Development and application of spatial and temporal statistical methods for unbiased wildlife sampling

Current methods of obtaining information on wildlife populations are based on monitoring programmes using periodic surveys. In most cases aerial techniques are applied. Reported numbers are, however, often biased and imprecise, making it difficult to use this information for management purposes. This thesis develops suitable statistical procedures to improve sampling of wildlife populations. It investigates survey and analysis procedures and proposes improvements and modifications to existing methods. Data analysed in the study originate two study areas in Kenya: Masai Mara National Reserve and Laikipia ecosystem.Chapter 1 gives a general introduction to the thesis. It formulates the motivation, objectives and scope of the research.Chapter 2 investigates different current sampling designs in aerial surveys, with particular focus on systematic and stratified random sampling. Sampling error is a major cause of biased and imprecise estimates of population parameters. Occurrence of several wildlife species in herds violates common assumptions in current sampling methods. This chapter investigates and discusses advantages and disadvantages of two common sampling designs in wildlife surveys: simple random sampling and a modified systematic sampling design known as systematic reconnaissance flights (SRF). It proposes an adaptive sampling design as an alternative that takes clustering of wildlife populations into account and uses criteria on observed animal counts to maximise sampling information. For such populations, the adaptive design is found to be more efficient than the common designs, showing a decrease in the standard error of up to 37%. The comparison focuses on three animal species of varying social behaviour: the elephant ( Loxodonta africana ), kongoni ( Alcelaphus buselaphus ) and wildebeest ( Connochaetes taurimus ).Chapter 3 integrates generalised linear modelling with geographic information systems to model abundance and distribution of wildlife in space and time. The chapter focuses on the distribution of elephant during nine successive surveys. It analyses their temporal and spatial distribution and relates these to 12 environmental variables using generalised linear modelling. A principal component analysis identifies five principle components, thereby reducing dimensionality in the data. The number of variables explaining elephant abundance is subject to large variations during successive surveys with a minimum of four and maximum of eight variables. In general, variables related to the protected reserve have more influence on elephant abundance. This chapter also develops a simple distance measure to calculate spatial correlation for wildlife data obtained through aerial surveys by quantifying clustering for different animal species. The procedure is illustrated by data on elephant, kongoni, wildebeest and zebra ( Equus burchelli ). The measure captures clustering in the wildebeest and zebra, which have over 20 times more observations within short distances compared to the other two species.Chapter 4 introduces more modern statistical procedures and applies them for better management of wildlife by addressing three key issues: determination of abundance, modelling of animal distributions and variability of diversity in both space and time. Prior information is incorporated in Markov Chain Monte Carlo (MCMC) methods and used to improve estimates of abundance. The new abundance estimates are up to 35% more accurate when compared to those obtained by the common Jolly II method. Modelling distribution is improved by developing a simple space-time procedure within a geographical information system, which includes modelling of autocorrelation in wildlife counts. Significant temporal changes in spatial patterns are found from a space-time analysis of elephant counts over a 20-year period, with strong interactions over 5 km and 6 months space and time separations, respectively. Spatial dependence is found to account for most variation when modelling species distribution. The chapter further proposes a diversity index suitable for monitoring changes in trend of large herbivores and based on transect data. The index is sensitive to both high abundance and species richness and is able to capture year to year variation. It indicates an overall marginal decrease in large herbivore diversity for in the Masai Mara ecosystem. The diversity index is easy be compute, thereby providing a handy tool for rapid decision making.Wildlife populations exhibit clustering in their distributions that is difficult to assess quantitatively by analysing transect data obtained from aerial surveys. Chapter 5 looks at this issue and analyses different clustering behaviour and characterises them using spatial point patterns analysis. This is made possible by the availability of a detailed data set, which gives geographic positions of each observed group of animals, leading to data that is amenable to spatial point pattern analysis. Nearest neighbour distance measures like the G -statistic and K -function are used to classify observed patterns as clustered, regular or completely random to correspond to three types of social behaviour, i.e. animals found in large herds, animals found in small to medium herds and solitary animals. Independence between species is tested using a multivariate extension of the K -function. Results show that spatial point patterns from Thomson's gazelle ( Gazellethomsoni ) and impala ( Aepyceros melampus ) come from strongly clustered populations. Clustering is explained for different wildlife species by relating observed patterns to environmental factors like vegetation type. This chapter demonstrates spatial point pattern analysis to be useful in determining and confirming species distribution patterns.In chapter 6 once more takes advantage of the detailed data set to develop a procedure that combines statistical simulation techniques and GIS to compare performance of the two common sampling designs, random and systematic, to the adaptive design. The intensive simulation in a GIS compares distribution, sampling and estimation of abundance. The chapter further assesses impact of sampling designs and intensities on estimates of population parameters from the three designs. Performance is compared by means of the root mean square errors at three increasing sampling intensities. Results show an increase in precision of estimates with increasing sampling intensity, while no significant differences are observed between estimates obtained with the two common sampling designs. The study demonstrates an increase in precision for the adaptive design, thereby stressing the importance of using such designs when sampling clustered populations.A brief outline is given in chapter 7 to aid a wildlife manager choose between different spatio-temporal techniques and other statistical methods introduced in earlier chapters of the thesis. This short chapter describes different scenarios encountered when making decisions related to the statistical aspects of wildlife management. This is made relevant by the fact that more and more data are collected in space and time and their proper analysis requires appropriate statistical attention. Selection of the right design and analysis method can result in significant savings in cost.In summary, this research shows that ecology can largely benefit from application of appropriate statistical techniques. In particular, estimation of population parameters like population size needs sound sampling strategies, while assumptions for each sampling design need to be carefully studied. Use of detailed data proved to be an important improvement in understanding spatial distribution of wildlife. This study suggests that it is better to first model spatial and temporal dependence, which is known to exist for many biological populations, before carrying out more detailed analysis. In general, this thesis shows that several existing techniques useful for studying dynamic populations can be extended and improved to provide tools that improve the information obtained from wildlife surveys.In conclusion, the following are main findings of this research:An adaptive sampling strategy as presented in this thesis is an extension to current sampling strategies that allows to sample clustered wildlife populations.Modelling of spatial dependence for individual wildlife species improves estimation of wildlife abundance.Modelling of spatial distributions of wildlife benefits from a further integration of statistical techniques in geographical information systems.An extension of current statistical methods with procedures to analyse spatio-temporal data allows assessing changes in wildlife populations in space and time.A simple diversity index as developed in this study shows a marginal decrease in diversity of large herbivores in the Masai Mara ecosystem.Data that are currently being collected by aerial transects are usually not sufficient for a rigorous statistical analysis. A higher resolution, in particular recording of individual animal locations, is necessary to model spatial distributions of wildlife using spatial point pattern analysis. If a spatial point pattern analysis is carried out, detailed information about the ecology of different species becomes available.</ul

Ecotourism and its ecological impact: A study of tourist developments in the Mara

The increased growth of wildlife tourism in Kenya over the last few decades has placed increasing demand and attention for the development and subsequent delivery of sustainable tourism. Today ecotourism ventures are perceived by many as a solution to the negative impacts of "traditional" wildlife tourism and thus a way to achieve ecologicalsustainability within the industry. To date however, there has been no attempt to qualifyand quantify any possible wildlife impacts of ecotourism - the basis of this research, usingthe Mara Ecosystem as a case study. Using WildKnowledge© software, this research recorded biotic and abiotic data from wildlife tourism developments of various sizes and assessed their anthropogenic impacts upon key ungulate species in the ecosystem over a three year period. The findings of this aspect of the research indicate that the effects of the tourism industry on wildlife are highly species specific. In particular Buffalo were most affected by differences in tourism seasonal variability (X2=5.040, df=l, p=O.025), distance to developments (X2=23.341, df=l, p=O.OOO) and group size (X2=7.998, df=1, p=O.005) between the different lodge types. In contrast, waterbuck and eland displayed similar patterns of disturbance irrespective of lodge type or tourism seasonal variability. Using historical species count data spanning a twenty year period, kernel density maps were constructed to demonstrate spatial changes in ungulate density and distribution patterns in relation to tourism growth. The resulting density maps revealed that while the national reserve offered a measure of security to wildlife, many ungulate species still heavily utilised their historical dispersal areas in the community lands. Interestingly, despite the tourism related land use changes demonstrated in the Mara's landscape, some species e.g. eland, displayed an increase in range size - to 4s0.5km2 in 2010 from 399.Skm2 in 2005 following the creation of wildlife conservancies in the surrounding ranches.Constructing site suitability models, the research explored how GIS modelling techniques can be employed to identify suitable locations for tourist accommodation, without compromising the ecological integrity of the wildlife areas where these facilities will be located. Employing two different bed occupancy models (conservancy model; 350 acres/bed and a current model; ; 174 acres/bed, derived from existing facilities), the Mara Ecosystem's ability to accommodate further tourism growth at low ecological cost was demonstrated. Application of the highest suitability criteria to select potential development sites revealed two suitable locations. A further 54 locations were identified as suitable for ecocamps and ecolodges on application of the second highest site selection suitability criteria. Importantly, the models employed clearly demonstrate that the majority of future ecotourism facilities be located outside the National Reserve in the group ranches if they are to have limited wildlife impact, as over-utilisation of any single sections of the ecosystem will lead to resource depletion and localized species loss. The results presented highlight the need for a more integrative approach to ecotourism provision. The utility of GIS based models to project the impacts of human disturbances on wildlife populations under different tourism scenarios is reinforced by this research. These suitability models are easily modified and can therefore be used under different planning scenarios in other wildlife areas in Kenya and the region. It is therefore hoped, that the results from this study will influence policy direction for tourism planning in wildlife areas for the Mara and other ecosystems, and be used to complement the country's tourism and wildlife bills which are about to be passed into law. This research concludes that although ecotourism plays an important role in environmental conservation, its ecological impacts on wildlife in receiving environments can be significant and should be a primary consideration in deciding upon the efficacy of individual proposals

Modelling cropland expansion and its drivers in Trans Nzoia County, Kenya

Population growth and increasing demand for agricultural production continue to drive global cropland expansions. These expansions lead to the overexploitation of fragile ecosystems, propagating land degradation, and the loss of natural diversity. This study aimed to identify the factors driving land use/land cover changes (LULCCs) and subsequent cropland expansion in Trans Nzoia County in Kenya. Landsat images were used to characterize the temporal LULCCs in 30 years and to derive cropland expansions using change detection. Logistic regression (LR), boosted regression trees (BRTs), and evidence belief functions (EBFs) were used to model the potential drivers of cropland expansion. The candidate variables included proximity and biophysical, climatic, and socioeconomic factors. The results showed that croplands replaced other natural land covers, expanding by 38% between 1990 and 2020. The expansion in croplands has been at the expense of forestland, wetland, and grassland losses, which declined in coverage by 33%, 71%, and 50%, respectively. All the models predicted elevation, proximity to rivers, and soil pH as the critical drivers of cropland expansion. Cropland expansions dominated areas bordering the Mt. Elgon forest and Cherangany hills ecosystems. The results further revealed that the logistic regression model achieved the highest accuracy, with an area under the curve (AUC) of 0.96. In contrast, EBF and the BRT models depicted AUC values of 0.86 and 0.77, respectively. The findings exemplify the relationships between different potential drivers of cropland expansion and contribute to developing appropriate strategies that balance food production and environmental conservation

Managing the Mount Kenya environment for people and elephants

Forests contain much global biodiversity, and over 90% of the worlds' poorest people depend on them. Few forests remain in East Africa, and these are vulnerable to further fragmentation from expanding settlement, and to over-exploitation by people and wildlife that become prone to over-crowding through isolation. Kenya contains 26 natural habitat fragments and only 3% of forest cover across five main forest blocks. These blocks form the main water towers in semi-arid Kenya on which people and wildlife, far beyond the protected boundaries, depend. Mount Kenya (MK) is the largest forest block, and the protection of its water catchment function is of national importance (Chapter 2). The five forest blocks in Kenya hold almost one third of the total of 28,806 elephants in Kenya, of which MK was estimated as having the largest highland elephant population with 2,911 (±640) individuals in 2001 (Chapter 3). Elephant estimates in forest are usually derived from dung count surveys, which are prone to bias and accordingly most often classed as C or D, in the range from A (best) to E (worst), in the African Elephant Database (AED). The MK elephant estimate described in this thesis was one of only two dung count estimates that were classed as quality B in the AED of 2002 (Chapter 3). Explanatory models based on the dung count data were integrated with a geographic information system (GIS) to develop the most advanced predictive seasonal distribution maps currently available for elephants in a forested environment (Chapter 4). Furthermore, least-cost elephant travel routes and foraging paths were digitally traced over cost surface images, developed from data on preferred elephant habitats in different seasons, physical barriers such as extreme slopes, and land use barriers such as farmland (Chapter 5). This enabled the location of elephant movements in relation to plantations inside the MK forest, and investigation of the relationship between measured tree damage in plantations and elephant movements (Chapter 5). Two areas where subsequently identified where elephant routes strayed from the forest into adjacent farmland, which was where most elephant crop damage was reported by farmers to Kenya Wildlife Service stations and outposts (Chapter 6). Elephants and people trespassing on each other's habitats is pronounced because MK is surrounded by a ring of small-scale farmers, totalling over 500,000 people living within 5,000m of the MK forest boundary on farms of 1.6ha on average (Chapter 6).Time-series analysis of satellite imagery of 1987,1995, and 2000 illustrated a gradual deterioration of MK land and resources, and results of an aerial survey conducted in 1999 showed high levels of illegal exploitation of land and resources (Chapter 7). However, management responsibility of the MK forest transferred from the Forestry Department to the Kenya Wildlife Service in July 2000, and time-series analysis of satellite images of 2000 and 2002 show regeneration of degraded MK land by 2002 (Chapter 8). Comparison of two aerial surveys conducted in 1999 and 2002, showed a significant reduction of illegal exploitation of forest resources on MK by 2002 (Chapter 8). Sound land use management plans are needed for MK to avoid deterioration of the forest by an over-crowded and confined elephant population, and by surrounding people. These plans need to address problems with longer term solutions, regardless of the short term disadvantages that they may entail (Chapter 9)

Understanding how land-use change in the Trans Mara District, Kenya is driving human-elephant conflict and elephant movement

Human-wildlife conflict is a global problem, due to habitat destruction and fragmentation, and it severely impacts the livelihoods of people and leads to the persecution and retributive killing of wildlife. In Kenya, human-elephant conflict is one of the most serious and challenging conservation issues. To successfully reduce conflict, management strategies and land-use planning must be informed and underpinned by robust evidence-based research. This thesis focused on understanding how land-use patterns and change in the Trans Mara District, Kenya, is driving human-elephant conflict and elephant movement. The aims of this thesis are to: (1) determine the implications of agricultural expansion on human-elephant conflict; (2) understand the seasonal, temporal and spatial drivers of crop raiding over time; and (3) investigate elephant pathway use and their role in human-elephant conflict. Methods used included risk mapping, landcover change scenario modelling, human-elephant conflict monitoring, fine scale spatial analysis of crop raiding using Generalised Additive Models, camera trapping, elephant sign surveys, qualitative focus groups and quantitative household surveys. The findings from this thesis show that the extent of agriculture land in the Trans Mara has increased by 42.5% between 2000 and 2015 and scenario modelling suggests that even with high future deforestation levels, large areas will remain susceptible to elephant crop raiding. The results also indicate that temporal, seasonal and spatial conflict trends are becoming less predictable, as crop raiding occurs throughout the year and affects crops at all stages of growth. This crop raiding has increased in frequency by 49% since 1999-2000 but has decreased in damage per incident by 83%, and increasingly involves a new group type consisting of elephant family units plus bulls. Results from this thesis also show that elephants used pathways between the Trans Mara and Masai Mara National Reserve at night, and that elephants preferred paths that had a high percentage of forest cover and were closer to farms, saltlicks and forest in the Trans Mara. In light of changing patterns of human-elephant conflict and landcover, land-use planning is crucial to balance the needs of humans and wildlife

INTEGRATED MODELING OF LAND USE AND CLIMATE CHANGE IMPACTS ON MULTISCALE ECOSYSTEMS OF CENTRAL AFRICAN WATERSHEDS

Assessment and management of ecosystem services demands diverse knowledge of the system components. Land use change occurring mainly through deforestation, expansion of agriculture and unregulated extraction of natural resources are the greatest challenges of the Congo basin and yet is central to supporting over 100 million people. This study undertook to implement an integrated modeling of multiscale ecosystems of central African watersheds and model the impact of anthropogenic factors on elephant population in Greater Virunga landscape. The study was conducted at varied scales, regional, landscape, and community. Regional study included watershed analysis and hydrological assessment using remotely sensed data implemented in Geographical information System. Species distribution modeling using generalized linear models using presence only and pseudo absence data was also done at regional scale. The major findings were that ecosystem services for the Congo basin are spatially varied and there are significant differences in their distribution of ecosystem services at a subwatershed level three and six and the distribution of endangered and threatened species is more concentrated in the central part of the basin and most of these species occur mainly inside protected areas. Poaching and sustained civil wars were the biggest threat to elephant conservation in the region. Improvement in law enforcement, monitoring, and increasing household incomes for communities living adjacent to protected areas would help to reduce the impact of poaching on elephant population dynamics. Climate change did not show immediate direct impacts on the elephant population, but thermal and latent heat effects could be occurring. A change in habitat, however, showed resulted in an upward trend in elephant population for all age classes. The local communities heavily depend on these ecosystem services for their livelihoods. It is therefore recommended that reasonable financing to strengthen law enforcement and monitoring is done, multiple policy options should be considered in order to maximize ecosystem benefits. Creation of partnerships and research networks need to be promoted. Existing transboundary collaboration initiatives should continue and be supported by the donor community in order to build peace and dram support for wildlife and wild habitat protection

Habitat fragmentation and metapopulation dynamics of the Angolan black and white colobus (Colobus angolensis palliatus) in coastal Kenya.

This study investigates the effects of habitat fragmentation on an Angola black-and-white colobus (Colobus angolensis palliatus) metapopulation in southern Kenya. 124 coastal forest fragments were surveyed in 2001. Fifty-five C. a. palliatus populations were found during this survey, (44% habitat patch occupancy), with an estimated national population estimate of 3,100 - 5,000 individuals. Colobus occurrence and density in this forest network was significantly linked to the spatial characteristics and quality of habitat patches. The heterogeneous landscape between habitat patches (matrix) was also found to be important, providing additional foraging habitat and connectivity between forest patches. The use of a spatially explicit metapopulation model (the incidence function model) provided a conceptual framework in which to explore future scenarios of habitat change. C. a. palliatus metapopulation persistence was found to be dependent upon the five largest forests in the network. Many of the colobus populations inhabiting unprotected forests were found to be on critical limits of population extinction. Population occupancy was also affected by the degradation or enhancement of the surrounding matrix

Assessing the underlying drivers of change over two decades of land use and land cover dynamics along the Standard Gauge Railway corridor, Kenya

We acknowledge funding from the UK Research and Innovation’s Global Challenges Research Fund (UKRI GCRF) through the Development Corridors Partnership project (project number: ES/P011500/1).Land cover has been modified by anthropogenic activities for thousands of years, although the speed of change has increased in recent decades, particularly driven by socio-economic development. The development of transport infrastructure can accelerate land use land cover change, resulting in impacts on natural resources such as water, biodiversity, and food production. To understand the interaction between land cover and social–ecological drivers, changing land cover patterns and drivers of change must be identified and quantified. This study documents land cover dynamics along the Standard Gauge Railway (SGR) corridor in Kenya and evaluates the underlying drivers of this change from 2000 to 2019. The study utilised GIS and remote sensing techniques to assess the land use and land cover changes along the SGR corridor, while correlational and regression analyses were used to evaluate various drivers of the changes. Results showed that built-up areas, bare lands, water bodies, croplands and forests increased by 144.39%, 74.73%, 74.42%, 9.32% and 4.85%, respectively, while wetlands, grasslands and shrub lands reduced by 98.54%, 67.00% and 33.86%, respectively. The underlying drivers responsible for these land use and land cover dynamics are population growth, urbanisation, economic growth and agro-ecological factors. Such land cover changes affect environmental sustainability, and we stress the need to adequately identify and address the cumulative social and environmental impacts of mega-infrastructure projects and their interacting investments. The findings of this study provide an evidence base for the evaluation of the social–ecological impacts of the SGR and the implementation of best practices that will lead to enhanced sustainability in the development corridors in Kenya and beyond.Publisher PDFPeer reviewe

A framework for adapting survey design through time for wildlife population assessment

Sampling strategies for monitoring the status and trends in wildlife populations are often determined before the first survey is undertaken. However, there may be little information about the distribution of the population and so the sample design may be inefficient. Through time, as data are collected, more information about the distribution of animals in the survey region is obtained but it can be difficult to incorporate this information in the survey design. This paper introduces a framework for monitoring motile wildlife populations within which the design of future surveys can be adapted using data from past surveys whilst ensuring consistency in design-based estimates of status and trends through time. In each survey, part of the sample is selected from the previous survey sample using simple random sampling. The rest is selected with inclusion probability proportional to predicted abundance. Abundance is predicted using a model constructed from previous survey data and covariates for the whole survey region. Unbiased design-based estimators of status and trends and their variances are derived from two-phase sampling theory. Simulations over the short and long-term indicate that in general more precise estimates of status and trends are obtained using this mixed strategy than a strategy in which all of the sample is retained or all selected with probability proportional to predicted abundance. Furthermore the mixed strategy is robust to poor predictions of abundance. Estimates of status are more precise than those obtained from a rotating panel design