# Living with elephants: A case study from Kavango-Zambezi transfrontier conservation area in Zimbabwe

Muhammad Faizan Usman, Martin Reinhardt Nielsen, Sébastien Le Bel, Maxwell Phiri, Patrice Grimaud

► To cite this version:

Muhammad Faizan Usman, Martin Reinhardt Nielsen, Sébastien Le Bel, Maxwell Phiri, Patrice Grimaud. Living with elephants: A case study from Kavango-Zambezi transfrontier conservation area in Zimbabwe. People and Nature, 2025, 7 (7), pp.1605-1618. 10.1002/pan3.70060. hal-05422619

HAL Id: hal-05422619

https://hal.science/hal-05422619v1

Submitted on 18 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

RESEARCH ARTICLE

<figure><img src="image_3.png" /></figure>

BRITISH
ECOLOGICAL
SOCIETY

People and Nature

# Living with elephants: A case study from Kavango-Zambezi Transfrontier Conservation Area in Zimbabwe

Muhammad Faizan Usman<fnref n="1" /> ID | Martin Reinhardt Nielsen<fnref n="2" /> ID | Sébastien Le Bel<fnref n="1" /> |
Maxwell Phiri<fnref n="3" /> | Patrice Grimaud<fnref n="4" />

**Correspondence**

Muhammad Faizan Usman

Email: muhammad-faizan.usman@cirad.fr

**Funding information**

Directorate-General for International
Partnerships

Handling Editor: Douglas Clark

**Abstract**

1. The Kavango-Zambezi Transfrontier Conservation Area (KaZa TFCA) is home to one of the largest populations of African savanna elephants (approximately 227,000), a species iconic for ecological balance but also a major driver of human-elephant conflict (HEC). This study investigates how spatial and socio-economic factors shape household perceptions and tolerance towards elephants in the Zimbabwean part of KaZa TFCA.

2. Using a survey sample of 668 households experiencing HEC, we analysed the impact of proximity to forests, protected areas and water points on conflict intensity and community responses.

3. Our findings reveal that households closer to these conflict-prone features experience higher exposure to elephants, resulting in significant tangible costs, such as crop loss and property damage, and increased emotional stress. Among these features, proximity to forests emerged as the strongest predictor of HEC, followed by protected areas, with water points having a lesser influence. Distance to these features was found to mediate tolerance through its effects on tangible costs, intangible benefits and mitigation efforts.

4. Intangible benefits, including an appreciation of elephants' ecological roles and aesthetic value, emerged as the strongest determinants of tolerance, highlighting the importance of nurturing positive perceptions. However, the absence of tangible benefits, such as tourism revenue or financial incentives, coupled with the tangible costs of living alongside elephants, such as crop damage, significantly undermine household tolerance.

5. The study stresses the need for spatially informed mitigation strategies, including participatory spatial planning, early-warning systems and livelihood diversification through community-based conservation initiatives. Establishing community conservancies in high-conflict wards could provide tangible benefits while empowering communities to engage in wildlife management. These findings highlight the importance of integrating conservation objectives with the socio-economic

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *People and Nature* published by John Wiley & Sons Ltd on behalf of British Ecological Society.

<footnote n="1">¹CIRAD, UPR Forêts et sociétés, ABIES
AgroParisTech, Montpellier Cedex 5,
France</footnote>

<footnote n="2">²Department of Food and Resource
Economics, University of Copenhagen,
Frederiksberg C, Denmark</footnote>

<footnote n="3">³CIRAD, UPR Forêts et sociétés, Harare,
Zimbabwe</footnote>

<footnote n="4">⁴CIRAD c/o ICRAF, Nairobi, Kenya</footnote>
realities of local communities, offering pathways for coexistence in biodiversity hotspots like KaZa TFCA.

KEYWORDS

community-based conservation, conservation psychology, human-elephant conflict (HEC), Kavango-Zambezi Transfrontier Conservation Area, sustainable coexistence, tolerance and perception, Wildlife Tolerance Model

# 1 | INTRODUCTION

The Kavango-Zambezi Transfrontier Conservation Area (KaZa TFCA) spans over 500,000 km² across five Southern African countries, hosting approximately 227,900 African savanna elephants (Bussière & Potgieter, 2023; Stoldt et al., 2020). In Zimbabwe, KaZa TFCA also supports over 375,000 people with an annual growth rate of approximately 1.5%, whose growing settlements increasingly overlap with elephant habitats, intensifying human-elephant interactions (Shanko et al., 2021; ZimStat, 2022). These interactions often result in crop raiding, property damage and threats to personal safety, often leading to severe economic and psychological burdens on communities (Compaore et al., 2020; Gross et al., 2021; Mumby & Plotnik, 2018; Usman et al., 2023). Such conflicts strain the livelihoods of vulnerable households while challenging conservation efforts to protect this flagship species (Granados & Weladji, 2012; van de Water & Matteson, 2018).

This spatial overlap, shaped by the dynamic relationship between settlement growth and elephant movements, highlights the importance of understanding how proximity to elephant habitats and key environmental features like forests and water sources affects local perceptions and tolerance (Naidoo et al., 2024; Redmore et al., 2020; Tiller et al., 2021). Elephants rely on these resources and their movement patterns often bring them into close contact with human settlements (Bucciarelli et al., 2024; Pozo et al., 2018; Sach et al., 2019). Communities near protected areas face higher risks of conflict, especially during harvest periods, when resource competition peaks (Buchholtz et al., 2019). These conflicts contribute to negative perceptions of elephants, complicating efforts to balance community welfare and conservation objectives (Redmore et al., 2023; Terada et al., 2021).

The recurring costs associated with crop loss, property damage and safety concerns often strain household tolerance of elephants, especially in communities experiencing frequent human-elephant conflict (HEC; Usman et al., 2023). Tolerance is shaped by a combination of economic and psychological factors, with psychological stress driven by repeated conflict and its unpredictability playing a key role (Frank, 2016; Kansky et al., 2021a; Saif et al., 2020). These impacts are closely linked to the direct economic losses that create persistent challenges for households already facing economic vulnerability (Mayberry et al., 2017; Nyumba et al., 2020). While economic and psychological factors are well recognized drivers of tolerance, spatial dimensions remain underexplored in human-wildlife conflict studies (Frank, 2016; Kansky et al., 2021a, 2021b; Slagle & Bruskotter, 2019; Williams et al., 2024). Understanding the spatial dimensions on the local level

is fundamental to developing pragmatic strategies that reduce conflict and promote coexistence (Terada et al., 2021).

Using the Zimbabwean part of the KaZa TFCA as a case study, this research aims to explore how spatial proximity to environmental and man-made features influences the frequency and intensity of HEC and how these insights can guide effective mitigation strategies. This paper, therefore, addresses two questions: (i) How do perceptions and attitudes of local communities near elephants vary based on proximity to conflict-prone environmental and man-made features? and (ii) How do spatial and socio-economic factors influence tolerance towards elephants in high-risk areas?

To answer these questions, we propose the following hypotheses:

**H1.** Communities closer to elephant habitats and conflict-prone features experience more frequent and intense HEC, leading to heightened negative perceptions toward elephants.

**H2.** Proximity to elephant habitats and conflict-prone features increases both tangible and intangible costs for communities, reducing overall tolerance towards elephants.

By testing these hypotheses, this study aims to improve the understanding of how spatial and socio-economic factors shape household perceptions and tolerance towards elephants. This foundational knowledge will assist communities and policy makers to identify high-risk areas and prioritize resources more effectively, paving the way for comprehensive mitigation strategies.

# 2 | THEORETICAL FRAMEWORK

This study adapts the Wildlife Tolerance Model (WTM) by Kansky et al. (2016), a framework widely used to examine human-wildlife conflict across diverse contexts. The WTM assumes that exposure to wildlife shapes perceptions of costs and benefits, ultimately influencing tolerance. To explore the role of proximity to protected areas, forests and water resources, the model is extended with additional context-specific variables (Figure 1).

The adapted model assumes that proximity to protected areas, forests and water resources affects mitigation strategies, which in turn influence exposure, experience, perceived costs and benefits and tolerance towards elephants.
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Wildlife Tolerance Model concerning proximity to protected area, forest and water resources (adapted from Kansky et al., 2016).</figcaption></figure>

Exposure is defined as the frequency of households (recorded in number of times) observing the elephants entering human-used areas across three spatial zones (village, household vicinity and property) during three seasons (dry, rainy and hot). Experience captures the number of positive or negative encounters with elephants.

Tangible costs include (1) monetary damage caused by the species and (2) the extent of expected future damage. Tangible benefits were excluded due to their negligible role in shaping attitudes, as communities reported no economic gains from elephants (Usman et al., 2023). Elephant meat was initially considered as a potential tangible benefit, but it was excluded from the final analysis due to its negligible role in influencing household attitudes towards elephants. Intangible costs encompass negative emotions (e.g. fear, stress) and perceptions of safety risks, vigilance and time wastage. Intangible

benefits include positive emotions (e.g. gratitude, trust) and perceptions of the elephant's ecological or symbolic value.

All intangible constructs are measured on a 5-point Likert scale, with 1 representing 'extremely weak' and 5 'extremely strong'.

# 3 | METHODS

## 3.1 | Study area

The study was conducted in Binga District, Zimbabwe, part of the greater KaZa TFCA shared between Angola, Botswana, Namibia, Zambia and Zimbabwe. The area is characterized by diverse landscapes encompassing expansive savannas and rugged terrain, mostly
Mopane and Terminalia trees, bisected by seasonal rivers (Grimaud et al., 2022). The climate is marked by three distinct seasons—hot, rainy and dry—influencing the movement and behaviour of wildlife, particularly elephants (Le Bel et al., 2022). Local communities traditionally rely on subsistence farming, which puts them at the forefront of conflict with wildlife due to the limited resources and shared spaces (Grimaud et al., 2022). Villages consist of clustered homesteads, with farms typically located at manageable distances from residential areas. The study focused on five wards: Sinampande (Ward 3), Sinasengwe (Ward 4), Sinakoma (Ward 5), Kariyangwe (Ward 15) and Chunga (Ward 22). These administrative units, overseen by elected councillors, are clusters of villages within the district's 25 wards. We selected these wards due to their ecological importance as wildlife habitats and the high prevalence of problems with wildlife, particularly with elephants. According to unofficial figures from local councillors, the total number of households in these wards was 7141, distributed as follows: Ward 3 (1119), Ward 4 (1051), Ward 5 (2073), Ward 15 (1835) and Ward 22 (1063). Figure 2 shows that wards 3, 4 and 5 are enclosed between Chizarira National Park and Chete Safari Area. Ward 15 is situated adjacent to Chizarira National Park, whereas Ward 22 shares its boundaries with Chete Safari Area and Lake Kariba. The proximity to these features makes the five wards a hotspot for HWC.

## 3.2 | Sampling

Due to the lack of an official household list, sampling was conducted using random sampling with villages treated as clusters (Omair, 2014). From five wards, 67 villages were randomly selected, and within each village, households were sampled using a random walk technique. An interval, calculated as the total number of households in the village divided by the target number of households to be surveyed, determined which households were skipped. Enumerators began at the village head's household, moving northward and interviewing every nth household. At village boundaries, they turned 45 degrees inward and continued until the target was reached.

This approach meant certain households were skipped based on the interval, with the number skipped varying by village household density. Using a 95% confidence interval, 5% margin of error, and 50% response distribution, a total of 1527 households were surveyed across the five wards (Israel, 1992).

## 3.3 | Data collection

A survey questionnaire was designed based on the theoretical framework to examine HEC dynamics, collecting demographic and socio-economic

<figure><img src="image_5.png" /><figcaption>FIGURE 2 Map of the study area.</figcaption></figure>
data from sampled households. Respondents reported their proximity to forests, protected areas and water bodies. Based on prior key-informant interviews, forests were defined as dense, uninhabited bushlands used for timber and non-timber products, including the state-owned Sijarira Forest (Figure 2). Water points were identified as wildlife-frequented sources of water, as perceived by households, while protected areas referred to territories under strict conservation regulations, specifically Chizarira National Park and Chete Safari Area (Figure 2). Distances were recorded as the walking time in minutes from the household to the nearest forest, protected area and water body.

The questionnaire assessed household exposure to HEC across three seasons: dry (harvest), rainy and hot. Exposure refers to the number of times households observed elephants entering human-used areas. Crop guarding, assessed specifically for the dry season (April–July) when crop-raiding risk is highest, refers to the time households actively spent protecting their crops. This seasonal focus aimed to capture HEC dynamics during peak conflict periods. Respondents were also asked about the types and diversity of mitigation solutions used to reduce conflict frequency and severity.

The questionnaire measured households' tolerance towards elephants using two approaches: support for lethal regulation and attitudes towards elephant population size. Support for lethal regulation was assessed under two scenarios: (i) if elephants were perceived as rare, and (ii) if they were perceived as abundant. It was measured across seven scenarios of escalating conflict:

(i) An elephant is seen in the bush far from villages or crops.

(ii) An elephant is near agricultural areas or neighbourhoods.

(iii) An elephant raids property or crops for the first time.

(iv) An elephant repeatedly raids property or crops but has never harmed anyone.

(v) An elephant threatens a child or adult.

(vi) An elephant injures a child or adult.

(vii) An elephant kills a child or adult.

Respondents received a score based on the earliest scenario they supported lethal intervention, with lower numbers meaning lower tolerance.

Tolerance towards elephant population size was measured across three scales: (i) in their district, (ii) in Zimbabwe and (iii) in Africa. Using Likert scale, respondents expressed whether they wanted the population to decrease a lot (1), stay the same (3) or increase a lot (5), with lower scores reflecting lower tolerance.

The survey questionnaire was developed in accordance with the EU General Data Protection Regulation and followed the ethical standards set by the Sustainable Wildlife Management (SWM) Programme. Prior to data collection, approval was obtained from the Binga Rural District Council, which also facilitated the process of recruiting appropriate enumerators to conduct the household surveys. Given the non-sensitive content of the questions, formal human-subjects approval with a reference number was not required. Each participant's involvement was secured through Free, Prior and Informed Consent (FPIC).

The questionnaire was designed in English and orally translated in the Tonga language when needed by native Tonga-speaking enumerators. Pre-testing of the questionnaire allowed for refinement of certain questions to align better with Tonga vocabulary. Respondents were selected based on their availability and familiarity with household dynamics, including the household head or another knowledgeable adult member. Only individuals aged 18 years and above, and of sound mind, were eligible to participate. No reward or compensation was provided for participation. Additionally, participants were informed that they had the option to discontinue the interview at any point. A copy of the questionnaire is available in Appendix S1.

## 3.4 | Data analysis

The analysis included 668 households (43.75% of the sample) that had experienced HEC. Due to the non-normal data distribution, the Kruskal–Wallis test was used to assess group differences, followed by Dunn's pairwise comparisons with Bonferroni correction (adjusted significance level: $\alpha = 0.0083$). Six pairwise comparisons were conducted: Group A versus B, C and D; Group B versus C and D and Group C versus D. Households were grouped separately for each key feature (water points, forests and protected areas) based on distance categories, meaning A–D classifications do not represent the same set of households across all features. Table S1 provides the distribution of households across these groups for each feature. Mitigation efforts were quantified for each household using a mitigation score, calculated as the sum of mitigation techniques employed.

A scaled tolerance index was created by averaging two measures: support for lethal regulation and tolerance towards elephant population size. The lethal regulation score (1–7 scale) was rescaled to a 1–5 scale using the formula:

$$x5 = (x7 - 1)(4/6) + 1$$

For this analysis, responses from the 'if population abundant' scenario were used, as they provided more stable distributions and clearer differentiation across groups in statistical tests.

Partial Least Squares Structural Equation Modelling (PLS-SEM) was performed using SmartPLS® version 4.1 to predict tolerance towards elephants and analyse relationships between latent variables. PLS-SEM is widely used in social sciences for handling complex models (Kansky et al., 2016; Lowry & Gaskin, 2014; Usman et al., 2023). Model validation was based on four criteria: (i) indicator reliability, (ii) composite reliability, (iii) convergent reliability and (iv) discriminant validity.

# 4 | RESULTS

## 4.1 | Description of the households

Respondents were 46.2% male and 53.7% female. Most households (98%) were of Tonga ethnicity, with 2% from Ndebele and Shona groups. Christians comprised 76% of households, whereas
24% practiced traditional beliefs. Average household size was 5.8±3.5 individuals, with a per capita income of USD 6.0±7.7 per month. Farming was the primary livelihood for 72% of respondents, with maize, sorghum and millet as staple crops. While all households engaged in some form of crop cultivation, the remaining 28% identified their primary occupation as self-employed individuals, teachers, local authorities, healthcare workers or those engaged in seasonal employment. Additionally, 70% owned vegetable gardens (mainly tomatoes and onions), and 36% had

fruit trees (bananas and mangoes). Table 1 summarizes the socio-demographic and economic profile of surveyed households and HEC incidence.

## 4.2 | Exposure to the elephant

Panel I of Figure 3 illustrates the interaction between people and elephants in terms of exposure and crop guarding time in different

<table><caption>TABLE 1 Distribution of interviewed households per ward.</caption><thead><tr><th></th><th>Ward 3</th><th>Ward 4</th><th>Ward 5</th><th>Ward 15</th><th>Ward 22</th><th>Total</th></tr></thead><tbody><tr><td><strong>Sampled households</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total households</td><td>1119</td><td>1051</td><td>2073</td><td>1835</td><td>1063</td><td><strong>7141</strong></td></tr><tr><td>Sampled households</td><td>287</td><td>290</td><td>340</td><td>320</td><td>290</td><td><strong>1527</strong></td></tr><tr><td>Households with HEC</td><td>35</td><td>168</td><td>184</td><td>131</td><td>150</td><td><strong>668</strong></td></tr><tr><td><strong>Economic characteristics</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average monthly per capita cash income (USD)</td><td>1.8 (±1.9)<br>(0, 1.1, 10)</td><td>7.6 (±7.7)<br>(0, 5, 50)</td><td>5.0 (±5.1)<br>(0, 3.3, 30)</td><td>5.9 (±8.5)<br>(0, 3.6, 75)</td><td>6.5 (±9.8)<br>(0, 3.5, 56.7)</td><td><strong>6.0 (±7.7)<br>(0, 3.3, 75)</strong></td></tr><tr><td>Average household size</td><td>5.9 (±3.0)<br>(2, 6, 18)</td><td>5.9 (±4.0)<br>(1, 5, 45)</td><td>6.1 (±4.0)<br>(1, 5, 39)</td><td>5.8 (±3.1)<br>(1, 5, 21)</td><td>5.5 (±2.5)<br>(1, 5, 20)</td><td><strong>5.8 (±3.5)<br>(1, 5, 45)</strong></td></tr><tr><td><strong>Gender of household heads and respondents</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female-headed households</td><td>26%</td><td>31%</td><td>28%</td><td>20%</td><td>21%</td><td><strong>25%</strong></td></tr><tr><td>Female respondents</td><td>51%</td><td>59%</td><td>46%</td><td>53%</td><td>59%</td><td><strong>54%</strong></td></tr><tr><td><strong>Education of the household</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average years of education (household head)</td><td>4.1 (±2.7)<br>(0, 5, 9)</td><td>5.5 (±3.5)<br>(0, 7, 19)</td><td>6.2 (±3.8)<br>(0, 7, 17)</td><td>5.9 (±3.9)<br>(0, 7, 17)</td><td>6.8 (±3.8)<br>(0, 7, 15)</td><td><strong>6.0 (±3.7)<br>(0, 7, 19)</strong></td></tr><tr><td>Average years of education (respondent)</td><td>4.5 (±2.9)<br>(0, 5, 10)</td><td>5.7 (±3.4)<br>(0, 7, 19)</td><td>6.2 (±3.8)<br>(0, 7, 13)</td><td>6.1 (±3.9)<br>(0, 7, 17)</td><td>6.6 (±3.8)<br>(0, 7, 15)</td><td><strong>6.0 (±3.6)<br>(0, 7, 19)</strong></td></tr><tr><td><strong>Religion</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Christian</td><td>80%</td><td>67%</td><td>90%</td><td>76%</td><td>66%</td><td><strong>76%</strong></td></tr><tr><td>Traditionalist</td><td>20%</td><td>33%</td><td>10%</td><td>24%</td><td>34%</td><td><strong>24%</strong></td></tr><tr><td><strong>Ethnicity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tonga ethnicity</td><td>100%</td><td>98%</td><td>96%</td><td>100%</td><td>100%</td><td><strong>98%</strong></td></tr><tr><td>Other ethnicities</td><td>0%</td><td>2%</td><td>4%</td><td>0%</td><td>0%</td><td><strong>2%</strong></td></tr><tr><td><strong>Agricultural crops and fruit</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Growing maize</td><td>86%</td><td>88%</td><td>80%</td><td>94%</td><td>53%</td><td><strong>79%</strong></td></tr><tr><td>Growing sorghum</td><td>37%</td><td>81%</td><td>79%</td><td>52%</td><td>91%</td><td><strong>75%</strong></td></tr><tr><td>Growing millet</td><td>83%</td><td>24%</td><td>30%</td><td>43%</td><td>27%</td><td><strong>33%</strong></td></tr><tr><td>Vegetable garden</td><td>91%</td><td>70%</td><td>49%</td><td>77%</td><td>84%</td><td><strong>70%</strong></td></tr><tr><td>Fruit trees</td><td>60%</td><td>33%</td><td>28%</td><td>46%</td><td>35%</td><td><strong>36%</strong></td></tr><tr><td><strong>Primary occupation</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Farmer</td><td>94%</td><td>73%</td><td>59%</td><td>53%</td><td>96%</td><td><strong>72%</strong></td></tr><tr><td>Self-employed</td><td>6%</td><td>7%</td><td>10%</td><td>42%</td><td>2%</td><td><strong>13%</strong></td></tr><tr><td>Seasonal employment</td><td>0%</td><td>7%</td><td>17%</td><td>0%</td><td>0%</td><td><strong>7%</strong></td></tr><tr><td>Other</td><td>0%</td><td>13%</td><td>14%</td><td>5%</td><td>2%</td><td><strong>8%</strong></td></tr></tbody></table>

Note: Average values are presented with their standard deviations in parentheses, followed by minimum, median and maximum values in brackets. Socio-economic demographics are reported only for households that experienced HEC. In some instances, the respondent differed from the household head when the household head was unavailable for the interview. Therefore, education and gender-related data are presented separately for household heads and respondents, as respondents were not always the household head.

Abbreviation: HEC, human–elephant conflict.
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

FIGURE 3 Panel I illustrates the relationship between distance, crop guarding time (harvest season) and elephant exposure, with red dotted lines indicating significant differences in exposure between groups. Panel II compares tolerance levels among households grouped by proximity to key features, with standard deviations shown as whiskers. Panel III presents average perceived intangible costs by proximity group, whereas Panel IV shows average perceived intangible benefits, both with whiskers representing standard deviations. Groups A, B, C and D are used across panels for consistency with the results in respective sections. Sample sizes for each group are as follows: Distance to Water Point—Group A (n=182), Group B (n=149), Group C (n=171) and Group D (n=166); Distance to Protected Area—Group A (n=144), Group B (n=187), Group C (n=149) and Group D (n=188); Distance to Forest—Group A (n=270), Group B (n=135), Group C (n=138) and Group D (n=125). Additional Likert scale visualizations for intangible costs, intangible benefits and tolerance are provided in the Supporting Information (Figures S4–S7).

seasons and at different distances from environmental features. Collectively, the median values presented in Table S3 support the trends observed in these graphs, revealing how certain spatial factors influence exposure to the elephant and the resultant vigilance required for crop protection.

### 4.2.1 | Distance to forest

Panel I of Figure 3 shows the association between distance to forests, protected areas and water points with exposure to elephants. Exposure significantly varied across distances from forests in all seasons: dry [H(3)=89.52, *p* < 0.0001], rainy [H(3)=75.26, *p* < 0.0001] and hot [H(3)=31.76, *p* < 0.0001]. Pairwise comparisons revealed that Groups A and B (less than 30 min from a forest) experienced significantly higher exposure than Groups C and D (more than 30 min away) in all seasons (Bonferroni-adjusted *p* < 0.0001).

Crop guarding time during the harvest season significantly varied across distances from forests [H(3)=22.15, *p* < 0.0001]. Dunn's pairwise comparisons revealed that Group A spent significantly

more time guarding crops compared only to Group C (*p* = 0.001) and Group D (*p* < 0.0001).

### 4.2.2 | Distance to protected area

The relationship between distance to protected areas and exposure to elephants is shown in Panel I of Figure 3. Significant differences in exposure were found between proximity categories across all three seasons: dry [H(3)=33.05, *p* < 0.0001], rainy [H(3)=44.75, *p* < 0.0001] and hot [H(3)=20.53, *p* = 0.0001]. Pairwise comparisons revealed that during the dry season, Group A experienced significantly higher exposure than only Groups B (*p* = 0.0001) and D (*p* < 0.0001). In the rainy season, Group A had significantly higher exposure than all groups—B (*p* < 0.0001), C (*p* < 0.0001) and D (*p* < 0.0001). Similarly, in the hot season, Group A had higher exposure than Groups B (*p* < 0.0001), C (*p* < 0.0001) and D (*p* < 0.0001). In brief, households within 80 min of protected areas faced significantly greater HEC exposure than those farther away.

For crop guarding, significant differences were also observed across proximity categories [H(3)=11.74, *p* = 0.0083]. Pairwise
comparisons showed Group A spent significantly more time guarding crops than Groups B (p < 0.002) and C (p < 0.005).

### 4.2.3 | Distance to water point

Panel I of Figure 3 shows the relationship between distance to water points and exposure to elephants. Significant differences in exposure were observed across distances for all three seasons: dry [H(3) = 10.68, p = 0.0136], rainy [H(3) = 22.31, p < 0.0001] and hot [H(3) = 27.46, p < 0.0001]. Pairwise comparisons revealed that Group D was significantly less exposed than Group A only during the dry season (p = 0.002), and less exposed than Groups A (p < 0.0001) and B (p = 0.001) only during the rainy season. In the hot season, Group D was significantly less exposed than Groups B (p < 0.0001) and C (p < 0.0001) only. These results indicate that exposure to elephants was significantly higher within 30 min of a water source.

Distance to water points also significantly influenced crop guarding time during the harvest period [H(3) = 34.35, p < 0.0001]. Households farthest from water points (Group D) spent significantly less time guarding crops than only Groups A (p < 0.0001) and B (p = 0.001).

## 4.3 | Intangible costs and benefits

Panels III and IV of Figure 3 compare perceived intangible costs and benefits of living with elephants based on household proximity to key features. No significant differences were observed between proximity groups for water points. However, proximity to forests and protected areas significantly influenced perceptions. Households closer to these features reported higher intangible costs, including negative emotions, while perceptions of intangible benefits, such as positive emotions, decreased with closer proximity.

### 4.3.1 | Perceived intangible costs

Panel III of Figure 3 shows significant differences in intangible costs between proximity groups for protected areas [negative emotions: H(3) = 15.19, p = 0.002; other intangible costs: H(3) = 11.11, p = 0.011] and forests [negative emotions: H(3) = 55.37, p < 0.0001; other intangible costs: H(3) = 18.84, p = 0.0003].

Pairwise comparisons for protected areas revealed that Group D reported significantly lower negative emotions than only Groups B (p = 0.006) and C (p = 0.0002). Group A reported significantly higher other intangible costs than Group C (p = 0.004) only.

For forests, Group A experienced significantly higher negative emotions compared to Groups B (p < 0.0001), C (p = 0.0003) and D (p < 0.0001). Similarly, Group A reported significantly higher other intangible costs than Groups B (p < 0.0001), C (p = 0.001) and D (p = 0.004).

### 4.3.2 | Perceived intangible benefits

Panel IV of Figure 3 compares intangible benefits across proximity groups. Significant differences in positive emotions were observed for protected areas [H(3) = 9.20, p = 0.027] and forests [H(3) = 111.50, p < 0.0001]. Other intangible benefits also differed significantly for protected areas [H(3) = 25.82, p < 0.0001] and forests [H(3) = 34.76, p < 0.0001].

Pairwise comparisons for protected areas showed Group A felt significantly lower positive emotions compared to Group B only (p = 0.003). Group A also reported higher perceptions of other intangible benefits compared only to Groups C (p = 0.001) and D (p < 0.0001).

For forests, Group A exhibited significantly fewer positive emotions than all groups—B, C and D (all p < 0.0001). Similarly, Group A reported significantly lower other intangible benefits compared to Groups B (p = 0.006), C (p < 0.0001) and D (p < 0.0001).

## 4.4 | Tolerance towards the elephant

Panel II of Figure 3 compares household tolerance by proximity to key features, showing that tolerance increases with distance from conflict-prone areas. Significant differences were observed for water points [H(3) = 21.18, p < 0.0001], protected areas [H(3) = 23.88, p < 0.0001] and forests [H(3) = 68.23, p < 0.0001].

For water points, pairwise comparisons showed Group A had significantly lower tolerance than Groups B (p = 0.002), C (p < 0.0001) and D (p = 0.001). For protected areas, Group A had significantly lower tolerance than Group C (p < 0.0001) only. For forests, Group A showed significantly lower tolerance than Groups B, C and D (all p < 0.0001).

## 4.5 | Factors affecting tolerance towards the elephant

Figure 4 presents the path model diagram showing relationships between latent variables. Model evaluation results met recommended limits for exploratory research (Table S4). Path coefficients, reported with standard deviations and significance levels, are detailed in Table S5, along with bootstrapped confidence intervals.

Figure 4 highlights the significant determinants of tolerance: intangible benefits (path coefficient = 0.484 ± 0.034 SD), tangible costs (–0.175 ± 0.039), mitigation efforts (–0.150 ± 0.033) and intangible costs (–0.088 ± 0.032). Intangible benefits were mediated by distance (0.278 ± 0.044) and exposure (–0.210 ± 0.038), whereas intangible costs were influenced only by mitigation efforts (–0.253 ± 0.041). Tangible costs were mediated by distance (–0.370 ± 0.036) and exposure (0.426 ± 0.045). Mitigation efforts were affected by distance (–0.133 ± 0.049), agricultural dependence (–0.103 ± 0.042) and exposure (0.099 ± 0.043), while exposure was mediated by distance (–0.205 ± 0.027) and agricultural dependence (0.183 ± 0.044). Financial capacity had no significant impact on latent variables.
<figure><img src="image_8.png" /><figcaption>FIGURE 4 Path model diagram from Partial Least Squares Structural Equation Modelling. Latent variables include AGRI (agricultural dependence), FIN (financial capacity), DIST (distance to environmental/man-made features), EXPO (exposure to elephants), MIT (mitigation efforts), CT (tangible costs), CI (intangible costs), BI (intangible benefits) and TOL (tolerance). Coefficients of determination (R²) are shown within circles. Solid lines represent significant path coefficients, and the values on those lines are path coefficients, whereas dotted lines indicate non-significant paths. The DIST construct is formative, comprising distances to forests, protected areas and water points, with weights indicating their relative contributions.</figcaption></figure>

For the distance construct, proximity to forests was the strongest contributor (weight = 0.847 ± 0.065), followed by protected areas (weight = 0.328 ± 0.121), with distance to water sources carrying a non-significant weight (0.084 ± 0.106).

$R^2$ values showed that intangible benefits, tangible costs and mitigation efforts accounted for 37.7% of the variation in tolerance. Exposure and distance explained 25.1% of the variation in tangible costs and 9.8% in intangible benefits. Exposure had a low $R^2$ (7.9%), reflecting limited variability explained by distance and agricultural dependence.

5 | DISCUSSION

5.1 | Tracing the footprints of conflict: Spatial insights into HEC

The role of spatial features emerged as a critical factor in managing HEC, as evidenced by the findings of this study, which support the hypothesis (H1) that communities closer to forests, protected areas and, to a lesser extent, water points experience significantly higher reported exposure to elephants (Panel I, Figures 3 and 4; Table S3).
<figure><img src="image_9.png" /></figure>

Among these factors, the distance to forests (in walking minutes) has the most profound influence on human-elephant interactions. Households situated closer to forests consistently reported more elephant encounters throughout the year, regardless of the season. This pattern also reflects the logical progression wherein higher conflict levels require increased guarding efforts, which, in turn, lead to greater exposure to elephants. During peak conflict season, households closer to forests allocated additional time to crop guarding, further intensifying these interactions. While forests serve as refuges for elephants (Sitati et al., 2003), they are also accessed by the local communities for extractive activities such as collection of fuelwood and other non-timber forest products, though these benefits are often informal and limited (Grimaud et al., 2022). A study in the Maasai Mara, Kenya, found that forests act as sanctuaries for elephants during the day, and a departure point for crop raiding occurring during the night, increasing the likelihood of HEC for households near forests (De La Torre et al., 2022; Sitati et al., 2003). These findings point out the importance of understanding the impact of geographical features on the frequency of HEC for crafting precise, spatially informed interventions for conflict management (Saha & Soren, 2024).

In-depth surveys can reveal spatial factors driving HEC, helping to draft effective mitigation strategies through participatory spatial planning. A study conducted in Zimbabwe advocates for fencing and the creation of buffer zones between the communities and a conservancy to respect the movements and habitats of elephants (Matseketsa et al., 2019). However, fencing has received significant criticism due to its potential to fragment elephant habitats and corridors as well as cause adverse ecological impacts, including the risk of animals becoming ensnared in these fences (Evans & Adams, 2016). Alternatively, the designation of buffer zones is a viable option that policymakers should consider (Sanare et al., 2022; Songhurst et al., 2016).

Moreover, well-informed spatial planning for HEC mitigation not only safeguards livelihoods and individual safety against potential elephant intrusions but also contributes to the conservation of elephant populations, as problematic individuals are instead usually culled as part of problem animal control operations (Hoare, 2012). Thus, integrating spatial planning into mitigation strategies following a landscape approach becomes crucial (Arts et al., 2017). Embracing a landscape approach implies recognizing that HEC is not confined to individual households or villages but extends across broader landscapes (Naha et al., 2019). It acknowledges the interconnection between various factors aggravating the conflicts, such as land use patterns, habitat fragmentation and resource availability (Buchholtz et al., 2020). Despite the need for holistic strategies to address the issues that contribute to conflicts on regional levels, there are actions that communities, especially the households living closer to conflict-prone features, can take collectively at the village level to reduce the conflict to a tolerable level.

For instance, there are various early-warning systems available that are effective in preventing HEC, ranging from sophisticated automated options to more cost-effective SMS-based manual systems

(Le Bel et al., 2014). In Zimbabwe, deploying chilli-based deterrents along corridors between elephant refuges and agricultural areas has shown effectiveness in preventing HEC (La Grange et al., 2022). Beehive fences have also proven effective at deterring elephants, both in Africa and Asia, while simultaneously offering the added benefit of producing honey for the communities (King et al., 2009, 2011, 2017; Van De Water et al., 2020). Other livelihood strategies, such as eco-tourism (Zingi et al., 2022) or sustainable harvesting of non-timber forest products (Mugari et al., 2024), could further reduce reliance on subsistence farming. This is particularly important given our findings that agricultural dependence increases exposure to elephants (Figure 4).

By integrating these livelihood strategies into broader spatial planning efforts, households can enhance resilience while promoting coexistence with wildlife (Zoysa, 2022). Targeted interventions should prioritize households closest to conflict-prone areas (Panel I, Figure 3) through participatory planning that identifies high-risk zones. By collectively focusing on these identified zones, households can then implement targeted interventions that reduce conflicts to a more tolerable level.

Finally, this study used walking times to measure distances rather than precise metric measurements. Doing so reflected the local realities and perceptions, which are fundamental in understanding household experiences and perceptions. However, walking times may vary between individuals due to factors such as age and gender, introducing potential variability. For more precise spatial planning, geographic information system (GIS)-based methods that incorporate GPS collar data and land-use-land-cover data could be used to further refine the role of spatial features in HEC dynamics (Gunawansa et al., 2024; Naidoo et al., 2024).

## 5.2 | Understanding tolerance to the elephant

We found that the relationship between communities and elephants is determined by the proximity of households to key environmental and man-made features. Consistent with previous research, communities living closer to conflict-prone areas, such as national parks and safari areas, tend to have lower tolerance towards wildlife (Saif et al., 2020; Usman et al., 2023). For instance, studies in Gabon and Nigeria found that proximity to national parks, combined with frequent crop damage, led to negative perceptions of wildlife (Digun-Aweto & Van Der Merwe, 2019; Terada et al., 2021). Proximity often translates to increased competition for resources, exacerbating HEC through crop raiding, property damage and even human casualties (Nsonsi et al., 2018).

These findings partially align with our second hypothesis (H2), which stated that proximity to elephant habitats and conflict-prone features increases tangible costs, subsequently reducing tolerance. While our PLS-SEM analysis indicates that exposure has a stronger direct effect on tangible costs than distance, distance remains relevant as it indirectly contributes to exposure levels (Figure 4). Moreover, the analysis revealed a weak link between intangible costs
and tolerance, indicating that their influence may be limited. While intangible costs were not directly linked to distance, their reduction was mediated by mitigation efforts. Conversely, increasing distance from conflict-prone areas positively influences household tolerance towards elephants. This trend is evident in our PLS-SEM results, which show that greater distance is associated with reduced tangible costs and enhanced perceptions of intangible benefits, such as an appreciation for elephants' ecological and aesthetic value. Word cloud analysis (Figure S1) supports this finding, with frequent mentions of 'elephant watching', suggesting that distance allows communities to view elephants more favourably, as symbols of biodiversity and ecosystem functioning (Makecha & Ghosal, 2017).

While our research highlights the role of distance, findings from a study in Kibale National Park in Uganda highlight the importance of considering elephant population density. Increased density has been found to aggravate HECs due to more frequent crop raiding, thereby negatively impacting local perceptions (MacKenzie et al., 2017). Therefore, it is important to account for both the spatial distribution of human settlements and the density of elephant populations when developing conservation strategies to address the root causes of conflict and promote coexistence. GIS-based tools have proven useful in this regard, such as in Kenya's Masai Mara and India's Sonitpur district, where GIS and remote sensing were used to predict HEC and protect elephant corridors through land-use planning (Hazarika & Saikia, 2013; Sitati et al., 2003).

Community-based conservation initiatives offer additional pathways to enhance tolerance. For instance, a study in Uganda's Kibale National Park found that involving local communities in decisions to implement strategies such as installing beehive fences and planting buffer crops, like tea and garlic, significantly improved their perceptions towards wildlife (Kolinski & Milich, 2021). Another study from Tanzania observed that involving the communities in management decisions yielded positive wildlife conservation outcomes of a magnitude similar to those observed in national parks (Kiffner et al., 2020). Similarly, Community-Based Natural Resource Management initiatives in other KaZa TFCA countries have demonstrated success by offering households tangible benefits, such as tourism revenue and participatory governance structures (Heffernan, 2022; Merz et al., 2024). However, such efforts are currently limited in the study area as communities report receiving no direct tangible benefits from the national park. Word cloud analysis (Figure S3) reflects this sentiment, with "wildlife conservation" frequently cited as a driver of conflict, suggesting frustration over policies that increase elephant presence without local benefits. Notably, wards 3, 4 and 5 in the study area have been identified as potential areas for a community conservancy, which could help address this by giving communities a greater role in governance (Grimaud et al., 2022). Future research could explore how such governance developments influence household perceptions and tolerance towards elephants.

PLS-SEM further provided insightful contributions to understanding what factors influence the tolerance level of the four proximity groups, highlighting the complex interplay of benefits, costs and mitigation efforts in shaping tolerance (Figure 4). The model highlighted

that intangible benefits are the most significant determinants of tolerance. This indicates that non-material values, such as emotional well-being and an appreciation of elephants' roles in ecological balance and community identity, play a central role in nurturing positive attitudes. This result aligns with the conservation psychology literature, emphasizing the importance of intrinsic values and emotional connections with wildlife (Fernández-Llamazares et al., 2020). The negative coefficient for tangible costs indicated that direct economic losses due to the elephants reduce tolerance. Word cloud analysis (Figure S2) reinforces this, with frequent mentions of 'crop damage' and 'property destruction' highlighting the financial burden imposed by HEC. These findings contradict a study conducted in rural Bangladesh, where intangible costs were found to significantly impact household tolerance, whereas tangible costs did not (Saif et al., 2020). This contrast highlights why it is important to understand the unique drivers of tolerance in each case, as factors influencing tolerance can vary significantly from one location to another. Finally, the negative coefficient for mitigation efforts may reflect frustration with ineffective and unreliable practices that fail to prevent elephant intrusions (Nelson et al., 2003).

The significant mediation effects that distance had on the relationships between tolerance and factors such as intangible benefits, tangible costs and mitigation efforts reinforce the spatial aspects of human-elephant interactions. Communities living further away from conflict zones are likely to experience fewer direct costs and more easily recognize the intrinsic value as well as broader ecological roles of elephants, demonstrating higher tolerance (Panel II of Figure 3). Researchers have explored and confirmed the effect that intangible benefits play in determining tolerance, but studies have not taken into consideration the extent to which distance mediates this effect (see Kansky et al., 2021a, 2021b; Saif et al., 2020; Teixeira et al., 2021; Usman et al., 2023). As we and other studies on the subject note, the importance of intangible benefits cannot be ignored. Intangible benefits repeatedly appear to be the strongest determinant of tolerance, leading to the conclusion that strong attention should be given to the intangible dimensions of human-elephant interactions along with the direct costs. However, we argue that to develop effective mitigation strategies, managers must consider the spatial dimension of conflict and how distance mediates other aspects determining tolerance.

6 | CONCLUSION

This study highlights the critical role of spatial features in shaping HEC and community tolerance towards elephants. Findings confirm that proximity to conflict-prone areas, such as forests, protected areas and water points, significantly increases exposure to elephants, leading to higher tangible costs and lower tolerance. Among these spatial factors, forests emerged as the most influential, serving both as key refuges for elephants and as sources of informal resources for local communities. These results call for the need for spatially informed mitigation strategies to balance conservation with community needs.
<figure><img src="image_10.png" /></figure>

Our analysis emphasizes the importance of intangible benefits, such as emotional well-being and an appreciation for elephants' ecological roles, as the strongest determinants of tolerance. While distance mediates the relationship between tolerance, tangible costs and mitigation efforts, communities living closer to conflict-prone areas face greater economic and emotional burdens, requiring targeted interventions. Strategies, such as participatory spatial planning, GIS-based tools and community-based conservation initiatives, are fundamental to addressing these challenges. By incorporating mitigation solutions—such as early warning systems, chilli deterrents and beehive fences—within broader landscape approaches, combined with alternative livelihoods such as ecotourism and non-timber forest products, it is possible to enhance the resilience of households and enable coexistence.

We also highlight the lack of tangible benefits, such as tourism revenue or participatory governance structures, as a significant limitation for enhancing household tolerance. Establishing community conservancies in high-conflict wards could potentially address these gaps by empowering households and providing financial incentives. Future research should explore how such governance reforms influence household perceptions and tolerance towards elephants. These efforts are essential for promoting coexistence, ensuring the conservation of elephants and safeguarding livelihoods in the KaZa TFCA region and beyond.

## AUTHOR CONTRIBUTIONS

Each author played a pivotal role in shaping the ideas and writing the text of this manuscript, bringing their individual perspectives and expertise to the collaborative work.

## ACKNOWLEDGEMENTS

The work is part of the Sustainable Wildlife Management Programme that is funded by the European Union and is being implemented by the French Agricultural Research Centre for International Development (CIRAD) and Food and Agriculture Organization (FAO) of the United Nations in Zimbabwe. The authors would like to acknowledge the Binga Rural District Council that assisted in nominating 17 enumerators to carry out the household survey. We would also like to thank Josias Mwinde, Shupa Muchimba and Daimon Mpande who assisted in the logistical arrangements for enumerators on behalf of the authors.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

## DATA AVAILABILITY STATEMENT

An anonymized version of the data from this study is available on the online repository (Dataverse) of the French Agricultural Research Centre for International Development (CIRAD) and can be accessed using the following DOI: https://doi.org/10.18167/DVN1/JZLKNI.

## ORCID

Muhammad Faizan Usman ID https://orcid.org/0000-0002-7167-2854

Martin Reinhardt Nielsen ID https://orcid.org/0000-0002-8941-6031

## REFERENCES

Arts, B., Buizer, M., Horlings, L., Ingram, V., Van Oosten, C., & Opdam, P. (2017). Landscape approaches: A state-of-the-art review. *Annual Review of Environment and Resources*, *42*, 439–463. https://doi.org/10.1146/annurev-environ-102016-060932

Bucciarelli, J. R., Pimm, S. L., Huang, R. M., Chase, M. J., Leggett, K., Bastos, A. D., & van Aarde, R. J. (2024). Local elephant movements, turning angles, and water access across a rainfall gradient in Southern Africa. *Biological Conservation*, *296*, 110669.

Buchholtz, E. K., Redmore, L., Fitzgerald, L. A., Stronza, A., Songhurst, A., & McCulloch, G. (2019). Temporal partitioning and overlapping use of a shared natural resource by people and elephants. *Frontiers in Ecology and Evolution*, *7*, 117.

Buchholtz, E. K., Stronza, A., Songhurst, A., McCulloch, G., & Fitzgerald, L. A. (2020). Using landscape connectivity to predict human-wildlife conflict. *Biological Conservation*, *248*, 108677.

Bussière, E. M. S., & Potgieter, D. (2023). KAZA elephant survey 2022. *Vol. I: Results*. KAZA TFCA Secretariat.

Compaore, A., Sirima, D., Hema, E. M., Doamba, B., Ajong, S. N., Di Vittorio, M., & Luiselli, L. (2020). Correlation between increased human–elephant conflict and poaching of elephants in Burkina Faso (West Africa). *European Journal of Wildlife Research*, *66*, 1–9.

De La Torre, J. A., Cheah, C., Lechner, A. M., Wong, E. P., Tuuga, A., Saaban, S., Goossens, B., & Campos-Arceiz, A. (2022). Sundaic elephants prefer habitats on the periphery of protected areas. *Journal of Applied Ecology*, *59*, 2947–2958. https://doi.org/10.1111/1365-2664.14286

Digun-Aweto, O., & Van Der Merwe, P. (2019). Community perceptions of the human-wildlife conflict: A case study of old Oyo National Park, Nigeria. *Biodiversity*, *20*, 118–131. https://doi.org/10.1080/14888386.2019.1664323

Evans, L. A., & Adams, W. M. (2016). Fencing elephants: The hidden politics of wildlife fencing in Laikipia, Kenya. *Land Use Policy*, *51*, 215–228.

Fernández-Llamazares, Á., Western, D., Galvin, K. A., McElwee, P., & Cabeza, M. (2020). Historical shifts in local attitudes towards wildlife by Maasai pastoralists of the Amboseli Ecosystem (Kenya): Insights from three conservation psychology theories. *Journal for Nature Conservation*, *53*, 125763.

Frank, B. (2016). Human–wildlife conflicts and the need to include tolerance and coexistence: An introductory comment. *Society & Natural Resources*, *29*, 738–743.

Granados, A., & Weladji, R. B. (2012). Human–elephant conflict around Bénoué National Park, Cameroon: Influence on local attitudes and implications for conservation. *Human Dimensions of Wildlife*, *17*, 77–90. https://doi.org/10.1080/10871209.2012.639133

Grimaud, P., Gumbo, D., & Le Bel, S. (2022). Towards sustainable wildlife management: An in-depth study for the promotion of community conservancies in Zambia and Zimbabwe. Food & Agriculture Org.

Gross, E. M., Lahkar, B. P., Subedi, N., Nyirenda, V. R., Klebelsberg, E., & Jakoby, O. (2021). Elephants in the village: Causes and consequences of property damage in Asia and Africa. *Conservation Science and Practice*, *3*, e343.

Gunawansa, T. D., Perera, K., Apan, A., & Hettiarachchi, N. K. (2024). Identifying human elephant conflict hotspots through satellite remote sensing and GIS to support conflict mitigation. *Remote Sensing Applications: Society and Environment*, *35*, 101261.

Hazarika, R., & Saikia, A. (2013). The pachyderm and the pixel: An assessment of elephant habitat suitability in Sonitpur, India. *International Journal of Remote Sensing*, *34*, 5317–5330. https://doi.org/10.1080/01431161.2013.787503
Heffernan, A. (2022). Development, conservation, empowerment: The trilemma of community-based natural resource management in Namibia. *Environmental Management, 69*, 480-491.

Hoare, R. (2012). Lessons from 15 years of human-elephant conflict mitigation: Management considerations involving biological, physical and governance issues in Africa. *Pachyderm, 51*, 60-74.

Israel, G. D. (1992). *Determining sample size*. University of Florida.

Kansky, R., Kidd, M., & Fischer, J. (2021a). Understanding drivers of human tolerance towards mammals in a mixed-use transfrontier conservation area in southern Africa. *Biological Conservation, 254*, 108947.

Kansky, R., Kidd, M., & Fischer, J. (2021b). Does money "buy" tolerance toward damage-causing wildlife? *Conservation Science and Practice, 3*, e262.

Kansky, R., Kidd, M., & Knight, A. T. (2016). A wildlife tolerance model and case study for understanding human wildlife conflicts. *Biological Conservation, 201*, 137-145.

Kiffner, C., Thomas, S., Speaker, T., O'Connor, V., Schwarz, P., Kioko, J., & Kissui, B. (2020). Community-based wildlife management area supports similar mammal species richness and densities compared to a national park. *Ecology and Evolution, 10*, 480-492. https://doi.org/10.1002/ece3.5916

King, L. E., Douglas-Hamilton, I., & Vollrath, F. (2011). Beehive fences as effective deterrents for crop-raiding elephants: Field trials in northern Kenya: Beehive fence field trials. *African Journal of Ecology, 49*, 431-439. https://doi.org/10.1111/j.1365-2028.2011.01275.x

King, L. E., Lala, F., Nzumu, H., Mwambingu, E., & Douglas-Hamilton, I. (2017). Beehive fences as a multidimensional conflict mitigation tool for farmers coexisting with elephants. *Conservation Biology, 31*, 743-752.

King, L. E., Lawrence, A., Douglas-Hamilton, I., & Vollrath, F. (2009). Beehive fence deters crop-raiding elephants. *African Journal of Ecology, 47*, 131-137. https://doi.org/10.1111/j.1365-2028.2009.01114.x

Kolinski, L., & Milich, K. M. (2021). Human-wildlife conflict mitigation impacts community perceptions around Kibale National Park, Uganda. *Diversity, 13*, 145.

La Grange, M., Matema, C., Nyamukure, B., & Hoare, R. (2022). The virtual fence dynamic: A breakthrough for low-cost and sustainable mitigation of human-elephant conflict in subsistence agriculture? *Frontiers in Conservation Science, 3*, 863180.

Le Bel, S., Chavernac, D., Mapuvire, G., & Cornu, G. (2014). FrontlineSMS as an early warning network for human-wildlife mitigation: Lessons learned from tests conducted in Mozambique and Zimbabwe. *The Electronic Journal of Information Systems in Developing Countries, 60*, 1-13. https://doi.org/10.1002/j.1681-4835.2014.tb00427.x

Le Bel, S., Jori, F., Nyirenda, V. R., Munyeme, M., Hanyire, T., Usman, M. F., Nsende, E., La Grange, M., & Mapuvire, G. (2022). Chapter 9: Human-wildlife interactions. In P. Grimaud, D. Gumbo, & S. Le Bel (Eds.), *Towards sustainable wildlife management: An in-depth study for the promotion of community conservancies in Zambia and Zimbabwe* (pp. 125-150). Food & Agriculture Org.

Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. *IEEE Transactions on Professional Communication, 57*, 123-146.

MacKenzie, C. A., Salerno, J., Hartter, J., Chapman, C. A., Reyna, R., Tumusiime, D. M., & Drake, M. (2017). Changing perceptions of protected area benefits and problems around Kibale National Park, Uganda. *Journal of Environmental Management, 200*, 217-228.

Makecha, R. N., & Ghosal, R. (2017). Elephant conservation: Reviewing the need and potential impact of cognition-based education. *International Journal of Comparative Psychology, 30*, 1-9.

Matseketsa, G., Muboko, N., Gandiwa, E., Kombora, D. M., & Chibememe, G. (2019). An assessment of human-wildlife conflicts in local

communities bordering the western part of save valley conservancy, Zimbabwe. *Global Ecology and Conservation, 20*, e00737.

Mayberry, A. L., Hovorka, A. J., & Evans, K. E. (2017). Well-being impacts of human-elephant conflict in Khumaga, Botswana: Exploring visible and hidden dimensions. *Conservation and Society, 15*, 280-291.

Merz, L., Kahler, J., Mwale, G., & Child, B. (2024). "Although elephants bring problems, they also bring benefits:" The complexities of human-wildlife coexistence. The Stacks.

Mugari, E., Nethengwe, N. S., & Gumbo, A. D. (2024). The utilization and contribution of timber and non-timber forest products to livelihoods under a changing climate in the Limpopo River basin. *Environmental Research Communications, 6*, 025005.

Mumby, H. S., & Plotnik, J. M. (2018). Taking the elephants' perspective: Remembering elephant behavior, cognition and ecology in human-elephant conflict mitigation. *Frontiers in Ecology and Evolution, 6*, 122.

Naha, D., Sathyakumar, S., Dash, S., Chettri, A., & Rawat, G. S. (2019). Assessment and prediction of spatial patterns of human-elephant conflicts in changing land cover scenarios of a human-dominated landscape in North Bengal. *PLoS One, 14*, e0210580.

Naidoo, R., Beytell, P., Brennan, A., Carter, J., Carter, K. D., Chamaillé-Jammes, S., Chilambe, B., Hoare, R., Iiyambo, N., Jooste, D., Karidozo, M., Kilian, J. W., Madhlamoto, D., Madiri, T. H., McCulloch, G., Monks, N., Mudimba, I., Ngwenya, N., Nyambe, N., ... Songhurst, A. (2024). Landscape connectivity for African elephants in the world's largest transfrontier conservation area: A collaborative, multi-scalar assessment. *Journal of Applied Ecology, 61*, 2483-2496. https://doi.org/10.1111/1365-2664.14746

Nelson, A., Bidwell, P., & Sillero-Zubiri, C. (2003). A review of human-elephant conflict management strategies. People & Wildlife, a Wildlife Conservation Research Unit, Born Free Foundation Partnership.

Nsonsi, F., Heymans, J., Diamouangana, J., Mavinga, F. B., & Breuer, T. (2018). Perceived human-elephant conflict and its impact for elephant conservation in northern Congo. *African Journal of Ecology, 56*, 208-215. https://doi.org/10.1111/aje.12435

Nyumba, T. O., Emenye, O. E., & Leader-Williams, N. (2020). Assessing impacts of human-elephant conflict on human wellbeing: An empirical analysis of communities living with elephants around Maasai Mara National Reserve in Kenya. *PLoS One, 15*, e0239545.

Omair, A. (2014). Sample size estimation and sampling techniques for selecting a representative sample. *Journal of Health Specialties, 2*, 142.

Pozo, R. A., Cusack, J. J., McCulloch, G., Stronza, A., Songhurst, A., & Coulson, T. (2018). Elephant space-use is not a good predictor of crop-damage. *Biological Conservation, 228*, 241-251.

Redmore, L., Katholo, I., Sene-Harper, A., Songhurst, A., McCulloch, G., & Stronza, A. (2023). The village, the elephant, and the state: Land access and vulnerability in rural Botswana. *Human Ecology, 51*, 237-249. https://doi.org/10.1007/s10745-023-00390-4

Redmore, L., Stronza, A. L., Songhurst, A., & McCulloch, G. (2020). Where elephants roam: Perceived risk, vulnerability, and adaptation in the Okavango Delta. *Ecology and Society, 25(4): 27. https://doi.org/10.5751/ES-12001-250427

Sach, F., Dierenfeld, E. S., Langley-Evans, S. C., Watts, M. J., & Yon, L. (2019). African savanna elephants (*Loxodonta africana*) as an example of a herbivore making movement choices based on nutritional needs. *PeerJ, 7*, e6260.

Saha, S., & Soren, R. (2024). Human-elephant conflict: Understanding multidimensional perspectives through a systematic review. *Journal for Nature Conservation, 79*, 126586.

Saif, O., Kansky, R., Palash, A., Kidd, M., & Knight, A. T. (2020). Costs of coexistence: Understanding the drivers of tolerance towards Asian elephants *Elephas maximus* in rural Bangladesh. *Oryx, 54*, 603-611.

Sanare, J. E., Valli, D., Leweri, C., Glatzer, G., Fishlock, V., & Treydte, A. C. (2022). A socio-ecological approach to understanding how land use challenges human-elephant coexistence in Northern Tanzania. *Diversity, 14*, 513.
<figure><img src="image_11.png" /></figure>

Shanko, G., Tona, B., & Adare, B. (2021). Human-wildlife conflict around Belo-Bira Forest, Dawro Zone, Southwestern Ethiopia. *International Journal of Ecology, 2021*, 1–7.

Sitati, N. W., Walpole, M. J., Smith, R. J., & Leader-Williams, N. (2003). Predicting spatial aspects of human–elephant conflict. *Journal of Applied Ecology, 40*, 667–677. https://doi.org/10.1046/j.1365-2664.2003.00828.x

Slagle, K., & Bruskotter, J. T. (2019). Tolerance for wildlife: A psychological perspective. In B. Frank, J. A. Glikman, & S. Marchini (Eds.), *Humanwildlife interactions: Turning conflict into coexistence* (pp. 85–106). Cambridge University Press.

Songhurst, A., McCulloch, G., & Coulson, T. (2016). Finding pathways to human–elephant coexistence: A risky business. *Oryx, 50*, 713–720.

Stoldt, M., Göttert, T., Mann, C., & Zeller, U. (2020). Transfrontier conservation areas and human-wildlife conflict: The case of the Namibian component of the Kavango-Zambezi (KAZA) TFCA. *Scientific Reports, 10*, 7964.

Teixeira, L., Tisovec-Dufner, K. C., Marin, G. D. L., Marchini, S., Dorresteijn, I., & Pardini, R. (2021). Linking human and ecological components to understand human-wildlife conflicts across landscapes and species. *Conservation Biology, 35*, 285–296. https://doi.org/10.1111/cobi.13537

Terada, S., Yobo, C. M., Moussavou, G.-M., & Matsuura, N. (2021). Human-elephant conflict around Moukalaba-doudou National Park in Gabon: Socioeconomic changes and effects of conservation projects on local tolerance. *Tropical Conservation Science, 14*, 194008292110267. https://doi.org/10.1177/19400829211026775

Tiller, L. N., Humle, T., Amin, R., Deere, N. J., Lago, B. O., Leader-Williams, N., Sinoni, F. K., Sitati, N., Walpole, M., & Smith, R. J. (2021). Changing seasonal, temporal and spatial crop-raiding trends over 15 years in a human-elephant conflict hotspot. *Biological Conservation, 254*, 108941.

Usman, M. F., Le Bel, S., Grimaud, P., & Nielsen, M. R. (2023). Evaluating the determinants of wildlife tolerance in the Kavango-Zambezi Transfrontier Conservation Area in Zimbabwe. *Journal for Nature Conservation, 75*, 126466.

Van De Water, A., King, L. E., Arkajak, R., Arkajak, J., Van Doormaal, N., Ceccarelli, V., Sluiter, L., Doornwaard, S. M., Praet, V., Owen, D., & Matteson, K. (2020). Beehive fences as a sustainable local solution to human-elephant conflict in Thailand. *Conservation Science and Practice, 2*(e260), e260. https://doi.org/10.1111/csp2.260

van de Water, A., & Matteson, K. (2018). Human-elephant conflict in western Thailand: Socio-economic drivers and potential mitigation strategies. *PLoS One, 13*, e0194736.

Williams, H. F., Leneuiyia, K. L., Mwalavu, B., Serem, G., Sempeyo, V., Pope, F., King, L. E., & Verissimo, D. (2024). The elephant queen: Can a nature documentary help to increase tolerance towards elephants? *People and Nature, 6*(10599): 762–774. https://doi.org/10.1002/pan3.10599

ZimStat. (2022). Zimbabwe 2022 population and housing census preliminary report (census report). Zimbabwe National Statistics Agency.

Zingi, G. K., Mpofu, A., Chitongo, L., Museva, T., Chivhenge, E., & Ndongwe, M. R. (2022). Ecotourism as a vehicle for local economic development: A case of Tsholotsho District Zimbabwe. *Cogent Social Science, 8*, 2035047. https://doi.org/10.1080/23311886.2022.2035047

Zoysa, M. D. (2022). Forest-based ecotourism in Sri Lanka: A review on state of governance, livelihoods, and forest conservation outcomes. *Journal of Sustainable Forestry, 41*, 413–439. https://doi.org/10.1080/10549811.2021.1943450

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Figure S1.** Word Cloud for positive experiences mentioned by the households (n = 26).

**Figure S2.** Word Cloud for negative experiences mentioned by the households (n = 605).

**Figure S3.** Word Cloud for the reasons mentioned by households for what is aggravating the elephant problem in the area (n = 669).

**Figure S4.** Likert scale response distribution for intangible cost.

**Figure S5.** Likert scale response distribution for intangible benefits.

**Figure S6.** Likert scale response distribution for support for lethal regulation (Tolerance).

**Figure S7.** Likert scale response distribution for desire for change in elephant population.

**Figure S8.** Relationship between distance to forest and average exposure during the last year.

**Figure S9.** Relationship between distance to protected area and average exposure during the last year.

**Figure S10.** Relationship between distance to water point and average exposure during the last year.

**Table S1.** Distribution of proximity groups according to their respective distances from key features.

**Table S2.** Results from the questionnaire survey reporting mean values for all the variables with their standard deviations in parentheses.

**Table S3.** Results from the questionnaire survey reporting median and min/max values for all the variables.

**Table S4.** Partial Least Squares Structural Equation Modelling results.

**Table S5.** Path coefficients of latent variables, 95% confidence levels and significance of path coefficients (Bootstrapping).

**Table S6.** Path coefficients of latent variables, standard deviations, T statistics, and p-values of path coefficients (Bootstrapping).

**Table S7.** Correlations between latent variables used in PLS-SEM path modelling.

**Appendix S1.** Questionnaire.

**How to cite this article:** Usman, M. F., Nielsen, M. R., Le Bel, S., Phiri, M., & Grimaud, P. (2025). Living with elephants: A case study from Kavango-Zambezi Transfrontier Conservation Area in Zimbabwe. *People and Nature, 7*, 1605–1618. https://doi.org/10.1002/pan3.70060