Striped hyena 9 0.007

44 African Buffalo 8 0.006

45 Goat 8 0.006

46 Honey badger 8 0.006

47 Sheep 8 0.006

48 Black-headed heron 7 0.006

49 Grey heron 7 0.006

50 Jackson's Mongoose 7 0.006

51 Domestic dog 6 0.005

52 Scrub hare 5 0.004

53 Waterbuck 5 0.004

54 Yellow baboon 5 0.004

55 African civet 4 0.003

56 Marabou stork 3 0.002

57 Ring-necked dove 3 0.002

58 African mourning dove 2 0.002

59 Heron species 2 0.002

60 Scaly Francolin 2 0.002

61 Black-headed batis 1 0.001

62 Common rabbit 1 0.001

63 Dwarf mongoose 1 0.001

64 Elephant Shrew species 1 0.001

65 Grant's gazelle 1 0.001

66 Holub's golden weaver 1 0.001

67 Nile monitor 1 0.001

68 Slate-coloured boubou 1 0.001

69 Slender Mongoose 1 0.001

70 Thomson Gazelle 1 0.001


4. DISCUSSION

4.1 Landcover classes and change

The observed increase in bare land in the study area is almost certainly a result of people clearing vegetation

for agriculture and building settlements to meet the demands of a growing population (Raphael B. B. Mwalyosi

1992; Sechambo 2001; Kiunsi & Meadows 2006; Bergh 2016; Schüßler, Lee, & Stadtmann 2018). This

interpretation is supported by the observed increase in agricultural land. The observed decline in grasslands

might be a threat to both livestock and wildlife in the KWC area because livestock and many wildlife species

depend on grasslands for forage (Hariohay 2013; Rija et al. 2013). The observed decline of water bodies in

the study area is probably best explained by climatic factors.

The observed increase in shrubland in the study area could be a result of the observed decline of woodlands,

and this is also probably caused, at least in part, by human activities, especially cutting trees for building

materials, charcoal, and firewood (Hariohay 2013; Njamasi 2015). For example, woodlands have declined in

the Eastern Selous Game Reserve as human activities have increased between 1978 and 2005 (Ntongani,

Munishi, & Mbilinyi 2010), and a global decline in woodlands and forests has been correlated with increased

agricultural land use (Ntongani et al. 2010; Skole & Tucker 1993).

4.2 Wildlife population distribution

Results of this study reveal extensive use of the KWC area by wildlife. Although there is considerable variance

in the size and distribution of different wildlife species populations, the aerial surveys, ground transect

surveys, and camera trap survey results clearly indicate that wildlife are using the area. Figure 15 depicts all

the aerial survey results compiled for this study in a single map (without indicating individual species

distributions), and it overlays on top of these results wildlife movement routes through the study area that

surveyors observed while conducting ground transect surveys. Figure 15 also shows an area that can be

considered a corridor buffer zone to protect wildlife and their presumed movement routes.


Figure 75. All species distribution and corridor buffer zone in the Kwakuchinja study area


5. CONCLUSION AND RECOMMENDATION

Increased human use of the KWC area especially for agriculture, livestock keeping, and constructing

settlements is creating significant threats to wildlife and could substantially reduce wildlife use of the corridor.

In particular, this study’s land use and land cover change analysis reveals that people are converting land to

agriculture at an alarming rate; the camera trap surveys detected cattle and people among the top 10 most

commonly photographed species; and the ground transect wildlife surveys indicate that cattle, sheep, and

goats outnumber even the most abundant wildlife species by an order of magnitude.

However, despite the high levels of human disturbance and livestock in the KWC area, the land cover and

land use change analysis, along with the aerial, ground transect, and camera trap surveys show that there

are still connections and habitats used by wildlife, including diverse wildlife in the Burunge WMA portion of

the wildlife corridor. The Kwakuchinja study area shows a clear pattern of habitat and wildlife distribution that

connects Lake Manyara National Park and Tarangire National Park via Burunge WMA and Manyara Ranch,

and from Burunge WMA toward the southern part of Lake Manyara. The distribution pattern may be

considered as representing the active and viable areas that wildlife use between these protected areas (See

Figure 15, Figure 76).

To provide additional protection for these areas, villagers would need to develop land use plans that check

the rampant agricultural expansion and limit grazing to more sustainable levels in the corridor area. In

addition, the tiny strip of land between Burunge WMA at Vilima Vitatu village could be widened to help

facilitate wildlife movement from the southern part of the WMA northward to the Minjingu and Magara

dispersal area. Future persistence of the Kwakuchinja wildlife corridor could also be enhanced by protecting

the nearby Makuyuni and Tarangire-Simanjiro corridors, which would help to further connect regional wildlife

populations in the Tarangire-Manyara ecosystem (See

Figure 86).


Figure 86. Tarangire-Manyara ecosystem and three important corridors in the region: Kwakuchinja, Makuyuni, and Tarangire-Simanjiro (Source: Wildlife corridors in Tanzania 2009)


6. REFERENCES

Amro, I., J. Mateos, M. Vega, R. Molina and A. K. Katsaggelos (2011). "A survey of classical methods and

new trends in pansharpening of multispectral images." EURASIP Journal on Advances in Signal

Processing 2011(1): 79.

Bergh, H. A. J. van den. (2016). The impacts of Maasai settlement on land cover, meteorological conditions

and wind erosion risk in northern Tanzania [Master thesis]. Retrieved March 12, 2019, from

http://dspace.library.uu.nl/handle/1874/349763

Bluwstein, J. (2018). From colonial fortresses to neoliberal landscapes in Northern Tanzania: a biopolitical

ecology of wildlife conservation. Journal of Political Ecology, 25(1), 144.

https://doi.org/10.2458/v25i1.22865

Buckland, S. T., & Elston, D. A. (1993). Empirical Models for the Spatial Distribution of Wildlife. Journal of

Applied Ecology, 30(3), 478–495. https://doi.org/10.2307/2404188

Brazilian Tapir Density in the Pantanal: A Comparison of Systematic Camera‐Trapping and Line‐Transect

Surveys - Trolle - 2008 - Biotropica - Wiley Online Library. (n.d.). Retrieved August 19, 2018, from

https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1744-7429.2007.00350.x

Buckland, S. T., & Elston, D. A. (1993). Empirical Models for the Spatial Distribution of Wildlife. Journal of

Applied Ecology, 30(3), 478–495. https://doi.org/10.2307/2404188

Burgman, S. F. (2000). Quantitative Methods for Conservation Biology. Springer.

Breiman, L. (2001). "Random forests." Machine learning 45(1): 5-32.

Chavez, P. S. (1996). "Image-based atmospheric corrections-revisited and improved." Photogrammetric

Engineering and Remote Sensing 62(9): 1025-1035.

Chavez, P. S., Jr. (1996). "Radiometric calibration of Landsat Thematic Mapper multispectral images."

Photogrammetric Engineering and Remote Sensing 55(9): 1285-1294.


Cutler, D. R., T. C. Edwards, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson and J. J. Lawler (2007).

"Random forests for classification in ecology." Ecology 88(11): 2783-2792.

Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration

coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of Environment,

113(5), 893-903.

Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric

Engineering and Remote Sensing, 62(9), 1025-1035.

Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data.

Remote sensing of environment, 37(1), 35-46.

Congedo, L. (2013). Semi Automatic Classification Plugin for QGIS. Sapienza University, Rome.

Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007).

Random forests for classification in ecology. Ecology, 88(11), 2783-2792.

Ekstrand, S. (1996). Landsat TM-based forest damage assessment: correction for topographic effects.

Photogrammetric Engineering and Remote Sensing, 62(2), 151-162.

Frakes, R. A., Belden, R. C., Wood, B. E., & James, F. E. (2015). Landscape Analysis of Adult Florida

Panther Habitat. PloS one, 10(7), e0133044.

Franklin, S. E., & Giles, P. T. (1995). Radiometric processing of aerial and satellite remote-sensing

imagery. Computers & Geosciences, 21(3), 413-423.

Frakes, R. A., R. C. Belden, B. E. Wood and F. E. James (2015). "Landscape Analysis of Adult Florida


Fewster, R. M., Buckland, S. T., Burnham, K. P., Borchers, D. L., Jupp, P. E., Laake, J. L., & Thomas, L.

(2009). Estimating the Encounter Rate Variance in Distance Sampling. Biometrics, 65(1), 225–236.

https://doi.org/10.1111/j.1541-0420.2008.01018.x

Franklin, S. E. and P. T. Giles (1995). "Radiometric processing of aerial and satellite remote-sensing

imagery." Computers & Geosciences 21(3): 413-423.

Hariohay, K. M. (2013). Impacts of human settlements and land use changes in the Kwakuchinja wildlife

corridor, Northern Tanzania. 43. Retrieved from https://brage.bibsys.no/xmlui/handle/11250/245238

Hendry, H. & Mann, C. 2017. Camelot- Intuitive Software for Camera Trap Data Management. Oryx, doi:

http://dx.doi.org/10.1101/203216

Jensen, J. R. (1996). "Thematic information extraction: Image classification." Introductory Digital Image

Processing: A Remote Sensing Perspective: 197-256.

James Gibbs, M. H. (2008). Problem-solving in Conservation Biology and Wildlife Management. Blackwell.

Jimmie R. Parrish, Frank P. Howe, & Russell E. Norvell. (2003). A SEVEN-YEAR

COMPARISON OF relative-abundance and distance-sampling methods | The Auk. Retrieved August 19,

2018, from http://www.bioone.org/doi/abs/10.1642/0004-

8038(2003)120%5B1013:ASCORA%5D2.0.CO%3B2

Jimmie R. Parrish, Frank P. Howe, & Russell E. Norvell. (2003). A SEVEN-YEAR COMPARISON OF

RELATIVE-ABUNDANCE AND DISTANCE-SAMPLING METHODS | The Auk. Retrieved August 19,

2018, from http://www.bioone.org/doi/abs/10.1642/0004-

8038(2003)120%5B1013:ASCORA%5D2.0.CO%3B2

Kissui, B., A. Lobora, and R. Tossi. (2019). Camera Trap Survey to Assess Mammalian Species Diversity

in the Burunge WMA and Kwakuchinja Wildlife Corridor. Unpublished Report.

http://dx.doi.org/10.1101/203216


Kiunsi, R. B., & Meadows, M. E. (2006). Assessing land degradation in the Monduli District, northern

Tanzania. Land Degradation & Development, 17(5), 509–525. https://doi.org/10.1002/ldr.733

Mwalyosi, R. B. B. (1991). Ecological evaluation for wildlife corridors and buffer zones for Lake Manyara

National Park, Tanzania, and its immediate environment. Biological Conservation, 57(2), 171–186.

https://doi.org/10.1016/0006-3207(91)90137-X

Mwalyosi, Raphael B. B. (1992). Land-use Changes and Resource Degradation in South–West Masailand,

Tanzania. Environmental Conservation, 19(2), 145–152. https://doi.org/10.1017/S0376892900030629

Ntongani, W. A., Munishi, P. K., & Mbilinyi, B. P. (2010). Land use changes and conservation threats in the

eastern Selous–Niassa wildlife corridor, Nachingwea, Tanzania. African Journal of Ecology, 48(4), 880-

887.

Njamasi, Y. R. (2015). The impact of human activities on wildlife in the Kwakuchinja migratory corridor -

Tarangire/Manyara ecosystem (tme), Northern Tanzania (Thesis). Sokoine University of Agric

Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014).

Good practices for estimating area and assessing the accuracy of land change. Remote sensing of

environment, 148, 42-57.

PCI (2015). PCI Geomatics Software. Canada.

Pons, X., Pesquer, L., Cristóbal, J., & González-Guerrero, O. (2014). Automatic and improved radiometric

correction of Landsat imagery using reference values from MODIS surface reflectance images.

International Journal of Applied Earth Observation and Geoinformation, 33, 243-254.

Pangelova, B. and J. Rogan (2006). "Land cover and land use change detection and analyses in Plovdiv,

Bulgaria, between 1986 and 2000." Proceedings from Annual Conference of the American Society for

Photogrammetry and Remote Sensing: 1-5.


Pons, X., L. Pesquer, J. Cristóbal and O. González-Guerrero (2014). "Automatic and improved radiometric

correction of Landsat imagery using reference values from MODIS surface reflectance images."

International Journal of Applied Earth Observation and Geoinformation 33: 243-254.

Pons, X. and L. Solé-Sugrañes (1994). "A simple radiometric correction model to improve automatic

mapping of vegetation from multispectral satellite data." Remote sensing of Environment 48(2): 191-

204.

Riaño, D., E. Chuvieco, J. Salas and I. Aguado (2003). "Assessment of different topographic corrections in

Landsat-TM data for mapping vegetation types (2003)." IEEE Transactions on geoscience and remote

sensing 41(5): 1056-1061.

Rodriguez-Galiano, V., M. Chica-Olmo, F. Abarca-Hernandez, P. M. Atkinson and C. Jeganathan (2012).

"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-

seasonal texture." Remote Sensing of Environment 121: 93-107.

Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo and J. P. Rigol-Sanchez (2012). "An

assessment of the effectiveness of a random forest classifier for land-cover classification." ISPRS

Journal of Photogrammetry and Remote Sensing 67: 93-104.

Shepherd, J. and J. Dymond (2003). "Correcting satellite imagery for the variance of reflectance and

illumination with topography." International Journal of Remote Sensing 24(17): 3503-3514.

Reed, D. N., Anderson, T. M., Dempewolf, J., Metzger, K., & Serneels, S. (2009). The spatial distribution of

vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on

vegetation patch characteristics. Journal of Biogeography, 36(4), 770-782.


Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An

assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS

Journal of Photogrammetry and Remote Sensing, 67, 93-104.

Schüßler, D., Lee, P. C., & Stadtmann, R. (2018). Analyzing land use change to identify migration corridors

of African elephants (Loxodonta africana) in the Kenyan-Tanzanian borderlands. Landscape Ecology,

33(12), 2121–2136. https://doi.org/10.1007/s10980-018-0728-7

Sechambo, F. (2001). Land use by people living around protected areas: the case of Lake Manyara

national park. Retrieved from http://41.73.194.134:8080/xmlui/handle/123456789/466

Skole, D., & Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon: satellite

data from 1978 to 1988. Science, 260(5116), 1905-1910.

Sutherland, W. J. (2006). Ecological Census Techniques. Cambridge University Press.

Thomlinson, J. R., Bolstad, P. V., & Cohen, W. B. (1999). Coordinating methodologies for scaling landcover

classifications from site-specific to global: Steps toward validating global map products. Remote

sensing of environment, 70(1), 16-28.

Jones, T. C. (January 2009). Wildlife Corridors in Tanzania. Arusha: Tanzania wildlife research

Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R., & Evangelista, P. H. (2017). A

survival guide to Landsat preprocessing. Ecology, 98(4), 920-932.


ANNEXES

Buffalo

Annex 1. Buffalo distribution in various surveyed years over Kwakuchinja study area


Elephant

Annex 2. Elephant distribution in various surveyed years over the Kwakuchinja study area


Giraffe

Annex 3. Giraffe distribution in various surveyed years over Kwakuchinja study area


Grant’s gazelle

Annex 4. Grant’s gazelle distribution in various surveyed years over Kwakuchinja study area


Impala

Annex 5. Impala density in various surveyed years over Kwakuchinja study area

(Note: In 2007 Lake Burunge was reduced in size during the survey period. The observed groups marked on

the map where Lake Burunge is located are not within the waterlogged area).


Wildebeest

Annex 6. Wildebeest distribution in various surveyed years over Kwakuchinja study area


Zebra

Annex 7. Zebra distribution in various surveyed years over Kwakuchinja study area

kwakuchinja wildlife corridor ecological viability …

Documents