ASSESSING THE IMPACTS OF BIOENERGY EXTRACTION AND HUMAN LAND USE OF THE BIODIVERSITY OF KAKAMEGA TROPICAL RAINFOREST, KENYA
Christopher Amutabi Kefa
A Thesis
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
August 2016
Committee:
Andrew Gregory, Advisor
Anita Simic
Kefa Otiso
Yu Zhou
© 2016
Christopher Amutabi Kefa
All Rights Reserved iii ABSTRACT
Andrew Gregory, Advisor
Tropical rainforests are globally recognized as important biodiversity areas. Most of these forests are situated in regions with high population density, high poverty and high unemployment which leave local people little choice but to use forests resources to survive. Consequently, tropical forests are rapidly declining due to deforestation and unsustainable consumptive utilization of their natural resources. One of the main challenges facing efficient management and protection of tropical forests is reconciling human needs of the forest resources with biodiversity conservation interests. The objective of this study was to examine coupled nature of how human use of the forest impact biodiversity and biodiversity influence where humans use the forest. The study investigated patterns of wood harvest across Kakamega Tropical Rainforest and quantified rate and amount of wood harvested from the forest. Point sample Timber Cruising methodology and pitfall trapping were used to assess trees and arthropods respectively to characterize the status of forest health. Results showed that wood harvesters preferred indigenous as opposed to non-indigenous wood and that indigenous wood was sold for a premium price. Moreover, natural forest areas that had indigenous and rare tree species were targeted by humans for wood extraction, suggesting a bidirectional influence of human use on forest biodiversity and biodiversity on human use. Conservation of Kakamega forest is linked to social and economic development of the people living near it. Consequently, market economies may be able to incentivize forest conservation since it seems to drive forest use. This thesis is divided into four chapters. Chapter one provides a general introduction about the study and the study area. Chapter two is a standalone entity and a peer review paper (short communication) that describes iv assessment of the rate and quantity of wood harvested from Kakamega forest. Similarly, Chapter
three which is also a standalone entity, explains forest health assessment, and how management of the forest and the two conservation interventions (tea belts and energy efficient cook stoves) have impacted forest health. Chapter four gives summary of the study findings, recommendations, study limitations, and suggestions for future research work. v
“The book of nature has no beginning, as it has no end. Open this book where you will, and at any period of your life, and if you have the desire to acquire knowledge you will find it of intense interest, and no matter how long or how intently you study the pages, your interest will not flag,
for in nature there is no finality”
Jim Corbett vi ACKNOWLEDGMENTS
I am highly grateful to my Masters Advisor Dr. Andrew J. Gregory, and research committee members: Dr. Anita Simic, Dr. Kefa Otiso and Dr. Yu Zhou for their guidance and commendable mentoring during the whole period of my study. I am grateful to BGSU and
Eco2librium Company Ltd for providing material, financial and technical support. I gratefully acknowledge Genetic Research in Applied Spatial Ecology (GRASE) Lab members for their support. I also thank Herbert Imbuka, Emma Spence, Gregory Brinkman, and Nadejda
Mirochnitchenko, who were technicians on this project. I thank all Eco2librium staff for their support during the entire study period, and in particular Herbert Imbuka for his assistance with field data collection.
My sincere gratitude goes to my wife Melissa for her support and encouragement throughout the period of my study, and especially for taking all parenting responsibilities for our children in my absence. My special regards to my son Eliud, daughters Valary and Janice for bearing with my absence gracefully. I am indebted to my Mum and late Dad (who passed on at the time I was starting my studies) for their encouragement and prayers. Many thanks to my brothers, sisters and friends for their encouragement and support.
I highly appreciate the Kenya National Commission for Science and Technology
(NACOSTI), Kenya Forest Service (KFS) and Kenya Wildlife Service (KWS) and the County government of Kakamega County for issuing permits that allowed me to undertake fieldwork. I also thank our collaborators at the National Museum of Kenya for administrative, logistical and technical support. I also thank the numerous anonymous research participants who spoke with us in the field and allowed us to weigh their wood bundles. vii TABLE OF CONTENTS
CHAPTER ONE: INTRODUCTION ...... 1
BACKGROUND ...... 1
GENERAL INTRODUCTION OF KAKAMEGA FOREST STUDY AREA ...... 3
The People ...... 3
Overview of The Kakamega Forest ...... 5
Biodiversity of Kakamega Forest ...... 5
Management of Kakamega Forest ...... 6
Other Conservation Interventions ...... 6
RESEARCH OBJECTIVES ...... 9
RESEARCH AUTHORIZATION AND PERMITS ...... 10
CHAPTER TWO: QUANTIFYING THE RATE OF SUBSISTENCE WOOD
HARVESTING FROM KAKAMEGA TROPICAL RAINFOREST IN KENYA ...... 11
ABSTRACT ...... 11
INTRODUCTION ...... 12
METHODS ...... 13
Study Area ...... 13
Data Collection ...... 13
Data Analysis ...... 16
RESULTS ...... 17
Descriptive Results (Wood Measurement) ...... 17
Statistical Results ...... 19
Wood Markets Results ...... 20 viii DISCUSSION ...... 21
CHAPTER THREE: HUMAN USE, BIODIVERSITY, AND FOREST
MANAGEMENT: ANALYSIS OF COUPLED NATURAL AND HUMAN SYSTEM
INTERACTIONS IN THE KAKAMEGA RAINFOREST OF KENYA ...... 24
ABSTRACT ...... 24
INTRODUCTION ...... 25
METHODS ...... 27
Study Site ...... 27
Sampling Procedure and Data Collection ...... 29
Forest Biodiversity ...... 29
Characterizing Biodiversity of the Forest ...... 34
Human Use of the Forest ...... 34
Land Management Outside and Inside the Forest ...... 35
SPATIAL DATA ANALYSIS ...... 35
Edge Effect...... 35
Kriging Interpolation ...... 36
Normalized Difference Vegetation Index (NDVI) ...... 37
Unsupervised Iso-Cluster Classification ...... 37
STATISTICAL DATA ANALYSIS ...... 38
RESULTS ...... 39
Descriptive Results ...... 39
Spatial Analysis Results ...... 39
Kriging Interpolation Results ...... 40 ix Statistical Analysis Results ...... 44
Conservation Priority Areas of Kakamega Forest ...... 47
DISCUSSION ...... 50
CHAPTER FOUR: CONCLUSION ...... 54
STUDY LIMITATIONS ...... 56
SUGGESTIONS FOR FUTURE RESEARCH ...... 57
LITERATURE CITED ...... 58
APPENDICES ...... 63
APPENDIX A: ADDITIONAL MATERIALS ...... 63
APPENDIX B: SUPPLEMENTARY TABLE OF WOOD MEASUREMENT
VARIABLES ...... 64
APPENDIX C: WOOD SPECIES IDENTIFIED IN WOOD SURVEY...... 65
APPENDIX D: MEASUREMENTS/VARIABLES FOR TIMBER CRUISE ...... 66
APPENDIX E: TREE SPECIES OBSERVED IN TIMBER CRUISE ...... 67
x LIST OF FIGURES
Figure Page
1-1. Kenya’s Population Trend, 1969-2009 (Source: KNBS, 2015) ...... 2
1-2. Location of Kakamega Forest in western Kenya ...... 4
1-3. Tea plantation adjacent to Kakamega Forest ...... 7
1-4. Left: Traditional 3-stone cooking method; Right: Energy efficient cook-stove ...... 8
1-5. Map of distribution of energy efficient cook-stoves around Kakamega Forest...... 9
2-1. Locations of wood measurement sites and wood markets ...... 14
2-2. Left: Women carrying wood from the forest, and Right: a field technician holding
wood bundle ...... 15
2-3. Left: A woman ready to weigh wood, and Right: Herbert (Eco2librium staff)
interviews a wood harvester ...... 15
2-4. Percent indigenous vs non-indigenous wood ...... 17
2-5. Frequency plots of (A)indigenous and (B) non-indigenous harvested wood ...... 18
2-6. Maximum distance of wood destinations by foot and motor vehicles...... 20
3-1. Map of the study area and Sampling locations ...... 30
3-2. (a) Left: measuring tree DBH and (b) right: Reading Visual obstruction (VOR) ... 31
3-3. Edge effect distance calculation...... 36
3-4. Edge effect analysis ...... 40
3-5. Kriging interpolation: (a) tree RWR, (b) Tree species richness, (c) Tree species
diversity, and (d) Selectivity index ...... 42
3-6. Kriging interpolation images: (e)Tree DBH, (f) NDVI (g) Arthropod RWR and
(h) Arthropod species diversity ...... 43 xi 3-7. Scatter plot of Distance to edge Vs RWR (trees) ...... 46
3-8. Scatter plot of Distance to edge Vs RWR (arthropods) ...... 47
3-9. Conservation priority regions of Kakamega Forest ...... 49
xii LIST OF TABLES
Table Page
2-1. Results of wood harvest surveys in Kakamega Forest, Kenya ...... 19
3-1. Timber cruise Variables ...... 32
3-2. Environmental Variables ...... 33
3-3. Mean values of the calculated indices and other variables ...... 44
3-4. βeta - values of predictor variables (DF = 1; num DF=3; den DF =23) ...... 45
3-5. Tukey's Studentized Range (HSD) results for individual variables ...... 47
xiii ACRONYMS
CFA ------Community Forest Association
DBH ------Diameter at Breast Height
GPS ------Global Positioning System
GRASE ------Genetic Research and Applied Spatial Ecology
HSRB ------Human Subjects Review Board
IUCN ------International Union for Conservation of Nature
KES ------Kenya Shillings
KFS ------Kenya Forest Service
KIFCON ------Kenya Indigenous Forest Conservation Program
KNBS ------Kenya National Bureau of Statistics
KWS ------Kenya Wildlife Service
LPG ------Liquid Petroleum Gas
MANOVA ------Multivariate Analysis of Variance
NDVI ------Normalized Difference Vegetation Index
NMK ------National Museums of Kenya
NTZDC ------Nyayo Tea Zone Development Corporation
PES ------Payment for Ecosystem Services
RWR ------Rarity Weighted Richness
USDA/FS ------United States Department of Agriculture/Forest Service
UTM ------Universal Transverse Mercator
VOR ------Visual Obstruction Reading 1 CHAPTER ONE: INTRODUCTION
BACKGROUND
Tropical rainforests play an important role in the conservation of biodiversity, climate regulation, habitat for wildlife, carbon sequestering, and regulation of the hydrological cycle among others. The economic benefit of services provided by rainforests globally is estimated between $1,581- $20,851 per hectare per year (Costanza et al., 2014). Tropical forests also provide a wide range of products to the local people, including firewood, vegetables, fruits, medicine, construction materials, as well as livestock fodder (Guthiga & Mburu, 2006). In
Africa, Guineo-Congolian tropical rainforests once formed a continuous belt from the coast of
Western Africa, covering large parts of Central Africa and extending to Western Kenya in East
Africa (Mitchell, 2004).
Today, the Guineo-Congolian rainforest exists in forest fragments that are surrounded by agricultural fields and human settlements (Tsingalia, 1988). The Kakamega rainforest in western
Kenya is the easternmost remnant of Guineo-Congolian rainforest and the only one of its type in
Kenya (Mutoko et al., 2015). The rate at which these rainforests are declining is alarming and there is need for urgent and effective conservations measures (Whitmore & Bierregaard, 1997).
The main challenge in managing rainforests is reconciling extraction needs of the local people with conservation interests. Most tropical rainforests are located in regions with high populations of poor people who rely on subsistence harvests of forest products to survive (Myer, 1992). In fact, most of these forests are under protection by state agencies but deforestation and decline of wildlife populations due to human activities continues to be a major conservation concern. (Di
Marco et al., 2014). 2 Since Kenya gained independence from Britain in 1963, the country has undergone a
rapid growth, both in terms of economic development and in population. Despite a reduction in
the overall poverty rate of Kenya from 1999 to 2009, in that same period over 9.5 million people were added to the population (Figure1-1). Between the years 2013-2014, environmental degradation arising from increased infrastructural development, new settlements in previously unpopulated areas, charcoal burning, and poor land-use planning within fragile ecosystems led to land fragmentation and loss of habitat for wildlife. In Kenya, pressures from increasing population resulted in continued interference with forest resources through encroachment and illegal logging (KNBS, 2015).
Figure 1-1. Kenya’s Population Trend, 1969-2009 (Source: KNBS, 2015)
The loss of forests has at least three consequences: (1) loss of local and global services, (2) loss of resources for humans, and (3) loss of biodiversity. The Kakamega Forest in western Kenya is perhaps one of the country’s most utilized forests due to the high population density in its vicinity (Matiru, 2000). 3
GENERAL INTRODUCTION OF KAKAMEGA FOREST STUDY AREA
The People
The Kakamega forest spans 23,000ha of Kakamega and Vihiga Counties in Western region of Kenya (Figure1-2). The Kakamega county has a population of over 1.6million people
(542people/km2) while Vihiga county has over 0.5million people (1,045 people/Km2) (KNBS,
2015). The majority of people living adjacent to Kakamega forest belong to the Luhya tribe, the
second largest ethnic group in Kenya. Luhya tribe has 18 clans, each having distinct dialect. The
main economic activities include small scale subsistence farming, small scale businesses, and
temporary or permanent employment. Most families depend on small farm plots where they
grow household staple foods but their farm harvests are not enough to sustain their subsistence
needs. Consequently, these families depend on forest products for their subsistence needs e.g.
wood, charcoal, building materials, fruits, mushrooms, traditional medicinal plants, bush meat,
grazing, and timber for making furniture (Kiplagat et al., 2008).
In most cases, people’s living standard is associated with their levels of education. Also,
education can represent a medium through which the social stratification and segmentation are
created among people or society (KNBS, 2009). According to KNBS (2015) report, only 18% of
the population of Kakamega County have acquired a high school or higher education. Majority
(61%) have primary level education and 21% have no formal education. In terms of housing,
18% of the residents of Kakamega County have homes with cement floors, while 80% have
earthen floors, and less than 1% have wood or tile floors. 4
Figure 1-2. Location of Kakamega Forest in western Kenya
Similarly, the type of cooking fuel or lighting fuel used by households is related to the socio-economic status of households. Clean energy sources cost more and are used by households with higher levels of income compared with less clean sources of fuel e.g. firewood.
The majority of people in Kakamega county (87%) use wood as the main source of fuel and more than 95% of this wood is harvested from the Kakamega forest. Only 0.5 % of the people use electricity, 1% use Liquid Petroleum Gas (LPG), 2% use paraffin, and 9% use charcoal
(KNBS, 2015). 5 Overview of The Kakamega Forest
The Kakamega Forest lies between 0°10′ and 0°21′ North and 34°47′ and 34°58′ East,
and has a varied topography with altitude ranging between 1,250m-2,000m above the sea level
(Tsingalia 1988). Kakamega forest receives annual rainfall of 1,500-2,000mm per year, with two
rainy seasons: the long rainy season which occurs between March and June, and the short rainy
season which occurs between July and October (Esther, 2014). November through February are
typically dry months. The mean maximum and minimum annual temperature ranges between
280C-320C and 110C-130C respectively.
Biodiversity of Kakamega Forest
The Kakamega forest is considered a biodiversity hotspot with numerous endemic plant and animal species. The vegetation of the forest includes closed indigenous forest, indigenous plantations, non-indigenous plantations, and grasslands. The forest harbors high diversity of
arthropods with more than 400 species of butterflies (20% of Kenya’s butterflies). A total of 72
dragonfly species which represent 42% of Kenya’s dragonfly fauna, and 170 ant species have
been identified and recorded in Kakamega forest. The forest has more than 400 species of plants
of which 150 represent the woody trees, shrubs, and vines (KIFCON, 1994).
A total of 7 species of primates are found in Kakamega forest namely: black and white
colobus (Colobus guereza), blue monkey (Cercopithecus mitis), red-tailed monkey
(Cercopithecus ascanius), vervet monkey (Chlorocebus pygerythrus), the rare De brazza’s monkey (Cercopithecus neglectus), olive baboons (Papio anubis) and the nocturnal slow moving
Potto (Perodicticus potto). The forest also has diverse assemblage of reptiles including over 36 known species of snakes e.g. Green Mamba (Dendroaspis angusticeps), Jameson´s Mamba
(Dendroaspis jamesoni), Forest Cobra (Naja melanoleuca) and Gaboon viper (Bitis gabonica) 6 (Wagner et al., 2008). The Kaimosi blind Snake (Afrotyphlops kaimosae) is found only in
Kakamega forest in Kenya. More than 400 of birds have been identified with at least a quarter of
them being forest specialists (Brooks et al., 1999). Some of the magnificent bird species include
the Great Blue Turaco (Corythaeola cristata), Black-billed Turaco (Tauraco schuetti) and Blue-
headed Bee-eater (Merops muelleri). Thus, Kakamega Forest has the third highest priority
ranking for conservation among forests in Kenya by the International Union for Conservation of
Nature (IUCN) (Wass, 1995).
Management of Kakamega Forest
Protection and Conservation of tropical forests is primarily dependent on sound and
effective management practices (Gardner et al., 2009; Myers et al., 2000). Kakamega forest has
been under protection status since 1933. In 1991, the Forest Department (currently KFS) and the
KWS entered into a memorandum of understanding to oversee management of forests in Kenya
whose biodiversity is threatened (Guthiga & Mburu, 2006). Currently, the Kakamega forest is
managed by these two government agencies: KFS collaborates with the Community Forest
Associations(CFA) to manage approximately 82% of the Forest and the KWS on the other hand
manages approximately 18% of the forest. The KFS management system is inclusive (incentive-
based) approach which allows the local people to legally harvest forest products with the
purchase of permit. The KWS applies exclusive (protective) approach which prohibits any form
of consumptive utilization of the forest products (Mburu & Birner, 2007).
Other Conservation Interventions
Several management interventions and conservation initiatives have been put up to
conserve the Kakamega forest and also reduce human dependence on forest resources (Esther et al., 2014). In 1984, the World Bank funded the Nyayo Tea Zone Development Corporation 7 (NTZDC), a semi-autonomous government organization, which planted a ~100m wide band of tea margin in sections of the KFS managed forest region (Matiru, 2000) (Figure1-3). The goal of establishing tea plantations was twofold: 1) create a buffer to protect the forest from human encroachment and over exploitation; and 2) to provide an alternative source of income to the local people through employment in tea and fuelwood plantations. Areas that were cleared by
Nyayo Tea Zones but were found to be unsuitable for tea cultivation were replanted with non- indigenous (Eucalyptus species) plantations for fuelwood (Matiru, 2000). Both of these goals were to support the conservation and protection of the Kakamega forest resources.
Figure 1-3. Tea plantation adjacent to Kakamega Forest
Source: http://cdn.ipernity.com/114/03/10/7150310.d0be820e.640.jpg?r2
Since 2010, Eco2librium, a B-corporation1 that applies business solutions to solve social and environmental problems has been involved in carbon offset projects in Kakamega region.
Carbon projects are important conservation programs because they simultaneously reduce
1 A certified B Corporation (B Corp) refers to a for-profit company certified by the nonprofit B Lab to meet rigorous standards of social and environmental performance, accountability and transparency (Gao et al., 2015). 8 demand for forest products, and provide funds for clean development while reducing global
atmospheric carbon dioxide (Turner et al., 2012). One of Eco2librium’s project activities
involves installation of ceramic stoves that are based on traditional 3-stone cooking method, but
are 1.5-3 times more efficient in their wood usage (Figure 1-4).
Figure 1-4. Left: Traditional 3-stone cooking method; Right: Energy efficient cook-stove
Source: http://www.myclimate.org/carbon-offset-projects/projekt/kenya-efficient-cook-stoves-
7138/
According to Myclimate report (2015), by the end of 2014, stoves project had installed >24,000 energy efficient cook-stoves in households adjacent to the Kakamega Forest, which translates to saving 100,000 tons of firewood or equivalent of 250ha of rainforest (Figure 1-5). 9
Figure 1-5. Map of distribution of energy efficient cook-stoves around Kakamega Forest
RESEARCH OBJECTIVES
An understanding of the degree of influence of human activities on biodiversity is critical knowledge for helping forest managers to make informed management decisions. The broad objectives of this study were twofold:
1) To quantify the rate of subsistence wood harvest from Kakamega tropical rainforest. The specific questions that were used to address this objective include: (a) how does KFS vs KWS 10 management influence wood harvest from Kakamega Forest? (b) What is the preferred species of
wood harvested from the forest? (c) What proportion of wood is cut and/or gathered? (d) What
are economic drivers of wood harvest and how far from the forest is Kakamega forest wood
used?
2) To assess how human use, management of the forest, and conservation interventions impact forest biodiversity. To adequately address this broad objective, this study looked at three specific objectives (a) Assess tree and arthropod species diversity and richness of Kakamega
Forest; (b) Determine if there are differences in biodiversity between KFS and KWS managed regions, and (c) Assess if conservation interventions (e.g. Forest management, energy efficient
cook-stoves or tea belts) impact Forest biodiversity.
RESEARCH AUTHORIZATION AND PERMITS
Prior to the start of field work, Bowling Green State University’s Human Subjects
Research Board (HSRB) reviewed the field methods and granted a waiver for approval review on
10-March, 2015. Appropriate permits from the legally governing bodies of Kenya and the
Kakamega Forest were also acquired, namely, Kenya National Commission for Science and
Technology (NACOSTI) – Ref. No: NACOSTI/P/15/6537/4622, Kenya Forest Service – Ref.
No: RESEA/1/KFS/VOL.II (25), Kenya Wildlife Service – Ref. No: KWS/BRM/5001, the
County Government of Kakamega – Ref. No: ED/12/1/169 and the State Department of
Education (Kakamega County) – Ref. No: WP/GA/29/17/VOL.II/2049.
11 CHAPTER TWO: QUANTIFYING THE RATE OF SUBSISTENCE WOOD HARVESTING
FROM KAKAMEGA TROPICAL RAINFOREST IN KENYA
ABSTRACT
One of the major threats to tropical forests throughout the world is the frequency and intensity with which local people use them for their subsistence. Kakamega Forest in Kenya is no
exception to this. Fuel wood harvest is one of the primary uses of Kakamega Forest. Although
the Kenya Forest Service and Kenya Wildlife Service have tried to regulate subsistence harvests
from the forest, high local human population density (>542 people/km2) and poverty, leave local
people little choice but to use forest resources to survive. We investigated patterns of human
wood use across Kakamega Forest. Our results indicated that wood harvesters preferred
indigenous as opposed to non-indigenous wood, as indigenous sold for a premium price.
Harungana madagascariensis and Psidium guajava represented the most harvested indigenous
and non-indigenous woods respectively. Our data further suggest that because market economies
drive forest use, then perhaps market economies can incentivize forest conservation. Regardless,
proper integration of economic forest conservation interventions, economic diversification, and
effective forest management are needed to protect the Kakamega Forest.
12 INTRODUCTION
Globally, rainforests are in sharp decline due to an increase of anthropogenic activities
(Tscharntke, et.al. 2010). The Kakamega Forest in western Kenya is the only tropical rainforest in the country and is the easternmost remnant of the Guineo-Congolian rainforest belt that once spanned the entire equatorial region of Africa (Kokwaro, 1988). At the turn of the 20th century, rainforest cover in Kenya was approximately 240,000 hectares, but due to severe deforestation and fragmentation, 23,000 hectares remain with approximately 10,000 hectares (42%) being indigenous forest (Mitchell, 2004). The Kakamega Forest is endowed with rich biodiversity and hosts a large number of rare animals and endemic plants (KIFCON, 1994).
A common theme throughout the developing world is high density populations of poor people living adjacent to tropical forest remnants and being reliant on subsistence harvests from the forest to survive (Myer, 1992). This is true of the Kakamega Forest. It is situated amid one of the most densely populated rural areas of Kenya (>542 people/km2) (KNBS, 2015), and a majority of this population (>52 %) survives on <$1USD/day (KNBS, 2009). Similarly, as a result of high population density and poverty, these people have little choice but to rely on subsistence harvest of forest products for food, income and fuel (Bleher et al., 2006).
In the Kakamega Forest, KFS seeks to incentivize conservation by allowing people to purchase a permit to legally harvest forest products. KWS prohibits all forms of consumptive harvesting within their jurisdiction of Kakamega Forest. To effectively implement KWS management system, penalties for offenders and lawbreakers are very stringent in the KWS managed area (Esther et al., 2014; Guthiga & Mburu, 2006; Wildlife Conservation &
Management Act 2013). Despite these KFS and KWS management systems, illegal activities like logging, charcoal burning and hunting still occur in the forest (Esther et al., 2014). 13 Wood is the most important energy source for people that live in rural areas in Kakamega
region and even the entire County. More than 87% of the entire population of Kakamega and
Vihiga counties use firewood as the main source of energy and Kakamega Forest is the main source of this wood (KNBS, 2015). However, there insufficient information regarding how much and at what rate local communities are harvesting (both legally and illegally) firewood and other forest products from Kakamega Forest. The objectives of this study were thus fourfold: 1)
Quantify the rate and amount of wood harvested from the Kakamega Forest; 2) Assess differences in wood harvest between KFS and KWS management zones; 3) Determine which, if any wood species are preferentially harvested; and 4) Assess the economic drivers of wood harvest and how far from the forest Kakamega wood is used.
METHODS
Study Area
At the Kakamega Forest, KFS collaborates with local Community Forest Associations to
manage approximately 19,700hectares (~82%) of the forest, and KWS manages the remaining
4300ha (~18% including Kisere forest Fragment). KFS allows the local people to collect fallen
dead branches of trees in specific sections of the forest after purchasing permit. Wood harvesting
from the forest is the primary source of cooking fuel for >95% of people living near Kakamega
forest. Permits can also be purchased to obtain other products from the KFS forest section e.g., cutting grass from the forest glades and grazing livestock in open grasslands. However, cases of illegal extraction of forest products occur in both KFS and KWS forest sections (Guthiga, 2008)
Data Collection
Between the months of March and August 2015, we walked along the forest edge and
forest trails and randomly stopped women leaving the forest with firewood (harvesters) and 14 asked them if we could weigh their wood bundles. We weighed headload bundles at 24 different locations (KFS=21; KWS=3) and recorded their GPS coordinates, date, and time of measurements for each location (Figure 2-1). Time and locations of wood measurements were varied to reduce avoidance bias. Researcher avoidance was common in wood harvest studies because many harvesters lacked permits, harvest restricted species, or cut wood (permits typically only allowed harvesters to gather already felled wood in non-indigenous forest
(Guthiga & Mburu, 2006).
Figure 2-1. Locations of wood measurement sites and wood markets
15 For each headload, we collected data on: wood mass, percent cut wood, percent
indigenous wood, species identification, headload bundle diameter, length and circumference
(Figure 2-2 & 2-3). We measured headload mass by weighing wood harvesters with and without
wood bundles, and subtracted harvester’s body weight.
Figure 2-2. Left: Women carrying wood from the forest, and Right: a field technician
holding wood bundle
Figure 2-3. Left: A woman ready to weigh wood, and Right: Herbert (Eco2librium staff) interviews a wood harvester
16 To reduce inter-observer bias in estimation of percent cut and percent indigenous wood,
we stratified percentages in to six equally distributed bins and recorded the median percentile of
each bin. While we measured wood bundles, a staff of Eco2librium interviewed harvesters asking them: 1) age of wood harvester, 2) family size, 3) number of wood collection trips per week, and 4) home village (Figure 2-3). Eco2librium shared their interview data with us for analysis. From these data we were able to approximate the distances travelled to harvest wood.
For all variables measured, see Appendix B. We followed a subset of measured wood bundles from field sites to households or wood markets. Eco2librium staff interviewed harvesters and wood buyers at wood markets asking: 1) how much they sold or charged for their wood; 2) how they transported their wood to and from markets; 3) if a wood harvest or selling permit was required; 4) what their other sources of income were.
Data Analysis
To determine the percentages of cut vs gathered, indigenous vs non-indigenous, distances walked, and frequencies of wood harvesting. We performed descriptive statistics using RStudio
(R Core Development Team 2015). We used Multivariate analysis of variance (MANOVA) in
SAS 9.4 statistical computer program to assess differences in wood harvesting between KFS and
KWS managed regions. Our input variables for MANOVA were wood mass, distance travelled to collect wood, number of collection trips per week, percent cut wood, percent indigenous wood, diameter of sampled woods. Results are presented in terms of figures, tables, charts, and percentages. 17 RESULTS
Descriptive Results (Wood Measurement)
We measured a total of 270 head bundles (KFS=235; KWS=35) and collected 240 field
interviews around and within Kakamega Forest (Figure 2.1). Mean headload mass for the survey
population was 29.21±14.14kg. On average, wood collectors made 3.18±1.732 trips to the forest
per week to collect wood. Most harvesters (99%) were females aged 6-68 years (median age:
KFS =24; KWS=13 years). We identified 47 different wood species in the headloads (Appendix
C) out of which, 71.6% of which were indigenous species (Figure 2-4).
Percent Indigenous vs non-indigenous wood
100.0 90.0 80.0 70.0 60.0 50.0
%wood 40.0 71.6 30.0 20.0 28.4 10.0 0.0 Indigenous Non-indigenous wood category
Figure 2-4. Percent indigenous vs non-indigenous wood
The most common indigenous harvested wood species was Harungana madagascariensis
(15% of all indigenous species harvested (Figure 2-5(A)). The most common non-indigenous
species harvested was the Guava tree (Psidium guajava), which represented 40% of all nonindigenous wood harvested (Figure 2-5(B)).
18
(A)
(B)
Figure 2-5. Frequency plots of (A)indigenous and (B) non-indigenous harvested wood
19 Statistical Results
Based on wood mass and other variables in wood measurement, there was a significant
difference in wood harvesting between KFS and KWS (MANOVA: F (28, 35) = 29.36, p<.0001;
Wilk's Λ = 0.464; Table 2.1). Wood harvesters in the KFS section of the forest carried heavier
bundles, made more collection trips, and walked longer distances to the forest than those in
KWS. There was a significant difference in mean mass of wood between the KFS and KWS
managed areas with heavier wood bundles leaving the KFS compared to KWS regions (t-test: t
(204) =2.971), p = 0.03). Though both the KFS and KWS management prohibited wood cutting in the forest, our results showed that 24% of the wood from KFS and 5% of the wood from KWS managed areas was cut. Percent cut wood between KF and KWS was significant (F = 5.49, p=0.0199). In addition, the size (diameter) of wood leaving the KWS (average diameter = 5.8cm) area was bigger in KWS than KFS (average diameter = 4.8cm) (Table2.1)
Table 2-1. Results of wood harvest surveys in Kakamega Forest, Kenya
Variable KFS (82%) KWS (18%) F- value p-value
Wood mass(kg) 29.9 ±7.6 22.2±3.8 10.31 0.0015*
Dist. from forest (m) 3045±1395 924±1221 92.38 <.0001*
Median age (years) 24 13 17.55 <.0001*
Trips per week 3.4±0.97 2.4±0.82 8.37 0.0042*
% cut wood 23.5 4.5 5.49 0.0199*
Family size (median) 5.5 6 0.14 0.7070
% indigenous wood 68 95 11.36 0.0009*
Debris diameter (cm) 4.8 5.8 0.85 0.3573
* Indicates parameters with a p-value < 0.05. 20 Wood Markets Results
Similarly, we collected 63 field interviews at 12 wood markets. Wood measurement results were consistent with the results from wood market interviews where Harungana
madagascariensis (25% of indigenous wood species in wood measurement) and Psidium
guajava (33% of non-indigenous species in measured wood) were the highly preferred
indigenous and non-indigenous woods respectively by buyers. There were four ways that wood
was transported to the markets: 1) by foot (73.4%), 2) motorcycles (9.4%), 3) tractors (9.4%),
and 4) bicycle (4.7%). The maximum distance Kakamega Forest wood was transported by foot
was 9km. However, when transported by motorcycles and tractors, Kakamega wood was sold as
far as Kisumu, ~50km from the forest (Figure 2-6).
Figure 2-6. Maximum distance of wood destinations by foot and motor vehicles
21 Wood was transported to the markets by either harvesters or by sellers. At the markets, harvesters sold wood directly to end users or to sellers, who then broke down the bundles by species and size, then collated them into new bundles. Indigenous wood was sold for a premium price and had higher demand compared to non-indigenous wood. For example, at Lubao market, a headload bundle of indigenous wood sold directly by a harvester cost KES ±200 ($±2) whereas a head bundle of non-indigenous sold for ~25% less (KES±150 ($±1.5). The most common wood buyers at the markets were hotels (56%). Other buyers included individuals (41%), schools
(2%), and churches (1%).
DISCUSSION
Our study had four key findings. First, for the first time, we quantified the differences in rate of wood harvest from KWS and KFS managed regions of Kakamega Forest. Second, we observed that at KWS managed area, wood harvesters were significantly younger and made fewer trips to the forest per week. This suggested that people were circumventing KWS prohibition rules and tough penalties on illegal wood harvesting by sending their children into the forest, as children were protected from arrest by law. Third, harvesters preferred to harvest indigenous as opposed to non-indigenous wood species. Preference for indigenous wood was driven by market values as wood buyers paid up to 25% more for indigenous wood. From the author’s long time personal experience in the area, indigenous wood was preferred because it burns consistently for longer time, has nicer aroma, and a higher heat intensity compared to non- indigenous wood. Lastly, Kakamega forest wood was used as far away as the City of Kisumu,
~50km from the forest.
Wood is ranked at the top among the forest products harvested from Kakamega forest due to its availability as a cheap source cooking fuel for most households (Kiplagat, 2008). KFS 22 regulate extraction of forest resources as an incentive for conservation. Given that >52% of
people around the forest were living in extreme poverty with limited or no other sources of
income, controlled harvesting of forest resources could act as a great incentive for forest
conservation if well managed. However, for it to work, the rate of extraction cannot exceed the
rate of regeneration or replacement (Brown, 2002). Maintaining a sustainable level of forest
products harvest is difficult given the area’s rapidly growing population and forest product
demand.
Conversely, KWS prohibit harvesting of wood and other forest products to ensure
minimal disruption of ecological processes in its area. Dead wood is crucial to forest health and ecological processes because it acts as a habitat and breeding site for bird and invertebrate species, and is food for fungi which breaks down wood and returns its nutrients to the ecosystem
(Pfeifer Et al. 2015; Vrška et al., 2015). All these are key processes for natural forest regeneration and maintenance of its diversity (Travaglini et al., 2007).
Our market survey showed that indigenous wood was preferred and sold at a premium
price. This suggested that selective harvest of indigenous wood from the forest was likely to be
driven by high market demand and other factors. Consequently, areas with natural forest and
indigenous trees e.g. KWS area was at high risk of being targeted for wood extraction. To further
complicate matters, many times the people charged with enforcing wood harvesting laws are often implicated in illegal logging/harvesting activities. For example, Mabaruk and Wesangula
(2015) reported that Sandalwood worth KES 20 million (approximately USD $20,000) were impounded on December 12, 2015 while being transported in a police truck by an off-duty police officer. Such blatant disregard for the law by those entrusted with its enforcement was perhaps the most difficult challenge facing Kenya’s forest conservation. 23 Regardless, the findings suggested that the more restrictive laws and harsher penalties were likely to be a better approach for conserving forest biodiversity in Kenya although sustainable alternative cooking fuels for local people would be required. At the same time, since market economies seemed to be driving deforestation, then perhaps market economies could incentivize forest conservation by diversifying local income; thereby, reducing demand for forest resources. For example, projects that use business solutions to address forest conservation, to improve livelihoods, and reduce demand for forest products (Lung & Espira, 2015). Nonetheless, proper integration of economic forest conservation, diversification of economic incentives, and effective forest management are necessary for the protection of Kakamega forest for posterity.
24 CHAPTER THREE: HUMAN USE, BIODIVERSITY, AND FOREST MANAGEMENT:
ANALYSIS OF COUPLED NATURAL AND HUMAN SYSTEM INTERACTIONS IN THE
KAKAMEGA RAINFOREST OF KENYA
ABSTRACT
Despite efforts to protect tropical rainforests as important ecological systems, reliance on
their resources by local people for subsistence continues to be a conservation challenge.
Kakamega tropical rainforest in western Kenya is located amid one of the most densely
populated rural areas. Approximately 82% of the forest area is managed by the Kenya Forest
Service (KFS) in collaboration with the Community Forest Associations (CFA), and the
remaining ~18% of the forest is managed by the Kenya Wildlife Service (KWS). KFS and CFA
allow controlled harvesting of forest products by local people if they purchase a permit.
Conversely, the KWS bans all forms of harvest in the portion of the forest under their
jurisdiction. We assessed the coupled nature of human use of the forest impacting biodiversity
and biodiversity influencing where humans use the forest; specifically, we evaluated two
regional conservation initiatives: tea plantations and wood-efficient cook stoves. To characterize
forest health, timber cruising methodology and pitfall traps were used to assess tree and arthropod diversity respectively. KWS managed region had higher tree species diversity and rare trees than KFS managed areas (mean Rarity Weighted Richness: KWS= 2.4±0.71,
KFS=1.34±1.18; F=5.01, p = 0.03; mean Species diversity: KWS=1.64±0.31, KFS = 0.94±0.71;
F=6.47, p = 0.0158). In addition, rare indigenous trees species were targeted for human harvesting. Results suggest existence of a bidirectional influence of human use on forest
biodiversity, and forest biodiversity on human use. Conservation of Kakamega forest is linked to
social and economic development of the people living near it and therefore, its conservation can 25 be approached through integration of effective management, and enhancing economic empowerment of people living near it through provision of economic conservation incentives, and establishment of appropriate forest conservation interventions.
INTRODUCTION
Despite occupying only 7% of the earth’s total land surface, tropical rainforests sustain over 60% of known terrestrial animal and plant species (Dirzo 2003). Most tropical forests are located in regions with high population densities of poor people who rely on subsistence harvest of forest products to survive (Myer, 1992). Even with protection by state agencies, deforestation and decline of wildlife populations due to human activities continues to be a major conservation concern (Di Marco et al., 2014). The main challenge in managing rainforests in regions with high population is reconciling extraction needs of the local people with conservation interests.
In Africa, the Guineo-Congolian tropical rainforests once formed a continuous belt from the coast of Western Africa, across Central Africa and extending to Western Kenya in East
Africa. Today, the Guineo-Congolian rainforest exists in forest fragments that are surrounded by agricultural fields and human settlements. The rate at which these forests are deteriorating is alarming and there is need for urgent and effective conservations measures (Whitmore &
Bierregaard, 1997).
The Kakamega rainforest is the easternmost remnant of Guineo-Congolian rainforest and the only one of this type in Kenya (Mutoko et al., 2015). The Kakamega forest has been under protection status since 1933, but more than 50% of the forest has been lost over the past few decades, and this deforestation has largely been attributed to increasing population and poverty levels in the region (Bleher et al., 2004; Mitchell, 2014). Moreover, individual land holdings have declined generation to generation as population has skyrocketed, resulting in inadequate 26 harvest that are incapable of meeting local people’s basic needs (personal experience, Kefa).
Consequently, poor families who live near the Kakamega Forest are left with no option but to
harvest forest products to supplement their income. These products include fuel wood, charcoal,
building materials, fruits, mushrooms, traditional medicinal plants, bush meat, grazing, and
timber for making furniture (Kiplagat et al., 2008).
Protection and Conservation of tropical forests is primarily dependent on sound and
effective management practices (Gardner et al., 2009; Myers et al. 2000). Since designation as a
Trust forest in 1933, the Kakamega forest has had several different managements (Althof, 2005).
In 1991, the Forest Department (currently KFS) and the KWS entered into a memorandum of
understanding to oversee management of forests in Kenya whose biodiversity is threatened
(Guthiga & Mburu, 2006). The Kenya Forest Service (KFS) applies inclusive (incentive-based)
approach which allows the local people to legally harvest forest products with the purchase of a
permit at KES100 per month (KFS report, 2012). The Kenya Wildlife Service on the other hand,
applies exclusive (protective) approach which prohibits any form of consumptive utilization of
the forest products (Mburu & Birner, 2007). Restrictive laws used by KWS have been criticized,
because they do not benefit the local people, and instead incentivize people to break the law and
obtain needed forest products illegally (Kiplagat et al., 2008). Similarly, KFS’s inclusive
approach has been criticized because it is perceived to encourage overexploitation of the forest
resources, and likely to result to Tragedy of the Commons scenario in Kakamega Forest (Röhss
2012).
At the Kakamega forest, several management interventions and conservation initiatives
have been established to reduce human dependence on forest resources (Esther et al., 2014). In
1984, the World Bank funded the Nyayo Tea Zone Development Corporation (NTZDC), a semi- 27 autonomous government organization which planted a ~100m wide band of tea around KFS managed regions of the forest with the objectives of protecting the forest from human encroachment and over exploitation; and to provide an alternative source of income through employment in tea plantations. Another conservation initiative is implemented by Eco2librium
Company and it involves installation of ceramic stoves that are based on traditional 3-stone cooking method, but are 1.5-3 times more efficient in their wood usage. The goal of these projects is to promote conservation of Kakamega Forest.
Previous studies have used tree and arthropod species diversity and richness to characterize forest health (Lattin, 1993; Maleque et al., 2009). Arthropods are an ideal suite of species to use to measure human impacts on forests because they: have a relatively stable taxonomy, have high species richness, occur in most terrestrial environments, are readily available and easy to collect, and are sensitive to environmental changes (Kotze 2011). In this study, we assessed tree and arthropod species diversity and richness as bio-indicators of forest health and used them to characterize the status of forest health. We also evaluated if type of forest management, and conservation interventions have impacted forest conservation. Thus, the objectives of this study were threefold: 1) Assess tree and arthropod species diversity and richness of Kakamega Forest; 2) Determine if there are differences in biodiversity between KFS and KWS managed regions, and 3) Assess if conservation interventions (Forest management, energy efficient cook-stoves, and presence of tea plantations) impact forest biodiversity.
METHODS
Study Site
This study took place in the Kakamega tropical Rainforest in western Kenya. The
Kakamega Forest spans 23,000ha of Kakamega and Vihiga Counties. The Kakamega Forest lies 28 between 0°10′ and 0°21′ North and longitudes 34°47′ and 34°58′ East, and has a varied topography with altitude ranging between 1,250m-2,000m above the sea level (Tsingalia 1988).
Kakamega forest receives annual rainfall of 1,500-2,000mm per year, with two rainy seasons: long rainy season that occurs between March and June, and the short rainy season between July and October (Esther 2014). November through February are typically dry months. The mean maximum and minimum annual temperature ranges between 280C-320C and 110C-130C respectively.
The Kakamega Forest currently exist as six fragments. The largest forest fragment (main forest block) covers an area of 8,537 ha. Other fragments include Ikuywa (1,370ha), Yala
(1,199ha), Kisere (420ha), Malava (190ha) and Kaimosi (132ha) (Mitchell 2004). These forest fragments have been separated from the main forest block for at least 35 years (Mitchell 2004).
Approximately 1,592 ha of the forest is non-indigenous plantation (Guthiga & Mburu 2006). The
KFS is responsible for management of approximately 19,600 ha (82%) of the forest whereas the
KWS manages approximately 3,700ha (18%) of the forest, including the Kisere Forest fragment.
The Kakamega forest is considered a biodiversity hotspot with several endemic plants and animal species. The forest harbors 20% of Kenya’s butterflies (>400 species), >400 species of birds, 7 species of primates, 36 known species of snakes, of which many are of West African origin (Wagner et al., 2008), and over 400 species of plants, 150 of which are trees (KIFCON
1994). Thus, the forest has the third highest priority ranking for conservation among forests in
Kenya by the International Union for Conservation of Nature (Wass, 1995).
The Kakamega forest is situated amid one of the most densely populated rural areas in
Kenya (542 people/km2), with >52 % of the people surviving on <$1USD/day (KNBS, 2015).
Most of these people rely directly on harvesting forest products for their subsistence (e.g. >90% 29 households around the forest use wood from the forest for fuel) (Bleher et al., 2004). Other forest
uses include charcoal, timber, medicine, meat, fruits, honey and grass for thatching houses and
for livestock fodder (Ouma et al., 2010). Regions adjacent to the forest have high agricultural
potential due to good climate and good soil conditions, but owing to high population, the
agricultural land is divided in to small parcels which people grow food crops on (e.g. maize and
beans) for household consumption and for sale at local markets. The two main cash crops in the
region are tea and sugarcane farming (Diwani et al., 2013).
Sampling Procedure and Data Collection
We collected field data from March to August 2015. The three categories of our data included:
(1) Forest Biodiversity (2) Human use of the forest and (3) Land management outside and inside
the forest
Forest Biodiversity
To measure forest biodiversity, we divided the Kakamega Forest into 2km by 2km square grids, and assigned a unique identification number to each grid. We then selected a random point in each grid and chose a subset of 36 grids (60% of the forest area) for sampling. We chose sites to ensure interdespersion of forest types between KFS and KWS forest areas, but otherwise were randomly selected. At each sample point, we sampled 0.1 ha (18m radius) plots. Using this sampling approach, we sampled 2% of the total forest area (Figure 3-1). We used Garmin 72h
handheld GPS units and compass to locate sampling sites in the forest. 30
Figure 3-1. Map of the study area and Sampling locations
At each 0.1 ha (~18m radius) plots, we conducted a standard point sample timber cruise following standard USDA/FS timber cruising guidelines (Hovind, & Rieck, 1970). Using 10mm wedge prism, we identified trees that we measured by selecting trees whose stems appeared to 31 overlap with the main tree stem at breast height when viewed through the prism (Hovind, &
Rieck, 1970). For each of the selected trees, we identified the tree species, measured diameters at breast-height (DBH) which we standardized at 1.5m above the ground (Figure 3-2a). At each site we also measured litter depth at four random locations, Visual Obstruction Readings (VOR) in each cardinal directions via a density board (Figure 3-2b), estimated percent coarse woody debris, and measured the diameter of all coarse woody debris >4cm diameter (Table 3.1). For a summary of all timber cruise and site specific covariates, see Appendix D).
Figure 3-2. (a) Left: measuring tree DBH and (b) right: Reading Visual obstruction (VOR)
In addition, we sampled arthropods at each site via pitfall traps (Southwood, 1978). We
laid 25 pitfall traps for ground dwelling arthropods at a spacing of 1m along straight random
bearing from the center of our timber cruise location. Each trap consisted of 2 stacked plastic cups (18oz) buried in the ground with the cup’s mouth level with the soil surface (Larsen &
Forsyth, 2005). We used two cups so that the top cup containing the samples could be easily 32 removed and replaced again after collection. We opened drainage holes at the bottom and sides of exterior and internal cups respectively to allow drainage of excess water especially during rains. The top cup was partially filled with water to prevent trapped arthropods from escaping.
We checked the traps and removed trapped individuals daily for three consecutive days. We preserved all trapped specimens in 50ml storage vials containing 95% ethanol. With the help of
Kenya National Museums (NMK) staff, we sorted, identified and counted all specimens at the
Kenya National Museums (NMK), and preserved a subset for genetic analysis.
Table 3-1. Timber cruise Variables
Variable Description How collected
Tree ID Genus and species name of trees Guidebook (Dalitz, 2011)
Distance in m from center of sampling Rangefinder (>5m), or Distance to tree location to measured tree tape measure <5m
Tree DBH Tree diameter at 130 cm above ground DBH meter
Litter depth Depth of litter at 5 locations Ruler
Woody debris diameter Diameter of samples of woody debris DBH meter
VOR Measurements in 4 cardinal directions. Density board
33 At each sampling location, we also collected standard environmental covariate data (Table3-2).
Table 3-2. Environmental Variables
Variable Description How collected
Time (EAT, Time zone :GMT+3 Garmin 72MP GPS
Date Date of measurement mm-dd-yr
GPS Coordinates (UTM, Zone 36N Garmin 72MP GPS
Temperature (temp) Degrees Celsius (0C) Kestrel 4500NV
Wind speed Meters per second Kestrel 4500NV
Elevation (Elec) Meters above sea level Kestrel 4500NV
Humidity (Hum) Percentage (%) Kestrel 4500NV
Barometric pressure (Bar) hectoPascals (hPa) Kestrel 4500NV
Noise dBC; Low (30-80dB), Slow Sound Pressure Level(SPL) meter
34 Characterizing Biodiversity of the Forest
To characterize forest biodiversity, we calculated five indices using the collected timber
cruise and arthropod data: 1) Rarity Weighted Richness (RWR); 2) Shannon diversity index; 3)
Evenness index; 4) Simpson’s index of diversity; and 5) Species richness.
Rarity-Weighted Richness (RWR) index (Albuquerque & Beier, 2015) was used to
identify the relative rarity of trees species at each sampled location.
Where ci is the number of sites occupied by species i, and the values are summed for the n
species that occur in that site. We calculated the RWR by first scoring each species as the inverse
of the number of sites where it occurs. A species found in only in one site receives the maximum
score (1.0), whereas a species that occurs in all the 36 sites received a lower score (1/35 or
0.029). The RWR score for any point then is the summation of all individual scores for all
species occurring at a site.
Human Use of the Forest
We used wood survey data and tree measurement data (timber cruise) to create a
selectivity score which is analogous to a chi-square for assessing if there is preference of
particular species of wood for use by humans, and how these preferred species are distributed in
the forest. To create selectivity score, we used wood survey data as observed values and tree
count data from timber cruise as expected values. We calculated species relative frequency
(freq) of occurrence by dividing the total number of trees (n) for each species at the site by the
total number of trees for all species (N) at the site (timber cruise data). We repeated the same procedure using wood measurement data to get the relative frequency of species observed for wood harvesting. 35 We stratified these frequencies separately for indigenous and non-indigenous species; and also for KFS and KWS forest sections. To calculate selectivity score for each species, we subtracted the observed frequency values (obs freq) from the expected frequency values (exp freq) for each species. The resulting values ranged between -1 to +1, where negative values represent less preferred species and positive values are preferred species. We further calculated average selectivity score for each sampling site by multiplying the total number of counted trees at the site by the corresponding species selectivity score, summed them up, then divided the result by the overall total number of trees counted at the site.
Where n is all species at the site, obs freq refers to observed frequency values for wood harvest; and exp freq is the species’ expected frequency values (timber cruise data); and N is the total number of trees counted at the site.
Land Management Outside and Inside the Forest
To assess the impacts of forest management on biodiversity, we considered three land management interventions which aim to protect and conserve the Kakamega Forest. First, which management agency has jurisdiction over the forest, KFS or KWS? Second, Presence/absence of tea belts (can a site be reached with or without walking through a tea belt?). Third, the number of stoves installed within 9 km buffer of the sampled site. We used a 9 km buffer as it represents the furthest distance travelled by wood harvesters to collect wood from the forest.
SPATIAL DATA ANALYSIS
Edge Effect
We assessed edge effects to determine if species richness, species diversity, and human use of the forest change as a function of distance from the forest edge. We first calculated 36 Euclidean distances of each of our random sampling points to the nearest forest boundary, then plotted graphs of these distances against our response variable (calculated species diversity and richness indices). We visually inspected each plot to identify asymptotic thresholds in the response factor to disturbance from the forest edge where impacts likely occurred. Based on these distances, we categorized the regions of the forest into three zones: (1) Forest Edge, (2)
Transition zone, and (3) Forest Interior (Figure 3-3). The Edge represents a region where impacts on the forest from the neighboring human settlements is high; the transition zone represents a region with an intermediate level of influence, and 3) The Core zone represents natural forest with little disturbance. We calculated the edge depth for KFS and KWS separately.
Figure 3-3. Edge effect distance calculation
Kriging Interpolation
We used ordinary Kriging interpolation to create surfaces for each of our response variables so as to visually explore interspersion and juxtaposition across the forest. Kriging was the most appropriate method over other methods of spatial interpolation because kriging yields 37 best cross-validated accuracies by accounting for the intra-class variability of the response variables (Hernandez-Stefanoni & Ponce-Hernandez, 2006). We created kriged surfaces for species diversity index (trees and arthropods); Average DBH (trees), and RWR (trees and arthropods). We also used kriging to create a surface of selectivity scores as a spatially explicit suitability or desirability index of where in the forest humans ought to prefer to harvest wood based on harvest data.
Normalized Difference Vegetation Index (NDVI)
Normalized Difference Vegetation Index (NDVI) which is a measure of greenness was used to evaluate greenness of forest vegetation. NDVI is a remote sensing index that gives a measure of greenness or vegetative cover of the area. NDVI is based on the spectral properties of green vegetation. Vegetation differs from other land surface as it reflects more in the near infrared wavelength and absorbs more in the red wavelength. NDVI uses red and infrared wavelengths to compute the greenness of vegetation. NDVI values ranges between -1 and 1.
High positive values of NDVI correspond to the actively growing dense vegetation cover.
Negative values on the other hand are associated with bare soil, snow, clouds or other non- vegetated areas. (Pettorelli et al., 2014). NDVI is calculated by the equation:
NDVI = where NIR is the near infrared
Landsat 8 OLI data acquired on 8th March, 2016 was used for NDVI calculation. Computation of NDVI was done using ENVI computer software version 5.2 (Exelis Visual Information
Solutions, Boulder, Colorado).
Unsupervised Iso-Cluster Classification
We then used an unsupervised Iso-Cluster Classification to cluster raster layers generated by Kriging interpolation to create a prediction surface of conservation priority areas of 38 the forest. The Iso-Cluster analysis identified those areas where we had high conservation value
(high tree diversity, large trees, high arthropod diversity) and high risk of human use (areas with high desirability to harvest). Areas with all high values, i.e. those areas where we had high endemism and a high potential for human harvesting were determined to be high priority areas for conservation action. We set 4 output classes this would yield four levels of conservation priority. All spatial analyses were performed in ArcMap 10.2 (Environmental Systems Research
Institute, Redding, CA).
STATISTICAL DATA ANALYSIS
To assess the influence of forest management and human activities on forest metrics, we used multivariate multiple regression in the computer program R (R-Core development team,
2013) to assess which variables influenced forest biodiversity. Prior to running any analyses, we tested for Multivariate Normality for both Trees and Arthropod Data. We created two independent regression models: one for trees and one for arthropods. For each of the regression models, we used values for RWR, Shannon Weiner index and species richness as the response variables. We regressed these against 13 predictor variables. We used backward stepwise variable selection methods to identify significant variables that we retained for the final model.
Multivariate multiple regression was appropriate for this analysis due to the nested nature of our variables, the presence of several response variables for each model, and also because some of these variables were both response and predictor variables in our two models (Rencher, 2002).
We used Multivariate Analyses of Variance (MANOVA) in SAS (Statistical Analysis
Software, Inc.) to determine if there were significant differences in tree and arthropod species richness, species diversity and RWR between KFS and KWS regions. We used Tukey's 39 Studentized Range Test to check for significance differences between KFS and KWS for each variable.
RESULTS
Descriptive Results
We sampled a total of 36 random locations but dropped one location in our analysis as it was an outlier (occurred in the glade). We therefore report on 35 locations [KFS=28(80%);
KWS=7(20%)] across the entire Kakamega Forest, which means we sampled both management units in proportion to the area of the forest they are responsible for. Sampling was randomized so that KFS and KWS would be sampled at an intensity proportionately equal to the area managed.
We counted a total of 43 tree species across the 35 sampling locations (indigenous = 39 and non- indigenous 4 species). All 43 species were observed in KFS managed forest (indigenous=39; non-indigenous =4) and 22 species were observed in KWS managed forest (indigenous =22 and non-indigenous = 0). For list of species identified, see Appendix E. We counted a total of 2,272 arthropods (KFS=1,863; KWS=409) belonging to 17 orders and 127 species.
Spatial Analysis Results
The KFS had a larger edge effect distance (Forest edge = 983±121m) compared to KWS
(Forest Edge=867±137m). Similarly, the transition zone in the KFS managed area was also wider compared to the KWS region. The distance to the forest interior was larger in KFS
(>2,100m) compared to KWS areas (>1,483m). The core area of the KFS forest was 6,330 hectares which represented 32.3% of the total area managed by the KFS. On the other hand, the core area of the KWS managed region covered 1,680 ha, representing 38.2% of the KWS area
(Figure 3-4). Of the total edge areas of 8,010ha, 79% was found in KFS and 21% was found in
KWS managed areas. 40
Figure 3-4. Edge effect analysis
Kriging Interpolation Results
By visual assessment of raster images created by Kriging interpolation, the region of the forest starting from the center of the forest to the northeastern part of the forest had higher species diversity and species richness (Figure3.5). Coincidentally, regions that had high tree species diversity and species rarity corresponded with areas that showed higher selectivity index.
In the KFS managed region, areas of high tree species diversity, rarity and big trees were located at the interior of the forest (core forest) whereas in the KWS region, high tree species diversity, rarity and big trees appeared to be distributed evenly (Figure 3.5). These results were consistent with edge effect analysis where KFS had a larger edge impact area (Figure 3-4). Southern part of the forest had lowest concentration of species diversity, rare species and big trees (Figure 3-5). 41 Kriging results further showed that there is high diversity and rarity of arthropod species in the
Eastern part of the forest which is located in the KFS managed region (Figure 3-6). These
regions also showed relatively high tree species diversity.
NDVI also showed that core zone of the forest and a large portion of KWS managed area had higher NDVI compared to areas at the forest edges and the southern part of the forest (Figure
3-6(f)).
42
(a) (b
(c) (d)
Figure 3-5. Kriging interpolation: (a) tree RWR, (b) Tree species richness, (c) Tree species diversity, and (d) Selectivity index
43
(e) (f)
(g) (h)
Figure 3-6. Kriging interpolation images: (e)Tree DBH, (f) NDVI (g) Arthropod RWR and
(h) Arthropod species diversity
44 Statistical Analysis Results
KWS had overall higher mean values for tree attributes including DBH (KWS
=68.208±30.445; KFS=42.089±25.413), RWR, species richness, and species diversity compared to the KFS managed region. In addition, the mean selectivity score for KWS (-0.360±0.353) region was higher than for KFS (-0.392±0.964) (Table3-3).
Table 3-3. Mean values of the calculated indices and other variables
Variable KFS: N=28(80%) KWS: N=7(20%)
Trees DBH(cm) 42.089±25.413 68.208±30.445
Trees RWR 1.344±1.181 2.393±0.708
Trees Shannon Weiner index (H’) 0.938±0.706 1.639±0.307
Trees Evenness index(E) 0.631±0.421 0.942±0.054
Trees Species richness(SR) 3.464±2.285 5.857±1.574
Trees Simpsons diversity index (1-D) -0.685±0.347 -0.697±0.177
Litter (cm) 4.104±1.889 4.679±1.118
Arthropods RWR 3.567±2.270 2.943±0.351
Arthropods 1-D -0.506±0.395 -0.697±0.177
Arthropods H’max 2.545±0.430 2.464±0.220
Arthropods H’ 1.942±0.512 1.851±0.243
Selectivity index -0.392±0.964 -0.360±0.353
Stoves 3156±1491 4037±506
Neither tree nor arthropod datasets violated multivariate normality. Our regression analysis retained nine predictor variables for trees, and seven variables for arthropods (Table3.4). 45 Table 3-4. βeta - values of predictor variables (DF = 1; num DF=3; den DF =23)
Model 1(Trees) β - values
Variable Wilk's Λ F-value FR FD RWR Pr(>F)
(Intercept) - - -2.176 -0.3662 0.2662 -
Dist. to edge 0.307 3.39 0.0002 0.0001 -0.0001 0.035
Selectivity 0.194 1.84 -0.494 -0.1484 -0.032 0.167
Noise 0.178 1.67 0.019 0.0068 0.0116 0.202
Arthropod H’ 0.204 1.97 -0.787 0.1041 -0.3873 0.147
Arthropod Hmax. 0.167 1.54 1.879 0.2089 1.0588 0.232
Arthropod 1-D 0.677 16.04 -5.478 -1.4244 -0.1523 <0.001
Forest type 0.685 16.69 -1.126 -0.6571 -2.113 <0.001
Dist. edge*Forest type 0.186 1.75 0.001 0.0002 0.0003 0.186
Arthropd 1-D*Forest type 0.230 2.29 3.942 0.6106 -0.1317 0.105
Model 2: Arthropods Wilk's Λ F-value AD AR RWR Pr(>F)
(Intercept) - - 2.613 20.9169 6.3105 -
Presence of Tea belt 0.291 3.43 -0.524 -5.5796 -1.9781 0.032 stoves 0.221 2.37 -0.0001 -0.001 -0.0006 0.095
Distance to edge 0.215 2.28 -0.0002 -0.0017 -0.0003 0.104
Selectivity 0.222 2.38 -1.537 -7.7194 -2.6933 0.093
Woody debris 0.192 1.98 0.012 0.0869 0.0251 0.143
FR 0.319 3.90 -0.07 1.4282 0.8418 0.021
FD 0.328 4.07 0.522 -1.9546 -1.4474 0.018 46 Distance to edge variable predicted both trees diversity, richness and rarity (Wilk’s Λ
=0.307; p=0.035) as well as arthropod diversity, richness and rarity (Wilk’s Λ =0.215; p=0.104).
The βeta-values associated with selectivity across all response variables were negative (Table
3.4). Presence of Tea plantations and stoves variables were not statistically significant in both
trees and arthropods model (Presence of tea: Wilk’s Λ =0.215, F = 3.43, p=0.032; and Stoves:
Wilk’s Λ =0.221, F = 2.37, p=0.095).
The maximum observed RWR for trees in KFS managed areas (RWR at 138m =2.0) was approximately equal to the minimum observed tree RWR in KWS (RWR at 467m=3.2417)
(Figure 3-7). The KFS managed areas had higher observed arthropod RWR values than the KWS
areas (Figure 3-8)
Figure 3-7. Scatter plot of Distance to edge Vs RWR (trees)
47
Figure 3-8. Scatter plot of Distance to edge Vs RWR (arthropods)
Conservation Priority Areas of Kakamega Forest
KWS managed region had higher mean tree species diversity and rare trees than KFS managed areas (Rarity Weighted Richness: KWS: mean = 2.39±0.71, KFS = mean=1.34±1.18;
F=5.01, p = 0.032; Species diversity: KWS: mean =1.64±0.31, KFS: mean= 0.94±0.71; F=6.47, p = 0.016). However, these differences were not significant (MANOVA: Wilk's Λ = 0.68, F (6,
28) = 2.47; p = 0.072) (Table3.4)
Table 3-5. Tukey's Studentized Range (HSD) results for individual variables
Variable Mean(KFS ) Mean(KWS) F- value p-value
Trees RWR 1.344 2.393 5.01 0.0321*
Arthropods RWR 3.567 2.944 0.51 0.4785
Trees SR 3.464 5.857 6.79 0.0136*
Arthropods SR 13.750 12.000 0.78 0.3847
Trees H’ 3.464 5.857 6.47 0.0158 *
Arthropods H’ 1.942 1.851 0.21 0.6514
N=35: KFS=28(80%); KWS= 7 (20%), * Indicates parameters with p-value < 0.05. 48 Similarly, Unsupervised Iso-Cluster classification of Kakamega Forest that was based on our forest health metrics and selectivity of species for human use showed that the forest area which was classified as high priority for conservation (high species richness, species diversity, high species rarity, and high human harvest preference) covered approximately 5,030ha (21.1% of the total forest area). This high priority area was located in both the KFS and KWS managed regions. About 2,675 ha (~53 %) of this high priority region was located within the KWS managed area and the remaining 2,357ha (47%) was located in the KFS region. Approximately
72.1% of the KWS managed area and approximately 9.8% of the total area under KFS jurisdiction were classified as very high priority conservation region (Figure 3-9).
49
Figure 3-9. Conservation priority regions of Kakamega Forest
50 DISCUSSION
We found that KFS managed area had lower tree diversity than KWS areas. Visual exploration of our kriging interpolation images of tree species diversity, rarity weighted richness and size of trees showed that the western and southern parts of the forest (which were under
KFS) had poor forest health status. These results were consistent with our edge effect analysis which also showed that KFS had a larger edge distance compared to the KWS managed area.
Status of forest health is associated with levels of forest destruction or extraction of forest products. Similarly, greater edge area was an indication of high human activities in these areas.
Unsustainable human use of the forest is one of the main factors that lead to loss of forest. In the
KFS managed area, local people are allowed to extract forest resources with the purchase of a permit. When extraction exceeds regeneration, then forest health status is impacted negatively.
Therefore, poor forest health status in KFS regions of the forest could be attributed to higher rate of extraction of forest products than they can be replaced. Human activities slows down seedlings recruitment, and natural forest regeneration (Farwig et al., 2008). Extraction of forest resources can also be heightened by the pressure from high population density in the neighboring areas hence increased demand of forest products. Previous studies have pointed out KWS region as being in a natural state compared to KFS and have attributed this to the exclusive management system practiced by the KWS (Guthiga & Mburu, 2008).
Despite KWS managed area having higher tree diversity, higher species richness, higher tree species rarity, and big trees compared to KFS, we found out that the KFS area had high arthropod diversity and richness. Even though the differences in arthropod diversity were not significant, these finding were inconsistent with previous findings that have reported KWS to have higher overall biodiversity compared to KFS. We attributed this inconsistency to a 51 limitation in our arthropod survey whereby most of arthropod sampling in the KWS area took
place during the dry spell while we waited for the clearance of permits to access KFS region. At
this time, most ground dwelling arthropods may not have been active due to extreme weather
conditions.
We found that both stoves and tea belts conservation initiatives did not positively impact
trees and arthropods. Myclimate (2015) report showed that by the end of 2014, the stove project had installed >24,000 energy efficient cook-stoves in households adjacent to the Kakamega
Forest, which translated to saving 100,000 tons of firewood or equivalent of 250ha of rainforest.
This showed a significant reduction of the total amount of wood harvested from the forest. Our finding could be explained in two ways. First, stove variable used in our analysis was based on the stove density within 9km radius of our sampling point, and was therefore a function of the number of households present hence population density. In addition, with a population growth rate of 2.12% and 3.3% for Kakamega and Vihiga counties respectively, the population was expected to increase (KNBS, 2009). This suggested that even though the stove project had reduced per capita wood consumption, the overall impact of population density on forest in those regions was still high. Second, because wood harvesting from Kakamega forest was influenced by market forces, therefore, a larger portion of what people harvest from the forest was being
sold to increase household income. Overall demand for wood was expected to increase with the
increasing population in the area.
The presence of tea plantation belts around the KFS managed area could only be creating
a physical barrier limiting encroachment of agricultural cultivation in the forest in regions where
they existed. The negative βeta - values associated with tea belt presence across our forest
biodiversity response variables in our regression analysis suggested that tea belts could be 52 increasing human use of the forests in forests adjacent to them. Previous studies have reported
that tea belts did not effectively prevent local people from entering and harvesting forest resources due to considerable use of the forest and high trail densities in the forest regions that border Nyayo tea zones (Lung et al., 2008; Cords, 2012; Esther et al, 2014).
We found that areas with high conservation value (in terms of large trees, high diversity) overlapped with areas of high selectivity. Thus we defined a high conservation priority area
(Figure 3-9). When we evaluated this highest conservation priority area, we found that only
~21% of the forest was classified as high priority, with 53% of the highest priority area being in
the KWS controlled area. Since this area had higher tree species diversity, and rare trees, it was
most likely to be targeted by humans for extraction of forest products, especially in the KFS
region where harvesting was legally allowed with the purchase of a permit. Our high
conservation priority area highlights the coupled nature of this system. Restricting KWS
management and other conservation interventions seemed to have helped create a zone of high
conservation value area, however this also created a zone with high desirability for harvest
because of a selective preference for rare or indigenous species which sell for a higher price.
This suggested that perhaps the exclusive management approach of KWS was the most effective
way to protect indigenous forest and rare species of trees.
The implication of our work was that conservation of the Kakamega forest was likely
linked to sustainable economic development across the region. The forest is an important
resource for the local people as it provides essential products and services. However, people
seemed to be overexploiting it from a legal and conservation stand point (DeBann 2003).
Therefore, one of the ways that may help to conserve the forest is by creating economic
incentives that can provide financial support, improve people’s livelihoods, or compensate them 53 for their conservation of the forest. Payment for Ecosystem Services (PES) schemes can be used
to incentivize local people not to use certain forest products (or specific species of trees) but
promote their conservation, creating a win-win synergy between conservation and economic empowerment (Turner et al., 2012). Total ban of consumptive use of the forest resources is perhaps the most efficient and quick to apply method of protecting and conserving indigenous and rare forest biodiversity. However, the former will provide a long term solution to conservation challenges though it requires longer period of time to mobilize resources
54 CHAPTER FOUR: CONCLUSION
The Kakamega forest is not only important for conservation of biodiversity but also for
providing ecosystem services and forest products to people. High population density, high
poverty levels and high unemployment among people living in regions surrounding the
Kakamega forest are among the main factors that contribute to forest degradation, as dependency
on natural resources is linked to poverty. Therefore, an understanding of the rate of extraction of
forest resources, distribution of these resources in the forest, and factors that drive resource
extraction is important for helping forest managers to make informed management and
conservation decisions.
This study set out to investigate the impacts of human use of the forest on forest health, specifically how bioenergy extraction from the Kakamega forest is impacting forest biodiversity.
The two broad objectives of this study as described in chapter one were: (1) to quantify the rate
of subsistence wood harvest from the Kakamega tropical rainforest, and (2) to assess the impacts
of human use of the forest on biodiversity. This study successfully quantified wood harvesting in
Kakamega Forest and stratified the harvest between KFS and KWS managed areas. The study
further assessed how wood harvesting and management interventions impact biodiversity and
health of Kakamega forest. This chapter will briefly review these two objectives and give
conclusions.
Objective 1: Quantifying the Rate of Subsistence wood harvest from Kakamega
Rainforest. This study gives a report on rate and frequency with which wood is extracted from
KFS and KWS managed areas. Majority of wood harvesters were women and girls, and this is
influenced by culture and traditions of people in the area. Indigenous wood was preferred for
harvesting compared to non-indigenous wood as indigenous wood had higher demand at the 55 markets and also it sold at a higher price. The end users of wood from Kakamega forest were not
only households near the forest but also hotels (restaurants), schools, churches and individuals in
urban areas. The study further documented how the differential value of indigenous and non-
indigenous wood species were possible drivers of human wood harvest practices within the
forest. In addition, Kakamega Forest wood was used up to 50Km away from the forest, a much
greater distance than previously thought or reported.
According to results, preference for indigenous wood was influenced by high demand
and high prices attached to indigenous wood. Based on the researcher’s(Kefa) local knowledge,
people also prefer indigenous wood because it burns for a longer period of time with consistent
heat which is ideal for cooking some of the local foods e.g. arrow roots and sweet potatoes. Also,
indigenous wood produces nicer aroma during burning.
Objective 2: Assessing how human use, management of the forest, and conservation
interventions impact forest biodiversity. We used the standard USDA/FS point sample timber
cruising methodology and pitfall trapping to assess tree and arthropod species diversity
respectively, as bio indicators of forest health. We evaluated the differences in diversity and
distribution of tree and arthropod species between the KFS and KWS management regions of the
forest. By using spatial analyses, specifically ordinary Kriging interpolation, we created raster surfaces for each of the response variables to visually explore interspersion and juxtaposition of our calculated biodiversity metrics across the forest. Then, we used Iso-Cluster Unsupervised
Classification to cluster raster layers generated by Kriging interpolation to create a prediction surface that shows areas of priority conservation value. Based on spatial analyses results, regions that had rare and indigenous trees coincided with regions that were targeted for harvest by 56 humans, and this suggested a bidirectional impact of human use of the forest on biodiversity and
the influence of biodiversity on human use of the forest.
With regard to presence of tea plantations as a conservation intervention, our findings were consistent with what previous studies had reported that the presence of tea belts was not significantly enhancing forest conservation. Tea plantations were ironically perceived as a restrictive barrier for wildlife and plant species than for local people, as regions with tea plantations were reported to have considerable use of the forest as evidenced by high trail densities (Esther et al., 2014; Lung et al., 2008). Stove project did not impact forest trees and arthropods. Stove density variable was a function of the number of households hence proportional to the population density. Even though the stove project could have reduced per capita wood consumption, the overall impact of high population on forest was still high. Also, a large portion of wood saved from household usage was sold to increase household income.
Establishment of schemes that can provide financial incentives to stop forest destruction is recommended to help in forest conservation. In addition, placing the high priority for conservation forest region under strict management preferably exclusive management system is recommended to safeguard the rare indigenous trees in those regions.
STUDY LIMITATIONS
Due to limited time, most of the data were collected during the long rainy season. Field
data collection started in March, only about three weeks before the onset of long rains.
Therefore, the dataset did not represent both dry and wet seasons adequately. Second, there was a
delay in acquisition of access permits to KFS forest section hence field data collection exercise
during the first three weeks of study (dry spell) took place in the KWS section. This was more
likely to create a bias in the data. 57
SUGGESTIONS FOR FUTURE RESEARCH
The findings of this study raised a number of questions that could be addressed in future.
First, since this study focused only on Kakamega forest in western Kenya, similar studies can be extended to other forests in the region (North and South Nandi forests) and even nationwide.
Findings from this study (Kakamega forest) and other regional forests can provide adequate knowledge necessary for creation of proper management framework and informed management decisions.
Second, remote sensing technology is a useful tool in monitoring forest biodiversity. A study that can examine the use of NDVI to monitor forest biodiversity is recommended.
Third, I observed that most women and girls carry heavy headloads through long distances. For example, a case where a 41-year-old woman, fending for a family of seven people, her body weight is 45kg carried ~ 69kg of head bundle through a distance of 2 km ~3 times every week. A social study that can examine the impact of wood harvesting on women’s health, and girl-child education is recommended for future research work.
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63 APPENDICES
APPENDIX A: ADDITIONAL MATERIALS
In the course of this study, we collected camera trapping data which were not been used for analysis in this thesis. These data were collected by deploying Bushnell HD game cameras along trails and at random points throughout the forest to record human activity at a location continuously for 3days. The goal was to use the data together with wood measurement data to characterize human use of the forest. The data were collected at 29 locations around the forest.
Unfortunately, two cameras were stolen in the forest and this interfered with continuity of this exercise.
64
APPENDIX B: SUPPLEMENTARY TABLE OF WOOD MEASUREMENT
VARIABLES
No. Variable Description 1. id Counting variable for individual wood measurement delimiter 2. Trail ID Trail id where wood measurement was taken--grouping variable defining number of surveys 3. UTM easting Measured using Garmin GPS 72H. Datum: (WGS 1983, Zone 36N) 4. UTM northing Measured using Garmin GPS 72H. Datum: (WGS 1983, Zone 36N) 5. Management Government Management agency in charge of the forest area where sampling was conducted 6. Date Date when the wood measurement was taken 7. Time Time of day wood measurement was taken(EAT - +3.00) 8. Length Length of wood bundle in cm 9. Width Width of wood bundle in cm 10. Circumference Circumference of wood bundle in cm 11. Ordinal Original ordinal value recorded for % indigenous wood in the bundle indigenous 12. Wood mass Wood mass in kilograms 13. Wood diameter Average diameter of sample woods in the head bundle 14. % indigenous Converted % indigenous wood in the bundle based on the median value of the range of percentages covered by the ordinal rank value 15. exotic Converted % exotic wood in the bundle based on the median value of the range of percentages covered by the ordinal rank value 16. Ordinal exotic Original ordinal value recorded for the % exotic wood in the bundle 17. Ordinal cut Original ordinal value recorded for the % cut wood in the bundle 18. % cut Converted % cut wood in the bundle based on the median value of the range of percentages covered by the ordinal rank value 19. Gender Gender of the wood collector. Either male or female 20. Age Age in years of the person carrying wood 21. Marital status Marital status of the wood harvester 22. village Name of the home village of the wood harvester 23. Distance Distance from the forest in km 24. Trips Trips per week 25. Family Size of the family
65
APPENDIX C: WOOD SPECIES IDENTIFIED IN WOOD SURVEY
No Wood species type 1. Acacia Abyssinia Indigenous 2. Acanthus spp Indigenous 3. Albizia grandibracteata Indigenous 4. Albizia gummifera Indigenous 5. Antiaris toxicaria Indigenous 6. Bischofia japonica Non-indigenous 7. Blighia unijigata Indigenous 8. Bridelia micrantha Indigenous 9. Celtis africana Indigenous 10. Celtis durandii Indigenous 11. Cordia africana Indigenous 12. Craibia brownii Indigenous 13. Croton macrostachyus Indigenous 14. Croton megalocarpus Indigenous 15. Croton silvaticus Indigenous 16. Cupressus lusitanica Non-indigenous 17. Diospyros abyssinica Indigenous 18. Dombeya spp Indigenous 19. Eucalyptus saligna Non-indigenous 20. Ficus exosperata Indigenous 21. Ficus lutea Indigenous 22. Ficus sycomorus Indigenous 23. Funtumia africana Indigenous 24. Harungana madagascariensis Indigenous 25. Lantana camara Non-indigenous 26. Maesa lanceolata Indigenous 27. Maesopsis eminii Indigenous 28. Markhamia lutea Indigenous 29. Olea capensis Indigenous 30. Persia americana Non-indigenous 31. Pinus patula Non-indigenous 32. Pittosporum manii Indigenous 33. Polyscias fulva Indigenous 34. Prunus africana Indigenous 35. Psidium guajava Non-indigenous 36. Sapium ellipticum Indigenous 37. Solanum mauritianum Non-indigenous 38. Strichinos usambarensis Indigenous 39. Strombosia schefleri Indigenous 40. Syzygium cuminii Indigenous 41. Teclea nobilis Indigenous 42. Tithonia tithonii Non-indigenous 43. Trema orientalis Indigenous 44. Trichilia emetica Indigenous 45. Trillepisium madagascariensis Indigenous 46. Vernonia spp Indigenous 47. Zanthoxylum gilletii Indigenous 66
APPENDIX D: MEASUREMENTS/VARIABLES FOR TIMBER CRUISE
Variable Description % debris Percent of coarse woody debris Air density(AD) Air density Barometric pressure h/Pa Debris diameter Diameter of sample woody debris. DBH standardized at 130cm above the ground in meters Elevation (elev) Meters above the sea level Forest type primary or secondary forest GPS (utme) UTM easting. Measured by Garmin GPS 72H. Datum: (WGS 1984, Zone 36N) GPS(utmn) UTM northing. Measured by Garmin GPS 72H. Datum: (WGS 1984, Zone 36N) Humidity(hum) Percent (%) Litter depth Centimeters (cm) Management(Mngt) KFS or KWS Marginal trees Non-measured trees identified by using 10mm wedge prism (stems align at the edge) Measured trees Measured trees identified by using 10mm wedge prism Non-measured trees Non-measured trees identified by using 10mm wedge prism (stems completely displaced) SPLmax Average maximum sound pressure level(SPL) readings SPLmin Average minimum sound pressure level(SPL) readings Temperature (temp) Degrees Centigrade (0C) Visual Obstruction Reading Average of the four Visual obstruction readings in cardinal directions (N,E,S & W) Wind Wind speed (m/s)
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APPENDIX E: TREE SPECIES OBSERVED IN TIMBER CRUISE
No Trees species Type 1. Albizia coriara Indigenous 2. Albizia grandibracteata Indigenous 3. Albizia gummifera Indigenous 4. Aningeria altissima Indigenous 5. Antiaris toxicaria Indigenous 6. Beqauertiodendron oblanceollatum Indigenous 7. Bischofia japonica Non-indigenous 8. Blighia unijigata Indigenous 9. Bridelia micrantha Indigenous 10. Celtis africana Indigenous 11. Celtis durandii Indigenous 12. Comberatum molle Indigenous 13. Cordia africana Indigenous 14. Craibia brownii Indigenous 15. Croton megalocarpus Indigenous 16. Cupressus lusitanica Non-indigenous 17. Diospyros abyssinica Indigenous 18. Eucalyptus saligna Non-indigenous 19. Ficus exosperata Indigenous 20. Ficus lutea Indigenous 21. Ficus natalensis Indigenous 22. Ficus sycomorus Indigenous 23. Ficus thonningii Indigenous 24. Funtumia africana Indigenous 25. Harungana madagascariensis Indigenous 26. Kigelia africana Indigenous 27. Maesopsis eminii Indigenous 28. Manilkara butugi Indigenous 29. Mung’ang’a Indigenous 30. Olea capensis Indigenous 31. Oncoba spinosa Indigenous 32. Polyscias fulva Indigenous 33. Premna angolensis Indigenous 34. Prunus africana Indigenous 35. Psidium guajava Non-indigenous 36. Strichinos usambarensis Indigenous 37. Synsepalum cerasiferum Indigenous 38. Syzygium cuminii Indigenous 39. Trichilia emetica Indigenous 40. Trillepisium madagascariensis Indigenous 41. Vitex keniensis Indigenous 42. Zanthoxyllum mildebraedii Indigenous 43. Zanthoxylum gilletii Indigenous