MAKERERE UNIVERSITY

IMPACTS OF SAND MINING ON SPECIES DIVERSITY OF VASCULAR PLANTS IN LWERA WETLAND, CENTRAL UGANDA

By

Sarah Kawala

Reg. No. 2015/HD02/545U

A Dissertation Submitted to the Directorate of Research and Graduate Training in Partial Fulfilment of the Requirements for the Degree of MSc. Environment and Natural Resources of Makerere University

February 2021

i ii

ACKNOWLEDGMENTS

Special thanks go to the National Environment Management Authority (NEMA) for the initial grant provided to carry out this research. My sincerest thanks also go to Associate Professor Gerald Eilu and Dr. Daniel Waiswa who with due diligence, ability and guidance supervised and reviewed this project to a successful completion. I also wish to extend my gratitude to Dr. Jerome Lugumira Ssebaduka for the wide ranging assistance and guidance offered during the data collection process and Dr. Enock Ssekuubwa for the assistance given during the analysis of the data. Lastly, I wish to thank the staff of NEMA, staff of Makerere College of Agricultural and Environmental Science, students of MSc. Environment and Natural Resource (2015) Makerere University and my family for the encouragement and support offered to enable me complete this project.

iii

Table of Contents DECLARATION ...... Error! Bookmark not defined. APPROVAL ...... i ACKNOWLEDGMENTS ...... iii Table of Contents ...... iv ABSTRACT ...... vi CHAPTER ONE ...... 1 INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Problem Statement ...... 3 1.3 Objectives and Hypotheses ...... 4 1.4 Justification ...... 4 CHAPTER TWO ...... 6 LITERATURE REVIEW ...... 6 2.1. Introduction ...... 6 2.2 Characterization of sand mining sites ...... 6 2.3 Plant species diversity and composition in disturbed areas ...... 8 2.4 Plant species Turnover (β diversity) in disturbed areas...... 10 CHAPTER THREE ...... 13 STUDY AREA AND METHODS ...... 13 3.1 Study Area ...... 13 3.1.1 Location ...... 13 3.1.2 Topography and soils ...... 14 3.1.3 Climate ...... 14 3.1.4 Vegetation ...... 14 3.1.5 Land use ...... 15 3.1.6 Major human activities in the wetland ...... 15 3.2 Methods ...... 16 3.2.1 Reconnaissance survey and sampling design ...... 16 3.2.2 Data collection ...... 18 3.2.3 Data analysis ...... 19 CHAPTER FOUR ...... 23 RESULTS ...... 23 4.1 Characterization of sand mined sites ...... 23

iv

4.2 Plant species Diversity, Composition and Turnover ...... 23 4.2.1 Plant Species Composition ...... 23 4.2.2 Relative abundance of plant forms and species at the study sites (Zones ...... 24 4.2.3 Species Diversity ...... 27 4.2.4 Plant Species Turn Over (β-diversity) ...... 27 CHAPTER FIVE ...... 30 DISCUSSION ...... 30 5.1 Characterization of sand mining sites ...... 30 5.2 Species diversity and spatial turnover ...... 31 CHAPTER SIX ...... 33 CONCLUSION AND RECOMMENDATIONS ...... 33 6.1. Conclusion ...... 33 6.2. Recommendations ...... 34 6.2.1 Recommendations for improved management and sustainable sand mining ...... 34 6.2.2 Recommendations for future research ...... 34 REFERENCES ...... 35 APPENDICES ...... 41 Appendix 1: Plant species composition of Lwera wetland ...... 41 Appendix 2: Data Sheet ...... 43 Appendix 3: ANOVA Tests ...... 46 Appendix 4: List of the Non-native species in Lwera wetland ...... 48

v

ABSTRACT

Wetlands provide enormous socio-economic and environmental benefits but they are threatened by various degradation activities of which sand mining is one of the most rampant and devastating in Uganda. This study was conducted in Lwera wetland in Central Uganda to assess the impacts of sand mining activities on the diversity and distribution of vascular plants. The objectives were: (i) To characterize the sand mining sites; (ii) To determine the species diversity of vascular plants; and (iii) To assess the Spatial Turnover (β diversity) of vascular plants in the sand mined areas. The wetland was stratified in four zones based on the density of pits: 1). High pit zone: 2). Medium pit zone; 3). Low pit zone; and 4). Virgin zone. In each zone, four plots of 200m by 200m were established. In each plot, five subplots of 1m by 1m were established giving a total of 80 quadrats. Plant species diversity was determined in these subplots. Characteristics of sand mined sites were determined within three sand mined zones out of the four. The results show that the high pit zone had a pit density of 0.248 while the low pit zone had a pit density 0.109. The average pit size ranged from 13, 314.2m2 in the low pit zone to 2, 144.8m2 in the high pit zone. A total of 75 species belonging to 25 families and 60 genera was recorded. The plant forms included the Herbs-39 spp., Climbers-9 spp., Grasses-9 spp., Sedges-7 spp., Shrubs-8 spp., and Creepers-3 spp. Plant diversity was highest in the High pit zone (Shannon-Wiener diversity index-H1 = 2.57); and lowest in the Virgin zone 1 (H = 1.7). The turnover was highest in the paired zones of high pit zone and Virgin zone (β1-J =80%) indicating that the similarity of plant species in the two zones was relatively low. Pit area (F = 6.710, df = 1, p = 0.005), Zone (mined vs unmined, F = 5.12, df = 1, p = 0.005) and pit density (F = 2.829, df = 1, p = 0.005) influenced the distribution of wetland species. Environmental variables induced by sand mining played an important role in influencing plant species diversity and though, this seems positive to the biological integrity of the sand mined sites, it is assumed that some ecologically and socio-economically valuable wetland plant species were lost. There is therefore a need to develop a strategy for sustainable use and management of wetlands for sand mining.

vi

CHAPTER ONE INTRODUCTION 1.1 Background The earth is experiencing unprecedented changes in its natural environment. These have led to the wide spread destruction and degradation of natural habitats including wetlands. Their implications for the sustainability of wetlands and other natural ecosystems are of global concern (Andrew, 1999). Wetlands, among other natural ecosystems, are valuable but fragile and their functions are diverse: ranging from climate regulation and biodiversity protection, to socio- economic benefits (Zedler & Kercher, 2005; Constanza et al., 1997). Critical services provided by wetlands include regulating atmospheric gases, sustaining native biota, sequestering carbon and maintaining water quality (Constanza et al., 1997). The benefits and values from this ecosystem are however undercut by increasing degradation. Global estimates indicate that over 50% of the world’s original wetland cover has disappeared, although this varies between countries (Mitsch & Gosselink, 2000) and this is partly attributed to man-made interruptions due to several reasons, in particular, the extraction of sand for industrialization, construction and urban expansion (Zedler & Kercher, 2005; NEMA, 2006).

Wetlands in Uganda, cover approximately 11% (26,600 km2) of the country’s total area (241, 500 km2); a drop from 13% in 2000 (MWE, 2001; Mbeche & Bagyenda, 2017). The drop is further complicated by unclear wetland boundaries and the legal definition of wetlands. The remaining wetlands face pressure from nutrient enrichment, hydrological alterations and invasive species of plants and animals mainly as result of multiple land use practices including unregulated harvesting of grass and papyrus, commercial sand and clay extraction, construction of large drainage channels, dumping of murram and other solid wastes, use of agrochemicals, and clearing of natural forests for agriculture (MWE, 2017). Wetlands with sand deposits are under threat, occasioned by the growing construction industry (NEMA, 2014), which has increased the rate of sand extraction.

Sand mining operations routinely modify the surrounding landscape by exposing previously undisturbed earthen materials (BRGM/RP-50319-FR, 2001; Uścinowicz et al., 2014). Studies undertaken in aquatic environments show that dredging and extraction of sand from some ecosystems conflicts with the functionality of these ecosystems hence destroying organisms and habitats which affects the composition and distribution of biodiversity and may lead to a shift in

1 species composition (Langer 2003; Krause, Diesing & Arlt, 2010; Desperez, Pearce & Le Bot, 2010). Therefore, issues concerning the location and regulation of sand extraction and its environmental effects are of local as well as global concern (Arun et al., 2006).

Sand mining in wetlands is taking place at a much faster rate than natural replenishments, and this has led to severe ecological disorders (Leeuw et al., 2010). Studies by Singh et al. (2007) and Peckenham, Thornton & Whalen (2009) show that environmental concerns result when the rate of extraction exceeds the rate at which natural processes generate the sand. Sand and gravel represent the highest volume of raw material used on earth after water and their use greatly exceeds natural renewal rates (UNEP, 2014). The demand for sand continues to increase even after many scholars recognized and understood the significant negative environmental impacts that the extraction of sand has on the wetland ecosystems (Kim, 2005; Arun et al., 2006; Marh & Panthania, 2008; Padmalal et al., 2008; Leeuw et al., 2010; and Tamang, 2013).

The increasing demand for sand in many parts of the world has been due to several developments taking place e.g. growth of new townships, expansion of highways, metro railways and other infrastructure projects (Singh et al., 2007). The U.S. Geological Survey (USGS) Mineral Commodity Summaries (2013) show that the building industry needs about six to seven times more tonnes of sand and gravel for each tonne of cement in the preparation of concrete, thus, the world’s use of sand is enormous.

There exists a large discrepancy between the magnitude of the problem on the wetlands and public awareness of it. Studies have shown that despite the colossal quantities of sand being used and the increasing dependence on them, the significant impact of their extraction on the environment has been ignored and remains largely unknown by the general public (UNEP, 2014). The absence of comprehensive global data on aggregate extraction of sand and gravel and its effects on environment has also undoubtedly contributed to the gap in knowledge and this has made environmental assessment of the cumulative impacts of sand mining very difficult hence translated into a lack of action (UNEP, 2014; Facts, 2015). In many developing countries, mining and dredging regulations are often established without scientific understanding of the consequences, and some projects are carried out without proper environmental impact assessments (Saviour, 2012). This is sometimes the case in Uganda.

2

The demand for sand in Uganda, has increased and so have the measures sought to meet the increasing demand. The production of sand was estimated at 3.5 million tonnes in 2015/16 and this is expected to increase alongside the escalating demands for concrete and mortar used in construction although specialized uses such as glass production may figure more prominently in the future (UNDP, 2018). On a large scale in Uganda, sand mining in wetlands is conducted using dredgers, which are fitted with hoses that pry the ground and are powered to suck hundreds of tonnes of sand in a single day. This creates pits of various sizes resulting into fragmentation of the landscape and its associated impacts on species diversity (Saviour, 2012; Andrew, 1999).

Therefore, as the issue of sand mining and its impacts on wetlands is important, there is a need to develop a clear understanding of the impacts created by sand mining and land use modification on plant species diversity in wetlands (Saunders, Hobbs & Margules, 1991) before designing criteria to improve conservation in already fragmented areas and areas whose landscapes have been modified as a result of sand mining activities. This can generate information which will be used by conservation managers in protecting sensitive ecosystems so that they maintain their natural functions. This study therefore focuses on generating information on how changes in land use of wetlands due to sand mining have affected and influenced plant species diversity.

Since the study area was characterized by grazing and cultivation as part of the wetland’s land use, disturbance of the wetland in this case only referred to sites with presence of sand mined pits, trucks and camp areas. Therefore, sites experiencing sand mining and have pits were considered disturbed whilst those without pits and had not undergone sand mining were considered undisturbed.

1.2 Problem Statement There is increasing mining of sand from wetlands in Uganda that is driven by the growing construction industry (UBOS, 2016), which could lead to alteration and depletion of wetland resources. Sand is a resource of universal importance that is found in fragile ecosystems such as wetlands. This therefore means that more sand-bearing land is being exposed to mining and many of the sand mines are not rehabilitated nor monitored for recovery. The wetlands are often under pressure from mechanized sand mining as excavation is often unplanned and rehabilitation rarely conducted. Plant species diversity at such sites has probably been tampered with to an unknown extent and the land degraded. The environmental impacts are also unclear to many stakeholders. Assessment of the impact of human-induced land-use modifications due to sand mining on species

3 assemblages has been a popular, albeit controversial research area in ecology and conservation with some studies indicating that it has led to loss of species contrary to other studies (Haila, 2002). This has left a gap in the knowledge on the impacts of sand mining on the plant species diversity of natural wetlands. The scientific understanding of the consequences of land use modification and ecosystem fragmentation due to sand mining on the ecological connectivity of plant species in wetlands, is also not direct and specific enough to trigger guidelines that are system and regional specific. This study therefore set out to contribute to the general scientific understanding of the impacts of sand mining on the plant species in wetlands, so as to inform the development of effective conservation strategies in lacustrine wetlands disturbed by sand mining.

1.3 Objectives and Hypotheses The overall objective of the study was to determine impacts of sand mining on the diversity of vascular plant species in wetlands.

Specific objectives of the study were to:

(i) Characterize the sand mining sites in Lwera wetland. (ii) Determine the species diversity of vascular plants. (iii) Assess the Spatial Turnover (β diversity) of vascular plants in the sand mined areas.

Hypotheses The study was premised on the following hypotheses: 1. The diversity of plant species in sand mined areas is not different from that in areas that have not been mined. 2. The extent of sand mining has no influence on the composition of plant species.

1.4 Justification Sand mining is a major issue that requires special attention in many developing countries given that the cost of environmental restoration in degraded sand mined ecosystems is appalling. Besides being very expensive, restoring an ecosystem to its original state is not possible. This study therefore was aimed at contributing to the understanding of the characteristics of sand mined sites in addition to the impacts of sand mining on the wetlands. Information from this study will be used by mining operators, in conjunction with cognizant resource agencies and regulators in the sand mining sector to develop specific effective conservation management strategies for sustainable use of Lacustrine

4 habitats upon which guidelines to assist permitting of sand mining would be based. This study as a contribution to the Sustainable Development Goals (i.e. SDGs-No.12 Ensure sustainable consumption and production patterns & SDGs-No.14 Conserve and sustainably use the oceans, seas and marine resources for sustainable development), aids development of recommendations and strategies to promote sustainable sand mining practices (consumption and production) that can be considered before and during the issuing of permits for sand mining.

5

CHAPTER TWO LITERATURE REVIEW

2.1. Introduction Ecological disturbance factors (e.g. sand mining, forest fires, floods, tree falls and grazing) influence the patterns of species diversity (Cadotte, 2007). Variation in the spatial and temporal patterns of disturbance generates a mosaic of patches in different successional stages, which positively affects species turnover (Hobbs & Huenneke, 1992); Cadotte, 2007; Limberger & Wickham 2012; and Vanschoenwinkel, Brendonck & Buschke, 2013). The frequency and intensity of disturbance are important factors when determining how disturbances will affect local plant communities (Hobbs & Huenneke, 1992). Sand mining as a disturbance in many wetlands has effects on the diversity of plant species. However, it is necessary to determine how its intensity affects plant diversity.

The process of sand mining is complex and involves five stages (Table 1). Activities undertaken at every stage during sand mining cause severe environmental impacts and can lead to major changes in the local flora and fauna, contaminate groundwater and air, change and disrupt the landscape, among others (Gavriletea, 2017).

Table 1. Stages in the life of a sand mine Mining Stages Process Description Precursors to mining 1 Prospecting Searching for sand deposits using multiple exploration techniques 2 Exploration Determining the possible size and value of the sand deposits using evaluation techniques such as drilling for bulk sampling. Mining proper 3 Developing Setting-up and commissioning facilities to extract, treat and transport sand 4 Exploitation Large scale sand production Post mining (Usually applies for licensed sand mines) 5 Closure and reclamation Returning the land to near its original state (Rehabilitation). Source: (Gavriletea, 2017)

2.2 Characterization of sand mining sites Characterization of landscapes is a collective term for systematic, area-covering identification, classification and /or character assessment of landscapes (Tudor, 2015). There are substantial 6 differences between landscape characterization methods, and no single method can address all dimensions of the landscape without important trade-offs. The choice of methodological approach directly determines the applicability and usefulness of the resulting typologies for applied purposes, therefore different landscape characterization methods are needed in order to address different purposes and user needs (Simensen, Halvorsen & Erikstad, 2018).

The forces for change in today’s land use for wetlands are rapidly altering and creating new landscapes. There are several impacts on wetlands that are quickening the rate of change; mining, agricultural expansion, buildings and road construction, soil erosion; to mention but a few. However, there is need to know the extent and acceptable degrees of loss or change in land use for modern society as this guides in decision making since a number of factors are considered, for example, visibility and aesthetics, the identity of place, loss of biodiversity and the loss of non- renewable resources (Gudmundur, Edda & Olafur, 2005).

Sand mining operations routinely modify the surrounding landscape by exposing previously undisturbed earthen materials (BRGM/RP-50319-FR, 2001). In Tamang (2013), erosion and deposition in water bodies as a result of excessive sand mining leads to alteration in the topography of the water bodies and affects their ecosystems. In rivers, it can also lead to squeezing of the river bed and receding the hill slopes. Extraction activities despite the magnitude, encroach rapidly on other land use categories and have both direct and indirect ecological impacts despite the size of the area used for sand mining (Teijpal et al., 2014). It was therefore necessary to characterize sand mining sites so as to determine the changes in land-use resulting from sand mining in Lwera wetland and also recommend strategies of how to regulate the activity.

Mining disturbs land surface areas, leaving huge open pits which are difficult physically and economically to rehabilitate at the time mining ceases (Tariro, 2013). Areas that experience sand mining are characterized by deep pits which keep increasing as the rate of extraction increases, deep gullies due to enhanced erosion, loss of vegetation and destruction of landscape (Musah, 2009; Amponsah-Dacosta & Mathada, 2017). These studies, however, do not explain the nature/characteristics of the pits created and how these affect the plant species diversity since it is assumed that several ecological changes occur as a result of changing the landscape.

7

Due to the high rates of erosion, the pits left behind keep expanding and such areas are faced with destruction of vegetation, expansion of rivers, ecosystems, crop fields and grazing lands (Tejpal et al., 2014). The Collapse of riverbanks is common at sites experiencing sand mining due to close proximity of sand mining activities to the rivers (Amponsah-Dacosta & Mathada, 2017). However, the studies do not give details of the induced changes for example, the depth and surface area of the pits in relation to the proximity to the rivers, and likewise the extent and magnitude of the changes in land-use.

Apart from creation of huge pits hence decreasing the total land area, most natural ecosystems have been fragmented into patches or fragments which vary in a different way, for example; in number, distribution, sizes, shape and spatial configuration. The resulting patterns from fragmentation and loss of ecosystems and their effects on different ecological processes vary considerably and change overtime at varying rates (Rutledge, 2003).

2.3 Plant species diversity and composition in disturbed areas Species diversity includes the richness and evenness of species and increases with habitat complexity (Heydari & Mahdavi, 2009; Monica et al., 2004). Species richness is the number of species in a unit of study, while Evenness describes the variability in species abundances. A community in which all species have approximately equal numbers of individuals (or similar biomasses) would be rated as extremely even and conversely, a large disparity in the relative abundances of species would result in the descriptor “uneven” (Magurran, 2004). Composition is the proportion of various plant species in relation to the total of a given area (U.S. Department of Agriculture, 1999).

Species movement and dispersal increases when environment features are similar in structure to the breeding patches (Watts et al., 2005). Species movement also depends on the species’ habitat adaptation, combined with the composition of the surrounding landscape and environmental conditions which encourage species dispersal, pollination and eventually establishment of species (Eycott & Watts, 2011; Pausas & Austin, 2001). The information, however, is broad and heterogeneous making the strength of the evidence fairly low and partly accounted for by the relative infrequency of dispersal events.

8

Sand mining requires clearing of large open lands before mining leading to loss of aesthetic value and furthermore, stripping the top few meters of soil decreases the fertility of the land, which affects the suitability of the soils to support some native vegetation (Tariro, 2013; Tejpal et al., 2014; Gavriletea, 2017). These studies, however, focused on native plant species richness and composition without considering the long-term effects following restoration or after abandoning the site. This study took into consideration the long-term effects of disturbance resulting from sand mining on plant species diversity (richness and evenness) and composition.

The most common environmental impact of sand mining as an ecological disturbance is the alteration of land use, most likely from underdeveloped or natural land to excavations in the ground (Langer, 2003) leading to a decrease in species richness (Barry & Marilyn, 2001; Gavriletea, 2017), however, once left to fallow and not disturbed for some time, the disturbed site is exposed to successional changes in vegetation which leads to regrowth of native and exotic species (Fonge et al., 2011). In addition to the previous studies undertaken, this study considered sand mining as a disturbance, and how species richness and composition varied for both the native and exotic species at the different sand mined sites.

The use of heavy equipment, processing plants, gravel stockpiles at or near the extraction site leads to destruction of vegetation and ecosystems (Ladlow, 2015). Considering that, habitat diversity, habitat disturbance and species interactions are some of the factors that influence the number and type of species coexisting in a place (Barry & Marilyn, 2001), this can significantly impact on the recruitment of certain species, which are reliant on these events for their long-term persistence on these terraces. In other words, a generation of recruitment may be lost, causing a gap in the population structure which can be exploited by other species (Ashraf et al., 2011).

Different land uses including sand mining (especially during the exploitation stage) result in fragmentation and loss of ecosystems affecting the diversity of specific plant species differently (Rutledge, 2003). Sand mining reduces the area of residual habitat fragments and may simultaneously increase isolation among fragments, increase the prevalence of within-fragment edge effects, and alter the matrix between remaining fragments (Didham, 2012; Koper et al., 2009). The diversity and composition of native vegetation are increasingly influenced by processes originating in modified areas, and typically, as ecosystem modification increases, more native vegetation is lost as land-use intensity increases (McIntyre & Hobbs, 1999).

9

The extraction of sand and gravel from rivers, wetlands and streams conflicts with the functionality of these ecosystems and has therefore resulted into serious environmental impacts (Langer, 2003). Sand mining results into water pollution and also causes changes in the chemical composition of the soils which could have negative effects or lead to the death of soil living organisms and the destruction or extinction of some plants in the area (Onoduku, et al., 2014; Gavriletea, 2017). This, however, does not take into consideration the regrowth of new plants in the area as a long-term effect on plant species richness and composition following the changes in the ecosystem habitat.

The plant species diversity among the impaired wetlands relates to the degree of disturbance within wetlands being highly degraded as compared to pristine wetlands; and the invasion by upland and exotic weeds in disturbed wetlands out-competes the socio-economically and ecologically important native species (Wondie, 2018) hence affecting the functionality of the wetland ecosystem. This, however, does not associate a specific disturbance like sand mining to the diversity of the plants in the wetlands but considers all disturbances within the wetland.

2.4 Plant species Turnover (β diversity) in disturbed areas. Turnover (β diversity) is the measure of the extent to which the diversity of two or more spatial units differ in terms of their species composition. Changes in vegetation are very dynamic in nature and therefore need to be monitored at regular intervals considering that change in habitat and the subsequent increasing isolation of plant species is one of the major contributing factors to the change and loss of biodiversity (Eycott & Watts, 2011).

Understanding how biodiversity is distributed and its relationship with the environment is crucial for conservation assessment. It also helps us to predict impacts of environmental changes and design appropriate management plans (Guillaume & Melodie, 2017). Spatial turnover or β-diversity is defined as the extent of differentiation of communities along transects or habitat gradients, and is often interchangeably used and defined as the variation in the species identities among sites (Whittaker, 1972). β-diversity as turnover measures the spatial change in community structure between samples along a gradient (Vellend, 2001). It is a measure of between-habitat diversity and can vary with scale, even when measures apparently independent of species richness are used (Magurran, 2004).

10

There are many hypotheses as to why there is species turnover, amongst which are the disturbance regimes from activities like sand mining (Kraft, 2011), and these are dependent on the scale of observation and the taxon, meaning that there is no one scale at which to describe populations or ecosystems (Whittaker, Willis & Field, 2001; Chase & Leibold, 2002).

Sand mining practices involve withdrawing water, altering land use, or changing the potential for recharge between aquifers which may affect water supplies locally within an area (Tejpal et al., 2014). The conversion of land to lakes through creation of huge pits may result into reduced supply of ground water by direct consumption of water from pits and through evaporative losses (Hurst, 2002). In areas of sand mining, this is a matter of great concern, however, these studies do not document the extent to which this has affected the plant species turnover, given that, it is likely to have an effect on the regrowth of the vegetation within an area since plant distribution relies heavily on the availability of water, hence affecting the species turnover.

Sand mining activities like dredging during exploitation of the resource are capable of altering the physical connectivity of landscapes due to habitat fragmentation hence cutting off migratory routes” (Andrew, 1999). This in due course affects the ability of some organisms to successfully navigate among several stepping stones or affects some processes of interest like dispersal of plant species (Hurst, 2002). This increases the ability of new plant species to occupy the new habitat or the native species to disappear hence changing the composition of the plant species in this environment (Tischendorf & Fahrig, 2000). The turn-over (beta diversity) of plant species is therefore an important ecological factor in fragmented and disturbed landscapes and understanding how a particular disturbance affects the turn-over or beta diversity determines how the activity causing the disturbance can be regulated.

The resultant pits from sand mining increase the rate of evaporation, hence lowering the water table, and consequently impacting on plant communities around the wetlands (Bradshaw, 1987). Vegetation removal and disturbance of land surfaces also leads to increased sedimentation and turbidity of water sources, and removal of soil as over burden alters the topography, and consequently the water flow patterns of the mined area (Zedler, 2000). In situations where restoration is not appropriately done, the disorientation and modification of wetlands due to sand mining may affect the plant species turnover hence permanent ecosystem disruption and biodiversity loss (Leeuw, et al., 2010).

11

Impacts of sand mining on vegetation indicate that re-growth in areas that have experienced extraction is excellent, though the number and quality of species appears to be different due to the wetter environments and probably other unknown factors resulting from extraction activities (Ladlow, 2015). However, additional studies are required to assess the specific species of plants that are re-growing in sand mined areas as compared to the plant species in the undisturbed surrounding areas. This would provide clearer data about the changes in species and turnover in disturbed areas.

After rehabilitation, plant populations in remnant habitat patches are forced to spread throughout the landscape due to sand mining disturbances, and the populations that inhabit isolated habitat patches are at a risk of local extinction (Didham, 2012). A network of habitat patches may allow species survival if the patches are interconnected by dispersal (Holyoak, Leibold & Holt, 2005) hence low turn-over. At the meta-population level, dispersal and colonization of new habitat patches is a very important mechanism for the long-term persistence of species, and it compensates for local extinctions, hence reducing species turnover.

Understanding how landscape characteristics affect plant species diversity and ecological processes is critical for mitigating effects of environmental change (Teja et al., 2012). However, many of the studies undertaken along the subject matter are generic. Most of these studies are not specific enough to communicate the actual impacts of sand mining on plant species diversity in wetlands while those that talk about changes in plant species diversity are not specific on the nature of disturbance, this therefore leaves a gap in knowledge on the impacts of sand mining on plant species diversity in wetlands. This study therefore was intended to bring out a clearer understanding of how sand mining induced changes on land use in the wetland and affected the plant species diversity so as to provide a basis for recommending strategies to regulate environmental change as a result of human activities like sand mining in wetlands.

12

CHAPTER THREE

STUDY AREA AND METHODS 3.1 Study Area 3.1.1 Location The study was carried out in Lwera wetland located in the Lake Victoria basin along Kampala- Masaka Highway. Geographically, Lwera wetland is located in the Lake Victoria catchment; stretching from 0º 5′11″N to 0º 17′S and 31º 38′40″and 32º 00′45″ East. The wetland area is approximately 235km2 and forms part of the boundary between Mpigi and Kalungu Districts. The expansive Lwera wetland is a major water catchment that connects several small permanent rivers as well as the permanent and seasonal wetlands. It drains directly into Lake Victoria. Extensive sand mining in Kalungu District occurs in Lukaya Town Council, and in Mpigi District, it is located in Nkozi Sub County (Figure-1).

Figure 1: Map of Lwera wetland and location of wetland in Uganda. Data Source: NEMA, 2019 13

3.1.2 Topography and soils The landscape is flat and the soils in the area are variable in type depending on the parent rock, climatic conditions and land use activities. Soil mapping reports show that the soils were developed from remnants of old lacustrine deposits that can be traced to the Pleistocene period. The soils in the study area are mainly hydromorphic comprising mainly sodium minerals and ferallitic mainly sandy clay loams. The top soils consist of traces of humus merging into yellow brown or brown sandy loam or loamy sands to the depth of 0.9m to 5.5m. The soils are under laid by rounded quartz pebbles and some places with a layer of Murram and massive laterite (NARO, 2015).

3.1.3 Climate The study area falls within the Lake Victoria climate zone receiving rainfall throughout the year with two rainfall peaks from April-May and October-November. Two relatively low rainfall periods are experienced between December-March and June-July. The climate in this zone is modified by maritime conditions i.e. proximity to Lake Victoria and location astride the equator (NEMA, 2010). The annual average rainfall received is between 1100mm - 1200mm with 100 - 110 rainy days. Trend analysis by Uganda National Meteorological Authority (UNMA), for the long rains of April to May based on 35 years (1981-2016) show no major fluctuation apart from an increasing trend from 2012-2016 (UNMA, 2016; MWE, 2017). The average maximum temperature does not exceed 30°C and the minimum does not fall below 10°C, with almost equal length of day and night throughout the year while relative humidity is generally low with the exception of lakeshore areas where it tends to rise, (MWE, 2017; UNMA, 2016).

3.1.4 Vegetation The vegetation of Lwera is partly permanent wetland vegetation terminating into seasonal wetlands. The wetland is a seasonally flooded wooded grassland and home to distinct plant communities that are well adapted to its natural environment. It is characterized by extensive vegetation like varied wooded grasslands with scattered shrubs and Acacia sp. The grassland is predominantly Imperata spp., Sporobolus spp and Hyparrhenia spp. It is also dominated by plants such as Cyperus spp., Loudetia spp., Phragmities spp, Typha spp., Phoenix aethipoicus, and Miscanthus sinensis because they are tolerant to soils that are acidic and deficient of plant nutrients (MWE, 2017).

14

3.1.5 Land use The wetland has largely been used for farming and sand mining (providing sand for the construction industry in Uganda) especially the section in Mpigi and Kalungu districts. Other land uses around and within the wetland consist mainly of activities such as hunting, settlement, fishing, cattle grazing, natural resource extraction (herbs, grass, sand, clay, firewood, poles, fish and water), cultural heritage (shrines), small scale cultivation, and social infrastructure such as roads (MWE, 2017). Consequently, large investments have been established in the area such as commercial farms, construction of leisure facilities at beaches, and huge extraction of wetland resources (sand and clay). These investments have boosted the economic status of the area but at the same time may have potential negative environment impacts and should therefore be monitored closely.

3.1.6 Major human activities in the wetland i. Sand mining Sand mining practices in Uganda have been to a large scale traditional where sand is scooped out of the ground using traditional tools, such as hoes, and spades. Around 2010, there was an upgrade of technology, where backhoes, excavators and bull dozers were increasingly used. Currently, on a large scale, sand mining is conducted using dredgers. This expanse of wetlands besides supporting the lake ecosystem, is rich in sand used in construction supplying several districts. However, there are several issues emerging out of the ongoing activities.

An estimated average of 80 trucks of sand are collected from the wetland per month with a market value of USD 17,920 per year. Levies on sand extraction were introduced in the financial year 2009/2010, in a bid to regulate the activity, estimating an equivalent of USD 1,280 to be collected per year (Mpigi District Local Government, 2010). Such returns may seem exciting attracting many into the business. However, sand is largely a non-renewable resource with short lived returns yet the processes involved leave devastating impacts on the environment. Field observations however, show that the activity is out of character and is likely to lead to changes in many habitats if not regulated comprehensively. ii) Cultivation The wetland supports agriculture and is used for cultivation of crops, mainly Ipomoea batatas (sweet potatoes), Solanum lycopersicum (tomatoes), Citrullus lanatus (water melon) and vegetables. In Uganda, Wetland edge gardening is permitted as long as it does not exceed 25% of

15 the total wetland area. These activities, however, in one way or another are as well likely to affect the plant diversity in the wetland. iii) Livestock Grazing The wetland is also widely used for grazing cattle and goats and this has also been found to be part and parcel of the landscape of Lwera wetland.

3.2 Methods 3.2.1 Reconnaissance survey and sampling design A reconnaissance field visit and a focus group discussion with the selected land users/communities were conducted prior to the field sampling. This included a walk and or drive around and within the area with a key informant to help familiarize with the study area and severity of the degradation from sand mining, and to understand the accessibility of the area. Sampling (Fig.1) covered sections of the wetland that had been extensively used for sand mining and those which had not been used for sand mining to serve as controls. The map of the area was obtained and transects aligned with environmental gradients (grad-sects) established from the South (Lakeshore) to the North of the wetland following the methods of Tropical Biology Association (2012), (Figure 2). ‘Grad sects” according to Wessels et al. (1998) and U.S. Department of Agriculture (1999) are transects usefully aligned with environmental gradients and these are hypothesized to be important in studies comparing plant species diversity in different land use areas. Using the map of the study area. strata were established and using the measuring tool on google earth, the sizes of the strata were obtained accordingly.

The wetland was stratified into four zones (1. High pit zone equivalent to 101-Hectares, 2. Medium pit zone equivalent to 101-Hectares, 3. Low pit zone equivalent to 201-Hectares and 4. Virgin zone equivalent to 184-Hectares) depending on the density of pits per zone/stratum. Used in this sense, strata are equivalent to zones of land areas that have or have not been used for sand mining and these comprised areas with sand mined pits and those without pits. In this study, areas experiencing cultivation and livestock grazing but were not being used for sand mining i.e. had no pits were considered undisturbed.

Out of the four zones/strata, three were established in areas of the wetland characterized by sand mining and one was established where sand mining had not taken place so as to test the hypothesis.

16

Within each zone, 3-transects were established, thus in total 12-transects were established. Along these transects, 4-independent plots of 200m x 200m were randomly established in each zone as replicates. In total 16-plots were established.

Within each 200m x 200m plot, sub-sampling was done in five randomly selected quadrats of 1m x 1m. Since a quadrat delimits an area in which vegetation cover can be estimated, plants counted and/or species listed (Cox, 1990), a quadrat which was 75% occupied by water was replaced. Thus 20-quadrats were sampled within each stratum and used for plant species diversity assessments making a total of 80-sampling points. The sampling design is as shown in Fig.2.

Figure 2: Sampling design showing the strata, transects, plots and quadrats

Within each zone, random sampling of pits was undertaken and wetland modifications due to sand mining (identification of pits and their sizes) were recorded. The zones were later on defined by the density of pits contained therein as High, Medium or Low pit zones and then the virgin zone which had not experienced sand mining.

Pit width was determined using a 30m measuring tape to take measurements of the pit boundaries, pit depth was measured using poles which were marked, measuring tape and canoes (boats) which

17 were used to navigate inside the pits with water. GPS was used for capturing coordinates and a digital camera to capture photographs. All the data was captured using standard data sheets which had been designed to ensure easy data collection and analysis. The strata/zones, Plots and Quadrats at each site were numbered sequentially, and were unique within the study. For example: • MDS/01/01/001 = Mpigi District survey/ Zone number one/ Plot one/ quadrat one. • MDS/01/02/002 = Mpigi District survey/ Zone number one/ Plot two/ quadrat two. • MDS/02/01/002 = Mpigi District survey/ Zone number two/ Plot one/ quadrat two.

The study area also presented some challenges which were taken into consideration, including;

 the location of sand mined sites and study pits on exclusively private owned property requiring constant negotiation for property access. Fortunately, data was collected with some NEMA officials which made it possible given NEMA’s mandate under the National Environment Act to access the sites.  limited accessibility to some sites mainly the virgin zone since some parts were flooded and some of the pits were in frequently flooded sites calling for the use of boats. In frequently flooded sites, limited accessibility issues made it difficult to take samples from some randomly selected areas.  Some sections of the wetland mainly the virgin zone and sand mined sites with abandoned pits were inhabited by lots of mosquitoes which created very harsh working conditions during data collection as a result of the continuous bites from the mosquitoes.

3.2.2 Data collection i) Objective 1. Characterization of Sand mined sites Within each zone, sand mined pits were identified, counted, measured (in terms of size/Area-A (m2) and Depth-D(m) and recorded. Pit depth was determined at 5-points within each pit and the average recorded. ii) Objective 2 and 3. Plant Species Diversity and Turnover (β - Diversity). In each 1m x 1m quadrat, plant species were identified and recorded for species richness. For each species, the number of individuals encountered in each quadrat was recorded. Identification, classification and description of the different species was done visually based on the flowers (size, shape, colour), fruits, leaves (color, shape, size, arrangement, and texture), roots and the stem/bark. Identification was done by recognition and comparison with previously collected specimen. The 18 plants were then classified into families, genera and species (Glimn-lacy & Kaufman, 2006). Plant samples were also categorized using growth habits, morphology, physiology and ecology to identify the different plant forms. These include Herbs (short-sized plant with soft, green, delicate stem without the woody tissues); Shrubs (medium-sized, woody plants with bushy, hard, and woody stems with many branches); Climbers (very thin, long and weak stem which cannot stand upright, and use external support to grow vertically and carry their weight); Creepers (small, viny plants that grow close to the ground); grasses (have hollow, circular stems with alternate, narrow leaves that have parallel veins and small, inconspicuous flowers. Stems have visible bulges or joints where the leaves attach (nodes) and they are usually hollow except at the nodes) and sedges (have edges and the stem is usually triangular in cross-section, sedge leaves emerge from a stem node in three directions).

Any new plants encountered and could not be identified were counted and collected for later identification. Duplicates were collected in cases where they provided better samples for identification. Materials/samples collected were covered in a counter book (A4) sized paper while paper envelopes were used for small specimens. Each specimen collected was tagged with a voucher label. Larger specimens were placed directly into plastic bags until ready to transfer to a press. Wet plants were lightly wrapped in newspapers to dry and the smaller ones were kept in small zip lock bags. Plants specimen were then pressed and afterwards taken to the Makerere University Herbarium (MHU) for identification. The identified samples were then used together with the others already identified to determine the species diversity (richness and evenness) and composition.

3.2.3 Data analysis i) Objective 1. Characterization of sand mined sites. The data on pit-dimensions were analyzed using descriptive statistical methods involving computation of the means to determine the average depth and area for each zone. The density of pits (d) was calculated using the total number of pits in each zone divided by the area/size of each zone. To determine the significance of the difference in the number of pits between the zones, One- way ANOVA was conducted using the means of the pit density and pit area, in which pairwise comparison of the zones was undertaken using Tukey’s Honestly Significant Difference test (Tukey’s HSD). The information obtained was used to characterize the different zones of the sand mined sites.

19 ii) Objective 2. Diversity and composition of plant species. 1 The Shannon-Wiener (H ) diversity indices, Margalef Indices (DMg) and Evenness (E) were computed to determine plant species diversity in the zones. The Shannon-Wiener Diversity Index (H1) measures the species diversity within the community of an ecosystem. The index assumes that individuals are randomly sampled from an infinitely large community and all species are represented in the sample (Magurran, 2004). It was used to calculate the species diversity and to compare the diversity between areas with pits and those without pits, based on the abundance of the species. The index also emphasizes the species richness component of diversity. The value of Shannon-Weiner Diversity Index lies between 1.5 and 3.5, only rarely does it exceed 4.5 (Bibi & Ali, 2013). It increases as both the richness and the evenness of the community increases, and a value near 4.6 indicates that the numbers of individuals are evenly distributed between all the 1 푠 1 species (Bibi & Ali, 2013). It was calculated using the formula; H = − ∑푖=1 푃푖퐿푛푃푖. Where; H =

Shannon-Wiener diversity index, s = number of plant species encountered, Pi = ni/N = relative th abundance of each species, ni = number of i species, N = total number of all individuals of all species (Carlo, Peter & Karline, 1998). To determine the significance of the diversity indices between the different zones, One-way ANOVA was conducted for the Shannon-wiener indices and pairwise comparison using Tukey’s HSD done. Margalef Index (DMg) was used to measure species richness using the formula DMg= (S-1/lnN) where S is the number of species, and N is the total number of individuals in the sample (Magurran, 2004).

Evenness (E) is the proportion of species present on site, and therefore an important component of diversity indices (Hill, 1973; Turchi et al., 1995). It is a measure of the relative abundance of the different species making up the richness of an area, therefore, the more equal species are in proportion to each other, the greater the evenness of the site. A site with low evenness indicates that a few species dominate the site. Note that species evenness ranges from zero to one, with zero signifying no evenness and one, a complete evenness. The Evenness (E) component of H1 was thus 퐻′ 퐻′ computed as follows: 퐸 = = Where: E= Evenness; H1max= Ln(S), S= total number 퐿푛(푆) 퐻′푚푎푥 of species in the sample.

Objective 3. Plants species turnover (Beta (β) - Diversity)

Turnover was determined using beta diversity which was assessed to measure the change and similarity in diversity of species from the four zones. It was calculated based on the types of species,

20 the numbers of species and the counts of individuals of those species using matching/mismatching components and thereby identifying the contribution of different sources of variation in species composition between a pair of zones/strata in the wetland (e.g. High and Low pit zones). It was calculated using the formula by Magurran (2004); β1-j=100*(1- βj), where βj is the Jaccard Coefficient of Similarity. The factor 100 was introduced, corresponding to values of 0 to 100% species turnover. A high beta diversity index (β1-j) and likewise the Turnover indicates a low level of similarity, while a low beta diversity index will show a high level of similarity.

The Jaccard coefficient of similarity index- JCS (βj) for paired strata/zones was calculated using the 푎 formula given by (Koleff, Gaston & Lennon, 2003) as βj= Where; a= total number of species 푎+푏+푐 that occur in two zones; b= total number of species that occur in zone b, but not in zone c; and c= total number of species that occur in zone c but not in zone b, as illustrated in Figure-3.

Figure 3: The possible spatial distribution of a species across a pair of sites

To determine the relationship between species composition and environmental factors, Canonical Correspondence Analysis (CCA) was conducted using the cca () function in R version 3.5.3 for windows (Borcard, Gillet, & Legendre, 2018). The CCA was based on a species matrix containing species abundances per plot and an environmental matrix containing zones (mined and unmined), pit density and pit surface area as environmental factors. To determine the proportion of variation explained by the canonical axes, the function RsquareAdj () was used to compute adjusted R2 which measures the explained variation (Borcard, Gillet, & Legendre, 2018). To visualize the CCA, a triplot was generated using the basic plot () function. The default scaling 2 was used such that plot scores are scaled to the relative Eigen values and species are weighted averages of plots (Borcard et al., 2018). To help entangle the species in the triplot, the plots were excluded and so the triplot became a biplot. To test for the significance of the environmental factors, a permutation test of the CCA model was conducted using the anova () function for 999 permutations. Since the CCA model was globally significant, a forward selection of explanatory variables was computed using ordistep

21

() function to identify the most important factors explaining patterns in the species (Borcard, Gillet, & Legendre, 2018).

22

CHAPTER FOUR RESULTS 4.1 Characterization of sand mined sites The pits were randomly distributed in all mined zones. The number of pits per unit area-hectares ranged between 0.109 in the low pit zone and 0.248 in the high pit zone. The high pit zone had the highest number of pits and likewise, the highest density of pits (Table 2).

Table 2: Density and size of pits in the Zones Zone Area No of pits Density of pits Average pit Average pit Stratum Zones (Hectares) per zone (d) area (m2) depth-D (m) 1 High pit zone 101 25 0.248 13314.2 2.09 2 Medium pit zone 101 19 0.188 6070.3 3.09 3 Low pit zone 201 22 0.109 2144.8 2.00 4 Virgin zone 184 0 0 0 0

The low pit zone had more pits than the medium pit zone but had the lowest density of pits per unit area. The average surface area of pits ranged from 13314.2m2 to 2144.8m2 with the high pit zone having the largest average pit area and the low pit zone having the smallest. The average depth of the pits ranged from 2m to 3.09m with the medium pit zone having the largest average pit depth. The ANOVA test showed that the pit density was significantly higher in the HPZ (0.23 ± 0.02, meant ± SD) than in MPZ (0.18 ± 0.02) and LPZ (0.12 ± 0.02), F=31.17, P˂0.001; and on running the pairwise comparison test (Tukey’s HSD), all the zones were different in terms of pit density and pit area, Appendix 3.

4.2 Plant species Diversity, Composition and Turnover 4.2.1 Plant Species Composition In total, 14,086 individuals were recorded comprising 75 species belonging to 25 families and 60 genera within a sampling area of 587ha (Table 3). Six plant forms were recorded including Herbs, Climbers, Creepers, Grasses, Sedges, and Shrubs (Appendix-1).

Among the 25 families recorded, Asteraceae had the highest number of genera and species followed by Fabaceae and Poaceae. Of the 25 families, 16 were represented by 1 species each and the remainder were represented by 2-15 species each (Table-3).

23

Table 3: Plant families, numbers of genera and species recorded from Lwera wetland. No. of Family No. of Genera No. of species Family No. of species Genera Amaranthaceae 1 1 Lythraceae 1 1 Apiaceae 1 1 Malvaceae 5 7 Apocynaceae 1 1 Melastomataceae 1 1 Asteraceae 14 15 Onagraceae 1 1 Campanulaceae 1 1 Pedaliaceae 1 1 Commelinaceae 2 2 Phyllanthaceae 1 1 Convolvulaceae 2 2 Poaceae 7 8 Cucurbitaceae 2 2 Rubiaceae 1 1 Cyperaceae 4 7 Thelypteridaceae 1 1 Dioscoreaceae 1 1 Typhaceae 1 1 Euphorbiaceae 1 2 Verbenaceae 1 1 Fabaceae 7 14 Vitaceae 1 1 Lamiaceae 1 1 ______

4.2.2 Relative abundance of plant forms and species at the study sites (Zones) The plant forms with the highest relative abundance were the grasses (52.38%) followed by the Sedges (23.54%) and the least were the shrubs (0.88%) (Tables 4-8). The Herbs had the largest number of species types (40 species) followed by the climbers and grasses.

Table 4: Relative abundance of Grasses in the four zones High pit Medium Low pit Virgin Total Rel. Code Scientific Name Family zone pit zone Zone Zone abundance(%) BS-69 Acrocerus zizanioides Poaceae 0 0 14 0 14 0.10 BS-34 Digitaria abyssinica Poaceae 12 12 68 0 92 0.65 BS-08 Digitaria velutina Poaceae 580 0 0 0 580 4.12 BS-05 Echinochloa pyramidalis Poaceae 646 176 82 249 1153 8.19 BS-79 Imperata cylindrical Poaceae 0 0 24 0 24 0.17 BS-20 Leersia hexandra Poaceae 1346 1024 928 1938 5236 37.17 BS-59 Lipocarpha sp Poaceae 0 62 0 0 62 0.44 BS-30 Setaria sphacelata Poaceae 202 0 0 0 202 1.43 BS-52 Typha sp Typhaceae 0 6 0 9 15 0.11 Total 52.38

Table 5: Relative abundance of Sedges in the four zones High pit Medium Low pit Virgin Total Rel. Code Scientific Name Family zone pit zone Zone Zone abundance(%) BS-80 Cyperus cyperoides Cyperaceae 0 44 140 0 184 1.31 BS-38 Cyperus denudatus Cyperaceae 16 124 144 269 553 3.93 BS-90 Cyperus polystachyos Cyperaceae 0 0 4 0 4 0.03 BS-16 Cyperus rotundus Cyperaceae 127 542 229 642 1540 10.93 BS-88 Fuirena spp Cyperaceae 0 6 6 0 12 0.09 BS-84 Kyllinga polyphylla Cyperaceae 0 160 145 0 305 2.15 BS-03 Mariscus sumatrensis Cyperaceae 719 0 0 0 719 5.10 Total 23.54 24

The grasses comprised nine species and were the most abundant plant form, with Leersia hexandra being the most abundant and Acrocerus zizanioides the least.

The Sedges comprised 7 types of plant species and the most abundant was the Cyperus rotundus followed by Mariscus sumatrensis. The least abundant was the Cyperus polystachyos.

Table 6: Relative abundance of Herbs in the four zones Code Scientific Name Family High Medium Low pit Virgin Total Rel. pit zone pit zone Zone Zone abundance(%) BS-81 Acmella caulirhiza Asteraceae 0 0 6 0 6 0.04 BS-19 Ageratum conyzoides Asteraceae 83 26 2 219 330 2.34 BS-72 Alysicarpus glumaceus Fabaceae 0 0 0 6 6 0.04 BS-51 Alysicarpus ferrugineus Fabaceae 0 2 0 0 2 0.01 BS-37 Alysicarpus rugosus Fabaceae 20 0 0 0 20 0.14 BS-56 Amaranthus spinosus Amaranthaceae 0 6 0 0 6 0.04 BS-58 Ammannia sp Lythraceae 0 6 0 0 6 0.04 BS-01 Bidens pilosa Asteraceae 112 0 0 0 112 0.80 BS-04 Commelina benghalensis Commelinaceae 480 20 4 18 522 3.71 BS-13 Conyza floribunda Asteraceae 18 14 66 0 98 0.70 BS-75 Crassocephalum montuosum Asteraceae 0 0 1 0 1 0.01 BS-21 Crassocephalum sp Asteraceae 11 8 0 6 25 0.18 BS-54 Crotalaria verrucose Fabaceae 4 2 0 0 6 0.04 BS-76 Desmodium hirtum Fabaceae 0 0 1 0 1 0.01 BS-77 Desmodium salicifolium Fabaceae 0 0 14 0 14 0.10 BS-33 Desmodium tortuosum Fabaceae 15 24 0 0 39 0.28 BS-23 Dissotis rotundifolia Melastomataceae 137 0 0 133 270 1.92 BS-45 Erlangea globose Asteraceae 7 2 0 0 9 0.06 BS-60 Ethulia conyzoides Asteraceae 0 10 0 0 10 0.07 BS-02 Euphorbia heterophylla Euphorbiaceae 71 6 0 0 77 0.55 BS-11 Euphorbia hirta Euphorbiaceae 184 0 0 0 184 1.31 BS-07 Galinsoga parviflora Asteraceae 6 0 0 0 6 0.04 BS-53 Gnaphalium sp Asteraceae 0 4 0 0 4 0.03 BS-74 Gomphocarpus physocarpus Apocynacea 0 0 1 0 1 0.01 BS-73 Hibiscus cannabinus Malvaceae 0 0 1 0 1 0.01 BS-40 Indigofera drepanocarpa Fabaceae 4 0 1 0 5 0.04 BS-36 Indigofera tinctoria Fabaceae 60 14 26 0 100 0.71 BS-25 Ludwigia repens Onagraceae 22 34 26 59 141 1.00 BS-66 Murdannia simplex Commelinaceae 0 0 2 0 2 0.01 BS-06 Phyllanthus niruri Phyllanthaceae 8 0 0 0 8 0.06 BS-67 Pluchea carolinensis Asteraceae 0 14 26 0 40 0.28 BS-12 Sesamum angustifolium Pedaliaceae 2 0 0 0 2 0.01 BS-82 Sida rhombifolia Malvaceae 0 4 10 0 14 0.10 BS-10 Synedrella nodiflora Asteraceae 20 0 0 0 20 0.14 BS-35 Tephrosia paniculata Fabaceae 118 0 0 0 118 0.84 BS-68 Tridax procumbens Asteraceae 0 0 6 0 6 0.04 BS-87 Triumfetta pilosa Malvaceae 0 10 10 0 20 0.14 BS-89 Triumfetta spp Malvaceae 0 2 2 0 4 0.03 BS-22 Thelypteris confluens Thelypteridaceae 119 24 7 169 319 2.26 BS-24 Urena lobata Malvaceae 52 12 20 101 185 1.31 Total 19.45

Out of the 40 species of herbs, the most abundant was the Commelina benghalensis followed by the Ageratum conyzoides and Dissotis rotundifolia. 25

Table 7: Relative abundance of Climbers and Creepers in the four zones High pit Medium Low pit Virgin Total Rel. Code Scientific Name Family zone pit zone Zone Zone abundance(%) BS-48 Cayratia ibuensis Vitaceae 0 2 0 0 2 0.01 BS-78 Dioscorea bulbifera Dioscoreaceae 0 0 4 0 4 0.03 BS-65 Diplocyclos palmatus Cucurbitaceae 0 0 2 0 2 0.01 BS-31 Glycine wightii Fabaceae 10 0 2 0 12 0.09 BS-15 Hewittia scandens Convolvulaceae 2 0 0 0 2 0.01 BS-26 Mikania cordata Asteraceae 3 0 0 2 5 0.04 BS-18 Monopsis stellarioides Campanulaceae 16 0 0 27 43 0.31 BS-17 Oldenlandia corymbosa Rubiaceae 146 0 0 12 158 1.12 BS-28 Zehneria scabra Cucurbitaceae 6 0 0 0 6 0.04 BS-86 Ipomoea cairica Convolvulaceae 0 6 6 0 12 0.09 BS-85 Mimosa pudica Fabaceae 0 2 2 0 4 0.03 BS-46 Centella asiatica Apiaceae 3 30 244 0 277 1.97 Total 3.75

The relative abundance of the climbers and creeper was 3.75% with the most abundant being the Centella asiatica and least were Cayratia ibuensis, Diplocyclos palmatus and Hewittia scandens.

Table 8: Relative abundance of Shrubs in the four zones High Medium Low pit Virgin Total Rel. Code Scientific Name Family pit zone pit zone Zone Zone abundance(%) BS-70 Abutilon longicuspe Malvaceae 0 0 4 0 4 0.03 BS-64 Lantana trifolia Verbenaceae 0 0 6 0 6 0.04 BS-50 Mimosa pigra Fabaceae 0 20 14 11 45 0.32 BS-47 Tithonia rotundifolia Asteraceae 0 2 8 0 10 0.07 BS-32 Desmodium sp Fabaceae 35 0 6 0 41 0.29 BS-83 Hibiscus sp Malvaceae 0 4 10 0 14 0.10 BS-29 Pycnostachys sp Lamiaceae 4 0 0 0 4 0.03 Total 0.88

The shrubs were the least abundant plant form in the study area and these comprised 7 types of plant species with a relative abundance of 0.88%. The most abundant was Mimosa pigra.

Table 9: Summary of taxa of plants in the four zones Taxa High pit zone Medium pit zone Low pit zone Virgin zone Number of Individual plants 5426 2466 2330 3864 Number of families 18 15 14 12 Number of genera 34 34 35 15 Number of species 39 38 44 16

When zone data were compared (Table 9), the high pit zone had the highest number of families followed by the medium pit zone; while the low pit zone had the highest number of species followed by the high pit zone. The virgin zone had the lowest number of families, genera and species.

26

4.2.3 Species Diversity Species Diversity ranged from 1.7 to 2.57 (Shannon Diversity Index, 퐻1) with the high pit zone having the highest diversity. Evenness (E) ranged from 0.569 to 0.7 and the most even zone was the high pit zone. Margalef (d) was highest in the low pit zone (Table 10).

Table 10: Diversity indices, evenness, and species richness in the different zones.

Zones Shannon-wiener (H') Evenness (E) Margalef (DMg ) High pit zone 2.57 0.700 4.419 Medium pit zone 2.07 0.569 4.737 Low pit zone 2.31 0.610 5.546 Virgin zone 1.70 0.614 1.816

The High Pit zone was the most diverse site with the most evenly distributed species while the virgin zone was the least diverse. The richest zones in terms of number of species (Margalef–d) was the Low pit zone followed by the Medium pit zone. The ANOVA test showed that the Shannon Diversity Index, 퐻1 was significantly higher in the HPZ (2.57 ± 0.02, meant ± SD) followed by the LPZ (2.31 ± 0.01) and was least in the Virgin Zone (1.73 ± 0.02), F=1995.27, P˂0.001. The Tukey’s HSD tests showed that the four zones were different in terms of diversity indices, Appendix 3.

4.2.4 Plant Species Turn Over (β-diversity) The most abundant species in the high pit zone were Leersia hexandra and Mariscus sumatrensis while in the medium pit zone, the most abundant species were L. hexandra and Cyperus rotundus. In the low pit zone, the most abundant species were L. hexandra and Centella asiatica. Lastly, in the Virgin zone, the most abundant species were L. hexandra and C. rotundus

Nine (9) species were common in all the four sites: Ludwigia repens, Urena lobata, Thelypteris confluens, Ageratum conyzoides, Commelina benghalensis, Cyperus denudatus, Echinochloa pyramidalis, C. rotundus and, L. hexandra. The most common plant species in all the four sites was L. hexandra.

The high pit zone had the highest number of individuals of non-natives species which included among others; D. tortuosum (137), Erlangea globose (719), Indigofera tinctoria (202) while the virgin zone was found to have least, Appendix 4.

27

Similarity The distance correlation (ward linkage) between the high pit zone (hpz) and medium pit zone (mpz) was minimal (0.68), and this shows that the high pit zone and the medium pit zone had relatively more plants species in common as compared to other paired zones. The virgin zone was less similar to all the other zones (Figure 4).

Figure 4: Linkage distances between the different strata/zones. The variables are: hpz= High pit zone, mpz= Medium pit zone, lpz=Low pit zone and vz= Virgin zone.

The turnover amongst the paired zones was generally high for all cases, however, the highest was in the paired zones-high pit zone and Virgin zone (β1-J =80%). The paired zones with the lowest turnover were the high pit zone and Medium pit zone (Table 11).

Table 11. Turnover among the different pairs of zones ZONES Low zone Medium zone High Zone Virgin zone Low pit zone 0 70.5 76.5 65.9 Medium pit zone 70.5 0 56.1 72.1 High pit Zone 76.5 56.1 0 80.0 Virgin zone 65.9 72.1 80.0 0

The CCA-biplot of species and environmental variables shows the relationship between wetland species distribution and environmental variables (Figure 5). Three axes CCA1 (46%), CCA2 (21%) and CCA3 (9%) explained the environmental gradients in species composition. Forward selection of environmental factors showed that pit area (F = 6.710, df = 1, p = 0.005), Zone (mined vs

28 unmined, F = 5.12, df = 1, p = 0.005) and pit density (F = 2.829, df = 1, p = 0.005) influenced the distribution of wetland species.

Figure 5. Canonical Correspondence Analysis (CCA) ordination biplot reflecting species’ distributions along gradients of environmental variables. The codes refer to species listed in Appendix 1.

The biplot shows that species like Alysicarpus glumaceus (Aly.glu) are restricted to the unmined zones. There are species like Leersia hexandra (Lee.hex), Echinochloa pyramidalis (Echi.pyr) that are generalists and appear in both mined and unmined zones. Certain species like Desmodium sp (Des.sp) and Glycine wightii (Gly.wig) prefer mined sites with high pit density and pit area. It is also observed that species like Centella asiatica (Cen.asi) and Tithonia rotundifolia (Tit.rot) are restricted to mined areas with low pit density and pit area.

29

CHAPTER FIVE DISCUSSION

5.1 Characterization of sand mining sites

The sand mined zones were characterized by large pits differing in surface area and depth. This could partially be explained by the presence of different mining companies or individuals operating at the different sites and employing different mining techniques. It could also have resulted from the erosion of pits making them expand which is similar to findings of Tejpal et al. (2014) and Musah (2009), that, sites which experience sand mining are characterized by deep pits which keep increasing as the rate of extraction increases, causing deep gullies because of erosion, loss of vegetation and destruction of the landscape. Mining disturbs the wetland surface, leaving open pits that are difficult to rehabilitate in physical and economic terms (Musah, 2009 and Tariro, 2013).

The high pit zone experienced the highest intensity of sand mining as compared to other zones and in this case was defined by the density of pits therein which was significantly higher than that in other zones. The summary statistics of density of pits and the average surface area showed that the high pit zone had more pits but shallow in terms of depth. This is because the zone was closest to the lakeshores, where operating heavy machines is difficult. This dictates that the pits are dug deeper where the water table is farther down and maintained shallower where the water table is close to the surface to guarantee safety (Akello, 2018). Therefore, the surface areas of pits in the high pit zone could be explained by the fact that depth inhibits deep mining and so the pits are made wider to maximize the sand excavated.

The average surface areas of pits in sand mined zones increased with the density of pits per zone depicting the intensity of sand mining. Pits in the sand mined zones reduced the total land area with the high pit zone undergoing the largest decrease in total land area. This is because it had pits with the largest average pit surface area and moreover, filled with water thereby lowering the total land area. This is similar to the conclusions of Rutledge (2003) that sand mining reduces the total land area. The decrease in total land area within the different sand mined zones was therefore attributed to the intensity of sand mining activities.

30

5.2 Species diversity and spatial turnover

The flora of Lwera wetland is comprised of a variety of plant species though the wetland cannot be termed as ‘Rich in diversity’ given that all the sites sampled had a Shannon Diversity value of less than 3.5. A community is said to be rich if it has a Shannon-Weiner Diversity value ≥3.5 (Kent & Coker, 1992).

The diversity at the different sites indicated that sand mined areas were more diverse with the high pit zone being the most diverse and most even. This could be explained by the intensity of sand mining at these sites as an ecological disturbance which had significantly impacted on the recruitment of certain native species and influenced other plant species like Erlangea globose, and Indigofera tinctoria that did not originally exist in the wetland. The heavy equipment and gravel stockpiles at or near the extraction sites could cause soil compaction, and increasing erosion by reducing soil infiltration and causing overland flow of water. This may have impacted on the recruitment of native species. This is also explained by the view that loss or change in habitat as a result of human activities sand mining on riparian vegetation may lead to a decline of native species and influence the growth of new species (Ashraf et al. 2011 and Koper et al. 2009).

The low pit zone was more species rich and diverse and compared to the medium pit zone. This zone like the high pit zone, also had more abandoned pits than the medium pit zone. The diversity and richness could have resulted from the fact that sand mining activities in the low pit zone were fewer and many pits had been abandoned by the miners for some time, thereby allowing for successional changes in the vegetation and appearance of many new plant species as lands had been left undisturbed for some time. Areas that have experienced an ecological disturbance and then left to fallow or are abandoned for some time are exposed to successional changes in vegetation leading to growth of native and exotic species (Fonge et al., 2011).

The virgin zone was the least diverse of all the sites and this could be attributed to naturogenic effects given that the top soils at this site had not been stripped for sand mining, which could have eventually affected the suitability of the soils to support the native vegetation (Tariro, 2013; Tejpal et al. (2014). In addition, the most abundant species at the virgin zone was Cyperus rotundus which is characteristic of most native wetland vegetation in Uganda.

31

The prevalence of certain species in particular zones could be a result of the degree of disturbance (density of pits, surface area of pits) within the mined zones (being highly degraded as compared to unmined zones). The disturbance of the soils and other environmental factors could have provided favorable environmental conditions that supported establishment of those species in such habitats. Similar findings were reported by Pausas & Austin (2001) and Wondie (2018), on species richness in relation to environment.

The spatial turn-over was generally high. However, it increased across all zones from the virgin zone to the high pit zone in line with pit density. High spatial turnover is an indication that the proportion of species shared between any two zones was very low. This could be attributed to the fact that sand mining activities created a different environment thereby increasing the possibility of other plant species occupying the modified habitat and/or the native species disappearing (Tischendorf & Fahrig, 2000). The spatial turnover between zones of the sand mined sites could also be hinged to the intensity of the disturbance (Hobbs & Huenneke, 1992) which creates different kinds of vegetation regrowth due to several factors resulting from extraction activities. This may have affected species turnover positively which tallies with findings in literature (e.g. Cadotte 2007, Ladlow 2015; Limberger & Wickham 2012, and Vanschoenwinkel, Brendonck & Buschke 2013).

32

CHAPTER SIX

CONCLUSION AND RECOMMENDATIONS

6.1. Conclusion Sand mining has resulted in destruction of vegetation and natural habitats by creating non-uniformly distributed, open pits that result in the overall reduction in vegetation cover. The pits are of various dimensions in depth and surface area, influenced by the distance of the mining site from the lakeshores. Zones that are closer to the lake have shallower pits than those farther away.

Sand mining has impacted on the diversity of plant species in the wetland. The magnitude of the impact is dependent on the intensity of the activity, with sites undergoing more sand mining experiencing a larger impact on the plant species diversity. The sites that had experienced sand mining had a higher diversity of vascular plants than those that had not due to the changes in the environmental conditions resulting from sand mining.

The spatial turn-over of plant species in the wetland is influenced by sand mining with sand mined areas supporting the growth of plant species that did not previously exist in the wetland. The impact on the spatial turnover is also dependent on the intensity of the activity and the density of pits therein. Sand mining increased the ability of new plant species to occupy the new habitat and/or the native species to disappear. Though this seems positive for biological integrity and rehabilitation of the pit-infested sites, sand mining has caused the loss and degradation of wetland species vital for ecosystem services and livelihood of local community. Low similarity in plant species composition between sand mined sites and the site that had no pits, indicates that changes in the wetland conditions due to sand mining also favors upland plant species.

Environmental variables induced by sand mining played an important role in influencing plant species composition. The differences in plant species observed across the sand mined and un mined zones and over other environmental variables such as density of pits and pit surface area should be considered when designing management strategies for sand mining in Lacustrine wetlands.

33

6.2. Recommendations 6.2.1 Recommendations for improved management and sustainable sand mining  The recommended density of pits per hectare should be limited to a maximum of 0.18 and the pit surface area of 6070m2. This is based on the result that the diversity within the medium pit zone was closer to the virgin zone. Sand mining guidelines should emphasize and specify the maximum number and size of pits to be permitted per unit area so as to regulate the activity.

 Sustainable sand mining techniques or guidelines are required. These should include restoration or rehabilitation of the sand mined areas using plant species that enable the wetlands to regain close to or their original wetland conditions and to perform their ecosystem services accordingly.

 In order to minimize interruptions in the wetland ecosystem functions, it is necessary that the restoration of each mined pit be done immediately with native vegetation before it is abandoned. Once abandoned without restoration, there is growth of various plant species that did not originally occur in the wetland.

 A rehabilitation strategy should be included in any management programme by the sand miners and continuous/ regular monitoring of the newly revegetated areas/ rehabilitated areas should be undertaken to identify long term performance of the planted species and to map out and control invasive species as these compete for vital resources like nutrients, light and water.

6.2.2 Recommendations for future research  Additional research and studies should be undertaken on the propagation methods for native plant species to be used in the rehabilitation of sand mined sites in the wetlands.

 Experiences can be borrowed from other areas where sand mining pits have been successfully rehabilitated such as in Australia and studies undertaken to customize this experience to the local situation in Uganda.

34

REFERENCES Akello, G. (2018). Interpreting the effects of sand mining in Lwera Wetland using landscape metrics and testing a rehabilitation approach. Kampala, Uganda. Amponsah-Dacosta, F., & Mathada, H. (2017). Study of sand mining and related environmental problems along the Nzhelele River in Limpopo Province of South Africa. (S. L. Wolkersdorfer C, Ed.) Mine Water and Circular Economy, 1259-1266. Andrew, F. B. (1999). Linkages in the landscape: Role of corridors and connectivity in wildlife conservation. Australia: IUCN; Deakin University, Clayton, Victoria 3168. Arun, P. R., Sreeja, R., Srecbha, S., Maya, K., & Padmalal, D. (2006). River sand mining and its impact on physical and biological environments of kerala rivers, Southwest coast of india. Eco-Chronicle, 1, 1-6. Ashraf, M. A., Maah, M. J., Yusoff, I., Wajid, A., & Mahmood, K. (2011). Sand Mining effects, causes and concerns: A case study from Bestari Jaya, Selangor, Peninsular Malaysia. Scientific Research and Essays, 6(6), 1216-1231. Barry, J., & Marilyn, D. (2001). Factors determining mammal species richness on habitat islands and isolates. Global Ecology and Biogeography, 9(1), 19-37. doi: 10.1046/j.1365- 2699.2000.00184.x. Bibi, F., & Ali, Z. (2013). Measurement of diversity indices of avian communities at Taunsa Barrage wildlife Sanctuary, Pakistan. Animal and Plant Sciences, 469-474. Borcard, D., Gillet, F., & Legendre, P. (2018). Numerical Ecology with R. Springer (second ed.). Cham, Switzerland : Springer International Publishing. Bradshaw, D. A. (1987). The reclamation of derelict land and the ecology of ecosystems, In: Jordan WR, III, Gilpin ME, Aber JD. Restoration Ecology: A synthetic approach to ecological research, 53-74. BRGM. (2001). Management of mining, quarrying and ore-processing waste in the European Union. Study made for DG Environment, European Commission. Co-ordination by P.Charbonnier. Cadotte, M. W. (2007). Competition–Colonisation trade-offs and disturbance effects at multiple scales. Ecology, 88(4), 823-829. Carlo, H. H., Peter, M. H., & Karline, S. (1998). Indices of diversity and evenness*. Oceanis , 24(4), 61-87. Chase, J. M., & Leibold, M. A. (2002). Spatial scale dictates the productivity-biodiversity relationship. Nature, 416, 427-430. Constanza, R., d'Arge, R., Groot, R. d., Farberk, S., Grasso, M., Hannon, B., . . . Marjan , V. (1997). The value of the world's ecosystem services and natural capital. Nature, 15(387), 253-260. doi:10.1016/S0921-8009(98)00020-2 Cox, G. (1990). Laboratory Manual of general ecology (6th ed.). Dubuque, Iowa: William C. Brown. 35

Desperez, M., Pearce, B., & Le Bot, S. (2010). The biological impact of over flowing sand aroung a marine aggregate extraction site: Dieppe (easten english channel). ICES journal of marine science, 67(2), 270-277. Didham, R. K., V., K., & R, M. E. (2012). Rethinking the conceptual foundation of habitat fragmentation research. Oikos(121), 161-170. Eycott, A., & Watts, K. (2011). Biodiversity in Fragmented landscapes: reviewing evidence on the effects of landscape features on species movement. Edinburgh: Forest Commission Information note (FCRN010), Forest commission. F. Bibi, & Z. Ali. (2013). Measurement of Diversity Indices of Avian Communities at Taunsa Barrage Wildlife sanctuary, Pakistan. The Journal of Animal & Plant Sciences, 23(2), 469- 474. Facts, G. (2015, October 30). The Mining of Sand, a Non-Renewable resource. Retrieved July 10, 2018, from http://www.greenfacts.org/en/sand-extraction/index.htm#1 Fonge, B. A., Focho, A. D., Egbe, E. A., & Tening, A. S. (2011). The effects of climate and edaphic factors on plant colonisation of lava flows onMount Cameroon. Journal of Ecology and theNatural Environment, 3(6), 255-267. Gavriletea, M. D. (2017). Environmental Impacts of Sand Exploitation. Faculty of Business, Department of Business, Romania: Babe¸s-Bolyai University, Cluj-Napoca 400084. Gudmundur, H., Edda, S. O., & Olafur, E. (2005). Effects of Afforestation on Ecosystems, Landscape and Rural Development. Iceland: Reykholt. Guillaume, L., & Melodie, A. M. (2017, 04 13). Capturing the contribution of rare and common species to turnover; a multi-site version of generalised dissimilarity modelling. Retrieved from https://methodsblog.com/2017/04/13/multi-site-generalised-dissimilarity-modelling Haila, Y. (2002). A Conceptual Genealogy of Fragmentation Research: From Island Biogeography to Landscape Ecology. Ecological applications, 321-334. Heydari, M., & Mahdavi, A. (2009). The Survey of Plant Species Diversity and Richness between Ecological Species Groups (Zagros ecosystem, Ilam). Journal of Applied Science, 9(4), 745- 751. doi:10.3923/jas.2009.745.751 Hill, M. O. (1973). Diversity and Evenness: A Unifying Notation and Its Consequences. Ecology, 54, 427-432. Hobbs, J. R., & Huenneke, F. L. (1992). Disturbance, Diversity, and Invasion: Implications for Conservation. Conservation Biology, 6(3), 324-337. Holyoak, M., Leibold , M. A., & Holt, R. D. (2005). Metacommunities: spatial dynamics and ecological communities. 307-330. Hurst, M. V. (2002). HydrologiceEffects of sand mining in Lake Country. ER Jahna Industries Inc. Florida: Independent Geological Services, Inc. Kent, M., & Coker, P. (1992). Vegetation Description and Analysis:,. A Practical Approach. CRC Press, Boca Raton Ann Arbor and Belhaven press, 1, 255. 36

Kim, C. (2005). Impact analysis of river aggregate mining on river environment. water engineering, 9(1), 45-48. Koleff, P., Gaston, K. J., & Lennon, J. J. (2003). Measuring beta diversity for presence –absence data. Journal of Animal Ecology, 72, 367-382. Koper, N., Smith, A., A., C., Francis, C. M., & Fahrig, L. (2009). Confronting Collinearity: Comparing methods for disentangling the effects of habitata loss and fragmentation. Landscape Ecology(24), 1271-1285. Kraft, N. (2011). Disentangling the drivers of beta diversity along latitudinal and elevational gradients. Science, 334, 1348-1348. Krause, C., Diesing, M., & Arlt, G. (2010). The physical and biological impact of sand extraction: A case study of the western Baltic sea. Journal of coastal research, 51, 215-226. Ladlow, C. (2015). An Assessment of the impacts of sand mining. Unguja, Zanzibar: Independent study project (ISP) collection. paper 2048. Langer, W. H. (2003). A General Overview of the Technology of In-Stream Mining of Sand and Gravel Resources, Associated Potential Environmental Impacts, and Methods to Control Potential Impacts. U.S. Department of the Interior | U.S. Geological Survey. U.S. Geological Survey Open-File Report 02-153. Retrieved from http://pubs.usgs.gov/of/2002/ofr-02-153 Leeuw, J. D., Shankmam, D., Wu, G., De Boer, W. F., Burnham, J., Quing, H., . . . Xiao, J. (2010). Strategic assessment of the magnitude and impacts of sand mininh in Poyang lake, China. Regional Environmental Change, 10, 95-102. Limberger , R., & Wickham, S. A. (2012). Disturbance and diversity at two spatial scales. Oecologia, 168(3), 785-95. Magurran, A. E. (2004). Measuring Biological Diversity. Malden, MA: Blackwell Science Ltd. Marh, B. S., & Panthania, S. S. (2008). Environmental degradation due to slaye mining: a case study of Khanyara Mines, Kangra District, Himachal Pradesh. Perspectives in Resources Management in developing countries, 2, (part 1: population, Resources and development), 232-243. Mbeche, R., & Bagyenda, R. (2017). Strategic Partnerships for wetland management: Lessons from Uganda. JKUAT Annual scientific conference. Nairobi: JKUAT. McIntyre, S., & Hobbs , R. (1999). A Framework for Conceptualizing Human Effects on Landscapes and Its Relevance to Management and Research Models. Conservation Biology(13), 1282 - 1292. doi:10.1046/j.1523-1739.1999.97509.x. Ministry of Water and Environment, W. M. (2017). Lake Nabugabo Wetlands System Ramsar Site. Kampala: Ministry of Water and Environment. Mitsch, W., & Gosselink, J. G. (2000). The Value of Wetlands: Importance of Scale and Landscape Setting. Ecological Economics, 35(1), 25-33. doi:10.1016/S0921-8009(00)00165-8

37

Monica, l. P., Roger, L. S., Tony, J. S., & Richard, E. E. (2004). Plant Species Diversity in a Grassland Plant Community: Evidence for forbs as a critical Management Consideration. Westrn North American Naturalist, 64(2), 219-230. Mpigi District Local Government. (2010). District state of the Environment. Mpigi District Local Government. Mpigi District Local Government, 2. 2., Water dept, 2., & Health dept, .. 2. (2010). Retrieved September 2016, from http://www.nemaug.org/district_reports/mpigi_2010_reports.pdf. Musah, J. A. (2009). Assessment of Sociological and Ecological Impacts of Sand and Gravel mining – a case study of east Gonja district (Ghana) and Gunnarsholt (Iceland). Ghana: unpublished. NARO. (2015/2016). Annual Report 2015/2016. Entebbe, Uganda: National Agricultural Research. NEMA. (2006). State of Environment Report for Uganda. Kampala, Uganda: National Environment Management Authority. NEMA. (2010). National State of the Environment Report for Uganda. National Environment Management Authority. NEMA. (2014). State of the Environment Report for Uganda. Kampala: National Environment Management Authority (NEMA). Onoduku, U., Oke, S., Essien, B., Idris, F., Umar, A., & Ahmed, A. (2014). Environmental Effects of Sand and Gravel Mining on Land and Soil in Luku, Minna, Niger State, North Central Nigeria. Geosciences and Geomatics, 2(2), 42-49. doi:10.12691/jgg-2-2-1 Padmalal, D., Maya, K., Sreebha, S., & Sreeja, R. (2008). Environmental effects of river sand mining: a case from the river catchment of Vembanad Lake southwest coast of India. Environmental Geology, 54, 879-889. Pausas, J. G., & Austin, M. P. (2001). Patterns of plant species richness in relation. Journal of Vegetation Science, 12(2), 153-166. Peckenham, J. M., Thornton, T., & Whalen, B. (2009, January). Sand and gravel mining: effects on ground water resources in Hancock county, Marine, USA. Environmental Geology, 56(6), 1103-1114. doi:10.1007/s00254-008-1210-7 Rutledge, D. (2003). Landscape indices as measures of the effects of fragmentation: can Pattern reflect processes? Wellington.27p: DOC Science Internal Series 98. New Zealand Department of Conservation. Saunders, D. A., Hobbs, R. J., & Margules, C. R. (1991). Biological Consequences of Ecosystem Fragmentation: A Review. Conservation Biology, 5(1), 18-32. Saviour, M. N. (2012). Environmental impacts of soil and sand mining: a review. International journal of science, environment and Technology, 1(3), 125-134. Simensen, T., Halvorsen, R., & Erikstad, L. (2018). Methods for landscape characterisation and mapping: A systematic review. Land Use Policy, 557-569.

38

Singh, G., Mehta, K. K., Sharma, R. C., Chawla, K. L., Joshi, P. K., & Yaduvanshi, N. P. (2007). Sand mining or no sand mining in agriculture fields in Haryana. Central Soil Salinity Research Institute. Karnal, Haryana, India: Technical report: 1-28. Tamang, L. (2013). Quarrying activities along the lower Balason River in Darjeeling district, West Bengal. Indian Journal of Spatial science, 4 (1), 1-8. Tariro, M. (2013). Case studies of environmental impacts of sand mining and gravel extraction for urban development in Gaborone. Gaborone: Unicersity of South Africa. Teijpal, M. S., Jaglan, B. S., Chaudhary, B. S., Karnal, & Haryanahence. (2014). Geo- Environmental Consequences of River Sand and Stone Mining: A Case Study of Narnaul Block, Haryana. 36(2), 1-16. Teja , T., Jason, M. T., Tatyana, A. R., Raphael, K. D., & Lenore, F. (2012). Landscape moderation of biodiversity patterns and processes. Biological Reviews, 8(3), 661-685. Doi:10.1111/j.1469-185X.2011.00216.x. Tejpal, Jaglan, M. S., Chaudhary, B. S., Karnal, & Haryana. (2014). Geo-Environmental Consquences of River Sand and Stone Mining: A case study of Narnaul Block, Haryana. 36(No. 2), 218-234. Tischendorf, L., & Fahrig, L. (2000). On the usage and measurement of landscape connectivity. Oikos, 90(1), 7-19. doi:https://doi.org/10.1034/j.1600-0706.2000.900102.x Tropical Biology Association . (2012, January 20). Developing Research Capacity among African Environmental Scientists. DRECA Research toolkit. Cambridge, United Kingdom: Tropical Biology Association. Tudor, C. (2015). landscape character assessment. Natural England: Landscape Institute. Turchi, M. G., Patricia, L. K., Dean, U., & Dale, H. (1995). Bird species richness in relation to isolation of Aspen Habitats. Wilson Bulletin, 107(3), 463-474. U.S. Department of Agriculture, F. S. (1999). Sampling Vegetation Attributes; Interagency Technical Reference. Colorado: U.S. Department of the Interior, Bureau of Land Management’s National Applied Resource Sciences Center. UBOS, U. (2016). STATISTICAL Abstract. Kampala: UBOS. Uganda National Meteorological Authority. (2016). National Weather Report 2016. . Kampala: Ministry of Water and Environment. UNDP. (2018). Baseline Assessment of development minerals in Uganda. Market Study and Value chain Analysis. United Nations Development Programme. UNEP. (2014). Ecosystem management, Environmental governance, Resource Effeciency; Sand, rarer than one thinks. UNEP Global Environmental Alert Services(GEAS). Uścinowicz, S., Jegliński, W., Miotk-Szpiganowicz, G., Pączek, U., Przezdziecki, P., & Szefler, K. (2014). Impact of sand extraction from the bottom of the Southern Baltic Sea on the relief and sediments of the seabed. OCEANOLOGIA(56), 857-880.

39

USGS. (2013). Mineral Commodity Summaries. Reston, VA: U.S Geological Survey. Vanschoenwinkel , B., Brendonck, L., & Buschke, F. (2013). Disturbance regime alters the impact of dispersal on alpha and beta diversity in a natural metacommunity. Ecology, 94, 2547-57. Vellend, M. (2001). Do commonly used indices of β‐diversity measure species turnover? Jounal of Vegetation Science, 2(4), 545-552. Watts, K., Humphrey, J. W., Griffiths, M., Quine, C., & Ray, D. (2005). Evaluating biodiversity in fragmented landscapes: principles. Edinburgh: Forest Commission Information Note (FCIN073). Forest Commission. Wessels, J. K., Van, J. A., Grimbeek, J. D., & Van der Linde, J. M. (1998). An evaluation of the gradsect biological survey method. Biodiversity and Conservation, 1093-1121. doi:10.1023/A:1008899802456 Whittaker, R. H. (1972). Evolution and Measurement of Species Diversity. Taxon, 21, 213-251. doi:http://dx.doi.org/10.2307/1218190 Whittaker, Robert, J., Willis, Katherine, J. K., Field, & Richard. (2001). scale and species richness: towards a general, hierarchical theory of species diversity. Journal of Biogeography, 4(28), 453-470. doi:doi:https://doi.org/10.1046/j.1365-2699.2001.00563.x Wondie, A. (2018, March 6). EcologicalconditionsandecosystemservicesofwetlandsintheLakeTanaArea,Ethiopia. Ecohydrology&Hydrobiology, 231-244. Zedler, J. (2000). Progress in wetland restoration ecology. Trends in ecology & evolution, 15(10), 402–407. Zedler, J., & Kercher, S. (2005). Wetland Resources: Status, Trends, Ecosystem Services, and Restoration. Annual Review of Environment and Resources, 15, 39-74.

40

APPENDICES

Appendix 1: Plant species composition of Lwera wetland

Family Genera Spp. codes Scientific Name Plant form 1 Malvaceae Abutilon Abu.lon Abutilon longicuspe Hochst. ex A. Rich. Shrub 2 Asteraceae Acmella Acm.cau Acmella caulirhiza (Delile) Herb 3 Poaceae Acrocerus Acr.ziz Acroceras zizanioides (Kunth) Dandy Grass 4 Asteraceae Ageratum Age.con Ageratum conyzoides (L.) Herb 5 Fabaceae Alysicarpus Aly.glu Alysicarpus glumaceus (Wall.) Herb 6 Fabaceae Alysicarpus Aly.fer Alysicarpus ferrugineus (Hochst. & Steud. ex A. Rich.) Herb 7 Fabaceae Alysicarpus Aly.rug Alysicarpus rugosus (Hochst. & Steud. ex A. Rich.) Herb 8 Amaranthaceae Amaranthus Ama.spi Amaranthus spinosus (L.) Herb 9 Lythraceae Ammania Amm.sp Ammannia sp (L.) Herb 10 Asteraceae Bidens Bid.pil Bidens pilosa (L.) Herb 11 Vitaceae Cayratia Cay.ibu Cayratia ibuensis (Hook. f.) Suess Climber 12 Apiaceae Centella Cen.asi Centella asiatica (L.) Urb. Creeper 13 Commelinaceae Benghalensis Com.ben Commelina benghalensis (L.) Herb 14 Asteraceae Conyza Con.flo Conyza floribunda (Carl Kunth) Herb 15 Asteraceae Crassocephalum Cra.mon Crassocephalum montuosum ((S. Moore) Milne-Redh. Herb 16 Asteraceae Crassocephalum Cra.sp Crassocephalum sp (Conrad Moench) Herb 17 Fabaceae Crotalaria Cro.sp Crotalaria verrucose (L.) Herb 18 Cyperaceae Cyperus Cyp.cyp Cyperus cyperoides (Britton, Nathaniel lord) Sedge 19 Cyperaceae Cyperus Cyp.den Cyperus denudatus (Vahl) Sedge 20 Cyperaceae Cyperus Cyp.pol Cyperus polystachyos (Rottb) Sedge 21 Cyperaceae Cyperus Cyp.rot Cyperus rotundus (Benth.) Sedge 22 Fabaceae Desmodium Des.hir Desmodium hirtum (Guill. & Perr.) Herb 23 Fabaceae Desmodium Des.sal Desmodium salicifolium (Poir.) DC. Herb 24 Fabaceae Desmodium Des.sp Desmodium sp (Desv.) Shrub 25 Fabaceae Desmodium Des.tor Desmodium tortuosum (Sw.) DC. Herb 26 Poaceae Digitaria Dig.aby Digitaria abyssinica (Hochst. ex A. Rich.) Stapf Grass 27 Poaceae Digitaria Dig.vel Digitaria velutina (Forssk.) P. Beauv Grass 28 Dioscoreaceae Dioscorea Dio.sp Dioscorea sp (L.) Climber 29 Cucurbitaceae Palmatus Dip.pal Diplocyclos palmatus (L.) C. Jeffrey Climber 30 Melastomataceae Dissotis Dis.rot Dissotis rotundifolia (Sm.) Triana Herb 31 Poaceae Echinochloa Ech.pyr Echinochloa pyramidalis (Lam.) Hitchc. & Chase Grass 32 Asteraceae Erlangea Erl.glo Erlangea globose (Robyns) Herb 33 Asteraceae Ethulia Eth.con Ethulia conyzoides (L. f.) Herb 34 Euphorbiaceae Euphorbia Eup.het Euphorbia heterophylla L. Herb 35 Euphorbiaceae Euphorbia Eup.hir Euphorbia hirta (L.C. Wheeler) Herb 36 Cyperaceae Fuirena Fui.sp Fuirena sp (L.) Sedge 37 Asteraceae Galinsoga Gal.par Galinsoga parviflora (Cav.) Herb 38 Fabaceae Glycine Gly.wig Glycine wightii (Graham ex Wight & Arn.) Verdc. Climber 39 Asteraceae Gnaphalium Gna.spp Gnaphalium spp (L.) Herb 40 Apocynacea Gomphocarpus Gom.phy Gomphocarpus physocarpus (E. Mey.) Herb 41 Convolvulaceae Hewittia Hew.sca Hewittia scandens (Milne) Mabb. Climber 42 Malvaceae Hibiscus Hib.can Hibiscus cannabinus (L.) Herb 43 Malvaceae Hibiscus Hib.spp Hibiscus spp (L.) Shrub 44 Poaceae Imperata Imp.cyl Imperata cylindrica (L.) Raeusch. Grass 45 Fabaceae Indigofera Ind.tin Indigofera tinctoria (L.) Herb 46 Fabaceae Indigofera Ind.dre Indigofera drepanocarpa (Bergman) Herb 47 Convolvulaceae Impomoea Ipo.cai Ipomoea cairica (L.) Sweet Creeper 48 Cyperaceae Kyllinga Kyl.sp Kyllinga sp (Peder Lauridsen Kylling) Sedge 49 Verbenaceae Lantana Lan.tri Lantana trifolia (L.) Shrub 50 Poaceae Leersia Lee.hex Leersia hexandra (Sw.) Grass 51 Poaceae Lipocarpha Lip.spp Lipocarpha spp Grass 52 Onagraceae Ludwigia Lud.rep Ludwigia repens (J.R. Forst.) Herb 53 Cyperaceae Mariscus Mar.sum Mariscus sumatrensis (Retz.) J. Raynal Sedge 41

Family Genera Spp. codes Scientific Name Plant form 54 Asteraceae Mikania Mik.cor Mikania cordata (Burm. f.) B.L. Rob. Climber 55 Fabaceae Mimosa Mim.pig Mimosa pigra (L.) Shrub 56 Fabaceae Mimosa Mim.pud Mimosa pudica (L.) creeper 57 Campanulaceae Monopsis Mon.ste Monopsis stellarioides (C. Presl) Urb. Climber 58 Commelinaceae Murdannia Mur.sim Murdannia simplex (Vahl) Brenan Herb 59 Rubiaceae Oldenlandia Old.sp Oldenlandia sp (L.) Climber 60 Phyllanthaceae Phyllanthus Phy.nir Phyllanthus niruri (L.) Herb 61 Asteraceae Plunchiea Plu.car Pluchea carolinensis (Jacq.) G. Don Herb 62 Lamiaceae Pycnostachys Pyc.spp Pycnostachys spp (l.) shrub 63 Pedaliaceae Sesamum Ses.ang Sesamum angustifolium (Oliv.) Engl. Herb Set.sph Setaria sphacelata (Schumach.) Stapf & C.E. Hubb. ex 64 Poaceae Setaria M.B. Moss Grass 65 Malvaceae Sida Sid.rho Sida rhombifolia (L.) Herb 66 Asteraceae Synedrella Syn.nod Synedrella nodiflora (L.) Gaertn. Herb 67 Fabaceae Tephrosia Tep.pan Tephrosia paniculata (Welw. ex Baker) Herb 68 Thelypteridaceae Thelypteris The.con Thelypteris confluens (Thunb.) C.V. Morton Herb 69 Asteraceae Tithonia Tit.rot Tithonia rotundifolia (Mill.) S.F. Blake Shrub 70 Asteraceae Tridax Tri.pro Tridax procumbens (L.) Herb 71 Malvaceae Triumfetta Tri.pil Triumfetta pilosa (Roth) Herb 72 Malvaceae Triumfetta Tri.sp Triumfetta sp (L.) Herb 73 Typhaceae Typha Typ.lat Typha latifolia (L.) Grass 74 Malvaceae Urena Ure.lob Urena lobate (L.) Herb 75 Cucurbitaceae Zehneria Zeh.sca Zehneria scabra (L. f.) Sond. Climber

42

Appendix 2: Data Sheet

SITE/ ZONE PHYSICAL DESCRIPTION SHEET

A: GENERAL INFORMATION

Recorder/Observer: Date: (dd. mm. year Stratum/Zone: e.g. (MDS/001/01):

GPS Eastings: GPS Northings: Elevation (m)

B. PHYSICAL DESCRIPTION 1. Area of zone (Hectares)………………………………………………… 2. General Landscape description: …………………………………………………………………………………

3. General Land-use: …………………………………………………………………………………

4. Presence of other forms of Land use apart from sand mining. (Yes/No). …………………..

5. If Yes, Mention them.

i) ………………………………………………………………..

ii) ………………………………………………………………..

iii) ……………………………………………………………………

6. Nature of Disturbance on site/zone (Tick where applicable) i) Access Tracks ii) Cleared Land iii) Bee Hives iv) Drains v) Borrow/ Sand pits vi) Fire Breaks vii) Off Road Vehicle viii) Rubbish dumping ix) Slashing x) Watering Points

43

7. In case of presence of Sand Pits, give details of pits: a) Number of pits found on site. ……………………………………………….. b) Density of pits per zone………………………………………………… c) Fill in the Tables below;

PIT Depth Surface Area Shape Of Pit Years of Existence No.

44

VEGETATION DESCRIPTION DATA SHEET

A: GENERAL INFORMATION Recorder/ Observer: Date: Stratum/Zone: Plot:

Quadrant: e.g. (MDS/001/01/01) GPS Eastings: GPS Northings:

Size of Quadrant…………………………... B: PLANT SPECIES IDENTIFICATION 1. Total Number of Plant Species Identified. .……………….

2. Total Number of Individual Plant identified. …………………………

Code Species Binomial name No. of Family Genus Plant form plants

45

Appendix 3: ANOVA Tests

One-way ANOVA: Pit_density versus Zone

Source DF SS MS F P Zone 2 0.027474 0.013737 31.17 0.000 Error 9 0.003966 0.000441 Total 11 0.031440

S = 0.02099 R-Sq = 87.39% R-Sq(adj) = 84.58%

Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev -+------+------+------+------HPZ 4 0.23450 0.02340 (----*----) LPZ 4 0.11750 0.01708 (----*---) MPZ 4 0.18200 0.02197 (---*----) -+------+------+------+------0.100 0.150 0.200 0.250

Pooled StDev = 0.02099

Tukey 95% Simultaneous Confidence Intervals All Pairwise Comparisons among Levels of Zone

Individual confidence level = 97.91%

Zone = HPZ subtracted from:

Zone Lower Center Upper ---+------+------+------+------LPZ -0.15846 -0.11700 -0.07554 (-----*-----) MPZ -0.09396 -0.05250 -0.01104 (-----*----) ---+------+------+------+------0.140 -0.070 0.000 0.070

Zone = LPZ subtracted from:

Zone Lower Center Upper ---+------+------+------+------MPZ 0.02304 0.06450 0.10596 (-----*-----) ---+------+------+------+------0.140 -0.070 0.000 0.070

One-way ANOVA: Pit_area versus Zone

Source DF SS MS F P Zone 2 14.4390 7.2195 256.78 0.000 Error 9 0.2530 0.0281 Total 11 14.6921

S = 0.1677 R-Sq = 98.28% R-Sq(adj) = 97.89%

Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ------+------+------+------+--- HPZ 4 3.1975 0.1391 (-*-) LPZ 4 0.5248 0.0172 (--*-) MPZ 4 1.6225 0.2543 (-*--) ------+------+------+------+--- 0.80 1.60 2.40 3.20 46

Pooled StDev = 0.1677

Tukey 95% Simultaneous Confidence Intervals All Pairwise Comparisons among Levels of Zone

Individual confidence level = 97.91%

Zone = HPZ subtracted from:

Zone Lower Center Upper -----+------+------+------+---- LPZ -3.0039 -2.6728 -2.3416 (--*-) MPZ -1.9062 -1.5750 -1.2438 (--*--) -----+------+------+------+---- -2.4 -1.2 0.0 1.2

Zone = LPZ subtracted from:

Zone Lower Center Upper -----+------+------+------+---- MPZ 0.7666 1.0978 1.4289 (--*--) -----+------+------+------+---- -2.4 -1.2 0.0 1.2

One-way ANOVA: Shannon versus Zone

Source DF SS MS F P Zone 3 1.529148 0.509716 1995.27 0.000 Error 12 0.003066 0.000255 Total 15 1.532213

S = 0.01598 R-Sq = 99.80% R-Sq(adj) = 99.75%

Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev -+------+------+------+------HPZ 4 2.5725 0.0185 (*) LPZ 4 2.3082 0.0055 *) MPZ 4 2.0776 0.0149 (*) VGZ 4 1.7310 0.0207 *) -+------+------+------+------1.75 2.00 2.25 2.50

Pooled StDev = 0.0160

Tukey 95% Simultaneous Confidence Intervals All Pairwise Comparisons among Levels of Zone

Individual confidence level = 98.83%

Zone = HPZ subtracted from:

Zone Lower Center Upper --+------+------+------+------LPZ -0.29786 -0.26429 -0.23073 *) MPZ -0.52837 -0.49481 -0.46124 (* VGZ -0.87502 -0.84146 -0.80789 (*) --+------+------+------+------0.80 -0.40 -0.00 0.40

Zone = LPZ subtracted from: 47

Zone Lower Center Upper --+------+------+------+------MPZ -0.26408 -0.23051 -0.19695 (*) VGZ -0.61073 -0.57716 -0.54360 (* --+------+------+------+------0.80 -0.40 -0.00 0.40

Zone = MPZ subtracted from:

Zone Lower Center Upper --+------+------+------+------VGZ -0.38021 -0.34665 -0.31309 (*) --+------+------+------+------0.80 -0.40 -0.00 0.40

Appendix 4: List of the Non-native species in Lwera wetland

Scientific Name HPZ MPZ LPZ Virgin Zone Total Bidens pilosa 2 0 0 0 2 Conyza floribunda 11 14 66 0 91 Crotalaria verrucose 6 2 0 0 8 Cyperus cyperoides 0 44 140 0 184 Desmodium hirtum 0 0 1 0 1 Desmodium salicafolium 0 0 14 0 14 Desmodium sp 127 0 6 0 133 Desmodium tortuosum 137 24 0 0 161 Erlangea globose 719 2 0 0 721 Ethulia conyzoides 0 10 0 0 10 Euphorbia heterophylla 2 6 0 0 8 Euphorbia hirta 8 0 0 0 8 Hibiscus cannabinus 0 0 1 0 1 Hibiscus sp 0 4 10 0 14 Imperata cylindrical 0 0 24 0 24 Indigofera tinctoria 202 14 26 0 242 Mikania cordata 71 0 0 2 73 Mimosa pigra 0 20 14 11 45 Mimosa pudica 0 2 2 0 4 Monopsis stellaroides 16 0 0 27 43 Phyllanthus niruri 4 0 0 0 4 Plunchiea carolinensis 0 14 26 0 40 Sesamum angustifolium 10 0 0 0 10 Sida rhombifolia 0 4 10 0 14 Tithonia rotundifolia 0 2 8 0 10

Data source: Invasive Species Compendium (https://www.cabi.org) & Global invasive species database (http://www.iucngisd.org)

48