Importance of wildlife conservation – Abstract
Human-elephant conflict is an often intractable problem that threatens the contribution of conservation interventions to human wellbeing and securing livelihoods in Africa and Asia. Local human populations living in key elephant ranges are among the world’s most poor and vulnerable people. In efforts to address this problem, previous studies have mainly focused on the direct impacts of conflict and applied standard regression models based on the assumption of individual-level homogeneity. More recently, human-elephant conflict has been seen to extend well beyond the physical, to the psychological and social sides of wellbeing. However, the impacts on human wellbeing have not been robustly explored, especially for local communities co-existing with elephants. We evaluated the impacts of conflicts on the wellbeing of local communities around the world-famous Masai Mara National Reserve in Kenya. We conducted 18 focus group discussions with 120 community members in different locations and administered a questionnaire survey to 367 sampled households from 26 sub-locations in Trans Mara. We used descriptive statistics with appropriate statistical tests, including propensity score matching, to evaluate the impacts of conflict on human wellbeing. Before matching, the results of the descriptive statistics showed differences between households experiencing conflicts and those without in terms of gender, age, education level, household size, benefiting from elephant conservation, main occupation and number of income sources. Our matching results indicate the existence of a significant negative and positive impacts on four and one of our eight wellbeing indicators for households that experienced conflicts, respectively. Better conflict mitigation approaches and conservation policies need to be adopted to realize the harmonious and concurrent development of ecological and wellbeing objectives.
Citation: Nyumba TO, Emenye OE, Leader-Williams N (2020) Assessing impacts of human-elephant conflict on human wellbeing: An empirical analysis of communities living with elephants around Maasai Mara National Reserve in Kenya. PLoS ONE 15(9):
Editor: Tunira Bhadauria, Feroze Gandhi Degree College, INDIA
Received: May 7, 2020; Accepted: September 9, 2020; Published: September 18, 2020
Copyright: © 2020 Nyumba et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: NT received financially supported by the Cambridge Commonwealth Trust through the Churchill/Sidney Sussex Southern African Cambridge Scholarship (https://www.cambridgetrust.org/about/cambridge-commonwealth-trust/). Additional funding came from the WildiZe Foundation (https://wildize.org/), the Wildlife Conservation Society (Tellus Leadership Scholarship) (https://www.wcs.org/about-us/grants/graduate-scholarship-program), the Wildlife Conservation Network (Schink Scholarship for Wildlife Conservation) (https://wildnet.org/tag/wcn-scholarship-program/), and Churchill College (Pennett Fund Grant and Lundgren Research Award) (https://www.chu.cam.ac.uk/student-hub/resources/financial-support/small-grants/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Competing interests: The authors have declared that no competing interests exist, financial or otherwise.
Importance of wildlife conservation – Introduction
In the global south, the struggle to alleviate poverty often entails finding a balance between use and preservation of natural resources [1, 2]. Under these circumstances, conservation has not only been portrayed as both a win-win solution for poverty alleviation and sustainable development, but also as a constraint on economic growth . In particular, conservation actions are expected to benefit human wellbeing, help secure livelihoods, yet pose little risk to the poor [2, 4, 5]. These benefits extend beyond human needs to how people use the resources to build their lives and achieve their notions of what it means to live well in their particular ecological context of wellbeing . Consequently, the need to understand local people’s aspirations for their wellbeing has become central to the success of conservation interventions at the local level [6, 7].
In Africa, changes in human populations and land-use patterns have led to an increasing overlap of critical areas of conservation importance, and widespread exclusion of large mammal species from their previous ranges [8, 9]. Coincidentally, these large mammals are also important conservation flagship species [10–13], whose role in conservation goes beyond other conservation surrogates like keystone or umbrella species that are selected for their ecological functions only . This is particularly true for African and Asian elephants (Loxodonta africana) and (Elephas maximus), respectively. Both species are commonly linked to some of the most intractable forms of conflict with rural communities [12, 14]. Human-elephant conflict (HEC) is not a new phenomenon in Africa [9, 15–18] and has evolved from being perceived as a nuisance to a major conservation concern across many elephant ranges [19, 20]. HEC is defined as “any interactions between human and elephants which result in negative effects on human social, economic or cultural life; elephant conservation or on the environment” . According to Madden , HEC can be considered as a phenomenon where elephants negatively impact on human wellbeing or where the actions of people are detrimental to the survival of elephants.
HEC follows diverse impact pathways on human wellbeing [21, 23–27]. These include direct threats to life and loss of productive assets such as livestock  and subsistence crises where extreme crop-raiding is prevalent . Furthermore, indirect threats caused by elephants can also impose curfews on school children and local residents through being closer to roads leading to schools and surrounding forests, further hindering access to essential social, cultural and economic services [29–31]. The impacts of other factors such as employment [32–34], income  and demographic variables [33, 36] have been highlighted in the literature. Furthermore, the interaction of these factors and underlying social, cultural, economic and conservation drivers are thought to contribute to complex and interdependent relationships between people and nature . Such interactions may impact on the local support for conservation and development projects based on the flow of positive benefits or negative social impacts on people’s wellbeing [38, 39]. Focusing on wellbeing offers a platform for evaluating the broader spectrum of gains and losses due to conservation interventions. In turn, this should ensure fair and just distribution of conservation benefits, and mitigation of conservation-related costs on local people [1, 37, 40, 41].
HEC represents one of the most intractable problems of conservation affecting human wellbeing in Africa and Asia. This distinction is epitomised in key elephant ranges where the local human populations comprise some of the world’s poorest and most vulnerable regarding food security, health, education, infrastructure and social institutions [8, 42]. In Kenya, HEC is widespread in all wildlife ranges across the country . However, key elephant ranges such as Trans Mara District (TM) along the south-western region are considered among the worst of HEC hotspots. Here both resident and migrating elephant populations from the adjacent Masai Mara National Reserve (MMNR) move into communal areas that are characterised by a mosaic of agriculture and elephant conservation activities [29, 44–46].
Human-elephant conflict has been documented in Kenya since the early 1990’s [e.g. 22, 42, 46–48]. These studies provide an invaluable foundation for the present research. However, their current limitations require further considerations for several reasons. Firstly, most previous studies have focused on direct measurable economic and material impacts of HEC, yet HEC is increasingly encompassing indirect, social, hidden and psychological sides of wellbeing [12, 29, 30, 49–52] and is highly differentiated [7, 29, 30, 50, 53, 54]. Furthermore, wellbeing is a multi-dimensional concept encompassing social, political, cultural, institutional and environmental dimensions [55, 56]. However, these studies tend to apply data aggregation approaches to assess mean impacts of HEC. In turn, this can mask inequalities , which nevertheless can be addressed by accounting for the differential impacts of HEC on human wellbeing .
Secondly, most previous studies have used ordinary regression models, which are assume homogeneity at the individual-level. However, the distribution of HEC is non-random, and this raises the problem of sample selection bias . These studies have compared the impacts of HEC on households that experience HEC to those that do not experience HEC. This approach could lead to selection bias, leading to overestimating or underestimating impacts of HEC. Therefore, a meaningful way of measuring the impacts of HEC would be to compare impacts on the same households with and without HEC as a means to addressing the selection bias. This would require different approaches to simulate the random experimental process and to estimate the treatment effect, based on the condition that the treatment group and control group are as similar as possible.
The objective of this study was to assess the impacts of HEC on the wellbeing of local residents in TM. Specifically, the study sought to examine three questions:
- What factors affect human wellbeing?
- What is the effect of the HEC on the wellbeing of local residents in TM?
- Compared to the control group, what is the effect of the HEC on the wellbeing of households experiencing HEC?
Importance of wildlife conservation – Materials and methods
This study was approved by the Ethics Review Group of the University of Cambridge, and the protocols used in the study were approved by the National Council for Science and Technology and Innovation of the republic of Kenya (Permit No. NACOSTI/P/14/0362/2798) and the Kenya wildlife Service (Permit No. KWS/BRM/5001). A total of 367 local residents were interviewed between 2014 and 2015 in the Mara Ecosystem, Kenya and informed consent was sought according to the University of Cambridge Research Ethics guidelines and strategies aimed at minimizing harm to the subject.
Description of the study area
Trans Mara (TM) District lies in the south-west of Kenya on the border with Tanzania (0°50′−1°50′S, 34°35′− 35°14′E). The district covers an area of some 2900 km2 and encompasses the western portion of the world-famous Masai Mara National Reserve (MMNR) (S1 Fig). Approximately 2200 km2 of TM is unprotected and is occupied by local communities separated from the protected MMNR by the steep Oloololo escarpment. Land use in TM comprises cultivation, livestock keeping, forests, rural and urban settlement, mining, and wildlife conservation and tourism . Wildlife-based tourism in the Mara ecosystem accounts for over 18% of the annual tourist visits to Kenya and is worth an estimated US$15–20 million . A range of private and communal wildlife-based enterprises including campsites, tented camps, airstrips, balloon safaris and lodges exist within the communal area. These facilities contribute to the economic standing of TM through direct and indirect employment in the catering, administrative and tour operations functions, among others. In addition, local residents sell food products to the lodges, as well as Maasai cultural items such as embroidery and woodcarvings . TM has a bimodal rainfall pattern from March to June and from November to December. The district experiences an annual average of between 1200–1500 mm of rainfall, with a north-south gradient of high to low rainfall across TM. The natural vegetation is a mosaic of Afro-montane, semi-deciduous and dry deciduous forests and Acacia savannah . However, many areas of TM have high agricultural potential and cultivation is widespread . The remaining forest provides refuge for a resident, unprotected population of 200–300 elephants that once ranged across most parts of TM district, but that now extend over 1,000 km2 [29, 63].
The human population of TM has witnessed a rapid increase estimated at 274,532 during the 2019 national census with a population growth rate of 3.3% compared to 2.2% in national estimates. The average household size in TM is 5.03 which is higher than the national household size of 4.4 . TM has seen a dramatic increase in agriculture, land sub-division and fencing of such parcels . With a population of about 300 resident elephant population and over 3000 transient animals, HEC has been inevitable in TM . The conflict between elephants and people over cultivated crops began with the immigration of non-Maasai into TM in the 1920s . As cultivation was increasingly introduced to TM District by these immigrants, so too did crop-raiding increase to become a perennial problem throughout the 1990s and 2000s. Both people and elephants suffer injury and death, and attitudes towards elephants in TM District are generally negative [29, 45, 66]. Rural communities receive little support from the national wildlife authority, Kenya Wildlife Service, because of limited resources and personnel [44, 62].
From 2003, the World Wide Fund (WWF) for Nature’s Kenya programme in TM initiated a series of HEC mitigation strategies which included community awareness and education and testing a set of farm-based HEC mitigation tools . These tools, including chilli-grease fences, tree-top watchtowers, thunderflashes, powerful torches, cowbells, fires and a standard wire fence erected on the elephant corridors, mainly sought to empower local communities to address HEC with little or no external support. Nyumba  established that these techniques increased the awareness of HEC mitigation approaches and created a legacy of improved attitudes towards elephants and capacity for HEC mitigation. Nevertheless, the overall uptake was limited. Since then various community based and research and conservation organisations such as the Mara Elephant project have continued to work with communities to expand the application of these techniques including aerial surveillance using helicopters to mitigate HEC and minimise illegal killing of elephants and other wildlife in the landscape. However, both the financial and non-financial costs of establishing and implementing HEC mitigation methods mean that they are often poorly or patchily implemented . As a result, local people have little faith in these methods [29, 45, 62]. Therefore, TM represents a model of a situation common across Africa where elephants and people co-exist in disharmony.
We conducted focus group discussions to identify and define the components of wellbeing among local residents in TM following White and Pettit  and Cahyat et al. . We used a combination of multi-stage and simple random sampling techniques to draw a sample of respondents  for our household interviews. Firstly, based on the specific administrative divisions falling within the elephant range in TM, and secondly by specific sub-locations where elephant presence was determined to be permanent, recent or erratic based on sightings during the past five years. Consequently, we focused on 26 sub-locations. Secondly, we clustered the households according to administrative boundaries. In this case, “sub-location” was assumed to be the unit of clustering. Finally, we drew a random sample of households from within each cluster based on proportional random sampling . We selected a total of 376 households which were considered adequate within a margin of error of 5% and a 95% confidence level. Here, a “household” is defined as a person or group of persons related or not, residing in the same homestead or compound. However, all household members do not necessarily live in the same dwelling unit, but do share the same cooking arrangements, and are answerable to the same household head .
We recruited local assistants to assist in the administration of the questionnaires. Although the assistants had some prior research experience, they were further trained in the use of the questionnaires to address the specific needs of this study. The questionnaire survey was administered to household heads. However, in their absence, another adult (>18years) was interviewed. While this might affect the comparability of the data gained, it was justified as a pragmatic choice given the realities of the fieldwork context. In all, 367 questionnaires were completed, with a response rate of 97.6%. The household survey recorded household socio-demographic data; human, material and social resources; wellbeing assessment; and interaction with elephants. The short version of the questionnaires can be found in supplementary materials (S1 Appendix).
To measure the impact of human-elephant conflict (HEC) on the wellbeing of rural households, we used the technique of Propensity Score Matching (PSM). This enabled us to extract from the sample of households that had experienced HEC a set of matching households that had not experienced HEC in all relevant pre-intervention characteristics, following Caliendo and Kopeinig  and Rosenbaum and Rubin . Households that had experienced HEC were considered the “treatment sample” whereas those that had not experienced HEC were considered the “control sample” that were used for comparison. Meanwhile, the change in wellbeing scores was considered the “impact of HEC” and was the outcome indicator. We attempted to estimate the average impact of treatment on treated (ATT) following Caliendo and Kopeinig . We were interested in two sets of variables: the outcome variable in this study referred to the wellbeing indicators and the choice of variables for estimating PSM.
Human wellbeing indicators.
The first step towards measuring wellbeing was the construction and validation of wellbeing indicators. First, we conducted a qualitative analysis of the focus group discussion data to identify broadly the conception and understanding of factors that constitute wellbeing for the TM residents. We then used IBM SPSS Statistics 22  software to conduct a series of statistical steps. These included the test for assumptions of normality and suitability of the data for factor analysis and factorial analytic techniques to reduce variables, validate and assign scores to the various wellbeing indicators. We generated 10 factors as depicted in (S2 Appendix). We tested the 10 factors for internal consistency using Cronbach’s alpha and inter-item correlations . We evaluated the Cronbach’s alpha coefficients using the guidelines suggested by George and Mallery [76: 231] where >0.9: Excellent; >0.8: Good; >0.7: Acceptable; >0.6: Questionable; >0.5: Poor, and <0.5: Unacceptable. Our results in (S2 Appendix) show that the Cronbach’s alpha coefficients for our items ranged from 0.07 to 0.81. Therefore, we established that only eight
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