Introduction

Human–elephant conflict (HEC) arises in all 50 elephant range countries, as hostile encounters between elephants and humans can lead to physical harm and occasionally deaths of members of either species (Shaffer et al. 2019; Hoare 1999). In Sri Lanka, HEC has emerged among the most severe social–ecological problems primarily due to frequent foraging activities of wild Sri Lankan elephants (Elephas maximus maximus) in crop fields, home gardens and grain storages. This foraging behaviour, which occurs within the elephant range regions confined to the Sri Lankan dry zone, results in significant economic loss, damage to houses and an atmosphere of fear among affected villagers (Fernando 2015; Fernando et al. 2019; Köpke et al. 2023; Gunawansa et al. 2023; de Silva et al. 2023).

According to official statistics, elephant killing in Sri Lanka met a record high in 2022, with 433 elephants reported dead (WNPS 2022). As stated in statistics of the Department of Wildlife Conservation (DWC), the number of elephants found dead in Sri Lanka has been significant since 2015 or earlier (Table 1). Statistics compiled by Charles Santiapillai (1994) indicate that the numbers were markedly lower in the 1950s and 1960s, when an annual average of 33 elephants was killed either intentionally by poachers or as a reaction to crop foraging. Gunshots are a major cause of death (Santiapillai 2013), and the use of hakka pattas—homemade grenades (WNPS 2019)—is on the rise. These explosives are hidden in vegetables such as cabbage, cucumber, or squash; when the elephants ingest the vegetables, the explosives go off, causing the elephant to bleed to death or starve. Other intentional causes of elephant deaths are poisoning or electrocution. As there is a low number of tuskers in Sri Lanka, poaching for ivory is not seen as a prevalent cause of elephant mortality in present-day Sri Lanka (WNPS 2021). Due to the protected status of the species, the killing of Sri Lankan elephants is illegal and hence sanctioned by fines and even imprisonment under the Fauna and Flora Protection Ordinance (Santiapillai 2013). Yet, prosecution of people harming elephants appears to be rare (Fernando 2015).

Table 1 Causes of Elephant Deaths 2015–2023
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Violent encounters with elephants take a toll on the human population. On average, 90 people die in encounters with elephants each year. Between 2010 and 2022, 1206 people died in Human–elephant encounters (WNPS 2019, 2020, 2021, 2022). In a nationwide survey, Fernando et al. (2019) find that humans live in 70% of the elephant ranges, and, consequently, human–elephant hostilities are prevalent throughout the entire dry zone, with several hotspots in North Central Province (NCP), Northwestern (NWP), Central (CP), and smaller parts of Eastern (EP) and South Province (SP).

The high number of wild elephants in Sri Lanka has strong implications for elephant conservation. The first elephant census in 2011 gave 5879 as the minimum number of elephants in Sri Lanka (DWC 2011). However, for methodological reasons, the real number of wild elephants might even be higher since counting wild elephants generally is a challenging endeavor. The main method is waterhole observation, and establishing total numbers is a matter of guesswork (Santiapillai 2013). Over 67% of elephants in Sri Lanka were reported living in the protected areas (PA) of the DWC, and almost 30% were reported living in protected areas under the administration of the Forest Conservation Department (FCD). The remaining 3% were living outside the protected land; however, the majority of the elephant range appears to be situated outside PAs (DWC 2011). Sri Lanka is an important site for the conservation of the Asian elephant (Elephas maximus), as it hosts a sizable number—about 10%—of the global population under the condition of high density (Fernando 2015; Santiapillai et al. 2010).

Deadly train accidents involving elephants occur frequently. Annual numbers average in the lower double-digits (Table 1). However, collisions with trains and road vehicles are a distinct problem in elephant conservation, as these can be considered accidental and therefore different from intentional killings. Thus, accidents are not discussed further in this article.

Considering the loss of lives, property damage, and negative impact on agricultural production, HEC embodies a real threat to rural lives and livelihoods in affected communities, especially those already burdened by poverty. In areas where HEC continues or increases, socio-economic conditions have become unstable. For the governance of human–wildlife conflict, it is of great importance to consider social factors (Dickmann 2010). Jadhav and Barua (2012) show for India that Human–elephant conflict threatens the mental health of marginalized people. Mayberry et al. (2017) come to similar conclusions in research on encounters with African savannah elephants (Loxondonta Africana) in Botswana, where fear and restricted mobility are described as side-effects of HEC. Nyirenda et al. (2018) show for a case study site in Zambia that social vulnerability of rural people to HEC can severely impact their livelihood strategies and has adverse effects on their resilience. As mentioned by Köpke et al. (2023) elsewhere, the interrelations of social vulnerability and Human–elephant conflict dynamics are under-researched.

In this context, elephant encounters take on the character of an ‘environmental hazard’ (Cutter 1996); vulnerability here is defined as either the degree to which people are at risk from environmental hazards, potential for loss or suffering harm, and includes the capability or lack of capability of such populations to deal with risk (for a more in-depth discussion, see below “Conceptual framework”). As emphasized by Gunawardhana (2018), HEC in Sri Lanka is not framed as a natural disaster and state-led mitigation efforts follow a different logic than disaster interventions. However, human–elephant encounters in Sri Lanka increasingly create a hazardscape (Mustafa 2005) in affected regions, where vulnerability emerges as a defining feature of rural communities—not only in terms of material, but also in socio-economic and cultural dimensions.

This paper, based on a multi-item large-N field survey, aims to investigate the causes of vulnerability to human–elephant conflict in affected parts of the Sri Lankan population in order to better understand the context of the phenomenon, and also to inform targeted policy interventions to alleviate the grievances from HEC. What are the important drivers of vulnerability to human–elephant conflict in affected regions, and the distribution of vulnerability in the research area are the research questions to be examined in this study.

The paper proceeds as follows: in the coming section, the theoretical approach is discussed, the conceptual framework is introduced, and the hypothesis is laid out. Also, the section describes the study region and the methodology. In the third section, the results are presented both in terms of descriptive statistics, as well as the results of the regression analyses. Then, the fourth section discusses the results and offers a comprehensive evaluation of the study and a number of policy recommendations.

Materials and methods

Conceptual framework

Sri Lanka’s population is overwhelmingly rural, with more than 80% living in the countryside (World Bank 2024). The rural poverty headcount—the percentage of rural people living in poverty measured by national poverty lines—has declined from 24.7 to 4.8 from 2002 to 2016, although pockets of poverty remain (CBSL 2019, p. 136). In particular in the research area (see section “Conceptual framework”), paddy (rice) farming and other agricultural activities are the main sources of income. This renders affected village communities vulnerable to negative outcomes of human–elephant encounters, especially crop foraging and post-harvest loss (such as damage to grain storages), as well as bodily harm.

Vulnerability is a central concept in social–ecological research, in particular pertaining questions of hazards, risks, and environmental change (e.g. climate change) (Scoville-Simonds and O’Brien 2018). Vulnerability describes the extent to which a person, household, community, or other societal group will be potentially touched or harmed by a particular hazard or threat. Vulnerability is closely linked to exposition, which means that there are factors that might either shield or expose someone to a given hazard. With regards to wildlife, the ranging grounds of the wild animals define the exposition. Sensitivity refers to the degree, once exposed, people react to a given hazard or threat. A third important dimension to vulnerability is the capacity to adapt, which denotes the ability to handle hazards (Adger 2006).

In the social sciences literature, ‘natural hazards’ and vulnerability to such hazards are seen as products of social and political factors (Blaikie et al. 1994), such as socio-economic and geographic factors, gender, and ethnic group affiliation. This conceptualization understands hazards not as a ‘natural’, but as politically charged and socially constructed phenomena (Hewitt 1995); at the same time, it is important to retain the sense that such environmental hazards are not solely constructed (Nygren and Rikoon 2008), but have serious, sometimes even devastating impacts on vulnerable populations in the ‘real’ world. Marginalization of rural people enhances their vulnerability and erodes resilience (Blaikie et al. 1994).

In several instances and different geographic contexts, a vulnerability lens has been applied to HEC (Nyirenda et al. 2018; Ogra 2008; Twitcher 2018). Where quantitative approaches were used, researchers either established complex general vulnerability indices (Cutter et al. 2003), made use of existing indices (Pereira et al. 2021) or modified existing ones (Aksha et al. 2019) to fit their purposes. In this study, instead of relying on a complex index, a conceptual framework was created with specific relevance and contextual information directly related to human–elephant encounters in Sri Lanka.

The foundation to the conceptual framework used in this study, based on an understanding of existing literature on human–elephant conflict in Sri Lanka (Köpke et al. 2021), lies in the theoretical assumption that human–elephant conflict is driven by factors covering the domains of the economic (including agricultural production), the environmental (including land-use and land-cover change), the political (regarding conservation governance and land-use planning policy) and the social (including cultural dimensions). These domains are translated into concrete independent variables.

Vulnerability to HEC is associated with livelihoods and the coping capacity of rural communities, highlighting the importance of socio-economic conditions (Thant et al. 2021; van de Water and Matteson 2018; Wilson et al. 2015). Further, land use and proximity of fields and settlements to elephant ranges is of utmost importance because it determines exposure to elephants (Hoare 1999; Shaffer et al. 2019; van de Water and Matteson 2018; Sitati et al. 2003; Neupane et al. 2017; Rathnayaka et al. 2022; Bharaty et al. 2022; Withanage et al. 2023). Under the ‘policy’ variable, factors may be understood that relate to the governance of conflict through mechanisms like compensation to damage and official recognition of property and crop damage (Sampson et al. 2021; Guru 2021). Finally, under ‘awareness’ this study categorizes the set of attitudes and practices that is linked to the mitigation of human–elephant conflict, to the perception of elephants and the potential for convivial encounters, and to preventive measures like insurance (Fernando et al. 2005; Talukda and Choudhury 2020; Nayak and Swain 2022).

According to the above-mentioned insights, vulnerability to HEC in the study framework, as the dependent variable, was analyzed in relation to the following independent variables: ‘socio-economic conditions’, ‘land use’, ‘policy’, and ‘awareness’. The questionnaire included a five-point Likert statement intended to capture independent variables. Those independent variables were derived by grouping items from the questionnaire, i.e. occupation, education, loss from crop damage, and the existence of protection mechanisms were grouped to create an indicator of socio-economic conditions, etc. The conceptual framework model for the present study is given below (Fig. 1), and detailed methodological descriptions are presented in the following subsections.

Fig. 1
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Conceptual framework used to model the vulnerability

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Study area

The research, as a part of the collaborative project between Rajarata University of Sri Lanka and University of Kassel, Germany, was conducted from 2018 to 2022 in dry zone areas in Sri Lanka. The key objectives of the project were to investigate the multiple factors, dynamics, and policies driving human–elephant conflict in Sri Lanka. The research team undertook the field survey across the areas covering Anuradhapura, Polonnaruwa, Vavuniya, Trincomalee, Ampara, Matale, Kurunegala, Puttalam, and Mannar districts including 89 Grama Niladhari (GN) divisions (Fig. 2)—GN being the local-level, lowest administrative unit in the country. The total area covered 26,921 km2—30% of regions of the country where HEC is most prevalent (Fernando et al. 2015).

Fig. 2
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Research areas within Sri Lanka (authors’ illustration based on open-source data)

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Survey and data collection

The survey was conducted using a questionnaire with both open-ended and closed-ended questions. The survey participants were selected from the GN divisions that are generally situated within the critical areas affected by the HEC problem. Based on studies from Nyhus et al. (2005) and Okech (2010), we define a critical area where the agricultural and residential areas of communities are located adjacent to a protected area, or newly settled communities encroaching on existing or previously existing wildlife habitat. Some of the GNs are located in between the elephant corridors. In each village, we selected participants for the survey after the pre-study about the village through the village officer (GN officer) and the farmer association or the civil organization in the village. In some cases, we used random sampling for selecting the survey study in the village. The participants in the survey, interviews, and focus group discussions (FGD) were people who had different socio-economic backgrounds.

We designed the survey with the objective of achieving multiple objectives within the main project, thereby facilitating the computation of diverse statistical data pertaining to various subjects that are oriented toward a similar objective. The surveys were completed by research assistants during face-to-face interviews. Some surveys were filled out during the group discussions and individual meetings. The total sample is 651 (N = 651). Among them, 400 were male participants and 251 were female participants. The FGDs and the survey data collection were at the community halls or meetings (farmers’ associations or death donation associations), agricultural farmland, common local shops, or at religious places, such as temples or churches or Hindu-Kovil. Ethical clearance for the study was provided by the Ethics Review Committee of Rajarata University Faculty of Applied Sciences (Ref.-No. ERC/07/22). Informed oral consent was obtained from all participants of the study.

The questionnaire was formulated based on five domains, namely vulnerability, socio-economic conditions, environmental change and land use, policy, and awareness (Table 6 in Appendix). These domains are interrelated and rationally combined with the research hypotheses, survey questions, and the findings from the literature review. To maximize the response rate and minimize the errors in the responses, questionnaires were filled in by independent researchers. In addition to the questionnaire survey, field observations were used as a qualitative approach to gain an in-depth understanding of the coping capacity of the rural community and exposed risk. Furthermore, during field observations and focus group discussions survey data and assigned Likert scale values were screened and verified before statistical analyses. The variables used in this study are in different scales of measurement. In the descriptive analysis, the data were presented with their original scales of measurement, but to model the vulnerability to HEC according to the conceptual framework (Fig. 1) variables were transformed to a Likert scale in order to perform statistical analysis (Jebb et al. 2021).

Data analysis

The transcripts of the individual interviews and the FGD were used as qualitative data in the analysis. MAXQDA 2022 was used for coding the answers to open-ended questions. The categorization system of qualitative analysis followed the theoretical framework described above and illustrated in Fig. 1. The survey data were also transferred to an MS Excel datasheet before the quantitative statistical analysis. The data was transferred to code book for statistical analysis. Descriptive statistical results for each category of predictor and criterion variables are presented in the Table 7 in Appendix. Analysis was performed using SPSS statistical package version 22. Demographic data and the socioeconomic background of the sample were presented using descriptive statistical methods. A Likert scale was used to measure the independent and dependent variables, for each Likert scale item, a numerical value was assigned to each response option (see Tables 6 and 7 in Appendix). The values ranged from 1 to 5, with 1 representing ‘Strongly Disagree’ and 5 representing ‘Strongly Agree.’ Then Likert scaled items were summarized to each variable in the conceptual framework taking the average of each variable. Then, correlations of independent variables (socio-economic conditions, environmental change and land use, policy, awareness) to the dependent variable (vulnerability) were estimated using the chi-squared method. The chi-squared method is the most suitable statistical measure for assessing the correlation between categorical variables (Turhan 2020).

Model specification

In this study we model the vulnerability to HEC by running a multiple regression analysis with correlated variables. Multiple regression analysis allows us to examine the impact of correlated independent variables on a dependent variable vulnerability of HEC. The significance of the multiple regression model was tested using the ANOVA test at the significance level of p = 0.05. Finally, the association between the level of vulnerability and the relationship between ‘occupation’ and ‘distance to nature’ were estimated using contingency tables.

Regression model

Then the proposed regression model is as follows, considering socio-economic conditions (× 1), land use (× 2), and awareness (× 3) as independent variables. The model summary is given in Table 2.

$$Y = \hat{\beta }_{0} + \hat{\beta }_{1} X_{3} + \hat{\beta }_{2} X_{3} + \hat{\beta }_{3} X_{3}$$
$$Vulnerability \, = \, f\left( {Socio{ - }economic \, conditions, \, land{ - }use, \, awareness} \right)$$
Table 2 Model summary
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Based on the model summary, the R-squared value serves as an indicator of the model's validity. In reference to Table 3 the R-squared (coefficient of determination) within the fitted model can elucidate 73.6% of the observed variability.

Table 3 Analysis of variance (ANOVA)
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According to the model summary, the R-square shows the validity of this model. When explaining this table (Table 2), the R-square (coefficient of determination) in the fitted model is able to explain 73.6% of the observed variability. The ANOVA table (Table 3) shows the significance of the overall regression model. If the significant level is close to zero, the regression is significant.

There are two hypotheses,

H0:

The fitted model is not significant.

H1:

The fitted model is significant.

As the p-value in the ANOVA table (Table 3) is less than 0.006, it can be concluded that this fitted model is significant at 95% confidence level; the null hypothesis is rejected.

Each coefficient in Table 4 represents the change in the vulnerability for a one-unit change in the corresponding independent variable. Based on the obtained coefficient values, it is evident that socio-economic conditions, land use practices, and awareness exhibit negative values. This suggests that all of these independent variables contribute to a reduction in vulnerability to the HEC.

Table 4 Model and coefficients
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Vulnerability mapping method

The level of vulnerability for each GN division was estimated using a developed multiple regression model. However, the independent variables were measured using Likert scales on an ordinal scale, which consequently means that the level of vulnerability is also represented on an ordinal scale. This involved analyzing predictor variables, to predict the vulnerability level of each GN division. Following the regression analysis, the resulting vulnerability values were integrated into a geo-database, employing ArcGIS (version 10.4) (ESRI, Redlands, CA, USA) software. To facilitate the spatial representation of vulnerability across the study area, a two-step process was employed.

Firstly, the X and Y coordinates of the center points of the GN divisions were utilized for data integration within the geodatabase in ArcGIS environment. These center points served as spatial reference points for associating vulnerability values with their corresponding GN divisions.

Subsequently, the estimated vulnerability values were interpolated to cover the entire study area using the Kriging interpolation method (Perera et al. 2019). Kriging, a widely utilized geostatistical technique, leverages spatial autocorrelation by creating weighted predictions based on surrounding measured values (Liu and Miao 2018). This process enables the generation of a continuous raster representation of vulnerability to HEC over the study area, facilitating spatial analysis and visualization.

Results

Descriptive Statistics

Figures 3, 4, 5, 6, 7, 8, 9 and 10 present the relevant results of the survey with regard to the research questions. Figure 3 displays clear evidence that respondents were primarily occupied as farmers, with 87% percent of respondents declaring ‘farmer’ to be their occupation, followed by ‘self-employed’ and ‘government-employed’ with 5% each and trailed by ‘unemployed’ (student, monk, no occupation) with 3%. Figure 4 indicates that more than half of the respondents (56.5%) had a mid-level education either up to Grade 8 or O-Levels, while 17.5% had an education up to A-Levels, and 22% had only low formal education up to maximum Grade 6. Only 4% of respondents had a higher education.

Fig. 3
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Key occupation of respondents

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Fig. 4
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Level of education of the respondents

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Fig. 5
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Impacts of elephant encounters as reported by respondents

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Fig. 6
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Type of housing of respondents

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Fig. 7
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Type of reaction to elephant encounters

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Fig. 8
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Assumptions of respondents regarding the main causes of increasing HEC in the previous 20 years

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Fig. 9
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Respondents in possession of a private electric fence

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Fig. 10
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Financing of private electric fence

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The impacts of adverse encounters with elephants are detailed in Fig. 5. Here, crop damage (83.6%) turns out to be the most prevalent consequence of encounters with elephants, with human damage (injury or death) a second (13.8%). Other kinds of damages are comparatively rare, such as damage to houses; damage to livestock was not recorded at all. The elephant-human conflict in affected area is felt more acutely due to crop damage, since the majority of the population is engaged in farming.

The type of housing (Fig. 6) is an important signifier of land use. 35.3% of respondents live in hereditary housing—houses that have been constructed on traditionally owned land that were granted or inherited by parents. 22.9% live in houses under the ‘Suvarnabhumi’ category—a form of government land grant (Marawila 2007). 17.7% of respondents said they lived in licensed (leased) houses, and 11.8% lived in new settlement houses under the Mahaweli resettlement scheme (Brun and Lund 2009), houses that were granted to the inhabitants but could not legally be sold or mortgaged. Only a small minority declared to live in informal (2.3%), reservation (1.4%), or temporarily licensed (0.3%) types of housing, while 8.3% of respondents have been filed under ‘other’. Most of the respondents had settled in accordance with the land laws with the legal permission of the government and there is no noticeable evidence that these respondents had encroached on forest areas for settlement or cultivation.

When asked about prevalent protection measures, responses were heterogeneous, reflecting different levels of engagement, knowledge of available strategies, and overall capacities to fend off elephant attacks. Figure 7 displays the different forms of reaction. Shouting (29.5%) and usage of fire (24.1%) make up the most common reactions. The planting of vegetation disliked by elephants, in particular limes (Santiapillai et al. 2010) and thorny shrubs, is a mid-term strategy employed by a number of respondents. Further measures include the digging of moats, the usage of bees, the use of elephant spells (ali mantra) (Köpke et al. 2023) and the use of an ancient cascade tank system (ellanga). Areas where these cascade tank systems (ellanga) are adequately safeguarded appear to be not as affected by HEC. This can be attributed to the fact that elephants do not traverse the natural barrier to reach the villages. However, in areas where the networked tank systems have been destroyed or compromised, the same level of security is not evident.

Figure 8 displays the assumptions or observations of respondents regarding the main cause of an increase in Human–elephant conflict over the last 20 years. Again, the responses are heterogeneous. Multiple answers were possible; among the most-cited causes, with between 12 and 13% each, respondents answered ‘elephants did not have enough food’, ‘drought’, (lack of proper construction of) ‘elephant fences’, and two causes specific to the respective locales, (noise from weapon training in the) ‘army training grounds’ and (the elephants being attracted by the) ‘garbage disposal areas near the village’. A frequent response was deforestation, either due to ‘development’ (6.1%), ‘illegal deforestation’ (4.2%) or ‘chena cultivation’ (5.8%)—a type of shifting cultivation. Another reason assumed by respondents was ‘water management issues’ (7%).

The above-mentioned reasons have affected the increase in Human–elephant conflict, among which the lack of proper maintenance of the elephant fence and the lack of food required by the elephants have led to the escalation of this conflict. Respondents also said that due to the construction of private electric fences, elephants’ usual paths (elephant corridors) were blocked and as a consequence, elephants moved through other lands. Accordingly, when planning elephant fences on private lands, it should be done with an understanding of the elephants’ route (see 3.4 below).

The presence of electric fences—seen as a largely effective mitigation mechanism for human–elephant conflict, despite some shortcomings—was a major concern. Less than a quarter of respondents (22.6%) claimed to possess a private electric fence, as shown in Fig. 9. Of these, the absolute majority (93.9%) had financed the fence through their own financial means, as presented in Fig. 10. Four persons (2.7%) had received assistance from a village association and 5 (3.4%) from the government; NGOs were not involved in the financing of private electrical fences.

Examining predictor variables for vulnerability

The Chi-square test was conducted to evaluate the relationship between the dependent variable, vulnerability, and each of the four independent variables: socio-economic conditions, land-use, policy, and awareness. This aimed to pinpoint significant predictor variables independently associated with vulnerability. A significance level of 0.05 was utilized to determine statistical significance. Table 5 provides the results of each chi-square test, including the chi-square value, degrees of freedom (df), and the corresponding two-tailed p-value, offering insight into the significance of the relationships observed.

Table 5 Summary of chi-square tests used to assess the statistical significance of independent variables in relation to vulnerability
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The results indicate varying degrees of significance across these variables. Specifically, socio-economic conditions and land use demonstrate statistically significant relationships with vulnerability, as evidenced by their respective Pearson Chi-Square values of 0.665 (p = 0.017) and 9.646 (p = 0.008). Conversely, policy exhibits no significant association with vulnerability, with a Pearson Chi-Square value of 1.687 (p = 0.918). However, awareness does show a statistically significant relationship, as indicated by a Pearson Chi-Square value of 0.086 (p = 0.046).

Spatial distribution of vulnerability levels

In the geodatabase, vulnerability levels for GN divisions are delineated on an ordinal scale, with the center coordinates of villages serving as spatial reference points. Figure 11 provides a visual representation of the spatial distribution of vulnerability levels to human–elephant Conflict within the study area. This map illustrates several distinct patterns and hotspots.

Fig. 11
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Mapping the spatial distribution of human–elephant conflict vulnerability: insights from multiple regression modeling (authors’ illustration based on the aggregated data)

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Notably, the highest concentration of vulnerability to HEC is observed in the Kurunegala, Anuradhapura, and Puttalam Districts. These regions are closely situated to protected areas and agricultural lands. The proximity to these protected areas suggests frequent incursions by elephants into adjacent agricultural lands, thereby exacerbating vulnerability levels. This distribution underscores the significant role of resource competition between elephants and humans in exacerbating conflict dynamics.

Further analysis of the vulnerability map reveals that Puttalam, Anuradhapura, Kurunegala, Matale, and Polonnaruwa districts are significantly affected by elephant-human conflict, while Mannar and Vavuniya districts experience comparatively lower levels of vulnerability. These findings align with observations that elephant corridors often intersect human settlements, main roads, or railways, exacerbating conflict incidences.

Field observations additionally highlight the presence of mini-isolated forest patches that were once connected as larger contiguous forests. Some elephant populations inhabit these mini-isolated forest areas. Conversely, Mannar, Vavuniya, and Ampara Districts exhibit lower vulnerability to HEC, likely due to factors such as habitat suitability and reduced human-elephant interaction.

Overall, the spatial distribution of vulnerability to HEC underscores the complex interplay between ecological factors, human activities, and conflict dynamics, necessitating targeted interventions for mitigating conflict and promoting coexistence between humans and elephants.

Perceived causes of vulnerability according to local communities

In this section, we analyze the direct statements during interviews with individuals and FGD during the field study. To verify some of the factors, we collected information from the respective institutes and authorities. Local communities indicate that there are few reasons to exaggerate the elephant attacks on the village. One of the key reasons that were highlighted by villagers in Singahpur in Walikanda DS Division in Polonnaruwa District is the privately owned large-scale fruit gardens such as marketable fruit crops like mangoes. Though these lands are the property of the Mahaweli Authority, the lands are leased to private entrepreneurs. Per interview data from the officials, the Mahaweli Authority conducted the environmental feasibility study and the private companies submitted their own environmental impact assessment before the project was initiated. However, the FGD interview data revealed the increasing severity of the elephant attacks on the adjacent villages after this large-scale private fruit gardens were set up. The electric fences of these gardens are well-constructed. The attack into the garden is rare according to the information from the guards in a few gardens. Some villagers claim that some of the gardens obstruct the elephant corridors or are located in the natural elephant habitats. As a result of these new geo– and natural-structural changes, elephants enter the villages and damage farmers’ agricultural lands and house properties.

"Rich people from Colombo open up acres of land and cultivate it with fences. These were forests before. When the elephants lose their forests, they come to the surrounding villages and destroy the crops. This harms the poor." – a villager from Sinhapura.

Private electric fences are becoming popular among locals to deal with the elephant attacks on private properties. As can be seen above in Fig. 10, 10.8% of respondents point out that their individual properties (including living premises or agricultural lands) are surrounded by private electric fences. These fences are privately bought from companies. However, per our observation, this type of private fence is owned by only a few villagers in a village. Here, qualitative data serves to amend the statistical data.

In a village in Thirappane GS of ​​Anuradhapura district, and in Kallikulama Village and Omanthei in Vavuniyawa District, some people are building private electric fences to protect their houses and land from wild elephants. In the course of the construction process, elephant corridors are blocked. In reaction to this, wild elephants choose alternative routes when previous routes between forest habitats and lakes (for drinking) are blocked by these private electric fences. Those alternative routes pass through settled areas where private electric fences have not been erected, and here wild elephants destroy crops and damage houses and harm humans. While observing these villages, it became evident to the researchers that the posts of the government-financed elephant fences were damaged by insects and hence lacked proper maintenance. Accordingly, villagers may have built private elephant fences as a reaction to the state of disrepair public fences were in. However, some of the locals criticised this type of private electric fence, mentioning that most villagers are facing heightened vulnerability due to these fences.

“People who have money built electric elephant fences around their land. Some houses are across elephant corridors. When elephants’ usual roads are blocked by houses, elephants cross other roads. Elephants destroy those houses.” – a villager from Thirappane

“See what happened to my land, elephant attacked last month, I do not have any private electric fence, few neighbors they build their own fences. I do not have enough money. However, I see this is not as a solution. These individual fences created a small safety island in the village and elephants could not reach there, so elephants now attacked others heavily. We need a holistic solution for all village This is very selfish way.”

– a villager from Vavuniyawa, DS Kallikulama, Village Omanthei

Meanwhile, the absence of social activities in the evenings and at night has caused social isolation and impacted the social bonds of the people in the village. This has also affected the education of school-going students, who are unable to attend school in the morning and to return home in the evening. The areas heavily affected by HEC are perceived as difficult and isolated, which has led to hesitancy among people to organize events like weddings, funerals, and festivals.

Discussion

The findings suggests that vulnerability to HEC is unevenly distributed both in spatial and social dimensions. The central mitigation mechanism, constructing of electrical fences, arises as a contested issue. Capacities to deter elephants from the property are connected to the availability of financial capital; less than a quarter of respondents were in possession of electric fences, and where those existed, they were primarily paid by private property owners with their own means. Policy interventions such as compensation payments were found to have a negligible impact on vulnerability. This points to the low availability of compensation payments for crop damage already noted more than a decade earlier by Santiapillai et al. (2010), and mirrors newer findings from Southern Sri Lanka (de Silva et al. 2023). The lack of adequate compensation transfers suggests that in order to reduce vulnerability, payments would have to be upscaled considerably.

Traditional mitigation mechanisms are largely in place. It is evident that these are both practical and adapted to the ecosystem. Yet in modern society, it is difficult for villagers to preserve the practice of those traditional methods, which are knowledge- or labour-intensive. Frustration about failing mitigation of HEC has been discussed by Köpke et al. (2023).

The study has some limitations. The research conditions did not allow for a gender differentiation of respondents due to the implicit assumption that respondents each spoke for their whole household. Hence, the gender dimension of vulnerability remains opaque in the scope of this study. Also, there are epistemological limits to the study since perpetrators of elephant killings, an illegal act, could not be expected to come forward and admit to their deeds. Hence, there is silence on a central and important element of HEC; namely the illegal killing of a threatened species. Uncovering the exact motivations and procedures of elephant killers would potentially necessitate time-consuming ethnographic fieldwork in the communities; authors are aware of only one study from West Africa where survey respondents admitted to taking part in poaching activities (Compaore et al. 2020).

Despite the above-mentioned limitations, the study could validate the model which shows that vulnerability was driven by a combination of socio-economic conditions, land-use and awareness. It thus contributes to the literature on Human–elephant conflict, in particular with regard to the social dimension of the problem.

The study has highlighted the importance of socio-economic conditions and land-use. Farming as a livelihood and proximity to elephant habitats are predicting vulnerability to HEC. This validates findings on the interrelations between farming livelihoods, geographic exposition, and human–wildlife conflict from different local contexts and involving different species (Bharathy et al. 2022; Pereira et al. 2021; Poornima et al. 2022). Housing categories and education also point to the identity of most affected people as members of the social group of farmers on small marginal plots, as is typical for large parts of the Sri Lankan dryland (Withanachchi et al. 2014) (ethnic, religious or caste identity were not queried in the survey, but the survey covered regions with different ethnic majority composition).

In accordance with other studies on HEC in Sri Lanka (Fernando et al. 2019; Santiapillai et al. 2010), crop damage was found to be the most prevalent negative impact of elephant encounters, followed by bodily harm. In addition to socio-economic conditions and land use as central components to measure vulnerability, awareness—and lack thereof—is a third relevant variable. Horgan and Kudavinage (2020) have addressed the lack of training in Sri Lankan farmers affected by human–wildlife conflict. With regards to our respondents, opinions on both the causes of elephant intrusions and the most adequate type of reaction are characterized by ambiguity. This suggests the need for awareness and education campaigns and the dissemination of current scientific insights on elephant behaviour.

Conclusion

Human–elephant conflict has been established as a severe social–ecological problem emerging in the Sri Lankan elephant range with adverse impacts on communities and households. However, the burdens of these impacts are not equally distributed. This study seeks to establish the components of vulnerability to HEC in affected regions. Based on the understanding of the literature, a conceptual framework was created as laid out above in section “Materials and methods”. In order to test the framework, a large-N survey (N = 651) was conducted among members of affected communities, and data was, after adequate transformations, used to conduct a multiple regression analysis. The study was able to identify geographic, social and cultural factors—namely land-use, socio-economic situation as well as problem awareness—as major contributing variables to vulnerability to HEC. Finally, a vulnerability map was created, pointing towards conflict hotspots in certain dry zone districts in North Western, North Central, Central and Northern Provinces.

The problem of human–elephant conflict persists in the affected areas, with several hundred elephant fatalities each year and bodily harm, human deaths, and, primarily, loss of income through elephant crop foraging in farming households as widespread consequences. The central mitigation measure, the erection of electric elephant fences, at times exacerbates the problem since elephant foraging behaviour is then unevenly distributed, increasing vulnerability of households and communities without economic capacities to set up electric fences by their own financial means.

The findings suggest that an upscaling of multiple intervention programs—compensation payments, construction of coordinated and ecologically sound barrier systems, awareness campaigns, and halting development programs in elephant corridors—is needed to de-escalate HEC. More research into the feasibility of upscaling alternative mitigation methods such as bee fences (King et al. 2018), citrus plant cultivation (Senarathna 2016; Salja and Famees 2022) or palmyra bio-fences (Perera et al 2013) is recommended. As far as possible, the active integration of communities in the scope of participatory schemes—community-based conservation, as already well-established in turtle conservation in Southern Sri Lanka (Rathnayake 2016)—should be mainstreamed in elephant conservation policy. A reduction of overall vulnerability to human–elephant conflict is absolutely necessary to enhance rural people’s safety and well-being and hence to decrease elephant mortality from encounters.