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Research article · 2025 · Full text

International Journal of Biology Sciences

Human-wildlife conflict mitigation and community-based conservation in protected area buffer zones

Torstein ArnesenErik JohansenSigrun Dahl

Volume 7(3) · Pages 117–120 · DOI: 10.33545/26649926.2025.v7.i3b.749 ↗

Research areasWildlife Ecology and ConservationConservation, Biodiversity, and Resource ManagementRangeland and Wildlife Management

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Abstract

Author-written

Living next to a protected area can feel like a blessing or a burden, depending on whether the wildlife stays inside the fence. This research examined the spatial patterns, economic costs, and community responses to human-wildlife conflict (HWC) in the buffer zones of three protected areas in western Norway: Hardangervidda National Park, Folgefonna National Park, and Stølsheimen Landscape Protection Area. Household surveys (n = 387), GPS-collared wildlife movement data, and compensation claim records spanning 2019-2023 were analysed. Crop raiding by red deer (Cervus elaphus) was the most frequent conflict type (42.8%), followed by livestock predation by wolverine and lynx (23.4%). Annual economic losses averaged NOK 34,700 per affected household. Conflict intensity declined exponentially with distance from the protected area boundary (r² = 0.78). Households participating in community-based conservation programmes reported 31.6% fewer conflict incidents than non-participants. These findings indicate that spatially targeted mitigation—focusing on the first 5 km of buffer zone—combined with community engagement can meaningfully reduce HWC while maintaining conservation goals.

Summary

Source-checked

Plain-language reading of the full paper, prepared with AI and checked against the text. Each point links to the authors' own words.

  1. Why it matters

    • Human-wildlife conflict (HWC) in farmland adjacent to reserves can lead to crop and livestock losses, which may rapidly diminish local support for conservation efforts. View evidence ↗

      Why it matters

      Human-wildlife conflict (HWC) in farmland adjacent to reserves can lead to crop and livestock losses, which may rapidly diminish local support for conservation efforts.

      In the paper's own words

      “When large herbivores and predators spill out of reserves into adjacent farmland, the resulting crop losses and livestock kills can erode local support for conservation faster than any poaching crisis”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • Tensions have increased in Scandinavia due to the dramatic rebound of wolverine and Eurasian lynx populations since the 1990s. View evidence ↗

      Why it matters

      Tensions have increased in Scandinavia due to the dramatic rebound of wolverine and Eurasian lynx populations since the 1990s.

      In the paper's own words

      “In Europe, the expansion of large carnivore populations under strict protection has amplified these tensions, particularly in Scandinavia where wolverine (Gulo gulo) and Eurasian lynx (Lynx lynx) populations have rebounded dramatically since the 1990s”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • Norway manages its protected area buffer zones using a mix of voluntary community programs, licensed culling, and financial compensation. View evidence ↗

      Why it matters

      Norway manages its protected area buffer zones using a mix of voluntary community programs, licensed culling, and financial compensation.

      In the paper's own words

      “Norway's buffer zone management model uses a combination of financial compensation, licensed culling quotas, and voluntary community programmes to balance conservation and livelihoods”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
  2. The question

    • The study aimed to examine community responses, economic costs, and spatial patterns of human-wildlife conflict in three western Norwegian protected area buffer zones. View evidence ↗

      The question

      The study aimed to examine community responses, economic costs, and spatial patterns of human-wildlife conflict in three western Norwegian protected area buffer zones.

      In the paper's own words

      “This research examined the spatial patterns, economic costs, and community responses to human-wildlife conflict (HWC) in the buffer zones of three protected areas in western Norway”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
  3. What they did

    • The research focused on the buffer zones of Folgefonna National Park, Hardangervidda National Park, and the Stølsheimen Landscape Protection Area. View evidence ↗

      What they did

      The research focused on the buffer zones of Folgefonna National Park, Hardangervidda National Park, and the Stølsheimen Landscape Protection Area.

      In the paper's own words

      “three protected areas in western Norway: Hardangervidda National Park, Folgefonna National Park, and Stølsheimen Landscape Protection Area”

      Page 1

      Found in: Study Area Description

      Read it in the full textOpen PDF at page 1 ↗
    • Between March and June 2023, a survey was sent to 850 farming households, with 387 households (a 45.5% response rate) participating. View evidence ↗

      What they did

      Between March and June 2023, a survey was sent to 850 farming households, with 387 households (a 45.5% response rate) participating.

      In the paper's own words

      “a structured household survey was distributed to 850 farming households in the buffer zones between March and June 2023, achieving a 45.5% response rate (n = 387)”

      Page 2

      Found in: Introduction

      Read it in the full textOpen PDF at page 2 ↗
    • The study utilized GPS movement data from 2019 to 2022 for 4 lynx, 7 wolverines, and 18 red deer. View evidence ↗

      What they did

      The study utilized GPS movement data from 2019 to 2022 for 4 lynx, 7 wolverines, and 18 red deer.

      In the paper's own words

      “GPS movement data from 18 collared red deer, 7 wolverines, and 4 lynx tracked between 2019 and 2022 were obtained from the Norwegian Environment Agency”

      Page 2

      Found in: Methods

      Read it in the full textOpen PDF at page 2 ↗
    • Loss records were independently verified using livestock compensation claims from 2019 to 2023. View evidence ↗

      What they did

      Loss records were independently verified using livestock compensation claims from 2019 to 2023.

      In the paper's own words

      “livestock compensation claims submitted to the Directorate for Nature Management between 2019 and 2023 provided independently verified loss records”

      Page 2

      Found in: Introduction

      Read it in the full textOpen PDF at page 2 ↗
  4. What they found

    • Red deer crop raiding was the most common conflict (42.8%), while livestock predation by lynx and wolverine was the second most common (23.4%). View evidence ↗

      What they found

      Red deer crop raiding was the most common conflict (42.8%), while livestock predation by lynx and wolverine was the second most common (23.4%).

      In the paper's own words

      “Crop raiding by red deer (Cervus elaphus) was the most frequent conflict type (42.8%), followed by livestock predation by wolverine and lynx (23.4%)”

      Page 1

      Found in: Results

      Read it in the full textOpen PDF at page 1 ↗
    • Affected households experienced an average annual economic loss of NOK 34,700. View evidence ↗

      What they found

      Affected households experienced an average annual economic loss of NOK 34,700.

      In the paper's own words

      “Annual economic losses averaged NOK 34,700 per affected household”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • Conflict intensity decreased exponentially as distance from the boundary increased (r² = 0.78), with 73% of all incidents occurring within the first 5 km. View evidence ↗

      What they found

      Conflict intensity decreased exponentially as distance from the boundary increased (r² = 0.78), with 73% of all incidents occurring within the first 5 km.

      In the paper's own words

      “Conflict intensity declined exponentially with distance from the protected area boundary (r² = 0.78)”

      Page 1

      “mitigation resources should be concentrated within the first 5 km of the protected area boundary, where 73% of all incidents occurred”

      Page 3

      Found in: Discussion

      Read it in the full textOpen PDF at page 1 ↗
    • Households in community-based conservation programs had 31.6% fewer incidents (3.0 ± 1.7 per year) compared to non-participants (4.4 ± 2.3 per year). View evidence ↗

      What they found

      Households in community-based conservation programs had 31.6% fewer incidents (3.0 ± 1.7 per year) compared to non-participants (4.4 ± 2.3 per year).

      In the paper's own words

      “Households participating in community-based conservation programmes reported 31.6% fewer conflict incidents than non-participants”

      Page 1

      “Participants in community conservation programmes reported significantly fewer incidents (3.0 ± 1.7 vs 4.4 ± 2.3 per year; p = 0.003)”

      Page 2

      Found in: Results

      Read it in the full textOpen PDF at page 1 ↗
    • Participants in community conservation programs held more positive attitudes toward wildlife, with a mean Likert score of 3.7 compared to 2.9 for non-participants. View evidence ↗

      What they found

      Participants in community conservation programs held more positive attitudes toward wildlife, with a mean Likert score of 3.7 compared to 2.9 for non-participants.

      In the paper's own words

      “more positive attitudes toward wildlife (mean Likert 3.7 vs 2.9; p < 0.001)”

      Page 2

      Found in: Results

      Read it in the full textOpen PDF at page 2 ↗
  5. What it means

    • The authors recommend increasing community program enrollment from 37.7% to at least 60%, alongside faster compensation, crop diversification advice, and targeted fencing. View evidence ↗

      What it means

      The authors recommend increasing community program enrollment from 37.7% to at least 60%, alongside faster compensation, crop diversification advice, and targeted fencing.

      In the paper's own words

      “Expanding programme enrollment, currently at 37.7%, to at least 60% of buffer zone households—combined with targeted fencing, crop diversification advice, and faster compensation processing—would strengthen the social license for conservation”

      Page 3

      Found in: Conclusion

      Read it in the full textOpen PDF at page 3 ↗
    • The 45.5% survey response rate may have introduced non-response bias, as households with more severe conflicts might have been more likely to respond. View evidence ↗

      What it means

      The 45.5% survey response rate may have introduced non-response bias, as households with more severe conflicts might have been more likely to respond.

      In the paper's own words

      “The 45.5% survey response rate introduces potential non-response bias—households with severe conflict experiences may have been more motivated to respond”

      Page 2

      Found in: Limitations

      Read it in the full textOpen PDF at page 2 ↗
    • Because program participation was self-selected, the observed 31.6% reduction in conflict cannot be attributed solely to the program without considering confounders like farm size. View evidence ↗

      What it means

      Because program participation was self-selected, the observed 31.6% reduction in conflict cannot be attributed solely to the program without considering confounders like farm size.

      In the paper's own words

      “because community programme participation was self-selected rather than randomised, the 31.6% reduction in conflict cannot be attributed solely to the programme without accounting for confounders like farm size and proximity to forest edges”

      Page 2

      Found in: Limitations

      Read it in the full textOpen PDF at page 2 ↗

Visual summary

3 visuals

Illustrations explain the paper; charts are replotted from the paper's own table values.

AI-generated illustration
AI-generated conceptual illustration (African elephants and rural farmland in a protected area buffer zone). It explains the idea and does not show study data.↗ Enlarge
Spatial Decay of Conflict
AI-generated conceptual illustration (African elephants and rural farmland in a protected area buffer zone). It explains the idea and does not show study data.

AI-generated conceptual illustration (African elephants and rural farmland in a protected area buffer zone). It explains the idea and does not show study data.

Open full-size image ↗

Spatial Decay of Conflict

AI-generated conceptual illustration (African elephants and rural farmland in a protected area buffer zone). It explains the idea and does not show study data.

View source evidence ↗
Replotted from source data
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.↗ Enlarge
Crop raid% and Predation%: Hardangervidda vs Folgefonna vs Stolsheimen
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Crop raid% and Predation%: Hardangervidda vs Folgefonna vs Stolsheimen

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

View source evidence ↗
Replotted from source data
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.↗ Enlarge
Loss (NOK): Hardangervidda vs Folgefonna vs Stolsheimen
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Loss (NOK): Hardangervidda vs Folgefonna vs Stolsheimen

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

View source evidence ↗

Figures & tables

From the original paper
Human-wildlife conflict incidents and economic losses in three buffer zones, 2019-2023Table 1Page 2Scatter plot of annual conflict incidents versus distance from protected area boundary, with fitted exponential decay curve (r² = 0.78).Figure 1Page 2Proportional breakdown of human-wildlife conflict types across all three buffer zones combined.Figure 2Page 3
FULL TEXT Original article · reflowed for reading

Introduction

Protected areas are conservation's greatest success story-and sometimes its most contentious. When large herbivores and predators spill out of reserves into adjacent farmland, the resulting crop losses and livestock kills can erode local support for conservation faster than any poaching crisis [1] . In Europe, the expansion of large carnivore populations under strict protection has amplified these tensions, particularly in Scandinavia where wolverine (Gulo gulo) and Eurasian lynx (Lynx lynx) populations have rebounded dramatically since the 1990s [2] . Norway's buffer zone management model uses a combination of financial compensation, licensed culling quotas, and voluntary community programmes to balance conservation and livelihoods [3] . But the effectiveness of these tools has rarely been evaluated together at the household level. Most published analyses focus on either compensation costs or predator ecology in isolation [4] . This research bridged that gap by combining household-level economic data, wildlife movement tracks, and survey-based attitudes across three protected area buffer zones in western Norway [5,6] .

Study Area Description

The three protected areas are located in the Vestland and former Hordaland counties of western Norway (59.8-60.6°N, 5.5-7.5°E). Hardangervidda National Park (3,422

Methods

Conflict incidents were georeferenced and mapped in QGIS v3.28. Spatial decay of conflict intensity with distance from protected area boundaries was modelled using negative exponential regression. Households were classified as participants or non-participants in community conservation programmes based on survey responses. Differences in conflict rates between groups were tested by Mann-Whitney U tests. Attitudes toward wildlife were measured on a 5point Likert scale and compared between participant groups using ordinal logistic regression. GPS collar data were analysed for boundary-crossing events and seasonal movement patterns using the adehabitatHR package in R [7] . All analyses used R version 4.3.1; significance was set at p < 0.05.

Limitations

The 45.5% survey response rate introduces potential nonresponse bias-households with severe conflict experiences may have been more motivated to respond. Self-reported economic losses may overestimate actual damage due to recall bias; we partially addressed this by cross-referencing with official compensation records. The GPS collar sample was small relative to total wildlife populations, limiting spatial generalisation. And because community programme participation was self-selected rather than randomised, the 31.6% reduction in conflict cannot be attributed solely to the programme without accounting for confounders like farm size and proximity to forest edges.

Results

Table Across all three buffer zones, the average household experienced 4.4 conflict incidents per year. Crop raiding by red deer dominated, particularly at Folgefonna (48.2%) where lower-elevation agricultural land abuts deer habitat. Livestock predation was highest at Hardangervidda (27.8%), corresponding to the wolverine and lynx concentration documented by collar data. Participants in community conservation programmes reported significantly fewer incidents (3.0 ± 1.7 vs 4.4 ± 2.3 per year; p = 0.003) and more positive attitudes toward wildlife (mean Likert 3.7 vs 2.9; p < 0.001).

Discussion

The exponential decay of conflict intensity with distance confirms a pattern seen in African and Asian buffer zones but rarely quantified in northern Europe [8,9] . The practical implication is clear: mitigation resources should be concentrated within the first 5 km of the protected area boundary, where 73% of all incidents occurred. Beyond 10 km, conflict dropped to background levels. Community programme participants experienced 31.6% fewer incidents, though the observational design makes it difficult to separate programme effects from pre-existing differences between participants and non-participants [10,11] . The dominance of crop raiding (42.8%) over livestock predation (23.4%) is notable because Norwegian policy discourse around HWC tends to focus on large carnivore predation. While wolverine and lynx kills attract media attention and higher per-incident compensation, red deer crop damage affects far more households and generates comparable cumulative economic losses [12,13] . Rebalancing mitigation investment to include deer-proof fencing and crop insurance alongside carnivore-focused interventions could improve overall conflict reduction.

Conclusion

This multi-source analysis of human-wildlife conflict in western Norwegian buffer zones reveals that crop raiding, not predator attacks, is the most frequent and widespread conflict type. Conflict intensity decays rapidly with distance from protected area boundaries, suggesting that spatially targeted within a 5 km buffer strip would capture the majority of incidents. Community-based conservation programmes are associated with both fewer conflicts and more positive attitudes toward wildlife. Expanding programme enrollment, currently at 37.7%, to at least 60% of buffer zone households-combined with targeted fencing, crop diversification advice, and faster compensation processing-would strengthen the social license for conservation in these landscapes [14][15][16][17][18][19][20][21] .

Figures and tables from the paper

Table 1. Human-wildlife conflict incidents and economic losses in three buffer zones, 2019-2023
Protected AreaHouseholdsIncidents/yrCrop raid%Predation%Loss (NOK)Programme%
Hardangervidda1874.8 ± 2.339.127.841,20038.5
Folgefonna1123.6 ± 1.948.218.628,40042.0
Stolsheimen885.2 ± 2.741.324.734,10031.8
Combined3874.4 ± 2.342.823.434,70037.7

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image
Human-wildlife conflict incidents and economic losses in three buffer zones, 2019-2023↗ Enlarge
Table 1
Human-wildlife conflict incidents and economic losses in three buffer zones, 2019-2023

Human-wildlife conflict incidents and economic losses in three buffer zones, 2019-2023

Open full-size image ↗
Original paper tableSource PDF · page 2 ↗
Figure 1. Scatter plot of annual conflict incidents versus distance from protected area boundary, with fitted exponential decay curve (r² = 0.78).
Scatter plot of annual conflict incidents versus distance from protected area boundary, with fitted exponential decay curve (r² = 0.78).↗ Enlarge
Figure 1
Scatter plot of annual conflict incidents versus distance from protected area boundary, with fitted exponential decay curve (r² = 0.78).

Scatter plot of annual conflict incidents versus distance from protected area boundary, with fitted exponential decay curve (r² = 0.78).

Open full-size image ↗
Original paper figureSource PDF · page 2 ↗
Figure 2. Proportional breakdown of human-wildlife conflict types across all three buffer zones combined.
Proportional breakdown of human-wildlife conflict types across all three buffer zones combined.↗ Enlarge
Figure 2
Proportional breakdown of human-wildlife conflict types across all three buffer zones combined.

Proportional breakdown of human-wildlife conflict types across all three buffer zones combined.

Open full-size image ↗
Original paper figureSource PDF · page 3 ↗

References

21 in the original paper
  1. Woodroffe R, Thirgood S, Rabinowitz A. People and Wildlife: Conflict or Co-Existence? Cambridge: Cambridge University Press; 2005.
  2. Chapron G, Kaczensky P, Linnell JDC, von Arx M, Huber D, Andren H, et al. Recovery of large carnivores in Europe's modern human-dominated landscapes. Science. 2014;346(6216):1517-1519.
  3. Linnell JDC, Breitenmoser U, Breitenmoser-Wursten C, Odden J, von Arx M. Recovery of Eurasian lynx in Europe: what we know and what we need. In: Bentham Science Publishers; 2009:72-91.
  4. Nyhus PJ. Human-wildlife conflict and coexistence. Annual Review of Environment and Resources. 2016;41:143-171.
  5. Dickman AJ. Complexities of conflict: the importance of considering social factors for effectively resolving human-wildlife conflict. Animal Conservation. 2010;13(5):458-466.
  6. Treves A, Karanth KU. Human-carnivore conflict and perspectives on carnivore management worldwide. Conservation Biology. 2003;17(6):1491-1499.
  7. Calenge C. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling. 2006;197(3-4):516-519.
  8. Wittemyer G, Elsen P, Bean WT, Burton AC, Brashares JS. Accelerated human population growth at protected area edges. Science. 2008;321(5885):123-126.
  9. Karanth KK, Gopalaswamy AM, DeFries R, Ballal N. Assessing patterns of human-wildlife conflicts and compensation around a central Indian protected area. PLoS ONE. 2012;7(12):e50433.
  10. Madden F. Creating coexistence between humans and wildlife: global perspectives on local efforts to address human-wildlife conflict. Human Dimensions of Wildlife. 2004;9(4):247-257.
  11. Redpath SM, Young J, Evely A, Adams WM, Sutherland WJ, Whitehouse A, et al. Understanding and managing conservation conflicts. Trends in Ecology and Evolution. 2013;28(2):100-109.
  12. Mysterud A, Meisingset EL, Langvatn R, Yoccoz NG, Stenseth NC. Climate-dependent allocation of resources to secondary sexual traits in red deer. Oikos. 2005;111(2):245-252.
  13. Stoen OG, Zedrosser A, Saebo S, Swenson JE. Inversely density-dependent natal dispersal in brown bears. Oecologia. 2006;148(2):356-364.
  14. Inskip C, Zimmermann A. Human-felid conflict: a review of patterns and priorities worldwide. Oryx. 2009;43(1):18-34.
  15. Barua M, Bhagwat SA, Jadhav S. The hidden dimensions of human-wildlife conflict: health impacts, opportunity and transaction costs. Biological Conservation. 2013;157:309-316.
  16. Kansky R, Kidd M, Knight AT. Meta-analysis of attitudes toward damage-causing mammalian wildlife. Conservation Biology. 2014;28(4):924-938.
  17. Lute ML, Carter NH. Are we coexisting with large carnivores or merely tolerating them? Conservation Letters. 2020;13(4):e12714.
  18. Bhatia S, Athreya V, Grenyer R, Macdonald DW. Understanding the role of representations of humanleopard conflict in Mumbai through media-Loss Perceptions Surveys. Conservation Biology. 2013;27(4):851-858.
  19. Berger J. The last mile: how to sustain long-distance migration in mammals. Conservation Biology. 2004;18(2):320-331.
  20. Frank B, Glikman JA, Marchini S. Human-Wildlife Interactions: Turning Conflict into Coexistence. Cambridge: Cambridge University Press; 2019.
  21. Khorozyan I, Waltert M. How long do anti-predator interventions remain effective? Patterns, thresholds and uncertainty. Royal Society Open Science. 2019;6(9):190826.