Socioeconomic pragmatism and the adoption of conservation-compatible livelihoods: evidence from park -adjacent communities in northern Tanzania

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This study examines the drivers of alternative occupation preferences among residents living adjacent to Tarangire National Park (TNP) in northern Tanzania, a landscape where pastoralist traditions, conservation priorities, and livelihood vulnerabilities intersect. Using a cross-sectional survey of 184 households and multinomial logistic regression, we assessed the influence of demographic, socioeconomic, and attitudinal factors on willingness to adopt conservation-compatible livelihoods (CCLs). Results reveal widespread dissatisfaction with current livelihoods (70%) and a strong preference for agribusiness (36%), followed by livestock keeping (26%) and artisanal work (19%). Livelihood satisfaction, gender, education, occupation, years of residence, and perceptions of park impacts significantly shaped occupational aspirations, while ecocentric values and land ownership were not predictive. Women and respondents with lower levels of formal education demonstrated markedly higher odds of preferring agribusiness, livestock, and artisanal enterprises. These findings suggest that livelihood decisions are primarily driven by socioeconomic pragmatism rather than environmental attitudes alone. We argue that conservation interventions should prioritize agribusiness value chains, artisanal skill development, and gender-responsive strategies to align biodiversity protection with community aspirations. By situating livelihood diversification within the realities of rural pragmatism, this study advances actionable insights for integrating poverty alleviation and conservation planning in East African protected area landscapes. Conservation-compatible livelihoods socioeconomic pragmatism Tarangire National Park pastoralist communities livelihood diversification Introduction Protected areas (PAs) remain central to global biodiversity conservation strategies, yet their success is inextricably linked to the livelihoods and perceptions of surrounding communities. Residents living adjacent to PAs both shape and are shaped by conservation outcomes: they can act as custodians who deter illegal exploitation of resources, or alternatively, as participants in activities that undermine conservation objectives (Lyakurwa et al., 2025 ; Wei et al., 2018 ; Oldekop et al., 2016 ). This duality underscores the need to understand how livelihood strategies in park-fringe communities influence, and are influenced by, conservation agendas. Traditional land-based livelihoods such as farming and pastoralism often compete directly with conservation goals through land conversion, overgrazing, or resource extraction (Abukari & Mwalyosi, 2018 ; Ykhanbai et al., 2014 ). While conservation education and awareness campaigns have been widely promoted to encourage pro-conservation behavior, evidence suggests that such approaches are rarely sufficient on their own to shift livelihood practices (Queiros, 2019 ; Nilsson et al., 2016 ; Brooks et al., 2013). Incentive-based conservation, such as benefit-sharing schemes and alternative income generation programs, has also been advanced as a means of fostering positive attitudes towards PAs (Nepal et al., 2022 ; Lindhjem et al., 2010 ), however, other studies indicate that incentives alone may be insufficient or even irrelevant in shaping long-term livelihood decisions (Swann & Richards, 2016 ; Waylen et al., 2010). Instead, choices are frequently rooted in socioeconomic pragmatism—where constraints and opportunities linked to education, gender, household economy, and occupation outweigh environmental attitudes in determining behavioral outcomes (Ren et al., 2022 ; Chaplin & Wyton, 2014 ). Against this backdrop, conservation-compatible livelihoods (CCLs) have gained traction as a framework for reconciling biodiversity conservation with rural development. CCLs are defined as livelihood activities pursued by PA-adjacent communities that provide income and sustenance without degrading ecosystem services or biodiversity (He & Jiao, 2023 ). By promoting sustainable resource use, value addition, and non-consumptive ecosystem functions, CCLs offer a convergence point where conservation priorities and local aspirations intersect (Scherr & McNeely, 2008 ). They are particularly vital in regions where poverty alleviation, equity, and resilience are integral to the legitimacy of conservation interventions (Costanza, 2014). At their core, CCLs recognize that conservation gains are unlikely to endure unless communities perceive tangible livelihood benefits and opportunities for self-reliant development (He & Jiao, 2023 ). Despite the conceptual appeal of CCLs, empirical evidence on the determinants of willingness to adopt such livelihoods remains limited, especially in pastoralist landscapes of East Africa where conservation-livelihood tensions are pronounced. In these contexts, demographic realities, gendered roles, occupational structures, and land tenure uncertainties interact in complex ways to shape livelihood decisions (Leslie & McCabe, 2013 ; Keane et al., 2016 ). Importantly, while ecocentric values may foster positive orientations towards conservation, there is growing recognition that socioeconomic factors are often stronger predictors of livelihood transformation than environmental attitudes alone (Sunstein & Reisch, 2014 ). This study addresses these knowledge gaps by examining livelihood preferences and their predictors among communities surrounding Tarangire National Park (TNP) in northern Tanzania. Specifically, it investigates the extent to which satisfaction with current livelihoods, gender, education, occupation, and perceptions of park impacts influence willingness to adopt alternative, conservation-compatible occupations. By situating livelihood decision-making within a framework of pragmatic socioeconomic considerations, this study contributes to a more nuanced understanding of how conservation interventions can be designed to align with community aspirations. The findings provide critical insights for integrating livelihood diversification into conservation planning, offering a pathway to reduce human–wildlife conflict, alleviate vulnerability, and enhance the resilience of social–ecological systems in protected area landscapes. This study therefore seeks to identify the determinants of willingness to adopt conservation-compatible livelihoods among communities surrounding Tarangire National Park, northern Tanzania. Specifically, it examines how gender, education, occupation, livelihood satisfaction, and perceptions of park impacts shape alternative occupation preferences. We hypothesize that socioeconomic factors—rather than environmental attitudes alone—are the strongest predictors of livelihood choices in this context. By testing these relationships, the study provides empirical evidence to inform the design of conservation strategies that are both socially pragmatic and ecologically sustainable. Materials and methods Study Area Tarangire National Park (TNP) is the fifth largest national park in Tanzania, after Ruaha, Serengeti, Katavi, Mikumi, and Mkomazi, covering approximately 2,850 km². The park lies between latitudes 3°40′S and 4°35′S and longitudes 35°50′E and 36°20′E, spanning three administrative districts: Monduli, Simanjiro, and Babati. It is situated about 118 km southwest of Arusha, the principal gateway to northern Tanzania’s safari circuit. The park is bordered by four Game Control Areas (GCAs): Simanjiro GCA to the east, Mkungunero GCA to the south, Lolkisale GCA to the northeast, and Mto wa Mbu GCA to the north. Tarangire is renowned for its high density of large mammals, hosting more than 58 species, including zebra ( Equus quagga ), wildebeest ( Connochaetes taurinus ), buffalo ( Syncerus caffer ), and elephant ( Loxodonta africana ) (TCP, 1997). This rich megafauna diversity makes it one of Tanzania’s key tourist destinations. Surrounding Tarangire are 42 local communities, predominantly composed of pastoralists and agro-pastoralists, with a minority engaged exclusively in crop farming. The Maasai, who are traditionally pastoralists, constitute the largest ethnic group in the area. These communities are critical stakeholders in conservation, as many large mammals within the park rely on resources located outside its boundaries for more than six months each year (Kangwana and Mako, 1998). The research was conducted in three villages (Kimotorok, Mamire and Mswakini Juu) fringing TNP. The villages were purposively selected to represent different socio-economic contexts along the park boundary. The Tarangire–Simanjiro ecosystem was selected because of its unique conservation–livelihood interface: it is a key migratory corridor for wildlife and simultaneously home to predominantly pastoralist and agro-pastoralist Maasai communities. The area is marked by recurring tensions between biodiversity conservation and human survival strategies (Bond et al., 2022; Baird, 2012), making it a suitable context to investigate livelihood pragmatism and willingness to pursue conservation-compatible alternatives. Study Design This study employed a cross-sectional survey design to capture the sociodemographic characteristics, livelihood profiles, and occupational aspirations of communities living adjacent to TNP in northern Tanzania. A cross-sectional approach was appropriate as it enabled the collection of quantitative and attitudinal data at a single point in time, offering a snapshot of livelihood dynamics while minimizing time and resource constraints (Taris et al., 2021). Sampling Strategy and Respondents The target population comprised household heads or adult decision-makers residing in the selected villages. Village household registers, validated with the assistance of local leaders, formed the sampling frame. Stratified random sampling was applied to ensure inclusion of diverse demographic groups, particularly by livelihood type. A total of 184 households were sampled, consistent with the recommended thresholds for multinomial regression analysis, which requires at least ten event per variable to ensure reliable estimation of odds ratios across multiple categories (Hosmer et al., 2023). Data Collection Methods Data were collected through a structured questionnaire administered via face-to-face interviews from January to March, 2017. The tool was developed in English, translated into Kiswahili, and pretested in a non-sample community to refine clarity, sequencing, and contextual sensitivity. Trained local enumerators, fluent in both English and Kiswahili languages, conducted the interviews under close supervision by the research team. The questionnaire was structured into four main sections: (i) Demographics and household profile, which captured information on gender, age, household size, education level, and years of residence in the village; (ii) Livelihood activities and satisfaction, focusing on respondents’ current occupations and their level of satisfaction with existing livelihoods that they engaged in; (iii) Environmental attitudes, assessed through Likert-scale statements designed to capture both ecocentric and anthropocentric orientations; and (iv) Occupational aspirations, which elicited preferences for alternative livelihoods, including agribusiness, livestock rearing, artisanal work, petty trading, and other occupations, with respondents asked to rank their top choices. In addition to closed-ended questions, open-ended prompts were used to elicit narratives about livelihood dissatisfaction, climate-related challenges, and perceptions of park impacts. This mixed-format design enriched quantitative data with contextual depth. Ethical Considerations This study was part of a broader PhD project, and the ethical approval was obtained from the University of Dar es Salaam (Ref. number: AB3/12(B)), and research clearance was obtained from the Tanzania National Parks (Ref. number: TNP/HO/E.20/08B). Prior to each interview, participants were informed of the study’s purpose, assured of confidentiality, and asked for written or verbal consent. Participation was voluntary, with respondents free to withdraw at any stage. Enumerators received three days of training on research ethics, interviewing techniques, and survey content. Daily debriefing sessions were held to resolve field challenges, and supervisors conducted random spot checks. Completed questionnaires were reviewed daily and double-entered into SPSS to minimize transcription errors. Data analysis Data analysis was conducted in SPSS version 20. Descriptive statistics summarized demographic characteristics, livelihood activities, and occupational aspirations. Multinomial logistic regression was employed to identify predictors of livelihood choice, as the dependent variable (preferred occupation) was categorical with multiple, unordered outcomes. Independent variables included livelihood satisfaction, gender, education, occupation, years of residence, land ownership, household size, age, environmental attitudes, and perceptions of park impacts. This method allowed the estimation of odds ratios and confidence intervals, providing nuanced insights into the socioeconomic and attitudinal determinants of alternative livelihood preferences. Results A total of 184 residents participated in the survey, of whom 126 (68%) were male and 59 (32%) female. Respondents were predominantly young to middle-aged, with more than half (n = 104, 56%) falling within the 20–40-year age category. The mean household size was 7.87 (SD = 4.01; range = 1–26). Educational attainment was generally low; nearly two-thirds (64%) had completed primary school and only 1% reported tertiary-level education (Table 1 ). Occupation was varied in surveyed communities with the majority of participants (n = 98, 53%) identified as pastoralists, while others were engaged in agro-pastoralism (18%) (Table 1 ). Despite this occupational diversity, dissatisfaction was widespread, with 70% (n = 116) reporting discontent with their current livelihood activities. With respect to environmental orientations, almost two-thirds of respondents (n = 104, 63%) expressed ecocentric values, agreeing that wild plants and animals should be protected even when such actions require personal sacrifices. Land ownership was limited, with only 31% (n = 56) reporting ownership of the land on which they practiced their livelihood activities. Regarding occupational aspirations, agribusiness—including commodity trading, processing, and value addition of staple crops such as maize, rice, cassava, and groundnuts—was the most preferred future occupation (36%). This was followed by livestock keeping (26%), artisanal work such as carpentry, masonry, beadwork, and leatherwork (19%), and other occupations including teaching, nursing, and service in the police or military (12%). Petty trading was the least preferred (7%). Table 1 Frequencies and percentages of variables used in the study Variable Levels Frequency Percentage Gender Female 59 32% male 125 68% 60 11 6% Highest level of education None 43 24% Primary 116 64% Secondary 20 11% Tertiary 2 1% Current job Pastoral 98 53% Crop farming 20 11% Ecotourism-related business 10 5% Agro-pastoral 33 18% Petty trading 13 8% Ecotourism business 10 5% Years in village 10 years 128 70% Satisfaction with current job Satisfied 56 30% Dissatisfied 116 70% Ecocentric values Yes 104 63% No 50 27% Don’t know 18 10% Land ownership Yes 56 31% No 125 69% Preferred job Agri-business 66 36% Livestock-keeping 48 26% Buying and selling 13 7% Artisanal work 35 19% Other 22 12% A multinomial logistic regression was conducted to identify factors influencing willingness to adopt alternative, conservation-compatible livelihood activities. The overall model was statistically significant compared with the intercept-only model, χ²(84) = 179.04, p < .001, indicating that the predictor set reliably distinguished among livelihood choices. Model fit indices suggested substantial explanatory power (Nagelkerke R² = .656; Cox & Snell R² = .622; McFadden R² = .330). Likelihood ratio tests (Table 2 ) revealed that livelihood satisfaction, gender, education, occupation, years of residence in the village, and perceived park impacts significantly influenced livelihood preferences. Conversely, household size, land ownership, ecocentric values, and age were not significant predictors. Table 2 Likelihood Ratio Tests for Predictors of Livelihood Choice (Multinomial Logistic Regression) Predictor χ² df p Livelihood satisfaction 18.90 4 .001 Gender 22.63 4 < .001 Education 23.07 12 .027 Occupation 49.61 20 < .001 Years in village 16.02 8 .042 Park effects 25.15 12 .014 Land ownership 9.45 4 .051 Household size 1.79 4 .774 Ecocentric values 4.65 4 .325 Age 15.69 12 .206 Note. Model fit indices: χ²(84) = 179.04, p < .001; Nagelkerke R² = .656; Cox & Snell R² = .622; McFadden R² = .330. Parameter estimates (Table 3 ) showed that respondents satisfied with their current livelihoods were significantly more likely to prefer agribusiness (OR = 3.62, p < .001), livestock keeping (OR = 3.19, p = .002), craft-making (OR = 5.12, p = .003), and petty trade (OR = 2.56, p = .012) relative to the “other” category. Gender exerted a strong effect, with females significantly more likely than males to prefer agribusiness (OR = 9.96, p = .004), livestock keeping (OR = 5.99, p = .026), and craft-making (OR = 18.47, p = .031). Lower levels of education were associated with increased odds of preferring agribusiness (OR = 22.15, p = .032), livestock keeping (OR = 19.25, p = .046), craft-making (OR = 58.15, p = .047), and petty trade (OR = 29.23, p = .023). Occupation status also differentiated livelihood choices, with individuals currently engaged in farming demonstrating greater odds of selecting agribusiness- and livestock-based activities (Table 3 ). Table 3 Selected Parameter Estimates for Predictors of Preferred Livelihood (Reference Category = “Other”) Predictor Outcome B SE Wald OR (Exp(B)) 95% CI for OR p Livelihood satisfaction Agribusiness 1.29 .37 12.17 3.62 1.76–7.45 < .001 Livestock 1.16 .38 9.24 3.19 1.51–6.75 .002 Craft making 1.63 .55 8.88 5.12 1.75–14.98 .003 Petty trading .94 .37 6.32 2.56 1.23–5.33 .012 Gender (female) Agribusiness 2.30 .79 8.46 9.96 2.12–46.90 .004 Livestock 1.79 .81 4.94 5.99 1.24–29.01 .026 Craft making 2.92 1.35 4.64 18.47 1.30–261.96 .031 Education (lower levels) Agribusiness 3.10 1.44 4.62 22.15 1.31–373.62 .032 Livestock 2.96 1.49 3.97 19.25 1.05–353.31 .046 Craft making 4.06 2.04 3.96 58.15 1.06–3184.26 .047 Petty trading 3.38 1.49 5.16 29.23 1.59–538.25 .023 Discussion This study sheds light on the livelihood dynamics of communities fringing Tarangire National Park (TNP) in Tanzania and their willingness to transition into alternative, conservation-compatible economic activities. The sociodemographic structure of respondents is predominantly male, youthful (20–40 years), and with large household sizes. This reflects the demographic realities of rural pastoralist societies in East Africa, where extended families and male-dominated labor remain central to local economies (Onyima, 2021 ). Education levels were generally low, with the majority having attained only primary education and very few reaching tertiary level, a trend consistent with other protected area-adjacent communities where access to advanced schooling is limited (Kiconco et al., 2025 ; Røskaft, 2021 ). The majority of residents identified as pastoralists, reaffirming the centrality of livestock keeping in the Simanjiro–Tarangire ecosystem in northern Tanzania. However, a striking 70% expressed dissatisfaction with their current livelihoods, indicating both vulnerability and openness to livelihood transformation. The vulnerabilities range from climate change impacts that manifest through water and forage scarcity for livestock and labour migration (Leslie & McCabe, 2013 ). Land use change and ambiguous land tenure arrangements pose significant challenges that exacerbate the dissatisfaction with existing livelihoods, which are predominantly pastoral in nature (Abukari & Mwalyosi, 2018 ). The lack of land tenure security constrains agricultural innovation and long-term investment prospects, particularly in other land-based businesses. Despite the potential challenges with land-based business, agribusiness emerged as the most preferred alternative occupation among the residents in TNP fringe communities, followed by livestock keeping and artisanal work. This finding signals a gradual shift in livelihood aspirations, as residents seek opportunities beyond traditional herding – the main livelihood activity with pastoralsts. Agribusiness reflects the increasing importance of value addition and market-oriented farming, with crops such as maize, rice, cassava, cashew and groundnuts, providing potential entry points for diversification. Notably, artisanal work (including carpentry, masonry, and beadwork) was also attractive to nearly a fifth of respondents, underscoring the latent demand for skill-based non-farm opportunities. This is particularly positive for the achievement of conservation goals since the artisanal work is likely to reduce pressure on forest and wildlife resources. The multinomial regression analysis identified significant predictors of livelihood preferences among respondents. Job satisfaction emerged as a strong determinant, with higher levels of satisfaction increasing the likelihood of selecting agribusiness, livestock rearing, and artisanal occupations as alternative livelihoods. This indicates that even individuals content with their current livelihoods recognize the potential advantages of diversification, particularly when new opportunities align with their existing skills and interests. Pronounced gender differences were also observed: women were significantly more likely than men to favor agribusiness, livestock keeping, and craft-based enterprises. This pattern reflects women’s longstanding engagement in small-scale farming, trade, and handicrafts, and underscores the necessity of gender-sensitive approaches in designing livelihood interventions. These results corroborate findings by Keane et al. ( 2016 ), who reported that women in communities adjacent to the Maasai Mara National Reserve, Kenya, placed relatively lower value on wage employment while showing stronger preferences for livestock-based livelihoods. Education also emerged as a significant determinant, with those possessing lower levels of education more likely to choose agribusiness, livestock, and artisanal occupations. This may reflect pragmatic livelihood choices, where low education often limits access to formal employment, steering residents towards familiar or attainable alternatives within agrarian and craft sectors. Similarly, Mukwedeya and Mudhara ( 2023 ) reported that among youth in Mashonaland East Province, Zimbabwe, higher levels of formal education were associated with a reduced likelihood of selecting agriculture as a primary livelihood. The similarity between their finding and that of the present study, may be attributable to similarity in the study areas, as both are agrarian areas predominantly occupied by Maasai. Occupation and years of residence in the village also shaped preferences, suggesting that long-term settlers and those already engaged in farming were more open to expanding or modifying their livelihood strategies. Interestingly, ecocentric values and land ownership did not significantly predict livelihood preferences, despite widespread recognition of the importance of biodiversity conservation. This indicates that livelihood decisions are driven more by socioeconomic pragmatism than by environmental attitudes alone. This point is consistently elaborated in earlier studies, that factors like income, capital, access to technology, and survival strategies, play a more decisive role than environmental attitudes alone in shaping livelihood decisions (Ren et al., 2022 ; Chaplin & Wyton, 2014 ; Sunstein & Reisch, 2014 ). Overall, the findings highlight a strong willingness among TNP-adjacent communities to embrace conservation-compatible livelihoods, but also reveal gendered, educational, and occupational nuances in these preferences. The strong preference for agribusiness and artisanal work among residents highlights the importance of livelihood diversification as a conservation strategy in TNP’s fringe communities. By promoting value-added agriculture and skill-based enterprises, conservation programs can reduce reliance on unsustainable pastoral practices that intensify human–wildlife conflict and habitat degradation. Targeted support for women, who showed higher willingness to adopt alternative livelihoods, can amplify conservation gains while advancing gender equity. Integrating livelihood development into conservation planning is therefore essential to foster community buy-in, mitigate pressures on park resources, and enhance long-term ecosystem resilience. The study concludes that interventions aiming to reduce conservation-livelihood conflicts should prioritize agribusiness development through training, credit access, and market linkages for crop value addition. Artisanal skills training and enterprise support, particularly targeting women and youth. Gender-responsive approaches that harness women’s stronger inclination toward conservation-compatible activities. Integration of livelihood support with conservation education, ensuring communities see tangible benefits from biodiversity protection. By addressing these areas, conservation managers and policymakers can create a win–win pathway that enhances rural livelihoods while safeguarding the ecological integrity of the Tarangire National Park. Declarations Ethical Approval: the interview protocol received approval from the from the University of Dar es Salaam (Ref. number: AB3/12(B)), and research clearance was obtained from the Tanzania National Parks (Ref. number: TNP/HO/E.20/08B). Verbal consent was obtained from the village heads and all participants before they took part in the study. Clinical trial number not applicable Funding: The study was supported by Trans-disciplinary Training for Resource Efficiency and Climate Change Adaptation in Africa (TRECCAfrica) scholarship. Author Contribution The author (HA) executed the study from conception and design, material preparation, data collection, data analysis, and data visualization, drafted the manuscript, revised, and approved the final version. Acknowledgement The data used in this paper is part of a doctoral project work that was made possible by the sponsorship of the Trans-disciplinary Training for Resource Efficiency and Climate Change Adaptation in Africa (TRECCAfrica). My sincere thanks go to the donors and the secretariat in Stellenbosch University, South Africa. I equally thank the local coordinator of the TRECCAfrica programme in the University of Dar es Salaam, Dr. James Lyimo for facilitating logistics throughout the studies. I thank authorities of the Tanzania National Parks (TANAPA) and Madam Gladys Ng’umbi, deputy chief park warden of the Tarangire National Park. I thank the Village Executive officers of Mamire, Mswakini and Kimotorok, all in Tanzania, for their support throughout the survey in their communities. References Abukari, H., & Mwalyosi, R. B. (2018). Comparing pressures on national parks in Ghana and Tanzania: The case of Mole and Tarangire National Parks. Global Ecology and Conservation , 15 , e00405. Chaplin, G., & Wyton, P. (2014). Student engagement with sustainability: Understanding the value–action gap. International journal of sustainability in higher education , 15 (4), 404–417. He, S., & Jiao, W. (2023). Conservation-compatible livelihoods: An approach to rural development in protected areas of developing countries. 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Automatically green: Behavioral economics and environmental protection. Harv Envtl L Rev , 38 , 127. Swann, E., & Richards, R. (2016). What factors influence the effectiveness of financial incentives on long-term natural resource management practice change? Evidence Base: A journal of evidence reviews in key policy areas , (2), 1–32. Wei, F., Wang, S., Fu, B., Zhang, L., Fu, C., & Kanga, E. M. (2018). Balancing community livelihoods and biodiversity conservation of protected areas in East Africa. Current Opinion in Environmental Sustainability , 33 , 26–33. Ykhanbai, H., Garg, R., Singh, A., Moiko, S. S., Beyene, C. E., Roe, D., & Flintan, F. E. (2014). Conservation and land grabbing in rangelands: Part of the problem or part of the solution?. Additional Declarations No competing interests reported. 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Residents living adjacent to PAs both shape and are shaped by conservation outcomes: they can act as custodians who deter illegal exploitation of resources, or alternatively, as participants in activities that undermine conservation objectives (Lyakurwa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Oldekop et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This duality underscores the need to understand how livelihood strategies in park-fringe communities influence, and are influenced by, conservation agendas.\u003c/p\u003e\u003cp\u003eTraditional land-based livelihoods such as farming and pastoralism often compete directly with conservation goals through land conversion, overgrazing, or resource extraction (Abukari \u0026amp; Mwalyosi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ykhanbai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While conservation education and awareness campaigns have been widely promoted to encourage pro-conservation behavior, evidence suggests that such approaches are rarely sufficient on their own to shift livelihood practices (Queiros, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nilsson et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Brooks et al., 2013). Incentive-based conservation, such as benefit-sharing schemes and alternative income generation programs, has also been advanced as a means of fostering positive attitudes towards PAs (Nepal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lindhjem et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), however, other studies indicate that incentives alone may be insufficient or even irrelevant in shaping long-term livelihood decisions (Swann \u0026amp; Richards, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Waylen et al., 2010). Instead, choices are frequently rooted in socioeconomic pragmatism\u0026mdash;where constraints and opportunities linked to education, gender, household economy, and occupation outweigh environmental attitudes in determining behavioral outcomes (Ren et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chaplin \u0026amp; Wyton, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, conservation-compatible livelihoods (CCLs) have gained traction as a framework for reconciling biodiversity conservation with rural development. CCLs are defined as livelihood activities pursued by PA-adjacent communities that provide income and sustenance without degrading ecosystem services or biodiversity (He \u0026amp; Jiao, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By promoting sustainable resource use, value addition, and non-consumptive ecosystem functions, CCLs offer a convergence point where conservation priorities and local aspirations intersect (Scherr \u0026amp; McNeely, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). They are particularly vital in regions where poverty alleviation, equity, and resilience are integral to the legitimacy of conservation interventions (Costanza, 2014). At their core, CCLs recognize that conservation gains are unlikely to endure unless communities perceive tangible livelihood benefits and opportunities for self-reliant development (He \u0026amp; Jiao, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the conceptual appeal of CCLs, empirical evidence on the determinants of willingness to adopt such livelihoods remains limited, especially in pastoralist landscapes of East Africa where conservation-livelihood tensions are pronounced. In these contexts, demographic realities, gendered roles, occupational structures, and land tenure uncertainties interact in complex ways to shape livelihood decisions (Leslie \u0026amp; McCabe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Keane et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Importantly, while ecocentric values may foster positive orientations towards conservation, there is growing recognition that socioeconomic factors are often stronger predictors of livelihood transformation than environmental attitudes alone (Sunstein \u0026amp; Reisch, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study addresses these knowledge gaps by examining livelihood preferences and their predictors among communities surrounding Tarangire National Park (TNP) in northern Tanzania. Specifically, it investigates the extent to which satisfaction with current livelihoods, gender, education, occupation, and perceptions of park impacts influence willingness to adopt alternative, conservation-compatible occupations. By situating livelihood decision-making within a framework of pragmatic socioeconomic considerations, this study contributes to a more nuanced understanding of how conservation interventions can be designed to align with community aspirations. The findings provide critical insights for integrating livelihood diversification into conservation planning, offering a pathway to reduce human\u0026ndash;wildlife conflict, alleviate vulnerability, and enhance the resilience of social\u0026ndash;ecological systems in protected area landscapes.\u003c/p\u003e\u003cp\u003eThis study therefore seeks to identify the determinants of willingness to adopt conservation-compatible livelihoods among communities surrounding Tarangire National Park, northern Tanzania. Specifically, it examines how gender, education, occupation, livelihood satisfaction, and perceptions of park impacts shape alternative occupation preferences. We hypothesize that socioeconomic factors\u0026mdash;rather than environmental attitudes alone\u0026mdash;are the strongest predictors of livelihood choices in this context. By testing these relationships, the study provides empirical evidence to inform the design of conservation strategies that are both socially pragmatic and ecologically sustainable.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eTarangire National Park (TNP) is the fifth largest national park in Tanzania, after Ruaha, Serengeti, Katavi, Mikumi, and Mkomazi, covering approximately 2,850 km\u0026sup2;. The park lies between latitudes 3\u0026deg;40\u0026prime;S and 4\u0026deg;35\u0026prime;S and longitudes 35\u0026deg;50\u0026prime;E and 36\u0026deg;20\u0026prime;E, spanning three administrative districts: Monduli, Simanjiro, and Babati. It is situated about 118 km southwest of Arusha, the principal gateway to northern Tanzania\u0026rsquo;s safari circuit.\u003c/p\u003e\u003cp\u003eThe park is bordered by four Game Control Areas (GCAs): Simanjiro GCA to the east, Mkungunero GCA to the south, Lolkisale GCA to the northeast, and Mto wa Mbu GCA to the north. Tarangire is renowned for its high density of large mammals, hosting more than 58 species, including zebra (\u003cem\u003eEquus quagga\u003c/em\u003e), wildebeest (\u003cem\u003eConnochaetes taurinus\u003c/em\u003e), buffalo (\u003cem\u003eSyncerus caffer\u003c/em\u003e), and elephant (\u003cem\u003eLoxodonta africana\u003c/em\u003e) (TCP, 1997). This rich megafauna diversity makes it one of Tanzania\u0026rsquo;s key tourist destinations.\u003c/p\u003e\u003cp\u003eSurrounding Tarangire are 42 local communities, predominantly composed of pastoralists and agro-pastoralists, with a minority engaged exclusively in crop farming. The Maasai, who are traditionally pastoralists, constitute the largest ethnic group in the area. These communities are critical stakeholders in conservation, as many large mammals within the park rely on resources located outside its boundaries for more than six months each year (Kangwana and Mako, 1998).\u003c/p\u003e\u003cp\u003eThe research was conducted in three villages (Kimotorok, Mamire and Mswakini Juu) fringing TNP. The villages were purposively selected to represent different socio-economic contexts along the park boundary. The Tarangire\u0026ndash;Simanjiro ecosystem was selected because of its unique conservation\u0026ndash;livelihood interface: it is a key migratory corridor for wildlife and simultaneously home to predominantly pastoralist and agro-pastoralist Maasai communities. The area is marked by recurring tensions between biodiversity conservation and human survival strategies (Bond et al., 2022; Baird, 2012), making it a suitable context to investigate livelihood pragmatism and willingness to pursue conservation-compatible alternatives.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eThis study employed a cross-sectional survey design to capture the sociodemographic characteristics, livelihood profiles, and occupational aspirations of communities living adjacent to TNP in northern Tanzania. A cross-sectional approach was appropriate as it enabled the collection of quantitative and attitudinal data at a single point in time, offering a snapshot of livelihood dynamics while minimizing time and resource constraints (Taris et al., 2021).\u003c/p\u003e\n\u003ch3\u003eSampling Strategy and Respondents\u003c/h3\u003e\n\u003cp\u003eThe target population comprised household heads or adult decision-makers residing in the selected villages. Village household registers, validated with the assistance of local leaders, formed the sampling frame. Stratified random sampling was applied to ensure inclusion of diverse demographic groups, particularly by livelihood type. A total of 184 households were sampled, consistent with the recommended thresholds for multinomial regression analysis, which requires at least ten event per variable to ensure reliable estimation of odds ratios across multiple categories (Hosmer et al., 2023).\u003c/p\u003e\n\u003ch3\u003eData Collection Methods\u003c/h3\u003e\n\u003cp\u003eData were collected through a structured questionnaire administered via face-to-face interviews from January to March, 2017. The tool was developed in English, translated into Kiswahili, and pretested in a non-sample community to refine clarity, sequencing, and contextual sensitivity. Trained local enumerators, fluent in both English and Kiswahili languages, conducted the interviews under close supervision by the research team.\u003c/p\u003e\u003cp\u003eThe questionnaire was structured into four main sections: (i) Demographics and household profile, which captured information on gender, age, household size, education level, and years of residence in the village; (ii) Livelihood activities and satisfaction, focusing on respondents\u0026rsquo; current occupations and their level of satisfaction with existing livelihoods that they engaged in; (iii) Environmental attitudes, assessed through Likert-scale statements designed to capture both ecocentric and anthropocentric orientations; and (iv) Occupational aspirations, which elicited preferences for alternative livelihoods, including agribusiness, livestock rearing, artisanal work, petty trading, and other occupations, with respondents asked to rank their top choices.\u003c/p\u003e\u003cp\u003eIn addition to closed-ended questions, open-ended prompts were used to elicit narratives about livelihood dissatisfaction, climate-related challenges, and perceptions of park impacts. This mixed-format design enriched quantitative data with contextual depth.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study was part of a broader PhD project, and the ethical approval was obtained from the University of Dar es Salaam (Ref. number: AB3/12(B)), and research clearance was obtained from the Tanzania National Parks (Ref. number: TNP/HO/E.20/08B). Prior to each interview, participants were informed of the study\u0026rsquo;s purpose, assured of confidentiality, and asked for written or verbal consent. Participation was voluntary, with respondents free to withdraw at any stage.\u003c/p\u003e\u003cp\u003eEnumerators received three days of training on research ethics, interviewing techniques, and survey content. Daily debriefing sessions were held to resolve field challenges, and supervisors conducted random spot checks. Completed questionnaires were reviewed daily and double-entered into SPSS to minimize transcription errors.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted in SPSS version 20. Descriptive statistics summarized demographic characteristics, livelihood activities, and occupational aspirations. Multinomial logistic regression was employed to identify predictors of livelihood choice, as the dependent variable (preferred occupation) was categorical with multiple, unordered outcomes. Independent variables included livelihood satisfaction, gender, education, occupation, years of residence, land ownership, household size, age, environmental attitudes, and perceptions of park impacts. This method allowed the estimation of odds ratios and confidence intervals, providing nuanced insights into the socioeconomic and attitudinal determinants of alternative livelihood preferences.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 184 residents participated in the survey, of whom 126 (68%) were male and 59 (32%) female. Respondents were predominantly young to middle-aged, with more than half (n\u0026thinsp;=\u0026thinsp;104, 56%) falling within the 20\u0026ndash;40-year age category. The mean household size was 7.87 (SD\u0026thinsp;=\u0026thinsp;4.01; range\u0026thinsp;=\u0026thinsp;1\u0026ndash;26). Educational attainment was generally low; nearly two-thirds (64%) had completed primary school and only 1% reported tertiary-level education (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Occupation was varied in surveyed communities with the majority of participants (n\u0026thinsp;=\u0026thinsp;98, 53%) identified as pastoralists, while others were engaged in agro-pastoralism (18%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Despite this occupational diversity, dissatisfaction was widespread, with 70% (n\u0026thinsp;=\u0026thinsp;116) reporting discontent with their current livelihood activities.\u003c/p\u003e\u003cp\u003eWith respect to environmental orientations, almost two-thirds of respondents (n\u0026thinsp;=\u0026thinsp;104, 63%) expressed ecocentric values, agreeing that wild plants and animals should be protected even when such actions require personal sacrifices. Land ownership was limited, with only 31% (n\u0026thinsp;=\u0026thinsp;56) reporting ownership of the land on which they practiced their livelihood activities. Regarding occupational aspirations, agribusiness\u0026mdash;including commodity trading, processing, and value addition of staple crops such as maize, rice, cassava, and groundnuts\u0026mdash;was the most preferred future occupation (36%). This was followed by livestock keeping (26%), artisanal work such as carpentry, masonry, beadwork, and leatherwork (19%), and other occupations including teaching, nursing, and service in the police or military (12%). Petty trading was the least preferred (7%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequencies and percentages of variables used in the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHighest level of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eCurrent job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePastoral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrop farming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEcotourism-related business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgro-pastoral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEcotourism business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eYears in village\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSatisfaction with current job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSatisfied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDissatisfied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEcocentric values\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLand ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003ePreferred job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgri-business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLivestock-keeping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuying and selling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtisanal work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA multinomial logistic regression was conducted to identify factors influencing willingness to adopt alternative, conservation-compatible livelihood activities. The overall model was statistically significant compared with the intercept-only model, χ\u0026sup2;(84)\u0026thinsp;=\u0026thinsp;179.04, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, indicating that the predictor set reliably distinguished among livelihood choices. Model fit indices suggested substantial explanatory power (Nagelkerke R\u0026sup2; = .656; Cox \u0026amp; Snell R\u0026sup2; = .622; McFadden R\u0026sup2; = .330).\u003c/p\u003e\u003cp\u003eLikelihood ratio tests (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that livelihood satisfaction, gender, education, occupation, years of residence in the village, and perceived park impacts significantly influenced livelihood preferences. Conversely, household size, land ownership, ecocentric values, and age were not significant predictors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLikelihood Ratio Tests for Predictors of Livelihood Choice (Multinomial Logistic Regression)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLivelihood satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears in village\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePark effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcocentric values\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote. Model fit indices: χ\u0026sup2;(84)\u0026thinsp;=\u0026thinsp;179.04, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Nagelkerke R\u0026sup2; = .656; Cox \u0026amp; Snell R\u0026sup2; = .622; McFadden R\u0026sup2; = .330.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eParameter estimates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that respondents satisfied with their current livelihoods were significantly more likely to prefer agribusiness (OR\u0026thinsp;=\u0026thinsp;3.62, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), livestock keeping (OR\u0026thinsp;=\u0026thinsp;3.19, p\u0026thinsp;=\u0026thinsp;.002), craft-making (OR\u0026thinsp;=\u0026thinsp;5.12, p\u0026thinsp;=\u0026thinsp;.003), and petty trade (OR\u0026thinsp;=\u0026thinsp;2.56, p\u0026thinsp;=\u0026thinsp;.012) relative to the \u0026ldquo;other\u0026rdquo; category. Gender exerted a strong effect, with females significantly more likely than males to prefer agribusiness (OR\u0026thinsp;=\u0026thinsp;9.96, p\u0026thinsp;=\u0026thinsp;.004), livestock keeping (OR\u0026thinsp;=\u0026thinsp;5.99, p\u0026thinsp;=\u0026thinsp;.026), and craft-making (OR\u0026thinsp;=\u0026thinsp;18.47, p\u0026thinsp;=\u0026thinsp;.031). Lower levels of education were associated with increased odds of preferring agribusiness (OR\u0026thinsp;=\u0026thinsp;22.15, p\u0026thinsp;=\u0026thinsp;.032), livestock keeping (OR\u0026thinsp;=\u0026thinsp;19.25, p\u0026thinsp;=\u0026thinsp;.046), craft-making (OR\u0026thinsp;=\u0026thinsp;58.15, p\u0026thinsp;=\u0026thinsp;.047), and petty trade (OR\u0026thinsp;=\u0026thinsp;29.23, p\u0026thinsp;=\u0026thinsp;.023). Occupation status also differentiated livelihood choices, with individuals currently engaged in farming demonstrating greater odds of selecting agribusiness- and livestock-based activities (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSelected Parameter Estimates for Predictors of Preferred Livelihood (Reference Category = \u0026ldquo;Other\u0026rdquo;)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (Exp(B))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI for OR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLivelihood satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgribusiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.76\u0026ndash;7.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLivestock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.51\u0026ndash;6.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCraft making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.75\u0026ndash;14.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.23\u0026ndash;5.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGender (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgribusiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.12\u0026ndash;46.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLivestock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.24\u0026ndash;29.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCraft making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.30\u0026ndash;261.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducation (lower levels)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgribusiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.31\u0026ndash;373.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLivestock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.05\u0026ndash;353.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCraft making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06\u0026ndash;3184.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePetty trading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.59\u0026ndash;538.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sheds light on the livelihood dynamics of communities fringing Tarangire National Park (TNP) in Tanzania and their willingness to transition into alternative, conservation-compatible economic activities. The sociodemographic structure of respondents is predominantly male, youthful (20\u0026ndash;40 years), and with large household sizes. This reflects the demographic realities of rural pastoralist societies in East Africa, where extended families and male-dominated labor remain central to local economies (Onyima, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Education levels were generally low, with the majority having attained only primary education and very few reaching tertiary level, a trend consistent with other protected area-adjacent communities where access to advanced schooling is limited (Kiconco et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; R\u0026oslash;skaft, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe majority of residents identified as pastoralists, reaffirming the centrality of livestock keeping in the Simanjiro\u0026ndash;Tarangire ecosystem in northern Tanzania. However, a striking 70% expressed dissatisfaction with their current livelihoods, indicating both vulnerability and openness to livelihood transformation. The vulnerabilities range from climate change impacts that manifest through water and forage scarcity for livestock and labour migration (Leslie \u0026amp; McCabe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Land use change and ambiguous land tenure arrangements pose significant challenges that exacerbate the dissatisfaction with existing livelihoods, which are predominantly pastoral in nature (Abukari \u0026amp; Mwalyosi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The lack of land tenure security constrains agricultural innovation and long-term investment prospects, particularly in other land-based businesses.\u003c/p\u003e\u003cp\u003eDespite the potential challenges with land-based business, agribusiness emerged as the most preferred alternative occupation among the residents in TNP fringe communities, followed by livestock keeping and artisanal work. This finding signals a gradual shift in livelihood aspirations, as residents seek opportunities beyond traditional herding \u0026ndash; the main livelihood activity with pastoralsts. Agribusiness reflects the increasing importance of value addition and market-oriented farming, with crops such as maize, rice, cassava, cashew and groundnuts, providing potential entry points for diversification. Notably, artisanal work (including carpentry, masonry, and beadwork) was also attractive to nearly a fifth of respondents, underscoring the latent demand for skill-based non-farm opportunities. This is particularly positive for the achievement of conservation goals since the artisanal work is likely to reduce pressure on forest and wildlife resources.\u003c/p\u003e\u003cp\u003eThe multinomial regression analysis identified significant predictors of livelihood preferences among respondents. Job satisfaction emerged as a strong determinant, with higher levels of satisfaction increasing the likelihood of selecting agribusiness, livestock rearing, and artisanal occupations as alternative livelihoods. This indicates that even individuals content with their current livelihoods recognize the potential advantages of diversification, particularly when new opportunities align with their existing skills and interests. Pronounced gender differences were also observed: women were significantly more likely than men to favor agribusiness, livestock keeping, and craft-based enterprises. This pattern reflects women\u0026rsquo;s longstanding engagement in small-scale farming, trade, and handicrafts, and underscores the necessity of gender-sensitive approaches in designing livelihood interventions. These results corroborate findings by Keane et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who reported that women in communities adjacent to the Maasai Mara National Reserve, Kenya, placed relatively lower value on wage employment while showing stronger preferences for livestock-based livelihoods.\u003c/p\u003e\u003cp\u003eEducation also emerged as a significant determinant, with those possessing lower levels of education more likely to choose agribusiness, livestock, and artisanal occupations. This may reflect pragmatic livelihood choices, where low education often limits access to formal employment, steering residents towards familiar or attainable alternatives within agrarian and craft sectors. Similarly, Mukwedeya and Mudhara (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that among youth in Mashonaland East Province, Zimbabwe, higher levels of formal education were associated with a reduced likelihood of selecting agriculture as a primary livelihood. The similarity between their finding and that of the present study, may be attributable to similarity in the study areas, as both are agrarian areas predominantly occupied by Maasai.\u003c/p\u003e\u003cp\u003eOccupation and years of residence in the village also shaped preferences, suggesting that long-term settlers and those already engaged in farming were more open to expanding or modifying their livelihood strategies. Interestingly, ecocentric values and land ownership did not significantly predict livelihood preferences, despite widespread recognition of the importance of biodiversity conservation. This indicates that livelihood decisions are driven more by socioeconomic pragmatism than by environmental attitudes alone. This point is consistently elaborated in earlier studies, that factors like income, capital, access to technology, and survival strategies, play a more decisive role than environmental attitudes alone in shaping livelihood decisions (Ren et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chaplin \u0026amp; Wyton, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sunstein \u0026amp; Reisch, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the findings highlight a strong willingness among TNP-adjacent communities to embrace conservation-compatible livelihoods, but also reveal gendered, educational, and occupational nuances in these preferences. The strong preference for agribusiness and artisanal work among residents highlights the importance of livelihood diversification as a conservation strategy in TNP\u0026rsquo;s fringe communities. By promoting value-added agriculture and skill-based enterprises, conservation programs can reduce reliance on unsustainable pastoral practices that intensify human\u0026ndash;wildlife conflict and habitat degradation. Targeted support for women, who showed higher willingness to adopt alternative livelihoods, can amplify conservation gains while advancing gender equity. Integrating livelihood development into conservation planning is therefore essential to foster community buy-in, mitigate pressures on park resources, and enhance long-term ecosystem resilience.\u003c/p\u003e\u003cp\u003eThe study concludes that interventions aiming to reduce conservation-livelihood conflicts should prioritize agribusiness development through training, credit access, and market linkages for crop value addition. Artisanal skills training and enterprise support, particularly targeting women and youth. Gender-responsive approaches that harness women\u0026rsquo;s stronger inclination toward conservation-compatible activities. Integration of livelihood support with conservation education, ensuring communities see tangible benefits from biodiversity protection.\u003c/p\u003e\u003cp\u003eBy addressing these areas, conservation managers and policymakers can create a win\u0026ndash;win pathway that enhances rural livelihoods while safeguarding the ecological integrity of the Tarangire National Park.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003cp\u003ethe interview protocol received approval from the from the University of Dar es Salaam (Ref. number: AB3/12(B)), and research clearance was obtained from the Tanzania National Parks (Ref. number: TNP/HO/E.20/08B). Verbal consent was obtained from the village heads and all participants before they took part in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe study was supported by \u003cem\u003eTrans-disciplinary Training for Resource Efficiency and Climate Change Adaptation in Africa\u003c/em\u003e (TRECCAfrica) scholarship.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author (HA) executed the study from conception and design, material preparation, data collection, data analysis, and data visualization, drafted the manuscript, revised, and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe data used in this paper is part of a doctoral project work that was made possible by the sponsorship of the Trans-disciplinary Training for Resource Efficiency and Climate Change Adaptation in Africa (TRECCAfrica). My sincere thanks go to the donors and the secretariat in Stellenbosch University, South Africa. I equally thank the local coordinator of the TRECCAfrica programme in the University of Dar es Salaam, Dr. James Lyimo for facilitating logistics throughout the studies. I thank authorities of the Tanzania National Parks (TANAPA) and Madam Gladys Ng\u0026rsquo;umbi, deputy chief park warden of the Tarangire National Park. I thank the Village Executive officers of Mamire, Mswakini and Kimotorok, all in Tanzania, for their support throughout the survey in their communities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbukari, H., \u0026amp; Mwalyosi, R. B. (2018). 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Automatically green: Behavioral economics and environmental protection. \u003cem\u003eHarv Envtl L Rev\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 127.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwann, E., \u0026amp; Richards, R. (2016). What factors influence the effectiveness of financial incentives on long-term natural resource management practice change? \u003cem\u003eEvidence Base: A journal of evidence reviews in key policy areas\u003c/em\u003e, (2), 1\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei, F., Wang, S., Fu, B., Zhang, L., Fu, C., \u0026amp; Kanga, E. M. (2018). Balancing community livelihoods and biodiversity conservation of protected areas in East Africa. \u003cem\u003eCurrent Opinion in Environmental Sustainability\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 26\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYkhanbai, H., Garg, R., Singh, A., Moiko, S. S., Beyene, C. E., Roe, D., \u0026amp; Flintan, F. E. (2014). Conservation and land grabbing in rangelands: Part of the problem or part of the solution?.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Conservation-compatible livelihoods, socioeconomic pragmatism, Tarangire National Park, pastoralist communities, livelihood diversification","lastPublishedDoi":"10.21203/rs.3.rs-7545654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7545654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProtected areas (PAs) play a pivotal role in global biodiversity conservation, yet their effectiveness often hinges on the livelihood decisions of surrounding communities. This study examines the drivers of alternative occupation preferences among residents living adjacent to Tarangire National Park (TNP) in northern Tanzania, a landscape where pastoralist traditions, conservation priorities, and livelihood vulnerabilities intersect. Using a cross-sectional survey of 184 households and multinomial logistic regression, we assessed the influence of demographic, socioeconomic, and attitudinal factors on willingness to adopt conservation-compatible livelihoods (CCLs). Results reveal widespread dissatisfaction with current livelihoods (70%) and a strong preference for agribusiness (36%), followed by livestock keeping (26%) and artisanal work (19%). Livelihood satisfaction, gender, education, occupation, years of residence, and perceptions of park impacts significantly shaped occupational aspirations, while ecocentric values and land ownership were not predictive. Women and respondents with lower levels of formal education demonstrated markedly higher odds of preferring agribusiness, livestock, and artisanal enterprises. These findings suggest that livelihood decisions are primarily driven by socioeconomic pragmatism rather than environmental attitudes alone. We argue that conservation interventions should prioritize agribusiness value chains, artisanal skill development, and gender-responsive strategies to align biodiversity protection with community aspirations. By situating livelihood diversification within the realities of rural pragmatism, this study advances actionable insights for integrating poverty alleviation and conservation planning in East African protected area landscapes.\u003c/p\u003e","manuscriptTitle":"Socioeconomic pragmatism and the adoption of conservation-compatible livelihoods: evidence from park -adjacent communities in northern Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 09:21:17","doi":"10.21203/rs.3.rs-7545654/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed474d00-77a0-4b1a-907b-951aa3384cee","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T12:41:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 09:21:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7545654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7545654","identity":"rs-7545654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}