Beyond visible losses: Documenting the invisible impacts of human-wildlife conflict on livelihood sustainability in Nepal's lowland protected areas

Beyond visible losses: Documenting the invisible impacts of human-wildlife conflict on livelihood sustainability in Nepal's lowland protected areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond visible losses: Documenting the invisible impacts of human-wildlife conflict on livelihood sustainability in Nepal's lowland protected areas Uma Dhungel, Sujata Shrestha, Tulasa Chaudhary, Bhagawan Raj Dhahal, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9318000/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Human-wildlife conflict (HWC) imposes well-documented visible costs on rural communities, yet its invisible impacts, manifested in psychosocial, economic, and social losses, remain systematically underrepresented in research and policy. We present a multi-site assessment of invisible HWC impacts on livelihood sustainability in Nepal, using the Sustainable Livelihood Framework (SLF). Through household surveys with 641 respondents across 60 settlements in the buffer zones of six lowland protected areas, we documented 16 types of invisible impacts spanning five livelihood capitals: human, social, natural, physical, and financial. Financial capital was most severely affected, with 66% of respondents reporting increased transaction and opportunity costs, 56% reporting lost productive labor time, and 37% reporting increased expenditures on conflict prevention measures. Psychological distress and trauma were the most prevalent human capital impact (65.2%), while social capital erosion, including negative conservation attitudes and community displacement, were least prevalent. OLS regression models revealed that protected area location was the dominant predictor of cumulative invisible impacts; respondents near Shuklaphanta and Bardiya national parks reported significantly higher invisible impacts than those near Chitwan. Notably, Dalit ethnicity was the strongest demographic predictor of financial invisible impacts, even after controlling for income and location, consistent with structural barriers to accessing compensation mechanisms. These findings demonstrate that invisible impacts are not supplementary to visible HWC losses but constitute a distinct and consequential dimension of livelihood insecurity. Formally integrating invisible impacts into HWC mitigation frameworks through mental health services, simplified claims procedures, and inclusive governance, is essential for advancing sustainable human-wildlife coexistence. invisible impacts sustainable livelihood framework conservation coexistence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Human-wildlife conflict (HWC) is harmful interactions between humans and wild animals that result in negative outcomes for both parties that poses a major and multifaceted challenge to sustainable livelihoods worldwide, particularly for rural communities residing in biodiverse regions, where local livelihood and well-being depend heavily on natural resources (Andrade and Rhodes 2012 ; Nyhus 2016 ; Braczkowski et al. 2023 ). Although HWC results in both visible and invisible losses, research and policy responses have predominantly focused on visible and readily quantifiable impacts of HWC, including crop destruction, livestock depredation, infrastructure damage and human injuries, and fatalities (Gemeda and Meles 2018 ; Ford et al. 2022 ; Yeshey et al. 2022; Galley and Anthony 2024 ). However, a growing body of evidence indicates that the consequences of HWC extend far beyond these tangible losses, encompassing a range of invisible impacts—costs that are psychosocial, temporally delayed, systemically diffuse, or simply unaccounted for in existing mitigation frameworks (Barua et al. 2013 ; Thondhlana et al. 2020 ; Ram et al. 2022 ). These invisible impacts have been variously described as ‘hidden costs’ (Barua et al. 2013 ), ‘invisible costs’ (Thondhlana et al. 2020 ), ‘non-physical effects’ (Khumalo and Yung 2015 ), and ‘negative wildlife contributions to people’ (Methorst et al. 2020 ). However, we used the term ‘invisible impacts’ in this study that manifest across multiple dimensions of human well-being: psychologically, through chronic fear, anxiety, post-traumatic stress, and sleep deprivation (Jadhav and Barua 2012 ; Galley and Anthony 2024 ); socially, through erosion of community cohesion, trust in conservation authorities, and social networks (Blackie 2023 ; Dickman 2010 ; Agrawal and Redford 2009 ); economically, through opportunity costs of crop and livestock guarding, bureaucratic burdens of compensation claims, and diversion of productive labor (Mackenzie and Ahabyona 2012 ; Barua et al. 2013 ); and educationally, through disruption of children's schooling due to fear of wildlife encounters or the need to assist in guarding activities (Mariki and Sengelela 2019 ; Montero-Botey et al. 2024 ). Although these invisible impacts are deeply interconnected with direct losses, they often have more enduring and cumulative consequences and are systematically overlooked in policy and practice (Ogra 2008 ; Galley and Anthony 2024 ). The burden of HWC—both visible and invisible—is not distributed evenly across social groups (Braczkowski et al. 2023 ). Communities living near protected areas face disproportionately higher risks of wildlife encounters and associated losses (Treves 2009 ; Baral et al. 2021a ), while agricultural and pastoral households tend to experience greater livelihood disruptions (Ramesh et al. 2020 ; Ram et al. 2022 ). Within these communities, socio-demographic factors including gender, age, education, income, and landholding size mediate the households’ capacity to cope with and recover from HWC impacts (Karanth et al. 2013 ; Karanth and Kudalkar 2017 ; Meyer and Börner 2022 ; Ram et al. 2022 ; Mbise and Senkondo 2024 ). Women, in particular, often bear a disproportionate share of invisible costs, facing increased physical and psychological burdens when male household members are absent for guarding responsibilities or labor migration (Ogra 2008 ; Tamang et al. 2014 ; Doubleday and Adams 2020 ). Likewise, economically marginalized households, with limited resources to invest in mitigation measures or alternative livelihoods, experience more severe and persistent invisible impacts (Khumalo and Yung 2015 ; Braczkowski et al. 2023 ). These patterns of disproportionate vulnerability highlight that invisible impacts are not merely an extension of visible losses; rather, they constitute a crucial, yet under-documented, dimension of livelihood insecurity in HWC-affected landscapes. Nepal, which ranks among the top ten countries globally for HWC incidence (Torres et al. 2018 ), offers a compelling context to examine these invisible impacts. In fiscal year 2080/81 (2023/2024 AD), a total of 4,899 cases of crop damage, 4,767 cases of livestock depredation, 103 human injuries, and 19 human deaths were reported within Nepal’s protected areas (DNPWC 2024). The southern lowland Tarai and Chure regions—a biodiversity-rich landscape that harbors Nepal's major protected areas and large populations of megafauna, including Asian elephants, one-horned rhinoceros, and Bengal tigers — are epicenters of this conflict (Ram et al. 2022 ). To address HWC, Nepal has implemented various mitigation measures, including compensation schemes, community-based monitoring, and conflict mitigation infrastructure (Ravenelle and Nyhus 2017 ; Sherchan et al. 2022 , Shrestha et al. 2025a ). Over the past five years, the government has distributed approximately NPR 645 million (~ US $ 4.9 million) in relief and compensation payments (Joshi 2024 ). However, these interventions focused almost exclusively on visible impacts such as compensating for human casualties, livestock depredation, and crop damage caused by 16 designated animal species (Sharma et al. 2021 ; Joshi 2024 ). While a few recent studies have documented HWC-induced stress and psychological distress in specific protected areas (Baral et al. 2021b ; Karki 2023 ), no study has systematically documented the full spectrum of invisible impacts across multiple protected areas of Nepal, nor examined how these impacts differentially affect distinct socio-demographic groups. To address this gap, we employ the Sustainable Livelihood Framework (SLF), a holistic analytical model that assesses how people secure and sustain their livelihoods through five interrelated forms of capital: human, social, natural, financial, and physical (Scoones 1998 ; Carney 1998 ; DFID 1999). Originally developed to understand poverty dynamics and rural development, the SLF has been widely applied to assess the livelihood consequences of external shocks and stressors, including climate change (Elasha et al. 2005 ), drought (Ndlovu and Mamba 2023 ), and natural disasters (Can et al. 2013 ). HWC functions as both a ‘persistent shock’ through acute events such as wildlife attacks, and an ‘adverse trend’ through chronic exposure to wildlife-related risks, that gradually erode livelihood capitals and compound pre-existing vulnerabilities (Yeshey et al. 2022; Ford et al. 2022 ; Smith 2024 ). The SLF thus provides a structured and integrative lens for identifying and categorizing the invisible dimensions of HWC, tracing how these impacts cascade across different forms of livelihood capitals, and revealing the policy and institutional gaps that allow such impacts to persist (Morse 2025 ). Specifically, this study aims to: (1) document and categorize the invisible impacts of HWC on the livelihoods of communities living in the buffer zones of six lowland protected areas in Nepal using the SLF framework; (2) assess the relative contribution of different types of invisible impact types to each form of livelihood capital; and (3) examine how socio-demographic factors shape community perceptions of these invisible impacts. By doing so, we demonstrate that invisible impacts of HWC are as consequential as visible losses for livelihood sustainability, and that their systematic integration into HWC mitigation policies is essential for improving human well-being and enhancing the long-term effectiveness of biodiversity conservation in Nepal and beyond. 2. MATERIALS AND METHODS 2.1. Study area The study was conducted across the buffer zones of Nepal’s six lowland protected areas from west to east: Shuklaphanta National Park, Bardiya National Park, Banke National Park, Chitwan National Park, Parsa National Park, and Koshi Tappu Wildlife Reserve. These protected areas collectively harbor Nepal's largest populations of Asian elephants ( Elephas maximus ), one-horned rhinoceros ( Rhinoceros unicornis ), Bengal tigers ( Panthera tigris ), and other megafauna that are primary drivers of HWC in the region. Buffer zone settlements adjacent to these areas are predominantly occupied by smallholder farming communities and indigenous groups whose livelihoods depend heavily on farming, livestock and forest resources (Acharya et al. 2016 ; Baral et al. 2021b ; Khatri et al. 2024 ). Previous studies have shown that communities living in Nepal’s lowland protected area landscapes experience particularly high vulnerability to HWC (Shrestha et al. 2025b ). Although these regions are among the most impacted and well-studied for visible HWC impacts, existing research has rarely examined the invisible dimensions of these conflicts in a systematic manner. 2.2. Data collection Household surveys were conducted among 641 households across 60 settlements located within 36 buffer zones of the six protected areas between February and April 2024. For the household surveys, a two-stage sampling strategy was employed. First, the buffer zones most affected by HWC within each protected area were identified through purposive sampling based on the maximum number of HWC incidents recorded in the DNPWC annual report for the preceding fiscal year. Within these buffer zones, 60 settlements with the highest HWC incidence were selected in consultation with heads of Buffer Zone User Committees (BZUCs). Second, households within the selected settlements were sampled randomly. A minimum sample size of 595 households was calculated using Cochran's formula (Cochran 1977 ), based on a total household population of 59,849, applying a 95% confidence level, 4% margin of error, and assuming maximum variability (p = 0.5). An additional 46 households were surveyed to account for potential non-response and data entry errors, yielding a final sample of 641 households. Household heads or adult household members (≥ 18 years) were selected as respondents. Each interview lasted 20–30 minutes and was conducted in the Nepali language. Prior to each interview, the study objectives were explained, and Free, Prior, and Informed Consent (FPIC) was obtained from respondents. Respondents were assured of anonymity and confidentiality and informed of their right to decline any question or withdraw from the survey at any point. The research permit was secured from the DNPWC (permission number: 2080/81/E215). The survey questionnaire comprised three sections of both open-ended and close-ended questions: (i) socio-demographic information, (ii) household experiences with HWC and types of conflict encountered or experienced, and (iii) perceived impacts of HWC on livelihoods and existing mitigation measures. 2.3. Data analysis Qualitative and quantitative responses related to HWC impacts were classified into the five livelihood capital categories of SLF based on typologies developed in previous studies (Barua et al. 2013 ; Kansky et al. 2014 ; Khumalo and Yung 2015 ; Thondhlana et al. 2020 ; Methorst et al. 2020 ). Impacts classified as direct or visible, including human injury and death, crop raiding, livestock depredation, and physical infrastructure damage, were excluded from the analysis. Only invisible impacts (n = 16) were retained and categorized into five capitals: human (n = 4), social (n = 3), natural (n = 3), physical (n = 3) and financial (n = 3). The detailed classification of behavioral responses, invisible impacts and their corresponding livelihood capital categories, along with supporting literature, are given in Table 1 . Table 1 Classification of invisible impacts of HWC by livelihood capital. Observed behavioral response Invisible impact Livelihood capital Analytical variable References Presence of wildlife causing chronic fear, anxiety, and stress; recurrent traumatic memories of past encounters Psychological distress and trauma symptoms consistent with post-traumatic stress Human Psychological distress Ogra 2008 ; Barua et al. 2013 ; Yeshey et al. 2022; Galley and Anthony 2024 Increased night guarding disrupting sleep patterns; heightened nocturnal vigilance Sleep deprivation affecting health and well-being Human Sleep disruption Barua et al. 2013 ; Khumalo and Yung 2015 ; Bond and Mkutu 2018 ; Manoa et al. 2020 ; Galley and Anthony 2024 Fatigue from night guarding reducing daytime work capacity and decision-making Reduced concentration and exhaustion lowering productivity Human Fatigue/productivity loss Ogra 2008 ; Barua et al. 2013 ; Gemeda and Meles 2018 ; Yeshey et al. 2022 Unsafe travel routes to school due to wildlife presence; children diverted to guarding duties School absenteeism and educational disruption Human Educational disruption Ogra 2008 ; Barua et al. 2013 ; Khumalo and Yung 2015 ; Yeshey et al. 2022; Halder et al. 2025; Galley and Anthony 2024 ; Pereira et al. 2024 Disagreements over management of problematic wildlife, community patrol obligations, and perceived unfairness in relief distribution Weakened social networks and erosion of community cohesion Social Weak social networks Agrawal and Redford 2009 ; Stevens et al. 2025 Erosion of trust in conservation authorities due to perceived unfair compensation, top-down management, and prioritization of wildlife over human needs Negative attitudes toward conservation efforts Social Negative conservation attitudes Hill and Wallace 2012 ; Regmi et al. 2013 ; Pechacek et al. 2013 ; Yeshey et al. 2022; Dahal et al. 2025 Abandonment of traditional lands, sacred sites, and cultural rituals to avoid wildlife encounters; outmigration of young males Community displacement, migration, and social instability Social Displacement/fragmentation Kusiluka et al. 2011 ; Manral et al. 2016 ; Thekaekara et al. 2021 ; Karki 2023 Switching from high-value traditional crops to less palatable, lower-value alternatives to reduce wildlife raiding Reduced agricultural income and food security Natural Reduced farm income /food security Galley and Anthony 2024 Abandonment of farming practices due to repeated crop raiding and wildlife intrusion at harvest time Decline in agricultural productivity Natural Declining agricultural productivity Madden 2004 ; Distefano 2005 ; Lamarque et al. 2009 Avoiding use of natural resources (forests, rivers, water points, sacred sites) due to wildlife threats and park restrictions Limited access to natural resources Natural Restricted resource access Padmakumar and Shanthakumar 2023 Building fences, watchtowers, improved grain storage, and concrete houses to replace wildlife-damaged traditional structures Increased labor and material costs for protective infrastructure Physical Protection labor / material costs Muruthi 2005 ; Eniang et al. 2011 ; Mojo et al. 2014 ; Yeshey et al. 2022 Rearing guard animals (dogs) and bearing associated costs including replacement when killed by predators Increased costs of rearing guard animals Physical Guard animal rearing costs Ogra 2008 ; Mackenzie and Ahabyona 2012 ; Barua et al. 2013 ; Yeshey et al. 2022 Building predator-proof corrals and keeping livestock inside houses for protection Increased investment in securing livestock assets Physical Livestock protection investment Yeshey et al. 2022 Time and resources spent filing compensation claims, follow-up visits to offices, document preparation, and delays awaiting payment Increased transaction and opportunity costs Financial Transaction/ opportunity costs Barua et al. 2013 ; Galley and Anthony 2024 Time lost to guarding fields, community patrol obligations, and night-watching duties Reduced productive labor time and income opportunities Financial Lost labor/income Ogra 2008 ; Barua et al. 2013 ; Khumalo and Yung 2015 ; Yeshey et al. 2022 Expenditure on deterrents (fencing wire, flashlights, loudspeakers, alarms) and conflict prevention measures Increased financial burden for conflict prevention Financial Conflict mitigation costs Yeshey et al. 2022; Galley and Anthony 2024 For each respondent, perceived impacts were coded as binary variables (1 = perceived, 0 = not perceived). A weighted mean score was calculated for each livelihood capital by dividing the sum of perceived impacts by the total number of impact types within that capital (4 for human capital, 3 for each of the other four capitals). A cumulative invisible impact score was then calculated by adding the five capital-level weighted means, yielding a continuous score ranging from 0 (no invisible impacts perceived) to a theoretical maximum where all impacts are perceived. The observed total cumulative impact score values ranged from 0 to 1.8 (mean = 0.51, SD = 0.34). To examine the influence of socio-demographic factors and protected area context on perceived invisible impacts, we employed ordinary least squares (OLS) regression models. Two sets of models were estimated: (1) An aggregate model, using the cumulative invisible impact score as the dependent variable; and (2) Capital-level models with each of the five capital-specific scores served as separate dependent variables. This approach enables the identification of predictors that influence specific livelihood capitals, patterns that may be obscured in aggregated analyses. Ten socio-demographic predictor variables, selected based on previous HWC studies (e.g., Karanth et al. 2013 ; Karanth and Kudalkar 2017 ; Ramesh et al. 2020 ; Ram et al. 2021 ; Meyer and Börner 2022 ; Mbise and Senkondo 2024 ): gender, age group, education level, primary livelihood, distance from PA, household size, ethnicity, annual household income, land holding size, and livestock number, were incorporated. All predictors were entered as categorical dummy variables. Protected area location was included as a control variable (with Chitwan NP as the reference category) to account for site-level differences in HWC intensity, wildlife composition, and institutional context. Reference categories were selected to represent the most common or baseline group: male for gender, age 18–40 for age, higher secondary or above for education, non-farming for livelihood, > 2km for distance, household size of 7–19 for household size, Brahmin/Chhetri for ethnicity, >NPR 300,000 for annual income, > 0.51 ha for land holding, and > 10 for livestock number. To ensure the robustness of the regression models, we checked multicollinearity using variance inflation factors (VIF), heteroscedasticity using the Breusch–Pagan test, and functional form misspecification using the Ramsey RESET test. All analyses were performed in R version 4.5.1 (R Core Team, 2024 ). Qualitative responses to open-ended questions were analyzed thematically to complement and contextualize the quantitative findings. Representative quotes were selected based on specificity, uniqueness, analytical relevance, and demographic representativeness, following established criteria for mixed-methods integration (Creswell and Plano 2017). 3. RESULTS 3.1. Socio-demographic information of the respondents A total of 641 respondents participated in the survey, with relatively balanced representation: Shuklaphanta NP (n = 104), Banke NP (n = 105), Bardiya NP (n = 116), Chitwan NP (n = 111), Parsa NP (n = 103), and Koshi Tappu WR (n = 102). The majority of respondents were female (58%, n = 373), belonged to the 41–60-year age group (43%), and were involved in farming as their primary livelihood (75%). Nearly half (47%) were illiterate, and most (63%) lived within 1 km of a protected area boundary. The predominant ethnic groups were Brahmin/Chhetri (45%) and Indigenous (44%), with Dalit comprising 8% of the sample. Most respondents (63%) held less than 0.51 hectares of land, and 37% reported an annual income of NPR 100,000 or less (~ USD 750). Full socio-demographic characteristics are presented in Table 2 . Table 2 Socio-demographic characteristics of respondents. Indicator Frequency (%) Gender (n = 641) Male 268 42 Female 373 58 Age (years) (n = 632) 18–40 255 40 41–60 272 43 > 60 105 17 Education (n = 634) Illiterate 298 47 Literate 115 18 Secondary level 164 26 Higher secondary or above 57 9 Primary livelihood option (n = 639) Farming 480 75 Non-farming 159 25 Distance of household from PA (n = 585) (km) 2 92 16 Household size (n = 641) 1–6 452 71 7–19 189 29 Ethnicity (n = 611) Brahmin/Chhetri 284 46 Indigenous 274 45 Dalit 49 8 Annual income (NPR) (n = 553) ≤ 100,000 206 37 100,001–300,000 168 30 > 300,000 179 32 Land unit (hectares) (n = 641) ≤ 0.51 405 63 > 0.51 236 37 Livestock number (n = 550) ≤ 5 301 55 6–10 165 30 >10 84 15 3.2. Perceived invisible impacts on livelihood capital 3.2.1. Human capital Four types of invisible impacts on human capital were identified. The most frequently reported invisible impacts were psychological distress and trauma (65.2%, n = 418), followed by sleep disruption (49%, n = 314), fatigue/productivity loss (31.8%, n = 204), and school absenteeism and educational disruption (13.3%, n = 85) (Fig. 2 ). Qualitative accounts revealed the depth of these impacts. One female respondent from Chitwan described how a single traumatic event produced chronic behavioral change: " I witnessed my neighbor being killed by a wild elephant right in front of my eyes. That incident traumatized me, and now I am afraid to step out of my house when dusk approaches " (CNP_60_F). The gendered burden of HWC on human capital was particularly evident among women managing households alone. A respondent from Bardiya, whose husband had migrated for work, described a cascade of interlinked invisible impacts: " My husband is in a Gulf country, and I live here in a nuclear family with my daughter. I must deal with both farm and non-farm responsibilities, but I remain anxious all day about my daughter's safety, livestock security, and during times when conflict surges, we are often forced to take refuge at a neighbor's house " (BNP_37_F). Night guarding emerged as a critical mechanism linking HWC to human capital erosion, with consequences that could escalate from invisible to fatal: " We lost our father-in-law while guarding against the elephant attack in the field at night " (BNP_58_F). The impacts on children extended beyond absenteeism to intergenerational livelihood disruption: " My husband died due to the attack of a wild boar, which not just separated our family but also stopped my elder son's study as he had to work to support the family living " (CNP_50_F). Another respondent reported: “ My daughter is in class 10; she has an extra class in the morning at her school. But due to the fear of animal attacks, she usually drops those extra classes. This is hampering her study ” (BNP_74_M). 3.2.2. Social capital Three types of invisible impacts on social capital were perceived. The most prevalent was a shift in attitudes, particularly hostile attitudes toward wildlife and conservation (26%, n = 164), followed by weakened social networks and cohesion (6%, n = 38), and community displacement and migration (2%, n = 13) (Fig. 2 ). The attitudinal shift was driven by a perception that conservation authorities prioritize wildlife over human welfare: " In the buffer zone area, human life is not valued. We don't want money as a relief. In fact, practical solutions are essential or legal permission should be granted to kill problematic animals " (BNP_32_M). Inequities in compensation access further eroded social cohesion as one respondent commented: " Only people with strong connections can expedite the process, while it is hard for female and old-aged people " (ShNP_40_M). HWC also emerged as a driver of cross-border labor migration, with cascading gendered consequences: " It is very difficult for us to do agriculture and livestock rearing, as we are not allowed to go to the forest for the collection of resources. So, we have no other alternative than going to India to earn money " (ShNP_40_F). Respondents further reported disruption to cultural and spiritual practices. An Indigenous elder (67-year-old) from the Gadwaline area of Thori Municipality, Parsa, described how wildlife intrusion has severed communities from sacred sites: " Due to the increased intrusion of wildlife in the forest, we are afraid to visit the Bhatta Baba temple located deep in the jungle. That temple holds immense significance in our religious beliefs. " (PNP_67_M). A Tharu community member similarly noted: " Fish is crucial in Tharu rituals, considered an offering to the gods, but due to the fear of wildlife, fishing has been disrupted, which has impacted our cultural practices " (BaNP_62_M). 3.2.3. Natural capital Three types of invisible impacts on natural capital were perceived. Limited or restricted access to natural resources was the most prevalent (22%, n = 142), followed by reduced agricultural income and food security (11%, n = 71), and declining agricultural productivity (7%, n = 46) (Fig. 2 ). Respondents described how HWC and park restrictions combined to force livelihood transitions: "Previously, we used to fulfill our household chores by selling firewood, but nowadays, due to increased conflict between the park and people and restrictions on resource collection, we are unable to collect them, which is not only causing difficulties in fulfilling our household needs but forcing us to switch to wage labor" (BaNP_20_F). HWC also caused subtle mechanisms of production loss: "As animal intrusion increased during harvesting times, we were forced to harvest pre-matured crops before proper ripening; this resulted in low-quality grains that couldn't be stored for long." Another respondent (CNP_65_M) said: “ We have sold all of our livestock due to the fear of going to the forest for fodder collection. ” This reduced production has also impacted on the household economy of the farmers, as they must further buy crops to fulfill the food demand and invest in agricultural byproducts to rear the livestock 3.2.4. Physical capital Three types of invisible impacts on physical capital were perceived. Increased investment in protective infrastructure, including labor and material costs, was the most prevalent (34%, n = 219), followed by increased costs for guarding animals (16%, n = 101), and increased investment in securing livestock assets (2%, n = 11) (Fig. 2 ). The forced transition from traditional to modern housing exemplified invisible trade-offs: " Once an elephant destroyed my house, which made me build a new household made up of concrete. After that, everyone is making a concrete house due to the fear of elephants, which is not our traditional type of house. This had decreased fear but increased debt " (BNP_35_F). Investments in mitigation could themselves be rendered worthless: " I invested NRS 50,000 to buy an improved breed of dog for guarding purposes, but a leopard killed it within a week of rearing " (BNP_55_M). 3.2.5. Financial capital Three types of invisible impacts on financial capital were perceived. Increased transaction and opportunity costs were the most prevalent (66%, n = 424), followed by reduced productive labor time or lost labor income (56%, n = 356), and increased cost for conflict prevention (37%, n = 235) (Fig. 2 ). The bureaucratic costs of seeking compensation emerged as a major invisible burden: " It took almost two weeks and more to file the complaint about the crop loss. The process and the requirements of documents are too tedious, and there is no guarantee of getting the compensation amount " (SNP_52_F). Some respondents — particularly women living alone — opted out of the compensation system entirely: " As I stay alone, I have not reported the loss till now because it is a very complex process and requires time as well as hassle and bustle " (ShNP_47_F). 3.3. Patterns of invisible impacts across protected areas and livelihood capitals The distribution of cumulative invisible impacts varied considerably across protected areas (Fig. 3 ). The highest cumulative invisible impact scores were observed in Shuklaphanta NP (mean = 0.72) and Bardiya NP (mean = 0.68), followed by Banke NP (mean = 0.58). In contrast, Koshi Tappu WR exhibited the lowest cumulative score (mean = 0.31). Across livelihood capitals, financial impacts were consistently the most pronounced (mean = 1.13), followed by human capital impacts (mean = 0.92). Physical and natural capital impacts were comparatively moderate, while social capital impacts were the lowest (mean = 0.12). 3.4. Socio-demographic and spatial predictors of invisible HWC impacts Aggregate model The OLS regression model with all ten socio-demographic predictors and protected area controls explained 26.3% of the variance in cumulative invisible impact scores (Adjusted R² = 0.237, F = 9.86, p < 0.001). All variance inflation factors were below 4.0, indicating no problematic multicollinearity. HC3 (Heteroskedasticity-Consistent) robust standard errors were used to address heteroscedasticity detected by the Breusch–Pagan test (p < 0.001). Protected area location was the dominant predictor. Relative to Chitwan NP, respondents near Shuklaphanta NP reported substantially higher cumulative invisible impacts (β = 0.362, p < 0.001), followed by Bardiya NP (β = 0.299, p < 0.001) and Banke NP (β = 0.231, p < 0.001). Koshi Tappu WR and Parsa NP did not differ significantly from Chitwan (Fig. 4 ). The magnitude of the PA coefficients was 3–6 times larger than any socio-demographic predictor, indicating that site-level context, including differences in wildlife composition, conflict intensity, and institutional arrangements, is a stronger determinant of perceived invisible impacts than individual-level characteristics. Among socio-demographic variables, only annual household income was a significant independent predictor. Notably, the direction of this effect diverged from bivariate expectations: respondents with income ≤ NPR 100,000 (β = −0.101, p NPR 300,000, after controlling for PA location and other covariates (Fig. 4 ). Gender, education, distance to PA, ethnicity, land size, and livestock number were not significant predictors of cumulative invisible impact after multivariate adjustment (p > 0.05 for all). Farming as primary livelihood (β = −0.054, p = 0.063), age 41–60 (β = −0.048, p = 0.099), and small household size (β = 0.050, p = 0.089) showed marginally significant associations. Capital-level models To examine whether socio-demographic predictors operate differentially across livelihood capitals, separate OLS models were estimated for each of the five capital-specific impact scores (Fig. 5 ). This analysis revealed substantially different predictor patterns across capitals. Financial capital was the best-explained model (R² = 0.234) and the only capital where socio-demographic factors had strong independent effects. Three findings were notable. First, Dalit ethnicity was the strongest demographic predictor across all models (β = 0.498, p < 0.001), indicating that Dalit respondents perceived dramatically higher financial invisible impacts than Brahmin/Chhetri respondents, even after controlling for income and PA location (Fig. 5 ). This effect was absent in all other capital models and invisible in the aggregate analysis. Second, education was significant only for financial capital: Literate (β = 0.393, p < 0.01) and Secondary-level (β = 0.331, p < 0.05) respondents perceived higher financial impacts than those with higher secondary education, suggesting that moderately educated households engage more with formal compensation processes but lack the networks to navigate them efficiently. Third, the income reversal identified in the aggregate model was concentrated in financial capital (β = −0.469 for low income, p < 0.001) (Fig. 5 ). Human capital (R² = 0.155) was driven predominantly by PA location: Shuklaphanta NP had the largest coefficient in any model (β = 1.000, p < 0.001), indicating that respondents there scored a full point higher on human capital impacts compared to Chitwan. Bardiya NP showed a similar pattern (β = 0.596, p < 0.001). Among demographic variables, only age 41–60 (β = −0.211, p < 0.05) and small household size (β = 0.167, p < 0.05) were significant. Gender was not a significant predictor of human capital impacts (β = 0.047, p = 0.56). Social capital (R² = 0.025) was not explained by any measured predictor, neither socio-demographic variables nor PA location reached significance, and the overall model was not statistically significant (F-test p = 0.80). This suggests that social capital erosion, including shifts in conservation attitudes and community cohesion, is a collective, community-level phenomenon experienced broadly across socio-demographic groups and PA contexts, rather than being individually determined. Natural capital (R² = 0.055) and physical capital (R² = 0.105) were primarily predicted by PA location, with no significant socio-demographic predictors. For natural capital, Koshi Tappu WR was the only significant PA effect (β = −0.149, p = 0.002), reflecting lower perceived natural capital impacts relative to Chitwan, likely due to the different wildlife–livelihood dynamics of this wetland reserve. For physical capital, Bardiya (β = 0.174, p < 0.001) and Banke (β = 0.149, p < 0.01) areas with high elephant populations showed significantly elevated impacts. 4. DISCUSSION This study provides the first systematic, multi-site assessment of the invisible impacts of human–wildlife conflict on livelihood sustainability in Nepal. Across 641 households in the buffer zones of six lowland protected areas, we documented 16 types of invisible impacts spanning five livelihood capitals. While previous research in Nepal has focused primarily on the direct and visible costs of HWC—such as crop damage, livestock depredation, and human injury or death (Lamichhane et al. 2018 ; Sharma et al. 2021 ; Baral et al. 2022 ; Shrestha et al. 2025a ; Dahal et al. 2025 )—our findings demonstrate that invisible impacts are pervasive, consequential, and unevenly distributed across both livelihood dimensions and geographic contexts. Protected area location emerged as the dominant predictor of invisible HWC impacts, explaining substantially more variation than any socio-demographic characteristic. Respondents near Shuklaphanta NP, Bardiya NP, and Banke NP reported significantly higher invisible impacts than those near Chitwan NP, Parsa NP, or Koshi Tappu WR. This spatial heterogeneity likely reflects differences in conflict intensity, wildlife assemblages, institutional capacity, and infrastructure development across sites. Shuklaphanta and the Bardiya–Banke complex harbor large and growing populations of Asian elephants, Bengal tigers, and leopards. Elephants are among the most destructive conflict species globally (Gross et al. 2017 ; Shaffer et al. 2019 ), while tigers are responsible for most human casualties in the Bardiya–Banke complex (Paudel et al. 2024 ). In Shuklaphanta, elephants and leopards are the primary attacking species (Pant et al. 2023 ). These species generate not only direct material losses but also acute psychological stress due to unpredictable and sometimes fatal encounters. By contrast, the nature and composition of conflict at Koshi Tappu, dominated by wild water buffalo and seasonal elephant intrusions, may generate more acute but less psychologically diffuse impacts compared to the tiger/elephant-dominated conflict at Shuklaphanta and Bardiya. Chitwan NP, despite experiencing frequent HWC, may benefit from more developed tourism infrastructure, stronger institutional presence, and a longer-established buffer zone management program that partially mitigates invisible impacts (Stapp et al. 2016 ). Buffer zone communities in Chitwan also maintain relatively positive attitudes toward wildlife and conservation despite decades of conflict () and have gradually normalized coping strategies that may reduce perceived risk (Ghimire et al. 2022 ). Our multivariate analysis also revealed a notable reversal in the income–impact relationship. Bivariate analysis suggested that lower-income households perceived greater invisible impacts, consistent with earlier studies (Khumalo and Yung 2015 ; Meyer and Börner 2022 ). However, after controlling for protected area location and other covariates, this relationship reversed: lower-income households reported lower cumulative invisible impact scores than higher-income households. This reversal occurred because poorer households in our sample were disproportionately located in protected areas with lower overall HWC intensity (Chitwan, Parsa, Koshi Tappu), whereas relatively wealthier households were concentrated in higher-conflict areas such as Shuklaphanta and Bardiya. Once spatial variation in conflict intensity was accounted for, the independent effect of income shifted direction. Several previous studies report that poorer, less educated, or female respondents perceive greater HWC impacts (e.g.,Ogra 2008 ; Karanth et al. 2013 ; Karanth and Kudalkar 2017 ). However, our findings suggest that such relationships may sometimes reflect unaccounted spatial heterogeneity in conflict exposure rather than purely individual-level characteristics. Future studies should therefore adopt multivariate frameworks that explicitly control for spatial context before attributing HWC perceptions to demographic factors. The capital-specific regression models further demonstrate that the determinants of invisible impacts differ substantially across livelihood dimensions, validating the use of the SLF as an analytical framework rather than merely a descriptive classification. Financial capital was the only dimension strongly shaped by socio-demographic factors. In particular, Dalit respondents reported significantly higher financial invisible impacts than Brahmin/Chhetri respondents, even after controlling for income, education, and protected area location. This was the strongest ethnicity effect observed in the analysis, yet it was not visible in the aggregate cumulative model. This pattern is consistent with well-documented structural barriers faced by Dalit communities in accessing government services and compensation mechanisms in Nepal (Amnesty International 2024 ). Caste-based discrimination can limit Dalits’ ability to navigate bureaucratic processes, access political networks that facilitate compensation, and advocate effectively for their claims—barriers reflected in our qualitative data describing politically mediated relief distribution. These findings resonate with Barua et al.'s ( 2013 ) argument that the hidden costs of HWC fall disproportionately on those lacking the social capital necessary to navigate institutional systems. Human capital impacts were driven primarily by protected area context, although household size also played a role: smaller households reported greater human capital impacts, likely because fewer members are available to share night-guarding responsibilities, concentrating sleep deprivation and fear among fewer individuals. This finding aligns with Galley and Anthony ( 2024 ), who identified sleep deprivation as one of the most significant yet overlooked opportunity costs of HWC. Interestingly, gender was not a statistically significant predictor in the multivariate model despite qualitative accounts highlighting gendered experiences of fear, stress, and vulnerability. This apparent contradiction suggests that while men and women may experience invisible impacts through different pathways—such as male labor migration, gendered care responsibilities, or restricted mobility—the overall magnitude of reported impacts may remain similar within the same conflict context. Gendered dimensions of invisible impacts may therefore reflect differences in mechanisms rather than intensity, highlighting the importance of gender-sensitive conflict mitigation strategies (Doubleday and Adams 2020 ; Adler et al. 2025 ). Social capital impacts were largely unexplained by either socio-demographic or spatial predictors. No variable in the model reached statistical significance, and the overall model was weak. This suggests that erosion of social capital may operate primarily as a collective phenomenon rather than an individual-level experience. When trust in conservation institutions declines or compensation systems are perceived as unfair, the social fabric of entire communities can be affected, regardless of individual household characteristics. This interpretation aligns with Dickman’s ( 2010 ) argument that HWC is fundamentally a social conflict, as well as Agrawal and Redford’s ( 2009 ) observation that conservation-related conflicts can undermine collective action and community cohesion. Beyond statistical patterns, qualitative evidence indicates that invisible impacts rarely occur in isolation. Instead, they cascade across livelihood capitals and accumulate over time. For example, a single elephant attack can trigger psychological trauma (human capital), require infrastructure reconstruction through debt (physical and financial capital), erode trust in conservation authorities perceived as prioritizing wildlife over people (social capital), and restrict access to forests and farmland through fear-based avoidance (natural capital). This cascading dynamic—described by Barua et al. ( 2013 ) as the “hidden architecture” of HWC costs—suggests that the true burden of conflict is multiplicative rather than simply additive, while Blackie ( 2023 ) explained stress and trauma caused by HWC also play a major role in reducing overall life satisfaction This study has several limitations. First, the analysis focuses on buffer zones of six lowland protected areas in the Tarai and Chure regions; results may not generalize to mid-hill or mountain protected areas where HWC dynamics differ substantially (Baral et al. 2021a ; Bista and Song 2022 ). Second, the dependent variable is perception-based and binary-coded (perceived vs. not perceived), capturing prevalence but not severity or intensity of impacts. Future research could employ graded severity scales (e.g., Likert-type measures) or validated psychometric instruments such as the PTSD Checklist (Weathers et al. 2013 ) to better quantify psychological impacts. Third, the Dalit subsample was relatively small (n = 49); although the financial capital effect was statistically strong, replication with a larger Dalit-focused sample would strengthen confidence in this finding. 5. CONCLUSION This study demonstrates that the invisible impacts of human–wildlife conflict on livelihood sustainability are pervasive, consequential, and often overlooked in existing policy frameworks. Across six lowland protected areas in Nepal, households reported invisible impacts affecting all five livelihood capitals, with financial impacts—such as transaction costs associated with compensation processes, lost productive labor time, and expenditures on conflict prevention—being the most prevalent. These impacts rarely occur in isolation; rather, they cascade across livelihood capitals and accumulate over time, potentially creating livelihood traps that conventional compensation mechanisms fail to address. While protected area context emerged as the strongest determinant of invisible impact perceptions, capital-specific analyses revealed distinct underlying drivers. In particular, Dalit ethnicity was strongly associated with financial capital impacts, while social capital erosion appeared to operate as a community-level phenomenon not fully explained by individual characteristics. These findings demonstrate that invisible impacts are not merely supplementary to visible HWC losses but constitute an integral component of the overall burden borne by conflict-affected communities. Formally recognizing invisible impacts within Nepal's compensation and governance frameworks and embedding them into mitigation strategies will be essential to achieving sustainable human-wildlife coexistence. Declarations The authors have no relevant financial or non-financial interests to disclose. Author Contribution All authors contributed to the study conception and design. Data collection and analysis were performed by Uma Dhungel and Uttam Babu Shrestha. The first draft of the manuscript was written by Uma Dhungel and Uttam Babu Shrestha and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement This work was supported by the Zoological Society of London Nepal office through the Darwin Initiative grant (Grant number: DAREX008). Data Availability The data that support the findings of this study are available on request from the corresponding author, [UBS] References Acharya KP, Paudel PK, Neupane PR, Köhl M (2016) Human-wildlife conflicts in Nepal: patterns of human fatalities and injuries caused by large mammals. PLoS ONE 11(9):e0161717. https://doi.org/10.1371/journal.pone.0161717 Adler KA, Gore ML, Wilkinson CE (2025) The gendered costs of human–wildlife conflict: A global systematic review. 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National Center for PTSD, U.S. Department of Veterans Affairs Yeshey, Ford RM, Keenan RJ, Nitschke CR (2022) Subsistence farmers’ understanding of the effects of indirect impacts of human wildlife conflict on their psychosocial well-being in Bhutan. Sustainability 14(21):14050. https://doi.org/10.3390/su142114050 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 04 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Studies","correspondingAuthor":false,"prefix":"","firstName":"Sujata","middleName":"","lastName":"Shrestha","suffix":""},{"id":618212451,"identity":"ad0670e9-6ed9-4f5e-a8a9-108a54f10283","order_by":2,"name":"Tulasa Chaudhary","email":"","orcid":"","institution":"Global Institute for Interdisciplinary Studies","correspondingAuthor":false,"prefix":"","firstName":"Tulasa","middleName":"","lastName":"Chaudhary","suffix":""},{"id":618212452,"identity":"a6fe0929-c121-47c4-91e6-e71c1e948b67","order_by":3,"name":"Bhagawan Raj Dhahal","email":"","orcid":"","institution":"Zoological Society of London","correspondingAuthor":false,"prefix":"","firstName":"Bhagawan","middleName":"Raj","lastName":"Dhahal","suffix":""},{"id":618212453,"identity":"4869a772-f19f-4127-847a-8c7d12385c68","order_by":4,"name":"Uttam Babu Shrestha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABF0lEQVRIiWNgGAWjYBCDBBDBzMBgw8DGDGKy4VELUQHWwtjMwJBGupbDMGHcOgzu9x/8dIOhLs+8vf3544Ka83Z97OwPGD6UHWYwl27AruUYM7N0DsPhYpkzZwybZxy7ndzGzGPAOOPcYQbLOQdwaWEAajmQOEMih7GZh+12MhszDwMzb9thBoMbCTht+Z3DUJc4Q/75w2aef+eAWtgfMP/Fr4UNaAsz0BYGw2betgN2wPAwYGbEo0XyWLKZdY7B4cQZPDmGs3n7khOADjM42HMuncfgDna/8B0++Ph2TgXQYezHH3zm+WZnL99//OGDH2XWcga3sYcY1HkIZiJIHch4HgYJPDqQgT2CSayWUTAKRsEoGO4AAFOVWdswC/5LAAAAAElFTkSuQmCC","orcid":"","institution":"Global Institute for Interdisciplinary Studies","correspondingAuthor":true,"prefix":"","firstName":"Uttam","middleName":"Babu","lastName":"Shrestha","suffix":""}],"badges":[],"createdAt":"2026-04-04 06:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9318000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9318000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106572820,"identity":"a5d67599-a642-4ec7-b4bc-7ba90096e684","added_by":"auto","created_at":"2026-04-10 04:10:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1010214,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Nepal's protected areas and buffer zones showing the surveyed settlements.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9318000/v1/16b1e39f5ca01b79b3a61575.png"},{"id":106572799,"identity":"1b58a31c-4aa9-43c5-b0b7-5433f190df66","added_by":"auto","created_at":"2026-04-10 04:10:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198718,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of households reporting each of 16 invisible human–wildlife conflict (HWC) impacts across six protected areas.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9318000/v1/63a4e81f555a31fa632e2ded.png"},{"id":106572822,"identity":"212f1321-5703-46f3-9f07-9066fb756310","added_by":"auto","created_at":"2026-04-10 04:10:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115886,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of cumulative invisible impact scores of HWC across six protected areas and by livelihood capitals.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9318000/v1/d2ae3bcf950fdaa9b61c3a5b.png"},{"id":106572866,"identity":"133fc47a-55ab-4cc1-bfa7-5b2370856548","added_by":"auto","created_at":"2026-04-10 04:10:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":183600,"visible":true,"origin":"","legend":"\u003cp\u003eOLS regression coefficients (β) and significance levels for predictors of cumulative invisible HWC impact scores.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9318000/v1/86017a8a6748ab66fc142603.png"},{"id":106572801,"identity":"9cfb3744-558e-4037-96c6-ceb254f4d7a6","added_by":"auto","created_at":"2026-04-10 04:10:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":409772,"visible":true,"origin":"","legend":"\u003cp\u003eOLS regression coefficients (β) and statistical significance for predictors of invisible impacts of HWC across livelihood capitals.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9318000/v1/a95e81cd7afca3a2c0a5676e.png"},{"id":106572890,"identity":"9b097bdf-eeca-4fe9-9f35-9d892569528e","added_by":"auto","created_at":"2026-04-10 04:10:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2815061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9318000/v1/cb365f1a-5371-4060-afa7-6a80c616274b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond visible losses: Documenting the invisible impacts of human-wildlife conflict on livelihood sustainability in Nepal's lowland protected areas","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eHuman-wildlife conflict (HWC) is harmful interactions between humans and wild animals that result in negative outcomes for both parties that poses a major and multifaceted challenge to sustainable livelihoods worldwide, particularly for rural communities residing in biodiverse regions, where local livelihood and well-being depend heavily on natural resources (Andrade and Rhodes \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nyhus \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Braczkowski et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although HWC results in both visible and invisible losses, research and policy responses have predominantly focused on visible and readily quantifiable impacts of HWC, including crop destruction, livestock depredation, infrastructure damage and human injuries, and fatalities (Gemeda and Meles \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ford et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yeshey et al. 2022; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, a growing body of evidence indicates that the consequences of HWC extend far beyond these tangible losses, encompassing a range of invisible impacts\u0026mdash;costs that are psychosocial, temporally delayed, systemically diffuse, or simply unaccounted for in existing mitigation frameworks (Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thondhlana et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ram et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese invisible impacts have been variously described as \u0026lsquo;hidden costs\u0026rsquo; (Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u0026lsquo;invisible costs\u0026rsquo; (Thondhlana et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u0026lsquo;non-physical effects\u0026rsquo; (Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and \u0026lsquo;negative wildlife contributions to people\u0026rsquo; (Methorst et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, we used the term \u0026lsquo;invisible impacts\u0026rsquo; in this study that manifest across multiple dimensions of human well-being: psychologically, through chronic fear, anxiety, post-traumatic stress, and sleep deprivation (Jadhav and Barua \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); socially, through erosion of community cohesion, trust in conservation authorities, and social networks (Blackie \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dickman \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Agrawal and Redford \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); economically, through opportunity costs of crop and livestock guarding, bureaucratic burdens of compensation claims, and diversion of productive labor (Mackenzie and Ahabyona \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); and educationally, through disruption of children's schooling due to fear of wildlife encounters or the need to assist in guarding activities (Mariki and Sengelela \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Montero-Botey et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although these invisible impacts are deeply interconnected with direct losses, they often have more enduring and cumulative consequences and are systematically overlooked in policy and practice (Ogra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe burden of HWC\u0026mdash;both visible and invisible\u0026mdash;is not distributed evenly across social groups (Braczkowski et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Communities living near protected areas face disproportionately higher risks of wildlife encounters and associated losses (Treves \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Baral et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), while agricultural and pastoral households tend to experience greater livelihood disruptions (Ramesh et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ram et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within these communities, socio-demographic factors including gender, age, education, income, and landholding size mediate the households\u0026rsquo; capacity to cope with and recover from HWC impacts (Karanth et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Karanth and Kudalkar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Meyer and B\u0026ouml;rner \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ram et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mbise and Senkondo \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Women, in particular, often bear a disproportionate share of invisible costs, facing increased physical and psychological burdens when male household members are absent for guarding responsibilities or labor migration (Ogra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tamang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Doubleday and Adams \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Likewise, economically marginalized households, with limited resources to invest in mitigation measures or alternative livelihoods, experience more severe and persistent invisible impacts (Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Braczkowski et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These patterns of disproportionate vulnerability highlight that invisible impacts are not merely an extension of visible losses; rather, they constitute a crucial, yet under-documented, dimension of livelihood insecurity in HWC-affected landscapes.\u003c/p\u003e \u003cp\u003eNepal, which ranks among the top ten countries globally for HWC incidence (Torres et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), offers a compelling context to examine these invisible impacts. In fiscal year 2080/81 (2023/2024 AD), a total of 4,899 cases of crop damage, 4,767 cases of livestock depredation, 103 human injuries, and 19 human deaths were reported within Nepal\u0026rsquo;s protected areas (DNPWC 2024). The southern lowland Tarai and Chure regions\u0026mdash;a biodiversity-rich landscape that harbors Nepal's major protected areas and large populations of megafauna, including Asian elephants, one-horned rhinoceros, and Bengal tigers \u0026mdash; are epicenters of this conflict (Ram et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To address HWC, Nepal has implemented various mitigation measures, including compensation schemes, community-based monitoring, and conflict mitigation infrastructure (Ravenelle and Nyhus \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sherchan et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Shrestha et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Over the past five years, the government has distributed approximately NPR 645\u0026nbsp;million (~\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e 4.9\u0026nbsp;million) in relief and compensation payments (Joshi \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, these interventions focused almost exclusively on visible impacts such as compensating for human casualties, livestock depredation, and crop damage caused by 16 designated animal species (Sharma et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Joshi \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While a few recent studies have documented HWC-induced stress and psychological distress in specific protected areas (Baral et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Karki \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), no study has systematically documented the full spectrum of invisible impacts across multiple protected areas of Nepal, nor examined how these impacts differentially affect distinct socio-demographic groups.\u003c/p\u003e \u003cp\u003eTo address this gap, we employ the Sustainable Livelihood Framework (SLF), a holistic analytical model that assesses how people secure and sustain their livelihoods through five interrelated forms of capital: human, social, natural, financial, and physical (Scoones \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Carney \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; DFID 1999). Originally developed to understand poverty dynamics and rural development, the SLF has been widely applied to assess the livelihood consequences of external shocks and stressors, including climate change (Elasha et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), drought (Ndlovu and Mamba \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and natural disasters (Can et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). HWC functions as both a \u0026lsquo;persistent shock\u0026rsquo; through acute events such as wildlife attacks, and an \u0026lsquo;adverse trend\u0026rsquo; through chronic exposure to wildlife-related risks, that gradually erode livelihood capitals and compound pre-existing vulnerabilities (Yeshey et al. 2022; Ford et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SLF thus provides a structured and integrative lens for identifying and categorizing the invisible dimensions of HWC, tracing how these impacts cascade across different forms of livelihood capitals, and revealing the policy and institutional gaps that allow such impacts to persist (Morse \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecifically, this study aims to: (1) document and categorize the invisible impacts of HWC on the livelihoods of communities living in the buffer zones of six lowland protected areas in Nepal using the SLF framework; (2) assess the relative contribution of different types of invisible impact types to each form of livelihood capital; and (3) examine how socio-demographic factors shape community perceptions of these invisible impacts. By doing so, we demonstrate that invisible impacts of HWC are as consequential as visible losses for livelihood sustainability, and that their systematic integration into HWC mitigation policies is essential for improving human well-being and enhancing the long-term effectiveness of biodiversity conservation in Nepal and beyond.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe study was conducted across the buffer zones of Nepal\u0026rsquo;s six lowland protected areas from west to east: Shuklaphanta National Park, Bardiya National Park, Banke National Park, Chitwan National Park, Parsa National Park, and Koshi Tappu Wildlife Reserve. These protected areas collectively harbor Nepal's largest populations of Asian elephants (\u003cem\u003eElephas maximus\u003c/em\u003e), one-horned rhinoceros (\u003cem\u003eRhinoceros unicornis\u003c/em\u003e), Bengal tigers (\u003cem\u003ePanthera tigris\u003c/em\u003e), and other megafauna that are primary drivers of HWC in the region.\u003c/p\u003e \u003cp\u003eBuffer zone settlements adjacent to these areas are predominantly occupied by smallholder farming communities and indigenous groups whose livelihoods depend heavily on farming, livestock and forest resources (Acharya et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Baral et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Khatri et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies have shown that communities living in Nepal\u0026rsquo;s lowland protected area landscapes experience particularly high vulnerability to HWC (Shrestha et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Although these regions are among the most impacted and well-studied for visible HWC impacts, existing research has rarely examined the invisible dimensions of these conflicts in a systematic manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection\u003c/h2\u003e \u003cp\u003eHousehold surveys were conducted among 641 households across 60 settlements located within 36 buffer zones of the six protected areas between February and April 2024. For the household surveys, a two-stage sampling strategy was employed. First, the buffer zones most affected by HWC within each protected area were identified through purposive sampling based on the maximum number of HWC incidents recorded in the DNPWC annual report for the preceding fiscal year. Within these buffer zones, 60 settlements with the highest HWC incidence were selected in consultation with heads of Buffer Zone User Committees (BZUCs). Second, households within the selected settlements were sampled randomly. A minimum sample size of 595 households was calculated using Cochran's formula (Cochran \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), based on a total household population of 59,849, applying a 95% confidence level, 4% margin of error, and assuming maximum variability (p\u0026thinsp;=\u0026thinsp;0.5). An additional 46 households were surveyed to account for potential non-response and data entry errors, yielding a final sample of 641 households. Household heads or adult household members (\u0026ge;\u0026thinsp;18 years) were selected as respondents. Each interview lasted 20\u0026ndash;30 minutes and was conducted in the Nepali language.\u003c/p\u003e \u003cp\u003ePrior to each interview, the study objectives were explained, and Free, Prior, and Informed Consent (FPIC) was obtained from respondents. Respondents were assured of anonymity and confidentiality and informed of their right to decline any question or withdraw from the survey at any point. The research permit was secured from the DNPWC (permission number: 2080/81/E215). The survey questionnaire comprised three sections of both open-ended and close-ended questions: (i) socio-demographic information, (ii) household experiences with HWC and types of conflict encountered or experienced, and (iii) perceived impacts of HWC on livelihoods and existing mitigation measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data analysis\u003c/h2\u003e \u003cp\u003eQualitative and quantitative responses related to HWC impacts were classified into the five livelihood capital categories of SLF based on typologies developed in previous studies (Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kansky et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thondhlana et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Methorst et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Impacts classified as direct or visible, including human injury and death, crop raiding, livestock depredation, and physical infrastructure damage, were excluded from the analysis. Only invisible impacts (n\u0026thinsp;=\u0026thinsp;16) were retained and categorized into five capitals: human (n\u0026thinsp;=\u0026thinsp;4), social (n\u0026thinsp;=\u0026thinsp;3), natural (n\u0026thinsp;=\u0026thinsp;3), physical (n\u0026thinsp;=\u0026thinsp;3) and financial (n\u0026thinsp;=\u0026thinsp;3). The detailed classification of behavioral responses, invisible impacts and their corresponding livelihood capital categories, along with supporting literature, are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eClassification of invisible impacts of HWC by livelihood capital.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved behavioral response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvisible impact\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLivelihood capital\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnalytical variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of wildlife causing chronic fear, anxiety, and stress; recurrent traumatic memories of past encounters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological distress and trauma symptoms consistent with post-traumatic stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychological distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOgra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yeshey et al. 2022; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased night guarding disrupting sleep patterns; heightened nocturnal vigilance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep deprivation affecting health and well-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSleep disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bond and Mkutu \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Manoa et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue from night guarding reducing daytime work capacity and decision-making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced concentration and exhaustion lowering productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatigue/productivity loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOgra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gemeda and Meles \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yeshey et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsafe travel routes to school due to wildlife presence; children diverted to guarding duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool absenteeism and educational disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEducational disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOgra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yeshey et al. 2022; Halder et al. 2025; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pereira et al. 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagreements over management of problematic wildlife, community patrol obligations, and perceived unfairness in relief distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakened social networks and erosion of community cohesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak social networks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgrawal and Redford \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Stevens et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErosion of trust in conservation authorities due to perceived unfair compensation, top-down management, and prioritization of wildlife over human needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative attitudes toward conservation efforts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative conservation attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHill and Wallace \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Regmi et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pechacek et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yeshey et al. 2022; Dahal et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbandonment of traditional lands, sacred sites, and cultural rituals to avoid wildlife encounters; outmigration of young males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity displacement, migration, and social instability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisplacement/fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKusiluka et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Manral et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Thekaekara et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karki \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwitching from high-value traditional crops to less palatable, lower-value alternatives to reduce wildlife raiding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced agricultural income and food security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduced farm income /food security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGalley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbandonment of farming practices due to repeated crop raiding and wildlife intrusion at harvest time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecline in agricultural productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeclining agricultural productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMadden \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Distefano \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lamarque et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvoiding use of natural resources (forests, rivers, water points, sacred sites) due to wildlife threats and park restrictions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited access to natural resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRestricted resource access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePadmakumar and Shanthakumar \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding fences, watchtowers, improved grain storage, and concrete houses to replace wildlife-damaged traditional structures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased labor and material costs for protective infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtection labor / material costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMuruthi \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Eniang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mojo et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yeshey et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRearing guard animals (dogs) and bearing associated costs including replacement when killed by predators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased costs of rearing guard animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuard animal rearing costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOgra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mackenzie and Ahabyona \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yeshey et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding predator-proof corrals and keeping livestock inside houses for protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased investment in securing livestock assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLivestock protection investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYeshey et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime and resources spent filing compensation claims, follow-up visits to offices, document preparation, and delays awaiting payment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased transaction and opportunity costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransaction/ opportunity costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime lost to guarding fields, community patrol obligations, and night-watching duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced productive labor time and income opportunities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLost labor/income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOgra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yeshey et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpenditure on deterrents (fencing wire, flashlights, loudspeakers, alarms) and conflict prevention measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased financial burden for conflict prevention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConflict mitigation costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYeshey et al. 2022; Galley and Anthony \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\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\u003eFor each respondent, perceived impacts were coded as binary variables (1\u0026thinsp;=\u0026thinsp;perceived, 0\u0026thinsp;=\u0026thinsp;not perceived). A weighted mean score was calculated for each livelihood capital by dividing the sum of perceived impacts by the total number of impact types within that capital (4 for human capital, 3 for each of the other four capitals). A cumulative invisible impact score was then calculated by adding the five capital-level weighted means, yielding a continuous score ranging from 0 (no invisible impacts perceived) to a theoretical maximum where all impacts are perceived. The observed total cumulative impact score values ranged from 0 to 1.8 (mean\u0026thinsp;=\u0026thinsp;0.51, SD\u0026thinsp;=\u0026thinsp;0.34).\u003c/p\u003e \u003cp\u003eTo examine the influence of socio-demographic factors and protected area context on perceived invisible impacts, we employed ordinary least squares (OLS) regression models. Two sets of models were estimated: (1) An aggregate model, using the cumulative invisible impact score as the dependent variable; and (2) Capital-level models with each of the five capital-specific scores served as separate dependent variables. This approach enables the identification of predictors that influence specific livelihood capitals, patterns that may be obscured in aggregated analyses.\u003c/p\u003e \u003cp\u003eTen socio-demographic predictor variables, selected based on previous HWC studies (e.g., Karanth et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Karanth and Kudalkar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ramesh et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ram et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meyer and B\u0026ouml;rner \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mbise and Senkondo \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): gender, age group, education level, primary livelihood, distance from PA, household size, ethnicity, annual household income, land holding size, and livestock number, were incorporated. All predictors were entered as categorical dummy variables. Protected area location was included as a control variable (with Chitwan NP as the reference category) to account for site-level differences in HWC intensity, wildlife composition, and institutional context.\u003c/p\u003e \u003cp\u003eReference categories were selected to represent the most common or baseline group: male for gender, age 18\u0026ndash;40 for age, higher secondary or above for education, non-farming for livelihood, \u0026gt; 2km for distance, household size of 7\u0026ndash;19 for household size, Brahmin/Chhetri for ethnicity, \u0026gt;NPR 300,000 for annual income, \u0026gt;\u0026thinsp;0.51 ha for land holding, and \u0026gt;\u0026thinsp;10 for livestock number.\u003c/p\u003e \u003cp\u003eTo ensure the robustness of the regression models, we checked multicollinearity using variance inflation factors (VIF), heteroscedasticity using the Breusch\u0026ndash;Pagan test, and functional form misspecification using the Ramsey RESET test. All analyses were performed in R version 4.5.1 (R Core Team, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQualitative responses to open-ended questions were analyzed thematically to complement and contextualize the quantitative findings. Representative quotes were selected based on specificity, uniqueness, analytical relevance, and demographic representativeness, following established criteria for mixed-methods integration (Creswell and Plano 2017).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Socio-demographic information of the respondents\u003c/h2\u003e \u003cp\u003eA total of 641 respondents participated in the survey, with relatively balanced representation: Shuklaphanta NP (n\u0026thinsp;=\u0026thinsp;104), Banke NP (n\u0026thinsp;=\u0026thinsp;105), Bardiya NP (n\u0026thinsp;=\u0026thinsp;116), Chitwan NP (n\u0026thinsp;=\u0026thinsp;111), Parsa NP (n\u0026thinsp;=\u0026thinsp;103), and Koshi Tappu WR (n\u0026thinsp;=\u0026thinsp;102). The majority of respondents were female (58%, n\u0026thinsp;=\u0026thinsp;373), belonged to the 41\u0026ndash;60-year age group (43%), and were involved in farming as their primary livelihood (75%). Nearly half (47%) were illiterate, and most (63%) lived within 1 km of a protected area boundary. The predominant ethnic groups were Brahmin/Chhetri (45%) and Indigenous (44%), with Dalit comprising 8% of the sample. Most respondents (63%) held less than 0.51 hectares of land, and 37% reported an annual income of NPR 100,000 or less (~\u0026thinsp;USD 750). Full socio-demographic characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eSocio-demographic characteristics of respondents.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (n\u0026thinsp;=\u0026thinsp;641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) (n\u0026thinsp;=\u0026thinsp;632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (n\u0026thinsp;=\u0026thinsp;634)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher secondary or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary livelihood option (n\u0026thinsp;=\u0026thinsp;639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance of household from PA (n\u0026thinsp;=\u0026thinsp;585) (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size (n\u0026thinsp;=\u0026thinsp;641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (n\u0026thinsp;=\u0026thinsp;611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrahmin/Chhetri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDalit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual income (NPR) (n\u0026thinsp;=\u0026thinsp;553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,001\u0026ndash;300,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 300,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand unit (hectares) (n\u0026thinsp;=\u0026thinsp;641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock number (n\u0026thinsp;=\u0026thinsp;550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Perceived invisible impacts on livelihood capital\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Human capital\u003c/h2\u003e \u003cp\u003eFour types of invisible impacts on human capital were identified. The most frequently reported invisible impacts were psychological distress and trauma (65.2%, n\u0026thinsp;=\u0026thinsp;418), followed by sleep disruption (49%, n\u0026thinsp;=\u0026thinsp;314), fatigue/productivity loss (31.8%, n\u0026thinsp;=\u0026thinsp;204), and school absenteeism and educational disruption (13.3%, n\u0026thinsp;=\u0026thinsp;85) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQualitative accounts revealed the depth of these impacts. One female respondent from Chitwan described how a single traumatic event produced chronic behavioral change: \"\u003cem\u003eI witnessed my neighbor being killed by a wild elephant right in front of my eyes. That incident traumatized me, and now I am afraid to step out of my house when dusk approaches\u003c/em\u003e\" (CNP_60_F). The gendered burden of HWC on human capital was particularly evident among women managing households alone. A respondent from Bardiya, whose husband had migrated for work, described a cascade of interlinked invisible impacts: \"\u003cem\u003eMy husband is in a Gulf country, and I live here in a nuclear family with my daughter. I must deal with both farm and non-farm responsibilities, but I remain anxious all day about my daughter's safety, livestock security, and during times when conflict surges, we are often forced to take refuge at a neighbor's house\u003c/em\u003e\" (BNP_37_F).\u003c/p\u003e \u003cp\u003eNight guarding emerged as a critical mechanism linking HWC to human capital erosion, with consequences that could escalate from invisible to fatal: \"\u003cem\u003eWe lost our father-in-law while guarding against the elephant attack in the field at night\u003c/em\u003e\" (BNP_58_F). The impacts on children extended beyond absenteeism to intergenerational livelihood disruption: \"\u003cem\u003eMy husband died due to the attack of a wild boar, which not just separated our family but also stopped my elder son's study as he had to work to support the family living\u003c/em\u003e\" (CNP_50_F). Another respondent reported: \u0026ldquo;\u003cem\u003eMy daughter is in class 10; she has an extra class in the morning at her school. But due to the fear of animal attacks, she usually drops those extra classes. This is hampering her study\u003c/em\u003e\u0026rdquo; (BNP_74_M).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Social capital\u003c/h2\u003e \u003cp\u003eThree types of invisible impacts on social capital were perceived. The most prevalent was a shift in attitudes, particularly hostile attitudes toward wildlife and conservation (26%, n\u0026thinsp;=\u0026thinsp;164), followed by weakened social networks and cohesion (6%, n\u0026thinsp;=\u0026thinsp;38), and community displacement and migration (2%, n\u0026thinsp;=\u0026thinsp;13) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe attitudinal shift was driven by a perception that conservation authorities prioritize wildlife over human welfare: \"\u003cem\u003eIn the buffer zone area, human life is not valued. We don't want money as a relief. In fact, practical solutions are essential or legal permission should be granted to kill problematic animals\u003c/em\u003e\" (BNP_32_M). Inequities in compensation access further eroded social cohesion as one respondent commented: \"\u003cem\u003eOnly people with strong connections can expedite the process, while it is hard for female and old-aged people\u003c/em\u003e\" (ShNP_40_M).\u003c/p\u003e \u003cp\u003eHWC also emerged as a driver of cross-border labor migration, with cascading gendered consequences: \"\u003cem\u003eIt is very difficult for us to do agriculture and livestock rearing, as we are not allowed to go to the forest for the collection of resources. So, we have no other alternative than going to India to earn money\u003c/em\u003e\" (ShNP_40_F). Respondents further reported disruption to cultural and spiritual practices. An Indigenous elder (67-year-old) from the Gadwaline area of Thori Municipality, Parsa, described how wildlife intrusion has severed communities from sacred sites: \"\u003cem\u003eDue to the increased intrusion of wildlife in the forest, we are afraid to visit the Bhatta Baba temple located deep in the jungle. That temple holds immense significance in our religious beliefs.\u003c/em\u003e\" (PNP_67_M). A Tharu community member similarly noted: \"\u003cem\u003eFish is crucial in Tharu rituals, considered an offering to the gods, but due to the fear of wildlife, fishing has been disrupted, which has impacted our cultural practices\u003c/em\u003e\" (BaNP_62_M).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Natural capital\u003c/h2\u003e \u003cp\u003eThree types of invisible impacts on natural capital were perceived. Limited or restricted access to natural resources was the most prevalent (22%, n\u0026thinsp;=\u0026thinsp;142), followed by reduced agricultural income and food security (11%, n\u0026thinsp;=\u0026thinsp;71), and declining agricultural productivity (7%, n\u0026thinsp;=\u0026thinsp;46) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRespondents described how HWC and park restrictions combined to force livelihood transitions: \u003cem\u003e\"Previously, we used to fulfill our household chores by selling firewood, but nowadays, due to increased conflict between the park and people and restrictions on resource collection, we are unable to collect them, which is not only causing difficulties in fulfilling our household needs but forcing us to switch to wage labor\"\u003c/em\u003e (BaNP_20_F). HWC also caused subtle mechanisms of production loss: \u003cem\u003e\"As animal intrusion increased during harvesting times, we were forced to harvest pre-matured crops before proper ripening; this resulted in low-quality grains that couldn't be stored for long.\"\u003c/em\u003e Another respondent (CNP_65_M) said: \u0026ldquo;\u003cem\u003eWe have sold all of our livestock due to the fear of going to the forest for fodder collection.\u003c/em\u003e\u0026rdquo; This reduced production has also impacted on the household economy of the farmers, as they must further buy crops to fulfill the food demand and invest in agricultural byproducts to rear the livestock\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Physical capital\u003c/h2\u003e \u003cp\u003eThree types of invisible impacts on physical capital were perceived. Increased investment in protective infrastructure, including labor and material costs, was the most prevalent (34%, n\u0026thinsp;=\u0026thinsp;219), followed by increased costs for guarding animals (16%, n\u0026thinsp;=\u0026thinsp;101), and increased investment in securing livestock assets (2%, n\u0026thinsp;=\u0026thinsp;11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe forced transition from traditional to modern housing exemplified invisible trade-offs: \"\u003cem\u003eOnce an elephant destroyed my house, which made me build a new household made up of concrete. After that, everyone is making a concrete house due to the fear of elephants, which is not our traditional type of house. This had decreased fear but increased debt\u003c/em\u003e\" (BNP_35_F). Investments in mitigation could themselves be rendered worthless: \"\u003cem\u003eI invested NRS 50,000 to buy an improved breed of dog for guarding purposes, but a leopard killed it within a week of rearing\u003c/em\u003e\" (BNP_55_M).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5. Financial capital\u003c/h2\u003e \u003cp\u003eThree types of invisible impacts on financial capital were perceived. Increased transaction and opportunity costs were the most prevalent (66%, n\u0026thinsp;=\u0026thinsp;424), followed by reduced productive labor time or lost labor income (56%, n\u0026thinsp;=\u0026thinsp;356), and increased cost for conflict prevention (37%, n\u0026thinsp;=\u0026thinsp;235) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe bureaucratic costs of seeking compensation emerged as a major invisible burden: \"\u003cem\u003eIt took almost two weeks and more to file the complaint about the crop loss. The process and the requirements of documents are too tedious, and there is no guarantee of getting the compensation amount\u003c/em\u003e\" (SNP_52_F). Some respondents \u0026mdash; particularly women living alone \u0026mdash; opted out of the compensation system entirely: \"\u003cem\u003eAs I stay alone, I have not reported the loss till now because it is a very complex process and requires time as well as hassle and bustle\u003c/em\u003e\" (ShNP_47_F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Patterns of invisible impacts across protected areas and livelihood capitals\u003c/h2\u003e \u003cp\u003eThe distribution of cumulative invisible impacts varied considerably across protected areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest cumulative invisible impact scores were observed in Shuklaphanta NP (mean\u0026thinsp;=\u0026thinsp;0.72) and Bardiya NP (mean\u0026thinsp;=\u0026thinsp;0.68), followed by Banke NP (mean\u0026thinsp;=\u0026thinsp;0.58). In contrast, Koshi Tappu WR exhibited the lowest cumulative score (mean\u0026thinsp;=\u0026thinsp;0.31). Across livelihood capitals, financial impacts were consistently the most pronounced (mean\u0026thinsp;=\u0026thinsp;1.13), followed by human capital impacts (mean\u0026thinsp;=\u0026thinsp;0.92). Physical and natural capital impacts were comparatively moderate, while social capital impacts were the lowest (mean\u0026thinsp;=\u0026thinsp;0.12).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Socio-demographic and spatial predictors of invisible HWC impacts\u003c/h2\u003e \u003cp\u003e \u003cb\u003eAggregate model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe OLS regression model with all ten socio-demographic predictors and protected area controls explained 26.3% of the variance in cumulative invisible impact scores (Adjusted R\u0026sup2; = 0.237, F\u0026thinsp;=\u0026thinsp;9.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All variance inflation factors were below 4.0, indicating no problematic multicollinearity. HC3 (Heteroskedasticity-Consistent) robust standard errors were used to address heteroscedasticity detected by the Breusch\u0026ndash;Pagan test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eProtected area location was the dominant predictor. Relative to Chitwan NP, respondents near Shuklaphanta NP reported substantially higher cumulative invisible impacts (β\u0026thinsp;=\u0026thinsp;0.362, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by Bardiya NP (β\u0026thinsp;=\u0026thinsp;0.299, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Banke NP (β\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Koshi Tappu WR and Parsa NP did not differ significantly from Chitwan (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The magnitude of the PA coefficients was 3\u0026ndash;6 times larger than any socio-demographic predictor, indicating that site-level context, including differences in wildlife composition, conflict intensity, and institutional arrangements, is a stronger determinant of perceived invisible impacts than individual-level characteristics.\u003c/p\u003e \u003cp\u003eAmong socio-demographic variables, only annual household income was a significant independent predictor. Notably, the direction of this effect diverged from bivariate expectations: respondents with income\u0026thinsp;\u0026le;\u0026thinsp;NPR 100,000 (β = \u0026minus;0.101, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and income NPR 100,001\u0026ndash;300,000 (β = \u0026minus;0.084, p\u0026thinsp;=\u0026thinsp;0.005) reported lower cumulative invisible impact scores than those earning\u0026thinsp;\u0026gt;\u0026thinsp;NPR 300,000, after controlling for PA location and other covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGender, education, distance to PA, ethnicity, land size, and livestock number were not significant predictors of cumulative invisible impact after multivariate adjustment (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). Farming as primary livelihood (β = \u0026minus;0.054, p\u0026thinsp;=\u0026thinsp;0.063), age 41\u0026ndash;60 (β = \u0026minus;0.048, p\u0026thinsp;=\u0026thinsp;0.099), and small household size (β\u0026thinsp;=\u0026thinsp;0.050, p\u0026thinsp;=\u0026thinsp;0.089) showed marginally significant associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCapital-level models\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo examine whether socio-demographic predictors operate differentially across livelihood capitals, separate OLS models were estimated for each of the five capital-specific impact scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This analysis revealed substantially different predictor patterns across capitals.\u003c/p\u003e \u003cp\u003eFinancial capital was the best-explained model (R\u0026sup2; = 0.234) and the only capital where socio-demographic factors had strong independent effects. Three findings were notable. First, Dalit ethnicity was the strongest demographic predictor across all models (β\u0026thinsp;=\u0026thinsp;0.498, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that Dalit respondents perceived dramatically higher financial invisible impacts than Brahmin/Chhetri respondents, even after controlling for income and PA location (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This effect was absent in all other capital models and invisible in the aggregate analysis. Second, education was significant only for financial capital: Literate (β\u0026thinsp;=\u0026thinsp;0.393, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Secondary-level (β\u0026thinsp;=\u0026thinsp;0.331, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) respondents perceived higher financial impacts than those with higher secondary education, suggesting that moderately educated households engage more with formal compensation processes but lack the networks to navigate them efficiently. Third, the income reversal identified in the aggregate model was concentrated in financial capital (β = \u0026minus;0.469 for low income, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman capital (R\u0026sup2; = 0.155) was driven predominantly by PA location: Shuklaphanta NP had the largest coefficient in any model (β\u0026thinsp;=\u0026thinsp;1.000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that respondents there scored a full point higher on human capital impacts compared to Chitwan. Bardiya NP showed a similar pattern (β\u0026thinsp;=\u0026thinsp;0.596, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among demographic variables, only age 41\u0026ndash;60 (β = \u0026minus;0.211, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and small household size (β\u0026thinsp;=\u0026thinsp;0.167, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significant. Gender was not a significant predictor of human capital impacts (β\u0026thinsp;=\u0026thinsp;0.047, p\u0026thinsp;=\u0026thinsp;0.56).\u003c/p\u003e \u003cp\u003eSocial capital (R\u0026sup2; = 0.025) was not explained by any measured predictor, neither socio-demographic variables nor PA location reached significance, and the overall model was not statistically significant (F-test p\u0026thinsp;=\u0026thinsp;0.80). This suggests that social capital erosion, including shifts in conservation attitudes and community cohesion, is a collective, community-level phenomenon experienced broadly across socio-demographic groups and PA contexts, rather than being individually determined.\u003c/p\u003e \u003cp\u003eNatural capital (R\u0026sup2; = 0.055) and physical capital (R\u0026sup2; = 0.105) were primarily predicted by PA location, with no significant socio-demographic predictors. For natural capital, Koshi Tappu WR was the only significant PA effect (β = \u0026minus;0.149, p\u0026thinsp;=\u0026thinsp;0.002), reflecting lower perceived natural capital impacts relative to Chitwan, likely due to the different wildlife\u0026ndash;livelihood dynamics of this wetland reserve. For physical capital, Bardiya (β\u0026thinsp;=\u0026thinsp;0.174, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Banke (β\u0026thinsp;=\u0026thinsp;0.149, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) areas with high elephant populations showed significantly elevated impacts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study provides the first systematic, multi-site assessment of the invisible impacts of human\u0026ndash;wildlife conflict on livelihood sustainability in Nepal. Across 641 households in the buffer zones of six lowland protected areas, we documented 16 types of invisible impacts spanning five livelihood capitals. While previous research in Nepal has focused primarily on the direct and visible costs of HWC\u0026mdash;such as crop damage, livestock depredation, and human injury or death (Lamichhane et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Baral et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shrestha et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Dahal et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u0026mdash;our findings demonstrate that invisible impacts are pervasive, consequential, and unevenly distributed across both livelihood dimensions and geographic contexts.\u003c/p\u003e \u003cp\u003eProtected area location emerged as the dominant predictor of invisible HWC impacts, explaining substantially more variation than any socio-demographic characteristic. Respondents near Shuklaphanta NP, Bardiya NP, and Banke NP reported significantly higher invisible impacts than those near Chitwan NP, Parsa NP, or Koshi Tappu WR. This spatial heterogeneity likely reflects differences in conflict intensity, wildlife assemblages, institutional capacity, and infrastructure development across sites. Shuklaphanta and the Bardiya\u0026ndash;Banke complex harbor large and growing populations of Asian elephants, Bengal tigers, and leopards. Elephants are among the most destructive conflict species globally (Gross et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shaffer et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e ), while tigers are responsible for most human casualties in the Bardiya\u0026ndash;Banke complex (Paudel et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Shuklaphanta, elephants and leopards are the primary attacking species (Pant et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These species generate not only direct material losses but also acute psychological stress due to unpredictable and sometimes fatal encounters. By contrast, the nature and composition of conflict at Koshi Tappu, dominated by wild water buffalo and seasonal elephant intrusions, may generate more acute but less psychologically diffuse impacts compared to the tiger/elephant-dominated conflict at Shuklaphanta and Bardiya. Chitwan NP, despite experiencing frequent HWC, may benefit from more developed tourism infrastructure, stronger institutional presence, and a longer-established buffer zone management program that partially mitigates invisible impacts (Stapp et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Buffer zone communities in Chitwan also maintain relatively positive attitudes toward wildlife and conservation despite decades of conflict () and have gradually normalized coping strategies that may reduce perceived risk (Ghimire et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur multivariate analysis also revealed a notable reversal in the income\u0026ndash;impact relationship. Bivariate analysis suggested that lower-income households perceived greater invisible impacts, consistent with earlier studies (Khumalo and Yung \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Meyer and B\u0026ouml;rner \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, after controlling for protected area location and other covariates, this relationship reversed: lower-income households reported lower cumulative invisible impact scores than higher-income households. This reversal occurred because poorer households in our sample were disproportionately located in protected areas with lower overall HWC intensity (Chitwan, Parsa, Koshi Tappu), whereas relatively wealthier households were concentrated in higher-conflict areas such as Shuklaphanta and Bardiya. Once spatial variation in conflict intensity was accounted for, the independent effect of income shifted direction.\u003c/p\u003e \u003cp\u003eSeveral previous studies report that poorer, less educated, or female respondents perceive greater HWC impacts (e.g.,Ogra \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Karanth et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Karanth and Kudalkar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, our findings suggest that such relationships may sometimes reflect unaccounted spatial heterogeneity in conflict exposure rather than purely individual-level characteristics. Future studies should therefore adopt multivariate frameworks that explicitly control for spatial context before attributing HWC perceptions to demographic factors.\u003c/p\u003e \u003cp\u003eThe capital-specific regression models further demonstrate that the determinants of invisible impacts differ substantially across livelihood dimensions, validating the use of the SLF as an analytical framework rather than merely a descriptive classification. Financial capital was the only dimension strongly shaped by socio-demographic factors. In particular, Dalit respondents reported significantly higher financial invisible impacts than Brahmin/Chhetri respondents, even after controlling for income, education, and protected area location. This was the strongest ethnicity effect observed in the analysis, yet it was not visible in the aggregate cumulative model. This pattern is consistent with well-documented structural barriers faced by Dalit communities in accessing government services and compensation mechanisms in Nepal (Amnesty International \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Caste-based discrimination can limit Dalits\u0026rsquo; ability to navigate bureaucratic processes, access political networks that facilitate compensation, and advocate effectively for their claims\u0026mdash;barriers reflected in our qualitative data describing politically mediated relief distribution. These findings resonate with Barua et al.'s (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) argument that the hidden costs of HWC fall disproportionately on those lacking the social capital necessary to navigate institutional systems.\u003c/p\u003e \u003cp\u003eHuman capital impacts were driven primarily by protected area context, although household size also played a role: smaller households reported greater human capital impacts, likely because fewer members are available to share night-guarding responsibilities, concentrating sleep deprivation and fear among fewer individuals. This finding aligns with Galley and Anthony (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who identified sleep deprivation as one of the most significant yet overlooked opportunity costs of HWC. Interestingly, gender was not a statistically significant predictor in the multivariate model despite qualitative accounts highlighting gendered experiences of fear, stress, and vulnerability. This apparent contradiction suggests that while men and women may experience invisible impacts through different pathways\u0026mdash;such as male labor migration, gendered care responsibilities, or restricted mobility\u0026mdash;the overall magnitude of reported impacts may remain similar within the same conflict context. Gendered dimensions of invisible impacts may therefore reflect differences in mechanisms rather than intensity, highlighting the importance of gender-sensitive conflict mitigation strategies (Doubleday and Adams \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Adler et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial capital impacts were largely unexplained by either socio-demographic or spatial predictors. No variable in the model reached statistical significance, and the overall model was weak. This suggests that erosion of social capital may operate primarily as a collective phenomenon rather than an individual-level experience. When trust in conservation institutions declines or compensation systems are perceived as unfair, the social fabric of entire communities can be affected, regardless of individual household characteristics. This interpretation aligns with Dickman\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) argument that HWC is fundamentally a social conflict, as well as Agrawal and Redford\u0026rsquo;s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) observation that conservation-related conflicts can undermine collective action and community cohesion.\u003c/p\u003e \u003cp\u003eBeyond statistical patterns, qualitative evidence indicates that invisible impacts rarely occur in isolation. Instead, they cascade across livelihood capitals and accumulate over time. For example, a single elephant attack can trigger psychological trauma (human capital), require infrastructure reconstruction through debt (physical and financial capital), erode trust in conservation authorities perceived as prioritizing wildlife over people (social capital), and restrict access to forests and farmland through fear-based avoidance (natural capital). This cascading dynamic\u0026mdash;described by Barua et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) as the \u0026ldquo;hidden architecture\u0026rdquo; of HWC costs\u0026mdash;suggests that the true burden of conflict is multiplicative rather than simply additive, while Blackie (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explained stress and trauma caused by HWC also play a major role in reducing overall life satisfaction\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the analysis focuses on buffer zones of six lowland protected areas in the Tarai and Chure regions; results may not generalize to mid-hill or mountain protected areas where HWC dynamics differ substantially (Baral et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Bista and Song \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, the dependent variable is perception-based and binary-coded (perceived vs. not perceived), capturing prevalence but not severity or intensity of impacts. Future research could employ graded severity scales (e.g., Likert-type measures) or validated psychometric instruments such as the PTSD Checklist (Weathers et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to better quantify psychological impacts. Third, the Dalit subsample was relatively small (n\u0026thinsp;=\u0026thinsp;49); although the financial capital effect was statistically strong, replication with a larger Dalit-focused sample would strengthen confidence in this finding.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study demonstrates that the invisible impacts of human\u0026ndash;wildlife conflict on livelihood sustainability are pervasive, consequential, and often overlooked in existing policy frameworks. Across six lowland protected areas in Nepal, households reported invisible impacts affecting all five livelihood capitals, with financial impacts\u0026mdash;such as transaction costs associated with compensation processes, lost productive labor time, and expenditures on conflict prevention\u0026mdash;being the most prevalent.\u003c/p\u003e \u003cp\u003eThese impacts rarely occur in isolation; rather, they cascade across livelihood capitals and accumulate over time, potentially creating livelihood traps that conventional compensation mechanisms fail to address. While protected area context emerged as the strongest determinant of invisible impact perceptions, capital-specific analyses revealed distinct underlying drivers. In particular, Dalit ethnicity was strongly associated with financial capital impacts, while social capital erosion appeared to operate as a community-level phenomenon not fully explained by individual characteristics.\u003c/p\u003e \u003cp\u003eThese findings demonstrate that invisible impacts are not merely supplementary to visible HWC losses but constitute an integral component of the overall burden borne by conflict-affected communities. Formally recognizing invisible impacts within Nepal's compensation and governance frameworks and embedding them into mitigation strategies will be essential to achieving sustainable human-wildlife coexistence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Data collection and analysis were performed by Uma Dhungel and Uttam Babu Shrestha. The first draft of the manuscript was written by Uma Dhungel and Uttam Babu Shrestha and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the Zoological Society of London Nepal office through the Darwin Initiative grant (Grant number: DAREX008).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author, [UBS]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcharya KP, Paudel PK, Neupane PR, K\u0026ouml;hl M (2016) Human-wildlife conflicts in Nepal: patterns of human fatalities and injuries caused by large mammals. 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Sustainability 14(21):14050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su142114050\u003c/span\u003e\u003cspan address=\"10.3390/su142114050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"invisible impacts, sustainable livelihood framework, conservation, coexistence","lastPublishedDoi":"10.21203/rs.3.rs-9318000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9318000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman-wildlife conflict (HWC) imposes well-documented visible costs on rural communities, yet its invisible impacts, manifested in psychosocial, economic, and social losses, remain systematically underrepresented in research and policy. We present a multi-site assessment of invisible HWC impacts on livelihood sustainability in Nepal, using the Sustainable Livelihood Framework (SLF). Through household surveys with 641 respondents across 60 settlements in the buffer zones of six lowland protected areas, we documented 16 types of invisible impacts spanning five livelihood capitals: human, social, natural, physical, and financial. Financial capital was most severely affected, with 66% of respondents reporting increased transaction and opportunity costs, 56% reporting lost productive labor time, and 37% reporting increased expenditures on conflict prevention measures. Psychological distress and trauma were the most prevalent human capital impact (65.2%), while social capital erosion, including negative conservation attitudes and community displacement, were least prevalent. OLS regression models revealed that protected area location was the dominant predictor of cumulative invisible impacts; respondents near Shuklaphanta and Bardiya national parks reported significantly higher invisible impacts than those near Chitwan. Notably, Dalit ethnicity was the strongest demographic predictor of financial invisible impacts, even after controlling for income and location, consistent with structural barriers to accessing compensation mechanisms. These findings demonstrate that invisible impacts are not supplementary to visible HWC losses but constitute a distinct and consequential dimension of livelihood insecurity. Formally integrating invisible impacts into HWC mitigation frameworks through mental health services, simplified claims procedures, and inclusive governance, is essential for advancing sustainable human-wildlife coexistence.\u003c/p\u003e","manuscriptTitle":"Beyond visible losses: Documenting the invisible impacts of human-wildlife conflict on livelihood sustainability in Nepal's lowland protected areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 04:09:35","doi":"10.21203/rs.3.rs-9318000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T12:29:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219011147434435896997603445434623038623","date":"2026-05-11T06:35:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37816565376803728491733481247530048407","date":"2026-05-10T07:10:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T02:56:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T02:55:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T11:48:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2026-04-04T06:08:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"815cc677-16b9-4049-af5b-132b6b8ba453","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T12:29:22+00:00","index":53,"fulltext":""},{"type":"reviewerAgreed","content":"219011147434435896997603445434623038623","date":"2026-05-11T06:35:32+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"37816565376803728491733481247530048407","date":"2026-05-10T07:10:32+00:00","index":50,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T03:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 04:09:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9318000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9318000","identity":"rs-9318000","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}